BioMedical Engineering OnLine

Published by Springer Nature
Online ISSN: 1475-925X
Learn more about this page
Recent publications
A: Left ventricular MW parameters of the normal group. B: Left ventricular MW parameters of uremic patients. In each group, the LV PSL, 17-segment bull's-eye representation of GWI, and the MW parameters were given, and the MW parameters included GWI, GCW, GWW, and GWE. MW: myocardial work; PSL: pressure–strain loops; GLS: global longitudinal strain; GWI: global work index; GCW: global constructive work; GWW: global waste work; GWE: global work efficiency
ROC curve analysis of the main MW parameters for identifying impaired LV myocardial function in uremic patients with preserved LV ejection fraction. On the left: ROC curve of GWW parameter (AUC = 0.925). On the right: ROC curve of GWE parameter (AUC = 0.966). GWE had better AUC than GWW with the sensitivity, specificity and optimal threshold of 89%, 96% and 92.5%, respectively. ROC: receiver operating characteristic; GWE: global work efficiency
Flowchart of study populations
Schematic diagram of the NIMWI measurement results. a Changes in systolic blood pressure measured by the cuff as a function of the global longitudinal strain of the left ventricle. Red curve: overall pressure and strain of the left ventricle. The area under curve represents the global myocardial work index of the left ventricle. b Column chart of the global constructive work and waste work of the left ventricle. c Bull’s-eye diagram of the 17-segment myocardial work index of the left ventricle. d Results of the NIMWI parameters. NIMWI: non-invasive myocardial work index
Background Cardiac damage is the leading cause of death in uremic patients. This study aimed to evaluate the application of non-invasive myocardial work index (NIMWI) by echocardiography in assessing the left ventricular (LV) systolic function in uremic patients. Methods Twenty-six uremic patients and 27 age- and sex-matched healthy volunteers were enrolled in the study. Except for the conventional echocardiographic parameters, the LV myocardial work (MW) parameters including GWI (myocardial global work index), GCW (global constructive work), GWW (global wasted work), and GWE (global work efficiency) were calculated in study participants. Differences in MW parameters between the uremic and normal groups were compared by independent-sample t -test. Receiver operating characteristic (ROC) curves were constructed for MW parameters to detect abnormal LV systolic function in uremic patients. Results Compared with the normal group, GWW was significantly increased and GWE decreased in the uremic group ( P < 0.05). Area under the curve (AUC) for GWE by the ROC analysis was 0.966. The best threshold, sensitivity and specificity values of GWE to detect abnormality of LV systolic function in uremic patients were 92.5%, 0.89 and 0.96, respectively. Conclusions NIMWI may be applied to assess the global MW of uremic patients. The presence of reduced GWE can help identify impaired left ventricular myocardial function in uremic patients with preserved LV ejection fraction with a high sensitivity and specificity.
Scatter plot of NH versus CSI. The plots represent the ABD quartiles. Blue: first quartile. Red: second quartile. Green: third quartile. Purple: fourth quartile
Seven foot features of the three-dimensional (3D) foot-scanning system. a Instep height (IH) and navicular height (NH). b Transverse arch height (TAH) and transverse arch width (TAW). c Great toe–first metatarsal head–heel (GFH) angle, fifth toe–fifth metatarsal head–heel (FFH) angle, and distance from the center line between the heel and the second-toe tip when the coordinate point of the talus head is projected onto the floor (ABD)
Graphical representation of the Chippaux–Smirak and Staheli indices (CSI and SI, respectively). a: interior of the first metatarsal head, b: exterior of the fifth metatarsal. Line (c–d): line of minimal length across the arch parallel to line (a–b). Line e–f: parallel line used to represent the heel width
Background Flat feet increase the risk of knee osteoarthritis and contribute to frailty, which may lead to worse life prognoses. The influence of the foot skeletal structure on flat feet is not yet entirely understood. Footprints are often used to evaluate feet. However, footprint-based measurements do not reflect the underlying structures of feet and are easily confounded by soft tissue. Three-dimensional evaluation of the foot shape can reveal the characteristics of flat feet. Therefore, foot shape evaluations have garnered increasing research interest. This study aimed to determine the correlation between the three-dimensional (3D) features of the foot and the measurement results of footprint and to predict the evaluation results of flat feet from the footprint based on the 3D features. Finally, the three-dimensional characteristics of flat feet, which cannot be revealed by footprint, were determined. Methods A total of 403 individuals (40–89 years) participated in this study. The proposed system was developed to identify seven skeletal features that were expected to be associated with flat feet. The loads on the soles of the feet were measured in a static standing position and with a digital footprint device. Specifically, two footprint indices were calculated: the Chippaux–Smirak index (CSI) and the Staheli index (SI). In the analysis, comparisons between male and female measurement variables were performed using the Student’s t test. The relationships between the 3D foot features and footprint index parameters were determined by employing the Pearson correlation coefficient. Multiple linear regression was utilized to identify 3D foot features that were strongly associated with the CSI and SI. Foot features identified as significant in the multivariate regression analysis were compared based on a one-way analysis of variance (ANOVA) with Tukey’s post hoc test. Results The CSI and SI were highly correlated with the instep height (IH) and navicular height (NH) of the 3D foot scanning system and were also derived from multiple regression analysis. In addition to the NH and IH, the indicators of the forefoot, transverse arch width, and transverse arch height were considered. In the flat foot group with CSI values above 62.7%, NH was 13.5% ( p < 0.001) for males and 14.9% ( p = 0.01) for females, and the axis of the bone distance was 5.3% ( p = 0.05) for males and 4.9% ( p = 0.10) for females. In particular, for CSI values above 62.7% and NH values below 13%, the axis of the bone distance was large and the foot skeleton was deformed. Conclusions Decreased navicular bone height could be evaluated with the 3D foot scanning system even when flat feet were not detected from the footprint. The results indicate that the use of quantitative indices for 3D foot measurements is important when evaluating the flattening of the foot. Trial registration number UMIN000037694. Name of the registry: University Hospital Medical Information Network Registry. Date of registration: August 15, 2019.
Background Refractive error detection is a significant factor in preventing the development of myopia. To improve the efficiency and accuracy of refractive error detection, a refractive error detection network (REDNet) is proposed that combines the advantages of a convolutional neural network (CNN) and a recurrent neural network (RNN). It not only extracts the features of each image, but also fully utilizes the sequential relationship between images. In this article, we develop a system to predict the spherical power, cylindrical power, and spherical equivalent in multiple eccentric photorefraction images. Approach First, images of the pupil area are extracted from multiple eccentric photorefraction images; then, the features of each pupil image are extracted using the REDNet convolution layers. Finally, the features are fused by the recurrent layers in REDNet to predict the spherical power, cylindrical power, and spherical equivalent. Results The results show that the mean absolute error (MAE) values of the spherical power, cylindrical power, and spherical equivalent can reach 0.1740 D (diopters), 0.0702 D, and 0.1835 D, respectively. Significance This method demonstrates a much higher accuracy than those of current state-of-the-art deep-learning methods. Moreover, it is effective and practical.
Relative absolute error of time-domain measures for all subjects
Relative absolute error of frequency-domain measures for all subjects and all methods
Frequency extraction using the derivative of the phase signal and its filtered version. The black solid line is the reference respiratory rate signal
Top: filtered BCG signal. Bottom: phase signal of Hilbert transform. At around 560s\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${560}\,\hbox {s}$$\end{document}, a motion artefact occurs which follows a change in signal level. The phase signal is independent of that change
Workflow of the proposed algorithms
Background Measuring the respiratory rate is usually associated with discomfort for the patient due to contact sensors or a high time demand for healthcare personnel manually counting it. Methods In this paper, two methods for the continuous extraction of the respiratory rate from unobtrusive ballistocardiography signals are introduced. The Hilbert transform is used to generate an amplitude-invariant phase signal in-line with the respiratory rate. The respiratory rate can then be estimated, first, by using a simple peak detection, and second, by differentiation. Results By analysis of a sleep laboratory data set consisting of nine records of healthy individuals lasting more than 63 h and including more than 59,000 breaths, a mean absolute error of as low as 0.7 BPM for both methods was achieved. Conclusion The results encourage further assessment for hospitalised patients and for home-care applications especially with patients suffering from diseases of the respiratory system like COPD or sleep apnoea.
Background CPT-11 (irinotecan) is one of the most efficient agents used for colorectal cancer chemotherapy. However, as for many other chemotherapeutic drugs, how to minimize the side effects of CPT-11 still needs to be thoroughly described. Objectives This study aimed to develop the CPT-11-loaded DSPE-PEG 2000 targeting EGFR liposomal delivery system and characterize its targeting specificity and therapeutic effect on colorectal cancer (CRC) cells in vitro and in vivo. Results The synthesized liposome exhibited spherical shapes (84.6 ± 1.2 nm to 150.4 nm ± 0.8 nm of estimated average sizes), good stability, sustained release, and enough drug loading (55.19%). For in vitro experiments, SW620 cells treated with CPT-11-loaded DSPE-PEG2000 targeting EGFR liposome showed lower survival extended level of intracellular ROS production. In addition, it generated an enhanced apoptotic cell rate by upregulating the protein expression of both cleaved-caspase-3 and cleaved-caspase-9 compared with those of SW620 cells treated with free CPT-11. Importantly, the xenograft model showed that both the non-target and EGFR-targeted liposomes significantly inhibited tumor growth compared to free CPT-11. Conclusions Compared with the non-target CPT-11-loaded DSPE-PEG2000 liposome, CPT-11-loaded DSPE-PEG2000 targeting EGFR liposome treatment showed much better antitumor activity in vitro in vivo. Thus, our findings provide new assets and expectations for CRC targeting therapy.
Supervised learning method applying to tumor classification. The flow chart illustrates the steps of building a classification model to differentiate brain neoplasms using supervised learning technique. Here, the problem was identified as a classification problem at the initial stage and then the necessary data was collected as the second step. Data pre-processing was executed as the third step and at the fourth step, the data set was split into training and testing sets. Then a suitable ML algorithm for the collected data was selected as the fifth step of the study flow and then, the selected algorithm was trained with the training data as the sixth step. Finally, the developed algorithm was evaluated with the test data and the hyperparameter of the developed model was tuned to reach the optimum accuracy level of the model
Final confusion matrix. The confusion matrix express the performance of the optimized benign malignant brain tumor brain tumor classification model over the test set
MRI ADC brain image of a 14-year-old female patient diagnosed with pilocytic astrocytoma which was radiologically and histo-pathologically identified as a benign tumor. The tumor area is surrounded by the ROI. The texture features were extracted form the selected area
ANOVA f-test results chart. ANOVA f-test score for attributes 0 to 15 are illustrated in the graph; mean pixel value of ADC 32.3343, Skewness 3.3444 Kurtosis 9.6250, GLCM Mean1 32.6372, GLCM mean2 29.1327, GLCM variance1 14.0761, GLCM variance2 27.5219 GLCM energy, GLCM Homogeneity 3.4572, 33.9675, GLCM Entropy 4.989, GLCM contrast 47.9462, GLCM Correlation 48.6392, GLCM prominence 15.4134, GLCM Shade 17.1677, Patient Age 9.4337 and Patient Gender 73.7926
Background Diffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors. Methods This prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed. Results According to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process. Conclusions This study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies.
Background Vulvar lichen sclerosus (VLS) is one of the most common clinical manifestations of vulva. Thirteen percent of women have symptomatic vulvar diseases. The aim of this study is to investigate the expression profile of circular RNA (circRNAs) in vulvar lichen sclerosus, and to identify the underlying core genes of VLS. Methods We removed rRNA for sequencing, and screened the differentially expressed messenger RNA (mRNAs), long non-coding RNA (lncRNAs) and single-stranded circRNA in 20 groups of VLS tissues and 20 groups of healthy female vulvar skin tissues. Bioinformatics analysis was used to analyze its potential functions. Results A total of 2545 differentially expressed mRNAs were assessed in VLS patients, of which 1541 samples were up-regulated and 1004 samples were down-regulated. A total of 1453 differentially expressed lncRNAs were assessed, of which 812 samples were up-regulated and 641 samples were down-regulated. A total of 79 differentially expressed circRNAs were assessed, of which 54 were up-regulated and 25 were down-regulated. The differential expression of circRNAs was closely related to biological processes and molecular functions. The differences in circRNAs were mainly related to the “human T-cell leukemia virus 1 infection” signaling pathway and the “axon guidance” signaling pathway. Conclusion The profile of abnormal regulation of circRNA exists in VLS. According to biological informatics analysis, the dysregulation of circRNAs may be related to the pathogenesis and pathological process of VLS.
Background Brain-controlled wheelchairs (BCWs) are important applications of brain–computer interfaces (BCIs). Currently, most BCWs are semiautomatic. When users want to reach a target of interest in their immediate environment, this semiautomatic interaction strategy is slow. Methods To this end, we combined computer vision (CV) and augmented reality (AR) with a BCW and proposed the CVAR-BCW: a BCW with a novel automatic interaction strategy. The proposed CVAR-BCW uses a translucent head-mounted display (HMD) as the user interface, uses CV to automatically detect environments, and shows the detected targets through AR technology. Once a user has chosen a target, the CVAR-BCW can automatically navigate to it. For a few scenarios, the semiautomatic strategy might be useful. We integrated a semiautomatic interaction framework into the CVAR-BCW. The user can switch between the automatic and semiautomatic strategies. Results We recruited 20 non-disabled subjects for this study and used the accuracy, information transfer rate (ITR), and average time required for the CVAR-BCW to reach each designated target as performance metrics. The experimental results showed that our CVAR-BCW performed well in indoor environments: the average accuracies across all subjects were 83.6% (automatic) and 84.1% (semiautomatic), the average ITRs were 8.2 bits/min (automatic) and 8.3 bits/min (semiautomatic), the average times required to reach a target were 42.4 s (automatic) and 93.4 s (semiautomatic), and the average workloads and degrees of fatigue for the two strategies were both approximately 20. Conclusions Our CVAR-BCW provides a user-centric interaction approach and a good framework for integrating more advanced artificial intelligence technologies, which may be useful in the field of disability assistance.
a Microfluidic tear component analysis platform proposed by Karns et al. [83] Reproduced with permission from Ref. [83]. Copyright 2011, American Chemical Society. b Colorimetric μPad device for tear electrolyte analysis proposed by Yetisen et al. [87]. Reproduced with permission from Ref. [87]. Copyright 2020, Royal Society of Chemistry. c Wearable intraocular pressure sensor and detection system based on contact lens proposed by Araci et al. [90] Reproduced with permission from Ref. [90]. Copyright 2019, Royal Society of Chemistry
a An in vitro eyeball model platform developed by a microfluidic eyeball cell chip was used to study pharmacokinetics proposed by Bennet et al. [92]. Reproduced with permission from Ref. [92]. Copyright 2018, Royal Society of Chemistry. b The human corneal barrier and blink reconstruction platform based on the microfluidic chip is used to study the development of ophthalmic drugs proposed by Abdalkader et al. [95] Reproduced with permission from Ref. [95]. Copyright 2020, Royal Society of Chemistry
Ocular diseases are closely related to the physiological changes in the eye sphere and its contents. Using biomechanical methods to explore the relationship between the structure and function of ocular tissue is beneficial to reveal the pathological processes. Studying the pathogenesis of various ocular diseases will be helpful for the diagnosis and treatment of ocular diseases. We provide a critical review of recent biomechanical analysis of ocular diseases including glaucoma, high myopia, and diabetes. And try to summarize the research about the biomechanical changes in ocular tissues (e.g., optic nerve head, sclera, cornea, etc.) associated with those diseases. The methods of ocular biomechanics research in vitro in recent years are also reviewed, including the measurement of biomechanics by ophthalmic equipment, finite element modeling, and biomechanical analysis methods. And the preparation and application of microfluidic eye chips that emerged in recent years were summarized. It provides new inspiration and opportunity for the pathogenesis of eye diseases and personalized and precise treatment.
Background To assess the feasibility and clinical utility of artificial intelligence (AI)-based screening for diabetic retinopathy (DR) and macular edema (ME) by combining fundus photos and optical coherence tomography (OCT) images in a community hospital. Methods Fundus photos and OCT images were taken for 600 diabetic patients in a community hospital. Ophthalmologists graded these fundus photos according to the International Clinical Diabetic Retinopathy (ICDR) Severity Scale as the ground truth. Two existing trained AI models were used to automatically classify the fundus images into DR grades according to ICDR, and to detect concomitant ME from OCT images, respectively. The criteria for referral were DR grades 2–4 and/or the presence of ME. The sensitivity and specificity of AI grading were evaluated. The number of referable DR cases confirmed by ophthalmologists and AI was calculated, respectively. Results DR was detected in 81 (13.5%) participants by ophthalmologists and in 94 (15.6%) by AI, and 45 (7.5%) and 53 (8.8%) participants were diagnosed with referable DR by ophthalmologists and by AI, respectively. The sensitivity, specificity and area under the curve (AUC) of AI for detecting DR were 91.67%, 96.92% and 0.944, respectively. For detecting referable DR, the sensitivity, specificity and AUC of AI were 97.78%, 98.38% and 0.981, respectively. ME was detected from OCT images in 49 (8.2%) participants by ophthalmologists and in 57 (9.5%) by AI, and the sensitivity, specificity and AUC of AI were 91.30%, 97.46% and 0.944, respectively. When combining fundus photos and OCT images, the number of referrals identified by ophthalmologists increased from 45 to 75 and from 53 to 85 by AI. Conclusion AI-based DR screening has high sensitivity and specificity and may feasibly improve the referral rate of community DR.
A Examples of spatial variance maps (var map) and their spatial features represented in histogram and K-space (positive frequencies only), from domain A and B data, respectively. B Examples of spatial variance maps resulting from the trained translation models 2D cycleGAN, reCycleGAN, and the 3D cycleGAN models. The translated examples are derived from 1-3 in (A). The image sequences are here represented by a variance map, its corresponding histogram, and K-space (2D Fourier transform, positive frequencies)
Examples of two sets of translated time signals (A and D), their corresponding power spectral densities (Psd) (B and E), and cross-correlation with the input simulated signal (C and F). The original signal as well as that the outputs from the different translations are shown in different colours in time traces. Content similarity was computed as the maximal cross-correlation between the translated signals and the original simulated signal. It can be seen that the translations of the 3D cycle GAN models were similar to the simulated signals, whereas the translations from the 2D GAN and the recycle-GAN models were not and came with large amount of noise. G Shows an example of an experimental time signal, and H its corresponding psd. It should be stressed that the oscillatory pattern is similar in simulated and experimental signals. ∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$*$$\end{document}= modification 2 (stride 1),∗∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$**$$\end{document} = modification 1+\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$+$$\end{document}2 (stride 1 and noise injection)
Exploration of the learned mapping of the generator model. A Examples of simulated (in silico), translated tissue velocity images, B their corresponding differences at three different time frames of an image sequence, and C is the similarity map computed as the sum of difference maps of the whole image sequence. D Similarity maps for three translated examples. E Similarity as a function of depth for all 64 translated examples. The similarity was low in superficial subcutaneous region. F Examples of experimental tissue velocity frames. It can be seen by visual inspection that the texture pattern of the similarity maps (D) shared similar features with the texture pattern of the experimental velocity maps (F)
A Illustration of the cycleGAN generator architecture GB\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G_{B}$$\end{document}, including two of the modifications made to the original model of cycleGAN [35]. B Illustration of the modification 1 made to the ResNet block in order to make the generator stochastic. Solid lines show the parts where modifications were made. The approach is similar to the StyleGAN model [45]
Examples of image sequences of skeletal intra-muscular contraction patterns from a cross-sectional image plane A of the biceps brachii muscle at a constant low force level. B Represents a simulated (in silico, domain A) image sequence, and in C its corresponding variance map (Var map), computed as the variance of the time signals at each pixel in the image-sequence. D Represents an experimental (in vivo, domain B) image sequence, and E its corresponding variation map. In both sequences, the oscillating behaviour of different spatial regions can be seen (putative contractions of motor units). The experimental images presented different spatial features as compared to the simulated ones. The time between consecutive frames is here 2 ms and images show colour-coded tissue velocity
Background Advances in sports medicine, rehabilitation applications and diagnostics of neuromuscular disorders are based on the analysis of skeletal muscle contractions. Recently, medical imaging techniques have transformed the study of muscle contractions, by allowing identification of individual motor units’ activity, within the whole studied muscle. However, appropriate image-based simulation models, which would assist the continued development of these new imaging methods are missing. This is mainly due to a lack of models that describe the complex interaction between tissues within a muscle and its surroundings, e.g., muscle fibres, fascia, vasculature, bone, skin, and subcutaneous fat. Herein, we propose a new approach to overcome this limitation. Methods In this work, we propose to use deep learning to model the authentic intra-muscular skeletal muscle contraction pattern using domain-to-domain translation between in silico (simulated) and in vivo (experimental) image sequences of skeletal muscle contraction dynamics. For this purpose, the 3D cycle generative adversarial network (cycleGAN) models were evaluated on several hyperparameter settings and modifications. The results show that there were large differences between the spatial features of in silico and in vivo data, and that a model could be trained to generate authentic spatio-temporal features similar to those obtained from in vivo experimental data. In addition, we used difference maps between input and output of the trained model generator to study the translated characteristics of in vivo data. Results This work provides a model to generate authentic intra-muscular skeletal muscle contraction dynamics that could be used to gain further and much needed physiological and pathological insights and assess and overcome limitations within the newly developed research field of neuromuscular imaging.
Bland–Altman and correlation analyses were performed to compare the Q-Ac interval values that were estimated by the two measurers a, and to compare model-based QT interval values with Q-Ac interval values of the two measurers b–c. QT and Q-Ac intervals were estimated beat by beat and the total number of heart beats was 309 from five fetal mice. a 95% of the values are within the limits of agreement (r = 0.93, P < 0.05). b 93% of the values are within the limits of agreement (r = 0.89, P < 0.05). c 92% of the values are within the limits of agreement (r = 0.91, P < 0.05)
A fECG and pulsed Doppler images were obtained from the fetal mouse by using different systems. In order to match Doppler images with fECG records, a signal generator was connected to both systems to input pulsed signals. B The pulse signal that was recorded with fECG is aligned with the same signal that was recorded simultaneously with DUS. Q-Ac intervals were calculated by drawing vertical lines from the Ac timings of DUS to fECG. The red asterisks in fECG indicate the estimated end of T waves. End of T waves were calculated from R(t), Eq. 3. Ac timings were determined by drawing tangent lines where blood flow crosses the base line
Background Abnormal prolongation in the QT interval or long QT syndrome (LQTS) is associated with several cardiac complications such as sudden infant death syndrome (SIDS). LQTS is believed to be linked to genetic mutations which can be understood by using animal models, such as mice models. Nevertheless, the research related to fetal QT interval in mice is still limited because of challenges associated with T wave measurements in fetal electrocardiogram (fECG). Reliable measurement of T waves is essential for estimating their end timings for QT interval assessment. Results A mathematical model was used to estimate QT intervals. Estimated QT intervals were validated with Q-aortic closure (Q-Ac) intervals of Doppler ultrasound (DUS) and comparison between both showed good agreement with a correlation coefficient higher than 0.88 ( r > 0.88, P < 0.05). Conclusion Model-based estimation of QT intervals can help in better understanding of QT intervals in fetal mice.
Feature selection using the four machine learning algorithms. S: selected feature; KNN: k-nearest neighbors; SVM: support vector machine; MLP: multilayer perceptron; RF: random forest. For the description of HRV indices (columns), please see the text
Confusion matrices for the best classification models (same shown in Table 4). KNN: k-nearest neighbors; SVM: support vector machine; MLP: multilayer perceptron; RF: random forest. Low: low-risk group; Intermediate: intermediate-risk group; High: high-risk group
Background Dysautonomia plays an ancillary role in the pathogenesis of Chronic Chagas Cardiomyopathy (CCC), but is the key factor causing digestive organic involvement. We investigated the ability of heart rate variability (HRV) for death risk stratification in CCC and compared alterations of HRV in patients with isolated CCC and in those with the mixed form (CCC + digestive involvement). Thirty-one patients with CCC were classified into three risk groups (low, intermediate and high) according to their Rassi score. A single-lead ECG was recorded for a period of 10–20 min, RR series were generated and 31 HRV indices were calculated. The HRV was compared among the three risk groups and regarding the associated digestive involvement. Four machine learning models were created to predict the risk class of patients. Results Phase entropy is decreased and the percentage of inflection points is increased in patients from the high-, compared to the low-risk group. Fourteen patients had the mixed form, showing decreased triangular interpolation of the RR histogram and absolute power at the low-frequency band. The best predictive risk model was obtained by the support vector machine algorithm (overall F1-score of 0.61). Conclusions The mixed form of Chagas' disease showed a decrease in the slow HRV components. The worst prognosis in CCC is associated with increased heart rate fragmentation. The combination of HRV indices enhanced the accuracy of risk stratification. In patients with the mixed form of Chagas disease, a higher degree of sympathetic autonomic denervation may be associated with parasympathetic impairment.
Background It is known that inflammatory bowel disease is the result of a defective immune system, and immunotherapy and biological therapy have gradually become important means to treat it. This paper focused on the bibliometric statistical analysis of the current research progress to summarize the research status of this field and analyze the research trends in recent years. Methods Two visualization tools, CiteSpace and VOSviewer, were used to explore the data of journals, institutions, countries/regions, authors, references, and keywords for the literature included in the Web of Science Core Collection from January 1, 2002, to December 31, 2021. Results A total of 312 papers were published in 120 journals by 603 institutions from 40 countries/regions, with 9463 co-cited references. The United States has the most publications with the highest total citations in the world. Inflammatory Bowel Diseases published the maximum number of papers, and Gastroenterology devoted the most co-citations to immunotherapy and biological therapy for IBD. In addition, we found that the studies before 2009 mostly focused on clinical trials while researchers have paid more attention to clinical management in therapy for IBD since 2009. Combination therapy and management of the treatment for the disease have become research hotspots. Conclusion The focus of immunotherapy and biotherapy for IBD has shifted from clinical trials to the management of the risks and benefits of immunotherapy.
Background Cutaneous electrogastrography (EGG) is a non-invasive technique that detects gastric bioelectrical slow waves, which in part govern the motility of the stomach. Changes in gastric slow waves have been associated with a number of functional gastric disorders, but to date accurate detection from the body-surface has been limited due to the low signal-to-noise ratio. The main aim of this study was to develop a flexible active-electrode EGG array. Methods: Two Texas Instruments CMOS operational amplifiers: OPA2325 and TLC272BID, were benchtop tested and embedded in a flexible linear array of EGG electrodes, which contained four recording electrodes at 20-mm intervals. The cutaneous EGG arrays were validated in ten weaner pigs using simultaneous body-surface and serosal recordings, using the Cyton biosensing board and ActiveTwo acquisition systems. The serosal recordings were taken using a passive electrode array via surgical access to the stomach. Signals were filtered and compared in terms of frequency, amplitude, and phase-shift based on the classification of propagation direction from the serosal recordings. Results: The data were compared over 709 cycles of slow waves, with both active cutaneous EGG arrays demonstrating comparable performance. There was an agreement between frequencies of the cutaneous EGG and serosal recordings (3.01 ± 0.03 vs 3.03 ± 0.05 cycles per minute; p = 0.75). The cutaneous EGG also demonstrated a reduction in amplitude during abnormal propagation of gastric slow waves (310 ± 50 µV vs 277 ± 9 µV; p < 0.01), while no change in phase-shift was observed (1.28 ± 0.09 s vs 1.40 ± 0.10 s; p = 0.36). Conclusion: A sparse linear cutaneous EGG array was capable of reliably detecting abnormalities of gastric slow waves. For more accurate characterization of gastric slow waves, a two-dimensional body-surface array will be required.
Background: Considering the estimate that thyroid cancer will become the fourth most prevalent type of tumor, improving its diagnosis is a necessity. The gold standard for evaluating thyroid nodules is ultrasound followed by biopsy. These tests, however, have limitations, especially in nodules smaller than 0.5 cm. Dynamic infrared thermography is an imaging method that does not require ionizing radiation or contrast injection. The aim of the study was to analyze the thermal behavior of thyroid nodules through infrared thermography using the cold stress protocol. Results: The Wilcoxon test showed thermal differences between groups (control and healthy, p < 0.001). The difference in the thermal behavior of the nodular tissues was evidenced by the longitudinal analysis. When comparing the nodules, it was possible to verify that the beginnings of tissue heating is significant (p = 0.001). In addition, the variability analysis showed a "well" effect, which occurred in period t-1 (pre-cooling time) to period t = 3 (time three minutes). Benign nodules had a variation ratio of 1.81 compared to malignant nodules. Conclusion: Benign nodules present a different thermal behavior than malignant nodules, and both present different behavior than normal tissue. For the analysis of nodules, the protocol used with cold stress, dynamic thermography and the inclusion of time t-1 were essential for the differentiation of nodules in the thyroid gland. Therefore, we recommend the continuance of these parameters for future studies. Methods: Thirty-three individuals with nodules in the thyroid region and nine healthy individuals participated in this descriptive exploratory study. In total, 42 nodules were evaluated, 11 malignant and 31 benign. The region of interest was exposed to cold stress for 30 s. First, the image was captured before the cold stress and subsequently, the images were assessed every 30 s, over a 10-min time period after cold stress. The perfusion and the thermal behavior of the tissues were evaluated by longitudinal analysis based on the number of pixels in each time period. The statistical tests of Wilcoxon, F-Snedecor and longitudinal models would assist in data analysis.
Background The minimum variance (MV) beamformer can significantly improve the image resolution in ultrasound imaging, but it has limited performance in noise reduction. We recently proposed the covariance matrix-based statistical beamforming (CMSB) for medical ultrasound imaging to reduce sidelobes and incoherent clutter. Methods In this paper, we aim to improve the imaging performance of the MV beamformer by introducing a new pixel-based adaptive weighting approach based on CMSB, which is named as covariance matrix-based adaptive weighting (CMSAW). The proposed CMSAW estimates the mean-to-standard-deviation ratio (MSR) of a modified covariance matrix reconstructed by adaptive spatial smoothing, rotary averaging, and diagonal reducing. Moreover, adaptive diagonal reducing based on the aperture coherence is introduced in CMSAW to enhance the performance in speckle preservation. Results The proposed CMSAW-weighted MV (CMSAW-MV) was validated through simulation, phantom experiments, and in vivo studies. The phantom experimental results show that CMSAW-MV obtains resolution improvement of 21.3% and simultaneously achieves average improvements of 96.4% and 71.8% in average contrast and generalized contrast-to-noise ratio (gCNR) for anechoic cyst, respectively, compared with MV. in vivo studies indicate that CMSAW-MV improves the noise reduction performance of MV beamformer. Conclusion Simulation, experimental, and in vivo results all show that CMSAW-MV can improve resolution and suppress sidelobes and incoherent clutter and noise. These results demonstrate the effectiveness of CMSAW in improving the imaging performance of MV beamformer. Moreover, the proposed CMSAW with a computational complexity of $$O(N^2)$$ O ( N 2 ) has the potential to be implemented in real time using the graphics processing unit.
Background Hypertension is known as a major factor for global mortality. We aimed to investigate the role of Cullin3 (CUL3) in the regulation of hypertension. Material and methods Human vascular smooth muscle cells (VSMCs) were treated with Angiotensin II (Ang II) to establish a hypertension in vitro model. Cell viability was detected by a cell counting kit-8 (CCK-8) assay. The content of reactive oxygen species (ROS) was evaluated by kit. Transwell assay and TUNEL staining were, respectively, used to assess cell migration and apoptosis. Additionally, the expression of sonic hedgehog (SHH) signaling-related proteins (SHH, smoothened homolog (Smo) and glioblastoma (Gli)) and CUL3 was tested with western blotting. Following treatment with Cyclopamine (Cycl), an inhibitor of SHH signaling, in Ang II-induced VSMCs, cell viability, migration, apoptosis and ROS content were determined again. Then, VSMCs were transfected with CUL3 plasmid or/and treated with sonic hedgehog signaling agonist (SAG) to explore the impacts on Ang II-induced VSMCs damage. In vivo, a hypertensive mouse model was established. Systolic blood pressure and diastolic blood pressure were determined. The histopathologic changes of abdominal aortic tissues were examined using H&E staining. The expression of SHH, Smo, Gli and CUL3 was tested with western blotting. Results Significantly increased proliferation, migration and apoptosis of VSMCs were observed after Ang II exposure. Moreover, Ang II induced upregulated SHH, Smo and Gli expression, whereas limited increase in CUL3 expression was observed. The content of ROS in Ang II-stimulated VSMCs presented the same results. Following Cycl treatment, the high levels of proliferation and migration in Ang II-treated VSMCs were notably remedied while the apoptosis and ROS concentration were further increased. Moreover, Cycl downregulated SHH, Smo, Gli and CUL3 expression. Above-mentioned changes caused by Ang II were reversed following SAG addition. Indeed, SAG treatment combined with restoration of CUL3 expression inhibited proliferation, migration, apoptosis and ROS level in Ang II-stimulated VSMCs. In vivo, SAG aggravated the histopathological changes of the aorta and with a worse tendency after both SAG intervention and CUL3 silencing. By contrast, SAG treatment and rebound in CUL3 expression alleviated the vascular damage. Conclusions Collectively, restoration of CUL3 gene expression protected against hypertension through enhancing the effects of SHH activation in inhibition of apoptosis and oxidative stress for hypertension and alleviating the dysfunction of VSMCs.
Background Although the powerful clinical effects of radiofrequency and microwave ablation have been established, such ablation is associated with several limitations, including a small ablation size, a long ablation time, the few treatment positioning, and biosafety risks. To overcome these limitations, biosafe and efficient magnetic ablation was achieved in this study by using biocompatible liquid gallium as an ablation medium and a contrast medium for imaging. Results Magnetic fields with a frequency ( f ) lower than 200 kHz and an amplitude ( H ) × f value lower than 5.0 × 10 ⁹ Am ⁻¹ s ⁻¹ were generated using the proposed method. These fields could generate an ablation size of 3 cm in rat liver lobes under a temperature of approximately 300 °C and a time of 20 s. The results of this study indicate that biomedical gallium can be used as a contrast medium for the positioning of gallium injections and the evaluation of ablated tissue around a target site. Liquid gallium can be used as an ablation medium and imaging contrast medium because of its stable retention in normal tissue for at least 3 days. Besides, the high anticancer potential of gallium ions was inferred from the self-degradation of 100 µL of liquid gallium after around 21 days of immersion in acidic solutions. Conclusions The rapid wireless ablation of large or multiple lesions was achieved through the simple multi-injection of liquid gallium. This approach can replace the currently favoured procedure involving the use of multiple ablation probes, which is associated with limited benefits and several side effects. Methods Magnetic ablation was confirmed to be highly efficient by the consistent results obtained in the simulation and in vitro tests of gallium and iron oxide as well as the electromagnetic specifics and thermotherapy performance comparison detailed in this study Ultrasound imaging, X-ray imaging, and magnetic resonance imaging were found to be compatible with the proposed magnetic ablation method. Self-degradation analysis was conducted by mixing liquid gallium in acidic solutions with a pH of approximately 5–7 (to imitate a tumour-containing microenvironment). X-ray diffraction was used to identify the gallium oxides produced by degraded gallium ions.
Background Near-infrared (NIR) autofluorescence detection is an effective method for identifying parathyroid glands (PGs) in thyroidectomy or parathyroidectomy. Fiber optical probes provide quantitative autofluorescence measurements for PG detection owing to its high sensitivity and high excitation light cut-off efficiency at a fixed detection distance. However, an optical fiber probe lacks the imaging capability and cannot map the autofluorescence distribution on top of normal tissue background. Therefore, there is a need for intraoperative mapping of PGs with high sensitivity and imaging resolution. Methods We have developed a fluorescence scanning and projection (FSP) system that combines a scanning probe and a co-axial projector for intraoperative localization and in situ display of PGs. Some of the key performance characteristics, including spatial resolution and sensitivity for detection, spatial resolution for imaging, dynamic time latency, and PG localization capability, are characterized and verified by benchtop experiments. Clinical utility of the system is simulated by a fluorescence-guided PG localization surgery on a tissue-simulating phantom and validated in an ex vivo experiment. Results The system is able to detect indocyanine green (ICG) solution of 5 pM at a high signal-to-noise ratio (SNR). Additionally, it has a maximal projection error of 0.92 mm, an averaged projection error of 0.5 ± 0.23 mm, and an imaging resolution of 748 μm at a working distance ranging from 35 to 55 cm. The dynamic testing yields a short latency of 153 ± 54 ms, allowing for intraoperative scanning on target tissue during a surgical intervention. The simulated fluorescence-guided PG localization surgery has validated the system’s capability to locate PG phantom with operating room ambient light interference. The simulation experiment on the PG phantom yields a position detection bias of 0.36 ± 0.17 mm, and an area intersection over unit (IoU) of 76.6% ± 6.4%. Fluorescence intensity attenuates exponentially with the thickness of covered tissue over the PG phantom, indicating the need to remove surrounding tissue in order to reveal the weak autofluorescence signal from PGs. The ex vivo experiment demonstrates the technical feasibility of the FSP system for intraoperative PG localization with accuracy. Conclusion We have developed a novel probe-based imaging and navigation system with high sensitivity for fluorescence detection, capability for fluorescence image reconstruction, multimodal image fusion and in situ PG display function. Our studies have demonstrated its clinical potential for intraoperative localization and in situ display of PGs in thyroidectomy or parathyroidectomy.
Despite numerous clinical trials and pre-clinical developments, the diagnosis of cracked tooth, especially in the early stages, remains a challenge. Cracked tooth syndrome is often accompanied by dramatic painful responses from occlusion and temperature stimulation, which has become one of the leading causes for tooth loss in adults. Current clinical diagnostical approaches for cracked tooth have been widely investigated based on X-rays, optical light, ultrasound wave, etc. Advances in artificial intelligence (AI) development have unlocked the possibility of detecting the crack in a more intellectual and automotive way. This may lead to the possibility of further enhancement of the diagnostic accuracy for cracked tooth disease. In this review, various medical imaging technologies for diagnosing cracked tooth are overviewed. In particular, the imaging modality, effect and the advantages of each diagnostic technique are discussed. What’s more, AI-based crack detection and classification methods, especially the convolutional neural network (CNN)-based algorithms, including image classification (AlexNet), object detection (YOLO, Faster-RCNN), semantic segmentation (U-Net, Segnet) are comprehensively reviewed. Finally, the future perspectives and challenges in the diagnosis of the cracked tooth are lighted.
Introduction Low- to high-energy impact trauma may cause from small fissures up to extended bone losses, which can be classified as closed or opened injuries (when they are visible at a naked eye). Objective The aim of this study was to investigate the feasibility of clinical diagnosis of bone trauma through medical infrared thermography, in a hospital emergency room. Methods Forty-five patients with suspected diagnosis of bone fracture were evaluated by means of medical infrared images, and the data correlated with the gold standard radiographic images, in the anteroposterior, lateral, and oblique views, at the orthopedic emergency department. The control group consisted of thermal images of the contralateral reference limb of the volunteers themselves. Data were acquired with a medical grade infrared camera in the regions of interest (ROIs) of leg, hand, forearm, clavicle, foot, and ankle. Results In all patients evaluated with a diagnosis of bone fracture, the mean temperature of the affected limb showed a positive difference greater than 0.9 °C (towards the contralateral), indicating the exact location of the bone trauma according, while the areas diagnosed with reduced blood supply, showed a mean temperature with a negative variation. Conclusion Clinical evaluation using infrared imaging indicates a high applicability potential as a tool to support quick diagnosis of bone fractures in patients with acute orthopedic trauma in an emergency medical setting. The thermal results showed important physiological data related to vascularization of the bone fracture and areas adjacent to the trauma well correlated to radiographic examinations.
Load–deformation curves. During the dynamic cyclic loading test, InterTan nail (Group B) provided increased stability compared with FNS (Group A) and three cannulated screws (Group C). Three cannulated screws provided less stability than FNS and InterTan nail
Deformation of holes in the distal-to-proximal direction. A FNS hole, B InterTan nail hole; C cannulated screw holes. No significant deformation of the InterTan nail hole was seen, indicating that InterTan nail had highest stability. Slight extrusion was visible at the distal and proximal direction of the FNS holes. All three cannulated screw holes showed deformation to varying degrees, with the most pronounced deformation below the calcar femorale
A Osteotomy was performed with an oscillating saw of 0.9 mm thickness under the guidance of the self-made 3D printed osteotomy mold. B Creating Pauwels type III femoral neck fracture models with an angle of 20° between the fracture line and the axis of the femoral shaft
Fixation of Pauwels type III femoral neck fractures with three internal fixation implants. A Fixation with FNS, B, C anteroposterior and lateral fluoroscopic views after FNS fixation, D fixation with InterTan nail, E, F anteroposterior and lateral fluoroscopic views InterTan nail fixation, G fixation with three cannulated screws, H, I anteroposterior and lateral fluoroscopic views after fixation with three cannulated screws
Biomechanical test setups. A Torsion test, the femurs were placed in an inclined fashion, torque around the central axis of the femoral neck was applied. B A-P bending test, the femur was placed horizontally, vertical compression force was applied to the front of the femoral head. C Axial compression test, femurs were positioned vertically in 10° adduction
Background: There are a variety of internal fixation methods for unstable femoral neck fractures (FNFs), but the best method is still unclear. Femoral neck system (FNS) is a dynamic angular stabilization system with cross screws, and is a new internal fixation implant designed for minimally invasive fixation of FNFs. In this study, we conducted a biomechanical comparison of FNS, InterTan nail and three cannulated screws for the treatment of Pauwels III FNFs and investigate the biomechanical properties of FNS. Methods: A total of 18 left artificial femurs were selected and randomly divide into Group A (fixation with FNS), Group B (fixation with InterTan nail) and Group C (fixation with three cannulated screws), with 6 specimens in each group. After creating Pauwels type III FNF models, the specimens in each were tested with non-destructive quasi-static tests, including torsion, A-P bending and axial compression tests. The average slope of the linear load-deformation curve obtained from quasi-static tests defines the initial torsional stiffness, A-P bending stiffness, and axial compression stiffness. After cyclic loading test was applied, the overall deformation of models and local deformation of implant holes in each group were assessed. The overall deformation was estimated as the displacement recorded by the software of the mechanical testing apparatus. Local deformation was defined as interfragmental displacement. Data were analyzed by one-way analysis of variance (ANOVA) followed by Bonferroni post hoc test using the SPSS software (version 24.0, IBM, New York, NY, USA). Correlation analysis was performed using Pearson's correlation analysis. Results: Group B exhibited significantly higher axial stiffness and A-P bending stiffness than the other two groups (P < 0.01), while Group A had significantly higher axial stiffness and A-P bending stiffness than Group C (P < 0.01). Groups A and B exhibited significantly higher torsional stiffness than Group C (P < 0.01), no statistical significance was observed between Groups A and B (P > 0.05). Group B exhibited significantly lower overall and local deformations than the other two groups (P < 0.01), while Group A had significantly lower overall and local deformations than Group C (P < 0.01). Correlation analysis revealed positive correlation between axial stiffness and A-P bending stiffness (r = 0.925, P < 0.01), torsional stiffness (r = 0.727, P < 0.01), between torsional stiffness and A-P bending stiffness; negative correlation between overall, local deformations and axial stiffness (r = - 0.889, - 0.901, respectively, both P < 0.01), and positive correlation between the two deformations (r = - 0.978, P < 0.01). Conclusion: For fixation of unstable FNFs, InterTan nail showed the highest axial stiffness and A-P bending stiffness, followed by FNS, and then three cannulated screws. Torsional stiffness of FNS was comparable to that of the InterTan nail. FNS, as a novel minimally invasive implant, can create good mechanical environment for the healing of unstable FNFs. Clinical studies are needed to confirm the potential advantages of FNS observed in this biomechanical study.
Learning process of the non-expert annotators. The figure shows the speed of the annotator in seconds per frame (SPF) over the annotation experience measured by the total number of annotated videos by that point for both our tool and CVAT
Effect of AI performance on annotation speed. Plotted are the speed of the annotators in seconds per frame over the AI performance given by its F1-score on a video-by-video basis, where the AI used for prediction is the same for each video. Every point is computed as the average over all annotators
Annotation framework for fast domain expert labeling supported by an automated AI prelabeling
Video Review UI. The figure shows the list of freeze frames, the corresponding child frames, and annotations within the image on the right side. In the bottom part of the view, the user can insert comments, open reports, delete classes, and see all individual classes. The diseased tissue is delineated via bounding boxes
Image annotation UI. The figure shows a list of all available frames on the left with labeling functionality for a specific annotation and the whole image. The image to be annotated is displayed on the right. The diseased tissue is delineated via bounding boxes
Background Machine learning, especially deep learning, is becoming more and more relevant in research and development in the medical domain. For all the supervised deep learning applications, data is the most critical factor in securing successful implementation and sustaining the progress of the machine learning model. Especially gastroenterological data, which often involves endoscopic videos, are cumbersome to annotate. Domain experts are needed to interpret and annotate the videos. To support those domain experts, we generated a framework. With this framework, instead of annotating every frame in the video sequence, experts are just performing key annotations at the beginning and the end of sequences with pathologies, e.g., visible polyps. Subsequently, non-expert annotators supported by machine learning add the missing annotations for the frames in-between. Methods In our framework, an expert reviews the video and annotates a few video frames to verify the object’s annotations for the non-expert. In a second step, a non-expert has visual confirmation of the given object and can annotate all following and preceding frames with AI assistance. After the expert has finished, relevant frames will be selected and passed on to an AI model. This information allows the AI model to detect and mark the desired object on all following and preceding frames with an annotation. Therefore, the non-expert can adjust and modify the AI predictions and export the results, which can then be used to train the AI model. Results Using this framework, we were able to reduce workload of domain experts on average by a factor of 20 on our data. This is primarily due to the structure of the framework, which is designed to minimize the workload of the domain expert. Pairing this framework with a state-of-the-art semi-automated AI model enhances the annotation speed further. Through a prospective study with 10 participants, we show that semi-automated annotation using our tool doubles the annotation speed of non-expert annotators compared to a well-known state-of-the-art annotation tool. Conclusion In summary, we introduce a framework for fast expert annotation for gastroenterologists, which reduces the workload of the domain expert considerably while maintaining a very high annotation quality. The framework incorporates a semi-automated annotation system utilizing trained object detection models. The software and framework are open-source.
Daytime average values of the six HRV indices: before (blue diamond) and after (green circle) RDN. The health threshold was defined according to previous analysis [15]
Corresponding Spearman rank-order correlation (ρ) coefficients of a. AE vs. HF and b EoE vs. LF of all 48 RR daytime interval series, including before and after RDN
a Inverted U shape of the EoE vs. AE plot from the previous study [22]. The diamond, circle, and triangle symbols are from 15 CHF, 18 healthy, and 53 AF subjects, respectively. The dashed curve is a quadratic fitting in each plot. b EoE vs. AE of all 48 1-h RR interval series before (blue diamonds) and after (green circles) RDN from the five subjects in this study
a Three RR interval series {xi} with the same length of 70 data points. b Shannon entropy sequences {yj⁽⁵⁾} of the three heart rate series for EoE and AE analyses at τ = 5 as an example of short data analysis
Background The current method to evaluate the autonomic balance after renal denervation (RDN) relies on heart rate variability (HRV). However, parameters of HRV were not always predictive of response to RDN. Therefore, the complexity and disorder of heart rhythm, measured by entropy of entropy (EoE) and average entropy (AE), have been used to analyze autonomic dysfunction. This study evaluated the dynamic changes in autonomic status after RDN via EoE and AE analysis. Methods Five patients were prospectively enrolled in the Global SYMPLICITY Registry from 2020 to 2021. 24-h Holter and ambulatory blood pressure monitoring (ABPM) was performed at baseline and 3 months after RDN procedures. The autonomic status was analyzed using the entropy-based AE and EoE analysis and the conventional HRV-based low frequency (LF), high frequency (HF), and LF/HF. Results After RDN, the ABPM of all patients showed a significant reduction in blood pressure (BP) and heart rate. Only AE and HF values of all patients had consistent changes after RDN (p < 0.05). The spearman rank-order correlation coefficient of AE vs. HF was 0.86, but AE had a lower coefficient of variation than HF. Conclusions Monitoring the AE and EoE analysis could be an alternative to interpreting autonomic status. In addition, a relative change of autonomic tone, especially an increasing parasympathetic activity, could restore autonomic balance after RDN.
Background The objective is to clarify the effect of alveolar cleft bone graft on maxillofacial biomechanical stabilities, the key areas when bone grafting and in which should be supplemented with bone graft once bone resorption occurred in UCCLP (unilateral complete cleft lip and palate). Methods Maxillofacial CAD (computer aided design) models of non-bone graft and full maxilla cleft, full alveolar cleft bone graft, bone graft in other sites of the alveolar cleft were acquired by processing the UCCLP maxillofacial CT data in three-dimensional modeling software. The maxillofacial bone EQV (equivalent) stresses and bone suture EQV strains under occlusal states were obtained in the finite element analysis software. Results Under corresponding occlusal states, the EQV stresses of maxilla, pterygoid process of sphenoid bone on the corresponding side and anterior alveolar arch on the non-cleft side were higher than other maxillofacial bones, the EQV strains of nasomaxillary, zygomaticomaxillary and pterygomaxillary suture on the corresponding side were higher than other maxillofacial bone sutures. The mean EQV strains of nasal raphe, the maximum EQV stresses of posterior alveolar arch on the non-cleft side, the mean and maximum EQV strains of nasomaxillary suture on the non-cleft side in full alveolar cleft bone graft model were all significantly lower than those in non-bone graft model. The mean EQV stresses of bilateral anterior alveolar arches, the maximum EQV stresses of maxilla and its alveolar arch on the cleft side in the model with bone graft in lower 1/3 of the alveolar cleft were significantly higher than those in full alveolar cleft bone graft model. Conclusions For UCCLP, bilateral maxillae, pterygoid processes of sphenoid bones and bilateral nasomaxillary, zygomaticomaxillary, pterygomaxillary sutures, anterior alveolar arch on the non-cleft side are the main occlusal load-bearing structures before and after alveolar cleft bone graft. Alveolar cleft bone graft mainly affects biomechanical stabilities of nasal raphe and posterior alveolar arch, nasomaxillary suture on the non-cleft side. The areas near nasal floor and in the middle of the alveolar cleft are the key sites when bone grafting, and should be supplemented with bone graft when the bone resorbed in these areas.
Background Integrin, beta-like 1 (ITGBL1) is involved in a variety of human malignancies. However, the information on the involvement of ITGBL1 in gastric carcinoma (GC) is limited. Hence, this study aimed further to explore the functions and mechanisms of ITGBL1 in GC. Methods First, multiple bioinformatics databases, including Oncomine, Tumor Immune Estimation Resource, UALCAN, and Kaplan–Meier Plotter, were used to predict the expression level and prognostic value of ITGBL1, as well as its association with immune infiltration and epithelial–mesenchymal transition (EMT) in GC. Quantitative reverse transcription–polymerase chain reaction and immunohistochemical analysis were used to detect the expression of ITGBL1 in both GC tissues and cells. Then, targeted silencing of ITGBL1 in GC cells was further used to examine the biological functions of ITGBL1. Results These databases revealed that ITGBL1 was overexpressed and affected the overall survival in GC. Besides, the expression of ITGBL1 positively correlated with immune-infiltrating cells and EMT-related markers. Subsequently, molecular biology experiments verified these predictions. In GC tissues and cells, ITGBL1 was notably overexpressed. Loss-of-function studies showed that the knockdown of ITGBL1 significantly suppressed migration and invasion but promoted apoptosis in MGC803 GC cells. Furthermore, the inhibition of ITGBL1 resulted in remarkably increased protein expression levels of cadherin 1, while the expression of Vimentin, Snail, and transforming growth factor-β1 was downregulated, indicating the initiation and progression of GC caused by ITGBL1 partly via inducing EMT. Conclusions To sum up, the findings indicated that ITGBL1 acted as a valuable oncogenic factor in GC.
Significant findings of BPV indices at supine rest in fallers and non-fallers
Significant findings of HRV and BPV indices at standing in fallers and non-fallers
Relationship between a CV-DBPV and DASS-21 anxiety score, b CV-DBPV and DASS-21 stress score, and c CV-SBPV and DASS-21 stress score during supine in both group of fallers and non-fallers
Relationship between a CV-SBPV and DASS-21 anxiety score, and b CV-DBPV and DASS-21 anxiety score during standing in both group of fallers and non-fallers
Background Falls among older adults have become a global concern. While previous studies have established associations between autonomic function indicator; heart rate variability (HRV) and blood pressure variability (BPV) with fall recurrence, as well as physical inactivity and psychological disorders as risk factors for falls, the influence of physical activity and psychological status on autonomic dysfunction observed among older fallers has not been adequately investigated. The aim of this study was to evaluate the relationship between psychological disorder and physical performance on the autonomic nervous system (ANS) in older fallers. We hypothesised that older fallers have poorer autonomic function, greater dependency on others and were associated with psychological disorders. Furthermore, we hypothesised that both physical performance and psychological status can contribute to the worsening of the autonomic function among the elderly. Methods In this cross-sectional survey, adults aged ≥ 60 years were recruited. Continuous non-invasive BP was monitored over 5 min of supine and 3 min of standing. Psychological status was assessed in terms of depression, anxiety, stress, and concern about falling, while functional status was measured using time-up-and-go, functional reach, handgrip and Lawton’s Instrumental Activities of Daily Life (IADL) scale. Results A total of 62 participants were recruited consisting of 37 fallers and 25 non-fallers. Multivariate analysis revealed that Lawton IADL was independently associated with systolic blood pressure variability (SBPV) and diastolic blood pressure variability (DBPV) during both supine (SBPV: r² = 0.080, p = 0.025; DBPV: r² = 0.064, p = 0.046) and standing (SBPV: r² = 0.112, p = 0.008; DBPV: r² = 0.105, p = 0.011), while anxiety score was independently associated with SBPV and DBPV during standing (SBPV: r² = 0.112, p = 0.009; DBPV: r² = 0.105, p = 0.011) as compared to the other parameters. Conclusion Our findings suggest that fallers had poorer ANS, greater dependence in IADLs, and were more anxious. IADL dependency and anxiety were the most predictive of autonomic dysfunction, and can be used in practice to identify poor autonomic function for the prevention of falls and cardiovascular diseases among older adults.
Background: Current ankle prostheses for people with unilateral transtibial amputation (TTA) or transfemoral amputation (TFA) are unable to mimic able-bodied performance during daily activities. A new mechanical ankle-foot prosthesis was developed to further optimise the gait of people with a lower-limb amputation. This study aimed to evaluate the Talaris Demonstrator (TD) during daily activities by means of performance-related, physiological and subjective outcome measures. Materials and methods: Forty-two participants completed a protocol assessing performance and functional mobility with their current prosthesis and the TD. The protocol comprised the L-test, 2 min of stair climbing, 2 min of inclined treadmill walking, 6 min of treadmill walking at 3 different speeds in consecutive blocks of 2 min, and a 3-m Backward Walk test (3mBWT). Heart rate was measured during each task, and oxygen uptake was collected during all tasks except for the L-test and 3mBWT. Time of execution was recorded on the L-test and 3mBWT, and the rate of perceived exertion (score = 6-20), fatigue and comfort (score = 0-100) were assessed after each task. Paired sample t-tests and Wilcoxon Signed-rank tests were performed to compare outcomes between prosthetic devices. Benjamini-Hochberg corrections were applied to control for multiple comparisons with a level of significance set at α = 0.05. Results: Subjects with a TTA (N = 28) were faster with their current prosthesis compared to the TD on the L-test and 3mBWT (p = 0.005). In participants with a TFA (N = 14), we observed a tendency towards a higher heart rate during the L-test and towards increased comfort during inclined walking, with the TD compared to the participants' current prosthetic device (0.05 < p < 0.10). Further, no significant results were observed. Conclusion: The Talaris Demonstrator is a novel state-of-the-art passive ankle-foot prosthesis for both people with a TTA and TFA. Subjective measures indicate the added value of this device, while overall task performance and intensity of effort do not differ between the Talaris Demonstrator and the current prosthesis. Further investigations unravelling both acute and more prolonged adaptations will be conducted to evaluate the TD more thoroughly.
Background Increasing attention has been paid to the potential relationship between gut and lung. The bacterial dysbiosis in respiratory tract and intestinal tract is related to inflammatory response and the progress of lung diseases, and the pulmonary diseases could be improved by regulating the intestinal microbiome. This study aims to generate the knowledge map to identify major the research hotspots and frontier areas in the field of gut–lung axis. Materials and methods Publications related to the gut–lung axis from 2011 to 2021 were identified from the Web of Science Core Collection. CiteSpace 5.7.R2 software was used to analyze the publication years, journals, countries, institutions, and authors. Reference co-citation network has been plotted, and the keywords were used to analyze the research hotspots and trends. Results A total of 3315 publications were retrieved and the number of publications per year increased over time. Our results showed that Plos One (91 articles) was the most active journal and The United States (1035 articles) published the most articles. We also observed the leading institution was the University of Michigan (48 articles) and Huffnagle Gary B, Dickson Robert P and Hansbro Philip M, who have made outstanding contributions in this field. Conclusion The Inflammation, Infection and Disease were the hotspots, and the regulation of intestinal flora to improve the efficacy of immunotherapy in lung cancer was the research frontier. The research has implications for researchers engaged in gut–lung axis and its associated fields.
Background Glioblastoma (GBM) is the most malignant grade of glioma. Highly aggressive characteristics of GBM and poor prognosis cause GBM-related deaths. The potential prognostic biomarkers remain to be demonstrated. This research builds up predictive gene targets of expression alterations in GBM utilizing bioinformatics analysis. Methods and results The microarray datasets (GSE15824 and GSE16011) associated with GBM were obtained from Gene Expression Omnibus (GEO) database to identify the differentially expressed genes (DEGs) between GBM and non-tumor tissues. In total, 719 DEGs were obtained and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for function enrichment analysis. Furthermore, we constructed protein–protein Interaction (PPI) network among DEGs utilizing Search Tool for the Retrieval of Interacting Genes (STRING) online tool and Cytoscape software. The DEGs of degree > 10 was selected as hub genes, including 73 upregulated genes and 21 downregulated genes. Moreover, MCODE application in Cytoscape software was employed to identify three key modules involved in GBM development and prognosis. Additionally, we used the Gene expression profiling and interactive analyses (GEPIA) online tool to further confirm four genes involving in poor prognosis of GBM patients, including interferon-gamma-inducible protein 30 (IFI30), major histocompatibility complex class II-DM alpha (HLA-DMA), Prolyl 4-hydroxylase beta polypeptide (P4HB) and reticulocalbin-1 (RCN1). Furthermore, the correlation analysis indicated that the expression of IFI30, an acknowledged biomarker in glioma, was positively correlated with HLA-DMA, P4HB and RCN1. RCN1 expression was positively correlated with P4HB and HLA-DMA. Moreover, qRT-PCR and immunohistochemistry analysis further validated the upregulation of four prognostic markers in GBM tissues. Conclusions Analysis of multiple datasets combined with global network information and experimental verification presents a successful approach to uncover the risk hub genes and prognostic markers of GBM. Our study identified four risk- and prognostic-related gene signatures, including IFI30, HLA-DMA, P4HB and RCN1. This gene sets contribute a new perspective to improve the diagnostic, prognostic, and therapeutic outcomes of GBM.
Background Reflexive responses to head–neck perturbations affect the injury risk in many different situations ranging from sports-related impact to car accident scenarios. Although several experiments have been conducted to investigate these head–neck responses to various perturbations, it is still unclear why and how individuals react differently and what the implications of these different responses across subjects on the potential injuries might be. Therefore, we see a need for both experimental data and biophysically valid computational Human Body Models with bio-inspired muscle control strategies to understand individual reflex responses better. Methods To address this issue, we conducted perturbation experiments of the head–neck complex and used this data to examine control strategies in a simulation model. In the experiments, which we call ’falling heads’ experiments, volunteers were placed in a supine and a prone position on a table with an additional trapdoor supporting the head. This trapdoor was suddenly released, leading to a free-fall movement of the head until reflexive responses of muscles stopped the downwards movement. Results We analysed the kinematic, neuronal and dynamic responses for all individuals and show their differences for separate age and sex groups. We show that these results can be used to validate two simple reflex controllers which are able to predict human biophysical movement and modulate the response necessary to represent a large variability of participants. Conclusions We present characteristic parameters such as joint stiffness, peak accelerations and latency times. Based on this data, we show that there is a large difference in the individual reflexive responses between participants. Furthermore, we show that the perturbation direction (supine vs. prone) significantly influences the measured kinematic quantities. Finally, ’falling heads’ experiments data are provided open-source to be used as a benchmark test to compare different muscle control strategies and to validate existing active Human Body Models directly.
Background This study explored the feasibility of radiofrequency (RF)-based radiomics analysis techniques for the preoperative prediction of programmed cell death protein 1 (PD-1) in patients with hepatocellular carcinoma (HCC). Methods The RF-based radiomics analysis method used ultrasound multifeature maps calculated from the RF signals of HCC patients, including direct energy attenuation (DEA) feature map, skewness of spectrum difference (SSD) feature map, and noncentrality parameter S of the Rician distribution (NRD) feature map. From each of the above ultrasound maps, 345 high-throughput radiomics features were extracted. Then, the useful radiomics features were selected by the sparse representation method and input into support vector machine (SVM) classifier for PD-1 prediction. Results and conclusion Among all the RF-based prediction models and the ultrasound grayscale comparative model, the RF-based model using all of the three ultrasound feature maps had the highest prediction accuracy (ACC) and area under the curve (AUC), which were 92.5% and 94.23%, respectively. The method proposed in this paper is effective for the meaningful feature extraction of RF signals and can effectively predict PD-1 in patients with HCC.
Background Due to the steadily increasing life expectancy of the population, the need for medical aids to maintain the previous quality of life is growing. The basis for independent mobility is a functional locomotor system. The hip joint can be so badly damaged by everyday wear or accelerated by illness that reconstruction by means of endoprostheses is necessary. Results In order to ensure a high quality of life for the patient after this procedure as well as a long service life of the prosthesis, a high-quality design is required, so that many different aspects have to be taken into account when developing prostheses. Long-term medical studies show that the service life and operational safety of a hip prosthesis by best possible adaptation of the stiffness to that of the bone can be increased. The use of additive manufacturing processes enables to specifically change the stiffness of implant structures. Conclusions Reduced implant stiffness leads to an increase in stress in the surrounding bone and thus to a reduction in bone resorption. Numerical methods are used to demonstrate this fact in the hip implant developed. The safety of use is nevertheless ensured by evaluating and taking into account the stresses that occur for critical load cases. These results are a promising basis to enable longer service life of prostheses in the future.
Examples of a normal ECG, b VF and c VT signal
The development procedures for the threshold- and the intelligent ML-based SAAs
Spectrum of SH and NSH signals of the SH and NSH ECG segments
Shock advice algorithm plays a vital role in the detection of sudden cardiac arrests on electrocardiogram signals and hence, brings about survival improvement by delivering prompt defibrillation. The last decade has witnessed a surge of research efforts in racing for efficient shock advice algorithms, in this context. On one hand, it has been reported that the classification performance of traditional threshold-based methods has not complied with the American Heart Association recommendations. On the other hand, the rise of machine learning and deep learning-based counterparts is paving the new ways for the development of intelligent shock advice algorithms. In this paper, we firstly provide a comprehensive survey on the development of shock advice algorithms for rhythm analysis in automated external defibrillators. Shock advice algorithms are categorized into three groups based on the classification methods in which the detection performance is significantly improved by the use of machine learning and/or deep learning techniques instead of threshold-based approaches. Indeed, in threshold-based shock advice algorithms, a parameter is calculated as a threshold to distinguish shockable rhythms from non-shockable ones. In contrast, machine learning-based methods combine multiple parameters of conventional threshold-based approaches as a set of features to recognize sudden cardiac arrest. Noticeably, those features are possibly extracted from stand-alone ECGs, alternative signals using various decomposition techniques, or fully augmented ECG segments. Moreover, these signals can be also used directly as the input channels of deep learning-based shock advice algorithm designs. Then, we propose an advanced shock advice algorithm using a support vector machine classifier and a feature set extracted from a fully augmented ECG segment with its shockable and non-shockable signals. The relatively high detection performance of the proposed shock advice algorithm implies a potential application for the automated external defibrillator in the practical clinic environment. Finally, we outline several interesting yet challenging research problems for further investigation.
Background Using embedded sensors, instrumented walkways provide clinicians with important information regarding gait disturbances. However, because raw data are summarized into standard gait variables, there may be some salient features and patterns that are ignored. Multiple sclerosis (MS) is an inflammatory neurodegenerative disease which predominantly impacts young to middle-aged adults. People with MS may experience varying degrees of gait impairments, making it a reasonable model to test contemporary machine leaning algorithms. In this study, we employ machine learning techniques applied to raw walkway data to discern MS patients from healthy controls. We achieve this goal by constructing a range of new features which supplement standard parameters to improve machine learning model performance. Results Eleven variables from the standard gait feature set achieved the highest accuracy of 81%, precision of 95%, recall of 81%, and F1-score of 87%, using support vector machine (SVM). The inclusion of the novel features (toe direction, hull area, base of support area, foot length, foot width and foot area) increased classification accuracy by 7%, recall by 9%, and F1-score by 6%. Conclusions The use of an instrumented walkway can generate rich data that is generally unseen by clinicians and researchers. Machine learning applied to standard gait variables can discern MS patients from healthy controls with excellent accuracy. Noteworthy, classifications are made stronger by including novel gait features (toe direction, hull area, base of support area, foot length and foot area).
Background Early diagnosis and continuous monitoring are the key to emergency treatment and intensive care of patients with acute ischemic stroke (AIS). Nevertheless, there has not been a fully accepted method targeting continuous assessment of AIS in clinical. Methods Near-field coupling (NFC) sensing can obtain the conductivity related to the volume of intracranial components with advantages of non-invasiveness, strong penetrability and real-time monitoring. In this work, we built a multi-parameter monitoring system that is able to measure changes of phase and amplitude in the process of electromagnetic wave (EW) reflection and transmission. For investigating its feasibility in AIS detection, 16 rabbits were chosen to establish AIS models by bilateral common carotid artery ligation and then were enrolled for monitoring experiments. Results During the 6 h after AIS, the reflection amplitude (RA) shows a decline trend with a range of 0.69 dB and reflection phase (RP) has an increased variation of 6.48° . Meanwhile, transmission amplitude (TA) and transmission phase (TP) decrease 2.14 dB and 24.29° , respectively. The statistical analysis illustrates that before ligation, 3 h after ligation and 6 h after ligation can be effectively distinguished by the four parameters individually. When all those parameters are regarded as recognition features in back propagation (BP) network, the classification accuracy of the three different periods reaches almost 100%. Conclusion These results prove the feasibility of multi-parameter NFC sensing to assess AIS, which is promised to become an outstanding point-of-care testing method in the future.
Flowchart of the study identification and selection process. BPV blood pressure variability
Background Mental illness represents a major global burden of disease worldwide. It has been hypothesised that individuals with mental illness have greater blood pressure fluctuations that lead to increased cardiovascular risk and target organ damage. This systematic review aims to (i) investigate the association between mental illness and blood pressure variability (BPV) and (ii) describe methods of BPV measurements and analysis which may affect pattern and degree of variability. Methods Four electronic databases were searched from inception until 2020. The quality assessment was performed using STROBE criteria. Studies were included if they investigated BPV (including either frequency or time domain analysis) in individuals with mental illness (particularly anxiety/generalised anxiety disorder, depression/major depressive disorder, panic disorder and hostility) and without hypertension. Two authors independently screened titles, abstracts and full texts. A third author resolved any disagreements. Results Twelve studies met the inclusion criteria. Three studies measured short-term BPV, two measured long-term BPV and seven measured ultra-short-term BPV. All studies related to short-term BPV using ambulatory and home blood pressure monitoring found a higher BPV in individuals with depression or panic disorder. The two studies measuring long-term BPV were limited to the older population and found mixed results. Mental illness is significantly associated with an increased BPV in younger and middle-aged adults. All studies of ultra-short-term BPV using standard cardiac autonomic assessment; non-invasive continuous finger blood pressure and heart rate signals found significant association between BPV and mental illness. A mixed result related to degree of tilt during tilt assessment and between controlled and spontaneous breathing were observed in patients with psychological state. Conclusions Current review found that people with mental illness is significantly associated with an increased BPV regardless of age. Since mental illness can contribute to the deterioration of autonomic function (HRV, BPV), early therapeutic intervention in mental illness may prevent diseases associated with autonomic dysregulation and reduce the likelihood of negative cardiac outcomes. Therefore, these findings may have important implications for patients' future physical health and well-being, highlighting the need for comprehensive cardiovascular risk reduction.
PRISMA flow diagram of search strategy. A flow diagram of our study based on the preferred reporting items for systematic review and meta-analyses (PRISMA) method
Background There are a number of clinical disorders that require mandibular reconstruction (MR). Novel three-dimensional (3D) printing technology enables reconstructions to be more accurate and beneficial to the patient. However, there is currently no evidence identifying which techniques are better suited for MR, based on the type of clinical disorder the patient has. In this study, we aim to compare 3D techniques with conventional techniques to identify how best to reconstruct the mandible based on the clinical cause that necessitates the reconstructive procedure: cancerous or benign tumours, clinical disorders, infection or disease and trauma or injury. Methods PubMed, Scopus, Embase and Medline were searched to identify relevant papers that outline the clinical differences between 3D and conventional techniques in MR. Data were evaluated to provide a clear outline of suitable techniques for surgery. Results 20 of 2749 papers met inclusion criteria. These papers were grouped based on the clinical causes that required MR into four categories: malignant or benign tumour resection; mandibular trauma/injury and other clinical disorders. Conclusions The majority of researchers favoured 3D techniques in MR. However, due to a lack of standardised reporting in these studies it was not possible to determine which specific techniques were better for which clinical presentations.
Background This study aims to analyze the effects of a novel dual-bearing shoulder prosthesis and a conventional reverse shoulder prosthesis on the deltoid and rotator cuff muscle forces for four different arm motions. The dual-bearing prosthesis is a glenoid-sparing joint replacement with a moving center of rotation. It has been developed to treat rotator cuff arthropathy, providing an increased post-operative functionality. Methods A three-dimensional musculoskeletal OpenSim® model of an upper body, incorporating a natural gleno-humeral joint and a scapula-thoracic joint developed by Blana et al. (J Biomech 41: 1714-1721, 2008), was used as a reference for the natural shoulder. It was modified by integrating first a novel dual-bearing prosthesis, and second, a reverse shoulder prosthesis into the shoulder joint complex. Four different arm motions, namely abduction, scaption, internal and external rotation, were simulated using an inverse kinematics approach. For each of the three models, shoulder muscle forces and joint reaction forces were calculated with a 2 kg weight in the hand. Results In general, the maximal shoulder muscle force and joint reaction force values were in a similar range for both prosthesis models during all four motions. The maximal deltoid muscle forces in the model with the dual-bearing prosthesis were 18% lower for abduction and 3% higher for scaption compared to the natural shoulder. The maximal rotator cuff muscle forces in the model with the dual-bearing prosthesis were 36% lower for abduction and 1% higher for scaption compared to the natural shoulder. Although the maximal deltoid muscle forces in the model with the dual-bearing prosthesis in internal and external rotation were 52% and 64% higher, respectively, compared to the natural shoulder, the maximal rotator cuff muscle forces were 27% lower in both motions. Conclusion The study shows that the dual-bearing shoulder prosthesis is a feasible option for patients with rotator cuff tear and has a strong potential to be used as secondary as well as primary joint replacement. The study also demonstrates that computer simulations can help to guide the continued optimization of this particular design concept for successful clinical outcomes.
Background Patient-specific lung mechanics during mechanical ventilation (MV) can be identified from measured waveforms of fully ventilated, sedated patients. However, asynchrony due to spontaneous breathing (SB) effort can be common, altering these waveforms and reducing the accuracy of identified, model-based, and patient-specific lung mechanics. Methods Changes in patient-specific lung elastance over a pressure–volume (PV) loop, identified using hysteresis loop analysis (HLA), are used to detect the occurrence of asynchrony and identify its type and pattern. The identified HLA parameters are then combined with a nonlinear mechanics hysteresis loop model (HLM) to extract and reconstruct ventilated waveforms unaffected by asynchronous breaths. Asynchrony magnitude can then be quantified using an energy-dissipation metric, E asyn , comparing PV loop area between model-reconstructed and original, altered asynchronous breathing cycles. Performance is evaluated using both test-lung experimental data with a known ground truth and clinical data from four patients with varying levels of asynchrony. Results Root mean square errors for reconstructed PV loops are within 5% for test-lung experimental data, and 10% for over 90% of clinical data. E asyn clearly matches known asynchrony magnitude for experimental data with RMS errors < 4.1%. Clinical data performance shows 57% breaths having E asyn > 50% for Patient 1 and 13% for Patient 2. Patient 3 only presents 20% breaths with E asyn > 10%. Patient 4 has E asyn = 0 for 96% breaths showing accuracy in a case without asynchrony. Conclusions Experimental test-lung validation demonstrates the method’s reconstruction accuracy and generality in controlled scenarios. Clinical validation matches direct observations of asynchrony in incidence and quantifies magnitude, including cases without asynchrony, validating its robustness and potential efficacy as a clinical real-time asynchrony monitoring tool.
Stronger effect of tidal volume changes on CO2 elimination during high-frequency oscillatory ventilation (HFOV) compared to conventional mechanical ventilation (CV) close to normocapnia. Tidal volume is presented as relative to normocapnic tidal volume VT norm. Fitted curves are plotted with 95% functional prediction intervals
Stronger effect of tidal volume changes on oxygenation during high-frequency oscillatory ventilation (HFOV) compared to conventional mechanical ventilation (CV) close to the normocapnic PaO2 level. Tidal volume is presented as relative to normocapnic tidal volume VT norm. Fitted curves are plotted with 95% functional prediction intervals
Comparison of changes in oxygenation relative to changes in PaCO2 during high-frequency oscillatory ventilation (HFOV) and conventional mechanical ventilation (CV). Fitted curves are plotted with 95% functional prediction intervals
of corresponding actions taken during the conventional mechanical ventilation (CV) phase and the high-frequency oscillatory ventilation (HFOV) phase of the crossover study. MAP mean airway pressure
Background The role of high-frequency oscillatory ventilation (HFOV) has long been debated. Numerous studies documented its benefits, whereas several more recent studies did not prove superiority of HFOV over protective conventional mechanical ventilation (CV). One of the accepted explanations is that CV and HFOV act differently, including gas exchange. Methods To investigate a different level of coupling or decoupling between oxygenation and carbon dioxide elimination during CV and HFOV, we conducted a prospective crossover animal study in 11 healthy pigs. In each animal, we found a normocapnic tidal volume ( V T ) after the lung recruitment maneuver. Then, V T was repeatedly changed over a wide range while keeping constant the levels of PEEP during CV and mean airway pressure during HFOV. Arterial partial pressures of oxygen (P a O 2 ) and carbon dioxide (P a CO 2 ) were recorded. The same procedure was repeated for CV and HFOV in random order. Results Changes in P a CO 2 intentionally induced by adjustment of V T affected oxygenation more significantly during HFOV than during CV. Increasing V T above its normocapnic value during HFOV caused a significant improvement in oxygenation, whereas improvement in oxygenation during CV hyperventilation was limited. Any decrease in V T during HFOV caused a rapid worsening of oxygenation compared to CV. Conclusion A change in P a CO 2 induced by the manipulation of tidal volume inevitably brings with it a change in oxygenation, while this effect on oxygenation is significantly greater in HFOV compared to CV.
Echo intensity (EI) values in the three muscles and the two age groups. Significant (P < 0.05) EI differences between younger and older adults are highlighted with an *. con contraction, GM gastrocnemius medius, TA tibialis anterior, VL vastus lateralis
Correlations between muscle architecture (MA) parameters. A The correlations between pennation angle and muscle thickness in three muscles in rest and contraction and two age groups. B The correlations between fascicle length and muscle thickness in the three muscles at rest and contraction and in the two age groups. Significant correlations are highlighted with an *
Representative ultrasound scan of the m. tibialis anterior at rest for a younger participant. The muscle architecture is color-annotated as follows, light blue/grey: upper and deeper aponeurosis; red: fascicle length; blue: pennation angle; green: muscle thickness
Example ultrasound scan of the m. tibialis anterior in contraction of a younger (left) and older adult (right). The yellow rectangles demonstrate the regions of interest selected for the echo intensity analysis. The mean pixel intensity of the regions of interested are displayed on the image
Background Age-related changes in muscle properties affect daily functioning, therefore a reliable assessment of such properties is required. We examined the effects of age on reliability, muscle quality and interrelation among muscle architecture (MA) parameters of the gastrocnemius medialis (GM), tibialis anterior (TA), and vastus lateralis (VL) muscles. Methods Three raters scored ultrasound (US) scans of 12 healthy younger and older adults, on fascicle length (FL), pennation angle (PA) and muscle thickness (MT). Intra- and inter-rater reliability of MA measures in rest and contraction was assessed by intraclass correlation coefficients (ICC) and standard error of measurements (SEM, SEM%). The relationship between MA parameters was examined using Pearson correlation coefficients. Muscle quality (MQ) was examined using mean pixel intensity. Results Reliability was moderate to excellent for TA in both groups (ICCs: 0.64–0.99, SEM% = 1.6–14.8%), and for VL in the younger group (ICCs: 0.67–0.98, SEM% = 2.0–18.3%). VL reliability was poor to excellent in older adults (ICCs: 0.22–0.99, SEM% = 2.7–36.0%). For GM, ICCs were good to excellent (ICCs: 0.76–0.99) in both groups, but GM SEM% were higher in older adults (SEM% Younger = 1.5–10.7%, SEM% Older = 1.6–28.1%). Muscle quality was on average 19.0% lower in older vs. younger adults. In both groups, moderate to strong correlations were found for VL FL and MT ( r ≥ 0.54), and TA PA and MT ( r ≥ 0.72), while TA FL correlated with MT ( r ≥ 0.67) in younger adults only. Conclusions In conclusion, age- and muscle-specificities were present in the relationships between MT and PA, and MT and FL at rest. Furthermore, the reliability of MA parameters assessed with 2D panoramic US is acceptable. However, the level of reliability varies with age, muscle and MA measure. In older adults notably, the lowest reliability was observed in the VL muscle. Among the MA parameters, MT appears to be the simplest and most easily reproducible parameter in all muscles and age groups.
Background and objective Mechanical ventilation (MV) is the primary form of care for respiratory failure patients. MV settings are based on general clinical guidelines, intuition, and experience. This approach is not patient-specific and patients may thus experience suboptimal, potentially harmful MV care. This study presents the Stochastic integrated VENT (SiVENT) protocol which combines model-based approaches of the VENT protocol from previous works, with stochastic modelling to take the variation of patient respiratory elastance over time into consideration. Methods A stochastic model of Ers is integrated into the VENT protocol from previous works to develop the SiVENT protocol, to account for both intra- and inter-patient variability. A cohort of 20 virtual MV patients based on retrospective patient data are used to validate the performance of this method for volume-controlled (VC) ventilation. A performance evaluation was conducted where the SiVENT and VENT protocols were implemented in 1080 instances each to compare the two protocols and evaluate the difference in reduction of possible MV settings achieved by each. Results From an initial number of 189,000 possible MV setting combinations, the VENT protocol reduced this number to a median of 10,612, achieving a reduction of 94.4% across the cohort. With the integration of the stochastic model component, the SiVENT protocol reduced this number from 189,000 to a median of 9329, achieving a reduction of 95.1% across the cohort. The SiVENT protocol reduces the number of possible combinations provided to the user by more than 1000 combinations as compared to the VENT protocol. Conclusions Adding a stochastic model component into a model-based approach to selecting MV settings improves the ability of a decision support system to recommend patient-specific MV settings. It specifically considers inter- and intra-patient variability in respiratory elastance and eliminates potentially harmful settings based on clinically recommended pressure thresholds. Clinical input and local protocols can further reduce the number of safe setting combinations. The results for the SiVENT protocol justify further investigation of its prediction accuracy and clinical validation trials.
Gastric disease is a major health problem worldwide. Gastroscopy is the main method and the gold standard used to screen and diagnose many gastric diseases. However, several factors, such as the experience and fatigue of endoscopists, limit its performance. With recent advancements in deep learning, an increasing number of studies have used this technology to provide on-site assistance during real-time gastroscopy. This review summarizes the latest publications on deep learning applications in overcoming disease-related and nondisease-related gastroscopy challenges. The former aims to help endoscopists find lesions and characterize them when they appear in the view shed of the gastroscope. The purpose of the latter is to avoid missing lesions due to poor-quality frames, incomplete inspection coverage of gastroscopy, etc., thus improving the quality of gastroscopy. This study aims to provide technical guidance and a comprehensive perspective for physicians to understand deep learning technology in gastroscopy. Some key issues to be handled before the clinical application of deep learning technology and the future direction of disease-related and nondisease-related applications of deep learning to gastroscopy are discussed herein.
Background Surges of COVID-19 infections have led to insufficient supply of mechanical ventilators (MV), resulting in rationing of MV care. In-parallel, co-mechanical ventilation (Co-MV) of multiple patients is a potential solution. However, due to lack of testing, there is currently no means to match ventilation requirements or patients, with no guidelines to date. In this research, we have developed a model-based method for patient matching for pressure control mode MV. Methods The model-based method uses a single-compartment lung model (SCM) to simulate the resultant tidal volume of patient pairs at a set ventilation setting. If both patients meet specified safe ventilation criteria under similar ventilation settings, the actual mechanical ventilator settings for Co-MV are determined via simulation using a double-compartment lung model (DCM). This method allows clinicians to analyse Co-MV in silico, before clinical implementation. Results The proposed method demonstrates successful patient matching and MV setting in a model-based simulation as well as good discrimination to avoid mismatched patient pairs. The pairing process is based on model-based, patient-specific respiratory mechanics identified from measured data to provide useful information for guiding care. Specifically, the matching is performed via estimation of MV delivered tidal volume (mL/kg) based on patient-specific respiratory mechanics. This information can provide insights for the clinicians to evaluate the subsequent effects of Co-MV. In addition, it was also found that Co-MV patients with highly restrictive respiratory mechanics and obese patients must be performed with extra care. Conclusion This approach allows clinicians to analyse patient matching in a virtual environment without patient risk. The approach is tested in simulation, but the results justify the necessary clinical validation in human trials.
FiO2 measurements in two oxygen sensor locations: before (blue line) and after (red) the buffer tank under 6 hPa pressure
A FiO2 levels necessary to keep oxygen saturation at normal levels in individual patients using two oxygen delivery systems. B PaCO2 levels in individual patients treated with two compared devices
A PaO2 levels in individual patients under two testing conditions. B SaO2 levels in individual patients under two testing conditions. C Mean PaO2 values in all patients cohort breathing by the aid of two compared devices. D Mean SaO2 values in all patients cohort breathing by the aid of two compared devices
Regression line with predictive intervals demonstrating the dependence of FiO2 from AaDO2. Data obtained from NIV (A) and CPAP (B) devices
A The scheme of the modification of a certified CPAP device. B Device 3D model C The final metal enclosure with CPAP device on top
Background The study aims at solving the problem with the limitations of the homecare CPAP equipment such as sleep apnea devices in the treatment of COVID-19 pneumonia. By adding an advanced, rapid-to-produce oxygenation module to existing CPAP devices we allow distributing healthcare at all levels, reducing the load on intensive care units, promoting treatment in the early stages at homecare. A significant part of the COVID-19 pneumonia patients requires not only an oxygen supply but also additional air pressure. Existing home care devices are able to create precise positive airway pressure, but cannot precisely measure supplied oxygen concentration. Either uses uncertified and potentially unsafe mechanisms. Results The developed system allows using certified and widely available CPAP (constant positive airway pressure) devices to perform the critical function of delivering pressure and oxygen to airways. CPAP device is connected to the designed add-on module that can provide predefined oxygen concentration in a precise and stable manner. Clinical test results include data from 12 COVID-19 positive patients. The device has been compared against certified NIV (non-invasive) equipment under 6–20 hPa pressure and 30–70% FiO 2 . Tests have proved that the developed system can achieve the same SaO 2 ( p = 0.93) and PaO 2 ( p = 0.80) levels as NIV with clinically insignificant differences. Test results show that the designed system can substitute NIV equipment for a significant part of COVID-19 patients while leaving existing NIV devices for unstable and critical patients. The system has been designed to be mass-produced while having medically certified critical components. Conclusion The clinical testing of the new device for oxygen supplementation of patients treated using simple CPAP devices looks promising and could be used for the treatment of COVID-19 pneumonia.
Background Mental workload is a critical consideration in complex man–machine systems design. Among various mental workload detection techniques, multimodal detection techniques integrating electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals have attracted considerable attention. However, existing EEG–fNIRS-based mental workload detection methods have certain defects, such as complex signal acquisition channels and low detection accuracy, which restrict their practical application. Methods The signal acquisition configuration was optimized by analyzing the feature importance in mental workload recognition model and a more accurate and convenient EEG–fNIRS-based mental workload detection method was constructed. A classical Multi-Task Attribute Battery (MATB) task was conducted with 20 participating volunteers. Subjective scale data, 64-channel EEG data, and two-channel fNIRS data were collected. Results A higher number of EEG channels correspond to higher detection accuracy. However, there is no obvious improvement in accuracy once the number of EEG channels reaches 26, with a four-level mental workload detection accuracy of 76.25 ± 5.21%. Partial results of physiological analysis verify the results of previous studies, such as that the θ power of EEG and concentration of O2Hb in the prefrontal region increase while the concentration of HHb decreases with task difficulty. It was further observed, for the first time, that the energy of each band of EEG signals was significantly different in the occipital lobe region, and the power of β1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta_{1}$$\end{document} and β2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta_{2}$$\end{document} bands in the occipital region increased significantly with task difficulty. The changing range and the mean amplitude of O2Hb in high-difficulty tasks were significantly higher compared with those in low-difficulty tasks. Conclusions The channel configuration of EEG–fNIRS-based mental workload detection was optimized to 26 EEG channels and two frontal fNIRS channels. A four-level mental workload detection accuracy of 76.25 ± 5.21% was obtained, which is higher than previously reported results. The proposed configuration can promote the application of mental workload detection technology in military, driving, and other complex human–computer interaction systems.
Background Osteoporosis is the major cause of bone weakness and fragility in more than 10 million people in the United States. This disease causes bone fractures in the hip or spine, which result in increasing the risk of disabilities or even death. The current gold standard in osteoporosis diagnostics, X-ray, although reliable, it uses ionizing radiations that makes it unfeasible for early and continuous monitoring applications. Recently, microwave tomography (MWT) has been emerging as a biomedical imaging modality that utilizes non-ionizing electromagnetic signals to screen bones’ electrical properties. These properties are highly correlated to bones’ density, which makes MWT to be an effective and safe alternative for frequent testing in osteoporosis diagnostics. Results Both the conventional and wearable simulated systems were successful in localizing the tibia and fibula bones in the enhanced MWT images. Furthermore, structure extraction of the leg’s model from the blind MWT images had a minimal error compared to the original one (L2-norm: 15.60%). Under five sequentially incremental bone volume fraction (BVF) scenarios simulating bones’ treatment procedure, bones were detected successfully and their densities were found to be inversely proportional to the real part of the relative permittivity values. Conclusions This study paves the way towards implementing a safe and user-friendly MWT system that can be wearable to monitor bone degradation or treatment for osteoporosis cases. Methods An anatomically realistic finite-element (FE) model representing the human leg was initially generated and filled with corresponding tissues’ (skin, fat, muscles, and bones) dielectric properties. Then, numerically, the forward and inverse MWT problems were solved within the framework of the finite-element method-contrast source inversion algorithm (FEM-CSI). Furthermore, image reconstruction enhancements were investigated by utilizing prior information about different tissues as an inhomogeneous background as well as by adjusting the imaging domain and antennas locations based on the prior structural information. In addition, the utilization of a medically approved matching medium that can be used in wearable applications, namely an ultrasound gel, was suggested. Additionally, an approach based on k-means clustering was developed to extract the prior structural information from blind reconstructions. Finally, the enhanced images were used to monitor variations in BVF. Graphical Abstract
Top-cited authors
Karel Svoboda
  • Howard Hughes Medical Institute
Maged N Kamel Boulos
  • University of Lisbon
Ray B Jones
  • University of Plymouth
Steve Wheeler
  • University of Plymouth
James Geoffrey Chase
  • University of Canterbury