Pontifical Bolivarian University

Background Atrial fibrillation (AF) and heart failure (HF) are highly prevalent conditions associated with significant morbidity and symptom burden. Hypothesis This study compared the evolution over time of functional class and quality of life (QoL) in patients with HF according to the presence of AF. Methods A retrospective cohort study was conducted at an outpatient heart failure clinic in Colombia, between 2020 and 2022. Functional class (based on the New York Heart Association classification) and QoL (measured by the Minnesota Living with Heart Failure Questionnaire), were analyzed at baseline, 3 months, 6 months, and the last visit. The simultaneous impact of AF and left ventricular ejection fraction was analyzed using a generalized estimation equation model. Results Among the 440 patients (median age 74 years, 56.6% men), 41.4% with AF, and 65.2% with reduced ejection fraction (HFrEF). Over time, functional class improved in both groups, with a more significant improvement in patients without AF. Patients with AF and HFrEF were more likely to remain in worse functional classes (OR: 2.77; 95% CI: 1.37–5.62). Similar trends were observed in QoL questionnaire, with sustained improvement after 3 months. However, AF negatively affected the physical dimension in patients with HFrEF, increasing the QoL questionnaire score by up to 4%. Conclusions The presence of AF and reduced ejection fraction was associated with a lesser improvement in functional class and physical dimension of QoL questionnaire, emphasizing the importance of early detection and management of AF as part of comprehensive HF care.

Sarcoidosis is an immune-mediated systemic disease characterized by the presence of non-caseating granulomas in various parts of the body in the absence of another defined etiology. Neurologic involvement (neurosarcoidosis), which occurs in 5-10% of patients with the disease, encompasses a range of clinical and histopathological manifestations that can lead to significant morbidity and mortality. We present a case of a young man with a history of chronic sinusitis, who developed sudden headache associated with seizures. After thorough clinical and paraclinical evaluation, the diagnosis of neurosarcoidosis was made once other neurovascular, infectious, metabolic, tumor-related, and immune-mediated etiologies were ruled out. Neurosarcoidosis can present as a large dural mass due to nodular pachymeningitis, which can be clinically indistinguishable from other entities such as neoplasms and granulomatosis with polyangiitis. Isolated central nervous system involvement in this entity is rare and usually it is associated with other systemic manifestations. More aggressive management is required to treat this form of sarcoidosis presentation. Neurosarcoidosis represents a diagnostic challenge and requires ruling out more common entities such as infectious and non-infectious causes like granulomatosis with polyangiitis.

In 2021, Colombia produced 214,700 t of Hass avocado, representing 32.46% of the country’s total avocado production, with 96,358 t (44%) exported. Dehydration allows food preservation, whereas freeze-drying does not affect the avocado’s organoleptic features, except for its texture. The Hass avocado variety was freeze-dried at different temperatures (-20 °C, -80 °C) and freezing times (6, 12, 18 h). Enzymatic Activity (EA) and rehydration properties, such as Hydration Kinetics (HK) and Water Retention Capacity (WRC), were analysed as response variables. Hydration kinetics was assessed in different immersion mediums at 20 °C, 40 °C, 60 °C, and 80 °C. Analysis of variance was performed to evaluate both the WRC and the EA. The treatments achieved a maximum moisture content of 3.45 ± 0.84%. The maximum HK value was reached into 6 hours of freezing at -20 °C and -80 °C. The Vmax for EA occurred at -20 °C for 6 h. Freeze-drying at -20 °C and 18 h resulted in the lowest EA value (0.000742 mmol.s-1), compared to the blank.

The objective of this article is to identify and prioritize technologies, innovations and new businesses related to the dairy agro-industrial chain that are expected to emerge by 2035. To do so, the two-round Delphi method was used and questionnaires were applied to 27 national and international experts. A technology tree was built with Python codes and libraries, consisting of 174 topics. Additionally, 39 variables were generated for scenarios in the Good Livestock Practices BPG; Research, Development and Innovation R&D&I; Sustainable Livestock and Agroindustry groups, as well as four hypotheses and a bet scenario, with the future objectives of sustainable specialization of forage production and mass production and standardization in collection centers. This can be achieved through projects on technologies and innovations prioritized in the Delphi method, including ultrasound, pulsed combustion drying, dairy-derived medicinal products, bioethanol produced from whey, artificial intelligence and selection assisted by molecular markers, electromembrane filtration technologies, whey protein concentrates, life cycle assessment, blockchain, neural networks and smart assays, among others. The opportunity that actors in the Science, Technology and Innovation system have in the chain for the development of programs, plans, public policies and open innovation challenges in the prioritized technologies is highlighted.

Oral drug administration is the preferred route for pharmaceuticals, accounting for ~90% of the global pharmaceutical market due to its convenience and cost-effectiveness. This study provides a comprehensive scientific and technological analysis of the latest advances in oral dosage forms for colon-targeted drug delivery. Utilizing scientific and patent databases, along with a bibliometric analysis and bibliographical review, we compared the oral dosage forms (technology) with the specific application of the technology (colon delivery) using four search equations. Our findings reveal a gap in the publications and inventions associated with oral dosage forms for colon release compared to oral dosage forms for general applications. While tablets and capsules were found the most used dosage forms, other platforms such as nanoparticles, microparticles, and emulsions have been also explored. Enteric coatings are the most frequently applied excipient to prevent the early drug release in the stomach with pH-triggered systems being the predominant release mechanism. In summary, this review provides a comprehensive analysis of the last advancements and high-impact resources in the development of oral dosage forms for colon-targeted drug delivery, providing insights into the technological maturity of these approaches. Graphical Abstract

Transient bone marrow edema (BME) syndrome, also known as transient migratory osteoporosis, is a rare clinical entity characterized by severe, temporary arthralgia in the lower extremities without trauma history. It presents as focal or regional radiographic osteopenia and as a hallmark BME signal on magnetic resonance imaging (MRI). The syndrome’s idiopathic nature is underscored by its spontaneous resolution and variability in clinical presentations, with inconsistent joint involvement. A clinical case of a 57-year-old male illustrates the condition’s characteristics: Intense, non-traumatic knee pain initially located in the medial femoral condyle with typical imaging findings on MRI, resolved through analgesia and weight-bearing restriction. The patient experienced recurrence in the lateral femoral condyle, again resolving with similar management. This case emphasizes the disorder’s heterogeneity and intends to raise awareness in the medical community, where lack of knowledge and standardized nomenclature has complicated its recognition, leading to unnecessary interventions.

Background Antimicrobial resistance (AMR) poses a worldwide health threat; quick and accurate identification of AMR enhances patient outcomes and reduces inappropriate antibiotic usage. The objective of this systematic review is to evaluate the efficacy of machine learning (ML) approaches in predicting AMR in critical and high-priority pathogens (CHPP), considering antimicrobial susceptibility tests in real-world healthcare settings. Methods The search methodology encompassed the examination of several databases, such as PubMed/MEDLINE, EMBASE, Web of Science, SCOPUS, and SCIELO. An extensive electronic database search was conducted from the inception of these databases until November 2024. Results After completing the final step of the eligibility assessment, the systematic review ultimately included 21 papers. All included studies were cohort observational studies assessing 688,107 patients and 1,710,867 antimicrobial susceptibility tests. GBDT, Random Forest, and XGBoost were the top-performing ML models for predicting antibiotic resistance in CHPP infections. GBDT exhibited the highest AuROC values compared to Logistic Regression (LR), with a mean value of 0.80 (range 0.77–0.90) and 0.68 (range 0.50–0.83), respectively. Similarly, Random Forest generally showed better AuROC values compared to LR (mean value 0.75, range 0.58–0.98 versus mean value 0.71, range 0.61–0.83). However, some predictors selected by these algorithms align with those suggested by LR. Conclusions ML displays potential as a technology for predicting AMR, incorporating antimicrobial susceptibility tests in CHPP in real-world healthcare settings. However, limitations such as retrospective methodology for model development, nonstandard data processing, and lack of validation in randomized controlled trials must be considered before applying these models in clinical practice.

Our study aimed to describe the transmission dynamics and genotypic diversity of Mycobacterium tuberculosis in people deprived of liberty (PDL) in four Colombian prisons. Our cohort study included 64 PDL with bacteriologically confirmed pulmonary tuberculosis diagnosed in four Colombian prisons. The 132 isolates were genotyped using 24-mycobacterial interspersed repeated units-variable number tandem repeats (MIRUs-VNTR). A cluster was defined when ≥2 isolates from different PDL had the same genotype. Tuberculosis acquired in prison was considered when ≥2 persons were within the same cluster and had an epidemiological link. We mapped the place of residence before incarceration and within prisons. We assessed overcrowding and ventilation conditions in the prison that had clusters. We found that the most frequent genotypes were LAM (56.8%) and Haarlem (36.4%), and 45.3% of the PDL diagnosed with tuberculosis were clustered. Most PDL diagnosed in prison came from neighborhoods in Medellin with a high TB incidence. M. tuberculosis infection acquired in prison was detected in 19% of PDL, 9.4% had mixed infection, 3.1% reinfection, and 1.6% relapse. Clusters only appeared in one prison, in cell blocks with overcrowding >100%, and inadequate ventilation conditions. Prisons require the implementation of effective respiratory infection control measures to prevent M. tuberculosis transmission.

The construction industry is a major contributor to environmental pollution, with cement production only accounting for nearly 8% of global CO2 emissions. Sustainable alternatives, such as bio-bricks incorporating agricultural waste, offer a promising solution to reduce emissions. This study investigates the development and optimization of bio-bricks using lignin as reinforcement in cementitious composites. A mixture design approach was applied to determine optimal proportions of cement, lignin, and bovine excreta, enhancing mechanical properties such as compressive and flexural strength while promoting sustainability. Response Surface Methodology (RSM) was used to model the effects of mixture components, revealing that a blend of 959 g of cement, 224 g of lignin, and 314 g of bovine excreta resulted in the best performance. Compressive strength reached ~1.7 MPa, demonstrating the composition viability for eco-friendly construction. The study highlights the bio-brick’s potential to mitigate the environmental impact by reducing reliance on traditional cement while integrating renewable materials.

The field of ophthalmology relies on digital image processing techniques, such as Optical Coherence Tomography (OCT), for diagnosing retinal diseases. However, manual interpretation of OCT images is time-consuming and prone to human error. This study developed a deep learning-based model to assist in diagnosing retinal pathologies from OCT images. A modified VGG16 architecture was trained on a dataset of OCT images to classify four retinal conditions: choroidal neovascularization, diabetic macular edema, drusen, and normal. Rigorous evaluation, including cross-validation and independent testing, demonstrated the model’s ability to achieve an accuracy of 95.19% and high precision (95.29%), recall (95.19%), and F1-score (95.20%). In addition, gradient-weighted class activation mapping was employed to visualize network decisions, and a graphical user interface was developed to enhance user interaction with the diagnostic tool. The developed model can potentially improve the early detection and diagnosis of retinal diseases, ultimately enhancing patient care.

Aortic dissection in pediatrics is an extremely rare condition, which is generally related to predisposing factors such as connective tissue disorders, congenital heart disease and systemic arterial hypertension. A 3-year-old girl, with a history of bicuspid aortic valve, hypoplasia of the aortic arch and repaired aortic coarctation at one month of age. She was admitted 2 months of atypical chest pain, dysphonia, and low tone of voice. The echocardiogram and CT angiography showed an image corresponding to a Stanford A aortic dissection, with false lumen perfusion that generated a aneurysmal dilation with a saccular morphology of 53 × 40 × 70 mm dimensions. The patient was taken to surgery, exposure of the ascending aorta, aneurysmal dissection, and replacement with a 22 mm supracoronary tube were performed. We present a case of a 3-year-old pediatric patient with Stanford A aortic dissection, subacute evolution, with successful repair.

Purpose Keratoconus affects patients' quality of life. No study has assessed the multivariate determinants of quality of life using the keratoconus end points assessment questionnaire (KEPAQ). Methods This study included patients with keratoconus with no history of ocular surgery, who underwent clinical evaluation and tomographic imaging using a dual Scheimpflug/Placido device (Galilei G6). Emotional and functional quality of life was assessed using the KEPAQ. Multiple linear regression models were constructed for each KEPAQ subscale to adjust for confounding variables. Results A total of 140 surgery-naïve patients with keratoconus were included, with a median age of 39 years and a male predominance (57.1%). For the KEPAQ-E subscale, the multivariate model was significant [F(84,10) = 2.79; adjusted R ² = 0.160, P = 0.005], showing that female sex (β = −0.41) and worse corrected distance visual acuity in the better-seeing eye (β = −0.29) were associated with lower quality of life. Including the functional subscale score significantly enhanced the model's performance (adjusted R ² = 0.464, β = 0.60). For the KEPAQ-F subscale, the model was also significant [F(84,10) = 2.37; adjusted R ² = 0.127, P = 0.016], with corneal astigmatism in the better-seeing eye (β = −0.30) reducing quality of life. Adding the emotional subscale score improved the model (adjusted R ² = 0.442, β = 0.62). Conclusions Female sex, reduced vision, and corneal astigmatism negatively affect quality of life in patients with keratoconus. Nonetheless, patients' perceived impairment as measured in 1 subscale is a stronger predictor of overall quality of life than clinical and tomographic factors alone.

Artificial intelligence (AI) is transforming orthopedic research by optimizing academic workflows, improving evidence synthesis , and expanding access to advanced data analysis tools. Generative AI models such as ChatGPT and GPT-4, alongside specialized platforms such as Consensus and SciSpace, empower researchers to refine search queries, enhance literature reviews, synthesize documents, and conduct advanced statistical analyses. These technologies enable the interpretation of large datasets, saving time and boosting efficiency. For orthopedic residents, AI is particularly impactful, revolutionizing their education and fostering greater independence in research. This review explores the key applications of AI as a research assistant in orthopedics, as well as its ethical considerations and challenges.

Erasmus syndrome (ES) is a rare condition characterized by the link between crystalline silica exposure, with or without silicosis, and systemic sclerosis (SSc). Although first noted over a century ago, its underlying mechanisms remain unclear. However, it is indistinguishable from idiopathic SSc in the general population. Its clinical presentation is heterogeneous, depending on the affected systems, with notable features, including skin fibrosis, microstomia, telangiectasia, Raynaud’s phenomenon, arthralgia, and interstitial lung disease. Currently, there is no unified consensus on its treatment; however, organ-specific therapy is a reasonable approach. We report the case of a 43-year-old miner diagnosed with diffuse cutaneous SSc, where ES was diagnosed after an exhaustive history was taken, occupational exposure was characterized, differential diagnoses were excluded, and radiological and histopathological evidence of pulmonary silicosis was presented.

Del Nido cardioplegia (DNC), a blood‐and‐crystalloid solution containing high and low concentrations of potassium and calcium, respectively, is used as a single‐dose antegrade infusion to induce immediate cardiac arrest in the surgery of patients with cardiovascular diseases requiring extracorporeal circulation. Adding cardioprotective molecules may further reduce the damage‐triggered ischemia/reperfusion (I/R) injury. Angiotensin‐(1–9) (Ang‐(1–9)) and angiotensin‐(1–7) (Ang‐(1–7)), members of the counter‐regulatory renin‐angiotensin system, have shown cardioprotective effects in cardiac hypertrophy and I/R models. This study aimed to evaluate the effects of Ang‐(1–9) and Ang‐(1–7), as adjuvants of the DNC, on cardioprotection and ventricular function in isolated rat hearts subjected to I/R and in cultured neonatal rat ventricular myocytes subjected to simulated I/R (sI/R). The addition of DNC and Ang‐(1–9) and Ang‐(1–7) decreased lactic dehydrogenase (LDH) release in cultured cardiomyocytes subjected to sI/R in comparison to those cardiomyocytes subjected to sI/R and incubated with DNC alone. Moreover, hearts treated with Ang‐(1–9) during reperfusion after DNC + I/R exhibited fewer arrhythmias and required less time to reach left ventricular developed pressure stability. Overall, reperfusion with DNC and Ang‐(1–9) improves the recovery of the left ventricular function of the heart.

Magnesium’s high storage capacity, with a theoretical value of about 7.6 wt.%, makes it a viable candidate for hydrogen storage. However, slow kinetics and strong thermodynamic stability lead to a rather high desorption temperature, usually above 350 °C. It has been demonstrated that nanosizing magnesium-based materials is a successful strategy for simultaneously improving the kinetic and thermodynamic characteristics of MgH2 during hydrogen absorption and desorption. MgH2 nanoparticles were obtained by microwave assisted synthesis. To the best of our knowledge, synthesis of MgH2 nanoparticles by this method has not been reported. It was possible to produce MgH2 nanoparticles smaller than 20 nm. MgO and Mg(OH)2 were also present in the produced nanoparticles, although these compounds may enhance the processes involved in the release and absorption of hydrogen.

Cardiac tumors, whether primary (mostly benign) or secondary (metastatic), are extremely rare, and very few cases of retiniform hemangioendothelioma have been documented since its initial diagnosis in 1994. We present a 67-year-old male who presented with pericarditis, recurrent pericardial and pleural effusions. On computed axial tomography, an oval lesion located on the superior aspect of the left pulmonary pericardial recess within the transverse sinus, adjacent to the trunk of the pulmonary artery. In the operating room, after dissection and resection of the mass, the histopathological diagnosis of retiniform hemangioendothelioma was confirmed.

The article on the relationship between dental functional status and sarcopenia used computed tomography-based methodologies. While the study offers valuable insights, we provide constructive comments on certain scientific and methodological aspects that warrant further consideration, including the classification of functional dentition, the subjectivity of evaluating ill-fitting dentures, and the need for multivariable modeling and consideration of confounding factors. We also suggest acknowledging the study's limitations, including its cross-sectional nature and potential reverse causality. Addressing these concerns could strengthen the study's scientific rigor and clinical relevance.

Objectives Nailfold videocapillaroscopy (NVC) is the gold standard for diagnosing systemic sclerosis (SSc) and differentiating primary from secondary Raynaud's phenomenon. The CAPI-Score algorithm, designed for simplicity, classifies capillaroscopy scleroderma patterns (CSPs) using a limited number of capillary variables. This study aims to develop a more advanced machine learning (ML) model to improve CSP identification by integrating a broader range of statistical variables while minimising examiner-related bias. Methods A total of 1,780 capillaroscopies were randomly and blindly analysed by 3–4 trained observers. Consensus was defined as agreement among all but one observer (partial consensus) or unanimous agreement (full consensus). Capillaroscopies with at least partial consensus were used to train ML-based classification models using CatBoost software, incorporating 24 capillary architecture-related variables extracted via automated NVC analysis. Validation sets were employed to assess model performance. Results Of the 1,490 capillaroscopies classified with consensus, 515 achieved full consensus. The model, evaluated on partial and full consensus datasets, achieved 0.912, 0.812, and 0.746 accuracy for distinguishing SSc from non-SSc, among SSc patterns, and between normal and non-specific patterns, respectively. When evaluated on full consensus only, accuracy improved to 0.910, 0.925, and 0.933. CAPI-Detect outperformed CAPI-Score, revealing novel capillary variables critical to ML-based classification. Conclusions CAPI-Detect, an ML-based model, provides an unbiased, quantitative analysis of capillary structure, shape, size, and density, significantly improving capillaroscopic pattern identification.

Background Human toxocariasis is a helminthic zoonosis caused by infection of Toxocara canis or T. cati. Humans can be infected by through ingestion of embryonated eggs from contaminated water, food or soil. Diagnosis is challenging, immunodiagnosis tests are commonly implemented with major pitfalls in the cross-reactivity with other pathogens, particularly in endemic areas. Methods With the aim of identify species-specific genes encoding for highly expressed antigenic proteins, a list of parasites that may infect humans and that might present similar clinical symptoms to T. canis infections was built. Only organisms whose genomes were completely sequenced and the proteome predicted were included. First, orthologous proteins were detected and the subcellular localization of T. canis proteins was predicted. In order to identify differentially expressed genes encoding proteins in larvae L3, pair-wise comparisons among transcriptomes from body parts and genders were performed. Finally, all secreted proteins classified as species-specific of T. canis , whose genes were upregulated in larvae L3 were included in an antigenic prediction. Results Twenty-eight parasites were included in the analyses, proteins of T. canis were clustered in 11,399 groups, however, 279 were species-specific groups which represent 816 proteins. Three hundred and twenty-two proteins were predicted to be secreted and upregulated in larvae L3, however, after filtering these proteins by their orthology inference, only three proteins met all the features included in this study (species-specific, upregulated, secreted, and antigenic potential). To conclude, our strategy in the study is a rational approach for discovering antigenic proteins to be used in diagnosis.