Lanzhou University Second Hospital

This study aims to comprehensively assess the clinical performance of the Versius robotic platform in the context of robot-assisted radical prostatectomy (RARP), focusing on its safety profile, practicality, and postoperative functional recovery, in order to support its integration into urologic cancer management. An extensive literature search was performed using databases including PubMed, Embase, Web of Science, the Cochrane Library, and ClinicalTrials.gov, covering publications up to December 2024. The eligible studies were those reporting on perioperative metrics or functional outcomes associated with Versius-assisted RARP. Data extraction and synthesis were carried out under a single-arm meta-analytic framework. The aggregated measures such as operative duration, intraoperative blood loss, length of hospitalization, complication frequency, positive surgical margin (PSM) rate, and continence outcomes were calculated using Stata 18 SE. The study heterogeneity was quantified via the I² statistic and sensitivity tests were performed to explore heterogeneity sources. A total of four studies comprising 145 patients were included. The pooled average duration of surgery was 190.63 min, with a mean blood loss of 320.35 mL. The rate of high-grade complications (Clavien–Dindo > II) was 7%, while the PSM rate stood at 32%. Continence recovery rates at 1, 2 and 3 months post-surgery were 43%, 65%, and 73%, respectively. Considerable inter-study heterogeneity was identified, possibly influenced by differences in surgical proficiency and Versius system configurations. The sensitivity analysis highlighted operative console time as a stable parameter, with reduced heterogeneity after removal of an outlier study. The study demonstrates that the Versius robotic platform achieves comparable oncological and functional results to conventional prostatectomy techniques, with the added advantage of potential cost savings, positioning it as a viable surgical option. Its modular structure and ergonomic enhancements present distinct benefits, although procedural variability persists. Additional high-quality, multi-institutional prospective studies are necessary to substantiate these preliminary findings and inform standardized surgical practices.

Background The aim of this study was to elucidate the molecular abnormalities in a four-generation Chinese family affected by congenital fibrinogen disorder (CFD). Case presentation The proband was a 5-year-old Chinese boy with CFD. Routine clotting tests revealed decreased plasma fibrinogen concentration in the proband and in his father and sister. Notably, the condition presented was clinically asymptomatic. Whole exome sequencing identified a heterozygous c.1299G > A mutation in exon 8 of the FGB gene, leading to p.Trp433* (TGG > TGA). Further Sanger sequencing revealed the presence of this mutation in his great-grandmother, grandfather, father, and sister as well. Conclusion The FGB gene variant c.1299G > A (p.Trp433*) across four consecutive generations is associated with CFD.

The recovery of patients with liver cancer (LC) after laparoscopic hepatolobectomy is often affected by malnutrition, low immune function, and inflammatory responses, which may lead to an increase in postoperative complications and delayed recovery. Therefore, choosing a reasonable nutritional support plan is crucial for improving the postoperative recovery of patients. Material and Method: One hundred and fifty patients with primary LC who underwent laparoscopic hepatolobectomy were grouped: nutritional group (NG) and control group (CG). The NG implemented early enteral nutrition (EN) combined with parenteral nutrition (PN) support within 24 h postoperatively, while the CG only received early EN support. The liver function, nutritional indicators, plasma endotoxin levels, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) score, and postoperative recovery of the subjects were assessed. Result: Compared with the CG, the NG suggested visible improvement in postoperative aspartate aminotransferase (AST) and alanine aminotransferase (ALT), and a marked increase in albumin (ALB), prealbumin (PA), and total protein (TP). In addition, the plasma endotoxin levels were visibly lower, and the postoperative time to first bowel movement was visibly shortened in the NG as against the CG. There was no statistically meaningful distinction in hospital stay, time to ambulation, and time to first flatus in the subjects. The total score of the EORTC QLQ-C30 scale in the NG was significantly higher than that in the CG after intervention (P = 0.012). The overall incidence of complications in the NG was 6.7% (5/75), which was significantly lower than the 17.3% (13/75) in the CG (P = 0.042). Conclusion: Early EN combined with PN support can visibly improve the liver function and nutritional status of patients with primary LC after laparoscopic hepatolobectomy, promote postoperative immune recovery, and shorten recovery time.

The A subunit of thyrotropin receptor (TSHR) is thought to be the crucial gene mediating stimulatory autoantibodies in Graves' diease (GD), but it remains unclear what the molecular basis of this pathological antibody response is. Stimulatory TSHR autoantibodies may induce activation of multiple signalling pathways in GD, modulate chemokine exposure and further stimulate immune imbalance. In this study, we prepared TSHR 289 protein by using insect baculovirus expression, adenovirus‐expressed TSHR289 immunised mice, and obtained three mouse anti‐TSHR monoclonal antibodies (mAbs), 1A4, 7C3 and 22B1, by the hybridoma technique. Flow assay and ELISA tests tested the activity and competitive binding of the mAbs. After mAbs stimulation of human thyrocytes, RT‐qPCR and ELISA were used to detect the expression of chemokine; Western blotting detected the expression of CCL19 and the level of phosphorylation of NF‐κB. Nanogram concentrations of the IgG mAbs 1A4, 7C3 and 22B1 and their Fab induce TSHR stimulation. TRAb in the serum of GD patients competitively inhibits the binding of HRP‐conjugated mAbs to TSHR on the coated plate. Injection of micrograms of 7C3 resulted in elevated serum thyroxine and columnar and papillary hyperplasia of thyroid follicular epithelial cells. All three mAbs induced distinct expression of CCL2, CCL19 and CCL5 by activating canonical and non‐canonical NF‐κB signalling pathways in human thyrocytes. Collectively, we obtained three mouse anti‐TSHR mAbs which provide an improved approach to characterise the molecular basis of this pathological response, and confirmed that stimulating antibodies activate NF‐κB, inducing chemokines involved in the autoimmune response.

To develop and validate a deep learning model based on three-dimensional features (DL_3D) for distinguishing lung adenocarcinoma (LUAD) from tuberculoma (TBM). A total of 1160 patients were collected from three hospitals. A vision transformer network-based DL_3D model was trained, and its performance in differentiating LUAD from TBM was evaluated using validation and external test sets. The performance of the DL_3D model was compared with that of two-dimensional features (DL_2D), radiomics, and six radiologists. Diagnostic performance was assessed using the area under the receiver operating characteristic curves (AUCs) analysis. The study included 840 patients in the training set (mean age, 54.8 years [range, 19–86 years]; 514 men), 210 patients in the validation set (mean age, 54.3 years [range, 18–86 years]; 128 men), and 110 patients in the external test set (mean age, 54.7 years [range, 22–88 years]; 51 men). In both the validation and external test sets, DL_3D exhibited excellent diagnostic performance (AUCs, 0.895 and 0.913, respectively). In the test set, the DL_3D model showed better performance (AUC, 0.913; 95% CI: 0.854, 0.973) than the DL_2D (AUC, 0.804, 95% CI: 0.722, 0.886; p < 0.001), radiomics (AUC, 0.676, 95% CI: 0.574, 0.777; p < 0.001), and six radiologists (AUCs, 0.692 to 0.810; p value range < 0.001–0.035). The DL_3D model outperforms expert radiologists in distinguishing LUAD from TBM. Question Can a deep learning model perform in differentiating LUAD from TBM on non-enhanced CT images? Findings The DL_3D model demonstrated higher diagnostic performance than the DL_2D model, radiomics model, and six radiologists in differentiating LUAD and TBM. Clinical relevance The DL_3D model could accurately differentiate between LUAD and TBM, which can help clinicians make personalized treatment plans.

Purpose Stroke-associated pneumonia (SAP), a critical complication of ischemic stroke, significantly worsens outcomes. Our aim was to identify SAP risk factors and develop a machine learning (ML) model for early risk stratification. Methods This retrospective study analyzed 574 ischemic stroke patients, divided into training (75%) and testing (25%) sets. Nine ML models were trained using 10-fold cross-validation, with performance evaluated by accuracy, AUC-ROC, and F1-score. Key predictors were interpreted via SHAP analysis. An interactive web tool was developed using the optimal model. Results SAP incidence was 32.4%. LightGBM demonstrated superior predictive performance (ranking score=54) without overfitting, identifying Monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), NIHSS score, age, aggregate index of systemic inflammation (AISI), and platelet-to-lymphocyte ratio (PLR) as the top predictors. Conclusion Our findings demonstrate that machine learning models exhibit strong predictive performance for SAP, with the LightGBM algorithm outperforming other approaches. The web-based prediction tool developed from this model provides clinicians with actionable insights to support real-time clinical decision-making.

With gene editing technology and immunosuppressive drugs, kidney xenotransplantation has developed rapidly in recent years. However, as the most cutting‐edge medical personnel, there are few reports on the acceptance and awareness of kidney xenotransplantation. This study conducted an online questionnaire survey on medical staff and constructed the first predictive model for the acceptance of kidney xenotransplantation by medical staff through univariate and multivariate analysis of each variable. Their acceptance rate is 72.8%, and it was found that the willingness to donate organs, awareness of kidney xenotransplantation, and residential areas are independent factors affecting their acceptance rate. In addition, the study also found that although healthcare workers have a high acceptance and willingness to share, their awareness of kidney xenotransplantation is relatively low. This reminds us that in order to increase public acceptance of xenotransplantation, the first step is to start with promoting xenotransplantation among medical personnel.

Background The combination chemotherapy of alpha-PD-1/PD-L1 has become the standard treatment option for some cancer patients. However, studies have shown that not all patients benefit from improved survival rates, especially the use of PD-1/PD-L1 inhibitors in combination with chemotherapy for progression-free survival (PFS) in patients with gastric or gastroesophageal cancer (GC/GEJC) remains highly controversial. To address this issue, we conducted a meta-analysis of randomized controlled trials (RCTs) aimed at comparing the efficacy of PD-1/PD-L1 inhibitors combined with chemotherapy versus chemotherapy in GC/GEJC patients. Method By searching relevant databases, RCTs published up to November 2024 were collected, and the hazard ratios (HR) and 95% confidence intervals (CI) of overall survival (OS) and PFS were calculated. Meanwhile, the odds ratios (OR) and 95% CI of treatment-related adverse events (TRAEs) were evaluated. Result A total of 6842 patients were included in seven trials. In the summary analysis of OS, compared with the chemotherapy group, the PD-1/PD-L1 inhibitor combined with the chemotherapy group showed significant improvement in OS (HR = 0.80; 95% CI = 0.76–0.85; p < 0.0001) and PFS (HR = 0.86; 95% CI = 0.71–0.81; p < 0.0001). Additionally, there were significant differences in the incidence of TRAEs (OR = 1.59; 95% CI = 1.21–2.02; p = 0.0001) and grade 3–4 TRAEs (OR = 1.43; 95% CI = 1.30–1.58; p < 0.0001). Conclusion When compared to chemotherapy, the combination of PD-1/PD-L1 inhibitors with chemotherapy improves survival but with higher toxicity risks, requiring careful benefit-risk evaluation in clinical practice.

Receptor-interacting protein kinase 1 (RIPK1)-mediated necroptosis, a newly identified mode of regulated cell death, represents a significant pathogenic mechanism in multiple neurodegenerative disorders. Substantial experimental evidence indicates that RIPK1 regulates necroptotic cell death pathways in both neuronal and glial cell populations through activation of the canonical RIPK3-MLKL signaling cascade, thereby exacerbating neuroinflammatory responses and accelerating neurodegenerative progression. The pathological relevance of this molecular pathway has been extensively validated across multiple major neurodegenerative disorders, including Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS). Pharmacological interventions targeting RIPK1 or its downstream effectors—particularly RIPK3 and MLKL—have demonstrated significant efficacy in mitigating disease-associated pathological manifestations. This highlights the RIPK1 signaling axis as a promising therapeutic target for neuroprotective strategies. Consequently, thorough investigation of RIPK1-mediated necroptosis in neurodegenerative settings holds considerable translational potential. Such inquiry deepens mechanistic understanding of disease pathogenesis while accelerating the advancement of innovative therapeutic approaches with direct clinical relevance.

Machine learning drives osteoporosis detection and screening with higher clinical accuracy and accessibility than traditional osteoporosis screening tools. This review takes a step-by-step view of machine learning for osteoporosis detection, providing insights into today’s osteoporosis detection and the outlook for the future. The early diagnosis and risk detection of osteoporosis have always been crucial and challenging issues in the medical field. With the in-depth application of artificial intelligence technology, especially machine learning technology in the medical field, significant breakthroughs have been made in the application of early diagnosis and risk detection of osteoporosis. Machine learning is a multidimensional technical system that encompasses a wide variety of algorithm types. Machine learning algorithms have become relatively mature and developed over many years in medical data processing. They possess stable and accurate detection performance, laying a solid foundation for the detection and diagnosis of osteoporosis. As an essential part of the machine learning technical system, deep-learning algorithms are complex algorithm models based on artificial neural networks. Due to their robust image recognition and feature extraction capabilities, deep learning algorithms have become increasingly mature in the early diagnosis and risk assessment of osteoporosis in recent years, opening new ideas and approaches for the early and accurate diagnosis and risk detection of osteoporosis. This paper reviewed the latest research over the past decade, ranging from relatively basic and widely adopted machine learning algorithms combined with clinical data to more advanced deep learning techniques integrated with imaging data such as X-ray, CT, and MRI. By analyzing the application of algorithms at different stages, we found that these basic machine learning algorithms performed well when dealing with single structured data but encountered limitations when handling high-dimensional and unstructured imaging data. On the other hand, deep learning can significantly improve detection accuracy. It does this by automatically extracting image features, especially in image histological analysis. However, it faces challenges. These include the “black-box” problem, heavy reliance on large amounts of labeled data, and difficulties in clinical interpretability. These issues highlighted the importance of model interpretability in future machine learning research. Finally, we expect to develop a predictive model in the future that combines multimodal data (such as clinical indicators, blood biochemical indicators, imaging data, and genetic data) integrated with electronic health records and machine learning techniques. This model aims to present a skeletal health monitoring system that is highly accessible, personalized, convenient, and efficient, furthering the early detection and prevention of osteoporosis.

In recent years, a growing number of studies have disclosed the substantial role of macrophages—key immune cells—in the pathological process of intervertebral disc degeneration. Researchers have categorised macrophage phenotypes into M1 and M2 polarisation, associating these polarisations with intervertebral disc degeneration. Essentially, macrophage phenotypes can be classified as either pro‐inflammatory or anti‐inflammatory. Induced by diverse factors, these distinct polarisation states exert contrary effects on disc injury and repair. Although numerous studies focus on the polarisation of macrophages and the cytokines they secrete in relation to intervertebral disc degeneration, these studies frequently neglect the relationship between the efferocytosis of macrophages and the progression of intervertebral disc degeneration. Efferocytosis is a specialised procedure in which phagocytes, such as macrophages, engulf and eliminate apoptotic cells. This process is crucial for maintaining tissue homeostasis and resolving inflammation. By effectively clearing these dying cells, efferocytosis helps prevent the release of potentially detrimental cellular contents, thereby facilitating healing and the resolution of inflammation. Simultaneously, macrophages digest the engulfed cell debris and release various cytokines that participate in tissue self‐repair. Therefore, this article presents an overview of the molecular mechanisms connecting macrophages and their efferocytosis activity to intervertebral disc degeneration, explores new directions for the utilisation of macrophages in the treatment of intervertebral disc degeneration, and discusses the future prospects for the development of therapeutic targets.

Colonoscopy is an important method for the prevention and early detection of colorectal cancer, commonly used to detect polyps associated with colorectal cancer. However, accurate polyp segmentation still faces significant challenges: (1) polyps of the same type exhibit diversity in size, color, and texture; (2) the boundaries between polyps and the surrounding mucosa are often unclear. To address these issues, we propose a Deep Hybrid Attention Network (Deep-HybridUNet) that aims to improve the segmentation accuracy of polyps in colonoscopy images. Our method first employs a Hybrid Attention Module (HAM), which enhances segmentation performance by strengthening channel responses, extracting salient spatial features, and reinforcing boundary regions. Next, convolutional residual blocks are used in place of traditional double convolution layers to simplify the feature propagation path. Finally, by introducing a Depthwise Pooling Module (DPM), we excavate deeper information, thereby improving segmentation accuracy and detail restoration capabilities. The experimental results demonstrate that Deep-HybridUNet significantly outperforms existing mainstream methods across several key performance metrics. On the CVC-ClinicDB dataset, it tackles the dual challenges of accurately segmenting irregularly shaped polyps and mitigating artifact interference, achieving an IoU of 0.8592, accuracy of 0.9901 and F1 score of 0.9394. Notably, the model excels in cross-domain adaptability, with IoU and Dice scores improving to 0.8910 and 0.9286, respectively, on the ISIC2018 dataset. These results not only validate the effectiveness of the model architecture, but also highlight its powerful feature representation capabilities and cross-domain generalization, offering a novel technological pathway for the universality of medical image segmentation.

This study aims to investigate the interaction between common gene and 5q- chromosome karyotype mutations and the immune microenvironment in myelodysplastic syndromes (MDS), and explore the potential prognostic value of immune markers in MDS. A total of 83 MDS patients treated at the Second Hospital of Lanzhou University between January 2019 and April 2024 were enrolled in this study. Patients were divided into mutation and wild-type groups based on gene mutations and the presence of 5q- chromosomal abnormalities. Co-mutations in MDS patients were analyzed. A total of 19 fine immune parameters were measured in the samples using flow cytometry and flow cytometric bead array (CBA) technology. Changes in immune markers between the mutation and wild-type groups were observed, and the correlation between lymphocyte subsets and cytokines in the mutation group was analyzed. Significant differences in immune markers were observed between the common gene mutation group and the wild-type group in MDS (P < 0.05). Correlations between lymphocyte subsets and cytokines were identified in ASXL1, RUNX1, SF3B1, TET2, TP53, U2AF1, and 5q- mutation groups. This study investigates the correlation between common MDS mutations and cytokine/lymphocyte subpopulations, preliminarily revealing the interaction between genetic factors and immune markers in myelodysplastic syndrome (MDS), and emphasizes the potential important role of the Th17/Treg axis in the tumor immune microenvironment of MDS, but does not verify the mechanism. The study suggests that fine immune parameters have potential prognostic value in MDS and may guide future MDS treatment strategies.

Background Chronic kidney disease (CKD), often coexisting with various systemic disorders, may increase the risk of falls. This study aimed to investigate the associations between grip strength and fall injuries among patients with CKD, and whether these associations differ by sociodemographic and lifestyle factors. Methods We included patients with CKD from the China Health and Retirement Longitudinal Study. Multivariable logistic regression was used to evaluate the association between handgrip strength and fall injuries. Receiver operating characteristic (ROC) was employed to evaluate the predictive ability of handgrip strength for fall injuries. Results A total of 657 participants with CKD were included, and the prevalence of fall injury rates was 26.5%. After adjustment, for each 1 kg increase in right handgrip strength, the fall incident rate decreased by 3% (OR 0.97, 95% CI 0.94 to 1.00, p=0.023). Further analysis revealed a negative linear association between right handgrip strength and fall injuries, and the area under the ROC curve was 0.606 (95% CI 0.558 to 0.654, p<0.001). Conclusions Our study found a negative linear correlation between right handgrip strength and fall injuries rate among patients with CKD. Right handgrip strength could serve as a simple, low-cost screening tool for identifying patients with CKD at elevated risk of falls.

Objective To determine the quantitative impact of renal pelvic pressure (RPP) parameters on irrigation fluid absorption dynamics during mini‐percutaneous nephrolithotomy (mPCNL). Materials and Methods In this prospective observational study, 50 patients undergoing mPCNL were enrolled. Continuous synchronised RPP monitoring was performed using a calibrated pressure transduction system, while fluid absorption was quantified via a clinical grade endoscopic surgical monitor. Analytical approaches included Pearson correlation analysis and simple linear regression modelling to characterise pressure absorption dynamics. Results The median (interquartile range [IQR]) baseline, mean and maximum RPP values were 12.0 (10.3–14.8) mmHg, 16.7 (14.6–23.1) mmHg and 60.0 (35.0–67.3) mmHg, respectively. The median (IQR) fluid absorption was 625.5 (270.8–1296.5) mL, corresponding to an absorption rate of 838.0 (385.9–1349.4) mL/h. Mean RPP exhibited a significant positive correlation with absorption rate (Pearson R = 0.54, P < 0.001), with linear regression modelling demonstrating a 54.2‐mL/h increase in absorption rate per 1‐mmHg rise in mean RPP (R² = 0.29, P < 0.001). Transient RPP spikes exceeding 100 mmHg occurred in 9% of cases, primarily linked to access sheath obstructions from calculus migration or blood clots. Conclusions This study presents the first quantitative model identifying mean RPP as a critical predictor of irrigation fluid absorption during mPCNL. Acute pressure surges driven by sheath obstruction underscore the necessity for real‐time RPP monitoring and optimised outflow strategies to minimise absorption‐related risks.

Background Accumulating evidence demonstrates that miRNAs exhibit enhanced diagnostic sensitivity and specificity compared to the conventional hepatocellular carcinoma (HCC) biomarker alpha-fetoprotein (AFP), particularly showing promising clinical utility in early-stage HCC detection. However, insufficient transparency in methodology reporting compromises the validity assessment of these findings and hinders their translational applications. In this study, we investigated the adherence to the STARD criteria in studies investigating the diagnostic accuracy of miRNA for liver cancer. In addition, the quality of reporting was examined to identify factors influencing the quality of reporting. Method A comprehensive search strategy was performed on the PubMed, EMBASE, Cochrane Library, and Web of Science, as of March 30, 2023. Clinical trials investigating the diagnostic value of miRNA for HCC diagnosis were included in the analysis. The eligible studies were checked against adherence to the STARD criteria. Factors determining quality of studies were evaluated through subgroup analyses. All statistical analyses were performed using SPSS (varsion25.0). Results Sixty-two eligible studies, all published between 2010 and 2022, were eventually included in the final analysis. The results revealed a moderate overall adherence to STARD 2015, with an average of 12.1 (52.6%) of the 23 items reported. Subgroup analysis revealed that adherence to the STARD 2015 varied among countries (USA 58.7%;Egypt 46.5%)(p<0.05). Conclusion The quality of studies reporting the diagnostic accuracy of miRNAs in HCC was average and did not increase after the publication of STARD 2015. Our findings rise awareness regarding the need to improve STARD standards, implying that more journals need to include STARD standards for relevant manuscripts. In addition, editorial and peer approval procedures should adopt measures that will aim to improve reporting quality.

Efferocytosis, the process by which phagocytes like macrophages and dendritic cells clear apoptotic cells, is crucial for maintaining tissue homeostasis. However, its function in bladder cancer (BLCA) remains unclear and warrants further exploration. This study seeks to establish a prognostic and treatment response signature based on efferocytosis-related genes (EFRGs) for bladder cancer patients. BLCA-related datasets were sourced from the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) and the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/). A comprehensive analysis was performed on 28 prognostic EFRGs. Clustering analysis was carried out using ConsensusClusterPlus. Prognostic differentially expressed genes (DEGs) were identified based on expression variations across the subtypes. A prognostic model was subsequently developed using least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. Lastly, a thorough analysis was conducted to explore the relationship between risk scores and the tumor immune microenvironment, somatic mutations, as well as responses to immunotherapy and chemotherapy. Consensus clustering revealed two efferocytosis subtypes, Cluster A and Cluster B, and identified 61 prognostic DEGs between them. A risk scoring model, incorporating four key DEGs—SERPINE2, DPYSL3, CTSE, and KRT16—was constructed and validated. This model successfully stratified patients into high-risk and low-risk groups, with high-risk patients showing worse prognosis, increased immune infiltration, and higher immune checkpoint gene expression. The risk scores also provide insights into patient responsiveness to treatment. In conclusion, we identified four key genes—SERPINE2, DPYSL3, CTSE, and KRT16—that can be used to develop a prognostic model for bladder cancer. These findings may provide valuable molecular targets for the clinical diagnosis and therapeutic strategies of bladder cancer. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-04037-w.