ObjectiveTo investigate the mutation status of the MET gene in tumor patients， with a focus on its expression in lung cancer tissues and its impact on patient prognosis， providing theoretical basis and potential targets for precise diagnosis and treatment of the tumors. MethodsA total of 1，627 tumor patient samples were collected from January 2021 to September 2025. High-throughput sequencing technology was used to conduct DNA-level gene detection on 137 tumor patients with MET mutations. The distribution of mutation sites in exons and introns， as well as the occurrence frequencies of MET gene amplification and gene fusion were analyzed， with particular attention paid to the mutation frequencies of each exon and the high-frequency variant types. Immunohistochemical staining was performed to analyze the expression levels of c-MET protein in tumor tissues of the 137 patients. Immunohistochemical analysis was conducted on tumor tissues of 9 patients with MET gene exon 14 skipping mutations to study the association between c-MET protein expression and this specific mutation type. RNA sequencing results， immunohistochemical staining data， and follow-up prognosis of lung cancer from the GEPIA2 database and TCGA database were comprehensively analyzed to explore the potential relationship between MET gene expression in tumor tissues and patient prognosis. ResultsAmong 1，627 tumor patients， 137 MET mutation patients were detected， including 93 males with an average age of （66.85±9.61） years and 44 females with an average age of （59.43±11.08） years. Lung cancer was the most common tumor type （117 cases）. MET gene expression was lower in breast cancer and glioblastoma multiforme， but higher in lung cancer and colorectal cancer as compared with the expression in normal tissues. Survival analysis showed that patients with high MET gene expression in lung adenocarcinoma had a shorter overall survival time than those with low expression. A total of 961 mutations were detected by next-generation sequencing， including 547 exon mutations and 374 intron mutations. MET gene amplification was found in 13 cases and gene fusion in 27 cases. The highest mutation frequency was in exon 2， followed by exon 5， exon 4， and exon 19. High-frequency variant types included c.2890C>A （p.L964M） on exon 4 and c.3028G>T （p.D1010Y） on exon 5. The c-MET protein expression was closely related to the type of gene mutation. Missense mutations often led to positive or strongly positive c-MET protein expression， while nonsense mutations often resulted in weak or absent c-MET protein expression. Among the 9 patients with MET gene exon 14 skipping mutations， the c-MET protein expression score was mostly 0 - 1， with only 1 case scoring 2. Among the 117 lung cancer patients， the incidence rate was higher in males than in females， and more common among stage Ⅲ-Ⅳ patients than among stage I-Ⅱ patients. Out of the 41 patients treated with surgery + targeted drugs （gutemitinib， savolitinib）， 8 cases of recurrence or metastasis were found during follow-up. ConclusionMET gene mutations exhibit specific mutation characteristics in different tumor types， and its expression levels show significant differences between various tumor tissues and normal tissues. It is closely related to the poor prognosis of lung adenocarcinoma patients. We found that c-MET protein expression is closely related to the type of MET gene mutation， especially in patients with MET gene exon 14 skipping mutations， which presents a characteristic expression pattern. This provides an important basis for the preliminary screening using immunohistochemistry in clinical practice.

Objective:To develop and validate a clinical warning model for severe pediatric community-acquired pneumonia (CAP) using electronic health records.Methods:A retrospective cohort study was conducted, analyzing clinical data of 15 750 children hospitalized for CAP at Wuhan Children′s Hospital between January 1, 2019, and December 31, 2023. Patient data were randomly split into training and testing sets at a 7∶3 ratio. Six supervised machine learning models were constructed in the training set, optimized using five-fold cross-validation, and evaluated in the testing set. Model performance was assessed using ROC-AUC, sensitivity, specificity, positive predictive value, negative predictive value, calibration curves, and clinical decision curve analysis at optimal thresholds. The best-performing model was selected, and SHapley Additive exPlanations (SHAP) were used to interpret feature importance. A program interface was developed based on the model results, enabling integration into clinical decision support systems for automated early warning.Results:A total of 15 750 participants, ranging in age from 28 days to 18 years, were included in the study. The median age was 2 years [interquartile range (IQR): 0-4 years], with 9 555 males (60.67%) and 6 195 females (39.33%). Among them, 2 211 (14.04%) developed severe pneumonia. In the prediction models, XGB outperformed other models with an ROC-AUC of 0.884 (95% CI: 0.870-0.898), sensitivity (0.803, 95% CI: 0.772-0.832), specificity (0.828, 95% CI: 0.816-0.839). Calibration analysis showed strong agreement between predicted and observed risks (Brier score: 0.081, 95% CI: 0.075-0.086). The analysis based on the SHAP method revealed that respiratory rate, heart rate, T-lymphocyte subsets, and red blood cell volume distribution width-SD are predictive factors for severe progression of community-acquired pneumonia (CAP) in children. Conclusion:An interpretable machine learning model was developed for the early detection and personalized treatment planning of severe CAP in children, providing valuable support to clinicians.

OBJECTIVE: Pneumoconiosis, a lung disease caused by irreversible fibrosis, represents a significant public health burden. This study investigates the causal relationships between gut microbiota, gene methylation, gene expression, protein levels, and pneumoconiosis using a multi-omics approach and Mendelian randomization (MR). METHODS: We analyzed gut microbiota data from MiBioGen and Esteban et al. to assess their potential causal effects on pneumoconiosis subtypes (asbestosis, silicosis, and inorganic pneumoconiosis) using conventional and summary-data-based MR (SMR). Gene methylation and expression data from Genotype-Tissue Expression and eQTLGen, along with protein level data from deCODE and UK Biobank Pharma Proteomics Project, were examined in relation to pneumoconiosis data from FinnGen. To validate our findings, we assessed self-measured gut flora from a pneumoconiosis cohort and performed fine mapping, drug prediction, molecular docking, and Phenome-Wide Association Studies to explore relevant phenotypes of key genes. RESULTS: Three core gut microorganisms were identified: Romboutsia ( OR = 0.249) as a protective factor against silicosis, Pasteurellaceae ( OR = 3.207) and Haemophilus parainfluenzae ( OR = 2.343) as risk factors for inorganic pneumoconiosis. Additionally, mapping and quantitative trait loci analyses revealed that the genes VIM, STX8, and MIF were significantly associated with pneumoconiosis risk. CONCLUSIONS This multi-omics study highlights the associations between gut microbiota and key genes ( VIM, STX8, MIF) with pneumoconiosis, offering insights into potential therapeutic targets and personalized treatment strategies.
Humans ; Male ; East Asian People/genetics* ; Europe ; Gastrointestinal Microbiome ; Lung ; Macrophage Migration-Inhibitory Factors/metabolism* ; Mendelian Randomization Analysis ; Multiomics ; Pneumoconiosis/microbiology* ; Intramolecular Oxidoreductases

Objective:To develop and validate a clinical warning model for severe pediatric community-acquired pneumonia (CAP) using electronic health records.Methods:A retrospective cohort study was conducted, analyzing clinical data of 15 750 children hospitalized for CAP at Wuhan Children′s Hospital between January 1, 2019, and December 31, 2023. Patient data were randomly split into training and testing sets at a 7∶3 ratio. Six supervised machine learning models were constructed in the training set, optimized using five-fold cross-validation, and evaluated in the testing set. Model performance was assessed using ROC-AUC, sensitivity, specificity, positive predictive value, negative predictive value, calibration curves, and clinical decision curve analysis at optimal thresholds. The best-performing model was selected, and SHapley Additive exPlanations (SHAP) were used to interpret feature importance. A program interface was developed based on the model results, enabling integration into clinical decision support systems for automated early warning.Results:A total of 15 750 participants, ranging in age from 28 days to 18 years, were included in the study. The median age was 2 years [interquartile range (IQR): 0-4 years], with 9 555 males (60.67%) and 6 195 females (39.33%). Among them, 2 211 (14.04%) developed severe pneumonia. In the prediction models, XGB outperformed other models with an ROC-AUC of 0.884 (95% CI: 0.870-0.898), sensitivity (0.803, 95% CI: 0.772-0.832), specificity (0.828, 95% CI: 0.816-0.839). Calibration analysis showed strong agreement between predicted and observed risks (Brier score: 0.081, 95% CI: 0.075-0.086). The analysis based on the SHAP method revealed that respiratory rate, heart rate, T-lymphocyte subsets, and red blood cell volume distribution width-SD are predictive factors for severe progression of community-acquired pneumonia (CAP) in children. Conclusion:An interpretable machine learning model was developed for the early detection and personalized treatment planning of severe CAP in children, providing valuable support to clinicians.

Objective:To investigate the efficacy and safety of darolutamide in the treatment of patients with metastatic hormone-sensitive prostate cancer.Methods:A retrospective analysis was conducted on 17 cases of prostate cancer patients who received treatment with darolutamide in combination with ADT at our hospital from January to December 2022. The median age was 70 (range: 56 to 92) years old. The median pre-treatment prostate-specific antigen (PSA) level was 63.50 (range: 29.16 to 700.74) ng/ml. Sixteen cases had a Gleason score of 8 or above, and 11 cases were classified as high tumor burden (with four or more bone metastases and/or visceral metastases). The patients were treated with darolutamide in combination with goserelin (10.8 mg, subcutaneous injection, every 12 weeks). The decrease in PSA levels was observed at 2 weeks and at 1, 2, 3, and 6 months post-treatment. The time to achieve a 50% decrease in PSA level (PSA50), a 90% decrease (PSA90), and a PSA level of ≤0.2 ng/ml was recorded.Adverse drug reactions were also documented.Results:All the 17 patients were followed up and continued to receive darolutamide at our center without any loss to follow-up. The median follow-up time was 11.4(8.9, 15.3)months. It showed a median PSA decrease from baseline of 83.33% at 2 weeks, 95.37% at 1 month, 96.71% at 2 months, 97.22% at 3 months, and 99.10% at 6 months. The median time to achieve PSA50, PSA90, and PSA ≤ 0.2 ng/ml were 1.3 (0.9, 1.7)months, 1.7 (1.2, 2.4)months, and 3.6 (2.9, 4.5)months respectively. Six patients with bone metastases experienced relief of metastatic lesions after treatment. Only one patient developed papules on the left upper limb, which were assessed as grade 1 rash, and the rash disappeared after three days treatment of topical application of hydrocortisone cream.Conclusions:Darolutamide could rapidly control and significantly reduce PSA levels in prostate cancer patients, with a favorable safety profile.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective:To develop an intelligent recognition and precise segmentation technique using ultrasonic images，and to enhance diagnostic efficiency and accuracy.Methods:A total of 1，430 patients diagnosed with uterine fibroids through transvaginal ultrasonography at the Maternal and Child Health Hospital of Guangming from November 2020 to October 2024 were retrospectively included. Ultrasonic images were manually annotated by two experienced physicians and reviewed by a senior expert. The Mask DINO deep learning model was used for lesion segmentation，and the segmentation results were optimized using ellipse fitting technology. Model performance was evaluated using the Dice coefficient，intraclass correlation coefficient（ICC），mean absolute error（MAE），and measurement accuracy.Results:In the test set of 286 cases，the average Dice coefficient of model prediction was 0.992，indicating extremely high segmentation accuracy. The average accuracy of lesion identification by the model was 0.909，with 241 correctly identified samples，19 basically correct samples，and 26 incorrect samples. In terms of long and short axis measurements，the ICC of the model's direct predictions were 0.871（short axis）and 0.784（long axis），with MAE of 0.436 cm（short axis）and 0.508 cm（long axis）. After optimization with ellipse fitting，the ICC increased to 0.893（short axis）and 0.866（long axis），and the MAE decreased to 0.191 cm（short axis）and 0.274 cm（long axis），the measurement accuracy improved significantly.Conclusions:The intelligent recognition and precise segmentation technique for uterine fibroids based on ultrasonic images constructed in this study performed excellently in lesion segmentation and measurement，it can significantly improve the efficiency and accuracy of diagnosis.

How to effectively achieve regenerative repair of large bone defects remains a significant chal-lenge in current clinical treatment.Traditional bone regeneration materials have yet to yield ideal results,while the emergence of nanomaterials offers new options for bone regeneration.In recent years,metal-or-ganic frameworks(MOFs)have been widely applied in fields such as biomedicine,among which zeolitic imidazolate framework-8 nanoparticles(ZIF-8 NPs)have been extensively studied.ZIF-8 NPs are easy to synthesize,with controllable particle sizes,large specific surface areas,high thermal stability,and pH responsiveness.The nanoscale size of ZIF-8 NPs can be utilized to adjust the mechanical properties of biomaterials,further aligning with the elastic modulus of bone tissue.Additionally,ZIF-8 NPs facilitate osteogenic differentiation,vascular regeneration,and exhibit antibacterial properties,while also serving as carriers for controlled release of drugs or biomacromolecules.This article summarizes the characteris-tics and biocompatibility of ZIF-8 NPs and reviews their application progress in the field of bone tissue re-generation,including the enhancement of mechanical properties,cell adhesion,osteoinductive activity,angiogenic ability,anti-inflammatory ability and antibacterial ability of bone regeneration materials.This review aims to provide references for advancing the preparation and application of ZIF-8-based bioactive materials.

Surgical methods for varicocele remain controversial. This study intends to evaluate the efficacy and safety of different surgical approaches for treating varicocele through a network meta-analysis (NMA). PubMed, Embase, Cochrane, and Web of Science databases were thoroughly searched. In total, 13 randomized controlled trials (RCTs) and 24 cohort studies were included, covering 9 different surgical methods. Pairwise meta-analysis and NMA were performed by means of random-effects models, and interventions were ranked based on the surface under the cumulative ranking curve (SUCRA). According to the SUCRA, microsurgical subinguinal varicocelectomy (MSV; 91.6%), microsurgical retroperitoneal varicocelectomy (MRV; 78.2%), and microsurgical inguinal varicocelectomy (MIV; 76.7%) demonstrated the highest effectiveness in reducing postoperative recurrence rates. In this study, sclerotherapy embolization (SE; 87.2%), MSV (77.9%), and MIV (67.7%) showed the best results in lowering the risk of hydrocele occurrence. MIV (82.9%), MSV (75.9%), and coil embolization (CE; 58.7%) were notably effective in increasing sperm motility. Moreover, CE (76.7%), subinguinal approach varicocelectomy (SV; 69.2%), and SE (55.7%) were the most effective in increasing sperm count. SE (82.5%), transabdominal laparoscopic varicocelectomy (TLV; 76.5%), and MRV (52.7%) were superior in shortening the length of hospital stay. The incidence rates of adverse events for MRV (0), SE (3.3%), and MIV (4.1%) were notably low. Cluster analyses indicated that MSV was the most effective in the treatment of varicocele. Based on the existing evidence, MSV may represent the optimal choice for varicocele surgery. However, selecting clinical surgical strategies requires consideration of various factors, including patient needs, surgeon experience, and the learning curve.
Humans ; Male ; Embolization, Therapeutic/methods* ; Microsurgery/methods* ; Randomized Controlled Trials as Topic ; Sclerotherapy/methods* ; Treatment Outcome ; Urologic Surgical Procedures, Male/methods* ; Varicocele/surgery*

Objective:To investigate the clinical outcomes of arthroscopic suture anchor fixation for acute bony Bankart lesions.Methods:Data of 49 patients with acute bone Bankart injury treated with suture anchor fixation under arthroscopy at the Affiliate Nanjing Drum Tower Hospital of Nanjing University School of Medicine from February 2019 to July 2024 were retrospectively analyzed, including 34 males and 15 females, aged 46.4±17.7 years (range, 18-81 years). Body mass index was 26.3±4.2 kg/m 2 (range, 19.8-37.9 kg/m 2). There were 17 patients with left shoulder and 32 patients with right shoulder. The time from injury to operation was 12.6±10.5 d (range, 1-50 d). CT three-dimensional reconstruction showed that the area of bone defect accounted for 20.1%±11.4% (range, 8%-49%) of the glenoid area. According to the degree of bone defect, there were 38 cases in the bone defect area ≤25% group and 11 cases in the bone defect area >25% group. All 49 patients received shoulder arthroscopic fixation with wire anchor. All 49 the patients were treated under shoulder arthroscopy using suture anchor technique. Using American Shoulder & Elbow Surgeon (ASES) score, University of California, Los Angeles (UCLA) score, Rowe score as well as shoulder range of motion to evaluate shoulder function. The position and healing of the fracture mass were evaluated with shoulder joint CT and three-dimensional reconstruction at immediate and final follow-up. Results:The incision healed in one stage and no infection occurred in all patients. All 49 patients were followed up for a period of 3 to 81 months, with an average follow-up of 23.8±20.2 months. At the final follow-up, the mean forward elevation, external rotation, and internal rotation improved from 126.1°±20.3°, 36.9°±14.0°, and 10±2 preoperatively to 167.1°±15.5°, 66.8°±8.3°, and 6±1 at the last follow-up, respectively ( P<0.05). At the final follow-up, the mean ASES score, UCLA score, and Rowe score were all significantly improved from 41.3±11.9, 14.2±6.1 and 46.9±14.7 preoperatively to 89.7±7.8, 31.5±3.6 and 92.4±7.4, respectively ( P<0.05). The differences between the final follow-up and preoperative values for forward elevation in the bone defect area >25% group and the bone defect area ≤25% group were 43°±20° and 41°±21°, respectively. The differences in lateral external rotation were 34°±8° and 29°±18°, while the differences in internal rotation (measured by hand-behind-back reach) were 5±2 and 4±2, respectively. None of these differences were statistically significant. The differences between the final follow-up and preoperative scores in the bone defect area >25% fracture fragment group and the bone defect area ≤25% group were as follows: ASES score, 50.9±14.4 vs. 47.7±12.8; UCLA score, 18.4±3.2 vs. 17.0±6.9; and Rowe score, 40.5±13.5 vs. 46.9±15.0. None of these differences were statistically significant. CT examination at the last follow-up showed that all fractures were healed, all patients in the bone defect area ≤25% group achieved good reduction, and 3 patients in the bone defect area >25% group had step-offs on the glenoid surface. Postoperative shoulder adhesion occurred in 5 patients, which was improved after intensive abduction and external rotation exercise. Conclusions:Arthroscopic suture anchor fixation is effective for the bone defect area ≤25% bony Bankart lesions, offering advantages of minimal invasiveness and rapid recovery. For lesions involving more than the bone defect area 25% of the glenoid surface, enhanced fixation strength is recommended to prevent fracture fragment displacement.