Objective:To assess the feasibility and efficacy of transperineal laser ablation(TPLA)with single laser fiber in treating benign prostatic obstruction(BPO).Methods:From April 2021 to March 2024,a total of 13 BPO patients were selected from Beijing Friendship Hospital.TPLA was performed using a single laser fiber,which was guided by transrectal biplane ultrasound.The single laser fiber was used to undergo TPLA under the guidance of trans-rectal dual-plane ultrasound.The intraoperative time,ablation time,energy consumption,indwelling time of catheter,and complications were observed.The postoperative 6 months was chosen as the cut-off point of follow-up,and the pre and postoperative changes of international prostate symptom score(IPSS),quality of life index(QoL),prostate volume(PV),residual urine volume(RUV)and the maximum urine flow rate(Qmax)were compared.Results:All 13 patients successfully underwent TPLA with single laser fiber.The average operation time was(55.1±18.3)min,and the average ablation time was(16.3±1.7)min,and average energy consumption was(3951.6±459.7)J,and the median value of indwelling time of catheter was 7(7,10)days.The number of postoperative complication was 2 cases,and both them belonged to Clavien-Dindo grade II complication.At the postoperative 6th month,the IPSS,QoL,PV,Qmax and RUV of all patients were improved,all of which were better than preoperative these indicators,and the differences were significant(t=12.102,-3.228,-3.181,-2.581,-2.936,P<0.05).Conclusion:The application of single laser fiber in conducting TPLA operation is feasibility at technical aspect,and it can achieve the therapeutic goals of improving patients'symptoms and enhancing their quality of life.Although its operational time is slightly longer than that of using multiple fibers simultaneously,it can effectively reduce the cost of expenditure for consumables.

Objective:To develop and evaluate a medical visual question answering (VQA) model for neonatal lung ultrasound (LUS) images to enhance intelligent auxiliary diagnosis of neonatal pulmonary diseases.Methods:Using data from neonates admitted to Beijing Obstetrics and Gynecology Hospital, Capital Medical University (January 2023 to December 2024), an image-question-answer dataset comprising 251 LUS images was constructed [43 pneumonia (17.1%), 42 neonatal respiratory distress syndrome (16.7%), 83 transient tachypnea (33.1%), and 83 normal (33.1%) images] with a four-tier medical question-answer framework. Building upon the Qwen2.5-VL-7B base model and integrating LoRA fine-tuning with chain-of-thought prompting, we developed the NLUS-VQA model to enhance visual-language semantic alignment and enable stepwise clinical reasoning, achieving efficient small-sample adaptation. Model performance was comprehensively assessed through natural language generation metrics (BLEU-4, ROUGE-1/2/L), qualitative evaluation of characteristic recognition, and clinical consistency analysis.Results:(1) Quantitative evaluation demonstrated that NLUS-VQA achieved scores of 22.38 (BLEU-4), 48.26 (ROUGE-1), 22.40 (ROUGE-2), and 37.20 (ROUGE-L), representing significant improvements over baseline models. (2) Qualitatively, the model exhibited strong performance in identifying lung consolidation, coalescent B-lines, and snowflake signs, with its chain-of-thought strategy enhancing clinical interpretability and answer accuracy. (3) Clinically, NLUS-VQA achieved a Cohen's Kappa coefficient of 0.78 and diagnostic accuracy of 80.8% (21/26), indicating substantial agreement with clinical experts.Conclusion:The NLUS-VQA model demonstrates robust interpretability in recognizing key sonographic patterns (e.g. lung consolidation, confluent B-lines, and snowflake signs), providing a scalable framework for small-sample medical image analysis, though diagnostic performance on complex conditions remains limited by dataset scale and minority class representation.

BACKGROUND: There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA). METHODS: This retrospective study included 413 patients hospitalized at Xinhua Hospital diagnosed with SP from June 1, 2015 to May 30, 2020. AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population. The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models, and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods. RESULTS: The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes. Patients with subphenotype-1 had milder infections ( P <0.001) than patients with subphenotype-2 and had lower 30-day ( P <0.001) and 90-day ( P <0.001) mortality, and lower in-hospital ( P = 0.001) and 2-year ( P <0.001) mortality. Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume (used to quantify ventilation) and oxygen saturation (used to reflect gas exchange), compared with patients with subphenotype-2. There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes ( P <0.001). Compared with patients with subphenotype-2, those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation, and their clinical outcomes were effectively improved after receiving invasive ventilation treatment. CONCLUSIONS A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.
Humans ; Tomography, X-Ray Computed/methods* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Artificial Intelligence ; Aged ; Pneumonia/diagnosis* ; Latent Class Analysis ; Adult

Objective To analyze the causes of infection and hemorrhage in patients with malignant obstructive jaundice(MOJ)after ultrasound-guided percutaneous transhepatic biliary drainage(PTBD).Methods A total of 420 patients with MOJ after PTBD in The Third Affiliated Hospital of Naval Medical University from Jun.2023 to Sep.2024 were enrolled,and their condition after PTBD,medical histories,preoperative examinations,and other clinical data were retrospectively analyzed.Univariate and multivariate logistic regression analyses were performed to explore the risk factors for postoperative infection and hemorrhage,and receiver operating characteristic(ROC)curves were plotted.Results Univariate logistic regression analysis showed that ascites,diuretics use,repeated punctures,hepatitis B virus(HBV)DNA and preoperative hemoglobin(Hb)levels,and neutrophil count(NE)were associated with infection after PTBD(all P＜0.05).Multivariate logistic regression analysis showed that ascites,diuretic use,repeated punctures,HBV DNA and preoperative Hb levels,and NE were the independent risk factors for infection after PTBD(all P＜0.05).Six factors including ascites,diuretics use,repeated punctures,HBV DNA and preoperative Hb levels,and NE were used to establish a prediction model.The area under the ROC curve of the model for predicting infection after PTBD was 83.1％(95％confidence interval[CI]75.5％-90.7％,P＜0.001).Univariate logistic regression analysis showed that liver tissue inflammation,ascites,preoperative peritoneal drainage,diuretics use,preoperative Hb and prealbumin(PA)levels were related to bleeding after PTBD(all P＜0.05).Multivariate logistic regression analysis showed that preoperative Hb and PA levels were independent risk factors for bleeding after PTBD.Preoperative Hb and PA levels were included to establish the prediction model.The area under the curve value for predicting bleeding after PTBD was 86.3％(95％CI 80.8％-91.9％,P＜0.001).Conclusion The prediction model for infection and hemorrhage after PTBD can facilitate early preventivon and intervention measures,thereby improving surgical safety.

Objective Exploring the components of complex scenarios of healthcare surges triggered by major epidemics to provide a theorical basis for building resilience in healthcare organizations.Methods A hybrid analysis method is used to summarize macro-meso-micro multi-level and multi-source heterogeneous information,extract the elements of complex scenarios of medical surge and evaluate the rationality.Fault Tree Analysis method is used to clarify the logical relationship between various scenario elements and construct scenario reasoning paths.Results 10 scenario states,11 disaster-bearing,24 emergency management and 23 scenario results are summarized and extracted to form the key elements of complex surge scenarios.Among them,M4 expansion and coordinated scheduling of key positions,B2 conventional drug inventory emergency/insufficient core treatment drugs,B emergency medical material transportation breakage,S3 disease symptom spectrum shift to severe disease,R13 prevention and control awareness laxity,and M5 media information dissemination management are the key driving factors that promote a major turning point in the scenario.The most positive scenario result is the orderly operation of the medical service system,and the most negative scenario result is the paralysis of the medical service system.Conclusion Medical institutions need to improve emergency plans based on the complex evolution scenarios of medical surges and agile governance capabilities targeting key turning points,focus on dynamically expanding and scheduling personnel in key positions,strengthen material rotation and reserve mechanisms,maintain smooth emergency logistics channels,and improve efficient management of media and public opinion,so as to comprehensively improve overall resilience.

Objective To explore the coupling mechanism between medical surge response resources and the spread of secondary risks during public health emergencies,as well as the effectiveness of relevant interventions.Methods Based on complex network theory,a dual-layer network model of medical resources and secondary events was constructed.The interactive feedback between medical resource status and secondary event risk,as well as the effects of network structure,were analyzed through MATLAB simulations,REPAST agent-based modeling,and mean-field analysis.Results Simulation and prediction results show that an increase in first-layer resource-deficient nodes significantly raises the activation rate and transmission speed of secondary events,while the clustering and spread of secondary events in the second layer,in turn,intensify resource depletion,creating a negative feedback loop.Mean-field analysis indicates a nonlinear positive correlation between the adequacy of medical resources and the likelihood of secondary events.Network structure analysis reveals that when the average node degree exceeds 8,resource allocation efficiency improves markedly.Conclusion There exists a dynamic coupling and bidirectional feedback relationship between medical resource status and secondary event risks.Enhancing the flexible allocation and responsiveness of medical resources,improving multi-sectoral collaborative monitoring and coordinated regulation,optimizing network connectivity and coordination mechanisms for resource distribution,and establishing dynamic monitoring and tiered early warning systems are key strategies for strengthening the resilience of healthcare systems and effectively containing the spread of secondary events.

Objective:To analyze the clinical characteristics and gene mutations of 4 patients with the male phenotype of 17α-hydroxylase /17, 20-lyase deficiency(17-OHD), in order to improve the recognition and appropriate management of atypical cases.Methods:A retrospective analysis was performed on the clinical features, biochemical findings, and gene mutations of 4 patients with the male phenotype of 17-OHD treated in our hospital between 2018 and 2023.Results:The social gender of all 4 patients with 17-OHD was male. None of the 4 patients had hypertension or hypokalemia, but all had micropenis and gynecomastia. Two patients had adrenal hyperplasia, while adrenal morphology was normal in the other two. One patient had decreased bone mass. There were typical changes in the steroid synthesis-related hormone spectrum: progesterone was significantly elevated in all 4 patients, 17-hydroxyprogesterone was not markedly abnormal, cortisol, dehydroepiandrosterone sulfate(DHEAS) and estradiol levels were low, and testosterone levels were also low.Conclusion:17-OHD is a rare type of congenital adrenal hyperplasia, with the male phenotype being even rarer. Early symptoms are often atypical, resulting in high rates of misdiagnosis and missed diagnosis. Patients without hypertension and hypokalemia are particularly prone to diagnostic confusion. Male patients with unexplained progesterone elevation, poor secondary sexual development, and gynecomastia should undergo timely steroid hormone profiling and genetic testing to avoid misdiagnosis and missed diagnosis.

A 44-year-old male presented with a 19-year history of urinary calculi and a 1-year history of polydipsia and weight loss. Laboratory tests revealed hyperparathyroidism and evidence of glucagonoma-associated diabetes. Imaging studies identified masses in the pancreatic head and body/tail, suggestive of glucagonoma and a parathyroid adenoma. Furthermore, the patient exhibited hypercalcitoninemia and elevated cortisol and adrenocorticotropic hormone levels. Genetic testing revealed a heterozygous MEN1 mutation [c.65T>G (p.Leu22Arg)], confirming the diagnosis of multiple endocrine neoplasia type 1 (MEN-1). The patient subsequently underwent near-total parathyroidectomy and total pancreatectomy. Postoperative immunohistochemical staining of the pancreatic tail tumor was positive for glucagon and calcitonin. The patient′s postoperative hormone levels (calcitonin, glucagon, adrenocorticotropic hormone, cortisol) normalized, suggesting a rare pancreatic neuroendocrine tumor (pNET) that was co-secreting multiple hormones. Postoperative management included pancreatic enzyme supplementation, calcium supplementation, vitamin D supplementation, and insulin for glycemic control. Follow-up evaluations at 10 months demonstrated a stable clinical condition, well-controlled blood glucose and biochemical parameters, and an acceptable quality of life. This case study highlights that the presence of pNETs should be considered in patients with MEN-1 and multiple abnormal hormone levels. Timely surgical management of the involved glands and postoperative complications can effectively improve prognosis.

Objective Exploring the components of complex scenarios of healthcare surges triggered by major epidemics to provide a theorical basis for building resilience in healthcare organizations.Methods A hybrid analysis method is used to summarize macro-meso-micro multi-level and multi-source heterogeneous information,extract the elements of complex scenarios of medical surge and evaluate the rationality.Fault Tree Analysis method is used to clarify the logical relationship between various scenario elements and construct scenario reasoning paths.Results 10 scenario states,11 disaster-bearing,24 emergency management and 23 scenario results are summarized and extracted to form the key elements of complex surge scenarios.Among them,M4 expansion and coordinated scheduling of key positions,B2 conventional drug inventory emergency/insufficient core treatment drugs,B emergency medical material transportation breakage,S3 disease symptom spectrum shift to severe disease,R13 prevention and control awareness laxity,and M5 media information dissemination management are the key driving factors that promote a major turning point in the scenario.The most positive scenario result is the orderly operation of the medical service system,and the most negative scenario result is the paralysis of the medical service system.Conclusion Medical institutions need to improve emergency plans based on the complex evolution scenarios of medical surges and agile governance capabilities targeting key turning points,focus on dynamically expanding and scheduling personnel in key positions,strengthen material rotation and reserve mechanisms,maintain smooth emergency logistics channels,and improve efficient management of media and public opinion,so as to comprehensively improve overall resilience.

Objective To explore the coupling mechanism between medical surge response resources and the spread of secondary risks during public health emergencies,as well as the effectiveness of relevant interventions.Methods Based on complex network theory,a dual-layer network model of medical resources and secondary events was constructed.The interactive feedback between medical resource status and secondary event risk,as well as the effects of network structure,were analyzed through MATLAB simulations,REPAST agent-based modeling,and mean-field analysis.Results Simulation and prediction results show that an increase in first-layer resource-deficient nodes significantly raises the activation rate and transmission speed of secondary events,while the clustering and spread of secondary events in the second layer,in turn,intensify resource depletion,creating a negative feedback loop.Mean-field analysis indicates a nonlinear positive correlation between the adequacy of medical resources and the likelihood of secondary events.Network structure analysis reveals that when the average node degree exceeds 8,resource allocation efficiency improves markedly.Conclusion There exists a dynamic coupling and bidirectional feedback relationship between medical resource status and secondary event risks.Enhancing the flexible allocation and responsiveness of medical resources,improving multi-sectoral collaborative monitoring and coordinated regulation,optimizing network connectivity and coordination mechanisms for resource distribution,and establishing dynamic monitoring and tiered early warning systems are key strategies for strengthening the resilience of healthcare systems and effectively containing the spread of secondary events.