Objective To analyze the effect of releasing the lower pulmonary ligament on right residual lung expansion after right upper lobe resection under different body mass index (BMI) levels. Methods The clinical data of patients who underwent thoracoscopic right upper lobe resection in the First Affiliated Hospital with Nanjing Medical University from 2021 to 2022 were retrospectively analyzed. Patients were divided into a group A (17 kg/m2＜BMI≤23 kg/m2), a group B (23 kg/m2＜BMI≤29 kg/m2) and a group C (BMI＞29 kg/m2) according to BMI. The presence of residual cavity was judged by chest X-ray at 7-10 days after operation, the degree of compensation change of the right main bronchus angle was measured, and the changes in lung volume were determined by CT three-dimensional reconstruction. Results A total of 157 patients who underwent thoracoscopic right upper lobe resection were included, including 71 males and 86 females, with an average age of (59.7±11.2) years. There were 50 patients in the group A, 75 patients in the group B, and 32 patients in the group C. In the group A, compared with those without releasing the lower pulmonary ligament, patients with releasing had a lower incidence of postoperative residual cavity (P=0.016), greater changes in bronchus angle (P<0.001), and smaller changes in lung volume (P<0.001). In the group B and C, there was no significant effect of releasing the lower pulmonary ligament on postoperative residual cavity, bronchus angle, and lung volume changes (P>0.05). Conclusion For patients with thin and long body shape and low BMI, releasing the lower pulmonary ligament is helpful to promote the expansion of the residual lung after right upper lobe resection and reduce the occurrence of postoperative residual cavity in patients.

Objective To explore the application value of three mainstream large language models in the diagnosis, differential diagnosis, and treatment decision support of the primary diseases related to pediatric liver transplantation. Methods Seventy-nine cases of pediatric liver transplantation-related diseases diagnosed through pathological or clinical follow-up data were collected from Renji Hospital, Shanghai Jiao Tong University School of Medicine or published high-quality case reports. These cases covered 25 types of primary diseases such as cholestatic liver disease, metabolic diseases, and tumors. Standardized prompts were used to input the case information into the DeepSeek-R1, ChatGPT-4o and Grok-3 models, and the accuracy of their preliminary diagnosis and differential diagnosis based on basic clinical data was evaluated. The final diagnosis accuracy and the response time after supplementary examination were also assessed, as well as the completeness and rationality of their analysis of disease treatment principles. Results In the initial diagnosis and differential diagnosis stage, the comprehensive accuracy of DeepSeek-R1 was the highest [72.1%, 95% confidence interval (CI) 61.4% - 80.8%], and there was a statistically significant difference in the comprehensive accuracy of the three models for initial diagnosis (P = 0.008). After adding further examination information, the final diagnosis accuracy of the three models increased, with DeepSeek-R1 at 88.6% (95% CI 79.7% - 93.9%), ChatGPT-4o at 87.3% (95% CI 78.2% - 93.0%), and Grok-3 at 78.5% (95% CI 68.2% - 86.1%). There was no statistically significant difference among the three models (P = 0.05). The scores given by experts for the treatment principles showed good consistency (Kappa = 0.769). In addition, the response time of ChatGPT-4o is shorter than that of the other two models [(24 ± 7) s]. Conclusions Large language models demonstrate good efficacy in the diagnosis and treatment decision-making process of various pediatric liver diseases, have a good application prospect for auxiliary diagnosis and decision support, and are expected to help improve the accuracy and efficiency of clinical diagnosis and treatment of pediatric liver transplantation-related primary diseases.

In recent years， repetitive transcranial magnetic stimulation （rTMS） has garnered significant attention as a therapeutic approach for sleep disorders in the elderly. However， the prevailing rTMS protocols are predominantly developed based on normative neurophysiological data derived from young adults and fail to incorporate individualized parameters tailored to the brain characteristics of the elderly. To address this gap， the consensus development group synthesized the latest evidence from 2010 to 2025 and established a standardized rTMS protocol specifically for elderly patients with sleep disorders. Adhering to the Appraisal of Guidelines for Research and Evaluation II （AGREE II） framework， systematically screened randomized controlled trials （RCTs） and systematic reviews regarding rTMS in the treatment of sleep disorders across various conditions. Meanwhile， the Grading of Recommendations Assessment， Development and Evaluation （GRADE） system was employed to rigorously grade the quality of evidence and the strength of recommendations. This consensus guideline delineates precise rTMS protocols for the management of sleep disorders in the elderly， highlights the adjustment of stimulation intensity according to scalp-cortex distance recommends either MRI‑guided neuronavigation or the Beam F3/F4 heuristic approach for accurate target localization， thereby providing precise rTMS intervention protocol for sleep disorders in the elderly， aiming to enhance clinical efficacy while ensuring treatment safety. ［Funded by National Key Research and Development Program （number， 2023YFC3603200）； General Program of Shenzhen Science and Technology Innovation Commission （number， JCYJ20240813112859008， JCYJ20240813112900002）； Youth Program of Shenzhen Kangning Hospital （number， KN2023A004）； www.guidelines-registry.cn number， PREPARE-2026CN530］

Objective To analyze the efficacy, safety, and learning curve of the three-lobe holmium laser enucleation of the prostate(HoLEP)in the treatment of benign prostatic hyperplasia(BPH)in a county-level hospital.Methods A retrospective analysis was conducted on the clinical data of 65 BPH patients who underwent the three-lobe HoLEP performed by a single surgeon at the Department of Urology, Yining County People's Hospital, during Dec.2023 and Jun.2024.The enucleation efficiency was calculated by dividing the weight of the enucleated prostatic tissue by the enucleation time.A case scatter diagram of enucleation efficiency was plotted according to the chronological order of the operations, and the learning curve was analyzed after fitting.Based on the inflection points of the learning curve, the learning process was divided into the initial learning, mastery, and proficiency phases.The basic clinical data, perioperative indicators, postoperative complications, and follow-up indicators were compared among the different learning phases.Results All 65 procedures were successfully completed.Marked by the enucleation efficiency reaching a plateau, cases 1-20 were defined as the initial learning phase, cases 21-40 as the mastery phase, and cases 41 onwards as the proficiency phase.The prostate volume in the proficiency phase was significantly larger than that in the initial learning and mastery phases(both P＜0.05).There were no statistically significant differences in other baseline characteristics among the three groups(all P＞0.05).The operation time [(105.50±19.12)min vs.(85.25±26.92)min vs.(69.00±23.58)min] and enucleation time [(76.90±14.19)min vs.(63.70±22.24)min vs.(48.80±20.48)min] showed a decreasing trend across the three groups(all P＜0.05).The enucleation efficiency in both the mastery and proficiency phases was significantly higher than that in the initial learning phase [(1.16±0.44)g/min vs.(0.85 ±0.25)g/min, P＜0.05;(1.36±0.49)g/min vs.(0.85±0.25)g/min, P＜0.05].The enucleation efficiency in the proficiency phase was slightly higher than that in the mastery phase, but the difference was not statistically significant(P= 0.389).There was no significant difference in the incidence of perioperative complications among the three groups(all P＞0.05).At the 6-month follow-up, the international prostate symptom score(IPSS), post-void residual(PVR), and maximum urinary flow(Qmax)were significantly improved compared to preoperative values in all three groups(all P＜0.05);however, no significant differences were observed among the three groups(all P＞0.05).Conclusion The three-lobe HoLEP for the treatment of BPH is safe and effective in a county-level hospital setting.Surgeons with some experience in endoscopic surgery can preliminarily master this technique after a learning period of approximately 20 procedures.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

Objective To investigate the clinical manifestations of traditional Chinese medicine(TCM)and their associations with TCM constitutions in individuals who have migrated to plateau areas,and to provide a scientific basis for plateau health management.Methods Migrants living in areas above 3000 m were selected as research subjects.Data were collected by using TCM symptom assessment scales and constitution assessment scales.Descriptive statistical analysis was conducted to determine the incidence and severity of symptoms among individuals with different migration durations,and core symptoms were identified.Factor analysis was performed by using SPSS software to extract symptom clusters and explore the correlation between core symptoms and TCM constitutions.Results Among individuals who migrated to plateau areas,the incidence of discomfort symptoms was 83.44％.The five most common symptoms were dry skin(67.94％),forgetfulness(56.03％),dry mouth(52.06％),yellow urine(48.73％),and insomnia(47.14％).In the top 10 symptoms with the highest increase in incidence,yellow urine(33.51％)and forgetfulness(26.33％)were both present in the top 10 symptoms across different migration durations.Factor analysis extracted 5,2,4,and 6 symptom clusters from the overall population,individuals who migrated within 1 year,those who migrated for 1-2 years,and those who migrated over 2 years,respectively.Qi-deficiency constitution(QDC),blood stasis constitution(BSC),qi stagnation constitution(QSC),phlegm-dampness constitution(PDC),and dampness-heat constitution(DHC)were significantly positively correlated with forgetfulness.Conclusion Migrating to plateau areas can induce discomfort symptoms,and both the number and incidence of symptoms increase with longer migration durations.The number and incidence of high-frequency symptoms(incidence≥30％)increase with prolonged migration time.There are differences in the composition and severity of symptom clusters across different migration durations.QDC,BSC,QSC,PDC,and DHC are closely related to forgetfulness and can be considered risk constitutions for forgetfulness.Timely attention to changes in symptom clusters and constitutions can help prevent and mitigate the occurrence and development of symptoms.

Colorimetric analysis is a detection and quantification method based on observable color changes in response to analytes,which offers significant advantages including visually detectable signals,straightforward operation,rapid response,and low cost.Consequently,it plays a crucial role in a variety of fields.With increasingly diverse and complex application,colorimetric analysis requires continuous improvement in sensitivity,adaptability to diverse detection environments,and complex data handling capabilities.In recent years,the development of artificial intelligence technology,particularly within its core domain of machine learning(ML),has led to significant advancements in colorimetric analysis.The ML-assisted colorimetric analysis enables high-throughput and high-sensitivity detection,alongside automated analysis,thereby providing novel strategies to overcome the inherent limitations.This review categorized machine learning techniques and summarized their application in colorimetric analysis,introducing two fundamental categories of supervised learning,and unsupervised learning based on the division of core learning paradigms.The research progress of ML-assisted colorimetric analysis in the fields of environmental monitoring,biochemical detection,and food safety were summarized.Finally,the current challenges facing by this research area were analyzed and the research prospect of ML-assisted colorimetric analysis was outlined.

This study was aimed at providing basic data for the control and prevention of Yersinia enterocolitica(Ye)infections.Ye isolates from stool samples collected from patients with diarrhea in a Beijing district between January 2019 and June 2024 were studied.Basic patient information and stool samples were collected,and quantitative polymerase chain reaction(qPCR)was applied to enriched cultures.Further analyses included virulence gene detection,whole-genome sequencing,and drug resistance detection.The detection rate of Ye was 0.76%(11/1 439),according to culture methods,thus yielding 12 Ye strains from distinct patients:11 isolated during the study period and 1 from 2017.The 12 Ye positive patients were 6-41 years of age,and their clinical presentations predominantly featured watery stools(66.67%,8/12)and loose stools(33.33%,4/12).The frequencies of nausea,vomiting,and fever were 41.67%(5/12),41.67%(5/12),and 8.33%(1/12),respectively.The drug resistance rates of Ye to TET,AMP,and NAL were 50.00%(6/12),33.33%(4/12),and 25.00%(3/12),respectively.One Ye strain exhibited multidrug resistance to ETP,MEM,TET,CIP,NAL,and AMP.According to qPCR detection of five common virulence genes,two Ye strains were identified as ystA+/ystB-type(ystA+/ystB-/ail+/yadA+/virF+),whereas ten strains were identified as ystA-/ystB+type(ystA-/ystB+/ail-/yadA-/virF-).VFDB database analysis based on genome sequences indicated that 12 Ye strains carried an average of 11 key virulence genes associated with adhesion,invasion,protease activity,and flagellar movement,and predicted 106 virulence genes and 12 virulence gene profiles.Only the two ystA+/ystB-Ye strains contained elements related to the TTSS and ABC transporter function.Detection of ystA-/ystB+Ye in stool isolation and culture of diarrhea cases might potentially have been missed in some cases,thus highlighting the importance of fluorescence PCR screening of fecal growth solutions to enhance isolation efficiency.Moreover,our findings revealed the genetic diversity of Ye isolated from diarrhea cases,thereby indicating the presence of multiple types of virulence genes within this pathogen.

BACKGROUND: Atrial fibrillation (AF) is a common cardiac arrhythmia in the elderly. This study aimed to evaluate urban-rural disparities in its prevalence and management in elderly Chinese. METHODS: Consecutive participants aged ≥ 65 years attending outpatient clinics were enrolled for AF screening using handheld single-lead electrocardiogram (ECG) from April 2017 to December 2022. Each ECG rhythm strip was reviewed from the research team. AF or uninterpretable single-lead ECGs were referred for 12-lead ECG. Primary study outcome comparison was between rural and urban areas for the prevalence of AF. The Student's t-test was used to compare mean values of clinical characteristics between rural and urban participants, while the Pearson's chi-square test was used to compare between-group proportions. Multivariate stepwise logistic regression analysis was performed to estimate the association between AF and various patient characteristics. RESULTS: The 29,166 study participants included 13,253 men (45.4%) and had a mean age of 72.2 years. The 7073 rural participants differed significantly (P ≤ 0.02) from the 22,093 urban participants in several major characteristics, such as older age, greater body mass index, and so on. The overall prevalence of AF was 4.6% (n = 1347). AF was more prevalent in 7073 rural participants than 22,093 urban participants (5.6% vs. 4.3%, P < 0.01), before and after adjustment for age, body mass index, blood pressure, pulse rate, cigarette smoking, alcohol consumption and prior medical history. Multivariate logistic regression analysis identified overweight/obesity (OR = 1.35, 95% CI: 1.17-1.54) in urban areas and cigarette smoking (OR = 1.62, 95% CI: 1.20-2.17) and alcohol consumption (OR = 1.42, 95% CI: 1.04-1.93) in rural areas as specific risk factors for prevalent AF. In patients with known AF in urban areas (n = 781) and rural areas (n = 338), 60.6% and 45.9%, respectively, received AF treatment (P < 0.01), and only 22.4% and 17.2%, respectively, received anticoagulation therapy (P = 0.05). CONCLUSIONS In China, there are urban-rural disparities in AF in the elderly, with a higher prevalence and worse management in rural areas than urban areas. Our study findings provide insight for health policymakers to consider urban-rural disparity in the prevention and treatment of AF.

Homo- or heterodimeric compounds that affect dimeric protein function through interaction between monomeric moieties and protein subunits can serve as valuable sources of potent and selective drug candidates. Here, we screened an in-house dimeric natural product collection, and panepocyclinol A (PecA) emerged as a selective and potent STAT3 inhibitor with profound anti-tumor efficacy. Through cross-linking C712/C718 residues in separate STAT3 monomers with two distinct Michael receptors, PecA inhibits STAT3 DNA binding affinity and transcription activity. Molecular dynamics simulation reveals the key conformation changes of STAT3 dimers upon the di-covalent binding with PecA that abolishes its DNA interactions. Furthermore, PecA exhibits high efficacy against anaplastic large T cell lymphoma in vitro and in vivo, especially those with constitutively activated STAT3 or STAT3^Y640F. In summary, our study describes a distinct and effective di-covalent modification for the dimeric compound PecA to disrupt STAT3 function.