ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

ObjectiveCleft palate (CP) is a common congenital deformity often associated with velopharyngeal insufficiency (VPI), which disrupts the physiological coupling between respiration and speech. Conventional clinical assessments, such as nasometry and spirometry, provide limited static data and fail to visualize the dynamic spatiotemporal distribution of lung ventilation during phonation. This study introduces spatiotemporal electrical impedance tomography (ST-EIT) to evaluate speech-respiratory functional features in CP patients compared to normal controls (NC). The aim is to characterize multi-domain respiratory patterns and to validate an interpretable machine learning framework for providing objective, quantitative evidence for clinical assessment. MethodsSeventy-five participants were enrolled in this study, comprising 37 patients with surgically repaired CP and 38 healthy volunteers matched for age, gender, and body mass index (BMI). All subjects performed standardized sustained phonation tasks while undergoing synchronous monitoring with a 16-electrode EIT system and a pneumotachograph. A comprehensive feature engineering pipeline was developed to extract physiological parameters across 3 complementary domains. (1) Temporal domain: including inspiratory/expiratory phase duration (tPhase), time constants (Tau), and inspiratory-to-expiratory time ratios (TI/TE); (2) airflow domain: comprising mean flow, peak flow, and instantaneous flow at 25%, 50%, and 75% of tidal volume; and (3) spatial domain: quantifying global and regional tidal impedance variation (TIV), global inhomogeneity (GI), and center of ventilation (CoV). Extreme Gradient Boosting (XGBoost) classifiers were trained using 5 distinct data sources (Spirometry, Nasometry, Inspiratory-EIT, Expiratory-EIT, and fused ST-EIT). Model performance was rigorously evaluated via stratified 5-fold cross-validation, and Shapley additive explanations (SHAP) were employed to quantify global and local feature contributions. ResultsThe CP group exhibited a distinct respiratory phenotype compared to controls. In the temporal domain, CP patients showed significantly shorter inspiratory (1.60 s vs.1.85 s, P<0.001) and expiratory phase durations (2.45 s vs. 3.95 s, P<0.001), indicating a rapid, shallow breathing rhythm. In the airflow domain, while inspiratory flows were comparable, the CP group demonstrated significantly elevated mean and peak flows during the expiratory phase (P<0.001), reflecting compensatory respiratory effort. Spatially, CP patients presented significant ventilation redistribution, characterized by higher regional TIV in the right-anterior (ROI1) and left-posterior (ROI4) quadrants, but lower TIV in the left-anterior (ROI2) quadrant. In terms of diagnostic accuracy, the multi-modal ST-EIT model achieved the highest performance (AUC: 0.915±0.012, Accuracy: 0.843±0.019, F1-score: 0.872±0.017), substantially outperforming models based on spirometry (AUC: 0.721) or nasometry (AUC: 0.625) alone. Interpretability analysis revealed that spatial domain features were the most critical, contributing 53.4% to the model’s decision-making, followed by temporal (25.0%) and airflow (21.6%) features. ConclusionST-EIT successfully captures the temporal, airflow, and spatial deviations in CP speech respiration that are undetectable by conventional methods—specifically, rapid phase transitions, hyperdynamic expiratory airflow, and regional ventilation heterogeneity. This study validates ST-EIT as a robust, non-invasive, and radiation-free tool for characterizing speech-respiratory dysfunction, offering high clinical value for bedside screening, rehabilitation planning, and longitudinal monitoring of patients with cleft palate.

Antimicrobial resistance (AMR) poses a critical global health threat. This review systematically examines AMR surveillance technology advances, from conventional culture methods to modern molecular diagnostics (e.g., whole-genome sequencing) and artificial intelligence-assisted approaches. It focuses on the current application of mass spectrometry, machine learning predictive models, and real-time surveillance networks. To address challenges including inadequate technical standardization, clinical translation barriers, and data-sharing limitations, we propose integrated ^"genotype-phenotype^" strategies and global standardization framework, while exploring future applications of CRISPR-based portable detection, single-cell sequencing, and blockchain technologies.

Objective To explore the technical process and clinical application of laparoscopic combined upper midline incision minimally invasive liver transplantation. Methods A retrospective analysis was conducted on 30 cases of laparoscopic combined upper midline incision minimally invasive liver transplantation. The cases were divided into cirrhosis group (15 cases) and liver failure group (15 cases) based on the primary disease. The surgical and postoperative conditions of the two groups were compared. Results All patients successfully underwent laparoscopic "clockwise" liver resection, with no cases of passive conversion to open surgery or intolerance to pneumoperitoneum. In 6 cases, the right lobe was relatively large, and the right hepatic ligaments could not be completely mobilized. One case required an additional reverse "L" incision during open surgery. All patients successfully completed the liver transplantation, with no major intraoperative bleeding, cardiovascular events, or other occurrences in the 30 patients. The model for end-stage liver disease (MELD) score in the cirrhosis group was lower than that in the liver failure group (P<0.001). There were no statistically significant differences between the two groups in terms of age, surgical time, blood loss, anhepatic phase, or cold ischemia time (all P>0.05). During the perioperative period, there was 1 case of hepatic artery embolism, 1 case of portal vein anastomotic stenosis, no complications of hepatic vein and inferior vena cava, and 3 cases of biliary anastomotic stenosis, all of which occurred in the liver failure group. Conclusions In strictly selected cases, the minimally invasive liver transplantation technique combining laparoscopic hepatectomy with upper midline incision for graft implantation has the advantages of smaller incisions, less bleeding, relatively easier operation, and faster postoperative recovery, which is worthy of clinical promotion and application.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that is challenging to treat and lacks targeted therapeutic drugs in the clinic. Natural active ingredients provide promising opportunities for discovering and developing targeted therapies for TNBC. This study investigated the effects of daurisoline on TNBC and elucidated its potential mechanisms. Using network pharmacology, a correlation was identified between daurisoline, derived from Menispermum dauricum, and breast cancer, particularly involving the Notch signaling pathway. The effects of daurisoline on the proliferation, migration, and apoptosis of MDA-MB-231 and MDA-MB-468 cells were evaluated in vitro. Additionally, the impact of daurisoline on the growth of MDA-MB-231 xenograft tumors in nude mice was assessed through in vivo experiments. Expression levels of Notch signaling pathway-related proteins, including Notch-1, NICD, PSEN-1, Bax, and Bcl-2, were examined using molecular docking and Western blotting to explore the underlying mechanisms of daurisoline’s anti-breast cancer effects. It was revealed that daurisoline could effectively inhibit the proliferation and migration of MDA-MB-231 and MDA-MB-468 cells and promote apoptosis. Furthermore, it significantly reduced the growth of subcutaneous tumors in nude mice. Notably, daurisoline could reduce the hydrolytic activity of γ-secretase by binding to the catalytic core PSEN-1, thereby inhibiting activation of the γ-secretase/Notch axis and contributing to its anti-TNBC effects.This study supported the development of naturally targeted drugs for TNBC and provided insights into the research on dibenzylisoquinoline alkaloids, such as daurisoline.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that is challenging to treat and lacks targeted therapeutic drugs in the clinic. Natural active ingredients provide promising opportunities for discovering and developing targeted therapies for TNBC. This study investigated the effects of daurisoline on TNBC and elucidated its potential mechanisms. Using network pharmacology, a correlation was identified between daurisoline, derived from Menispermum dauricum, and breast cancer, particularly involving the Notch signaling pathway. The effects of daurisoline on the proliferation, migration, and apoptosis of MDA-MB-231 and MDA-MB-468 cells were evaluated in vitro. Additionally, the impact of daurisoline on the growth of MDA-MB-231 xenograft tumors in nude mice was assessed through in vivo experiments. Expression levels of Notch signaling pathway-related proteins, including Notch-1, NICD, PSEN-1, Bax, and Bcl-2, were examined using molecular docking and Western blotting to explore the underlying mechanisms of daurisoline’s anti-breast cancer effects. It was revealed that daurisoline could effectively inhibit the proliferation and migration of MDA-MB-231 and MDA-MB-468 cells and promote apoptosis. Furthermore, it significantly reduced the growth of subcutaneous tumors in nude mice. Notably, daurisoline could reduce the hydrolytic activity of γ-secretase by binding to the catalytic core PSEN-1, thereby inhibiting activation of the γ-secretase/Notch axis and contributing to its anti-TNBC effects.This study supported the development of naturally targeted drugs for TNBC and provided insights into the research on dibenzylisoquinoline alkaloids, such as daurisoline.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that is challenging to treat and lacks targeted therapeutic drugs in the clinic. Natural active ingredients provide promising opportunities for discovering and developing targeted therapies for TNBC. This study investigated the effects of daurisoline on TNBC and elucidated its potential mechanisms. Using network pharmacology, a correlation was identified between daurisoline, derived from Menispermum dauricum, and breast cancer, particularly involving the Notch signaling pathway. The effects of daurisoline on the proliferation, migration, and apoptosis of MDA-MB-231 and MDA-MB-468 cells were evaluated in vitro. Additionally, the impact of daurisoline on the growth of MDA-MB-231 xenograft tumors in nude mice was assessed through in vivo experiments. Expression levels of Notch signaling pathway-related proteins, including Notch-1, NICD, PSEN-1, Bax, and Bcl-2, were examined using molecular docking and Western blotting to explore the underlying mechanisms of daurisoline’s anti-breast cancer effects. It was revealed that daurisoline could effectively inhibit the proliferation and migration of MDA-MB-231 and MDA-MB-468 cells and promote apoptosis. Furthermore, it significantly reduced the growth of subcutaneous tumors in nude mice. Notably, daurisoline could reduce the hydrolytic activity of γ-secretase by binding to the catalytic core PSEN-1, thereby inhibiting activation of the γ-secretase/Notch axis and contributing to its anti-TNBC effects.This study supported the development of naturally targeted drugs for TNBC and provided insights into the research on dibenzylisoquinoline alkaloids, such as daurisoline.

Objective To understand the willingness to engage in the combine medical care with old-age care institutions of medical students in vocational colleges and analyze their influencing factors.Methods By convenient sampling method,1 266 medical students from 3 vocational colleges in Hunan Province were select-ed as the research objects,and their occupational cognition and occupational willingness were investigated and analyzed.Through objective sampling,20 medical students who were not willing to work in medical institu-tions were selected for semi-structured interviews.Results 62.1%of medical students intend to work in med-ical institutions after graduation;The results of multi-factor analysis showed that the experience of caring for the elderly,understanding of the nursing institution,participating in the volunteer service of the nursing insti-tution,and cognition score of the combination of medical care and nursing were the influencing factors of med-ical students'career intention(P＜0.05).The qualitative interview found that professional identity,job abili-ty,salary,social security and promotion were the main influencing factors.Conclusion Medical students in medical nursing institutions do not have strong willingness,so they should improve their professional cogni-tion of old-age service,enhance their professional identity and post competency,fully implement salary,social security and professional title promotion,attract more medical students to engage in the combined service in-dustry,and promote the combined development of medical care.

Objective To investigate the risk factors for pneumonia complicating infectious mononucleosis(IM)in adults and construct a nomogram prediction model.Methods A retrospective analysis was conducted on 198 IM patients admitted to the Second Affiliated Hospital of Air Force Medical University from January 2015 to December 2021.Patients were divided into pneumonia group(n=52)and non-pneumonia group(n=146)based on whether pulmonary infection occurred during hospitalization.The baseline data(age,gender,place of onset,etc.),clinical manifestations(maximum body temperature,lymph node enlargement,splenomegaly,etc.),and inflammatory indicators[white blood cell count(WBC),C-reactive protein(CRP),etc.]were compared between the two groups.Kaplan-Meier curves were plotted to analyze the key indicators affecting the hospital stay of IM patients.Multivariate logistic regression was used to analyze the independent risk factors for pneumonia complicating IM in adults and construct a nomogram prediction model based on the identified risk factors.The predictive efficacy of the model was evaluated using the receiver operating characteristic(ROC)curve and the consistency of the model was assessed using the calibration curve.The fit of the model was evaluated using the Hosmer-Lemeshow test.Additionally,the sensitivity,specificity,and accuracy of the model were assessed using confusion matrix.Results Compared with non-pneumonia group,the pneumonia group had a significantly higher proportion of patients from rural areas,with body mass index(BMI)≥24 kg/m2,smoking history,hepatomegaly,fever duration of≥7 d,as well as increased total hospitalization costs and average daily hospitalization costs,and prolonged hospital stay(P<0.05).The proportion of patients with a history of antibiotic use was lower in the pneumonia group(P<0.05).Kaplan-Meier survival analysis showed that patients from rural areas,with BMI≥24 kg/m2,smoking history,no prophylactic use of antibiotics,fever duration≥7 d,and hepatomegaly had significantly prolonged hospital stays(P<0.05).Multivariate logistic regression analysis revealed that living in a rural area(OR=4.089,P<0.05),hepatomegaly(OR=4.082,P<0.05),and elevated WBC(OR=1.205,P<0.05)were independent risk factors for pneumonia complicating IM in adults,while the prophylactic use of antibiotics(OR=0.142,P<0.05)was an independent protective factor.The area under the ROC curve of the constructed nomogram prediction model was 0.827(95%CI 0.762-0.892),and the slope of the calibration curve was close to 1,and the Hosmer-Lemeshow test showed χ2=5.299,P=0.725,indicating good consistency and fit of the prediction model.The results of the confusion matrix assessment showed that the sensitivity of the model was 0.669(0.624-0.773),the specificity was 0.827(0.724-0.930),and the accuracy was 0.732(0.665-0.793).Conclusion The nomogram prediction model based on place of onset,hepatomegaly,the prophylactic use of antibiotics and WBC has excellent fit and discrimination,providing an effective quantitative tool for prognosis assessment of IM.