Objective: To analyze factors impacting mental health status of medical students based on ecological systems theory, so as to provide reference for the mental health promotion system for medical students. Methods: In June 2024, 1 760 medical school students randomly selected from 19 different kinds of medical colleges in eastern China by stratified cluster were surveyed using questionnaires and expert interviews. Descriptive statistics, cross analysis, hierarchical linear regression analysis, structural equation models were used for data analysis. Results: Medical school students had higher satisfaction with the school (65.85%) and a great sense of perceived social support (57.16%). Furthermore, 91.14% of the students had normal interpersonal relationships. However, 44.89% reported that their mental health was impacted by high level of depression. The hierarchical linear regression analysis showed that the mental health outcomes of the medical students were positively predicted by higher perceived social support scores ( β =-11.40), institutional satisfaction ( β =-4.85 ), and lower help seeking stigma scores ( β =9.31) ( P <0.05). The structural equation modeling showed that the status of both perceived social support and self help seeking stigma had significant impacts on depression severity ( β =-0.32, -0.53) and interpersonal relationship sensitivity ( β =-0.31, 0.58) among medical students ( P <0.01).Through expert interviews, collaborations between the school and the tripartite organization (families, universities and society) was of growing importance. Conclusions Perceived social support and self stigma have a significant impact on the mental health status of medical students. The problem of self stigma of medical students should be paid attention to. Therefore, families, universities and society should work together to improve the mental health of medical students.

Objective To explore the application of Mini-CEX oriented by nurses'core competencies in the clinical teaching of intern nursing students.Methods A total of 50 students,interning in the First Affiliated Hospital of Kunming Medical University from January 1,2023 to April 30,2024,were randomly divided into experimental group and control group,with 25 students in each group.The control group was taught by traditional clinical teaching methods,and the experimental group by Mini-CEX.The data indicators of the two groups of intern nursing students were analyzed and compared at entry into and exit from the department,respectively.Results The scores of the two groups were higher than those at entry into the department(P<0.05),and the intern nursing students in the experimental group had excellent scores in nursing consultation,nursing examination,nursing diagnosis,nursing measures,humanistic care,organizational effectiveness,health consultation,and overall evaluation,and the scores were higher than those of the control group(P<0.05).The improvement in all the scores was also better than that of the control group(P<0.05).Conclusion Mini-CEX,which is oriented to the nurse'core competencies of nurses,can improve the clinical nursing abilities of intern nursing students and enhance the cultivation effect of clinical practice skills.

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 evaluate the application efficiency of the artificial prenatal sonography quality assurance system（PSAIS）in the quality control of fetal image quality during the first trimester（11-14 weeks），and to assist the quality control of fetal ultrasound examinations in the first trimester in Liaoning Province using this system.Methods:One hundred fetuses（2 757 ultrasound images）from early pregnancy ultrasound screenings at the Shengjing Hospital Affiliated to China Medical University in November 2022 were retrospectively randomly selected as the test set. The performance of PSAIS were evaluated by 3 obstetric ultrasound experts. First，the experts' sectional classification was used as the gold standard，the accuracy of PSAIS in classifying standard sections was assessed；the consistency between PSAIS and expert ratings was evaluated using the intraclass correlation coefficient（ICC）. The difference of time between PSAIS and expert quality control was analyzed. Finally，PSAIS was used to evaluate the quality of early pregnancy fetal ultrasound examinations at various levels of hospitals in Liaoning province，involving a total of 35 hospitals. Ten early pregnancy fetal ultrasound screening cases were randomly selected from each hospital. The specific evaluation content included the number of stored ultrasound images per examination，the completion rate of standard sections，image quality scores for each examination and individual images，excellence rates and pass rates. The assessment results were statistically described and analyzed.Results:The accuracy rate of PSAIS classification reached to 88.25%（706/800）；the ICC between PSAIS and expert ratings was 0.978. PSAIS analysis showed that the time for each early pregnancy fetal ultrasound examination was less than the average time required by experts（3.0 s vs. 63.5 s， P<0.05）.Quality control checks revealed statistically significant differences among hospitals at various levels in terms of the number of stored images，completeness of sections，scoring，excellence rate，and pass rate（all P<0.05）. Among them，tertiary hospitals performed better than secondary hospitals，private hospitals outperformed public ones，and specialized hospitals excelled over general hospitals. In the evaluation of section standardization，the fetal median sagittal plane had the highest completion rate［99.43%（348/350）］，while the umbilical cord abdominal wall entry transverse plane scan had the lowest completion rate［31.14%（109/350）］. Conclusions:PSAIS can reliably assess the quality of early pregnancy fetal ultrasound examinations，significantly enhancing the efficiency of quality control. There are noticeable differences among hospitals at various levels in Liaoning Province regarding the image quality of early pregnancy fetal ultrasounds，and there are also some common issues in standardizing sections. The quality of obstetric ultrasound images still needs further improvement.

Migration is one of the core biological characteristics of migratory birds. The nutrient, energy, and biological flows triggered during this process can facilitate the cross-regional spread of parasites and induce changes in their community structures. This article systematically reviews the interaction between migratory birds and parasites, including the prevalence characteristics of parasites in migratory birds, the vector role of migratory birds in parasite transmission, and the potential impact of parasites on hosts, ecosystems, and humans, in order to provide theoretical support for biodiversity conservation and prevention and control of zoonosis.

Migration is one of the core biological characteristics of migratory birds. The nutrient, energy, and biological flows triggered during this process can facilitate the cross-regional spread of parasites and induce changes in their community structures. This article systematically reviews the interaction between migratory birds and parasites, including the prevalence characteristics of parasites in migratory birds, the vector role of migratory birds in parasite transmission, and the potential impact of parasites on hosts, ecosystems, and humans, in order to provide theoretical support for biodiversity conservation and prevention and control of zoonosis.

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 evaluate the application efficiency of the artificial prenatal sonography quality assurance system（PSAIS）in the quality control of fetal image quality during the first trimester（11-14 weeks），and to assist the quality control of fetal ultrasound examinations in the first trimester in Liaoning Province using this system.Methods:One hundred fetuses（2 757 ultrasound images）from early pregnancy ultrasound screenings at the Shengjing Hospital Affiliated to China Medical University in November 2022 were retrospectively randomly selected as the test set. The performance of PSAIS were evaluated by 3 obstetric ultrasound experts. First，the experts' sectional classification was used as the gold standard，the accuracy of PSAIS in classifying standard sections was assessed；the consistency between PSAIS and expert ratings was evaluated using the intraclass correlation coefficient（ICC）. The difference of time between PSAIS and expert quality control was analyzed. Finally，PSAIS was used to evaluate the quality of early pregnancy fetal ultrasound examinations at various levels of hospitals in Liaoning province，involving a total of 35 hospitals. Ten early pregnancy fetal ultrasound screening cases were randomly selected from each hospital. The specific evaluation content included the number of stored ultrasound images per examination，the completion rate of standard sections，image quality scores for each examination and individual images，excellence rates and pass rates. The assessment results were statistically described and analyzed.Results:The accuracy rate of PSAIS classification reached to 88.25%（706/800）；the ICC between PSAIS and expert ratings was 0.978. PSAIS analysis showed that the time for each early pregnancy fetal ultrasound examination was less than the average time required by experts（3.0 s vs. 63.5 s， P<0.05）.Quality control checks revealed statistically significant differences among hospitals at various levels in terms of the number of stored images，completeness of sections，scoring，excellence rate，and pass rate（all P<0.05）. Among them，tertiary hospitals performed better than secondary hospitals，private hospitals outperformed public ones，and specialized hospitals excelled over general hospitals. In the evaluation of section standardization，the fetal median sagittal plane had the highest completion rate［99.43%（348/350）］，while the umbilical cord abdominal wall entry transverse plane scan had the lowest completion rate［31.14%（109/350）］. Conclusions:PSAIS can reliably assess the quality of early pregnancy fetal ultrasound examinations，significantly enhancing the efficiency of quality control. There are noticeable differences among hospitals at various levels in Liaoning Province regarding the image quality of early pregnancy fetal ultrasounds，and there are also some common issues in standardizing sections. The quality of obstetric ultrasound images still needs further improvement.

This study summarizes the application of automatic recognition technologies for patient-ventilator asynchrony(PVA)during mechanical ventilation.In the early stages,the method of setting rules and thresholds relied on manual interpretation of ventilator parameters and waveforms.While these methods were intuitive and easy to operate,they were relatively sensitive in threshold setting and rule selection and could not adapt well to minor changes in patient status.Subsequently,machine learning and deep learning technologies began to emerge and develop.These technologies automatically extract and learn data characteristics through algorithms,making PVA detection more robust and universal.Among them,logistic regression,support vector machines,random forest,hidden Markov models,convolutional autoencoders,long short-term memory networks,one-dimensional convolutional neural networks,etc.,have all been successfully used for PVA recognition.Despite the significant advancements in feature extraction through deep learning methods,their demand for labelled data is high,potentially consuming significant medical resources.Therefore,the combination of reinforcement learning and self-supervised learning may be a viable solution.In addition,most algorithm validations are based on a single dataset,so the need for cross-dataset validation in the future will be an important and challenging direction for development.

Objective To investigate the synergistic effect of Shugan Huatan Sanjie prescription(SHSF)and paclitaxel on MCF-7/PTX cells of breast cancer and its mechanism.Methods The PTX-resistant breast cancer cell line MCF-7/PTX was established by continuous induction of low concentration.The effects of different concentrations of PTX on MCF-7 and MCF-7/PTX cells.Then the proliferation of MCF-7/PTX cells by SHSF containing serum were detected by MTT assay,and the drug resistance index(RI)and reversion times were calculated according to the IC50 value.MCF-7/PTX cells were divided into blank control group,PTX group(45 nmol·L-1 PTX),SHSF group(7.5％SHSF drug-containing serum)and PTX+SHSF group(45 nmol·L-1 PTX+7.5％SHSF drug-containing serum).The cells were treated with drugs for 24 h.The apoptosis level of each group was detected by flow cytometry.The expression levels of apoptotic proteins Bax,Bcl-2,p-Akt(ser473),Akt,p-mTOR(Ser2448)and mTOR were detected by Western blot.Results The PTX-resistant cell line MCF-7/PTX was established successfully,and the RI value was 6.70.The proliferative activity of MCF-7/PTX cells decreased in a concentration-dependent manner with the increase of SHSF drug-containing serum concentration.And the resistance reversal ratio of 7.5％SHSF serum to MCF-7/PTX cells was 3.48.Compared with blank control group,the apoptosis levels of MCF-7/PTX cells in PTX group and SHSF group were significantly increased(P＜0.01),the protein expression level of Bax was significantly up-regulated(P＜0.01),and the protein expression levels of Bcl-2,p-Akt/Akt and p-mTOR/mTOR were significantly down-regulated(P＜0.05);Compared with PTX group,the apoptosis level in PTX+SHSF group was significantly increased(P＜0.01),the expression level of Bax protein was significantly up-regulated(P＜0.01),and the expression levels of Bcl-2,p-Akt and p-mTOR protein were significantly down-regulated(P＜0.01).Conclusion Shugan Huatan Sanjie Recipe promotes apoptosis by inhibiting Akt/mTOR pathway,and thus plays a synergistic effect with paclitaxel on MCF-7/PTX cells of breast cancer.