Objective:To investigate the promoting effect of individualized instruction combined with a blended learning model (IIBLM) in continuing training of neurology.Methods:A total of 93 trainees who received continuing training in Department of Neurology, The First Affiliated Hospital of Sun Yat-sen University, from August 2022 to December 2024 were enrolled as subjects. The 50 trainees registered since January 2024 were enrolled as observation group and received IIBLM teaching, including sub-specialty modular training, a cycle-adaptive cultivation system, a "mutual-selection" mentorship program, an on/off-line dual-track curriculum system, a dynamic course allocation mechanism based on "mutual selection", and a competency growth evaluation system, while the 43 trainees registered before January 2024 were enrolled as control group and received traditional teaching. A questionnaire survey and comprehensive competency assessments were performed to evaluate the effect of teaching, and the t-test, the chi-square test, and the qualitative analysis were used for statistical analysis. Results:Compared with the control group, the experimental group showed systematic improvements in clinical contents, theoretical curriculum, faculty competency, and workflow arrangement during continuing training, with a significant difference in the score of workflow arrangement between the two groups [(9.58±0.67) vs. (9.07±1.44), t=-2.13, P=0.037]. The observation group had a score of (97.70±1.30) for dynamic course allocation, an overall satisfaction rate of 97.15%, and a course benefit rate of 97.55%. The qualitative analysis showed that the trainees in the control group mainly complained of course monotony, while those in the observation group expected to enhance interdisciplinary integration and the cultivation of scientific research abilities. In addition, the results of competency assessment showed a continuous improvement in teaching effect after reform, with no significant difference. Conclusions:IIBLM teaching effectively enhances professional qualities, clinical competency, and the degree of satisfaction with courses among the trainees receiving continuing training, and it also revealed the necessity of interdisciplinary collaborative teaching and the integration of research and clinical practice.

Objective To investigate the relationship between non-high-density lipoprotein cho-lesterol(non-HDL-C)and cardiovascular disease(CVD)in maintenance hemodialysis(MHD)pa-tients.Methods A total of 124 MHD patients were enrolled and divided into CVD group(53 pa-tients)and non-CVD group(71 patients)based on the presence or absence of CVD.Clinical data be-tween the two groups were compared.Additionally,patients were divided into severe calcification group[coronary artery calcification score(C ACS)≥ 400,40 patients]and non-severe calcification group(CACS＜400,84 patients)based on CACS,and clinical data between these two groups were also compared.Multivariate Logistic regression analysis was used to explore the independent risk fac-tors for CVD in MHD patients,and the predictive performance of related indicators for CVD in MHD patients was assessed using receiver operating characteristic(ROC)curves.Results The levels of serum total cholesterol,low-density lipoprotein cholesterol(LDL-C),and non-HDL-C were higher,while the level of high-density lipoprotein cholesterol(HDL-C)was lower in the CVD group compared with the non-CVD group(P＜0.05).The levels of serum total cholesterol,LDL-C,and non-HDL-C were higher,and the level of HDL-C was lower in the severe calcification group compared with the non-severe calcification group(P＜0.05).Multivariate Logistic regression analysis showed that high levels of LDL-C and non-HDL-C were both independent risk factors for CVD in MHD patients(P＜0.05).ROC curve analysis showed that the areas under the curve for predicting CVD in MHD patients were 0.858 and 0.723 for non-HDL-C and LDL-C,respectively,and non-HDL-C had high-er specificity and Youden index than LDL-C.Conclusion Elevated non-HDL-C level is an inde-pendent risk factor and has high predictive performance for CVD in MHD patients.

Objective:To develop CNN-Dinov2, a deep learning-based automatic classification model for Gram-stained images, enabling rapid diagnosis of three prevalent Gram-negative bacilli in bloodstream infections: Escherichia coli ( E.coli), Klebsiella pneumoniae ( K.pneumoniae), and Pseudomonas aeruginosa ( P.aeruginosa). Methods:This evaluation study analyzed 1 425 Gram-stained microscopic images from patients with bloodstream infections at Houjie Hospital, in Dongguan City, collected between January 2023 and January 2024. The images, all positive for blood culture and identified as target strains, were categorized into Escherichia coli (419 images), Klebsiella pneumoniae (411 images), Pseudomonas aeruginosa (413 images), and other Gram-negative bacilli (182 images). They were randomly split into a training set (1 141 images), a validation set (141 images), and a test set (143 images) in an 8∶1∶1 ratio. A hybrid CNN-Dinov2 model was developed by integrating ResNet′s local feature extraction with Dinov2′s global pre-trained features, followed by a fully connected layer. The model was optimized by inputting the preprocessed images and adjusting parameters through loss calculation and backpropagation. AlexNet, Dinov2, and ResNet18 served as control models. The models′ classification performance was assessed using accuracy, precision, weighted F1 score, and recall rate, derived from the confusion matrix. The PR curve and AP value further evaluated each model′s classification capability across the four image categories. Results:The CNN-Dinov2 model achieved a training accuracy of 99.74%, a validation accuracy of 98.12%, and a validation loss of 0.070 6, demonstrating robust generalization without overfitting. Validation metrics revealed superior performance with an accuracy of 98.60%, precision of 98.65%, a weighted F1 score of 98.60%, and a recall rate of 98.60%, outperforming other models. The confusion matrix confirmed its strong classification capability, with the highest sum of diagonal values for identifying four types of bacteria. The macro average precision (AP) values under the precision-recall (PR) curves were all 1, indicating excellent discrimination across all categories. Overall, the CNN-Dinov2 model exhibited the best performance among the four models evaluated.Conclusion:This study successfully developed CNN-Dinov2, an automated classification model for Gram staining images. It offers valuable support for the rapid diagnosis of bloodstream infections caused by Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa, demonstrating practical utility.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Objective:To investigate the relationship between macrophage activation related factors and clini-cal symptoms of schizophrenia(SCZ).Methods:Outpatient or inpatient SCZ patients(n=166)and normal con-trols(n=71)meeting the diagnostic criteria of DSM 4th edition were selected as subjects.The psychopathological symptoms were assessed by the Positive and Negative Syndrome Scale(PANSS),and the concentrations of α-Na-Galases,MAF and IL-18 were determined by enzyme-linked immunosorbent assay(ELISA).The correlation be-tween biological indicators and clinical symptoms was analyzed and the mediation effect was tested.Results:The concentrations of α-NaGalases(P＜0.001)and MAF(P＜0.01)in SCZ group were lower than those in normal control group.In SCZ group,IL-18 was negatively correlated with α-NaGalases concentration(r=-0.24,P＜0.01).α-NaGalases was positively correlated with MAF concentration(r=0.67,P＜0.001),and the total score of PANSS positive symptom scale was positively correlated with IL-18(r=0.21,P＜0.05)and MAF concentration(r=0.22,P＜0.01).The mediating effect of α-NaGalases and MAF was statistically significant,and the relative mediating effect accounted for 25.47％.Conclusion:The increase of IL-18 level may indicate the occurrence of positive symptoms of schizophrenia,and α-NaGalases and MAF may negatively regulate the inflammatory damage effect of IL-18 on SCZ,thereby reducing the positive symptoms.

Objective:To investigate the relationship between macrophage activation related factors and clini-cal symptoms of schizophrenia(SCZ).Methods:Outpatient or inpatient SCZ patients(n=166)and normal con-trols(n=71)meeting the diagnostic criteria of DSM 4th edition were selected as subjects.The psychopathological symptoms were assessed by the Positive and Negative Syndrome Scale(PANSS),and the concentrations of α-Na-Galases,MAF and IL-18 were determined by enzyme-linked immunosorbent assay(ELISA).The correlation be-tween biological indicators and clinical symptoms was analyzed and the mediation effect was tested.Results:The concentrations of α-NaGalases(P＜0.001)and MAF(P＜0.01)in SCZ group were lower than those in normal control group.In SCZ group,IL-18 was negatively correlated with α-NaGalases concentration(r=-0.24,P＜0.01).α-NaGalases was positively correlated with MAF concentration(r=0.67,P＜0.001),and the total score of PANSS positive symptom scale was positively correlated with IL-18(r=0.21,P＜0.05)and MAF concentration(r=0.22,P＜0.01).The mediating effect of α-NaGalases and MAF was statistically significant,and the relative mediating effect accounted for 25.47％.Conclusion:The increase of IL-18 level may indicate the occurrence of positive symptoms of schizophrenia,and α-NaGalases and MAF may negatively regulate the inflammatory damage effect of IL-18 on SCZ,thereby reducing the positive symptoms.

Objective:To investigate the promoting effect of individualized instruction combined with a blended learning model (IIBLM) in continuing training of neurology.Methods:A total of 93 trainees who received continuing training in Department of Neurology, The First Affiliated Hospital of Sun Yat-sen University, from August 2022 to December 2024 were enrolled as subjects. The 50 trainees registered since January 2024 were enrolled as observation group and received IIBLM teaching, including sub-specialty modular training, a cycle-adaptive cultivation system, a "mutual-selection" mentorship program, an on/off-line dual-track curriculum system, a dynamic course allocation mechanism based on "mutual selection", and a competency growth evaluation system, while the 43 trainees registered before January 2024 were enrolled as control group and received traditional teaching. A questionnaire survey and comprehensive competency assessments were performed to evaluate the effect of teaching, and the t-test, the chi-square test, and the qualitative analysis were used for statistical analysis. Results:Compared with the control group, the experimental group showed systematic improvements in clinical contents, theoretical curriculum, faculty competency, and workflow arrangement during continuing training, with a significant difference in the score of workflow arrangement between the two groups [(9.58±0.67) vs. (9.07±1.44), t=-2.13, P=0.037]. The observation group had a score of (97.70±1.30) for dynamic course allocation, an overall satisfaction rate of 97.15%, and a course benefit rate of 97.55%. The qualitative analysis showed that the trainees in the control group mainly complained of course monotony, while those in the observation group expected to enhance interdisciplinary integration and the cultivation of scientific research abilities. In addition, the results of competency assessment showed a continuous improvement in teaching effect after reform, with no significant difference. Conclusions:IIBLM teaching effectively enhances professional qualities, clinical competency, and the degree of satisfaction with courses among the trainees receiving continuing training, and it also revealed the necessity of interdisciplinary collaborative teaching and the integration of research and clinical practice.

Objective:To develop CNN-Dinov2, a deep learning-based automatic classification model for Gram-stained images, enabling rapid diagnosis of three prevalent Gram-negative bacilli in bloodstream infections: Escherichia coli ( E.coli), Klebsiella pneumoniae ( K.pneumoniae), and Pseudomonas aeruginosa ( P.aeruginosa). Methods:This evaluation study analyzed 1 425 Gram-stained microscopic images from patients with bloodstream infections at Houjie Hospital, in Dongguan City, collected between January 2023 and January 2024. The images, all positive for blood culture and identified as target strains, were categorized into Escherichia coli (419 images), Klebsiella pneumoniae (411 images), Pseudomonas aeruginosa (413 images), and other Gram-negative bacilli (182 images). They were randomly split into a training set (1 141 images), a validation set (141 images), and a test set (143 images) in an 8∶1∶1 ratio. A hybrid CNN-Dinov2 model was developed by integrating ResNet′s local feature extraction with Dinov2′s global pre-trained features, followed by a fully connected layer. The model was optimized by inputting the preprocessed images and adjusting parameters through loss calculation and backpropagation. AlexNet, Dinov2, and ResNet18 served as control models. The models′ classification performance was assessed using accuracy, precision, weighted F1 score, and recall rate, derived from the confusion matrix. The PR curve and AP value further evaluated each model′s classification capability across the four image categories. Results:The CNN-Dinov2 model achieved a training accuracy of 99.74%, a validation accuracy of 98.12%, and a validation loss of 0.070 6, demonstrating robust generalization without overfitting. Validation metrics revealed superior performance with an accuracy of 98.60%, precision of 98.65%, a weighted F1 score of 98.60%, and a recall rate of 98.60%, outperforming other models. The confusion matrix confirmed its strong classification capability, with the highest sum of diagonal values for identifying four types of bacteria. The macro average precision (AP) values under the precision-recall (PR) curves were all 1, indicating excellent discrimination across all categories. Overall, the CNN-Dinov2 model exhibited the best performance among the four models evaluated.Conclusion:This study successfully developed CNN-Dinov2, an automated classification model for Gram staining images. It offers valuable support for the rapid diagnosis of bloodstream infections caused by Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa, demonstrating practical utility.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

To investigate the genomic features and perform cluster analysis of Carbapenem-resistant Klebsiella pneumoniae (CRKP) to provide an experimental basis for guiding the prevention and treatment of CRKP infections.A retrospective case-cohort study was conducted on 19 non-redundant CRKP strains isolated from the Tenth Affiliated Hospital of Southern Medical University between January and June 2023. Whole genome sequencing (WGS) and multilocus sequence typing (MLST) were performed to compare genomic features and analyze the resistance genes and homology of the strains.The results showed that the 19 CRKP strains were isolated from 8 different clinical departments, mainly from respiratory specimens. The whole genome sequencing revealed that the genomic lengths of CRKP ranged from 4.90 to 5.85 Mbp, with contigs N50 values>20 kb for each genome. The median overall GC content was 57.0% (50.4%-57.1%). Comparative genomic analysis identified three regions with high genomic variability. WGS detected 32 resistance genes across 11 categories. All 19 strains carried carbapenem resistance genes ( blaKPC-2 and blaOXA-48), blaTEM-1B extended-spectrum β-lactamase resistance genes, qnrS1 quinolone resistance gene, and fosA fosfomycin resistance gene, with each strain carrying only one carbapenemase gene. The detection rate of blaKPC-2 was 94.7% (18/19). MLST identified three sequence types: ST11, ST437 and ST147, with ST11 being predominant (89.5%, 17/19). Clustering analysis based on acquired resistance genes revealed three clonal transmission patterns among strains 72 and 90, and strains 88, 84, 66 and 79.In conclusion, CRKP strains carry multiple resistance genes, and clustering analysis indicating that nosocomial clonal transmission is closely related to acquired resistance genes. The ST11- blaKPC-2 type strain is the predominant clone. Strengthened surveillance and effective control strategies are necessary to reduce nosocomial transmission of CRKP.