ObjectiveTo explore the academic characteristics of contemporary renowned Chinese medicine masters in treating diabetic kidney disease （DKD） from the perspectives of principles， methods， formulas， and medications. MethodsIn strict accordance with the Systematic Review of Text and Opinion （SrTO） process developed by the Joanna Briggs Institute （JBI）， an Australian evidence-based healthcare center， the databases including China National Knowledge Infrastructure （CNKI）， VIP Database， Wanfang Data， and China Biomedical Literature Service System （SinoMed） were searched. Based on predefined inclusion and exclusion criteria， text information extraction， quality evaluation， and text information synthesis were conducted sequentially. The data were analyzed and presented in the form of text and figures. ResultsA total of 215 articles related to 43 contemporary renowned experts in the fields of Chinese medicine nephrology and endocrinology were included. The study found that the academic thoughts of these masters in the treatment of DKD are extensive， involving multiple levels such as disease understanding， therapeutic strategies， formula application， and medication use. In terms of disease understanding， the primary pathogenesis is characterized by deficiency in the root and excess in the manifestation. It is emphasized that internal factors， such as congenital endowment deficiency， interact with external factors such as improper diet， emotional disturbances， invasion of exogenous pathogens， and delayed or inappropriate treatment， to jointly induce the disease. This further gives rise to various pathogenetic theories， including obstruction of renal collaterals by blood stasis， toxin-induced damage to renal collaterals， latent wind disturbing the kidney， and internal heat leading to mass formation. In terms of therapeutic strategies and medication use， the principal treatment method is to replenish Qi and nourish Yin. Stage-based and syndrome-differentiated treatments are advocated. Flexible use of insect-derived drugs and wind-dispelling drugs is emphasized， along with proficiency in applying classical formulas and drug pairs. Integrated internal and external treatments， as well as the combined application of multiple therapeutic approaches， are commonly employed for comprehensive management. Meanwhile， the concept of "preventive treatment of disease" is upheld， and individualized long-term management of patients is advocated. ConclusionThrough the SrTO process， the academic thoughts of contemporary renowned Chinese medicine masters in the treatment of DKD have been systematically and standardly synthesized， providing a scientific and standardized basis for future theoretical exploration.

ObjectiveTo explore the academic characteristics of contemporary renowned Chinese medicine masters in treating diabetic kidney disease （DKD） from the perspectives of principles， methods， formulas， and medications. MethodsIn strict accordance with the Systematic Review of Text and Opinion （SrTO） process developed by the Joanna Briggs Institute （JBI）， an Australian evidence-based healthcare center， the databases including China National Knowledge Infrastructure （CNKI）， VIP Database， Wanfang Data， and China Biomedical Literature Service System （SinoMed） were searched. Based on predefined inclusion and exclusion criteria， text information extraction， quality evaluation， and text information synthesis were conducted sequentially. The data were analyzed and presented in the form of text and figures. ResultsA total of 215 articles related to 43 contemporary renowned experts in the fields of Chinese medicine nephrology and endocrinology were included. The study found that the academic thoughts of these masters in the treatment of DKD are extensive， involving multiple levels such as disease understanding， therapeutic strategies， formula application， and medication use. In terms of disease understanding， the primary pathogenesis is characterized by deficiency in the root and excess in the manifestation. It is emphasized that internal factors， such as congenital endowment deficiency， interact with external factors such as improper diet， emotional disturbances， invasion of exogenous pathogens， and delayed or inappropriate treatment， to jointly induce the disease. This further gives rise to various pathogenetic theories， including obstruction of renal collaterals by blood stasis， toxin-induced damage to renal collaterals， latent wind disturbing the kidney， and internal heat leading to mass formation. In terms of therapeutic strategies and medication use， the principal treatment method is to replenish Qi and nourish Yin. Stage-based and syndrome-differentiated treatments are advocated. Flexible use of insect-derived drugs and wind-dispelling drugs is emphasized， along with proficiency in applying classical formulas and drug pairs. Integrated internal and external treatments， as well as the combined application of multiple therapeutic approaches， are commonly employed for comprehensive management. Meanwhile， the concept of "preventive treatment of disease" is upheld， and individualized long-term management of patients is advocated. ConclusionThrough the SrTO process， the academic thoughts of contemporary renowned Chinese medicine masters in the treatment of DKD have been systematically and standardly synthesized， providing a scientific and standardized basis for future theoretical exploration.

Background The contamination of public baths with Legionella pneumophila contamination has become a growing public health concern in recent years. However, research on its association with bacterial community characteristics in water samples remains limited. The integration of 16S rRNA sequencing and random forest modeling provides a new approach to elucidate the bacterial community characteristics of public bath water and their association with Legionella pneumophila contamination. Objective To investigate the bacterial community structure and diversity of public bath water in Shanghai, explore the association between Legionella pneumophila contamination and bacterial community characteristics, and identify key bacterial genera associated with contamination, thereby providing a scientific basis for formulating hygiene management regulations for public bath water. Methods From February to March 2023, water samples were collected from ten public baths in Shanghai which were selected based on business scale, regional distribution, and functional differences. Water quality parameters were evaluated, and the samples were categorized into Legionella-positive and Legionella-negative groups based on the detection results of Legionella pneumophila. The bacterial community structure, α-diversity, and β-diversity were analyzed using 16S rRNA sequencing. Redundancy analysis (RDA) was employed to examine the relationship between physicochemical factors and bacterial community diversity. A random forest model was employed to identify key bacterial genera distinguishing the two groups, with the importance of genera being evaluated based on the mean decrease accuracy (MDA). Results The oxygen consumption in the Legionella-positive group was significantly lower than that in the Legionella-negative group (mean values: 1.85 mg·L⁻¹ vs. 6.81 mg·L⁻¹, P< 0.05), while no significant differences were observed in other physicochemical indicators. The sequencing results revealed a total of 27 bacterial phyla and 454 bacterial genera, with Proteobacteria (63.00%) being the dominant phylum. The dominant genera included Pelomonas (8.50%), Acidovorax (8.13%), Mycobacterium (7.93%), and Acinetobacter (6.59%). The α-diversity analysis indicated that bacterial community richness (Chao1 and ACE indices) was significantly higher in the Legionella-positive group than in the Legionella-negative group (P<0.01). The β-diversity analysis showed no significant difference in the bacterial community structure between the two groups (P>0.05). The RDA analysis demonstrated that the bacterial community diversity was positively correlated with pH and negatively correlated with oxygen consumption and free residual chlorine. The RDA1 and RDA2 explained 23.92% and 21.30% of the bacterial community diversity, respectively. The random forest model identified 20 key genera significantly influencing the microbial community distribution between the two groups, including unclassified_Bradyrhizobiaceae (MDA=2.42), Meiothermus (MDA=2.37), and Flavihumibacter (MDA=2.26). Conclusion The diversity of bacterial communities in public bath water is influenced by pH, oxygen consumption, and free residual chlorine. Samples contaminated with Legionella pneumophila exhibit greater microbial richness and contain characteristic key bacterial genera that contribute to community differences. Machine learning random forest technology helps identify these distinctive key bacterial genera. The findings provide a basis for carrying out risk early warning strategies in such settings.

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.

Objective:To analyze the contamination characteristics and pathogenic potential of Listeria monocytogenes (LM) in pre-packaged and refrigerated ready-to-eat cooked meat products in Chengdu City. Methods:From September 2022 to May 2023, pre-packaged and refrigerated ready-to-eat cooked meat products were collected from six districts and counties in Chengdu City. Qualitative and quantitative determination of LM was performed on these samples. Whole genome sequencing was carried out on the isolated strains. Different ST strains were selected for cell adhesion and invasion experiments. The results were expressed as adhesion rate and invasion rate. One-way ANOVA was used for comparison between groups, and the Dunnett t-test was used for pairwise comparison. Results:A total of 145 samples were collected, and LM was detected and isolated in 29 samples. The total detection rate was 20.00%, and the detection rate of braised pork was the highest (68.18%). The contamination level of LM in 9 samples (31.03%) was greater than 100.00 MPN/g. The 29 strains of Listeria monocytogenes belonged to 7 STs, including ST 3 (27.59%), ST 8 (17.24%) and ST 87 (13.79%). The strain of ST 87 carried Listeria pathogenicity islands 4 (LIPI-4), which was a highly virulent strain. The medium and high virulence strains accounted for 79.31%. Some moderately virulent and highly virulent strains of ST 8 and ST 87 were closely related to clinical patient strains. Some LM isolates of the same ST type had little SNP differences (1-6) in the same manufacturer at different stages. In vitro cell experiments showed that the highly virulent strain ST 87 had the strongest adhesion and invasion ability towards Caco-2 cells. Conclusion:The pre-packaged and refrigerated cooked meat products have a high contamination rate. Some samples have high contamination levels and carry medium and high virulence strains, posing potential health risks to human beings. LM residues continue to persist in some manufacturers′ production and processing stages.

The drug resistance of the carbapenem-resistant Enterobacteriaceae(CRE)strains was mainly induced by multiple approaches such as production of carbapenemases,increase of bacterial outer membrane permeability,activation of active efflux pump system,formation of biofilm and drug modifying mechanisms.Those mecha-nisms involve deletion,mutation,insertion and posttranscriptional modification of relevant encoding genes,which may affect the susceptibility of the CRE strains to antibiotics.At present,the conventional clinical thera-pies include the use of traditional antibiotics,either the one-drug use or combined use of drugs.The development of novel antibacterial therapy is under way.The epidemiological characteristics of CRE infections,drug resist-ance mechanisms,current and prospective treatment strategies for CRE infections(covering new application of the drugs in available,the novel drugs such as ceftazidime/avibactam,meropenem/vaborbactam and imipenem/rele-bactam)were deeply reviewed in this article,so as to provide reliable reference for clinical prevention,control and treatment of CRE infections.

Objective:To analyze the impact of the universal coverage,integration and DRG/DIP payment method reform of Basic Medical Insurance(BMI)on the equity of the health service utilization and resource allocation.Methods:The 1998-2022 panel data on health service utilization and resources were collected for China.Health equity is measured using the Concentration Index(CI)and Theil index,and the impact of BMI reform on health equity is analyzed by BP neural network and ITSA.Results:Universal coverage,integration,and DRG/DIP payment reform of BMI had a positive effect on the number of health workers,beds,and admissions(P＜0.001),and inhibited the growth of per capita hospitalization costs(P＜0.05).The CI for the number of health workers and beds declined by 0.004 and 0.003 per year after universal coverage of BMI;and both still declined by 0.002 per year with a 5-year lag(P＜0.05).The CI for the hospital admissions fell by 0.014 immediately after the DRG/DIP payment reform and by 0.012 per year thereafter(P＜0.001).Conclusion:BMI reforms not only can contribute to improve the health service utilization and resources,but also it can also improve health equity.In addition,it has a lagged effect on improving health equity.

BACKGROUND:It has been proved that induced pluripotent stem cells can differentiate into cardiomyocytes,but there are few reports on the maturity of differentiated cardiomyocytes.OBJECTIVE:To explore the effect of prolonging the induced differentiation time on the morphology,sarcomere length,binuclear cell content,cardiac gene expression,cardiac protein expression,and mitochondrial function of cardiomyocytes derived from induced pluripotent stem cells.METHODS:Bone morphogenetic protein 4,CH IR 99021,and IWR1 were used to induce pluripotent stem cells to differentiate into cardiomyocytes,and differentiated cardiomyocytes were collected on days 20 and 40 respectively.The expression levels of cardiac genes and proteins in differentiated cardiomyocytes were detected by RT-PCR and immunofluorescence,respectively.LAS X image analysis software was used to analyze the morphology and sarcomere length of differentiated cardiomyocytes.MitoTracker Green FM mitochondrial staining was used to detect total mitochondria.JC-1 mitochondrial staining was used to detect mitochondrial membrane potential.RESULTS AND CONCLUSION:Differentiated card io myocytes on day 40 had longer cell circumference and sarcomere length,and larger cell area than those on day 20(P＜0.05).The proportion of multinucleated cells rose sharply from about 16％on day 20 to about 29％on day 40(P＜0.05).Differentiated cardiomyocytes on day 40 had gene expression levels that were more similar to those of the primary cardiomyocytes,and the expression levels of SERCA2A,Cx-43,and α-MHC genes were significantly higher than on day 20(P＜0.05).Compared with the differentiated cardiomyocytes on day 20,the expression density of TNNT2 and α-MHC protein was relatively higher,the distribution density of mitochondria was larger,and the number of functional mitochondria was greater on day 40(P＜0.05).The results show that prolonging the induced differentiation time can increase the maturity of differentiated cardiomyocytes by increasing the length of sarcomere and the number of functional mitochondria,as well as improving the expression levels of cardiac genes and proteins.

Objective:To investigate the effect and mechanism of Jiedu Xiaoying Patch in rats with Hashimoto's thyroiditis (HT).Methods:Totally 32 rats were randomly divided into a blank group of 8 rats and a model group of 24 rats. The HT rat model was prepared by freely drinking 0.064% sodium iodide solution in the modeling module. 24 successfully modeled rats were randomly divided into model group, selenium yeast group, and patch group, with 8 rats in each group. Starting from the 9th week, the application group applied Jiedu Xiaoying Patch to the surface projection area of the thyroid gland in the neck of rats for 6 hours, once a day, for a total of 6 weeks; the selenium yeast group was orally administered with 21 μg/ml selenium yeast solution at a dose of 0.5 ml/100 g, while the blank group, model group, and patch group were orally administered with equal volumes of physiological saline solution once a day for a total of 6 weeks. The levels of TGAb,TPOAb, Sema 5A, and IL-17A in rat serum were detected by ELISA. The changes of thyroid tissue was observed with HE staining. The relative expression levels of plexin-A1 and plexin-B3 were determined through RT-PCR.Results:Compared with the model group, the levels of TPOAb, TGAb, Sema 5A, and IL-17A decreased ( P<0.05), and the expressions of plexin-A1 and plexin-B3 decreased in the selenium yeast group and the patch group ( P<0.05). The thyroid follicles in the model group were severely damaged, with a large number of lymphocytes infiltrating the interstices; the thyroid follicular structure of the selenium yeast group was relatively intact, and lymphocyte infiltration was reduced compared to the model group. The thyroid follicular structure of the patch group was basically intact, with a small amount of lymphocyte infiltration observed. Conclusion:Jiedu Xiaoying Patch can significantly reduce the levels of TPOAb and TGAb in HT rats. The mechanism may be related to reducing the content of Sema 5A, inhibiting the expressions of receptors plexin-A1 and plexin-B3, reducing the production of inflammatory cytokines such as IL-17A, and inhibiting immune and inflammatory responses.

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.