OBJECTIVE To investigate the distribution and drug resistance of multidrug-resistant bacteria in the neonatal intensive care unit of a three-A children's hospital in Henan Province,and to provide reference for ational drug use in clinical practice.METHODS Clinical specimens from hospitalized newborns in neonatal intensive care unit from a three-A children's hospital from Jan.1,2019 to Dec.31,2023 were subjected to etiological exam-ination and drug sensitivity test,and to analyze the distribution and drug resistance of multidrug-resistant bacteri-a in hospitalized newborns.RESULTS During the 5-year period,1139 strains of multidrug-resistant bacteria were i-solated,including 229 gram-positive bacteria(20.11％)and 910 gram-negative bacteria(79.89％).There were 92 strains of methicillin-resistant Staphylococcus aureus(MRSA)(accounting for 8.08％),57 strains(accounting for 5.00％)of methicillin-resistant coagulase-negative Staphylococcus epidermidis and 28 strains(accounting for 2.46％)of methicillin-resistant coagulase-negative human Staphylococcus.370 strains(accounting for 32.48)of carbapenem-resistant Klebsiella pneumoniae(CRKP),268 strains(accounting for 23.53％)of extenspectrum β-lactamase-producing Escherichia coli and 85 strains(accounting for 7.46％)of K.pneumoniae,there were 767 sputum specimens(67.34％),160 blood specimens from peripheral intravenous puncture and central venous cath-eterization(PICC)(14.05％),63 bronchoalveolar lavage fluid specimens(5.53％),29 secretion specimens(eye and wound secretions)(2.54％),and 120 other specimens(10.54％).K.pneumoniae and E.coli producing su-per-broad spectrum β-lactamase,CRKP and MRSA were the main drug-resistant bacteria.CONCLUSION The sit-uation of drug resistance in neonatal intensive care unit is serious,therefore monitoring bacterial resistance should be strengthened according to the clinical laboratory results,and antibiotics should be applied rationally.

Obesity is a common chronic metabolic disease mainly characterized by excessive accumulation of adipose tissue.Recently,the global prevalence of obesity-related metabolic diseases has increased significantly,seriously affecting the physical and mental health of patients.Adipokines are pleiotropic bioactive substances secreted by adipose tissue,which have physiological functions such as regulating energy metabolism,inflammatory response and insulin sensitivity.Abnormal hyperplasia of adipose tissue can induce chronic inflammatory responses in the body,stimulate the production or secretion disorders of adipokines,and alter glucose and lipid homeostasis,thereby leading to the occurrence and development of obesity-related metabolic diseases.However,the specific mechanism remains unclear.Exercise prescription is a planned exercise guidance program based on the results of the patient's physical fitness test to achieve the expected goal by adopting the prescribed exercise methods.In recent years,previous studies have found that exercise prescriptions can regulate adipokines,thereby preventing and treating obesity-related metabolic disorders,which may become a potential treatment for obesity-related metabolic diseases in clinical practice.This article reviews the mechanism and clinical effect of targeted regulation of adipokines by exercise prescriptions in the treatment of obesity-related metabolic disorders,in order to provide some new ideas and directions for finding new therapies for obesity-related metabolic diseases.

Objective To investigate the efficacy of an interpretable integrated learning model combining clinical indicators,CT image features and radiomics features for the differential diagnosis of lung squamous cell carcinoma and adenocarcinoma,so as to provide references for clincal treatment decisions.Methods A retrospective analysis was conducted on clinical and imaging data from 220 patients(231 lesions)with primary non-small cell lung cancer at Jiangjin Central Hospital of Chongqing(Center 1)and 83 patients(84 lesions)at Chongqing General Hospital(Center 2).In Center 1,the squamous cell carcinoma group consisted of 60 patients(60 lesions),while the adenocarcinoma group included 160 patients(171 lesions).In Center 2,the squamous cell carcinoma group comprised 18 patients(18 lesions),and the adenocarcinoma group involved 65 patients(66 lesions).The patients were categorized into squamous cell carcinoma and adenocarcinoma groups based on pathological findings.Center 1 was randomly partitioned into a training set and a validation set at a 7∶3 ratio,while Center 2 served as the independent test set.Firstly,a deep learning model,VB-Net,was used to automatically segment the tumor region on the lung window image;secondly,the SMOTE(synthetic minority oversampling technique)method was used to balance the categories in the training set and standardize the extracted features with Z-scores;thirdly,the least absolute shrinkage and selection operator(LASSO)were used to select the optimal radiomics features and calculate the radiomics score(Radscore),and univariate and multivariate logistic regression was used to screen clinical indicators and independent clinical factors for differentiating lung squamous cell carcinoma and adenocarcinoma in CT image features;finally,three ensemble learning algorithms(AdaBoost,Bagging decision tree and XGBoost)were used to combine independent clinical factors and Radscore to construct the model.The receiver operating characteristic(ROC)curve was used to evaluate the diagnostic performance of the models.SHAP technique was used to analyze the feature contribution and model decision-making process.Results Among the evaluated ensemble models,AdaBoost and Bagging decision trees demonstrated overfitting tendencies.In contrast,the XGBoost model showed the best performance,achieving AUC values of 0.939,0.887 and 0.853 in the training,validation and independent test sets,respectively.SHAP indicated that Radscore was the most important feature affecting the performance of the model.The decision diagram enabled the visualization of the diagnostic process of the model.Conclusion The interpretable integrated learning model based on clinical indicators,CT image and radiomics features is expected to non-invasively diagnose lung squamous cell carcinoma and adenocarcinoma before treatment and assist clinicians make treatment decisions as early as possible.[Chinese Medical Equipment Journal,2025,46(7):12-20]

Obesity is a common chronic metabolic disease mainly characterized by excessive accumulation of adipose tissue.Recently,the global prevalence of obesity-related metabolic diseases has increased significantly,seriously affecting the physical and mental health of patients.Adipokines are pleiotropic bioactive substances secreted by adipose tissue,which have physiological functions such as regulating energy metabolism,inflammatory response and insulin sensitivity.Abnormal hyperplasia of adipose tissue can induce chronic inflammatory responses in the body,stimulate the production or secretion disorders of adipokines,and alter glucose and lipid homeostasis,thereby leading to the occurrence and development of obesity-related metabolic diseases.However,the specific mechanism remains unclear.Exercise prescription is a planned exercise guidance program based on the results of the patient's physical fitness test to achieve the expected goal by adopting the prescribed exercise methods.In recent years,previous studies have found that exercise prescriptions can regulate adipokines,thereby preventing and treating obesity-related metabolic disorders,which may become a potential treatment for obesity-related metabolic diseases in clinical practice.This article reviews the mechanism and clinical effect of targeted regulation of adipokines by exercise prescriptions in the treatment of obesity-related metabolic disorders,in order to provide some new ideas and directions for finding new therapies for obesity-related metabolic diseases.

OBJECTIVE To investigate the distribution and drug resistance of multidrug-resistant bacteria in the neonatal intensive care unit of a three-A children's hospital in Henan Province,and to provide reference for ational drug use in clinical practice.METHODS Clinical specimens from hospitalized newborns in neonatal intensive care unit from a three-A children's hospital from Jan.1,2019 to Dec.31,2023 were subjected to etiological exam-ination and drug sensitivity test,and to analyze the distribution and drug resistance of multidrug-resistant bacteri-a in hospitalized newborns.RESULTS During the 5-year period,1139 strains of multidrug-resistant bacteria were i-solated,including 229 gram-positive bacteria(20.11％)and 910 gram-negative bacteria(79.89％).There were 92 strains of methicillin-resistant Staphylococcus aureus(MRSA)(accounting for 8.08％),57 strains(accounting for 5.00％)of methicillin-resistant coagulase-negative Staphylococcus epidermidis and 28 strains(accounting for 2.46％)of methicillin-resistant coagulase-negative human Staphylococcus.370 strains(accounting for 32.48)of carbapenem-resistant Klebsiella pneumoniae(CRKP),268 strains(accounting for 23.53％)of extenspectrum β-lactamase-producing Escherichia coli and 85 strains(accounting for 7.46％)of K.pneumoniae,there were 767 sputum specimens(67.34％),160 blood specimens from peripheral intravenous puncture and central venous cath-eterization(PICC)(14.05％),63 bronchoalveolar lavage fluid specimens(5.53％),29 secretion specimens(eye and wound secretions)(2.54％),and 120 other specimens(10.54％).K.pneumoniae and E.coli producing su-per-broad spectrum β-lactamase,CRKP and MRSA were the main drug-resistant bacteria.CONCLUSION The sit-uation of drug resistance in neonatal intensive care unit is serious,therefore monitoring bacterial resistance should be strengthened according to the clinical laboratory results,and antibiotics should be applied rationally.

Objective To investigate the efficacy of an interpretable integrated learning model combining clinical indicators,CT image features and radiomics features for the differential diagnosis of lung squamous cell carcinoma and adenocarcinoma,so as to provide references for clincal treatment decisions.Methods A retrospective analysis was conducted on clinical and imaging data from 220 patients(231 lesions)with primary non-small cell lung cancer at Jiangjin Central Hospital of Chongqing(Center 1)and 83 patients(84 lesions)at Chongqing General Hospital(Center 2).In Center 1,the squamous cell carcinoma group consisted of 60 patients(60 lesions),while the adenocarcinoma group included 160 patients(171 lesions).In Center 2,the squamous cell carcinoma group comprised 18 patients(18 lesions),and the adenocarcinoma group involved 65 patients(66 lesions).The patients were categorized into squamous cell carcinoma and adenocarcinoma groups based on pathological findings.Center 1 was randomly partitioned into a training set and a validation set at a 7∶3 ratio,while Center 2 served as the independent test set.Firstly,a deep learning model,VB-Net,was used to automatically segment the tumor region on the lung window image;secondly,the SMOTE(synthetic minority oversampling technique)method was used to balance the categories in the training set and standardize the extracted features with Z-scores;thirdly,the least absolute shrinkage and selection operator(LASSO)were used to select the optimal radiomics features and calculate the radiomics score(Radscore),and univariate and multivariate logistic regression was used to screen clinical indicators and independent clinical factors for differentiating lung squamous cell carcinoma and adenocarcinoma in CT image features;finally,three ensemble learning algorithms(AdaBoost,Bagging decision tree and XGBoost)were used to combine independent clinical factors and Radscore to construct the model.The receiver operating characteristic(ROC)curve was used to evaluate the diagnostic performance of the models.SHAP technique was used to analyze the feature contribution and model decision-making process.Results Among the evaluated ensemble models,AdaBoost and Bagging decision trees demonstrated overfitting tendencies.In contrast,the XGBoost model showed the best performance,achieving AUC values of 0.939,0.887 and 0.853 in the training,validation and independent test sets,respectively.SHAP indicated that Radscore was the most important feature affecting the performance of the model.The decision diagram enabled the visualization of the diagnostic process of the model.Conclusion The interpretable integrated learning model based on clinical indicators,CT image and radiomics features is expected to non-invasively diagnose lung squamous cell carcinoma and adenocarcinoma before treatment and assist clinicians make treatment decisions as early as possible.[Chinese Medical Equipment Journal,2025,46(7):12-20]

Aim To investigate the dynamic time-course changes in neuronal cytoskeleton after acute ischemia and reperfusion in rats. Methods Reperfusion was performedin rats by blocking the middle cerebralarteryfor 90 min, then therats wereobserved and collected at different time points. The brain damage wasobserved by Nissl staining,and neurobehavioural function was evaluated with neurological deficit score and forelimb placement test. The cellular changes in the alternations of cytoskeletal elements including microtubule associated protein 2 (MAP2) and neurofilament heavy chain (NF-H) were observed by immunohistochemistry staining and Western blot. Impaired axons, dendrites and cytoskeletal alternations were detected by electron microscope. Results Brain damage and neurobehavioural function were gradually aggravated with the prolongation of reperfusion. Brain damage appeared earlier and more severe in striatum than in cortex. Moreover, decreased MAP2-related and increased NF-H-related immunoreactive intensities were found in the ischemic areas. Impaired cytoskeletal arrangement and reduced dense were indicated. Damaged cytoskeletal components such as microtubules and neurofilament arrangement, decreased axonal filament density, and swelled dendrites were observed after cerebral ischemia reperfusion by ultrastructural observations. Conclusions Different brain regions have diverse tolerance to ischemia-reperfusion injury. Major elements of neuronal cytoskeleton show dynamic responses to ischemia and reperfusion, which may further contribute to brain damage and neurological impairment following MCAO and reperfusion.

This study aimed to investigate the effect of Flos Farfarae (FF) fumigation on cigarette smoke-induced lung injury mice, and analyze the metabolic profile of lung tissue by metabolomics. All animal experiments were conducted under the guidance and approval of the Animal Ethics Review Committee of Shanxi University (Approval number: SXULL2019014). By using HS-GC-MS to analyze volatile components of Flos Farfarae, 23 compounds were identified. The results showed that FF fumigation improved the lung tissue morphology of cigarette smoke-induced lung injury mice, lowered the levels of interleukin 6 (IL-6) and interleukin-1β (IL-1β). The lung tissue samples were applied for metabolomic analysis based on UHPLC-QTOF MS, the results showed that 70 metabolites were changed in the lung tissue of mice after cigarette exposure, and 35 of them could be regulated, including lysophosphatidylcholine (LPC), 12-HETE, adenosine, and xanthine. These metabolites, such as LPC, 12-HETE, adenosine, and xanthine were mainly associated with the body's inflammatory response. It was observed that these metabolites are primarily involved in purine metabolism, arachidonic acid metabolism, phospholipid metabolism, and pyruvate metabolism pathways. These findings suggest that the volatile terpenoids in the FF may regulate the metabolites associated with the inflammatory response in the lung tissue, such as lysophosphatidylcholine, 12-HETE, and adenosine, which could further alleviate lung inflammation induced by cigarette smoke through the metabolic pathways of purine metabolism and others. This study proved the scientific basis of the traditional application of FF fumigation, and provided a theoretical basis for the further product development.

Aim To investigate the dynamic changes of neuronal cells at different time points following acute cerebral ischemia-reperfusion injury by establishing a model of brain ischemia-reperfusion injury.Methods Thirty male Sprague-Dawley(SD)rats were ran-domly divided into six groups:sham group and cere-bral ischemia-reperfusion injury(IR)groups at differ-ent time points.Focal cerebral ischemia-reperfusion injury model was established using the middle cerebral artery occlusion(MCAO)technique.The Longa sco-ring method was used to assess neurobehavioral scores in rats.After successful model preparation,routine paraffin sections were made,and TUNEL staining and immunohistochemistry staining with NeuN antibody were performed to observe cell apoptosis and neuronal cell survival,respectively.Immunohistochemistry stai-ning was also performed to investigate the changes in glial fibrillary acidic protein(GFAP)as a marker for astrocytes,ionized calcium-binding adapter molecule 1(IBA-1)as a marker for microglia,and CD31 as a marker for endothelial cells at different time points.Results No significant changes were observed in neu-ronal cells of the sham group at different time points.In the cerebral ischemia-reperfusion injury groups,cell apoptosis was activated at IR3h and increased in quan-tity with morphological damage as time progressed.Ne-uN+neurons showed signs of ischemic injury after IR3h,with abnormal cell morphology.From 12 h,Ne-uN+neurons decreased in a time-dependent manner and reached their peak severity at 24 h.GFAP+astro-cytes decreased significantly after IR3h,while poorly labeled GFAP+astrocytes increased at IR 6 h and al-most disappeared in the infarcted area at 24 h and 48 h.The number of IBA-1+microglia-positive cells de-creased at IR3h,and their volume increased at IR6h.Microglial cell death was observed in the infarcted area at IR12h.CD31+endothelial cells around the infarc-ted cortex and striatum increased significantly after IR3h and persisted until 48 h.Conclusions After cerebral ischemia-reperfusion injury,the number of ap-optotic cells increases with the prolongation of time,and NeuN+neurons exhibit the most severe damage at 24 h.GFAP+astrocytes and microglial cells gradually die over time.The number of CD31+endothelial cells increases significantly around the infarcted cortex and striatum after 3 h of reperfusion and persists until 48 h.