This paper has summarized current studies on retinitis pigmentosa （RP） from two perspectives， which were disease mechanism insights from western medicine， and etiology， pathogenesis and therapeutic principles in traditional Chinese medicine （TCM）. From the perspective of western medicine， the understanding of RP has evolved from an early single-factor explanation focusing on pathogenic gene mutations to a more comprehensive， multifactorial model involving genetic abnormalities， photoreceptor cell degeneration， retinal pigment epithelium dysfunction， oxidative stress， inflammatory responses， microenvironmental imbalance， and vascular insufficiency. In TCM， RP is considered to be a disorder manifested in the eyes but rooted in dysfunction of the zang-fu （脏腑） organs. Its pathogenesis is primarily associated with congenital insufficiency， liver-kidney depletion， impaired spleen function of transportation and transformation， and stasis obstructing the meridians. The core pathological mechanism can be summarized as "deficiency complicated by stasis"， emphasizing holistic imbalance， dynamic disease progression， and syndrome differentiation and treatment. The differences and complementarities between the two medical systems in understanding disease progression are further discussed. RP is proposed to be more appropriately regarded as a complex disease characterized by long-term progression， stage-dependent evolution， and persistent decompensation. Western medicine contributes to elucidating the molecular mechanisms underlying RP and provides the foundation for targeted therapeutic interventions， whereas TCM offers a holistic perspective for understanding disease evolution through the assessment of overall functional status， disease-stage stratification， and long-term syndrome-based regulation.

This paper has summarized current studies on retinitis pigmentosa （RP） from two perspectives， which were disease mechanism insights from western medicine， and etiology， pathogenesis and therapeutic principles in traditional Chinese medicine （TCM）. From the perspective of western medicine， the understanding of RP has evolved from an early single-factor explanation focusing on pathogenic gene mutations to a more comprehensive， multifactorial model involving genetic abnormalities， photoreceptor cell degeneration， retinal pigment epithelium dysfunction， oxidative stress， inflammatory responses， microenvironmental imbalance， and vascular insufficiency. In TCM， RP is considered to be a disorder manifested in the eyes but rooted in dysfunction of the zang-fu （脏腑） organs. Its pathogenesis is primarily associated with congenital insufficiency， liver-kidney depletion， impaired spleen function of transportation and transformation， and stasis obstructing the meridians. The core pathological mechanism can be summarized as "deficiency complicated by stasis"， emphasizing holistic imbalance， dynamic disease progression， and syndrome differentiation and treatment. The differences and complementarities between the two medical systems in understanding disease progression are further discussed. RP is proposed to be more appropriately regarded as a complex disease characterized by long-term progression， stage-dependent evolution， and persistent decompensation. Western medicine contributes to elucidating the molecular mechanisms underlying RP and provides the foundation for targeted therapeutic interventions， whereas TCM offers a holistic perspective for understanding disease evolution through the assessment of overall functional status， disease-stage stratification， and long-term syndrome-based regulation.

Abnormal expression of CMTM4 protein is closely related to tumour occurrence,development and prognosis.Although the important role of CMTM4 in tumours has been gradually manifested,its spe-cific mechanism of action in cervical cancer remains unclear.The aim of this study was to investigate the relationship between CMTM4 expression and clinicopathological features in cervical cancer and study its mechanism.Immunohistochemistry and Western blot were used to detect the expression level of CMTM4 in cancer tissues and paracancerous tissues,and it was found that CMTM4 was significantly under-ex-pressed in cervical cancer tissues(P＜0.001).The chi-square test analysed the relationship between high and low CMTM4 expression and the clinical and pathological characteristics of cervical cancer pa-tients and results found that CMTM4 expression was correlated with the number of births,HPV infection status,pathological type,FIGO stage and lymph node metastasis.Data from Western blot and RT-qPCR found that CMTM4 protein and mRNA levels in HaCaT cells were significantly higher than that of C-33A cells,HeLa cells,U14 cells,and HT-3 cells.Among them,the most significant change in CMTM4 ex-pression was observed in C-33A cells,so the C-33A cell line was selected for subsequent overexpression experiments.CCK-8 analysis found that the proliferation ability of C-33A cervical cancer cells in the pcDNA-CMTM4 group was significantly lower than that in the pcDNA-NC group(P＜0.001).Flow cy-tometry and Western blot results indicated that CMTM4 overexpression promoted apoptosis(P＜0.001),significantly increased Bax(P＜0.001)and cleaved caspase 3(P＜0.05)protein levels,and significant-ly decreased Bcl-2 protein level(P＜0.01).Western blot results further found that CMTM4 overexpres-sion significantly reduced the protein levels of p-PI3K(P＜0.001)and p-AKT(P＜0.01),but did not affect the protein levels of PI3K and AKT(P＞0.05).The above findings indicated that CMTM4 gene expression was down-regulated in cervical cancer,and CMTM4 overexpression inhibited cervical cancer cell proliferation and induced apoptosis by inhibiting the PI3K/AKT pathway.Therefore,CMTM4 may be used as a biological marker for screening cervical cancer.

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]

Objective:To explore whether LBP3 exerts anti-tumor effects by promoting production of short-chain fatty acids(SCFAs)by intestinal microbiota and regulating function of polymorphonuclear myeloid-derived suppressor cells(PMN-MDSC).Methods:A subcutaneous H22 liver cancer model was employed to assess anti-tumor activity of LBP3 and its regulatory effects on PMN-MDSC.Pseudo-sterile tumor-bearing mouse model was used to investigate role of intestinal microbiota in tumor suppression of LBP3.Fecal microbiota transplantation(FMT)was conducted to explore immune regulatory role of LBP3-modulated flora.Serum SCFAs levels in tumor-bearing mice were quantified using liquid chromatography-mass spectrometry,and effect of SCFAs butyrate on arginase 1(Arg-1)expression was evaluated in vitro.Results:Both low-dose(125 mg/kg)and high-dose(250 mg/kg)LBP3 signifi-cantly inhibited tumor growth in H22 tumor-bearing mice,also led to a marked reduction in proportion of PMN-MDSC in both spleen and tumor,a reduced proportion of Treg in lymphoid tissues,a decrease in Arg-1 level within tumor,infiltration of CD8+T cells into tumor was significantly enhanced.However,these effects of LBP3 were did not observed in pseudo-sterile mice,while the above changes could be reproduced after fecal supernatant transplantation in high-dose LBP3 treatment group,suggesting a crucial role for gut microbiota.Furthermore,co-expression of Ly6G and SCFA receptor GPR43 in tumor was also observed.LBP3 treatment resulted in increased levels of SCFAs,particularly butyrate,in both blood and tumor tissues.In vitro,butyrate was shown to inhibit Arg-1 expression in MSC-2 cells,further supporting hypothesis that SCFAs mediate immune-modulatory effects of LBP3.Conclusion:LBP3 exerts its anti-tumor effects by promoting SCFA production,which subsequently inhibits function of PMN-MDSC.This highlights LBP3's potential as an immunomodulatory agent in cancer therapy.

Purpose To evaluate the value of preoperative MRI-based radiomic models for assessing tumor-infiltrating CD8+T-cell expression in glioblastoma patients,and to identify the most stable and efficient radiomic feature region for predicting prognosis following immunotherapy.Materials and Methods This retrospective study included 150 patients with histopathologically confirmed glioblastoma from Lanzhou University Second Hospital(January 2018 to April 2022).Tumor-infiltrating CD8+T-cell expression was quantitatively assessed using immunohistochemical staining,with patients stratified into CD8-high and CD8-low expression groups based on overall survival.A total of 1 185 radiomic features were extracted from each patient's contrast-enhanced T1C and T2WI images,covering the original tumor region and sequentially expanded peritumoral regions(2.5 mm,5.0 mm,7.5 mm,10.0 mm,12.5 mm,15.0 mm morphological dilation of tumor core+peritumoral area).Feature selection was performed using variance threshold,minimum redundancy maximum relevance,and least absolute shrinkage and selection operator methods.XGBoost classifier was employed to construct clinical,radiomic,and clinical-radiomic multimodal combined prediction models.Diagnostic performance was evaluated using receiver operating characteristic curve analysis.Results The radiomic model based on tumor expansion of 7.5 mm(tumor+peritumoral region)demonstrated optimal predictive performance.The clinical-radiomic multimodal combined model showed superior predictive capability compared to clinical and radiomic models alone,achieving an area under the curve of 0.991 and accuracy of 99.0%in the training set,and area under the curve of 0.840 with accuracy of 80.0%in the validation set.Conclusion MRI radiomics provides a feasible approach for evaluating tumor-infiltrating CD8+T-cell expression in glioblastoma patients,offering potential for preoperative prognosis prediction.

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.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43％(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38％,9.73％,and 8.47％,respectively.The prevalence of CRE strains in Escherichia coli was 1.99％.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)％,followed by blood(20.88±3.40)％and urine(18.40±3.45)％.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)％.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.

Objective To characterize the changing species distribution and antibiotic resistance profiles of respiratory isolates in hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods Commercial automated antimicrobial susceptibility testing systems and disk diffusion method were used to test the susceptibility of respiratory bacterial isolates to antimicrobial agents following the standardized technical protocol established by the CHINET program.Results A total of 589 746 respiratory isolates were collected from 2015 to 2021.Overall,82.6％of the isolates were Gram-negative bacteria and 17.4％were Gram-positive bacteria.The bacterial isolates from outpatients and inpatients accounted for(6.0±0.9)％and(94.0±0.1)％,respectively.The top microorganisms were Klebsiella spp.,Acinetobacter spp.,Pseudomonas aeruginosa,Staphylococcus aureus,Haemophilus spp.,Stenotrophomonas maltophilia,Escherichia coli,and Streptococcus pneumoniae.Each microorganism was isolated from significantly more males than from females(P＜0.05).The overall prevalence of methicillin-resistant S.aureus(MRSA)was 39.9％.The prevalence of penicillin-resistant S.pneumoniae was 1.4％.The prevalence of extended-spectrum β-lactamase(ESBL)-producing E.coli and K.pneumoniae was 67.8％and 41.3％,respectively.The overall prevalence of carbapenem-resistant E.coli,K.pneumoniae,Enterobacter cloacae,Pseudomonas aeruginosa,and Acinetobacter baumannii was 3.7％,20.8％,9.4％,29.8％,and 73.3％,respectively.The prevalence of β-lactamase was 96.1％in Moraxella catarrhalis and 60.0％in Haemophilus influenzae.The H.influenzae isolates from children(＜18 years)showed significantly higher resistance rates to β-lactam antibiotics than the isolates from adults(P＜0.05).Conclusions Gram-negative bacteria are still predominant in respiratory isolates associated with serious antibiotic resistance.Antimicrobial resistance surveillance should be strengthened in clinical practice to support accurate etiological diagnosis and appropriate antimicrobial therapy based on antimicrobial susceptibility testing results.