AIM: To identify the primary active components and underlying mechanisms of Yijing Decoction(YJD)in treating early diabetic retinopathy(DR)based on liquid chromatography-mass spectrometry and network pharmacology.METHODS: Active components of YJD were characterized through LC-MS. Components with optimal ADME(absorption, distribution, metabolism, excretion)properties were selected as key bioactive candidates. Network pharmacology approaches were employed to predict YJD-DR therapeutic targets. Protein-protein interaction(PPI)networks, gene ontology(GO)enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis were subsequently conducted to predict core targets and networks. Critical targets and pathways were experimentally validated through Western blot.RESULTS: Ten core therapeutic targets were identified, including TNF, Alb, EGFR, STAT3, PTGS2, ESR1, PPAR, MMP9, TLR4, and MAPK. YJD was related to cancer-related signaling, fluid shear stress and atherosclerosis, and neurodegenerative diseases, encompassing key biological processes such as inflammatory response regulation, programmed cell death activation, and enhanced cell migration. Furthermore, Western blot analysis confirmed that YJD significantly inhibited high glucose-induced phosphorylation of STAT3(P-STAT3/STAT3)and ERK(P-ERK/ERK)in rat retinal microvascular endothelial cells.CONCLUSION: This study revealed YJD's pharmacodynamical basis and its multi-component, multi-target, and multi-paths pharmacology. YJD exerts therapeutic effects on DR by coordinately regulating critical signaling pathways and alleviating intraocular inflammation, thus preserving retinal vascular endothelial cells, maintaining blood-retinal barrier integrity, and facilitating retinal neurovascular repair.

Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.

Objective To quantitatively assess the association of short-term exposure to polycyclic aromatic hydrocarbons (PAHs) in ambient fine particulate matter (PM2.5) with residents mortality. Methods A time-stratified case-crossover study was conducted from 2020 to 2022 among 10606 non-accidental residents by using the Guangzhou Cause of Death Surveillance System in Conghua District, Guangzhou. Exposure levels of PAHs in PM_2.5 and meteorological data during the study period were obtained from the Center for Disease Control and Prevention in Conghua District and the China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0), respectively. Conditional Poisson regression model was used to estimate the exposure-response association between PAHs and the mortality risk. Results Fluoranthene, chrysene, benzo[k]fluoranthene, benzo[a]pyrene, and indeno[1,2,3-cd]pyrene were significantly associated with an increased risk of mortality. For every one interquartile range increase in exposure levels, the non-accidental mortality risks increased by 8.33% (95% CI: 1.80%, 15.27%), 4.67% (95% CI: 1.86%, 7.57%), 6.07% (95% CI: 2.08%, 10.21%), 4.62% (95% CI: 1.85%, 7.47%), and 4.70% (95% CI: 0.53%, 9.03%), respectively. The estimated non accidental deaths attributable to exposure to fluoranthene, chrysene, benzo[k]fluorine, benzo[a]pyrene and indine[1,2,3-cd]pyrene were 5.91%, 6.08%, 6.51%, 6.46%, and 4.21%, respectively. Conclusions Short-term exposure to PAHs in PM_2.5, including fluoranthene, chrysene, benzo[k]fluoranthene, benzo[a]pyrene and indine[1,2,3-cd]pyrene, was significantly associated with an increased risk of mortality among residents.

Objective To construct a risk prediction model for post-stroke dysphagia(PSD)based on clinical and laboratory indicators of elderly stroke patients with explainable machine learning.Methods A retrospective analysis was conducted on 3994 stroke patients hospitalized in Depart-ment of Neurology of First Medical Center of Chinese PLA General Hospital from October 2010 to December 2021.Among them,the 1390 cases admitted during January 2019 and December 2021 were assigned into an external validation set,and the 2604 cases admitted during October 2010 to January 2019 were into a training group.Those from the training group were further divided into a training set(1823 cases)and an internal validation set(781 cases)in a 7∶3 ratio,and also grouped into a PSD subgroup(773 cases)and a non-PSD group(1831 cases).With occurrence of swallowing difficulties as an endpoint,risk prediction models were constructed using random for-est(RF),eXtreme Gradient Boosting(XGBoost),Support Vector Machine(SVM),and logistic regression.ROC curve analysis was employed to evaluate the performance of our models.After the optimal model was selected,SHAP was employed to interpret feature contributions.Results There were significant differences in muscle strength,right/left-sided stroke,and area of brain in-jury between the PSD and the non-PSD groups(P＜0.01).The PSD group had obviously larger proportions of hypertension,diabetes,and drinking history,increased neutrophil counts,and de-creased levels of potassium and albumin when compared with the non-PSD group(P＜0.05,P＜0.01).Multivariate logistic regression analysis showed that age,drinking history,diabetes,hyper-tension,muscle strength grade,area of brain injury,hemispheric stroke,neutrophil count,and al-bumin and potassium levels were risk factors for PSD(P＜0.05,P＜0.01).The external validation results showed that the area under curve value of the RF model,XGBoost model,SVM model,and our logistic model was 0.883,0.902,0.877,and 0.868,respectively.The distribution of SHAP value showed that drinking history,hypertension and diabetes were positively correlated with PSD risk;Muscle strength was negatively correlated with the risk;Age growth was positively correlated with the risk;Subtentorial lesions showed stronger predictive efficacy than supratentorial lesions and entire lesions;The bilateral and right-sided stroke had higher risk for PSD than the left-sided stroke.Conclusion The model based on the XGBoost model shows best performance in predicting the risk for swallowing disorders in elderly patients after stroke.

Objective To investigate the distribution and antimicrobial resistance profiles of common pathogens isolated from cerebrospinal fluid(CSF)in CHINET program from 2015 to 2021.Methods The bacterial strains isolated from CSF were identified in accordance with clinical microbiology practice standards.Antimicrobial susceptibility test was conducted using Kirby-Bauer method and automated systems per the unified CHINET protocol.Results A total of 14 014 bacterial strains were isolated from CSF samples from 2015 to 2021,including the strains isolated from inpatients(95.3％)and from outpatient and emergency care patients(4.7％).Overall,19.6％of the isolates were from children and 80.4％were from adults.Gram-positive and Gram-negative bacteria accounted for 68.0％and 32.0％,respectively.Coagulase negative Staphylococcus accounted for 73.0％of the total Gram-positive bacterial isolates.The prevalence of MRSA was 38.2％in children and 45.6％in adults.The prevalence of MRCNS was 67.6％in adults and 69.5％in children.A small number of vancomycin-resistant Enterococcus faecium(2.2％)and linezolid-resistant Enterococcus faecalis(3.1％)were isolated from adult patients.The resistance rates of Escherichia coli and Klebsiella pneumoniae to ceftriaxone were 52.2％and 76.4％in children,70.5％and 63.5％in adults.The prevalence of carbapenem-resistant E.coli and K.pneumoniae(CRKP)was 1.3％and 47.7％in children,6.4％and 47.9％in adults.The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)and Pseudomonas aeruginosa(CRPA)was 74.0％and 37.1％in children,81.7％and 39.9％in adults.Conclusions The data derived from antimicrobial resistance surveillance are crucial for clinicians to make evidence-based decisions regarding antibiotic therapy.Attention should be paid to the Gram-negative bacteria,especially CRKP and CRAB in central nervous system(CNS)infections.Ongoing antimicrobial resistance surveillance is helpful for optimizing antibiotic use in CNS infections.

Objective To investigate the antimicrobial resistance of clinical isolates from elderly patients(≥65 years)in major medical institutions across China.Methods Bacterial strains were isolated from elderly patients in 52 hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program during the period from 2015 to 2021.Antimicrobial susceptibility test was carried out by disk diffusion method and automated systems according to the same CHINET protocol.The data were interpreted in accordance with the breakpoints recommended by the Clinical and Laboratory Standards Institute(CLSI)in 2021.Results A total of 514 715 nonduplicate clinical isolates were collected from elderly patients in 52 hospitals from January 1,2015 to December 31,2021.The number of isolates accounted for 34.3％of the total number of clinical isolates from all patients.Overall,21.8％of the 514 715 strains were gram-positive bacteria,and 78.2％were gram-negative bacteria.Majority(90.9％)of the strains were isolated from inpatients.About 42.9％of the strains were isolated from respiratory specimens,and 22.9％were isolated from urine.More than half(60.7％)of the strains were isolated from male patients,and 39.3％isolated from females.About 51.1％of the strains were isolated from patients aged 65-＜75 years.The prevalence of methicillin-resistant strains(MRSA)was 38.8％in 32 190 strains of Staphylococcus aureus.No vancomycin-or linezolid-resistant strains were found.The resistance rate of E.faecalis to most antibiotics was significantly lower than that of Enterococcus faecium,but a few vancomycin-resistant strains(0.2％,1.5％)and linezolid-resistant strains(3.4％,0.3％)were found in E.faecalis and E.faecium.The prevalence of penicillin-susceptible S.pneumoniae(PSSP),penicillin-intermediate S.pneumoniae(PISP),and penicillin-resistant S.pneumoniae(PRSP)was 94.3％,4.0％,and 1.7％in nonmeningitis S.pneumoniae isolates.The resistance rates of Klebsiella spp.(Klebsiella pneumoniae 93.2％)to imipenem and meropenem were 20.9％and 22.3％,respectively.Other Enterobacterales species were highly sensitive to carbapenem antibiotics.Only 1.7％-7.8％of other Enterobacterales strains were resistant to carbapenems.The resistance rates of Acinetobacter spp.(Acinetobacter baumannii 90.6％)to imipenem and meropenem were 68.4％and 70.6％respectively,while 28.5％and 24.3％of P.aeruginosa strains were resistant to imipenem and meropenem,respectively.Conclusions The number of clinical isolates from elderly patients is increasing year by year,especially in the 65-＜75 age group.Respiratory tract isolates were more prevalent in male elderly patients,and urinary tract isolates were more prevalent in female elderly patients.Klebsiella isolates were increasingly resistant to multiple antimicrobial agents,especially carbapenems.Antimicrobial resistance surveillance is helpful for accurate empirical antimicrobial therapy in elderly patients.

To explore the protective effect of Angelica sinensis polysaccharide(ASP)on leptospiro-sis induced by pathogenic Leptospira infection,the golden hamster model of leptospirosis was se-lected for the experiment.The Leptospira and Leptospira+ASP groups were intraperitoneally injected with Leptospira interrogans serovar Lai strain 56601(1 × 10 6 per hamster).After infec-tion,the Leptospira+ASP group was injected intraperitoneally with ASP(50 mg/kg)for three consecutive days,while the Leptospira group was injected intraperitoneally with normal saline for three days.The experiment employed methods such as daily observation of the clinical symptoms of golden hamsters,statistics of the survival status of each group of golden hamsters,pathological damage of liver,kidney,and lung,bacterial load in organs,and the expression of inflammatory cy-tokines(IL-1β and TNF-α).The results indicated that ASP could effectively alleviate the clinical symptoms of the infected hamsters,enhance the survival rate,ameliorate the pathological damage of the body,reduce the bacterial load in various organs,and mitigate tissue inflammation.This study demonstrated for the first time that ASP has a protective effect on leptospirosis,providing medication guidance for the clinical treatment of leptospirosis.

Purpose To explore the feasibility and clinical value of deep learning-based image reconstruction technology in 5.0T MRI for nasopharyngeal carcinoma.Materials and Methods A prospective study was conducted on 50 newly diagnosed nasopharyngeal carcinoma patients from August to December 2024 at Sun Yat-sen University Cancer Center.5.0T MRI was performed to scan the nasopharynx region.Routine scanning protocols included transverse T2WI,transverse T1WI,transverse contrast-enhanced T1WI and coronal fat-suppressed contrast-enhanced T1WI sequences.Based on these standard scanning protocols,DeepRecon deep learning reconstruction technology with different levels(grade 1-5)was applied,generating a total of 24 sets of images.Qualitative evaluation employed a Likert scale(5-point system)for subjective scoring on lesion detection,lesion edge clarity,artifacts and overall image quality.Quantitative evaluation was performed using the signal-to-noise ratio and contrast-to-noise ratio to objectively assess the quality of the 24 image sets.Differences in qualitative and quantitative indicators between different groups were compared,while the Kappa coefficient was used to analyze the consistency of subjective evaluations by two radiologists.Results In the qualitative assessment of 24 image sets from four MRI sequences(with and without DeepRecon reconstruction),DeepRecon images(grade 2-4)significantly outperformed traditional images in all features except for artifact reduction(Z=-12.11--6.23,all P<0.001).Images reconstructed at DeepRecon grade 3 had the highest overall score and the best image quality.Furthermore,compared with traditional images,DeepRecon images(grade 2-5)demonstrated significantly improved signal-to-noise ratio for both lesions and the lateral pterygoid muscle(t=-15.67--3.44,Z=-6.09--4.63,all P<0.01).In addition,in the transverse T2WI,transverse contrast-enhanced T1WI and coronal fat-suppressed contrast-enhanced T1WI images with DeepRecon reconstruction(grade 2-5),the contrast-to-noise ratio(lesion/lateral pterygoid muscle)also showed significant improvement compared to traditional images(t=-12.71--3.19,Z=-6.08--4.47,all P<0.001).The inter-observer agreement for the overall subjective quality score between the two radiologists was good(Kappa=0.75-0.82,all P<0.01).Conclusion DeepRecon deep learning reconstruction technology significantly increases the signal-to-noise ratio and resolution of traditional magnetic resonance images of nasopharyngeal cancer,improving image clarity and bringing more possibilities for the advancement of imaging diagnosis.

Objective To propose an improved Y OLOv8-based visible small target detection method to solve the problems of the UAV visible light system in accuracy and timeliness when applied to measuring small targets.Methods A YOLOv8 network consisting of Backbone,Neck and Head was used as the base framework to construct an AGC-YOLO model.Firstly,a convolutional block attention module(CBAM)was incorporated into Backbone to improve the feature expression of the model;secondly,some traditional convolution modules were replaced with the changeable kernel convolution module AKconv to reduce the network parameters;finally,a Gold-YOLO module was involved in Neck to enhance the detection ability for targets with different sizes.VisDrone2019 dataset was used to carry out ablation and comparison experiments,and the efficacy of the AGC-YOLO model for detecting small targets was evaluated in terms of mean average precision(mAP),frames per second(FPS),giga floating-point operations per second(GFLOPs)and parameters.Results The AGC-YOLO model had the FPS,GFLOPs and parameters being 31.90,9.20 and 11.30 M respectively,meeting the real-time detection speed requirements of drones(FPS not lower than 30),in which the mAP50(the mAP with the intersection over union being 0.5)was increased by 15％,6％and 5％when compared with those of the lightweight YOLOv8n,Ghost-YOLO and YOLO-BiFPN models.Conclusion The method proposed behaves well in speed,decreased parameters and precision,and is worthy promoting for UAV visible small target detection.[Chinese Medical Equipment Journal,2025,46(1):1-6]

Purpose This study aims to investigate the clinicopathological features of diffuse pulmonary menin-gotheliomatosis(DPM).Methods The clinical data of 10 patients with DPM undergoing video-assisted thoracic sur-gery(VATS)were collected,and their clinical and pathological characteristics were analyzed using immunohistochem-istry.Results The detection rate of DPM was 1.19‰,with 90％of the patients being female.DPM predominantly occurred in the age range of 40-60 years,with an average age at diagnosis of 50.7 years.Most patients had no smok-ing history.Pathological diagnosis combined with imaging findings was the main method for diagnosing DPM.80％of the patients were prone to concurrent early-stage invasive pulmonary adenocarcinoma.Laboratory indicators,including pulmonary function,were generally normal.Chest CT showed diffuse multiple ground-glass opacity or cystic nodules in both lungs,with the number of nodules in both lungs ranging from dozens to hundreds,and the maximum diameter of the nodules was 2-6 mm.The median volume and CT value of the pulmonary nodules were 35.32 mm3 and-566 HU,respectively.Pathological features mainly included multiple meningothelial-like nodules observed under the micro-scope.Immunophenotypically,CD56,EMA,PR,and vimentin were often positive.Conclusion DPM is a rare lung disease with no obvious clinical symptoms,and is more common in middle-aged and elderly women.Diffuse multiple nodules in both lungs are its main imaging features.Most DPM patients are complicated with lung adenocarcinoma,and regular follow-up is recommended.