Objective To investigate the performance of serum epithelial fatty acid-binding protein (E-FABP) and epithelial neutrophil-activating peptide-78 (ENA-78) levels combined with computed tomography angiography (CTA) in assessing the severity of coronary atherosclerotic (CA) stenosis in patients with coronary heart disease (CHD). Methods A total of 160 CHD patients admitted to our hospital between October 2021 and October 2023 were enrolled. Based on coronary angiography results, patients were divided into a mild stenosis group (65 cases) and a moderate-to-severe stenosis group (95 cases). All patients underwent CTA. Serum levels of E-FABP and ENA-78 were measured using ELISA. Receiver operating characteristic curves were plotted to determine the diagnostic cutoff value of CTA combined with serum E-FABP and ENA-78 for assessing CA stenosis severity. A fourfold table was used to evaluate the diagnostic performance of CTA combined with serum E-FABP and ENA-78. Results CTA identified 64 patients with stenosis <50% and 96 patients with stenosis ≥50%. The agreement rates between CTA and coronary angiography were 87.69% in the mild group and 92.63% in the moderate-to-severe group. Serum levels of E-FABP and ENA-78 were significantly higher in the moderate-to-severe group than in the mild group (P<0.05). According to receiver operating characteristic analysis, the areas under the curve for E-FABP, ENA-78, and CTA in diagnosing CA stenosis severity were 0.828, 0.855, and 0.901, respectively. The areas under the curve for their combined use was 0.986. The diagnostic accuracies of CTA, E-FABP, and ENA-78 were 90.63%, 83.75%, and 83.23%, respectively, while the combined approach achieved a diagnostic accuracy of 93.13%. Conclusion Serum levels of E-FABP and ENA-78 are significantly elevated in CHD patients with moderate-to-severe CA stenosis. The combination of CTA with E-FABP and ENA-78 can improve the accuracy of assessing the severity of CA stenosis.

Purpose To investigate the sex differences of white matter damage in Alzheimer's disease(AD)and their association with cognitive impairment.Materials and Methods This retrospective study included 88 AD patients(48 females),71 amnestic mild cognitive impairment(aMCI)patients(39 females),and 95 healthy controls(63 females)recruited from the Memory Disorder Clinic at the First Affiliated Hospital of Anhui Medical University from September 2017 to July 2024.High-resolution three-dimensional T1 structure images and diffusion tensor imaging images were all obtained from each participant.The mean diffusivity(MD)and fractional anisotropy(FA)values of each white matter region were obtained,and the two-way ANOVA analysis was conducted to investigate brain regions with interaction effects between groups and sexes,those brain regions were then chosen as regions of interest for further correlation analysis with a series of cognitive scale scores.Results In terms of FA values,the right posterior corona radiata,right anterior limb of the internal capsule and left corticospinal tract showed interaction between sexes and cognitive groups(F=4.764,3.812,5.937,all P<0.05).The FA value of AD group was significantly lower than that of healthy control and aMCI group(all P<0.05),but there was no significant difference between healthy control and aMCI group(except the right anterior limb of the internal capsule,P=0.018).In AD group,FA values were significantly higher in women than in men in the previously described brain regions(all P<0.05),while there was no significant difference in FA values between male and female in healthy control and aMCI groups(except the left corticospinal tract,P<0.001).In terms of MD values,the right anterior limb of the internal capsule,right superior corona radiata and left external capsule showed interaction effect between sexes and cognitive groups(F=8.581,3.680,7.218,all P<0.05).The MD value of AD group was significantly higher than that of aMCI group(P<0.001),and aMCI group was higher than that of healthy control group(all P<0.05).In AD group,the MD values in the above brain regions were significantly higher in males than those in females(all P<0.01),while no significant difference was found between males and females in healthy control and aMCI groups(except for the left external capsule,P<0.05).For correlation analysis,the AD group was dimidiated into two groups by sex,the scores of the Montreal cognitive assessment,the Mini Mental state examination and the verbal fluency test of the female patient group were positively correlated with the FA values of the right posterior corona radiate(r=0.372,P=0.009;r=0.345,P=0.016;r=0.383,P=0.007),while the Mini Mental state examination and the verbal fluency test scores of female AD patient group were negatively correlated with the MD values of the right superior corona radiata(r=-0.360,P=0.012;r=-0.360,P=0.003).Conclusion Compared to the healthy control and MCI groups,white matter damage in AD patients shows sex differences and is associated with general cognitive and language functions impairment in female AD patients.

Objective:To analyze the population structure, quantity of rodents, fleas and epidemiological characteristics of animal plague in the plague foci of Erenhot in Inner Mongolia Autonomous Region, and to provide a basis for scientific prevention and control of plague.Methods:The descriptive epidemiological method was used to analyze the surveillance and epidemic data of plague in Erenhot from 2013 to 2023 (from the plague prevention and control management information system of the Chinese Center for Disease Control and Prevention, and the summary of plague surveillance data in Inner Mongolia Autonomous Region from 2013 to 2023), including rat density, nocturnal rat capture, flea infection of rodents, bacteriological and serological detection, etc.Results:From 2013 to 2023, a total of 418 hm 2 of plague foci were investigated in Erenhot, 1 054 rats were caught, and the density of rats was 2.52/hm 2, among which Meriones unguiculatus was the dominant species ( n = 480), and the density was 1.15/hm 2. The capture rate of nocturnal rodents was 6.38% (1 291/20 250). The flea infestation rate of rodents was 35.53% (1 491/4 197), with a flea index of 1.25. Xenopsylla conformity is the dominant flea species ( n = 1 886). Animal plague outbreaks occurred in 7 years, with 11 positive rodents and 10 groups of positive fleas identified by the bacteriology tests, and a total of 21 strains of Yersinia pestis isolated. Eleven positive sera samples were detected by serology. Conclusion:The rodent density in the plague foci of Erenhot is relatively low, but the flea index is high, the animal epidemic is active, facing a significant risk of imported human and animal plague epidemics.

Objective To evaluate the quality of Uncaria Hook(UH)in the market.Methods Twenty-nine batches of UH were tested by legal standards(character,identification,inspection,and extract detection).The TLC method was used to explore the identification of the control herbs,pilocarcinine,dehydropilocarcinine,pilocarcinine and isocarcinine.The HPLC method was used to establish the characteristic spectrum of UH.Then cluster analysis,PCA-X,and OPLS-DA analysis methods were used to perform statistical analysis on the common peak area of unguttine,and determine the contents of heicoside,iso-dehydrounguttine,iso-unguttine,iso-unguttine,and unguttine,to screen out the key quality control components of UH and evaluate their quality.Results Among the 29 batches of samples,23 batches were qualified,and the qualified rate was 79.31％.The results of the TLC analysis showed that the chemical constituents of different base sources were very different.No pilocarcinine was detected in Uncaria macrophylla no hydropilocarcinine was detected in Uncaria sinensis,and four components were not detected in Uncaria hirsuta.Only the samples with the base source of the plant showed corresponding spots in the chromatographic position with the control drug and the control substance.The results of HPLC showed that the chemical composition and content of different base sources of UH were very different.No hydrouncinine and pilocarcinine were detected in Uncaria macrophylla.No iso-dehydrouncinine,dehydrouncinine,and dehydropilocarcinine were detected in Uncaria sinensis.And only heicoside was detected in Uncaria sinensis,and the content of heicoside was low.Based on statistical analysis,there were differences among different batches of UH,and three key quality control ingredients,iso-dehydrounguocarcinoma,dehydrounguocarcinoma,and iso-unguocarcinoma that may lead to quality differences of unguocarcinoma were found.Conclusions Overall,the UH is of poor quality,the current processing standards and testing standards can not ensure the quality of the product,affecting the safety and effectiveness of clinic use.The chemical composition categories and contents of different base sources of UH are very different,which increases the difficulty of formulating a unified quality standard.

Objective:To establish machine learning models to predict visual improvement and anatomical response after anti-vascular endothelial growth factor (VEGF) treatment in patients with diabetic macular edema (DME).Methods:A multi-algorithm machine learning predictive modeling study based on retrospective clinical data was conducted.A total of 225 patients with DME who received their first intravitreal anti-VEGF injection at Fuzhou University Affiliated Provincial Hospital were enrolled between January 2023 and April 2025.According to data completeness, 204 cases were included in the visual recovery prediction model and 201 cases were included in the anatomical response prediction model.Baseline data included optical coherence tomography (OCT) features and blood biomarkers.The primary outcomes were defined as an improvement of ≥1 line in visual acuity and a reduction of ≥20% in central retinal thickness (CRT) after anti-VEGF treatment.Feature selection was performed using univariate logistic regression and Lasso regression.Four machine learning algorithms, logistic regression (LR), decision tree, multilayer perceptron, and random forest, were trained and validated.Model performance was evaluated using accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and decision curve analysis.The best-performing model was further interpreted using SHAP analysis, and a nomogram was constructed for clinical application.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Fuzhou University Affiliated Provincial Hospital (No.K2025-03-064).Written informed consent was obtained from each subject.Results:Among the 37 baseline variables, five key predictors were identified for the outcome of ≥20% CRT reduction: baseline CRT, baseline CRT ≥400 μm, presence of subretinal fluid (SRF), disorganization of the retinal inner layers (DRIL), and integrity of the ellipsoid zone (EZ).Among the four models, the LR model had the best performance, with an accuracy of 0.88, sensitivity of 0.94, specificity of 0.70, and an AUC of 0.94 (95% confidence interval [ CI]: 0.87-1.00).SHAP analysis showed that baseline CRT ≥400 μm, DRIL, SRF, and baseline CRT contributed positively to the outcome, while EZ integrity was a negative predictor for CRT reduction.For the outcome of ≥1-line visual improvement, two key predictors were identified: baseline best corrected visual acuity (BCVA) and EZ integrity.Both baseline BCVA and EZ integrity were negative predictors for ≥1-line visual improvement.The LR model also had the best performance in the internal validation cohort, with an accuracy of 0.71, sensitivity of 0.67, specificity of 0.75, and an AUC of 0.76 (95% confidence interval [ CI]: 0.61-0.91).A visual nomogram was developed based on the selected predictors and the best-performing model.By converting patient-specific clinical characteristics into scores, clinicians can calculate a total score and estimate the probability of achieving a reduction of ≥20% in CRT and a ≥1-line improvement in visual acuity after anti-VEGF therapy. Conclusions:Machine learning-based model building can effectively predict visual and anatomical response following anti-VEGF treatment in DME patients.Logistic regression shows robust predictive performance for both outcomes.Identification of key predictors, especially OCT features such as EZ integrity, SRF, and DRIL, may aid in guiding treatment expectation assessment and personalized intervention strategies.Nomogram constructed in this study shows good clinical applicability and may serve as a decision-support tool to improve the precision of DME management.

Objective:To establish machine learning models to predict visual improvement and anatomical response after anti-vascular endothelial growth factor (VEGF) treatment in patients with diabetic macular edema (DME).Methods:A multi-algorithm machine learning predictive modeling study based on retrospective clinical data was conducted.A total of 225 patients with DME who received their first intravitreal anti-VEGF injection at Fuzhou University Affiliated Provincial Hospital were enrolled between January 2023 and April 2025.According to data completeness, 204 cases were included in the visual recovery prediction model and 201 cases were included in the anatomical response prediction model.Baseline data included optical coherence tomography (OCT) features and blood biomarkers.The primary outcomes were defined as an improvement of ≥1 line in visual acuity and a reduction of ≥20% in central retinal thickness (CRT) after anti-VEGF treatment.Feature selection was performed using univariate logistic regression and Lasso regression.Four machine learning algorithms, logistic regression (LR), decision tree, multilayer perceptron, and random forest, were trained and validated.Model performance was evaluated using accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and decision curve analysis.The best-performing model was further interpreted using SHAP analysis, and a nomogram was constructed for clinical application.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Fuzhou University Affiliated Provincial Hospital (No.K2025-03-064).Written informed consent was obtained from each subject.Results:Among the 37 baseline variables, five key predictors were identified for the outcome of ≥20% CRT reduction: baseline CRT, baseline CRT ≥400 μm, presence of subretinal fluid (SRF), disorganization of the retinal inner layers (DRIL), and integrity of the ellipsoid zone (EZ).Among the four models, the LR model had the best performance, with an accuracy of 0.88, sensitivity of 0.94, specificity of 0.70, and an AUC of 0.94 (95% confidence interval [ CI]: 0.87-1.00).SHAP analysis showed that baseline CRT ≥400 μm, DRIL, SRF, and baseline CRT contributed positively to the outcome, while EZ integrity was a negative predictor for CRT reduction.For the outcome of ≥1-line visual improvement, two key predictors were identified: baseline best corrected visual acuity (BCVA) and EZ integrity.Both baseline BCVA and EZ integrity were negative predictors for ≥1-line visual improvement.The LR model also had the best performance in the internal validation cohort, with an accuracy of 0.71, sensitivity of 0.67, specificity of 0.75, and an AUC of 0.76 (95% confidence interval [ CI]: 0.61-0.91).A visual nomogram was developed based on the selected predictors and the best-performing model.By converting patient-specific clinical characteristics into scores, clinicians can calculate a total score and estimate the probability of achieving a reduction of ≥20% in CRT and a ≥1-line improvement in visual acuity after anti-VEGF therapy. Conclusions:Machine learning-based model building can effectively predict visual and anatomical response following anti-VEGF treatment in DME patients.Logistic regression shows robust predictive performance for both outcomes.Identification of key predictors, especially OCT features such as EZ integrity, SRF, and DRIL, may aid in guiding treatment expectation assessment and personalized intervention strategies.Nomogram constructed in this study shows good clinical applicability and may serve as a decision-support tool to improve the precision of DME management.

Objective To evaluate the quality of Uncaria Hook(UH)in the market.Methods Twenty-nine batches of UH were tested by legal standards(character,identification,inspection,and extract detection).The TLC method was used to explore the identification of the control herbs,pilocarcinine,dehydropilocarcinine,pilocarcinine and isocarcinine.The HPLC method was used to establish the characteristic spectrum of UH.Then cluster analysis,PCA-X,and OPLS-DA analysis methods were used to perform statistical analysis on the common peak area of unguttine,and determine the contents of heicoside,iso-dehydrounguttine,iso-unguttine,iso-unguttine,and unguttine,to screen out the key quality control components of UH and evaluate their quality.Results Among the 29 batches of samples,23 batches were qualified,and the qualified rate was 79.31％.The results of the TLC analysis showed that the chemical constituents of different base sources were very different.No pilocarcinine was detected in Uncaria macrophylla no hydropilocarcinine was detected in Uncaria sinensis,and four components were not detected in Uncaria hirsuta.Only the samples with the base source of the plant showed corresponding spots in the chromatographic position with the control drug and the control substance.The results of HPLC showed that the chemical composition and content of different base sources of UH were very different.No hydrouncinine and pilocarcinine were detected in Uncaria macrophylla.No iso-dehydrouncinine,dehydrouncinine,and dehydropilocarcinine were detected in Uncaria sinensis.And only heicoside was detected in Uncaria sinensis,and the content of heicoside was low.Based on statistical analysis,there were differences among different batches of UH,and three key quality control ingredients,iso-dehydrounguocarcinoma,dehydrounguocarcinoma,and iso-unguocarcinoma that may lead to quality differences of unguocarcinoma were found.Conclusions Overall,the UH is of poor quality,the current processing standards and testing standards can not ensure the quality of the product,affecting the safety and effectiveness of clinic use.The chemical composition categories and contents of different base sources of UH are very different,which increases the difficulty of formulating a unified quality standard.

With the increasing maturity of artificial intelligence(AI),the number of clinical cases ap-plying AI in cerebrovascular interventional diagnosis and treatment has been progressively increasing.After reviewing relevant literature published since 2011,this article provides a preliminary summary of AI applica-tions in cerebrovascular interventional diagnosis and treatment to date.Current AI applications in this field predominantly focus on preoperative planning,with some postoperative applications and limited intraoperative applications,while demonstrating diversified algorithmic approaches.For future algorithm development,main-stream scholars predominantly focus on constructing intraoperative automated decision-making models and de-veloping training algorithms for junior interventional physicians.AI undoubtedly holds broad application pros-pects in cerebrovascular interventional procedures.

Objective: To construct a scientific and perfect evaluation index system of infectious disease prevention and control ability in colleges and universities, so as to provide reference tools for colleges and universities to effectively respond to infectious disease. Methods: The initial framework of the evaluation index system of infectious disease prevention and control ability in colleges and universities was constructed by using literature analysis method. Experts familiar with infectious disease prevention and control or school health work were selected to conduct two rounds( n =16，18) of Delphi expert consultation for determining the evaluation index system. Analytical hierarchy process was used to calculate the index weights and combined weights. About 198 prevention and control personnel were conveniently selected from 3 universities in Inner Mongolia Autonomous Region to comprehensively evaluate the evaluation indicators by using fuzzy comprehensive evaluation method. Results: After two rounds of Delphi consultation questionnaire, the effective recovery rates were 80.0% and 90.0%, the expert authority levels were 0.89 and 0.86, the expert harmony coefficients for Kendall W were 0.166 and 0.310, and the variation coefficient of each index was <0.25. Finally, the evaluation index system of infectious disease prevention and control ability of colleges and universities included 4 first level indicators, 14 second level indicators and 75 third level indicators. The weights of prevention and monitoring and early warning, organizational system guarantee, emergency management, rehabilitation and summary were 0.176, 0.476, 0.268 and 0.080, respectively. The top 3 weights of the secondary indexes were 0.623 for infectious disease surveillance and early warning, 0.595 for loss assessment and 0.370 for emergency response. The score of fuzzy comprehensive evaluation of the index system of infectious disease prevention and control ability in colleges and universities was 79.148, suggesting a high level. Conclusion The established evaluation index system of infectious disease prevention and control ability in colleges and universities is scientific and reasonable, which is conducive to provide tool reference for the evaluation of infectious disease prevention and control ability in colleges and universities.

Various therapeuti modailities have been engineered for lung cancer treatment, but their clinic application is severely impeded by the poor therapy efficiency and immunosuppressive microenvironment. Herein, we fabricated a library of small molecule redox-labile nanoparticles (NPs) (i.e., diPTX-2C NPs, diPTX-2S NPs, and diPTX-2Se NPs) by the self-assembly of dimer paclitaxel (PTX) prodrug, and then utilized these NPs with the traditional Chinese medicine (TCM) Qi-Yu-San-Long-Fang (Q) for effective chemoimmunotherapy on Lewis lung carcinoma (LLC)-bearing mice models. Under the high concentration of glutathione (GSH) and H₂O₂, diPTX-2Se NPs could specifically release PTX in cancer cells and exert a higher selectivity and toxicity than normal cells. In LLC tumor-bearing mice, oral administration of Q not only effectively downregulated programmed death ligand-1 (PD-L1) expression, but also remodeled the immunosuppressive tumor immune microenvironment via the increase of CD4⁺ T and CD8⁺ T cell proportion and the repolarization of M2 into M1 macrophages in tumor tissues, collectively achieving superior synergistic treatment outcomes in combination with intravenous PTX prodrug NPs. Besides, we found that the combination regimen also demonstrated excellent chemoimmunotherapeutic performances on low-dose small established tumor and high-dose large established tumor models. This study may shed light on the potent utilization of Chinese and Western-integrative strategy for efficient tumor chemoimmunotherapy.