ObjectiveTo investigate the association between immunoglobulin G （IgG）/immunoglobulin M （IgM） ratio and prognosis in patients with initially unresectable hepatocellular carcinoma （iuHCC） receiving TTP triple therapy with transcatheter arterial chemoembolization （TACE）， tyrosine kinase inhibitor （TKI）， and programmed cell death protein-1 （PD-1） inhibitors. MethodsA retrospective analysis was performed for the clinical data of 151 iuHCC patients who received TTP triple therapy in Department of Hepatobiliary Surgery， Guangxi Medical University Cancer Hospital， from November 2019 to December 2022， and according to IgG/IgM ratio， they were divided into high IgG/IgM group （IgG/IgM ratio >13.23） and low IgG/IgM group （IgG/IgM ratio ≤13.23）. The t-test was used for comparison of continuous data between groups， and the chi-square test was used for comparison of categorical data between groups. The Kaplan-Meier method and the log-rank test were used for survival analysis， and the Cox proportional hazards model was used to investigate the potential influencing factors for overall survival （OS）. ResultsThe 151 patients had a median OS of 26.7 months （95% confidence interval ［CI］： 19.8-not reached） and a median progression-free survival of 12.5 months （95%CI： 10.4 — 15.8）. The objective response rate was 83.4% and the disease control rate was 94.0%. There were no significant differences in baseline data between the high IgG/IgM group and the low IgG/IgM group （all P>0.05）. There was a significant difference in median OS between the high IgG/IgM group and the low IgG/IgM group （20.6 months vs not reached， P=0.016）. In both the high IgG/IgM group and the low IgG/IgM group， salvage hepatectomy was significantly associated with the improvement in OS （χ²=8.297 and 10.307， both P<0.05）. The multivariate analysis showed that high IgG/IgM ratio （hazard ratio ［HR］=1.799， 95%CI： 1.077 — 3.006， P=0.025）， baseline alpha-fetoprotein >400 ng/mL （HR=1.762， 95%CI： 1.017 — 3.050， P=0.043）， and BCLC stage （HR=2.265， 95%CI： 1.212 — 4.232， P=0.010） were independent influencing factors for OS. ConclusionHigh IgG/IgM ratio is associated with a poorer prognosis in iuHCC patients receiving TTP triple therapy， and salvage hepatectomy has a potential value in improving the prognosis of patients with a high IgG/IGM ratio.

As the core vehicle for preserving and transmitting traditional Chinese medicine（TCM） academic thought and clinical experience， the establishment of a robust quality evaluation system for TCM clinical case reports is a crucial component in the current standardization and modernization of TCM. Based on the practical experience of constructing the China Clinical Cases Library of Traditional Chinese Medicine by the China Association of Chinese Medicine， this study conducted a comprehensive analysis of critical challenges， including insufficient authenticity and unfocused evaluation criteria. It proposed a three-dimensional evaluation framework grounded in the structure-process-outcome logic， encompassing three dimensions of authenticity and standardization， characteristics and advantages， application and translational impact. This framework integrated 12 key evaluation indicators in a systematic manner. The model preserved the academic characteristics of TCM syndrome differentiation and treatment， while aligning with modern scientific research standards， achieving a balance between individualized TCM experience and standardized evaluation. Concurrently， this study provided theoretical foundations and methodological guidance for evaluating the quality of TCM clinical cases， contributing significantly to the inheritance of TCM knowledge， evidence-based practice， and the reform of talent evaluation mechanisms.

Background The quality of occupational health technical services is directly linked to the protection of workers' health rights and the efficacy of occupational disease prevention and control. However, the industry still faces critical challenges: sporadic instances of institutional non-compliance and persistent irregularities in professional practice continue to undermine overall service performance. Objective To assess the current quality status of occupational health technical services in Zhejiang Province and propose countermeasures for quality improvement, providing a scientific basis for policy optimization and service delivery quality enhancement. Methods A total of 69 occupational health technical service institutions in Zhejiang Province that obtained formal accreditation as of April 30, 2024, were sampled, including 3 public institutions and 66 private institutions (comprising 3 formerly Class-A, 28 formerly Class-B, 11 formerly Class-C, and 24 newly certified institutions). Following the Technical Protocol for Quality Monitoring of Occupational Health Technical Service in Zhejiang Province and the Technical Protocol for Proficiency Testing of Occupational Health Detection in Zhejiang Province, a quality assessment task force comprising national and provincial experts was established. Evaluation was conducted across four dimensions: qualification maintenance and compliance, standardization of technical services, authenticity of technical services, and proficiency testing, utilizing a combination of document review, on-site inspections, and technical skill assessments. Results The occupational health technical service institutions in Zhejiang Province were predominantly private entities (82.5%), with significant disparities in overall service quality. The pass rates for qualification maintenance and compliance, technical service standardization, technical service authenticity, and the excellence rate for laboratory proficiency testing were 81.5%, 80.7%, 97.3%, and 90.4%, respectively. Regarding qualification maintenance, the pass rates for "environmental conditions" (49.8%, 56.7%) and "instrumentation and equipment" (58.2%、65.6%) were significantly lower for formerly Class-C and newly certified institutions compared to other categories. In terms of technical standardization, "standardized on-site inspections" recorded the lowest pass rate (67.4%), with newly certified institutions at only 48.0%. Regarding technical service authenticity, formerly Class-C institutions exhibited issues such as missing raw chromatograms for blank samples (85.7% pass rate). In laboratory proficiency testing, public and formerly Class-A institutions achieved 100% excellence rates, but the performance of formerly Class-C and newly certified institutions was comparatively weak; specifically, the failure rate for organic analysis in formerly Class-C institutions reached 20%; the failure rate for dust testing items in newly certified institutions was 10.3%. Conclusion The overall quality of occupational health technical services in Zhejiang Province still requires significant improvement, particularly in basic institutional conditions, the standardization of on-site inspections, and laboratory proficiency in organic and dust analysis. Formerly Class-C and newly certified institutions should be the primary focus of quality management efforts. Differentiated regulatory strategies are recommended, alongside strengthening interim and ex-post supervision to gradually enhance the quality of occupational health technical services across all institutions.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

This study investigated the potential pathogenicity and genetic characteristics of ST314 Salmonella Kentucky(S.Ken-tucky)isolates from a broiler slaughterhouse.Antimicrobial susceptibility testing and whole-genome sequencing(WGS)were used to determine antimicrobial resistance,virulence factors,and the presence of antimicrobial resistance genes(ARGs)and mobile genetic elements(MGEs)among the isolates.The three multidrug resistant(MDR)isolates exhibited high resistance to multiple antimicrobial agents.The F4-2S strain exhibited resistance to 14 drugs across seven categories,whereas the F4T strain showed resistance to 13 drugs in the same number of categories.In contrast,the Y23 strain was resistant to nine drugs in six categories.Notably,F4-2S dem-onstrated high homology with F4T:both possessed 13 ARGs distributed across nine categories,in addition to a wide range of virulence factors,including secretion systems and effector proteins.The presence of IncR and IncX1 plasmids significantly enhanced both the antimicrobial resistance and pathogenicity of the isolates.The genome map of Y23 revealed a chromosome alongside two plasmids.The chromosome containedonly one resistance gene but several virulence factors,including the type III secretion system(T3SS),which is crucial for bacterial invasion.The plasmid pY23-1 contained eight types of 19 ARGs.Comparative analysis indicated that pY23-1 ex-hibited high homology with pZ1323SSL0055 and pSAL-045,all of which contained multiple ARGs,thus suggesting critical roles of these genes in the evolution of bacterial resistance.In conclusion,ST314 S.Kentucky demonstrated a complex mechanism of resis-tance coupled with significant pathogenic potential.The ARGs and MGEs in the plasmid contributed to the emergence and dissemina-tion of antimicrobial resistance.The multiple virulence factors present in the chromosome may be key factors driving the increasing virulence of ST314 S.Kentucky.

RNA-binding protein human antigen R(HuR)is a protein product of the embryonic lethal abnormal vision gene(ELAVL).It is widely expressed in human cells and primarily regulates mRNA stability through post-transcriptional mecha-nisms,particularly by binding to AU-enriched elements(AR-Es).Recent studies have indicated that HuR interacts with non-coding RNAs to participate in the regulation of gene expression,including long non-coding RNAs,circular RNAs,microRNAs,and vault RNAs.The interactions between HuR and these ncR-NAs play a crucial role in the occurrence and development of va-rious diseases,including tumors.Since there are already reviews summarizing the research on tumors,this review mainly focuses on summarizing the role of HuR-ncRNA interactions in diseases other than tumors.

AIM To study the effects of total flavonoids of Dracocephalum Moldavica L.(TFDM)on reducing the inflammatory response of RAW264.7 macrophages induced by ox-LDL via the nuclear factor κB(NF-κB)/NOD-like receptor 3(NLRP3)signaling pathway.METHODS The RAW264.7 macrophages cultured in vitro were divided into the normal group,the model group(50 μg/mL ox-LDL),the TFDM group(100 μg/mL TFDM+50 μg/mL ox-LDL),the NF-κB inhibitor group(10 μmol/L Bay11-7821+50 μg/mL ox-LDL)and the TFDM+NF-κB inhibitor group(100 μg/mL TFDM+10 μmol/L Bay11-7821+50 μg/mL ox-LDL).The cells had their viability assessed by CCK-8 method;their ROS expression detected by the ROS kit;their mRNA expressions of NF-κB p65,NLRP3,Caspase-1,IL-18 and IL-1β detected by RT-qPCR;their protein expressions of NF-κB p65,IκBα,NLRP3,pro-Caspase-1,Caspase-1,IL-18 and IL-1β by Western blot;their protein expressions of NF-κB p65 and NLRP3 detected using immunofluorescence method.RESULTS Compared with the normal group,the model group showed increased ROS expression(P＜0.01);increased mRNA expressions of NF-κB p65,NLRP3,Caspase-1,IL-18 and IL-1β(P＜0.05,P＜0.01);decreased protein expressions of IκBα and cytoplasmic NF-κB p65(P＜0.01);increased protein expressions of nuclear NF-κB p65,NLRP3,Caspase-1,IL-1 β and IL-18(P＜0.01);and increased fluorescence intensity of NF-κB p65 and NLRP3(P＜0.01).Compared with the model group,the groups intervened with either TFDM or TFDM+inhibitor displayed decreased ROS expression(P＜0.01);the groups administrated with TFDM or NF-κB inhibitor,or TFDM+inhibitor showed decreased mRNA expressions of NF-κB p65,NLRP3,Caspase-1,IL-18 and IL-1β(P＜0.05,P＜0.01),increased protein expressions of IκBα and cytoplasmic NF-κB p65(P＜0.05,P＜0.01),decreased protein expressions of nuclear NF-κB p65,NLRP3,Caspase-1,IL-1β and IL-18(P＜0.05,P＜0.01),and decreased fluorescence intensity of NF-κB p65 and NLRP3(P＜0.01).There existed no significant group difference between the TFDM group and the NF-κB inhibitor group(P＞0.05).The TFDM+inhibitor group demonstrated decreased mRNA expressions of IL-1βand IL-18(P＜0.05),increased IκBα protein expression(P＜0.05),decreased protein expressions of nuclear NF-κB p65,NLRP3,Caspase-1,IL-1 β and IL-18(P＜0.05),and decreased fluorescence intensity of NLRP3 protein(P＜0.05).CONCLUSION TFDM can inhibit the ox-LDL-induced inflammatory response of RAW264.7 macrophages,and the mechansism may be associated with the reduced ROS expression and inflammatory factors due to the inhibited activation of the NF-κB/NLRP3 signaling pathway.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.