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 analyze and evaluate the early warning efficacy of D-dimer/albumin ratio(DAR)combined with interleukin-6(IL-6)and fibrin degradation products(FDP)in the postoperative treatment of acute Stanford type A aortic dissection(ATAAD).Methods A retrospective cohort study was conducted on 284 ATAAD patients who underwent the Sun's procedure at our hospital from July 2024 to March 2025.Patients were divided into a non-adverse outcome group(n=196)and an adverse outcome group(n=88)based on the occurrence of postop-erative complications within 30 days,including acute renal failure requiring dialysis,secondary thoracotomy for hemostasis,severe neurological complications,multiple organ failure,or all-cause mortality.Preoperative baseline data,perioperative parameters,and laboratory indicators were collected via the electronic medical record system.The Mann-Whitney U test was used to compare the differences between groups for continuous variables that did not conform to the normal distribution,and Chi-square test or Fisher's exact test was selected for statistical difference analysis according to the frequency distribution characteristics of categorical variables.On the basis of univariate analysis,multivariate logistic regression analysis was used to screen independent risk factors.Results Statistically significant differences were observed between the non-adverse and adverse outcome group in age,cardiopulmonary bypass time,lactate dehydrogenase(LDH),IL-6,D-dimer(D-D),FDP,and DAR levels(P<0.05).Multivariate analysis revealed that DAR,IL-6,D-D,FDP,and prolonged cardiopulmonary bypass time were independent risk factors for adverse postoperative outcomes(P<0.05).Combined detection analysis demonstrated that the combination of DAR,IL-6,FDP,and cardiopulmonary bypass time yielded the highest predictive efficacy,with an area under the ROC curve of 0.886(95％CI:0.846～0.927).Conclusion The combination of DAR,IL-6,FDP,and cardio-pulmonary bypass time effectively predicts adverse postoperative outcomes in ATAAD patients.This biomarker panel may serve as a robust predictive tool for postoperative risk stratification.

OBJECTIVE: This study aimed to investigate the effect of Astragalus (AST) on osteoporosis (OP) and the downstream mechanisms. METHODS: Human bone marrow-derived mesenchymal stem cells (hBMSCs) were induced to differentiate into osteogenic cells. After transfection with relevant plasmids, cell proliferation, cell cycle progression, and apoptosis were assessed. Alizarin red staining was used to detect calcium nodules in the cells, alkaline phosphatase (ALP) staining was used to detect ALP activity in the cells, and quantitative reverse transcription-polymerase chain reaction and western blotting were used to determine RUNX2 and Osterix expression levels. An OP rat model was established using ovariectomy and micro-computed tomography scanning. Hematoxylin and eosin staining and Masson's trichrome staining were used to evaluate the pathological conditions of bone tissues, while immunohistochemistry was conducted to detect RUNX2 in bone tissues. RESULTS: AST promoted the osteogenic differentiation of BMSCs, reduced miR-181d-5p expression levels, and increased SOX11 expression levels. Restoring miR-181d-5p expression or reducing SOX11 expression levels reversed the effects of AST on the osteogenic differentiation of hBMSCs. miR-181d-5p was found to target SOX11 in hBMSCs. AST improved OP in rats, and miR-181d-5p overexpression or SOX11 inhibition reversed the therapeutic effects of AST on OP in rats. CONCLUSION AST promoted the osteogenic differentiation of hBMSCs and alleviated OP by targeting SOX11 via miR-181d-5p.
Osteogenesis/drug effects* ; Animals ; MicroRNAs/genetics* ; Mesenchymal Stem Cells/drug effects* ; Osteoporosis/drug therapy* ; Humans ; Cell Differentiation/drug effects* ; Astragalus Plant/chemistry* ; Rats ; Rats, Sprague-Dawley ; Female ; SOXC Transcription Factors/genetics* ; Plant Extracts/pharmacology* ; Cells, Cultured ; Drugs, Chinese Herbal/pharmacology*

Objective To analyze and evaluate the early warning efficacy of D-dimer/albumin ratio(DAR)combined with interleukin-6(IL-6)and fibrin degradation products(FDP)in the postoperative treatment of acute Stanford type A aortic dissection(ATAAD).Methods A retrospective cohort study was conducted on 284 ATAAD patients who underwent the Sun's procedure at our hospital from July 2024 to March 2025.Patients were divided into a non-adverse outcome group(n=196)and an adverse outcome group(n=88)based on the occurrence of postop-erative complications within 30 days,including acute renal failure requiring dialysis,secondary thoracotomy for hemostasis,severe neurological complications,multiple organ failure,or all-cause mortality.Preoperative baseline data,perioperative parameters,and laboratory indicators were collected via the electronic medical record system.The Mann-Whitney U test was used to compare the differences between groups for continuous variables that did not conform to the normal distribution,and Chi-square test or Fisher's exact test was selected for statistical difference analysis according to the frequency distribution characteristics of categorical variables.On the basis of univariate analysis,multivariate logistic regression analysis was used to screen independent risk factors.Results Statistically significant differences were observed between the non-adverse and adverse outcome group in age,cardiopulmonary bypass time,lactate dehydrogenase(LDH),IL-6,D-dimer(D-D),FDP,and DAR levels(P<0.05).Multivariate analysis revealed that DAR,IL-6,D-D,FDP,and prolonged cardiopulmonary bypass time were independent risk factors for adverse postoperative outcomes(P<0.05).Combined detection analysis demonstrated that the combination of DAR,IL-6,FDP,and cardiopulmonary bypass time yielded the highest predictive efficacy,with an area under the ROC curve of 0.886(95％CI:0.846～0.927).Conclusion The combination of DAR,IL-6,FDP,and cardio-pulmonary bypass time effectively predicts adverse postoperative outcomes in ATAAD patients.This biomarker panel may serve as a robust predictive tool for postoperative risk stratification.

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.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective To explore the clinical characteristics of patients with alcoholic liver disease(ALD)of various traditional Chinese medicine(TCM)syndrome types.Methods A retrospective analysis was conducted in 129 patients with alcoholic liver disease who met the inclusion and exclusion criteria in Foshan Hospital of Traditional Chinese Medicine from 2018 to 2022.The general data of the patients as well as their TCM syndrome types and clinical information of liver and kidney function,blood lipid,liver transient elastography during the hospital visit were collected.The distribution of TCM syndrome types in ALD patients was analyzed,and the clinical characteristics of the ALD patients with various TCM syndrome types were explored.Results(1)Of the 129 patients,128(99.22%)were male and only one(0.78%)was female,the average age was(48.71±11.50)years old,and the average body mass index(BMI)was(23.82±3.98)kg·m-2.(2)Damp-heat accumulation syndrome was most common syndrome type in ALD patients,with a total of 70 cases(54.26%),and then came liver depression and spleen deficiency syndrome(24 cases,18.60%),internal obstruction of phlegm-damp syndrome(22 cases,17.05%),liver-kidney sufficiency syndrome(7 cases,5.43%),phlegm interweaved with blood stasis syndrome(3 cases,2.33%),and internal accumulation of blood stasis syndrome(3 cases,2.33%).(3)The analysis of clinical characteristics by non-parametric rank sum test showed that there were no statistically significant differences in BMI,alcohol consumption,aspartate aminotransferase(AST),gamma-glutamyltransferase(GGT),total bilirubin(TBIL),alkaline phosphatase(ALP),triglyceride(TG),liver stiffness measurement(LSM),and controlled attenuation parameter(CAP)which reflects the fat content of liver in ALD patients with various TCM syndrome types(P＜0.05 or P＜0.01).The prominent features were as follows:patients with the 4 types of liver depression and spleen deficiency,internal obstruction of phlegm-damp,phlegm interweaved with blood stasis,and internal accumulation of blood stasis had a BMI exceeding the standard(＞24 kg·m-2),whereas patients with damp-heat accumulation syndrome and liver-kidney deficiency syndrome,which accounted for 54.26%of the sample size,had a BMI within the normal range(23.03 kg·m-2 and 21.42 kg·m-2,respectively),and the BMI of these two types differed from that(26.44 kg·m-2)of the internal obstruction of phlegm-damp syndrome(P＜0.01),suggesting that more than half of the ALD patients had the normal BMI;moreover,the patients with internal obstruction of phlegm-damp also had the highest values of serum TG(2.69 mmol/L)and CAP(292 db/m)except for the highest BMI,indicating that patients with internal obstruction of phlegm-damp syndrome had a more serious degree of obesity and hepatic fat infiltration than those with other syndrome types;the levels of AST and GGT,which separately reflect the chronic inflammatory injury of liver and bile duct cell injury,were significantly increased in the patients with damp-heat accumulation syndrome and liver-kidney deficiency syndrome,and the LSM value of these two types of patients was also the highest in all of the syndrome types,the differences being all statistically significant(P＜0.05 or P＜0.01).Conclusion Damp-heat accumulation syndrome is the main TCM syndrome type of ALD patients,the degree of fatty infiltration of the liver and overweight of ALD patients are not corresponded to the severity of illness,and there are some differences in the clinical indicators of ALD patients with various TCM syndrome types.However,with cross reference to the data of the four diagnostic examinations of TCM and the clinical indicators,the accuracy of the TCM diagnosis of ALD is expected to be increased.

Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laboratory diagnosis of viral encephalitis is a worldwide challenge.Recently,high-throughput sequencing technology has provided new tools for diagnosing central nervous system infections.Thus,In this study,we established a multipathogen detection platform for viral encephalitis based on amplicon sequencing. Methods We designed nine pairs of specific polymerase chain reaction(PCR)primers for the 12 viruses by reviewing the relevant literature.The detection ability of the primers was verified by software simulation and the detection of known positive samples.Amplicon sequencing was used to validate the samples,and consistency was compared with Sanger sequencing. Results The results showed that the target sequences of various pathogens were obtained at a coverage depth level greater than 20×,and the sequence lengths were consistent with the sizes of the predicted amplicons.The sequences were verified using the National Center for Biotechnology Information BLAST,and all results were consistent with the results of Sanger sequencing. Conclusion Amplicon-based high-throughput sequencing technology is feasible as a supplementary method for the pathogenic detection of viral encephalitis.It is also a useful tool for the high-volume screening of clinical samples.

Humans ; Lymphoma, Large B-Cell, Diffuse/genetics* ; Herpesvirus 4, Human/genetics* ; Mutation ; Epstein-Barr Virus Infections/pathology* ; Hepatitis A Virus Cellular Receptor 2/genetics*

The modernization and development of traditional Chinese medicine has led to higher standards for the quality of traditional Chinese medicine products. The extraction process is a crucial component of traditional Chinese medicine production, and it directly impacts the final quality of the product. However, the currently relied upon methods for quality assurance of the extraction process, such as simple wet chemical analysis, have several limitations, including time consumption and labor intensity, and do not offer precise control of the extraction process. As a result, there is significant value in incorporating near-infrared spectroscopy (NIRS) in the production process of traditional Chinese medicine to improve the quality control of the final products. In this study, we focused on the extraction process of Xiao'er Xiaoji Zhike oral liquid (XXZOL), using near-infrared spectra collected by both a Fourier transform near-infrared spectrometer and a portable near-infrared spectrometer. We used the concentration of synephrine, a quality control index component specified by the pharmacopoeia, to achieve rapid and accurate detection in the extraction process. Moreover, we developed a model transfer method to facilitate the transfer of models between the two types of near-infrared spectrometers (analytical grade and portable), thus resolving the low resolution, poor performance, and insufficient prediction accuracy issues of portable instruments. Our findings enable the rapid screening and quality analysis of XXZOL onsite, which is significant for quality monitoring during the traditional Chinese medicine production process.