ObjectiveTo explore the mechanism of Sangpi Zhike prescription in treating cough after respiratory syncytial virus （RSV） infection through the "lung-intestine co-treatment" approach using network pharmacology and animal experimental validation. MethodsActive ingredients and targets of Sangpi Zhike prescription were retrieved from the Traditional Chinese Medicine Systems Pharmacology （TCMSP） database. Disease targets were obtained from GeneCards and Online Mendelian Inheritance in Man（OMIM） databases. Protein-protein interaction （PPI） networks and drug-component-target networks were constructed using overlapping targets between drugs and diseases to identify core targets. Gene ontology（GO） and Kyoto encyclopedia of genes and genomes（KEGG） pathway enrichment analyses were performed on the overlapping targets. Sixty mouse models were established： 10 as the normal group， and the remaining mice were infected with RSV via slow nasal drip of RSV suspension， with cough induced using capsaicin solution. After modeling， mice were divided into a model group， a Montelukast Sodium group （1 mg·kg^-1·d^-1）， and low， medium， and high dose groups of Sangpi Zhike prescription （4.875，9.75，and 19.5 g·kg^-1·d^-1）， with 10 mice per group. From day 14 after RSV infection， the normal and model groups received saline via gavage， while other groups received corresponding drug treatments once daily for 5 d. Hematoxylin-eosin（HE） staining was used to observe pathological changes in lung and intestinal tissue. The protein content of extracellular signal-regulated kinase 1/2 （ERK1/2） and phosphorylated （p）-ERK1/2 in the lung and colon tissue of mice was detected by Western blot. Real-time polymerase chain reaction（Real-time PCR） detected ERK1/2 mRNA expression in lung and intestinal tissue. Immunohistochemistry assessed p-MEK1/2， p-ERK1/2， p-c-Fos protein levels， and inflammatory cytokines interleukin（IL）-4 and （TNF）-α in lung and colon tissue. ResultsNetwork pharmacology identified 184 active ingredients and 684 targets in Sangpi Zhike prescription， with 1 344 RSV-related disease targets and 209 overlapping targets. Core targets included TNF， Fos， and Jun. KEGG enrichment revealed 179 pathways， primarily mitogen-activated protein kinase（MAPK）， cancer， TNF， and IL-17 signaling pathways. Animal experiments showed that， compared to those of the normal group， the lung tissue sections of the model group showed typical inflammatory damage， infiltration of inflammatory cells， rupture of alveolar septa， extensive alveolar fusion， and disruption of tight junctions between single-layer columnar epithelial cells in the intestinal tissue. The values of p-ERK1/2 and ERK1/2 in lung and intestinal tissue were significantly increased （P<0.01）， and the expression level of ERK1/2 mRNA was significantly elevated （P<0.01）. The levels of ERK1/2， p-MEK1/2， p-ERK1/2， p-c-Fos， IL-4， and TNF-α along the ERK pathway were significantly increased （P<0.05， P<0.01）. Compared to the model group， Sangpi Zhike prescription groups showed reduced lung and intestinal inflammation， decreased p-ERK1/2/ERK1/2 ratios （P<0.05，P<0.01）， lower ERK1/2 mRNA levels， and downregulated ERK pathway proteins （P<0.05，P<0.01）. ConclusionSangpi Zhike prescription alleviates cough and intestinal symptoms after RSV infection via the "lung-intestine co-treatment" mechanism by suppressing expression levels of ERK1/2， p-MEK1/2， p-ERK1/2， p-c-Fos， IL-4， and TNF-α on ERK pathway components， thereby mitigating lung and intestinal pathological damage.

Objective:To explore the incidence characteristics and correlation analysis of Western dryness syndrome in pulmonary tuberculosis patients in Moyu County,Hetian Prefecture,Xinjiang,and to provide a basis for the prevention and treatment strategies and effectiveness evaluation of pulmonary tuberculosis in Xinjiang.Methods:89 confirmed cases of pulmonary tuberculosis in Moyu County,Hotian Prefecture,Xinjiang from October 2023 to October 2024 were selected as the case group,and 117 healthy individuals matched with the case group in terms of gender,age,and education level in Moyu County,Hotian Prefecture,Xinjiang during the same period were selected as the control group.The general information and incidence of Northwest dryness syndrome between two groups were compared.The incidence and severity of Northwest dryness syndrome in tuberculosis patients,as well as the main and secondary syndrome scores of Northwest dryness syndrome were analyzed.The influencing factors of pulmonary tuberculosis were analyzed.The scores of the main syndrome of Northwest dryness syndrome(lung wei kong pi dryness syndrome)and the concurrent syndrome of Northwest dryness syndrome(heart kidney yin deficiency syndrome,lung heart spleen wind fire dryness syndrome,liver kidney essence blood deficiency syndrome,spleen stomach dampness syndrome,spleen stomach yin deficiency syndrome)between the control group and the case group were compared.The correlation between pulmonary tuberculosis and the on set of Northwest dryness syndrome were analyzed by Spearman rank sum test.Results:There was no significant difference between the control group and the case group in terms of body mass index(BMI),age,education level,gender,etc.(P＞0.05).The incidence of Northwest dryness syndrome in the case group was significantly higher than that in the control group(P＜0.05).The Northwest Dry Syndrome Scale score in the severe group was higher than that in the mild and moderate groups,and the Northwest Dry Syndrome Scale score in the moderate group was higher than that in the mild group(P＜0.05).Among 89 patients with pulmonary tuberculosis,the main syndrome of lung wei kong pi dryness syndrome had the highest number of cases,totaling 42 cases(47.19％).The highest number of concurrent syndromes were heart kidney yin deficiency syndrome,totaling 36 cases(40.45％),followed by spleen stomach yin deficiency syndrome,totaling 35 cases(39.33％).The results showed that suffering from Northwest dryness syndrome was a risk factor for pulmonary tuberculosis(P＜0.05).There were significant differences in the scores of the main syndrome of Northwest dryness(Lung Wei Kong Pi dryness syndrome),the concurrent syndrome of Northwest dryness(Lung Heart Spleen Wind Fire dryness syndrome),and the concurrent syndrome of Northwest dryness(Spleen Stomach Yin Deficiency syndrome)between the control group and the case group(P＜0.05).There was no significant difference in the scores of Northwest dryness syndrome(liver and kidney essence and blood deficiency syndrome),Northwest dryness syndrome(heart and kidney yin deficiency syndrome),and Northwest dryness syndrome(spleen and stomach dampness syndrome)between the control group and the case group(P＞0.05).The score of pulmonary tuberculosis syndrome was positively correlated with various types of Northwest dryness syndrome(P＜0.05),and there was a significant correlation at the 0.001 level between pulmonary Wei Kong Pi dryness syndrome,pulmonary heart spleen wind fire dryness syndrome,and spleen stomach dampness syndrome(P＜0.05).The correlation between liver and kidney essence and blood deficiency syndrome and spleen and stomach yin deficiency syndrome is significant at the 0.01 level(P＜0.05).The correlation between heart and kidney yin deficiency syndrome is significant at the 0.05 level(P＜0.05).Conclusion:The northwest dryness syndrome is related to the occurrence of pulmonary tuberculosis in Moyu County,Hotan Prefecture,Xinjiang,and is a risk factor for the occurrence of pulmonary tuberculosis.There is a significant positive correlation between the pulmonary tuberculosis syndrome score and various types of northwest dryness syndrome,among which the lung wei kong pi dryness syndrome,lung heart spleen wind fire dryness syndrome,and spleen stomach dampness syndrome have the most significant impact on the pulmonary nucleus syndrome score.

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.

Aim To explore the mechanism of Jiawei Shaofu Zhuyu decoction in antagonizing endometriosis fibrosis by regulating mitophagy.Methods After the animal model was constructed,the syndrome was evalu-ated by general condition,organ water content and ther-mal imaging.The curative effect was evaluated by the weight of ectopic focus and the degree of adhesion.The pathological changes were compared using HE stai-ning,transmission electron microscopy,Masson and Sir-ius red staining.The expression of PINK1 and Parkin was detected by immunohistochemistry.The expression of mRNA and protein was determined by qPCR and Western blot,and the level of serum ROS was detected by ELISA.Results The autonomic activity of model mice was weakened,the water content of organs rose,and the temperature of limbs and lower abdomen was reduced by thermal imaging.HE staining showed obvi-ous hyperplasia of ectopic epithelium and glands.Transmission electron microscopy showed mitochondrial and endoplasmic reticulum structure damage,and nor-mal autophagy structure disappeared.Masson and Siri-us red staining showed increased collagen deposition;immunohistochemistry showed decreased expression of PINK1 and Parkin in ectopic foci.qPCR and Western blot showed that the expression of PINK1,Parkin,Bec-lin1,LC3 mRNA and protein in ectopic foci of model mice decreased,the expression of p62 mRNA and pro-tein increased,and serum ROS increased.The syn-drome performance of model mice was improved after the intervention of Jiawei Shaofu Zhuyu decoction;the inflammatory infiltration of ectopic foci was relieved,the morphology of mitochondria and endoplasmic retic-ulum was restored,and normal autophagy structure ap-peared.The degree of collagen deposition and fibrosis was reduced;the mRNA and protein expression of PINK1,Parkin,Beclin1 and LC3 increased.The ex-pression of p62 mRNA and protein decreased,and the level of ROS decreased.Conclusions Jiawei Shaofu Zhuyu decoction can improve the fibrosis of ectopic le-sions in mice with endometriosis of cold-dampness sta-sis syndrome,which may be related to the regulation of mitophagy.

This study analyzed the variations and evolution characteristics of influenza B Victoria(BV)virus in the Yellow River Delta region of China during 2021-2024.Throat swabs were collected from people with influenza-like illness(ILI)from 2021 to 2024 in Binzhou and Dongying,China.Viral isolation was performed,and 22 representative influenza BV isolates were selected for whole genome sequencing.Phylogenetic analysis of whole-genome sequences was performed in MegAlign and MEGA software.A total of 27 674 samples were obtained,and the overall positivity rate of influenza virus(A/H3N2,A/H1N1,BV)was 11.1％.Our surveillance data indicated that influenza B virus was detected in 2 years,which showed positivity rates of 28.2％and 1.7％,respectively.Statistically significant differences in the positivity rates of influenza BV viruses were observed(x2=3 641.791,P＜0.001).The median pairwise sequence identities ranged from 98.7％to 99.3％for eight segments of 22 viral sequences.The isolates for the monitoring years 2021-2024 were located in clades V1A.3a.1 and V1A.3a.2.Intra-lineage reassortments were discovered in B/shandongbincheng17/2022.The NA gene of one isolate exhibited an increase in the 488NLTV N-glycoproteome site.The K338R mutation occurred in the PA gene.Three locus deletion or insertion mutations occurred in the MP gene.The BV influenza epidemic was prevalent every other year;the intensity ranged from strong to weak;and the duration ranged from long to short in the Yellow River Delta region of China during 2021-2022.Influ-enza B virus formed intra-lineage reassortments,and showed significant mutations in NA gene,PA gene,and MP gene.

Monoamine neurotransmitters mainly include serotonin,dopamine,epinephrine,and norepinephrine.They play an indispensable regulatory role in key physiological activities such as emotion,sleep,and memory within the central nervous system.Precise detection of these neurotransmitters holds great significance in the field of neuroscience research.Detection methods for monoamine neurotransmitters encompass high-performance liquid chromatography,mass spectrometry,capillary electrophoresis,fluorescence spectroscopy,and electrochemical methods,etc.Compared with other methods,electrochemical methods offer advantages such as high sensitivity,good selectivity,low cost,strong portability,convenient operation,and capability for in vivo real-time detection.This article reviewed recent research progress in electrochemical detection of monoamine neurotransmitters,focusing on a retrospective and summary from three aspects:sensor electrode materials,detection of various monoamine neurotransmitters,and in vivo real-time analysis.Furthermore,the future development of electrochemical sensors for monoamine neurotransmitters was prospected.

Aim To study the protective effect of the silibinin derivative Sil-1 on acute myocardial ischemia in SD rats and its mechanism of action.Methods Af-ter 18 hours of oxygen-glucose deprivation and treat-ment of H9c2 cells,the protective effect of Sil-1 on rat cardiomyocytes was examined.SD rats were treated 30 minutes before surgery,followed by 24 h ligation of the left anterior descending coronary artery.The cardiopro-tective effects of Sil-1 and its mechanisms for improving myocardial ischemic injury were investigated using pro-teomics technology.Results In vitro,compared with the control group,the activity of H9c2 cells in the mod-el group showed reduced cell viability,increased dead cells,elevated ROS and higher levels of LDH and in-flammatory cytokines TNF-α,IL-1β and IL-6 in the culture medium.Sil-1 could improve the above condi-tions to different degrees.In vivo,compared with the control group,rats in the model group showed signifi-cantly higher T waves on electrocardiogram,significant ischemic areas in the heart section,disorganized ar-rangement of cardiomyocytes,increased inflammatory factor infiltration and elevated CK,CK-MB,LDH and inflammatory factors TNF-α,IL-6 and IL-1β.Besides,NF-κB phosphorylation levels in myocardial tissue in-creased.Sil-1 improved the above conditions to varying degrees.The results of proteomics showed that 90 pro-teins were found between the control vs model group and the Sil-1 vs model group,and KEGG enrichment a-nalysis showed that MAPK,chemokines,VEGF and other signaling pathways were abundant.Western blot results showed that Sil-1 blocked the phosphorylation of ERK,JNK and p38 MAPK.Conclusions Sil-1 inhib-its the MAPK pathway by blocking the phosphorylation of JNK,ERK,and p38 MAPK,and achieves a protec-tive effect on rats with acute myocardial infarction.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

OBJECTIVE: To investigate possible associations between physical function assessment scales, such as Short Physical Performance Battery (SPPB) and Berg Balance Scale (BBS), with all-cause mortality in acute decompensated heart failure (ADHF) patients. METHODS: A total of 108 ADHF patients were analyzed from October 2020 to October 2022, and followed up to May 2023. The association between baseline clinical characteristics and all-cause mortality was analyzed by univariate Cox regression analysis, while for SPPB and BBS, univariate Cox regression analysis was followed by receiver operating characteristic curves, in which the area under the curve represented their predictive accuracy for all-cause mortality. Incremental predictive values for both physical function assessments were measured by calculating net reclassification index and integrated discrimination improvement scores. Optimal cut-off value for BBS was then identified using restricted cubic spline plots, and survival differences below and above that cut-off were compared using Kaplan-Meier survival curves and the log-rank test. The clinical utility of BBS was measured using decision curve analysis. RESULTS: For baseline characteristics, age, female, blood urea nitrogen, as well as statins, angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, or angiotensin receptor-neprilysin inhibitors, were predictive for all-cause mortality for ADHF patients. With respect to SPPB and BBS, higher scores were associated with lower all-cause mortality rates for both assessments; similar area under the curves were measured for both (0.774 for SPPB and 0.776 for BBS). Furthermore, BBS ≤ 36.5 was associated with significantly higher mortality, which was still applicable even adjusting for confounding factors; BBS was also found to have great clinical utility under decision curve analysis. CONCLUSIONS BBS or SPPB could be used as tools to assess physical function in ageing ADHF patients, as well as prognosticate on all-cause mortality. Moreover, prioritizing the improvement of balance capabilities of ADHF patients in cardiac rehabilitation regimens could aid in lowering mortality risk.

OBJECTIVE: To investigate the therapeutic effect of Xiangshao Granules (XSG) on post-stroke depression (PSD) and explore the underlying mechanisms. METHODS: Forty-three C57BL/6J mice were divided into 3 groups: sham (n=15), PSD+vehicle (n=14), and PSD+XSG (n=14) groups according to a random number table. The PSD models were constructed using chronic unpredictable mild stress (CUMS) after middle cerebral artery occlusion (MCAO). The sham group only experienced the same surgical operation, but without MACO and CUMS stimulation. The XSG group received XSG (60 mg/kg per day) by gavage for 4 weeks. The mice in the sham and vehicle groups were given the same volume of 0.9% saline at the same time. The body weight and behavior tests including open field test, sucrose preference test, tail suspension test, and elevated plus-maze test, were used to validate the PSD mouse model. Real-time fluorescence quantitative polymerase chain reaction (RT-qPCR), enzyme-linked immunosorbent assay (ELISA), and immunofluorescence staining were used to evaluate the anti-inflammatory effects of XSG. The potential molecular mechanisms were explored and verified through network pharmacology analysis, Nissl staining, Western blot, ELISA, and RT-qPCR, respectively. RESULTS: The body weight and behavior tests showed that MCAO combined with CUMS successfully established the PSD models. XSG alleviated neuronal damage, reduced the expressions of pro-apoptotic proteins Caspase-3 and B-cell lymphoma-2 (BCL-2)-associated X (BAX), and increased the expression of anti-apoptotic protein BCL-2 in PSD mice (P<0.05 or P<0.01). XSG inhibited microglial activation and the expressions of pro-inflammatory cytokines including tumor necrosis factor-α, interleukin (IL)-1 β, and IL-6 via the toll-like receptor 4/nuclear factor kappa-B signaling pathway in PSD mice (P<0.05 or P<0.01). Furthermore, XSG decreased the expression of indoleamine 2,3-dioxygenase1 (IDO1) and increased the concentration of 5-hydroxytryptamine in PSD mice (P<0.05 or P<0.01). CONCLUSION XSG could reverse the anxiety/depressionlike behaviors and reduce the neuronal injury in the hippocampus and prefrontal cortex of PSD mice, which may be a potential therapeutic agent for PSD.
Animals ; Indoleamine-Pyrrole 2,3,-Dioxygenase/metabolism* ; Depression/etiology* ; Drugs, Chinese Herbal/therapeutic use* ; Hippocampus/metabolism* ; Male ; Mice, Inbred C57BL ; Prefrontal Cortex/pathology* ; Microglia/metabolism* ; Stroke/drug therapy* ; Disease Models, Animal ; Mice ; Behavior, Animal/drug effects*