Irritable bowel syndrome with diarrhea （IBS-D）， as a prototypical disorder involving the microbiota-gut-brain axis， remains poorly understood in terms of its pathogenesis， posing ongoing challenges for clinical diagnosis. Omics technologies， leveraging their high-throughput detection and systematic analysis advantages， has emerged as a critical tool for deciphering the complex mechanisms underlying traditional Chinese medicine （TCM） treatment of IBS-D. This systematic review summarizes the research progress of transcriptomics， proteomics， metabolomics， microbiomics， and multi-omics integration techniques in TCM interventions for IBS-D. In single-omics studies， transcriptomics using techniques like RNA-seq， reveals the regulatory mechanisms of TCM on IBS-related signaling pathways. Proteomics， leveraging quantitative technologies such as 2D-difference gel electrophoresis and tandem mass tag， identifies differentially expressed proteins and elucidates the action targets of TCM in treating IBS-D. Metabolomics， employing methods like UPLC-Q-TOF-MS and LC-MS/MS， discovers metabolic pathways regulated by TCM to improve metabolic disturbances in IBS-D. Microbiomics， based on 16S rRNA sequencing， confirms that TCM can reshape the gut microbiota structure and restore the intestinal microecological balance， thereby improving IBS-D. Multi-omics integration further overcomes the limitations of single-omics approaches by synthesizing information from transcriptomics， proteomics， metabolomics， and microbiomics， enabling a more comprehensive and systematic elucidation of the complex mechanisms underlying TCM treatment for IBS-D. In the future， research related to IBS-D should be advanced from three aspects： stratified clinical research based on TCM syndrome types， multi-omics integration verification mechanisms， and emerging omics to explore new mechanisms， providing more innovative ideas for the precise diagnosis and treatment of this disease.

Objective: To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine (TCM) with machine learning algorithms. The proposed model seeks to establish a TCM-informed tool for early depression screening, thereby bridging traditional diagnostic principles with modern computational approaches. Methods: The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1, 2022 to October 1, 2023, as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group. Videos of 3 – 10 s were captured using a Xiaomi Pad 5, and the TCM spirit and expressions were determined by TCM experts (at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions). Basic information, facial images, and interview information were collected through a portable TCM intelligent analysis and diagnosis device, and facial diagnosis features were extracted using the Open CV computer vision library technology. Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data, TCM spirit and expression features, and facial diagnosis feature parameters of the two groups, to compare the differences in TCM spirit and expression and facial features. Five machine learning algorithms, including extreme gradient boosting (XGBoost), decision tree (DT), Bernoulli naive Bayes (BernoulliNB), support vector machine (SVM), and k-nearest neighbor (KNN) classification, were used to construct a depression recognition model based on the fusion of TCM spirit and expression features. The performance of the model was evaluated using metrics such as accuracy, precision, and the area under the receiver operating characteristic (ROC) curve (AUC). The model results were explained using the Shapley Additive exPlanations (SHAP). Results: A total of 93 depression patients and 87 healthy individuals were ultimately included in this study. There was no statistically significant difference in the baseline characteristics between the two groups (P > 0.05). The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows. (i) Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls (P < 0.05), with characteristic features such as sad expressions, facial erythema, and changes in the lip color ranging from erythematous to cyanotic. (ii) Depressed patients exhibited significantly lower values in facial complexion L, lip L, and a values, and gloss index, but higher values in facial complexion a and b, lip b, low gloss index, and matte index (all P < 0.05). (iii) The results of multiple models show that the XGBoost-based depression recognition model, integrating the TCM “spirit-expression” diagnostic framework, achieved an accuracy of 98.61% and significantly outperformed four benchmark algorithms—DT, BernoulliNB, SVM, and KNN (P < 0.01). (iv) The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm, the complexion b value, categories of facial spirit, high gloss index, low gloss index, categories of facial expression and texture features have significant contribution to the model. Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model, offering a novel paradigm for objective depression diagnosis.

ObjectiveTo establish an adjusted Serfling regression model to estimate the excess cases and the excess epidemic period of hand-foot-mouth disease (HFMD) in Beijing from 2011 to 2019, so as to provide data support and decision-making basis for HFMD prevention and control. MethodsThe weekly number of HFMD cases in Beijing from 2011 to 2019 was utilized for adjusted the Serfling regression model. Then the adjusted model was used to fit the baseline and epidemic threshold of HFMD in Beijing from 2011 to 2019, calculating the excess cases and determining the excess epidemic period. ResultsA total of 279 306 cases of HFMD were reported in Beijing from 2011 to 2019, with the climax of the disease occurring in summer and autumn. After adjusting the fitting R² of the Serfling regression model to 0.773, a total of 10 excess epidemic periods totaling 92 weeks were estimated, mainly occurring in summer. The highest number of excess cases during an excess epidemic period was found in 2014 (1 272 cases, 95%CI: 990‒1 554), accounting for 65.04% of the actual cases (95%CI: 50.62%‒79.46%). ConclusionThe adjusted Serfling regression model fits well and can be utilized for early warning of HFMD and estimating the disease burden caused by HFMD.

Objective To analyze the economic cost of self-monitoring of gestational diabetes mellitus, and provide a basis for measuring the economic burden of gestational diabetes mellitus, and to provide a reference for the formulation of intervention development and the adjustment of resource allocation. Methods The individual economic cost of self-monitoring for gestational diabetes mellitus was measured based on a decision tree model, and the total economic cost of self-monitoring for gestational diabetes mellitus in Beijing was estimated. The uncertainty of the model parameters was analyzed using one-way sensitivity analysis. Results The average individual economic cost of gestational diabetes self-monitoring was 1184 RMB, and the individual cost incurred by choosing different types of blood glucose meters ranged from 403 to 18 000 RMB. The average individual economic cost of finger-stick blood glucose monitoring was 606 RMB and the average individual economic cost of continuous glucose monitoring was 2 374 RMB. The total economic cost of gestational diabetes self-monitoring in Beijing was 23.818 0 million RMB, and the total economic cost incurred by choosing different types of blood glucose meters ranged from 0.292 5 to 9.027 9 million RMB. The proportion of the finger-stick blood glucose monitoring had the greatest impact on the robustness of the results. Conclusion Finger-stick blood glucose monitoring is still the dominant self-monitoring method and is less costly than continuous glucose monitoring. Self-monitoring of pregnant women with gestational diabetes mellitus incurs certain economic cost and causes an economic burden on society.

OBJECTIVE To analyze chemical components of Yao ethnic medicine Pittosporum pauciflorum， establish its fingerprint and investigate the spectrum-effect relationship of its anti-inflammatory effect. METHODS UHPLC-Q-Orbitrap-MS technology was used to analyze the chemical components of P. pauciflorum （batch S6）. The fingerprints for 10 batches of P. pauciflorum from different producing areas in Guangxi Province （batches S1-S10） were established by HPLC， and similarity assessment and chemometric pattern recognition analysis were conducted. RAW264.7 inflammatory cell model was induced by lipopolysaccharide， and the anti-inflammatory activity of P. pauciflorum was investigated. Using inhibition rates of nitric oxide （NO）， tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6） and IL-1β as efficacy indicators， grey relational analysis and partial least squares regression analysis were adopted to evaluate the spectrum-effect relationship of the anti-inflammatory effect of P. pauciflorum. RESULTS There were 60 chemical components， including flavonoids， phenolic acids， lipids， etc.， identified in P. pauciflorum. The fingerprints for 10 batches of P. pauciflorum showed 14 common peaks，with similarity values ranging from 0.883 to 0.991. Three common peaks were assigned neochlorogenic acid （peak 5）， chlorogenic acid （peak 7）， and syringaldehyde （peak 10）. The classification results of the systematic clustering analysis and the principal component analysis were basically consistent. Batches S1 to S10 of P. pauciflorum significantly reduced the levels of NO， TNF-α， IL-6 （except for batch S5） and IL-1β in the cell supernatant （P＜0.05 or P＜0.01）. Inhibition rates of above inflammatory indexes were 10.26%-39.96%， 14.96%-31.36%， 1.38%-21.27%， 18.54%-28.00%， respectively. The contents of neochlorogenic acid， syringaldehyde， as well as the components corresponding to peaks 1， 3， 9， 12 and 14，exhibited a strong correlation with the anti-inflammatory effects of P. pauciflorum. CONCLUSIONS The present study has analyzed the chemical components of P. pauciflorum and established HPLC fingerprints for 10 batches of P. pauciflorum. Each batch of medicinal herbs demonstrates certain anti- inflammatory activities， among which neochlorogenic acid， syringaldehyde， and the components corresponding to peaks 1， 3， 9， 12 and 14 are likely to be the active anti-inflammatory components.

BACKGROUND: Molecular subtyping is an essential complementarity after pathological analyses for targeted therapy. This study aimed to investigate the consistency of next-generation sequencing (NGS) results between circulating tumor DNA (ctDNA)-based and tissue-based in non-small cell lung cancer (NSCLC) and identify the patient characteristics that favor ctDNA testing. METHODS: Patients who diagnosed with NSCLC and received both ctDNA- and cancer tissue-based NGS before surgery or systemic treatment in Lung Cancer Center, Sichuan University West China Hospital between December 2017 and August 2022 were enrolled. A 425-cancer panel with a HiSeq 4000 NGS platform was used for NGS. The unweighted Cohen's kappa coefficient was employed to discriminate the high-concordance group from the low-concordance group with a cutoff value of 0.6. Six machine learning models were used to identify patient characteristics that relate to high concordance between ctDNA-based and tissue-based NGS. RESULTS: A total of 85 patients were enrolled, of which 22.4% (19/85) had stage III disease and 56.5% (48/85) had stage IV disease. Forty-four patients (51.8%) showed consistent gene mutation types between ctDNA-based and tissue-based NGS, while one patient (1.2%) tested negative in both approaches. Patients with advanced diseases and metastases to other organs would be suitable for the ctDNA-based NGS, and the generalized linear model showed that T stage, M stage, and tumor mutation burden were the critical discriminators to predict the consistency of results between ctDNA-based and tissue-based NGS. CONCLUSION ctDNA-based NGS showed comparable detection performance in the targeted gene mutations compared with tissue-based NGS, and it could be considered in advanced or metastatic NSCLC.
Humans ; Carcinoma, Non-Small-Cell Lung/pathology* ; Circulating Tumor DNA/blood* ; High-Throughput Nucleotide Sequencing/methods* ; Female ; Male ; Lung Neoplasms/pathology* ; Middle Aged ; Mutation/genetics* ; Aged ; Adult ; Aged, 80 and over

This article reviews the role of different types of T lymphocyte subpopulations in pathological cardiac fibrosis remodeling. T helper 17 (Th17) cells are implicated in promoting the development of pathological cardiac fibrosis remodeling, while regulatory T (Treg) cells exert an immunosuppressive functions as negative regulators, attributing to their interleukin-10 (IL-10) secretion and functional phenotype. Th1 and Th2 cells are involved in different stages of the inflammatory response in pathological cardiac fibrosis remodeling, and their influence varies according to the pathological mechanisms of different cardiac diseases. In addition, CD8⁺ T cells regulate the activation and polarization of macrophages, promote the secretion of granzyme B, induce cardiomyocyte apoptosis, and aggravate cardiac fibrosis post-myocardial infarction. Considering the limitation of cytokine modulation in clinical therapy of heart failure, targeting T-cell co-stimulatory molecules emerges as a promising strategy for treating pathologic cardiac remodeling. Future research will explore chimeric antigen receptor modified T cells (CAR-T cells) technology and targeted regulation of Treg cells quantity and phenotype, for both of which have the potential to become effective methods for treating heart disease.
Humans ; Fibrosis ; T-Lymphocytes, Regulatory/immunology* ; Ventricular Remodeling/immunology* ; Myocardium/immunology* ; Animals ; Th17 Cells/immunology* ; Interleukin-10/metabolism* ; Th1 Cells/immunology* ; Th2 Cells/immunology*

The etiology of nervous system diseases is complicated, posing significant harm to patients and often resulting in poor prognoses. In recent years, the role of dopaminergic system in nervous system diseases has attracted much attention, and its complex regulatory mechanism and therapeutic potential have been gradually revealed. This paper reviews the role of dopaminergic neurons, the neurotransmitter dopamine, dopamine receptors and dopamine transporters in neurological diseases (including Alzheimer's disease, Parkinson's disease and schizophrenia), with a view to further elucidating the disease mechanism and providing new insights and strategies for the treatment of neurological diseases.
Humans ; Dopamine/metabolism* ; Nervous System Diseases/physiopathology* ; Parkinson Disease/physiopathology* ; Receptors, Dopamine/metabolism* ; Dopaminergic Neurons/physiology* ; Dopamine Plasma Membrane Transport Proteins/metabolism* ; Alzheimer Disease/physiopathology* ; Schizophrenia/physiopathology* ; Animals

This study aimed to explore the pharmacological mechanism of Yourenji Capsules(YRJ) in improving osteoporosis by combining network pharmacology and proteomics technologies. The SD rats were randomly divided into a blank control group and a 700 mg·kg~(-1) YRJ group. The rats were subjected to gavage administration with the corresponding drugs, and the blank serum, drug-containing serum, and YRJ samples were compared using ultra performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry(UPLC-Q-TOF-MS/MS) to analyze the main components absorbed into blood. Network pharmacology analysis was conducted based on the YRJ components absorbed into blood to obtain related targets of the components and target genes involved in osteoporosis, and Venn diagrams were used to identify the intersection of drug action targets and disease targets. The STRING database was used for protein-protein interaction(PPI) network analysis of potential target proteins to construct a PPI network. Gene Ontology(GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment were performed using Enrichr to investigate the potential mechanism of action of YRJ. Ovariectomy(OVX) was performed to establish a rat model of osteoporosis, and the rats were divided into a sham group, a model group, and a 700 mg·kg~(-1) YRJ group. The rats were given the corresponding drugs by gavage. The femurs of the rats were subjected to label-free proteomics analysis to detect differentially expressed proteins, and GO functional enrichment and KEGG pathway enrichment analyses were performed on the differentially expressed proteins. With the help of network pharmacology and proteomics results, the mechanism by which YRJ improves osteoporosis was predicted. The analysis of the YRJ components absorbed into blood revealed 23 bioactive components of YRJ, and network pharmacology results indicated that key targets involved include tumor necrosis factor(TNF), tumor protein p53(TP53), protein kinase(AKT1), and matrix metalloproteinase 9(MMP9). These targets are mainly involved in osteoclast differentiation, estrogen signaling pathways, and nuclear factor-kappa B(NF-κB) signaling pathways. Additionally, the proteomics analysis highlighted important pathways such as peroxisome proliferator-activated receptor(PPAR) signaling pathways, mitogen-activated protein kinase(MAPK) signaling pathways, and β-alanine metabolism. The combined approaches of network pharmacology and proteomics have revealed that the mechanism by which YRJ improves osteoporosis may be closely related to the regulation of inflammation, osteoblast, and osteoclast metabolic pathways. The main pathways involved include the NF-κB signaling pathways, MAPK signaling pathways, and PPAR signaling pathways, among others.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Osteoporosis/metabolism* ; Proteomics ; Rats ; Rats, Sprague-Dawley ; Network Pharmacology ; Female ; Protein Interaction Maps/drug effects* ; Capsules ; Humans ; Signal Transduction/drug effects*