Anxiety disorder is a highly prevalent psychological illness, and research has shown that obesity is a significant risk factor for its development. This study explored the ameliorative effects and mechanisms of saponins from Panax japonicus(SPJ) on anxiety disorder in mice fed a high-fat diet(HFD). Fifty C57BL/6J mice were randomly divided into normal control diet(NCD) group, HFD group, and low-and high-dose SPJ groups. At week 12, six mice from the HFD group were further divided into a control group(treated with DMSO) and an exogenous fibroblast growth factor 21(FGF21) group(administered rFGF21). The anxiety-like behavior of the mice was assessed using the open field test and elevated plus maze test. Hematoxylin-eosin(HE) staining and oil red O staining were performed to observe pathological changes in the liver and adipose tissue. Glucose metabolism was evaluated through the glucose tolerance test(GTT) and insulin tolerance test(ITT). Western blot analysis was performed to detect the expression of FGF21 and its downstream-related proteins in the liver and cortex, along with the expression of brain-derived neurotrophic factor(BDNF), disks large homolog 4(DLG4), and synaptophysin(SYP) in the cortex. Real-time quantitative fluorescent PCR(qPCR) was used to detect the expression of FGF21 and its receptor genes in the liver and cortex. Immunofluorescence staining was employed to examine the expression of neuronal activator c-Fos, FGF21, and the FGF21 co-receptor β-klotho in the cerebral cortex. The results showed that SPJ significantly improved the frequency of activity in the open arms of the elevated plus maze and the central area of the open field in HFD mice, up-regulated the expression of BDNF, DLG4, and SYP, and effectively alleviated anxiety-like behaviors in HFD mice. Compared with the NCD group, HFD mice exhibited up-regulated expression of FGF21 in the liver and cerebral cortex, while the expression of fibroblast growth factor receptor 1(FGFR1) and β-klotho was significantly down-regulated, suggesting that HFD mice exhibited FGF21 resistance. SPJ markedly up-regulated the β-klotho levels in HFD mice, reversing FGF21 resistance. Further comparison with exogenously administered FGF21 revealed that SPJ activates brain cortical regions in a consistent manner, and additionally, SPJ promotes the number and colocalization of c-Fos and β-klotho positive cells in the brain cortex. In summary, SPJ effectively alleviates anxiety-like behaviors in HFD mice. Its mechanism is associated with up-regulation of β-klotho expression in the brain, reversal of FGF21 resistance, and subsequent activation of neurons in the cerebral cortex and amygdala.
Animals ; Diet, High-Fat/adverse effects* ; Fibroblast Growth Factors/genetics* ; Mice ; Male ; Panax/chemistry* ; Mice, Inbred C57BL ; Anxiety/etiology* ; Saponins/administration & dosage* ; Brain-Derived Neurotrophic Factor/genetics* ; Humans ; Liver/metabolism* ; Drugs, Chinese Herbal/administration & dosage*

Traditional Chinese medicine(TCM) decoction pieces are a core carrier for the inheritance and innovation of TCM, and their quality and safety are critical to public health and the sustainable development of the industry. Conventional quality control models, while having established a well-developed system through long-term practice, still face challenges such as relatively long inspection cycles, insufficient objectivity in characterizing complex traits, and urgent needs for improving the efficiency of integrating multidimensional quality information when confronted with the dual demands of large-scale production and precision quality control. With the rapid development of artificial intelligence, machine learning can deeply analyze multidimensional data of the morphology, spectroscopy, and chemical fingerprints of decoction pieces by constructing high-dimensional feature space analysis models, significantly improving the standardization level and decision-making efficiency of quality evaluation. This article reviews the research progress in the application of machine learning in the processing, production, and rapid quality evaluation of TCM decoction pieces. It further analyzes current challenges in technological implementation and proposes potential solutions, offering theoretical and technical references to advance the digital and intelligent transformation of the industry.
Machine Learning ; Drugs, Chinese Herbal/standards* ; Quality Control ; Medicine, Chinese Traditional/standards* ; Humans

Objective Based on 25 indicators including immune factors, cell count classification, and smear results of the knee joint fluid, machine learning models were established to distinguish between osteoarthritis (OA) and rheumatoid arthritis (RA). Methods 100 OA and 40 RA patients scheduled for total knee arthroplasty were enrolled respectively. Each patient's knee joint fluid was collected preoperatively. Nucleated cells were counted and classified. The expression levels of immune factors, including tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β), IL-6, IL-8, IL-15, matrix metalloproteinase 3 (MMP3), MMP9, MMP13, rheumatoid factor (RF), serum amyloid A (SAA), C-reactive protein (CRP), and others were measured. Smears and microscopic classification of all the immune factors were performed. Independent influencing factors for OA or RA were identified using univariate binary logistic regression, Lasso regression, and multivariate binary logistic regression. Based on the independent influencing factors, three machine learning models were constructed which are logistic regression, random forest, and support vector machine. Receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to evaluate and compare the models. Results A total of 5 indicators in the knee joint fluid were screened out to distinguish OA and RA, which were IL-1β(odds ratio(OR)=10.512, 95× confidence interval (95×CI) was 1.048-105.42, P=0.045), IL-6 (OR=1.007, 95×CI was 1.001-1.014, P=0.022), MMP9 (OR=3.202, 95×CI was 1.235-8.305, P=0.017), MMP13 (OR=1.002, 95× CI was 1-1.004, P=0.049), and RF (OR=1.091, 95×CI was 1.01-1.179, P=0.026). According to the results of ROC, calibration curve and DCA, the accuracy (0.979), sensitivity (0.98) and area under the curve (AUC, 0.996, 95×CI was 0.991-1) of the random forest model were the highest. It has good validity and feasibility, and its distinguishing ability is better than the other two models. Conclusion The machine learning model based on immune factors in the knee joint fluid holds significant value in distinguishing OA and RA. It provides an important reference for the clinical early differential diagnosis, prevention and treatment of OA and RA.
Humans ; Arthritis, Rheumatoid/metabolism* ; Machine Learning ; Male ; Female ; Middle Aged ; Aged ; Synovial Fluid/immunology* ; Osteoarthritis, Knee/metabolism* ; Knee Joint/metabolism* ; ROC Curve ; Diagnosis, Differential

Sjogren's syndrome (SS) is a common autoimmune disorder that primarily targets exocrine glands, leading to hallmark manifestations of xerostomia and xerophthalmia, with potential progression to multisystem involvement. The rapid advances in omics technologies-including metabolomics, proteomics, and transcriptomics-have yielded substantial insights into SS pathophysiology. This review consolidates current evidence on omics-derived biomarkers in SS. Studies consistently implicate aberrant glucose metabolism, neutrophil-derived enzyme activity, mitochondrial bioenergetic impairment, ferroptosis, and apoptotic pathways as central to SS development. These findings refine our understanding of disease mechanisms and the heterogeneity of therapeutic responses. Hydroxyproline has emerged as a candidate marker for distinguishing SS from IgG4-related disease, whereas distinct cytokine and chemokine signatures may enable earlier diagnosis. Genomic analyses demonstrate a robust association between expression of the rs11797 locus and SS-related lymphomagenesis, and several genes controlling DNA methylation represent promising therapeutic targets. Collectively, these findings lay the groundwork for personalized risk stratification and intervention in SS. The review concludes by summarizing existing progress and outlining priorities for future omics-based investigations.
Humans ; Sjogren's Syndrome/diagnosis* ; Biomarkers/analysis* ; Metabolomics/methods* ; Proteomics/methods* ; Genomics ; Multiomics

Atmospheric particulate matter has an association with respiratory system inflammation, and low molecular weight hyaluronic acid (LMW-HA) is a key biomarker of inflammatory cascade reaction. This review summarized the possible pathways and biomarkers of atmospheric particulate matter causing respiratory system inflammation through high molecular weight hyaluronic acid (HMW-HA)/LMW-HA imbalance, including the synthesis and decomposition of HA, the reduction of particulate matter and HMW-HA, the increase of LMW-HA, and the relationship between LMW-HA and respiratory system inflammation. Furthermore, inhibitors and therapeutic drugs targeting certain biomarkers were further listed. This review could shed light on the mechanism of respiratory system inflammation caused by atmospheric particulate matter and the weak points that need attention in subsequent research.

Atmospheric particulate matter has an association with respiratory system inflammation, and low molecular weight hyaluronic acid (LMW-HA) is a key biomarker of inflammatory cascade reaction. This review summarized the possible pathways and biomarkers of atmospheric particulate matter causing respiratory system inflammation through high molecular weight hyaluronic acid (HMW-HA)/LMW-HA imbalance, including the synthesis and decomposition of HA, the reduction of particulate matter and HMW-HA, the increase of LMW-HA, and the relationship between LMW-HA and respiratory system inflammation. Furthermore, inhibitors and therapeutic drugs targeting certain biomarkers were further listed. This review could shed light on the mechanism of respiratory system inflammation caused by atmospheric particulate matter and the weak points that need attention in subsequent research.

Objective To explore the value of vesical imaging reporting and data system(VI-RADS)combined with absolute tumor-wall contact length(ABTCL)and actual tumor-wall contact length(ACTCL)in diagnosing muscle invasive bladder cancer(MIBC).Methods The MRI data of 113 patients with pathologically confirmed bladder cancer(BCa)were analyzed retrospectively.All patients underwent conventional MRI,diffusion weighted imaging(DWI)and dynamic contrast enhanced(DCE)MRI before sur-gery.Two radiologists independently evaluated MRI images based on VI-RADS score,and measured quantitative parameters,inclu-ding ABTCL and ACTCL.The Chi-square test was used to compare the difference of VI-RADS scores between MIBC and non-mus-cle invasive bladder cancer(NMIBC).Quantitative parameters between MIBC and NMIBC were compared by Mann-Whitney U test.The receiver operating characteristic(ROC)curve was used to evaluate the diagnostic value of VI-RADS,quantitative parameters and VI-RADS combined with quantitative parameters in the diagnosis of MIBC.Results VI-RADS,ABTCL and ACTCL had significant differences between MIBC and NMIBC(P＜0.05).The area under the curve(AUC)for VI-RADS,ABTCL and ACTCL in diagno-sing MIBC were 0.89,0.76 and 0.77,respectively.There was no significant difference between the AUC for ABTCL and ACTCL(P＞0.05).The AUC for VI-RADS combined with ABTCL or ACTCL in diagnosing MIBC was 0.93,higher than that of only VI-RADS(P＜0.05).Conclusion The combination of VI-RADS with either ABTCL or ACTCL can effectively improve the diagnostic performance of MIBC.ABTCL obtainedby linear measurement is easier to implement in clinical practice than ACTCL obtained by curved measurement.

OBJECTIVE: To detect the relative expression of IGLL1 (immunoglobulin lambda-like polypeptide 1) mRNA in bone marrow of children with T-cell acute lymphoblastic leukemia (T-ALL), and analyze its correlation with the clinical characteristics and prognosis of the patients, so as to clarify the clinical significance of IGLL1 in pediatric T-ALL patients. METHODS: A total of 56 pediatric T-ALL patients hospitalized in Children's Hospital of Soochow University from June 2012 to December 2017 and treated with CCLG-ALL 2008 regimen were selected. Transcriptome sequencing technology was used to detect the transcription level of IGLL1 gene in children with T-ALL. According to 25% of the IGLL1 transcription level (cutoff value:448), the enrolled children were divided into IGLL1 low expression group (17 cases) and IGLL1 high expression group (39 cases). Combined with clinical data, the correlation between the expression level of IGLL1 and prognosis of the patients was analyzed. RESULTS: The comparative analysis showed that the transcription level of IGLL1 was not correlated with the clinical characteristics of the patients, such as sex, age, bone marrow blast, white blood cell (WBC) count at initial diagnosis. The 5-year OS rate of patients with high IGLL1 expression was significantly higher than that of patients with low IGLL1 expression (76.9%±6.7% vs 47.1%±12.1%, P =0.018). Further comparison of relapse-free survival (RFS) rate between the two groups showed that the 5-year RFS rate of patients with high IGLL1 expression was higher than that of patients with low IGLL1 expression, but the difference between the two groups was not statistically significant (P =0.095). Multivariate COX analysis was conducted on common clinical prognostic factors (age, sex, WBC count at diagnosis, prednisone response on the 7th day, bone marrow response on the 15th day after treatment) and IGLL1 expression level, and the results showed that IGLL1 expression (P =0.012) and prednisone response (P =0.017) were independent risk factors for overall survival in pediatric T-ALL patients. CONCLUSION In pediatric T-ALL, the OS rate of children with high expression of IGLL1 gene was significantly higher than that of children with low expression of IGLL1 gene, and the expression level of IGLL1 gene was an independent factor affecting the survival of children with T-ALL, which suggests that IGLL1 is a marker of good clinical prognosis of children with T-ALL.
Child ; Humans ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Clinical Relevance ; Disease-Free Survival ; Precursor T-Cell Lymphoblastic Leukemia-Lymphoma/genetics* ; Prednisone/therapeutic use* ; Prognosis ; Recurrence ; Immunoglobulin Light Chains, Surrogate/genetics*

Circular RNAs are novel non-coding RNAs with multiple biological functions, which can participate in biological processes such as the occurrence, development, invasion, and metastasis of liver cancer, as well as drug resistance of liver cancer. This article reviews the roles and mechanisms of circR-NAs in chemotherapy resistance, targeted therapy resistance and immunotherapy resistance in liver cancer, in order to provide new ideas for solving liver cancer resistance.

OBJECTIVE: This study aimed to determine the HIV-1 subtype distribution and HIV drug resistance (HIVDR) in patients with ART failure from 2014 to 2020 in Hainan, China. METHODS: A 7-year cross-sectional study was conducted among HIV/AIDS patients with ART failure in Hainan. We used online subtyping tools and the maximum likelihood phylogenetic tree to confirm the HIV subtypes with pol sequences. Drug resistance mutations (DRMs) were analyzed using the Stanford University HIV Drug Resistance Database. RESULTS: A total of 307 HIV-infected patients with ART failure were included, and 241 available pol sequences were obtained. Among 241 patients, CRF01_AE accounted for 68.88%, followed by CRF07_BC (17.00%) and eight other subtypes (14.12%). The overall prevalence of HIVDR was 61.41%, and the HIVDR against non-nucleoside reverse transcriptase inhibitors (NNRTIs), nucleotide reverse transcriptase inhibitors (NRTIs), and protease inhibitors (PIs) were 59.75%, 45.64%, and 2.49%, respectively. Unemployed patients, hypoimmunity or opportunistic infections in individuals, and samples from 2017 to 2020 increased the odd ratios of HIVDR. Also, HIVDR was less likely to affect female patients. The common DRMs to NNRTIs were K103N (21.99%) and Y181C (20.33%), and M184V (28.21%) and K65R (19.09%) were the main DRMs against NRTIs. CONCLUSION The present study highlights the HIV-1 subtype diversity in Hainan and the importance of HIVDR surveillance over a long period.
Humans ; Reverse Transcriptase Inhibitors/therapeutic use* ; HIV-1/genetics* ; Cross-Sectional Studies ; Phylogeny ; Anti-HIV Agents/therapeutic use* ; Drug Resistance, Viral/genetics* ; HIV Infections/epidemiology* ; Mutation ; China/epidemiology* ; Prevalence ; Genotype