Background: Adrenocortical hormones, particularly 11-oxygenated androgens, are pivotal in female reproductive health and fertility. Standardized detection kits and population-specific reference intervals are lacking in China, hindering related clinical applications. Methods: A HPLC-tandem mass spectrometry (HPLC-MS/MS) pipeline was developed, rigorously validated, and applied to simultaneously quantify corticosterone, cortisone, cortisol, 18-OH cortisol, androstenedione (A4), 11β-hydroxyandrostenedione (11-OH A4), dehydroepiandrosterone, and dehydroepiandrosterone sulfate in serum samples from 455 reproductive-aged women (18–45 yrs) in Guangxi, China. Age-dependent concentration trends were analyzed, and reference intervals stratified by age (2.5th to 97.5th percentiles) were established. Correlations with body-composition metrics, ethnicity, and the menstrual cycle were investigated. Results: The HPLC-MS/MS method demonstrated high precision (intra- and inter-assay CVs < 15%), accuracy, and sensitivity. All eight hormones exhibited significant age-related declines (P < 0.001 for seven hormones; P = 0.001 for 11-OH A4). Notably, 11-OH A4 levels were significantly lower in the 35–45-yr (3.05 nmol/L) and 25–34-yr (3.09 nmol/L) age groups than in the 18–24-yr (3.57 nmol/L) age group, whereas no significant difference was observed between the 35–45-yr and 25–34-yr age groups. Weak negative correlations were observed between the body mass index and corticosterone and cortisone levels, whereas ethnicity and the menstrual cycle showed no significant associations with hormone levels. Conclusions We developed an HPLC-MS/MS-based method for simultaneously quantifying eight adrenocortical hormones, including 11-OH A4, and defined age-specific reference intervals for reproductive-aged Chinese women. These findings advance the clinical utility of adrenocortical hormones in diagnosing and managing reproductive disorders.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

Objective To investigate the epidemiological characteristics of malaria and provide a basis for developing improved prevention and control measures. Methods Data were obtained from the Chinese Disease Prevention and Control Information System. Malaria surveillance data for Nanning City from January 1,2023, to December 31,2024, were exported from the Infectious Disease Reporting Information Management Subsystem. The characteristics of the two groups of malaria cases were compared. Results A total of 103 imported malaria cases were reported in Nanning City in 2024, representing a 38.32% decrease compared with the same period of the previous year. No statistically significant difference were observed between cases reported in 2023 and 2024 in terms of average age, gender ratio, proportion of parasite species, and monthly reporting distribution;however, statistically significant differences were found in the proportion of reporting areas and current residence areas(χ2= 13.572 and 10.355, respectively; P = 0.001 and 0.035, respectively). The proportion of cases reported in Shanglin County and the proportion of cases residing in Shanglin County were both lower than those during the same period of the previous year. Conclusions The high aggregation of imported malaria cases in Nanning City has decreased. Medical institutions in areas other than Shanglin County should strengthen their vigilance against malaria.

BackgroundIndividuals with chronic multimorbidity exhibit a high prevalence of sleep disturbances， which impair their physical and psychological well-being and impede chronic disease management. Although sleep quality is closely influencing with psychological functioning， empirical evidence regarding the longitudinal trajectories of sleep quality and their influencing factors in this population remains insufficient. ObjectiveTo investigate the longitudinal trajectories of sleep quality and associated factors among patients with chronic multimorbidity from hospitalization through 6 months post-discharge， aiming to facilitate the early identification of high-risk individuals and the development of personalized sleep interventions. MethodsA total of 228 inpatients with chronic multimorbidity， primarily with cardiovascular and metabolic chronic conditions， were enrolled via random sampling at The Third People's Hospital of Fuyang from January 2023 to September 2024. Baseline assessments were conducted during hospitalization using the general socio-demographic questionnaire， the Sleep Dysfunction Rating Scale （SDRS）， the Ruminative Response Scale （RRS）， and the Hospital Anxiety and Depression Scale （HADS）. Sleep quality was further evaluated using the SDRS at 1-， 3-， and 6-month post-discharge. Latent class growth model was employed to delineate trajectories of sleep quality. Multivariate logistic regression analysis was subsequently performed to identify influencing factors of sleep quality. ResultsA total of 201 patients with chronic diseases were included.Based on four predefined assessment time points （baseline， 1 month， 3 months， 6 months post-discharge）， the latent class growth model identified three distinct trajectories of sleep quality： persistent high sleep disturbance （n=72， 35.82%）， moderate sleep disturbance-progression （n=73， 36.32%）， and low sleep disturbance （n=56， 27.86%）. Multivariate logistic regression analysis revealed that， compared with the low sleep disturbance group， multiple factors increased the odds of membership in the persistent high sleep disturbance group： per capita monthly household income of <3 000 yuan （β=13.131， P<0.01） or 3 000–5 000 yuan （β=5.913， P<0.05）， aged 45–65 years （β=9.536， P<0.05）， presence of 3 （β=7.792， P<0.05） or >3 comorbid chronic conditions （β=6.626， P<0.05）， as well as higher RRS （β=0.334， P<0.01） and HADS score （β=1.628， P<0.01）. Additionally， patients aged 45–65 years （β=2.777， P<0.05）， with 3 （β=3.802， P<0.01） or >3 comorbid chronic conditions （β=2.463， P<0.01）， and with higher RRS （β=0.111， P<0.01） and HDAS scores （β=0.350， P<0.05） also demonstrated significantly increased odds of belonging to the moderate sleep disturbance–progression group. ConclusionThere is significant population heterogeneity in sleep quality trajectories among patients with chronic multimorbidity， with age， per capita household monthly income， number of comorbidities， rumination， and anxiety/depression symptoms being identify as the primary influencing factors.

ObjectiveTo explore the mechanisms through which Si Junzitang （SJZD） ameliorates Alzheimer's disease （AD） induced by scopolamine （SCOP） in mice and the PC12 cell model induced by H₂O₂ based on the Kelch-like ECH-associated protein 1 （Keap1）/nuclear factor erythroid 2-related factor 2 （Nrf2）/heme oxygenase-1 （HO-1） signaling pathway. MethodsIn the animal experiments， an AD model was established in mice by intraperitoneal injection of SCOP （3 mg·kg^-1）. Morris water maze and open field tests （OFT） were conducted to assess learning and memory abilities. Hematoxylin-eosin （HE） staining and Nissl staining were performed to observe pathological changes in neurons. Immunofluorescence was used to detect amyloid β-protein_1-42 （Aβ_1-42） expression， and immunohistochemistry was employed to detect phosphorylated （p）-Tau expression. Transmission electron microscopy （TEM） was employed to observe ultrastructural changes in hippocampal neurons and synapses. Biochemical methods were used to measure the levels of acetylcholine （ACh）， acetylcholinesterase （AChE）， superoxide dismutase （SOD）， malondialdehyde （MDA）， catalase （CAT）， and lactate dehydrogenase （LDH）. Real-time PCR and Western blot were employed to measure the mRNA and protein levels of molecules in the Keap1/Nrf2/HO-1 pathway in the hippocampus. In the cell experiments， a PC12 cell model of oxidative damage model was established with H₂O₂. Cell count kit-8 （CCK-8） assays and flow cytometry were adopted to measure cell viability and apoptosis rates， and Western blot was employed to quantify the expression levels of proteins in the Keap1/Nrf2/HO-1 pathway. ResultsThe animal experiments showed that compared with the model group， the SJZD and donepezil groups showed shortened escape latency （P<0.01）， increased time in the target quadrant and platform crossings， and increased movement distance and duration in the central area of the open field （P<0.05， P<0.01）. HE and Nissl staining showed more organized neurons and increased Nissl bodies in the drug intervention groups （P<0.05， P<0.01）， and the Aβ_1-42 and p-Tau expression levels were downregulated （P<0.05， P<0.01）. TEM revealed reduced ultrastructural damage in hippocampal neurons and synapses in the drug intervention groups. In addition， the drug intervention groups showed declined levels of MDA， LDH， and AChE （P<0.05， P<0.01）， elevated levels of SOD， CAT， and ACh （P<0.05， P<0.01）， reduced Keap1 expression and increased Nrf2， HO-1， and NQO1 expression in the hippocampus （P<0.05， P<0.01）. The cell experiments showed that compared with the model group， the SJZD-containing serum increased the cell viability （P<0.05， P<0.01）， and decreased total apoptosis rates （P<0.05， P<0.01）. The drug intervention groups showed upregulated protein levels of Nrf2 and HO-1 and downregulated protein level of Keap1 （P<0.05， P<0.01）. ConclusionSJZD demonstrates protective effects against SCOP-induced AD in mice and H₂O₂-induced damage in PC12 cells through antioxidant mechanisms mediated by the Keap1/Nrf2/HO-1 pathway.

In this paper, near-infrared spectroscopy(NIRS) was employed to analyze 129 batches of commercial products of Reduning Injection. The batch reporting rate was estimated according to the report of Reduning Injection in the direct adverse drug reaction(ADR) reporting system of the drug marketing authorization holder of the Center for Drug Reevaluation of the National Medical Products Administration(National Center for ADR Monitoring) from August 2021 to August 2022. According to the batch reporting rate, the samples of Reduning Injection were classified into those with potential risks and those being safe. No processing, random oversampling(ROS), random undersampling(RUS), and synthetic minority over-sampling technique(SMOTE) were then employed to balance the unbalanced data. After the samples were classified according to appropriate sampling methods, competitive adaptive reweighted sampling(CARS), successive projections algorithm(SPA), uninformative variables elimination(UVE), and genetic algorithm(GA) were respectively adopted to screen the features of spectral data. Then, support vector machine(SVM), logistic regression(LR), k-nearest neighbors(KNN), naive bayes(NB), random forest(RF), and artificial neural network(ANN) were adopted to establish the risk prediction models. The effects of the four feature extraction methods on the accuracy of the models were compared. The optimal method was selected, and bayesian optimization was performned to optimize the model parameters to improve the accuracy and robustness of model prediction. To explore the correlations between potential risks of clinical use and quality test data, TreeNet was employed to identify potential quality parameters affecting the clinical safety of Reduning Injection. The results showed that the models established with the SVM, LR, KNN, NB, RF, and ANN algorithms had the F1 scores of 0.85, 0.85, 0.86, 0.80, 0.88, and 0.85 and the accuracy of 88%, 88%, 88%, 85%, 91%, and 88%, respectively, and the prediction time was less than 5 s. The results indicated that the established models were accurate and efficient. Therefore, near infrared spectroscopy combined with machine learning algorithms can quickly predict the potential risks of clinical use of Reduning Injection in batches. Three key quality parameters that may affect clinical safety were identified by TreeNet, which provided a scientific basis for improving the safety standards of Reduning Injection.
Spectroscopy, Near-Infrared/methods* ; Drugs, Chinese Herbal/administration & dosage* ; Machine Learning ; Algorithms ; Humans ; Quality Control

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*

This study explored the drying kinetics of Salviae Miltiorrhizae Radix et Rhizoma(SM), established the suitable models simulating the drying kinetics, and then analyzed the dynamic changes of active components during the drying processes with different methods, aiming to provide a basis for the establishment of suitable drying methods and the quality control of SM. The drying kinetics were studied based on the drying curve, drying rate, moisture effective diffusion coefficient, and drying activation energy, and the appropriate drying kinetics model of SM was established. The drying performance of different methods, such as hot air drying, infrared drying, and microwave drying of SM was evaluated, and the changes in the content of 10 salvianolic acids and 6 tanshinones during drying were analyzed by UPLC-TQ-MS. The Technique for Order Preference by Similarity to an Ideal Solution(TOPSIS) was employed to evaluate the quality of SM dried with different methods. The results showed that the drying rate and moisture effective diffusion coefficient of SM increased with the rise in drying temperature, and the maximum drying rates of different methods were in the order of microwave drying > infrared drying > hot air drying, slice > whole root. The drying rate decreased with the rise in temperature and the extension of drying time. The activation energy of hot air drying was higher than that of infrared drying in SM. The most suitable model for simulating the drying process of SM was the Page model. The TOPSIS results suggested infrared drying at 50 ℃ was the optimal drying method for SM. During the drying process, the content of salvianolic acids increased in different degrees with the loss of moisture, among which salvianolic acid B showed the largest increase of 44 times compared with that in the fresh medicinal material. Tanshinones also existed in the fresh herb of SM, and the content of tanshinone Ⅱ_A increased by 3 times after drying. The results provided a basis for the establishment of suitable drying methods and the quality control of SM.
Salvia miltiorrhiza/chemistry* ; Desiccation/methods* ; Drugs, Chinese Herbal/chemistry* ; Rhizome/chemistry* ; Kinetics ; Quality Control ; Abietanes

The chemical constituents from leaves of Cyclocarya paliurus were isolated and purified by chromatography on silica gel, C_(18) reverse-phase silica gel, and Sephadex LH-20 gel, as well as semi-preparative high-performance liquid chromatography. Six compounds were identified by UV, IR, NMR, MS, calculated ECD, and comparison with literature data as cyclopaloside D(1), boscialin(2),(5R,6S)-6-hydroxy-6-[(E)-3-hydroxybut-1-enyl]-1,1,5-trimethylcyclohexanone(3), 3S,5R-dihydroxy-6R,7-megastigmadien-9-one(4), 3S,5R-dihydroxy-6S,7-megastigmadien-9-one(5), and gingerglycolipid A(6), respectively. Among them, compound 1 was identified as a new tetralone glycoside, and compounds 2-6 were isolated from leaves of C. paliurus for the first time. Furthermore, compound 1 exhibited strong antioxidant activity, with the IC_(50) of(454.20±31.81)μmol·L~(-1) and(881.82±42.31)μmol·L~(-1) in scavenging DPPH and ABTS free radicals, respectively.
Plant Leaves/chemistry* ; Glycosides/isolation & purification* ; Juglandaceae/chemistry* ; Tetralones/isolation & purification* ; Drugs, Chinese Herbal/isolation & purification*