OBJECTIVE To investigate the effects and mechanisms of Lycium ruthenicum Murr. in improving sleep. METHODS Network pharmacology was employed to identify the active components of L. ruthenicum and their associated disease targets， followed by enrichment analysis. A caffeine‑induced zebrafish model of sleep deprivation was established ， and the zebrafish were treated with L. ruthenicum Murr. extract （LRME） at concentrations of 0.1， 0.2 and 0.4 mg/mL， respectively； 24 h later， behavioral changes of zebrafish and pathological alterations in brain neurons were subsequently observed. The levels of inflammatory factors [interleukin-6 （IL-6）， IL-1β， IL-10， tumor necrosis factor-α （TNF-α）]， oxidative stress markers [superoxide dismutase （SOD）， malondialdehyde （MDA）， glutathione peroxidase （GSH-Px）， catalase （CAT）]， and neurotransmitters [5- hydroxytryptamine （5-HT）， γ-aminobutyric acid （GABA）， glutamic acid （Glu）， dopamine （DA）， and norepinephrine （NE）] were measured. The protein expression levels of protein kinase B1 （AKT1）， phosphorylated AKT1 （p-AKT1）， epidermal growth factor receptor （EGFR）， B-cell lymphoma 2 （Bcl-2）， sarcoma proto-oncogene，non-receptor tyrosine kinase （SRC）， and heat shock protein 90α family class A member 1 （HSP90AA1） in the zebrafish were also determined. RESULTS A total of 12 active components and 176 intersecting disease targets were identified through network pharmacology analysis. Among these， apigenin， naringenin and others were recognized as core active compounds， while AKT1， EGFR and others served as key targets； EGFR tyrosine kinase inhibitor resistance signaling pathway was identified as the critical pathway. The sleep improvement rates in zebrafish of LRME low-， medium-， and high-dose groups were 54.60%， 69.03% and 77.97%， 开发。E-mail：hjp_yft@163.com respectively， while the inhibition ratios of locomotor distance were 0.57， 0.83 and 0.95， respectively. Compared with the model group， the number of resting counts， resting time and resting distance were significantly increased/extended in LRME medium- and high-dose groups （P＜0.05）. Neuronal damage in the brain was alleviated. Additionally， the levels of IL-6， IL-1β， TNF-α， MDA， Glu， DA and NE， as well as the protein expression levels of AKT1， p-AKT1， EGFR， SRC and HSP90AA1， were markedly reduced （P＜0.05）， while the levels of IL-10， SOD， GSH-Px， CAT， 5-HT and GABA， as well as Bcl-2 protein expression， were significantly elevated （P＜0.05）. CONCLUSIONS L. ruthenicum Murr. demonstrates sleep-improving effects， and its specific mechanism may be related to the regulation of inflammatory responses， oxidative stress， neurotransmitter balance， and the EGFR tyrosine kinase inhibitor resistance signaling pathway.

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.

BACKGROUND: While emotional distress, encompassing anxiety and depression, has been associated with negative clinical outcomes, its impact across various clinical departments and general hospitals has been less explored. Previous studies with limited sample sizes have examined the effectiveness of specific treatments (e.g., antidepressants) rather than a systemic management strategy for outcome improvement in non-psychiatric inpatients. To enhance the understanding of the importance of addressing mental health care needs among non-psychiatric patients in general hospitals, this study retrospectively investigated the impacts of emotional distress and the effects of early detection and management of depression and anxiety on hospital length of stay (LOS) and rate of long LOS (LLOS, i.e., LOS >30 days) in a large sample of non-psychiatric inpatients. METHODS: This retrospective cohort study included 487,871 inpatients from 20 non-psychiatric departments of a general hospital. They were divided, according to whether they underwent a novel strategy to manage emotional distress which deployed the Huaxi Emotional Distress Index (HEI) for brief screening with grading psychological services (BS-GPS), into BS-GPS ( n = 178,883) and non-BS-GPS ( n = 308,988) cohorts. The LOS and rate of LLOS between the BS-GPS and non-BS-GPS cohorts and between subcohorts with and without clinically significant anxiety and/or depression (CSAD, i.e., HEI score ≥11 on admission to the hospital) in the BS-GPS cohort were compared using univariable analyses, multilevel analyses, and/or propensity score-matched analyses, respectively. RESULTS: The detection rate of CSAD in the BS-GPS cohort varied from 2.64% (95% confidence interval [CI]: 2.49%-2.81%) to 20.50% (95% CI: 19.43%-21.62%) across the 20 departments, with a average rate of 5.36%. Significant differences were observed in both the LOS and LLOS rates between the subcohorts with CSAD (12.7 days, 535/9590) and without CSAD (9.5 days, 3800/169,293) and between the BS-GPS (9.6 days, 4335/178,883) and non-BS-GPS (10.8 days, 11,483/308,988) cohorts. These differences remained significant after controlling for confounders using propensity score-matched comparisons. A multilevel analysis indicated that BS-GPS was negatively associated with both LOS and LLOS after controlling for sociodemographics and the departments of patient discharge and remained negatively associated with LLOS after controlling additionally for the year of patient discharge. CONCLUSION Emotional distress significantly prolonged the LOS and increased the LLOS of non-psychiatric inpatients across most departments and general hospitals. These impacts were moderated by the implementation of BS-GPS. Thus, BS-GPS has the potential as an effective, resource-saving strategy for enhancing mental health care and optimizing medical resources in general hospitals.
Humans ; Retrospective Studies ; Male ; Length of Stay/statistics & numerical data* ; Female ; Middle Aged ; Adult ; Psychological Distress ; Inpatients/psychology* ; Aged ; Anxiety/diagnosis* ; Depression/diagnosis*

The study aims to examine the effects and potential mechanisms of disulfiram (DSF) on cardiac hypertrophic injury, focusing on the role of transforming growth factor-β-activated kinase 1 (TAK1)-mediated pan-apoptosis (PANoptosis). H9C2 cardiomyocytes were treated with angiotensin II (Ang II, 1 µmol/L) to establish an in vitro model of myocardial hypertrophy. DSF (40 µmol/L) was used to treat cardiomyocyte hypertrophic injury models, either along or in combination with the TAK1 inhibitor, 5z-7-oxozeaenol (5z-7, 0.1 µmol/L). We assessed cell damage using propidium iodide (PI) staining, measured cell viability with CCK8 assay, quantified inflammatory factor levels in cell culture media via ELISA, detected TAK1 and RIPK1 binding rates using immunoprecipitation, and analyzed the protein expression levels of key proteins in the TAK1-mediated PANoptosis pathway using Western blot. In addition, the surface area of cardiomyocytes was measured with Phalloidin staining. The results showed that Ang II significantly reduced the cellular viability of H9C2 cardiomyocytes and the binding rate of TAK1 and RIPK1, significantly increased the surface area of H9C2 cardiomyocytes, PI staining positive rate, levels of inflammatory factors [interleukin-1β (IL-1β), IL-18, and tumor necrosis factor α (TNF-α)] in cell culture media and p-TAK1/TAK1 ratio, and significantly up-regulated key proteins in the PANoptosis pathway [pyroptosis-related proteins NLRP3, Caspase-1 (p20), and GSDMD-N (p30), apoptosis-related proteins Caspase-3 (p17), Caspase-7 (p20), and Caspase-8 (p18), as well as necroptosis-related proteins p-MLKL, RIPK1, and RIPK3]. DSF significantly reversed the above changes induced by Ang II. Both 5z-7 and exogenous IL-1β weakened these cardioprotective effects of DSF. These results suggest that DSF may alleviate cardiac hypertrophic injury by inhibiting TAK1-mediated PANoptosis.
Animals ; MAP Kinase Kinase Kinases/physiology* ; Rats ; Myocytes, Cardiac/pathology* ; Disulfiram/pharmacology* ; Cardiomegaly ; Apoptosis/drug effects* ; Cell Line ; Angiotensin II ; Necroptosis/drug effects* ; Interleukin-1beta/metabolism* ; Receptor-Interacting Protein Serine-Threonine Kinases/metabolism* ; Lactones ; Resorcinols ; Zearalenone/administration & dosage*

In this experiment, self-assembled nanoparticles(SANs) were prepared by the pH-driven method, and Her-HCP SAN was constructed by using herpetrione(Her) and Herpetospermum caudigerum polysaccharides(HCPs). The average particle size and polydispersity index(PDI) were used as evaluation indexes for process optimization, and the quality of the final formulation was evaluated in terms of particle size, PDI, Zeta potential, and microstructure. The proposed Her-HCP SAN showed a spheroid structure and uniform morphology, with an average particle size of(244.58±16.84) nm, a PDI of 0.147 1±0.014 8, and a Zeta potential of(-38.52±2.11) mV. Her-HCP SAN significantly increased the saturation solubility of Her by 2.69 times, with a cumulative release of 90.18% within eight hours. The results of in vivo unidirectional intestinal perfusion reveal that Her active pharmaceutical ingredient(API) is most effectively absorbed in the jejunum, where both K_a and P_(app) are significantly higher compared to the ileum(P<0.001). However, the addition of HCP leads to a significant reduction in the P_(app) of Her in the jejunum(P<0.05). Furthermore, the formation of the Her-HCP SAN results in a notably lower P_(app) in the jejunum compared to Her API alone(P<0.001), while both K_a and P_(app) in the ileum are significantly increased(P<0.001, P<0.05). The absorption of Her-HCP SAN at different concentrations in the ileum shows no significant differences, and the pH has no significant effect on the absorption of Her-HCP SAN in the ileum. The addition of the transporter protein inhibitors(indomethacin and rifampicin) significantly increases the absorption parameters K_a and P_(app) of Her-HCP SAN in the ileum(P<0.05,P<0.01), whereas the addition of verapamil has no significant effect on the intestinal absorption parameters of Her-HCP SAN, suggesting that Her may be a substrate for multidrug resistance-associated protein 2 and breast cancer resistance proteins but not a substrate of P-glycoprotein.
Nanoparticles/metabolism* ; Polysaccharides/pharmacokinetics* ; Intestinal Absorption/drug effects* ; Animals ; Rats ; Particle Size ; Drugs, Chinese Herbal/pharmacokinetics* ; Male ; Rats, Sprague-Dawley ; Drug Carriers/chemistry* ; Drug Compounding ; Cucurbitaceae/chemistry*

To determine the optimal cultivation duration and harvest period for cultivated Notopterygium incisum and promote its industrial development, this study established a characteristic chromatographic profile of cultivated N. incisum and employed chemometrics combined with entropy-weighted grey correlation analysis to assess differences in agronomic traits and quality indicators across different cultivation years and harvest periods. By comparing with reference substances, ten common peaks were identified, including chlorogenic acid, p-coumaric acid, ferulic acid, marmesinin, nodakenin, isochlorogenic acid B, notopterol, phenethyl ferulate, isoimperatorin, and falcarindiol. The similarity between the characteristic chromatographic profiles of N. incisum at different cultivation years and the reference profile was all above 0.932. Principal component analysis(PCA) and orthogonal partial least squares discriminant analysis(OPLS-DA) revealed that the quality of 1-to 3-year-old cultivated N. incisum was highly dispersed and unstable, whereas the quality of 4-year-old cultivated N. incisum remained relatively stable across different harvest periods. This suggests that the accumulation of relevant compounds in the medicinal material had reached a plateau, confirming that the optimal cultivation period for N. incisum is four years. Entropy-weighted grey correlation analysis indicated that the quality of 4-year-old cultivated N. incisum across different harvest periods ranked from highest to lowest as follows: November, December, October, August, July, and September, demonstrating that November is the optimal harvest time. The findings of this study establish the suitable cultivation duration and optimal harvest period for N. incisum, providing a scientific basis for cultivation guidance and quality standardization.
Chromatography, High Pressure Liquid/methods* ; Apiaceae/chemistry* ; Entropy ; Chemometrics/methods* ; Drugs, Chinese Herbal/chemistry* ; Principal Component Analysis ; Quality Control

Acute lung injury(ALI) is a critical clinical condition primarily characterized by refractory hypoxemia and infiltration of inflammatory cells in lung tissue, which can progress into a more severe form known as acute respiratory distress syndrome(ARDS). Immune cells and inflammatory cytokines play important roles in the progression of the disease. Due to its unclear pathogenesis and the lack of effective clinical treatments, ALI is associated with a high mortality rate and severely affects patients' quality of life, making the search for effective therapeutic agents particularly urgent. Ginseng Radix et Rhizoma, the dried root of the perennial herb Panax ginseng from the Araliaceae family, contains active ingredients such as saponins and polysaccharides, which possess various pharmacological effects including anti-tumor activity, immune regulation, and metabolic modulation. In recent years, studies have shown that ginsenosides exhibit notable effects in reducing inflammation, ameliorating epithelial and endothelial cell injury, and providing anticoagulant action, indicating their comprehensive role in alleviating lung injury. This review summarizes the pathogenesis of ALI and the molecular mechanisms through which ginsenosides act at different stages of ALI development. The aim is to provide a scientific reference for the development of ginsenoside-based drugs targeting ALI, as well as a theoretical basis for the clinical application of Ginseng Radix et Rhizoma in the treatment of ALI.
Ginsenosides/pharmacology* ; Humans ; Acute Lung Injury/immunology* ; Animals ; Panax/chemistry* ; Drugs, Chinese Herbal

OBJECTIVE: To establish and validate a novel diabetic retinopathy (DR) risk-prediction model using a whole-exome sequencing (WES)-based machine learning (ML) method. METHODS: WES was performed to identify potential single nucleotide polymorphism (SNP) or mutation sites in a DR pedigree comprising 10 members. A prediction model was established and validated in a cohort of 420 type 2 diabetic patients based on both genetic and demographic features. The contribution of each feature was assessed using Shapley Additive explanation analysis. The efficacies of the models with and without SNP were compared. RESULTS: WES revealed that seven SNPs/mutations ( rs116911833 in TRIM7, 1997T>C in LRBA, 1643T>C in PRMT10, rs117858678 in C9orf152, rs201922794 in CLDN25, rs146694895 in SH3GLB2, and rs201407189 in FANCC) were associated with DR. Notably, the model including rs146694895 and rs201407189 achieved better performance in predicting DR (accuracy: 80.2%; sensitivity: 83.3%; specificity: 76.7%; area under the receiver operating characteristic curve [AUC]: 80.0%) than the model without these SNPs (accuracy: 79.4%; sensitivity: 80.3%; specificity: 78.3%; AUC: 79.3%). CONCLUSION Novel SNP sites associated with DR were identified in the DR pedigree. Inclusion of rs146694895 and rs201407189 significantly enhanced the performance of the ML-based DR prediction model.
Diabetic Retinopathy/diagnosis* ; Humans ; Machine Learning ; Male ; Female ; Polymorphism, Single Nucleotide ; Middle Aged ; Exome Sequencing ; Aged ; Adult ; Pedigree ; Diabetes Mellitus, Type 2/complications* ; Genetic Predisposition to Disease ; Mutation

OBJECTIVES: To investigate the prognostic value of molecular minimal residual disease (Mol-MRD) monitored before and after allogeneic hematopoietic stem cell transplantation (HSCT) in pediatric acute myeloid leukemia (AML). METHODS: Clinical data of 71 pediatric AML patients who underwent HSCT between August 2016 and December 2023 were analyzed. Mol-MRD levels were dynamically monitored in MRD-positive patients, and survival outcomes were evaluated. RESULTS: No significant difference in the 3-year overall survival (OS) rate was observed between patients with pre-HSCT Mol-MRD ≥0.01% and <0.01% (77.3% ± 8.9% vs 80.4% ± 7.9%, P=0.705). However, patients with pre-HSCT Mol-MRD <1.75% had a significantly higher 3-year OS rate than those with Mol-MRD ≥1.75% (86.6% ± 5.6% vs 44.4% ± 16.6%, P=0.020). The median Mol-MRD level in long-term survivors was significantly lower than in non-survivors [0.61% (range: 0.04%-51.58%)] vs 10.60% (range: 1.90%-19.75%), P=0.035]. Concurrent flow cytometry-based MRD positivity was significantly higher in non-survivors (80% vs 24%, P=0.039). There was no significant difference in the 3-year overall survival rate between patients with Mol-MRD ≥0.01% and those with <0.01% at 30 days post-HSCT (P=0.527). For children with Mol-MRD <0.22% at 30 days post-HSCT, the 3-year overall survival rate was 80.4% ± 5.9%, showing no significant difference compared to those with molecular negativity (87.0% ± 7.0%) (P=0.523). CONCLUSIONS Patients with pre-HSCT Mol-MRD <1.75% or post-HSCT Mol-MRD <0.22% may achieve long-term survival outcomes comparable to Mol-MRD-negative cases through HSCT and targeted interventions.
Humans ; Hematopoietic Stem Cell Transplantation ; Neoplasm, Residual ; Leukemia, Myeloid, Acute/genetics* ; Child ; Male ; Female ; Child, Preschool ; Prognosis ; Adolescent ; Infant ; Transplantation, Homologous