Objective: To explore the early effects of birth weight at different gestational ages on blood pressure trajectory among primary school students, so as to provide evidence for incorporating gestational age birth weight into individualized early warning and intervention strategies for childhood hypertension. Methods: From May to November 2023, a purposeful sampling method was used to recruit 1 676 students in grade 1-3 from three primary schools in a certain urban district of Chongqing. Follow up assessments were conducted in May 2024(T1), November 2024(T2), and May 2025(T3). General demographic and birth related information were collected via self administered questionnaires, while height, weight and blood pressure were obtained through physical examinations. Linear mixed effects model was used to analyze the associations between birth weight at different gestational ages and blood pressure trajectories. Results: During the T1-T3 period, the systolic blood pressure of boys were 98.5 (93.0, 104.5 ),98.5 (93.5, 105.0), and 97.5 (92.5, 103.5)mmHg, respectively, while the diastolic blood pressure were 60.5 (56.5, 65.0), 61.5 ( 57.0 , 65.5), and 60.0 (56.0, 64.0)mmHg, respectively. For girls, the systolic blood pressure were 95.5 (90.0, 102.0),95.5 (90.5, 101.5), and 96.0 (90.5, 101.5)mmHg, respectively, and the diastolic blood pressure were 60.5 (56.0, 64.5 ),61.5 (57.5, 65.5), and 59.5 (56.0, 63.0)mmHg, respectively. Through Friedman test within both boys and girls, diostolic blood pressure were statistically significant across three measurements( χ 2=48.85，81.54，both P <0.01). The proportion of normal blood pressure increased , and the proportion of prehypertension and hypertension decreased with time( χ 2=39.72，25.62，both P < 0.01 ). Linear mixed effects model analysis revealed that after adjusting for age, sex, household income monthly, parental education, family history of hypertension and maternal pregnancy complications, large for gestational age had significantly higher trajectories of systolic ( β = 1.50) and diastolic( β =0.94) blood pressure compared to appropriate for gestational age(both P <0.01). Conclusion Large for gestational age is associated with elevated blood pressure trajectories during school age, and it may be considered as an early indicator for individualized screening and intervention for childhood hypertension.

Objective To explore the construction of heart preservation model of empty beating donor based on extracorporeal membrane oxygenation (ECMO). Methods From January 2022 to August 2023, 20 Guangxi Bama miniature pigs weighing 25-30 kg were selected, half male and half female. Under general anesthesia and heparinization, a midline thoracotomy was performed. The pericardium was cut after freeing the anterior and posterior vena cavae, and a perfusion needle was inserted near the brachiocephalic artery in the ascending aorta, connected to a blood collection bag to collect 500-600 mL of blood. The anterior and posterior vena cavae were ligated, the aorta was blocked and perfused with HTK solution to stop the heart beating. The superior and inferior vena cavae were cut off, the right pulmonary vein was decompressed, the aorta and left and right pulmonary arteries and veins were cut off, and the whole heart was removed. An ECMO device was used to continuously perfuse a cardioprotective solution mainly composed of oxygenated warm blood, maintaining the isolated pig heart beating for 8 hours, monitoring (once/hour) ECMO perfusion parameters, blood gas indicators, perfusate electrolytes, inflammatory factors, myocardial enzymes, myoglobin, and troponin levels. Myocardial tissue was taken for hematoxylin-eosin (HE) staining to observe myocardial cell damage and evaluate the quality of heart preservation. Results Among the 20 isolated beating pig hearts, 17 successfully resumed beating, 3 experienced ventricular fibrillation, resuscitated after intracardiac electrical defibrillation, and all 20 pig hearts successfully beat for 8 hours. There was no statistical difference in ECMO perfusion parameters, blood gas indicators, perfusate electrolytes, and inflammatory factors at each time point (P>0.05). There were statistical increases in myocardial enzymes, myoglobin, and troponin levels (P<0.05). HE staining results suggested that there was no severe myocardial damage. Conclusion ECMO technology can be used for pig heart preservation with good results, and this study provides experimental evidence for improving heart preservation research in clinical heart transplantation.

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.

Processing for deodorization is widely used in the production of animal-derived Chinese medicinal materials. In this study, Heracles Neo ultra-fast gas-phase electronic nose combined with chemometrics was employed to analyze the overall odor difference of Cervi Cornu Pantotrichum(focusing on that derived from Cervus nippon Temminck in this study) before and after processing. The results showed that the electronic nose effectively distinguished between the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. HS-GC-MS was used to identify and quantify the volatile components in the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum, and 35 and 37 volatile components were detected in the medicinal materials and decoction pieces, respectively. The medicinal materials and decoction pieces contained 28 common volatile components contributing to the odor of Cervi Cornu Pantotrichum. The odor activity value(OAV) of each volatile component was calculated based on the olfactory threshold and relative content. The results showed that there were 17 key odor substances such as isovaleraldehyde, 2-methylbutanal, isobutyraldehyde, hexanal, and methanethiol in the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. All of them had bad odor and were the main source of the odor of Cervi Cornu Pantotrichum. The results of principal component analysis(PCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) showed that there were significant differences in volatile components between the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. Based on the thresholds of P<0.05 and Variable Importance in Projection(VIP)>1, 21 differential volatile odor components were screened out. Among them, isopentanol, isovaleraldehyde, 2-methylbutanal, n-nonanal, and dimethylamine were the key differential odor compounds between the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. The odor compounds and their relative content reduced, and some flavor substances such as esters were produced after processing with wine, which was the main reason for the reduction of the odor after processing of Cervi Cornu Pantotrichum.
Odorants/analysis* ; Electronic Nose ; Gas Chromatography-Mass Spectrometry/methods* ; Animals ; Volatile Organic Compounds/analysis* ; Deer ; Drugs, Chinese Herbal/chemistry*

ObjectiveTo investigate and analyze an outbreak of rotavirus infectious diarrhea in a prison in Chongqing Municipality, to provide a basis for adult rotavirus surveillance and prevention, and to explore the public health problems in special settings. MethodsA retrospective survey was conducted to collect and analyze data on individual cases with diarrheal disease on-site. The clinical characteristics, as well as the temporal, spatial and geographical distribution patterns of the epidemic were described. Multi-pathogen detection tests were conducted both on diarrhea cases and environmental samples, with viral genotyping performed on positive samples. A case-control analysis was performed to identify the causes of the outbreak, and an SEIR model was adopted to predict the outbreak trend and evaluate the effectiveness of interventions. ResultsA total of 65 cases were found among the inmates, with an attack rate of 2.03%. The predominant clinical manifestations included diarrhea (89.23%), watery stool (73.85%), and dehydration (18.46%). The epidemic curve indicated a “human-to-human” transmission pattern, with an average incubation period of 5‒6 days. The attack rates among chefs in the main canteen (80.00%, 8/10) and caterers (28.33%, 17/60) were significantly higher than those of other inmates （P<0.05）. Multi-pathogen polymerase chain reaction (PCR) testing detected positive for group A rotavirus, with the viral genotyping identified as G9P [8] strain. Factors such as unprotected "bare-handed" food distribution among cases with diarrhea (OR=9.512, 95%CI: 4.261‒21.234) and close contact with diarrhea cases (OR=3.656, 95%CI: 1.719‒7.778) were the possible cause of the outbreak. The SEIR model (r₀=5, α=0.3, β₁=0.08, β₂=0.04) was constructed using prison inmates as susceptible population, aiming at fitting the initial transmission trend of the outbreak, and the epidemic rate declined rapidly after intervention measures were implemented (r_t≈0). ConclusionThis rare rotavirus infection diarrhea outbreak among adults in confined settings suggests that the construction of public health prevention and control systems in prison may be overlooked. Cross infection during meal processing and distribution in the canteens of such settings is likely to be the cause of the outbreak. Given the potential neglect of public heath system construction in special settings, it is imperative to enhance the surveillance and monitoring of rotavirus and other intestinal multi-pathogens among adults, as well as the construction of public health prevention and control systems in these special settings.

Good endometrial receptivity is an essential factor for embryo implantation,and gene expression in endometrial tissue during the window of implantation(WOI)is closely related to receptivity.Transcriptome sequencing technology enables the identification of gene expression profiles of endometrium during different menstrual phases,as well as microRNAs and long-chain non-coding RNA sequences involved in regulating gene expression.Combining this technology with bioinformatics analysis provides a better understanding of specific gene expression during the receptive period and offers technical support for studying its regulatory mechanism.Moreover,gene expression profiles of the endometrium during different menstrual phases hold significant clinical application value for accurately assessing endometrium receptivity in infertility patients and those with repeated implantation failure,thereby guiding individualized embryo transfer strategies.This review summarizes the progress of transcriptome sequencing in evaluating human endometrial receptivity and discusses future research directions.This review aims to understand the complex molecular mechanisms of endometrial receptivity formation and regulation from the transcriptional level,in order to improve the implantation rate of embryos in assisted reproductive technology and reduce the abortion rate.

Objective To investigate the risk factors influencing the prolonged length of hospital stay after transjugular intrahepatic portosystemic shunt(TIPS)in patients with ruptured esophagogastric variceal bleeding(EGVB)and to construct a risk prediction model.Methods The clinical data of 215 patients with EGVB,who received TIPS at a certain grade ⅢA hospital in Nanjing of China from January 1,2020 to January 30,2023,were retrospectively analyzed.According to whether the postoperative hospitalization stay was prolonged or not,the patients were divided into prolonged group(n=67)and normal group(n=148).Multivariate logistic regression analysis was sued to analyze the independent risk factors for prolonged postoperative hospitalization stay,and the risk factors were used as predictors for constructing the nomogram model.Results Multivariate logistic regression analysis showed that history of splenectomy,early TIPS performance,NRS 2002 Nutritional Risk Screening Score ≥3 points,and Barthel index score≤40 points were the independent risk factors for prolonged postoperative hospitalization stay in patients with cirrhotic EGVB after receiving TIPS(P＜0.05).The area under receiver operating characteristic(ROC)curve of the model constructed on the basis of independent risk factors was 0.743,which was higher than that of early TIPS performance,history of splenectomy,NRS 2002 score,and Barthel index score.The Hosmer-Lemeshow test obtained P=0.723,indicating that this model had a good fit degree.Conclusion The prediction model established in this study can be used for cirrhotic EGVB patients to predict the risk of prolonged postoperative hospitalization stay,and this model has good discrimination and calibration,besides,it can bring some clinical benefits to patients.

Objective To investigate the prognosis of patients with negative sentinel lymph nodes(SLN)after neoadjuvant chemotherapy(NAC)for breast cancer who are exempt from axillary lymph node dissection(ALND)and evaluate its safety in clinical treatment.Methods Clinical data of 2,163 patients initially diagnosed with breast cancer and having negative SLNs after NAC were se-lected from the SEER database from 2018 to 2021.Among them,373 patients underwent only SLN biopsy(SLNB group),and 1,790 patients underwent ALND(ALND group).Propensity score matching(PSM)was used to control for confounding factors,and the Kaplan-Meier method was em-ployed to analyze the overall survival rate.Univariate and multivariate Cox regression analyses were conducted to identify prognostic factors influencing the exemption from ALND in patients with negative SLNs after NAC for breast cancer.Results Before PSM,significant differences were observed be-tween the two groups in terms of clinical tumor stage,molecular subtype,estrogen receptor(ER)sta-tus,progesterone receptor(PR)status,human epidermal growth factor receptor 2(HER-2)status,efficacy of NAC,and breast surgery method(P＜0.05).After PSM,363 patients were included in each group.Univariate Cox regression analysis after PSM revealed that age,clinical tumor stage,and ER status were associated with overall survival(OS)of patients(P＜0.05).There was no sig-nificant difference in OS between patients who underwent SLNB and those who underwent ALND(P＞0.05).Multivariate Cox regression analysis indicated that age and clinical tumor stage were independent factors influencing OS in patients with negative SLNs after NAC.Survival curve analysis after PSM showed no statistically significant difference in overall survival rate between the SLNB and ALND groups(P=0.278).Conclusion Exemption from ALND in patients with negative SLNs af-ter NAC is feasible and does not affect the overall survival rate of patients.

OBJECTIVE: This study investigated the antidepression mechanisms of Xiaoyaosan (XYS), a classic Chinese prescription, from the perspective of energy metabolism in the brain and intestinal tissues. METHODS: Chronic unpredictable mild stress model-a classic depression rat model-was established. Effects of XYS on behaviors and gastrointestinal motility of depressed rats were investigated. Effects of XYS on energetic charge (EC), adenosine triphosphate-related enzymes, and key enzymes of energy metabolism in both hippocampus and jejunum tissues of depressed rats were investigated using high-performance liquid chromatography, biochemical analysis, and real-time quantitative polymerase chain reaction, respectively. Spearman correlation analysis was conducted to construct a correlation network of "behavior-brain energy metabolism-intestinal energy metabolism" of depression. RESULTS: XYS significantly reduced the abnormal behaviors that observed in depressed rats and increased the EC and the activity of Na⁺-K⁺-adenosine triphosphatase (ATPase) and Ca²⁺-Mg²⁺-ATPase in hippocampus and jejunum tissues of depressed rats. XYS restored the key energetic pathways that had been interrupted by depression, including glycolysis, tricarboxylic acid cycle, and oxidative phosphorylation. Furthermore, XYS exhibited antidepressive effects in terms of regulating energy metabolism in tissues of both brain and intestine. CONCLUSION XYS significantly corrected the disturbances in EC and energy metabolism-related enzymes of both brain and intestinal tissues, alleviating both core and concomitant symptoms of depression. The current findings underscore the role of energy metabolism in the antidepressive activity of XYS, providing a fresh perspective on depression, and novel research strategies for revealing the mechanism of actions of traditional Chinese medicines on multi-site and multi-symptom diseases. Please cite this article as: Xiao MT, Wang SY, Wu XL, Zhao ZY, Wang HM, Liu HM, Qin XM, Liu XJ. Antidepressant mechanism of Xiaoyaosan: A perspective from energy metabolism of the brain and intestine. J Integr Med. 2025; 23(6):706-720.
Animals ; Energy Metabolism/drug effects* ; Antidepressive Agents/therapeutic use* ; Drugs, Chinese Herbal/therapeutic use* ; Brain/drug effects* ; Male ; Depression/metabolism* ; Rats ; Rats, Sprague-Dawley ; Intestines/drug effects* ; Hippocampus/drug effects*