ObjectiveTo address the challenges of unstructured classical Chinese expressions， nested entity relationships， and limited annotated data in famous traditional Chinese medicine（TCM） case records， this study proposes a joint relation extraction framework that integrates data augmentation and entity mapping， aiming to support the construction of TCM diagnostic knowledge graphs and clinical pattern mining. MethodsWe developed an annotation structure for entities and their relationships in TCM case texts and applied a data augmentation strategy by incorporating multiple ancient texts to expand the relation extraction dataset. A cascade binary tagging framework for relation triple extraction（CasRel） model for TCM semantics was designed， integrating a pre-trained bidirectional encoder representations from transformers（BERT） layer for classical TCM texts to enhance semantic representation， and using a head entity-relation-tail entity mapping mechanism to address entity nesting and relation overlapping issues. ResultsExperimental results showed that the CasRel model， combining data augmentation and entity mapping， outperformed the pipeline-based Bert-Radical-Lexicon（BRL）-bidirectional long short-term memory（BiLSTM）-Attention model. The overall precision， recall， and F₁-score across 12 relation types reached 65.73%， 64.03%， and 64.87%， which represent improvements of 14.26%， 7.98%， and 11.21% compared to the BRL-BiLSTM-Attention model， respectively. Notably， the F₁-score for tongue syndrome relations increased by 22.68%（69.32%）， and the prescription-syndrome relations performed the best with the F₁-score of 70.10%. ConclusionThe proposed framework significantly improves the semantic representation and complex dependencies in TCM texts， offering a reusable technical framework for structured mining of TCM case records. The constructed knowledge graph can support clinical syndrome differentiation， prescription optimization， and drug compatibility， providing a methodological reference for TCM artificial intelligence research.

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.

ObjectiveTo examine the influences of Shenfu injection on the endogenous metabolic byproducts in the myocardium of the rat model exhibiting chronic heart failure， thus deciphering the therapeutic mechanism of the Qi-reinforcing and Yang-warming method. MethodsSD rats were randomly allocated into a control group and a modeling group. Chronic heart failure with heart-Yang deficiency syndrome in rats was modeled by multi-point subcutaneous injection of isoproterenol， and the rats were fed for 14 days after modeling. The successfully modeled rats were randomized into model， Shenfu injection （6.0 mL·kg^-1）， and trimetazidine （10 mg·kg^-1） groups and treated with corresponding agents for 15 days. The control group and the model group were injected with equal doses of normal saline， and the samples were collected after the intervention was completed. Cardiac color ultrasound was performed. Hematoxylin-eosin （HE） staining was used to observe histopathological morphology， and the serum level of N-terminal pro-brain natriuretic peptide （NT-proBNP） was assessed by enzyme-linked immunosorbent assay （ELISA）. The mitochondrial morphological and structural changes of cardiomyocytes were observed by transmission electron microscopy， and the metabolic profiling was carried out by ultra high performance liquid chromatography-quantitative exactive-mass spectrometry （UHPLC-QE-MS）. Differential metabolites were screened and identified by orthogonal partial least squares-discriminant analysis （OPLS-DA） and other methods， and then the MetaboAnalyst database was used for further screening. The relevant biological pathways were obtained through pathway enrichment analysis. The receiver operating characteristic （ROC） curve was established to evaluate the diagnostic value of each potential biomarker for myocardial injury and the evaluation value for drug efficacy. ResultsThe results of color ultrasound showed that Shenfu Injection improved the cardiac function indexes of model rats （P<0.05）. The results of HE staining showed that Shenfu injection effectively alleviated the pathological phenomena such as myocardial tissue structure disorder and inflammatory cell infiltration in model rats. The results of ELISA showed that Shenfu injection effectively regulated the serum NT-proBNP level in the model rats. Transmission electron microscopy （TEM） showed that Shenfu injection effectively restored the mitochondrial morphological structure. The results of metabolomics showed that the metabolic phenotypes of myocardial samples presented markedly differences between groups. Nine differential metabolites could be significantly reversed in the Shenfu injection group， involving three metabolic pathways： pyruvate metabolism， histidine metabolism， and citric acid cycle （TCA cycle）. The results of ROC analysis showed that the area under the curve （AUC） values of all metabolites were between 0.75 and 1.0， indicating that the differential metabolites had high diagnostic accuracy for myocardial injury， and the changes in their expression levels could be used as potential markers for efficacy evaluation. ConclusionShenfu injection significantly alleviated the damage of cardiac function， myocardium， and mitochondrial structure in the rat model of chronic heart failure with heart-Yang deficiency syndrome by ameliorating energy metabolism remodeling. Reinforcing Qi and warming Yang is a key method for treating chronic heart failure with heart-Yang deficiency syndrome.

Objective: To explore the effects of personality and sleep quality with psychological distress of junior and senior high school stduents, so as to provide a reference basis for precise interventions of junior and senior high school students mental health. Methods: In October 2023, a convenience sampling method was used to select 9 034 students aged 12-17 from Shiyan City as the study subjects. The Pittsburgh Sleep Quality Index (PSQI) and Kessler Psychological Distress Scale (K10) were used to collect information on sleep quality and psychological distress of junior and senior high school stduents. Between group comparison was conducted by using t-test and Chi-square test. Generalized linear models were employed to analyze the interaction and joint effects of personality and sleep quality on psychological distress. Results: The generalized linear model analysis showed that the interaction between personality and sleep quality on psychological distress was statistically significant of junior and senior high school students(effect size=0.80, P <0.01). The general linear model analysis indicated that, after adjusting for variables such as age, gender, screen time, and daily sitting time with the extroverted and good sleep quality group as the reference, the introverted and poor sleep quality group had the largest mean difference in psychological distress scores (difference=0.51, P <0.05). When stratified by sleep quality, psychological distress scores were higher in the introverted and neutral personality groups with both poor and good sleep quality compared to the extroverted group (poor sleep quality: introverted difference=3.71, neutral difference=1.14; good sleep quality: introverted difference=2.23, neutral difference=0.57, all P < 0.05). When stratified by personality, psychological distress scores were higher in the poor sleep quality groups for introverted, neutral, and extroverted individuals compared to their good sleep quality counterparts (differences=8.66, 7.83, 7.34, all P < 0.05 ). Conclusions Personality and sleep quality have interactive and joint effects on psychological distress of junior and senior high school stduents. Personalized psychological interventions should be developed based on personality and sleep quality.

This paper systematically reviewed the concept of authentication studies on traditional Chinese medicine （TCM） classics and the research achievements of scholars across historical and contemporary periods. We categorized the authentication studies on TCM classics into four types， including work-oriented authentication research， metho-dological studies on authentication， extended authentication research， and single-book authentication. Multiple methods were applied comprehensively， including investigating bibliographic documents of successive dynasties， analyzing the academic contents of medical books， studying the textual characteristics of medical books， examining the cited references in medical books， verifying the biographies of authors， and analyzing the interpolations and accretions in medical books， to distinguish the authenticity of TCM classics. The academic value of authenticity identification of TCM classics is concluded in three aspects，i.e. it serves as an important means to distinguish authenticity from falsehood in TCM classics， an important guarantee for inheriting the essence of TCM literature， and a key to unlocking the academic treasure trove of TCM classics and achieving inheritance-based innovation， which will lay a solid documentary foundation for constructing identification methodologies and standardized systems.

Obstructive sleep apnea (OSA) is an increasingly widespread sleep-breathing disordered disease, and is an independent risk factor for many high-risk chronic diseases such as hypertension, coronary heart disease, stroke, arrhythmias and diabetes, which is potentially fatal. The key to the prevention and treatment of OSA is early diagnosis and treatment, so the assessment and diagnostic technologies of OSA have become a research hotspot. This paper reviews the research progresses of severity assessment parameters and diagnostic technologies of OSA, and discusses their future development trends. In terms of severity assessment parameters of OSA, apnea hypopnea index (AHI), as the gold standard, together with the percentage of duration of apnea hypopnea (AH%), lowest oxygen saturation (LSpO₂), heart rate variability (HRV), oxygen desaturation index (ODI) and the emerging biomarkers, constitute a multi-dimensional evaluation system. Specifically, the AHI, which measures the frequency of sleep respiratory events per hour, does not fully reflect the patients’ overall sleep quality or the extent of their daytime functional impairments. To address this limitation, the AH%, which measures the proportion of the entire sleep cycle affected by apneas and hypopneas, deepens our understanding of the impact on sleep quality. The LSpO₂ plays a critical role in highlighting the potential severe hypoxic episodes during sleep, while the HRV offers a different perspective by analyzing the fluctuations in heart rate thereby revealing the activity of the autonomic nervous system. The ODI provides a direct and objective measure of patients’ nocturnal oxygenation stability by calculating the number of desaturation events per hour, and the biomarkers offers novel insights into the diagnosis and management of OSA, and fosters the development of more precise and tailored OSA therapeutic strategies. In terms of diagnostic techniques of OSA, the standardized questionnaire and Epworth sleepiness scale (ESS) is a simple and effective method for preliminary screening of OSA, and the polysomnography (PSG) which is based on recording multiple physiological signals stands for gold standard, but it has limitations of complex operations, high costs and inconvenience. As a convenient alternative, the home sleep apnea testing (HSAT) allows patients to monitor their sleep with simplified equipment in the comfort of their own homes, and the cardiopulmonary coupling (CPC) offers a minimal version that simply analyzes the electrocardiogram (ECG) signals. As an emerging diagnostic technology of OSA, machine learning (ML) and artificial intelligence (AI) adeptly pinpoint respiratory incidents and expose delicate physiological changes, thus casting new light on the diagnostic approach to OSA. In addition, imaging examination utilizes detailed visual representations of the airway’s structure and assists in recognizing structural abnormalities that may result in obstructed airways, while sound monitoring technology records and analyzes snoring and breathing sounds to detect the condition subtly, and thus further expands our medical diagnostic toolkit. As for the future development directions, it can be predicted that interdisciplinary integrated researches, the construction of personalized diagnosis and treatment models, and the popularization of high-tech in clinical applications will become the development trends in the field of OSA evaluation and diagnosis.

OBJECTIVE To establish a interview outline design process and quality control evaluation method based on grounded theory， providing ideas for qualitative research interview outline design in medical fields. METHODS A literature review was conducted to understand the current research status； a preliminary interview outline was developed around the research content. The triangulation method， group evaluation， expert review and pre-interview were adopted to execute the interview outline and conduct quality control. The evaluation indicators and target values were formulated （an average score for the overall quality evaluation of all indicators ≥4.5， and an average score for individual indicators ≥4.00） to evaluate the effect of the interview outline. Taking the research on the mechanism of physicians’ prescribing behavior under the background of Diagnosis Related Groups （DRGs） payment as an example， the methodological contents of above interview outline were applied in practical research. RESULTS The interview outline included basic information and interview questions. The interview questions were divided into three parts：influencing factors survey， promoting and hindering factors of standardizing physician prescription behavior， and communication， with a total of 12 questions. After being reviewed by members of the research group， experts review and pre- interview， a total of 9 people participated in the quality control evaluation of the interview outline. The overall evaluation score was 4.94 （＞4.50）， and the average score of each indicator was greater than 4.00， indicating that the quality of the outline met the requirements for the interview and could be used for the formal interview. CONCLUSIONS The established interview outline design and quality control method based on grounded theory provides ideas for the qualitative research interview outline design in the medical field， and lays the foundation for further using grounded theory to study the influencing factors and mechanisms of physician prescription behavior under the DRG background.

Methods: This study enrolled 40 patients with AIS, 20 patients with congenital scoliosis (CS), and 20 patients with spinal degenerative disease (SDD). All patients underwent open posterior surgery in our hospital, and a paravertebral muscle (multifidus muscle) biopsy was performed intraoperatively. This study included many indexes that describe muscle, especially dystrophin staining. The above pathological results were compared among the AIS, CS, and SDD groups. The correlation between the Cobb angle and Nash–Moe classification and the above pathological results was analyzed in patients with AIS. Results: Significant reductions in the dystrophin staining of dystrophin-1 (p<0.001), dystrophin-2 (p<0.001), and dystrophin-3 (p<0.001) were observed in the AIS group than in the CS and SDD groups. The higher the Nash–Moe classification in the AIS group, the more significant the loss of dystrophin-2 (p=0.042) in the convex paraspinal muscles. Additionally, a significantly positive correlation was observed between the reductions of dystrophin-2 on the concave side of the AIS group and Cobb angle (p=0.011). Conclusions Dystrophin protein deficiency in the paraspinal muscles plays a crucial role in AIS formation and progression. The severity of scoliosis in patients with AIS is correlated with the extent of dystrophin loss in the paravertebral muscles. Therefore, dystrophin dysfunction may be relevant to AIS occurrence and development.

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

Epidermal growth factor receptor-tyrosine kinase inhibitor （EGFR-TKI） represent a class of small-molecule targeted therapeutics for oncology treatment， and serve as first-line therapy for advanced non-small cell lung cancer （NSCLC） with EGFR- sensitive mutations， with representative agents including gefitinib， dacomitinib， and osimertinib. In clinical practice， dose adjustment of EGFR-TKI may be required for cancer patients under special circumstances such as drug combinations or hepatic/ renal impairment. Physiologically-based pharmacokinetic （PBPK） model， capable of predicting pharmacokinetic （PK） processes in humans， has emerged as a vital tool for clinical dose optimization. This article sorts the modeling methodologies， workflows， and commonly used software tools for PBPK model， and summarizes the current applications of PBPK model in EGFR-TKI precision therapy as of June 30， 2024. Findings demonstrate that PBPK modeling methods commonly employ the “bottom-up” approach and the middle-out approach. The process typically involves four steps： parameter collection， compartment selection， model validation， and model application. Commonly used software for modeling includes Simcyp， GastroPlus， and open-source software such as PK- Sim. PBPK model can be utilized for predicting drug-drug interactions of EGFR-TKI co-administered with metabolic enzyme inducers or inhibitors， acid-suppressive drugs， or traditional Chinese and Western medicines. It can also adjust dosages in conjunction with genomics， predict PK processes in special populations （such as patients with liver or kidney dysfunction， pediatric patients）， evaluate the efficacy and safety of drugs， and extrapolate PK predictions from animal models to humans.