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.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Objective To summarize the epidemiological and clinical features of infectious diseases of the central nervous system(CNS)by a single-center analysis.Methods A retrospective analysis was conducted on the data of 1247 cases of CNS infectious diseases diagnosed and treated in the First Medical Center of PLA General Hospital from 2001 to 2020.Results The data for this group of CNS infectious diseases by disease type in descending order of number of cases were viruses 743(59.6％),Mycobacterium tuberculosis 249(20.0％),other bacteria 150(12.0％),fungi 68(5.5％),parasites 18(1.4％),Treponema pallidum 18(1.4％)and rickettsia 1(0.1％).The number of cases increased by 177 cases(33.1％)in the latter 10 years compared to the previous 10 years(P<0.05).No significant difference in seasonal distribution pattern of data between disease types(P>0.05).Male to female ratio is 1.87︰1,mostly under 60 years of age.Viruses are more likely to infect students,most often at university/college level and above,farmers are overrepresented among bacteria and Mycobacterium tuberculosis,and more infections of Treponema pallidum in workers.CNS infectious diseases are characterized by fever,headache and signs of meningeal irritation,with the adductor nerve being the more commonly involved cranial nerve.Matagenomic next-generation sequencing improves clinical diagnostic capabilities.The median hospital days for CNS infectious diseases are 18.00(11.00,27.00)and median hospital costs are ￥29,500(￥16,000,￥59,200).The mortality rate from CNS infectious diseases is 1.6％.Conclusions The incidence of CNS infectious diseases is increasing last ten years,with complex clinical presentation,severe symptoms and poor prognosis.Early and accurate diagnosis and standardized clinical treatment can significantly reduce the morbidity and mortality rate and ease the burden of disease.

Objects To explore the effectiveness and safety of using the Cardio-O-Fix Plug occluder in the treatment of muscular ventricular septal defect(mVSD)in children.Methods 14 patients with mVSD were taken to the cardiology department of First Affiliated Hospital of Kunming Medical University from July 2015 to June 2021 as research subjects.They were divided into two groups:14 children who received Cardi-O-Fix Plug occluder as the experimental group,and 10 children who received Cardi-O-O-Fix mVSD occluder as the control group.Electrocardiogram and transthoracic echocardiography were used to evaluate the occlusive efficacy and incidence of complications 1 day after surgery and 1 month,3 months,and 6 months of follow-up.Results Among the 24 pediatric patients,22 cases were successfully occluded,and 2 cases were unsuccessful(1 in the experi-mental group and 1 in the control group).The success rate of the experimental group was 92.8%(13/14),while the success rate of the control group was 90.0%(9/10).The average surgical duration of the experimental group was(71.93±14.85)minutes,while the average surgical duration of the control group was(90.70±19.78)minutes.There was a significant statistical difference between the two groups(P<0.05).Both the experimental group and the control group did not experience serious complications during surgery and follow-up.There was no significant difference in cardiac ultrasound indicators(including left ventricular ejection fraction,left ventricular end-diastolic diameter,and pulmonary artery pressure)between the two groups at different time points(P>0.05).Conclusion Trans-catheter closure of mVSD using Cardi-O-Fix Plug occluder in children is both safe and effective.The incidence of arrhythmia is low in the short,medium and long term.

Bacterial biofilms gave rise to persistent infections and multi-organ failure, thereby posing a serious threat to human health. Biofilms were formed by cross-linking of hydrophobic extracellular polymeric substances (EPS), such as proteins, polysaccharides, and eDNA, which were synthesized by bacteria themselves after adhesion and colonization on biological surfaces. They had the characteristics of dense structure, high adhesiveness and low drug permeability, and had been found in many human organs or tissues, such as the brain, heart, liver, spleen, lungs, kidneys, gastrointestinal tract, and skeleton. By releasing pro-inflammatory bacterial metabolites including endotoxins, exotoxins and interleukin, biofilms stimulated the body’s immune system to secrete inflammatory factors. These factors triggered local inflammation and chronic infections. Those were the key reason for the failure of traditional clinical drug therapy for infectious diseases.In order to cope with the increasingly severe drug-resistant infections, it was urgent to develop new therapeutic strategies for bacterial-biofilm eradication and anti-bacterial infections. Based on the nanoscale structure and biocompatible activity, nanobiomaterials had the advantages of specific targeting, intelligent delivery, high drug loading and low toxicity, which could realize efficient intervention and precise treatment of drug-resistant bacterial biofilms. This paper highlighted multiple strategies of biofilms eradication based on nanobiomaterials. For example, nanobiomaterials combined with EPS degrading enzymes could be used for targeted hydrolysis of bacterial biofilms, and effectively increased the drug enrichment within biofilms. By loading quorum sensing inhibitors, nanotechnology was also an effective strategy for eradicating bacterial biofilms and recovering the infectious symptoms. Nanobiomaterials could intervene the bacterial metabolism and break the bacterial survival homeostasis by blocking the uptake of nutrients. Moreover, energy-driven micro-nano robotics had shown excellent performance in active delivery and biofilm eradication. Micro-nano robots could penetrate physiological barriers by exogenous or endogenous driving modes such as by biological or chemical methods, ultrasound, and magnetic field, and deliver drugs to the infection sites accurately. Achieving this using conventional drugs was difficult. Overall, the paper described the biological properties and drug-resistant molecular mechanisms of bacterial biofilms, and highlighted therapeutic strategies from different perspectives by nanobiomaterials, such as dispersing bacterial mature biofilms, blocking quorum sensing, inhibiting bacterial metabolism, and energy driving penetration. In addition, we presented the key challenges still faced by nanobiomaterials in combating bacterial biofilm infections. Firstly, the dense structure of EPS caused biofilms spatial heterogeneity and metabolic heterogeneity, which created exacting requirements for the design, construction and preparation process of nanobiomaterials. Secondly, biofilm disruption carried the risk of spread and infection the pathogenic bacteria, which might lead to other infections. Finally, we emphasized the role of nanobiomaterials in the development trends and translational prospects in biofilm treatment.

ObjectiveTo explore the syndrome elements and evolving patterns of patients with hepatitis B virus-related acute on chronic liver failure （HBV-ACLF） at different stages. MethodsClinical information of 1,058 hospitalized HBV-ACLF patients， including 618 in the early stage， 355 in the middle stage， and 85 in the late stage， were collected from 18 clinical centers across 12 regions nationwide from January 1， 2012 to February 28， 2015. The “Hepatitis B-related Chronic and Acute Liver Failure Chinese Medicine Clinical Questionnaire” were designed to investigate the basic information of the patients， like the four diagnostic information （including symptoms， tongue， pulse） of traditional Chinese medicine （TCM）， and to count the frequency of the appearance of the four diagnostic information. Factor analysis and cluster analysis were employed to determine and statistically analyze the syndrome elements and patterns of HBV-ACLF patients at different stages. ResultsThere were 76 four diagnostic information from 1058 HBV-ACLF patients， and 53 four diagnostic information with a frequency of occurrence ≥ 5% were used as factor analysis entries， including 36 symptom information， 12 tongue information， and 5 pulse information. Four types of TCM patterns were identified in HBV-ACLF， which were liver-gallbladder damp-heat pattern， qi deficiency and blood stasis pattern， liver-kidney yin deficiency pattern， and spleen-kidney yang-deficiency pattern. In the early stage， heat （39.4%， 359/912） and dampness （27.5%， 251/912） were most common， and the pattern of the disease was dominated by liver-gallbladder damp-heat pattern （74.6%， 461/618）； in the middle stage， dampness （30.2%， 187/619） and blood stasis （20.7%， 128/619） were most common， and the patterns of the disease were dominated by liver-gallbladder damp-heat pattern （53.2%， 189/355）， and qi deficiency and blood stasis pattern （27.6%， 98/355）； and in the late stage， the pattern of the disease was dominated by qi deficiency （26.3%， 40/152） and yin deficiency （20.4%， 31/152）， and the patterns were dominated by qi deficiency and blood stasis pattern （36.5%， 31/85）， and liver-gallbladder damp-heat pattern （25.9%， 22/85）. ConclusionThere are significant differences in the distribution of syndrome elements and patterns at different stages of HBV-ACLF， presenting an overall trend of evolving patterns as "from excess to deficiency， transforming from excess to deficiency"， which is damp-heat → blood stasis → qi-blood yin-yang deficiency.

Herein,a new multifunctional hydrogel wound dressing was fabricated for the first time based on the crosslinking between catechol-modified chitosan(CHI-C)and bismuth sulfide-gold nanocluster nanoparticles(Bi2S3-Au NCs NPs)by simple stirring at room temperature within 1 min.Benefit from the good biocompatibility of CHI-C and excellent photothermal abilily of Bi2S3-Au NCs NPs,it could achieve postoperative photothermal therapy of tumor residual tissue and wound healing.More importantly,the as-prepared hydrogel with fluorescent property could accurately monitor the postoperative wound filling in real-time,which was critical to wound healing,especially for irregular wounds.The smart hydrogel was expected to provide a new perspective for preventing and reducing cancer recurrence and wound infection after surgery.

Near infrared spectroscopy(NIRS)technology combined with chemometrics algorithms has been widely used in quantitative and qualitative analysis of food and medicine.However,traditional chemometrics methods,especially linear classification methods,often yield unsatisfactory results when addressing multi-class classification problems.Convolutional neural network(CNN)is adept at extracting deep-level features from data and suitable for handling non-linear relationships.The modeling performance of CNN depends on the size and diversity of sample,while the collection and preprocessing of NIRS sample data is often time-consuming and labor-intensive.This study proposed a NIRS qualitative analysis method based on data augmentation strategies and CNN.The data augmentation strategy included two steps.Firstly,applying Bootstrap resampling and generative adversarial network(GAN)methods to augment three NIRS datasets(Medicine,coffee and grape).Secondly,combining the original samples(Y)with the Bootstrap augmented samples(B)and GAN augmented samples(G)to obtain three augmented datasets(Y-B,Y-G and Y-B-G).Based on this,a CNN model structure suitable for these datasets was designed,consisting of 2 one-dimensional convolutional layers,1 max-pooling layer,and 1 fully connected layer.The results showed that compared to the optimal models of partial least squares discriminant analysis(PLS-DA),support vector machine(SVM),and back propagation neural network(BP),the CNN model based on Y-B dataset achieved average accuracy improvements of 3.998%,9.364%,and 4.689%for medicine(Binary classification);the CNN model based on the Y-B-G dataset achieved average accuracy improvements of 6.001%,2.004%,and 7.523%for coffee(7-class classification);and the CNN model based on the Y-B dataset achieved average accuracy improvements of 33.408%,51.994%,and 34.378%for grapes(20-class classification).It was evident that the models established based on data augmentation strategies and CNN demonstrated better classification accuracy and generalization performance with different datasets and classification categories.

Objective To detect the expression changes of interleukin-10(IL-10)and transforming growth factor-β1(TGF-β1)during the development of deep vein thrombosis in mice,and to explore the application value of them in thrombus age estimation.Methods The mice in the experimental group were subjected to ligation of inferior vena cava.The mice were sacrificed by excessive anesthesia at 1 d,3 d,5 d,7 d,10 d,14 d and 21 d after ligation,respectively.The inferior vena cava segment with thrombosis was extracted below the ligation point.The mice in the control group were not ligated,and the inferior vena cava segment at the same position as the experimental group was extracted.The ex-pression changes of IL-10 and TGF-β1 were detected by immunohistochemistry(IHC),Western blot-ting and real-time qPCR.Results IHC results revealed that IL-10 was mainly expressed in monocytes in thrombosis and TGF-β1 was mainly expressed in monocytes and fibroblast-like cells in thrombosis.Western blotting and real-time qPCR showed that the relative expression levels of IL-10 and TGF-β1 in each experimental group were higher than those in the control group.The mRNA and protein levels of IL-10 reached the peak at 7 d and 10 d after ligation,respectively.The mRNA expression level at 7 d after ligation was 4.72±0.15 times that of the control group,and the protein expression level at 10 d after ligation was 7.15±0.28 times that of the control group.The mRNA and protein levels of TGF-β1 reached the peak at 10 d and 14 d after ligation,respectively.The mRNA expression level at 10 d after ligation was 2.58±0.14 times that of the control group,and the protein expression level at 14 d after ligation was 4.34±0.19 times that of the control group.Conclusion The expressions of IL-10 and TGF-β1 during the evolution of deep vein thrombosis present time-dependent sequential changes,and the expression levels of IL-10 and TGF-β1 can provide a reference basis for thrombus age estimation.

Pharmacokinetics of traditional Chinese medicine(TCM)is a discipline that adopts pharmacokinetic research methods and techniques under the guidance of TCM theories to elucidate the dynamic changes in the absorption,distribution,metabolism and excretion of active ingredients,active sites,single-flavour Chinese medicinal and compounded formulas of TCM in vivo.However,the sources and components of TCM are complex,and the pharmacodynamic substances and mechanisms of action of the majority of TCM are not yet clear,so the pharmacokinetic study of TCM is later than that of chemical medicines,and is far more complex than that of chemical medicines,and its development also confronts with challenges.The pharmacokinetic study of TCM originated in the 1950s and has experienced more than 70 years of development from the initial in vivo study of a single active ingredient,to the pharmacokinetic and pharmacodynamic study of active ingredients,to the pharmacokinetic study of compound and multi-component of Chinese medicine.In recent years,with the help of advanced extraction,separation and analysis technologies,gene-editing animals and cell models,multi-omics technologies,protein purification and structure analysis technologies,and artificial intelligence,etc.,the pharmacokinetics of TCM has been substantially applied in revealing and elucidating the pharmacodynamic substances and mechanisms of action of Chinese medicines,research and development of new drugs of TCM,scientific and technological upgrading of large varieties of Chinese patent medicines,as well as guiding the rational use of medicines in clinics.Pharmacokinetic studies of TCM have made remarkable breakthroughs and significant development in theory,methodology,technology and application.In this paper,the history of the development of pharmacokinetics of TCM and the progress of cutting-edge research was reviewed,with the aim of providing ideas and references for the pharmacokinetics of TCM and related research.