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.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

The differences in chemical quality characteristics between Xinjiang Rehmannia glutinosa and R. glutinosa were analyzed to provide a theoretical basis for the introduction and quality control of R. glutinosa. In this study, the high performance liquid chromatography(HPLC) fingerprints of 6 batches of Xinjiang R. glutinosa and 10 batches of R. glutinosa samples were established. The content of iridoid glycosides, phenylethanoid glycosides, monosaccharides, oligosaccharides, and polysaccharides in Xinjiang R. glutinosa and R. glutinosa was determined by high performance liquid chromatography-diode array detection(HPLC-DAD), high performance liquid chromatography-evaporative light scattering detection(HPLC-ELSD), and ultraviolet-visible spectroscopy(UV-Vis). The determination results were analyzed with by chemical pattern recognition and entropy weight TOPSIS method. The results showed that there were 19 common peaks in the HPLC fingerprints of the 16 batches of R. glutinosa, and catalpol, aucubin, rehmannioside D, rehmannioside A, hydroxytyrosol, leonuride, salidroside, cistanoside A, and verbascoside were identified. Hierarchical cluster analysis(HCA) and principal component analysis(PCA) showed that Qinyang R. glutinosa, Mengzhou R. glutinosa, and Xinjiang R. glutinosa were grouped into three different categories, and eight common components causing the chemical quality difference between Xinjiang R. glutinosa and R. glutinosa in Mengzhou and Qinyang of Henan province were screened out by orthogonal partial least squares discriminant analysis(OPLS-DA). The results of content determination showed that there were glucose, sucrose, raffinose, stachyose, polysaccharides, and nine glycosides in Xinjiang R. glutinosa and R. glutinosa samples, and the content of catalpol, rehmannioside A, leonuride, cistanoside A, verbascoside, sucrose, and glucose was significantly different between Xinjiang R. glutinosa and R. glutinosa. The analysis with entropy weight TOPSIS method showed that the comprehensive quality of R. glutinosa in Mengzhou and Qinyang of Henan province was better than that of Xinjiang R. glutinosa. In conclusion, the types of main chemical components of R. glutinosa and Xinjiang R. glutinosa were the same, but their content was different. The chemical quality of R. glutinosa was better than Xinjiang R. glutinosa, and other components in R. glutinosa from two producing areas and their effects need further study.
Rehmannia/classification* ; Drugs, Chinese Herbal/chemistry* ; Chromatography, High Pressure Liquid/methods* ; Quality Control

Biomedical big data, characterized by its massive scale, multi-dimensionality, and heterogeneity, offers novel perspectives for disease research, elucidates biological principles, and simultaneously prompts changes in related research methodologies. Biomedical ontology, as a shared formal conceptual system, not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research. In this review, we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties, highlighting how technological advancements are enabling the more comprehensive use of ontology information. Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list. Deep learning, on the other hand, represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction. With the continuous evolution of big data technologies, the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.
Deep Learning ; Biological Ontologies ; Humans ; Big Data ; Biomedical Research

PURPOSE: Deep vein thrombosis (DVT) of the left and right lower extremities was treated in the same way, but the left and right extremities received different levels of attention. This study aimed to investigate the differences between the right and left lower extremity deep vein thrombosis (LEDVT). METHODS: Clinical characteristics of LEDVT patients from July 2020 to June 2022 were retrospectively analyzed to compare the incidence of LEDVT on different limbs, demographics, predisposing factors, and anatomical characteristics. The exclusion criteria were bilateral LEDVT and recurrent thrombosis. Measured data was analyzed using independent samples t-test or Mann-Whitney test. Count data were analyzed by Chi-square test. A p < 0.05 was considered a statistically significant difference. RESULTS: There were 478 patients included in this study and the ratio of left to right LEDVT on the left and right limbs was 3.16:1 (363:115). Left LEDVT predominantly affected female, with the major aged > 50 years (50 - 60 years: 16.80%; > 60 years: 57.30%). The primary predisposing factor was iliac vein compression syndrome, with iliofemoral thrombosis being the main type. Male patients with LEDVT on the right limb were predominant and the age of onset was usually ≤ 60 years (52.17%). The main predisposing factor was recent surgery or trauma (< 30 days) and femoropopliteal thrombosis was the main type. In more detail, the left iliac vein was compressed mainly in the proximal segment, and the right iliac vein was compressed mainly in the intermediate and distal segments. Recent surgery or trauma to the locomotor system and genitourinary system often induced LEDVT. CONCLUSION The incidence of LEDVT on the left is significantly higher than that on the right. LEDVT on different sides has different characteristics, which is crucial for prevention and diagnosis in the relevant population so there are also differences in treatment of the affected limbs.
Humans ; Venous Thrombosis/etiology* ; Male ; Female ; Middle Aged ; Lower Extremity/blood supply* ; Retrospective Studies ; Adult ; Aged ; Age Factors ; Sex Factors ; Risk Factors ; Acute Disease ; Incidence ; Aged, 80 and over ; Young Adult ; Adolescent

OBJECTIVE: To investigate the effect of zedoarondiol on neovascularization of atherosclerotic (AS) plaque by exosomes experiment. METHODS: ApoE^-/- mice were fed with high-fat diet to establish AS model and treated with high- and low-dose (10, 5 mg/kg daily) of zedoarondiol, respectively. After 14 weeks, the expressions of anti-angiogenic protein thrombospondin 1 (THBS-1) and its receptor CD36 in plaques, as well as platelet activation rate and exosome-derived miR-let-7a were detected. Then, zedoarondiol was used to intervene in platelets in vitro, and miR-let-7a was detected in platelet-derived exosomes (Pexo). Finally, human umbilical vein endothelial cells (HUVECs) were transfected with miR-let-7a mimics and treated with Pexo to observe the effect of miR-let-7a in Pexo on tube formation. RESULTS: Animal experiments showed that after treating with zedoarondiol, the neovascularization density in plaques of AS mice was significantly reduced, THBS-1 and CD36 increased, the platelet activation rate was markedly reduced, and the miR-let-7a level in Pexo was reduced (P<0.01). In vitro experiments, the platelet activation rate and miR-let-7a levels in Pexo were significantly reduced after zedoarondiol's intervention. Cell experiments showed that after Pexo's intervention, the tube length increased, and the transfection of miR-let-7a minics further increased the tube length of cells, while reducing the expressions of THBS-1 and CD36. CONCLUSION Zedoarondiol has the effect of inhibiting neovascularization within plaque in AS mice, and its mechanism may be potentially related to inhibiting platelet activation and reducing the Pexo-derived miRNA-let-7a level.
Animals ; MicroRNAs/genetics* ; Exosomes/drug effects* ; Plaque, Atherosclerotic/genetics* ; Neovascularization, Pathologic/genetics* ; Human Umbilical Vein Endothelial Cells/metabolism* ; Humans ; Blood Platelets/drug effects* ; Apolipoproteins E/deficiency* ; Thrombospondin 1/metabolism* ; CD36 Antigens/metabolism* ; Platelet Activation/drug effects* ; Male ; Mice ; Mice, Inbred C57BL

OBJECTIVE: To investigate the association between long-term exposure to ambient fine particulate matter (PM _2.5) and its constituents and the risk of incident type 2 diabetes mellitus (T2DM), and to examine the modification roles of overweight status. METHODS: This prospective study included 27,507 adults living in rural China. The annual mean residential exposure to PM _2.5 and its constituents was estimated using a satellite-based statistical model. Cox models were used to estimate the risk of T2DM associated with PM _2.5 and its constituents. Stratified analysis quantified the role of overweight status in the association between PM _2.5 constituents and T2DM. RESULTS: Over a median follow-up of 9.4 years, 3,001 new T2DM cases were identified. The hazard ratio ( HR) for a 10 μg/m ³ increase in ambient PM _2.5 was 1.30 (95% confidence interval [ CI]: 1.17, 1.45). Among the constituents, the strongest association was observed with black carbon. Being overweight significantly modified the association between certain constituents and the risk of T2DM. Participants who were overweight and exposed to the highest quartile of PM _2.5 constituents had the highest risk of T2DM ( HR: 2.46, 95% CI: 2.04, 2.97). CONCLUSIONS Our findings indicate that PM _2.5 was associated with an increased risk of T2DM, with black carbon potentially being the primary contributor. Being overweight appeared to enhance the association between PM _2.5 and T2DM. This suggests that controlling both PM _2.5 exposure and overweight status may reduce the burden of T2DM.
Humans ; Diabetes Mellitus, Type 2/chemically induced* ; China/epidemiology* ; Particulate Matter/analysis* ; Overweight/epidemiology* ; Female ; Male ; Middle Aged ; Rural Population ; Air Pollutants/analysis* ; Adult ; Prospective Studies ; Environmental Exposure/adverse effects* ; Aged ; Risk Factors

Time-of-flight secondary ion mass spectrometer(TOF-SIMS)is a highly sensitive surface analysis instrument with high spatial resolution.Traditional TOF-SIMS instruments for sample targets use high field extraction methods.Although the ion collection efficiency is high,it is prone to issues such as low-energy ion beam defocusing,sample morphology sensitivity,and organic molecule ion dissociation.This study aimed to develope an efficient low-field ion extraction system suitable for TOF-SIMS with a continuous beam source.The SIMION simulation software was used to construct a model of the secondary ion optical extraction system.The key factors affecting the extraction efficiency were studied,and the structural parameters of the extraction cone were optimized.Using an indium target as the sample,an experimental test of the performance of the ion extraction system was carried out on the TOF-SIMS instrument.The influences of the voltages of the ion extraction cone and the single lens on the ion extraction efficiency were consistent with the simulation results.By adopting the technology of deflection and coaxial dynamic compensation,the imaging field of view of the ion extraction system was increased to 500 μm×500 μm.The energy window of the ion extraction system reached 10 eV,and the large imaging depth of field of 400 μm was achieved.In the test of a 5 mg/L cholesterol thin film sample,the signal-to-noise ratio of the characteristic peak[M-OH]+reached 4453.The results showed that this low-field secondary ion extraction system effectively improved the performance of the continuous beam TOF-SIMS instrument.

Objective To analyse correlation between automatic quantification of emphysema and lung function based on artificial intelligence(AI)model algorithm by chest computed tomography(CT)in patients with chronic obstructive pulmonary disease(COPD).Methods The clinical and imaging data of hospitalized COPD patients who received chest CT plain scan in Taizhou Hospital of Zhejiang Province,Enze Hospital of Taizhou Enze Medical Center(Group)from December 2020 to May 2021 were retrospectively collected,patients were classified into five levels of ventilator-function decline.By using the AI model,the extent of emphysema lesions in COPD patients were calculated,low-attenuation areas below-950HU were identified and their low attenuation area percentage(LAA％)were calculated.Combined with the output results of AI model and whether each variable met the characteristics of normal distribution,Pearson correlation coefficient between percentage of measured forced expiratory volume at the end of 1 second to estimated value(FEV1％)and LAA％of each lung lobe,and the Spearman correlation coefficient between FEV1 as a percentage of forced vital capacity(FEV1/FVC)and LAA％of each lung lobe in patients with different COPD grades were calculated respectively.Results There was a negative correlation between total lung LAA％and FEV1/FVC in moderate COPD(r=-0.632,P=0.001).Total lung LAA％in very severe COPD was negatively correlated with both FEV1/FVC and FEV1％(r=-0.562,P=0.045 and r=-0.701,P=0.004).The results of lung segment analysis showed that LAA％of the left upper lung lobe was more strongly correlated with pulmonary function indicators in extremely severe COPD(r=-0.650,P=0.016 andr=-0.731,P=0.002).The correlation between left inferior lobe LAA％and FEV1/FVC was stronger correlation in patients with moderate COPD(r=-0.712,P=0.000).In smoking patients,LAA％was moderate correlated with FEV1(r=-0.534,P=0.006),and LAA％was moderate correlated with FEV1/FVC(r=-0.564,P=0.003).Conclusion AI-based emphysema quantification results have a good correlation with FEV1/FVC and FEV1％,which can provide strong support for the diagnosis and classification of COPD based on CT plain scan images.

Biochemistry,as a fundamental course for science and engineering majors related to biology and chemistry,holds a significant position in the curriculum.The course team at Dalian Minzu University is committed to teaching innovation,adopting the outcome-based education(OBE)concept for teaching de-sign and incorporating ideological and political elements,in order to achieve the dual goals of knowledge transmission and value guidance.The team has established a three-dimensional teaching goal of"knowl-edge,morality,and ability",covering"consolidating core knowledge,cultivating moral sentiment,and enhancing innovation ability".Through a multi-dimensional integrated teaching method of"three integra-tions and five combinations",multiple rounds of teaching practice have been carried out in the applied chemistry major using"classification and specificity of enzyme"as an example.The output of teaching re-sults and survey questionnaires show that students highly recognize the teaching design and its"process-based learning"evaluation method,fully reflecting the student-centered teaching idea.Research has shown that OBE design combined with ideological and political elements can effectively promote students' knowl-edge acquisition,moral growth,and innovation ability improvement in the course of Biochemistry.This teaching design not only helps students construct correct worldviews,outlooks on life,and values,but also significantly enhances their innovative thinking and practical abilities.This teaching design can not only ef-fectively improve the teaching quality of the course,but also provide new perspectives and ideas for the teaching design of Biochemistry,realizing the organic integration of professional knowledge imparting and i-deological and political education,and has certain innovation and practical significance.