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.

Following the release of the Technical Requirements on Quality Control and Standard Establishment of Traditional Chinese Medicine Formula Granules by the National Medical Products Administration in 2021, Chinese Pharmacopoeia Commission has promulgated 296 national drug standards so far, and most provinces have started the work of establishing provincial standards as supplements. The promulgation of standards fostered high-quality development of the industry. Since the implementation of national and provincial standards for more than three years, enterprises have gained deep understanding and hands-on experiences on the characteristics, technical requirements, production process, and quality control of traditional Chinese medicine(TCM) formula granules. Meanwhile, challenges have emerged restricting the high-quality development of this industry, including how to formulate quality control strategies for medicinal materials and decoction pieces, how to reduce manufacturing costs, and how to improve the pass rate and product stability under high standards. Based on the work experiences from standard management and process research, this article analyzed the distribution of sources, processing methods, dry extract rate ranges, process requirements for volatile oil-containing decoction pieces, control measures of safety indices, characteristics and trends of setting characteristic chromatograms or fingerprints, characteristics and trends of setting content ranges, and main differences between national standards and provincial standards. On the one hand, this article aims to present main characteristics for deeply understanding different indicators in standards and provide basic ideas for establishing quality and process control systems. On the other hand, from the perspective of industrial practice, suggestions are put forward on the important aspects that need to be focused on in the quality and process control of TCM formula granules.
Drugs, Chinese Herbal/chemistry* ; Quality Control ; Medicine, Chinese Traditional/standards* ; China ; Drug Industry/standards*

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.

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.

Objective To improve and refine the relevant regulations and guiding principles of warnings on drug instructions and labels in China.Methods This paper sorted out the drug instructions of small molecule anti-tumor drugs listed by the U.S.Food and Drug Administration(FDA)from 2005 to 2022,included the drugs mentioned in the QT interval prolongation risk,analyzed the clinical research and QT research results,and sorted out the identification and warning rules of the instructions.Results A total of 35 drugs were included,4 drugs wrote the risk of QT interval prolongation in the black box warning,21 drugs were wrote in the warning and precautions position,6 drugs were wrote in the adverse reaction section,and 2 drugs were only described under clinical pharmacology section.According to the severity of the QT interval prolongation caused by the drug and whether there were serious clinical consequences,they were displayed in the warnings(black box warnings),precautions(warnings and precautions)and adverse reactions in the instructions.Conclusion The aim of this article is to provide a reference for the writing of QT risk warning information of the instructions of domestic drug production enterprises and regulatory departments.It is recommended to clarify the severity of drug safety and the location of the instructions in clinical research,and continue to carry out safety monitoring and update the instructions in time after listing.

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

To evaluate the therapeutic effect of a modified Devine procedure with a subcutaneous sliding fixation method for the treatment of congenital concealed penis, we retrospectively selected 45 patients with congenital concealed penises who were admitted to General Hospital of Ningxia Medical University (Yinchuan, China) between September 2020 and November 2023. In all cases, the penis was observed to be short, and retracting the skin at the base revealed a normal penile body, which immediately returned to its original position upon release. All patients underwent the modified Devine procedure with subcutaneous sliding fixation and completed a 12-week postoperative follow-up. A statistically significant increase in penile length was observed postoperatively, with the median length increasing from 4.0 (interquartile range [IQR]: 3.5-4.8; 95% confidence interval [CI]: 3.9-4.4) cm to 8.0 (IQR: 7.8-8.0; 95% CI: 7.7-7.9) cm, with P < 0.001. The parents were satisfied with the outcomes, including increased penile length, improved hygiene, and enhanced esthetics. Except for mild foreskin edema in all cases, no complications (such as infections, skin necrosis, or penile retraction) were observed. The edema was resolved within 4 weeks after the operation. This study demonstrates that the modified Devine procedure utilizing the subcutaneous sliding fixation method yields excellent outcomes with minimal postoperative complications, reduced penile retraction, and high satisfaction rates among patients and their families.
Humans ; Male ; Penis/abnormalities* ; Retrospective Studies ; Urologic Surgical Procedures, Male/methods* ; Treatment Outcome ; Child ; Plastic Surgery Procedures/methods*

Objective:To integrate and analyze the trend of the disease burden of tracheal,bronchus,and lung cancer(TBL)attributable to secondhand smoke in China from 1990 to 2021 and to analyze future projections,aiming to provide data support for the prevention and treatment of TBL in China.Methods:Based on the global burden of disease(GBD)2021 database,TBL with ICD-10 disease classification C33,C34-C34.92 was studied.Using secondhand smoke as a risk factor,the data on TBL mortality and disability-adjusted life year(DALY)due to secondhand smoke in China from 1990 to 2021 were further age-standardized.Using Joinpoint 4.7.1 regression analysis model to calculate annual percentage change(APC)and average annual percentage change(AAPC),Hiplot software was used to plot disease burden data for different ages and genders,and R 4.3.1 software was used to construct a grey model GM(1,1)to predict the predicted value and trend of TBL disease burden attributed to secondhand smoke in China from 2022 to 2031.Results:From 1990 to 2021,the TBL mortality rate,age-standardized mortality rate,and DALY rate attributed to secondhand smoke in China increased from 1.76/100 000,2.63/100 000,and 49.43/100 000 to 4.08/100 000,2.80/100 000,and 95.57/100 000,respectively;the growth was 131.18%,6.45%,and 93.34%;the age-standardized DALY rate decreased from 65.04/100 000 to 63.32/100 000 with the reduction of 2.65%.The results of the Joinpoint regres-sion showed that the AAPC(95%CI)of mortality,age-standardized mortality rate,and DALY rate for TBL were 2.75(2.58-2.93)%,0.16(0.11-0.21)%,and 2.15(2.11-2.18)%,respectively,with an overall increasing trend;the AAPC(95%CI)of age-standardized DALY rate was-0.14(-0.40-0.12)%,with an overall fluctuating and unchanged trend and it was higher in males than in females.In both 1990 and 2021,the TBL mortality rate attributable to secondhand smoke in China gradually increased with age,and the DALY rate first increased and then slowed down with age.The main groups of the burden of disease were the elderly and males.The grey prediction model GM(1,1)showed that the age-standardized mortality rate of TBL attributable to secondhand smoke from 2022 to 2031 showed a slow increasing trend,and the predicted value in 2031 would increase to 2.95/100 000.The age-standardized DALY showed a slow decreasing trend,and the predicted value in 2031 would decrease to 63.83/100 000.Conclusions:From 1990 to 2021,the TBL mortality,age-standardized mortality,and DALY rates attributable to secondhand smoke in China increased,and the age-standardized DALY rate decreased.Men and the elderly are the main groups affected by TBL.Appropriate measures should be formulated to reduce exposure to and contact with secondhand smoke,tak-ing into account gender and age differences.Additionally,efforts should be made to strengthen secondhand smoke prevention and public health education.

Objective To investigate the impacts of midazolam(MDZ)on the proliferation,migration,and invasion of esophageal cancer(EC)cells by regulating the monocyte chemotactic protein-1(CCL2)-C-C chemokine receptor 2(CCR2)signaling pathway.Methods QRT-PCR method was applied to determine the expression of CCL2 and CCR2 mRNA in EC tissue,adjacent cancer tissue,human normal esophageal epithelial cell HEEC,and EC cell Eca-109.MTT assay and colony formation were applied to measure cell proliferation.Scratch test,Transwell test,and TUNEL method were applied to determine cell migration,invasion,and apoptosis,respectively.The expression of CCL2-CCR2 signaling pathway proteins was determined using Western blot method.Results Compared with adjacent cancer tissues and normal human esophageal epithelial cells(HEEC),the mRNA and protein expression levels of CCL2 and CCR2 in cancer tissues and Eca-109 cells were increased(P＜0.05).Compared with the control group,the OD450 value,colony formation number,scratch healing rate,and invasive cell count of Eca-109 cells in the MDZ-L group,MDZ-M group,and MDZ-H group decreased,while the proportion of TUNEL positive cells increased(P＜0.05).Compared with the MDZ-H group,the OD450 value,colony formation number,scratch healing rate,and number of invasive cells in the MDZ-H+GW0742 group all increased,while the proportion of TUNEL positive cells decreased(P＜0.05).Compared with the control group,protein and mRNA expressions of CCL2 and CCR2 proteins in Eca-109 cells in the MDZ-L group,MDZ-M group,and MDZ-H group decreased(P＜0.05).Compared with the MDZ-H group,the MDZ-H+GW0742 group showed an increase in the expression of CCL2 and CCR2 proteins in Eca-109 cells(P＜0.05).Conclusion MDZ can inhibit the proliferation,migration,and invasion of EC cells by inhibiting the activation of the CCL2-CCR2 signaling pathway.