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.

Based on commonly used acupoints in the clinical acupuncture treatment of functional dyspepsia （FD）， this study systematically analyzes the therapeutic differences and synergistic effects between local and distal point selection. It also examines the suitability of primary acupoint selection for different FD subtypes， postprandial distress syndrome （PDS） and epigastric pain syndrome （EPS）. The findings suggest that a combination of local and distal acupoints may be more appropriate as primary points for PDS， whereas local acupoints alone may be more suitable for EPS. Additionally， the study explores the impact of various factors， such as stimulation techniques， needling order， intensity or stimulation parameters， and depth， on the efficacy of acupuncture. It concludes that the intrinsic properties of acupoints are the primary determinants of therapeutic direction. Other factors mainly influence the magnitude rather than the direction of the effect. Future research may further investigate how different acupoint combinations， local versus distal， affect the treatment outcomes of FD subtypes， providing new insights for clinical acupuncture prescriptions.

Objective To investigate the prevalence of work-related musculoskeletal disorders (WMSDs) in passenger drivers and its influencing factors. Methods A total of 951 passenger drivers in Guangdong Province were selected as the research subjects using the judgmental sampling method. A Musculoskeletal Injury Questionnaire was employed to assess the prevalence of WMSDs in the past year. Results The prevalence of WMSDs in passenger drivers was 41.11%. The result of multivariable logistic regression analysis showed that married drivers had a higher risk of WMSDs than single drivers (P<0.05). The lower the frequency of physical exercise, the longer the driving time per week, the longer the continuous driving time, the more restricted the driving working space, the poorer the foot comfort during driving, and the more affected the normal meal, the higher the risk of WMSDs (all P<0.05). The risk of WMSDs in drivers with sleep time ≤ 8.0 h/d was higher than that in drivers with sleep time > 8.0 h/d (P<0.01), and the risk of WMSDs in drivers with the same posture for a long time on the shoulder was higher than that in drivers without this poor working posture (P<0.01). Conclusion WMSDs were prevalent among passenger drivers, which was associated with demographic and adverse ergonomic factors. Intervention on lifestyle and adverse ergonomic factors could further reduce the risk of WMSDs of passenger drivers.

Background Intraseasonal variation in acute health effects of extreme heat remains insufficiently investigated. Emergency ambulance calls (EACs) may offer timely insights into the population's health during such extreme heat events. Objective To analyze intraseasonal variation in the association between extreme heat and hourly EACs during summer in Dezhou City, Shandong Province, China. Methods We collected data on all-cause hourly EACs in Dezhou City from 2021 to 2022 and assigned hourly temperature and humidity data (with a spatial resolution of 0.0625° × 0.0625°) to call addresses. Summer in this study was defined as from June to September each year, with June to July considered as early summer and August to September as late summer. Extreme heat was defined as the 99th percentile of the temperature range during the summer. We employed a time-stratified case-crossover design using conditional logistic regression integrating distributed-lag nonlinear models to compare the association between extreme heat and the risk of hourly EACs in both early and late summer periods. Results A total of 80389 EACs were recorded in Dezhou City during the study period. The analysis revealed a U-shaped association between hourly ambient temperature and EACs during summer, with the most significant effect observed at lag 0-30 h. Using the optimal temperature of 20.0°C as a reference, the cumulative odds ratio (OR) (lag 0-120 h) for extreme heat was 1.55 (95%CI: 1.40, 1.71) throughout summer. The cumulative effect of extreme heat was higher in late summer (OR=2.38, 95%CI: 1.91, 2.97) than in early summer (OR=1.37, 95%CI: 1.22, 1.54) (P<0.0001). Additionally, individuals aged 60 years and above had a higher risk throughout summer (OR=1.98, 95%CI: 1.70, 2.30) compared to those under 60 years (OR=1.23, 95%CI: 1.06, 1.42) (P<0.0001). Conclusion Intraseasonal variation is observed in the strength of association between extreme heat and hourly EACs during summer in Dezhou City. The higher risk observed in late summer than in early summer indicates that repeated exposures to heat may escalate health risks, and older adults are more vulnerable.

Objective:To explore if miR-133a is involved in the occurrence and development of hepatocellular carcinoma(HCC)via regulating G6PD.Methods:Bioinformatics analysis predicted the binding sites of miR-133a and G6PD;RT-PCR or western blot was used to assess the expres-sion of miR-133a and G6PD in HCC tissues and the adjacent normal tissues;CCK-8 and flow cy-tometry assays were performed to evaluate the effects of miR-133a/G6PD on cell proliferation,apop-tosis;Fluorescent reporter gene and western blot assays were used to assess the effect of miR-133a on G6PD expression.Results:miR-133a expression was decreased in HCC tissues while G6PD was increased(P0.01);Up-regulation of miR-133a significantly reduced G6PD expression(P＜0.01);up-reg-ulation of miR-133a inhibited cell growth and promoted cell apoptosis(P＜0.05),whereas these effects induced by miR-133a over-expression were all abolished when G6PD was up-regulated(P＜0.01).Conclusion:miR-133a represses the occurrence and development of HCC via targeting G6PD.

In recent years, nucleic acid therapy, as a revolutionary therapeutic tool, has shown great potential in the treatment of genetic diseases, infectious diseases and cancer. Lipid nanoparticles (LNPs) are currently the most advanced mRNA delivery carriers, and their emergence is an important reason for the rapid approval and use of COVID-19 mRNA vaccines and the development of mRNA therapy. Currently, mRNA therapeutics using LNP as a carrier have been widely used in protein replacement therapy, vaccines and gene editing. Conventional LNP is composed of four components: ionizable lipids, phospholipids, cholesterol, and polyethylene glycol (PEG) lipids, which can effectively load mRNA to improve the stability of mRNA and promote the delivery of mRNA to the cytoplasm. However, in the face of the complexity and diversity of clinical diseases, the structure, properties and functions of existing LNPs are too homogeneous, and the lack of targeted delivery capability may result in the risk of off-targeting. LNPs are flexibly designed and structurally stable vectors, and the adjustment of the types or proportions of their components can give them additional functions without affecting the ability of LNPs to deliver mRNAs. For example, by replacing and optimizing the basic components of LNP, introducing a fifth component, and modifying its surface, LNP can be made to have more precise targeting ability to reduce the side effects caused by treatment, or be given additional functions to synergistically enhance the efficacy of mRNA therapy to respond to the clinical demand for nucleic acid therapy. It is also possible to further improve the efficiency of LNP delivery of mRNA through machine learning-assisted LNP iteration. This review can provide a reference method for the rational design of engineered lipid nanoparticles delivering mRNA to treat diseases.

The anti-inflammatory effect of simplified Zhiqin Decoction was observed by using lipopolysaccharide (LPS)-induced inflammation mouse model. The main chemical constituents and the main mechanism of action of simplified Zhiqin Decoction were predicted by network pharmacology. Animal experiments verified the anti-inflammatory mechanism of simplified Zhiqin Decoction (this experiment was approved by the Animal Experiment Ethics Committee of Southwest University, approval number: IACUC-20210825-02). Simplifying Zhiqin Decoction has a significant anti-inflammatory effect on inflammatory mice, can significantly improve the overall macro shape of mice, reduce body temperature, water intake, increase the number of autonomous activities; alleviate liver, lung, spleen, thymus inflammation and pathological damage; decrease tumor necrosis factor-α (TNF-α), interleukin (IL)-1β and IL-6, nitric oxide (NO), prostaglandin E₂ (PGE₂) content in serum and urine. The contents of serum immune factors IgG, IgA and IgM were increased. Network pharmacology predicted 66 potential anti-inflammatory active components of simplified Zhiqin Decoction involving 132 inflammatory targets, and the key anti-inflammatory signaling pathway involved PI3K/AKT, TNF, JAK-STAT, etc. Animal experiments show that simplified Zhiqin Decoction can significantly reduce the expression of JAK2/STAT-PI3K/AKT-NF-κB-TNF signaling pathway related proteins in lung tissue of inflammatory mice. The results of this study showed that simplified Zhiqin Decoction had significant anti-inflammatory effects, and its main anti-inflammatory components were quercetin, β-sitosterol, kaempferol, wogonin, stigmasterol, luteolin, neobaicalein, etc. The main mechanism of its anti-inflammatory action is that it significantly inhibits the expression of JAK2/STAT-PI3K/AKT-NF-κB-TNF signaling pathway protein.

Background Occupational stress is an important factor affecting the health of working population, wellness is a topic that has received much attention in recent years, and sleep quality and wellness have a certain impact on occupational stress. Objective To understand the current situation of occupational stress among locomotive engineers in in the Yangtze River Delta, explore the influence of well-being and sleep quality on occupational stress, and provide a theoretical basis for occupational stress intervention among locomotive engineers. Methods Sampling was conducted from October to December 2022 using stratified random sampling. The Chinese version of the Brief Occupational Tension Questionnaire based on the Job Demand-Control Model (JDC model), the World Health Organization 5-item Well-Being Index (WHO-5), and the Pittsburgh Sleep Quality Index (PSQI) scale were used to investigate the occupational stress, well-being, and sleep quality of locomotive engineers. Logistic regression was used to analyze the effects of sleep quality and well-being on occupational stress, and additive and multiplicative interaction models were applied to evaluate the interactive effects of well-being and sleep quality on occupational stress. Results A total of 8243 questionnaires were collected from five locomotive companies, and after excluding unqualified questionnaires, a total of 7976 questionnaires were included in the analysis. The number of locomotive engineers in the Yangtze River Delta reporting positive occupational stress was 4633 (58.09%), the number of low-level well-being was 4949 (62.05%), and the number of poor sleep quality was 4129 (51.77%). The distribution of occupational stress varied by age, length of service, region, technical level, nature of vehicle, education, per capita monthly family income, marital status, smoking, drinking, number of exercise sessions per week, quality of sleep, work shifts, and length of commute (P<0.01). The results of logistic regression showed that a lower level of occupational stress was associated with >11000 yuan per capita monthly household income (OR=0.795, 95%CI: 0.656, 0.964), high level of well-being (OR=0.477, 95%CI: 0.430, 0.528), and good sleep quality (OR=0.588, 95%CI: 0.531, 0.651); a higher level of occupational stress was associated with night shifts: 1 or fewer night shifts (OR=1.707, 95%CI: 1.312, 2.221), 2 to 3 night shifts (OR=2.649, 95%CI: 2.111, 3.325), and 4 or more night shifts (OR=2.804, 95%CI: 2.202, 3.571); a higher level of occupational stress was associated with 60 min or more commuting time (OR=1.353, 95%CI: 1.111, 1.646). Regarding interaction between poor sleep quality and low well-being on occupational stress, the relative excess risk ratio (RERI), attributable risk index (API), and synergy index (SI) were 1.451 (1.059, 1.844), 0.417 (0.327, 0.507), and 2.407 (1.787, 3.241), respectively, and the result of the multiplicative interaction was an OR of 1.546 (95%CI: 1.262, 1.893). Conclusion The prevalence of occupational stress among locomotive engineers in the Yangtze River Delta region is generally at a medium level compared with other industries. Good sleep quality reduces the risk of occupational stress to a greater extent than high well-being, and poor sleep quality and low well-being can interact and further increase the risk of occupational stress.

BACKGROUND:Our previous studies found that the polypeptide of Cornus cervi Colla can promote bone growth,which has a good application prospect in the treatment of bone diseases.However,how Cornus cervi Colla works in the body and the principle are not clear. OBJECTIVE:To study the in vivo distribution and tracing of Cornus cervi Colla using fluorescence labeling and tracer technique. METHODS:Cornus cervi Colla was fluorescently labeled using fluorescein isothiocyanate,and the labeling results were detected by fluorescence imaging and UV spectral scanning.Successfully labeled Cornus cervi Colla was injected into mice by gavage,and the absorption of Cornus cervi Colla into blood was detected by laser confocal microscopy,and the distribution of Cornus cervi Colla in mice was detected by small animal in vivo imager.The distribution of Cornus cervi Colla in the mice was detected by laser confocal microscopy.Samples were taken from serum and bone at the time of the strongest fluorescence,and gel electrophoresis was carried out on serum and bone tissue protein solutions,and the components of Cornus cervi Colla absorbed into target organs were determined by secondary mass spectrometry. RESULTS AND CONCLUSION:The fluorescent markers were successfully separated by dextran gel chromatography,and the fluorescence imaging and ultraviolet spectrum scanning proved that the labeling was successful,and the fluorescence substitution degree of FITC-labeled Cornus cervi Colla was 0.953%.The fluorescence intensity of the components of Cornus cervi Colla in the blood showed that Cornus cervi Colla was most distributed in serum after oral administration for 2 hours.The fluorescence images of mice at different times were the same as those of bilateral femur and tibia,indicating that Cornus cervi Colla could play a role by entering the bone.Compared with UniProt database,secondary mass spectrometry showed that the peptide was a characteristic fragment of decorin.It is proved that decorin in Cornus cervi Colla can enter the bone to play a therapeutic role.

BACKGROUND:Establishing a nomogram prediction model for postoperative pulmonary infection in hip fractures and taking early intervention measures is crucial for improving patients'quality of life and reducing medical costs. OBJECTIVE:To construct a nomogram risk prediction model of postoperative pulmonary infection in elderly patients with hip fracture,and provide theoretical basis for feasible prevention and early intervention. METHODS:Case data of 305 elderly patients with hip fractures who underwent surgical treatment at Wuxi Traditional Chinese Medicine Hospital Affiliated to Nanjing University of Chinese Medicine between January and October 2020(training set)were retrospectively analyzed.Using univariate and multivariate logistic regression analysis and Hosmer-Lemeshow goodness of fit test,receiver operating characteristic curve was utilized to analyze the diagnostic predictive efficacy of independent risk factors and joint models for postoperative pulmonary infections.Tools glmnet,pROC,and rms in R Studio software were applied to construct a nomogram model for predicting the risk of postoperative pulmonary infection in elderly patients with hip fractures,and calibration curves were further drawn to verify the predictive ability of the nomogram model.Receiver operating characteristic curves,calibration curves,and decision curves were analyzed for 133 elderly patients with hip fractures(validation set)receiving surgery at the same hospital from November 2022 to March 2023 to further predict the predictive ability of the nomogram model. RESULTS AND CONCLUSION:(1)The postoperative pulmonary infection rate in elderly patients with hip fractures in this group was 9.18%(28/305).(2)Single factor and multivariate analysis,as well as forest plots,showed that preoperative hospitalization days,leukocyte count,hypersensitive C-reactive protein,and serum sodium levels were independent risk factors(P<0.05).The Hosmer-Lemeshow goodness of fit test showed good fit(χ2=4.57,P=0.803).Receiver operating characteristic curve analysis was conducted on the independent risk factors and their joint models mentioned above,and the differentiation of each independent risk factor and joint model was good,with statistical significance(P<0.05).(3)The graphical calibration method,C-index,and decision curve were used to validate the nomogram prediction model.The predicted calibration curve was located between the standard curve and the acceptable line,and the predicted risk of the nomogram model was consistent with the actual risk.(4)The validation set used receiver operating characteristic curve,graphic calibration method,and decision curve to validate the prediction model.The results showed good consistency with clinical practice,indicating that the model had a good fit.The nomogram risk prediction model constructed for postoperative pulmonary infection in elderly patients with hip fractures has good predictive performance.The use of the nomogram risk prediction model can screen high-risk populations and provide a theoretical basis for early intervention.