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.

This paper aimed to systematically evaluate the effects of hypoxic training at different fraction of inspired oxygen (FiO₂) on body composition, glucose metabolism, and lipid metabolism in obese individuals, and to determine the optimal oxygen concentration range to provide scientific evidence for personalized and precise hypoxic exercise prescriptions. A systematic search was conducted in the Cochrane Library, PubMed, Web of Science, Embase, and CNKI databases for randomized controlled trials and pre-post intervention studies published up to March 31, 2025, involving hypoxic training interventions in obese populations. Meta-analysis was performed using RevMan 5.4 software to assess the effects of different fraction of inspired oxygen (FiO₂≤14% vs. FiO₂>14%) on BMI, body fat percentage, waist circumference, fasting blood glucose, insulin, HOMA-IR, triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C), with subgroup analyses based on oxygen concentration. A total of 22 studies involving 292 participants were included. Meta-analysis showed that hypoxic training significantly reduced BMI (mean difference (MD)=-2.29,95%CI: -3.42 to -1.17, P<0.000 1), body fat percentage (MD=-2.32, 95%CI: -3.16 to -1.47, P<0.001), waist circumference (MD=-3.79, 95%CI: -6.73 to -0.85, P=0.01), fasting blood glucose (MD=-3.58, 95%CI: -6.23 to -0.93, P=0.008), insulin (MD=-1.60, 95%CI: -2.98 to -0.22, P=0.02), TG (MD=-0.18, 95%CI: -0.25 to -0.12, P<0.001), and LDL-C (MD=-0.25, 95%CI: -0.39 to -0.11, P=0.000 3). Greater improvements were observed under moderate hypoxic conditions with FiO₂>14%. Changes in HOMA-IR (MD=-0.74, 95%CI: -1.52 to 0.04,P=0.06) and HDL-C (MD=-0.09, 95%CI: -0.21 to 0.02, P=0.11) were not statistically significant. Hypoxic training can significantly improve body composition, glucose metabolism, and lipid metabolism indicators in obese individuals, with greater benefits observed under moderate hypoxia (FiO>14%). As a key parameter in hypoxic exercise interventions, the precise setting of oxygen concentration is crucial for optimizing intervention outcomes.

Post-orgasmic illness syndrome (POIS) is a rare condition characterized by the rapid onset of extreme fatigue, flu-like symptoms, difficulty concentrating, depression, nasal congestion, rhinorrhea, itchy eyes, and other physical and psychological discomforts following ejaculation. This report presents the outcomes of two patients with POIS who underwent a two-year course of autologous seminal plasma subcutaneous cluster immunotherapy. Treatment efficacy was assessed using methods such as the symptom Visual Analogue Scale (VAS), the Union Physio-Psycho-Social Assessment Questionnaire (UPPSAQ)-70, and the Short Form 36 Health Survey (SF-36). The results suggest that autologous seminal plasma subcutaneous cluster immunother-apy may be a safe and effective therapeutic approach for POIS.

Objective This study aimed to investigate the potential relationship between urinary metals copper (Cu), arsenic (As), strontium (Sr), barium (Ba), iron (Fe), lead (Pb) and manganese (Mn) and grip strength. Methods We used linear regression models, quantile g-computation and Bayesian kernel machine regression (BKMR) to assess the relationship between metals and grip strength.Results In the multimetal linear regression, Cu (β=-2.119), As (β=-1.318), Sr (β=-2.480), Ba (β=0.781), Fe (β= 1.130) and Mn (β=-0.404) were significantly correlated with grip strength (P < 0.05). The results of the quantile g-computation showed that the risk of occurrence of grip strength reduction was -1.007 (95％ confidence interval:-1.362, -0.652; P < 0.001) when each quartile of the mixture of the seven metals was increased. Bayesian kernel function regression model analysis showed that mixtures of the seven metals had a negative overall effect on grip strength, with Cu, As and Sr being negatively associated with grip strength levels. In the total population, potential interactions were observed between As and Mn and between Cu and Mn (Pinteractions of 0.003 and 0.018, respectively).Conclusion In summary, this study suggests that combined exposure to metal mixtures is negatively associated with grip strength. Cu, Sr and As were negatively correlated with grip strength levels, and there were potential interactions between As and Mn and between Cu and Mn.

Objective To compare the efficacy of renal proximal renal artery denervation(pRDN)and full-length renal artery denervation(fRDN)for treatment of hypertension.Methods Fifty-six hypertensive patients were enrolled and randomly assigned to full-length renal artery denervation group(n=25)and proximal renal artery denervation group(n=31).After the procedure,24-hour ambulatory blood pressure monitoring(24 h-ABPM)at 6 months and office blood pressure at 12 months was recorded for statistical analysis.Results The blood pressure at follow-up reduced significantly in both groups,while there was no significant difference between groups.The baseline office blood pressure in fRDN group and pRDN group was(180±15)/(104±10)mmHg and(180±12)/(103±8)mmHg,respectively,which decreased to(142±9)/(82±7)mmHg and(143±10)/(83±6)mmHg at 12 months postoperatively(P＜0.001 within groups and P＞0.05 between groups).The baseline 24 h-ABPM in the two groups was(162±13)/(95±8)mmHg and(160±12)/(94±8)mmHg,respectively,which decreased to(142±11)/(83±7)mmHg and(141±8)/(81±7)mmHg at 6 months postoperatively(P＜0.001 within groups and P＞0.05 between groups).However,there was no significant difference in the reduction of office blood pressure and ambulatory blood pressure between the two groups.No treatment-related adverse events were observed.Conclusions pRDN has similar antihypertensive effect to fRDN.

AIM To study the chemical constituents from the leaves of Citrus reticulata Blanco and their anti-inflammatory activities.METHODS The 85%ethanol extract from the leaves of C.reticulata was isolated and purified by silica gel,D101 macroporous resin,MCI,ODS and Sephadex LH-20,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The Griess method was used to determine their inhibitory activities on lipopolysaccharide-induced NO production in macrophages RAW 264.7 cells.The mice foot swelling inflammation model induced by carrageenan was established,and the levels of IL-1β,TNF-α were detected.RESULTS Twelve compounds were isolated and identified as nobiletin(1),tangeretin(2),5-demethylinoblitin(3),5,4'-dihydroxy-6,7,8,3'-tetramethoxy flavone(4),5-hydroxy-7,8,3',4'-trimethoxyflavanone(5),3,5,6,7,8,3',4'-heptamethoxyflavanone(6),hesperetin(7),5-hydroxy-6,7,3',4'-tetramethoxyphenone(8),β-balsam alcohol(9),stigmaster-5-en-3β-alcohol(10),p-hydroxybenzaldehyde(11),vanillin(12).Compounds 1,4,6,7,10 and 12 had strong inhibitory activites on NO release in LPS-induced RAW 264.7 cells,and the IC50 values were(25.21±2.10),(37.77±0.50),(38.19±1.58),(21.89±1.73),(43.81±1.18),(47.98±2.55),(41.23±1.11),(43.80±1.43)μmol/mL,respectively.Compounds 2-3 reduced IL-1β and TNF-α levels(P＜0.05,P＜0.01).CONCLUSION Compounds 6-7,9 are isolated from this plant for the first time.Compounds 1-4,8 exhibit strong in vitro anti-inflammatory activities,and compounds 2-3 exhibit significant in vivo anti-inflammatory activities.

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.

Patient 1,a 12-day-old female infant,presented with fever,cough,dyspnea,and elevated infection markers,requiring respiratory support.Metagenomic next-generation sequencing(mNGS)of blood and bronchoalveolar lavage fluid revealed Legionella pneumophila(LP),leading to diagnoses of LP pneumonia and LP sepsis.The patient was treated with erythromycin for 15 days and azithromycin for 5 days,resulting in recovery and discharge.Patient 2,an 11-day-old female infant,presented with dyspnea,fever,elevated infection markers,and multiple organ dysfunction,requiring mechanical ventilation.mNGS of blood and cerebrospinal fluid indicated LP,leading to diagnoses of LP pneumonia,LP sepsis,and LP intracranial infection.The patient was treated with erythromycin for 19 days and was discharged after recovery.Neonatal LP pneumonia lacks specific clinical symptoms,and azithromycin is the preferred antimicrobial agent.The use of mNGS can provide early and definitive diagnosis for severe neonatal pneumonia of unknown origin.

Objective To explore the efficiency of artificial intelligence and radiomics-assisted X-ray in diagnosis of lumbar osteoporotic vertebral compression fractures(OVCF).Methods The clinical data of 455 patients diagnosed as lumbar OVCF by MRI in our hospital were selected.The patients were divided into the training group(n=364)and the validation group(n=91),X-ray films were extracted,the image delineation,feature extraction and data analysis were carried out,and the artificial intelligence radiomics deep learning was applied to establish a diagnostic model for OVCF.After verifying the effectiveness of the model by receiver operating characteristic(ROC)curve,area under the curve(AUC),calibration curve,and decision curve analysis(DCA),the efficiencies of manual reading,model reading,and model-assisted manual reading of X-ray in the early diagnosis of OVCF were compared.Results The ROC curve,AUC and calibration curve proved that the model had good discrimination and calibration,and excellent diagnostic performance.DCA demonstrated that the model had a higher clinical net benefit.The diagnostic efficiency of the manual reading group:the accuracy rate was 0.89,the recall rate was 0.62.The diagnostic efficiency of the model reading group:the accuracy rate was 0.93,the recall rate was 0.86,the model diagnosis showed good predictive performance,which was significantly better than the manual reading group.The diagnostic efficiency of the model-assisted manual reading group:the accuracy rate was 0.92,the recall rate was 0.72,and the recall rate of the model-assisted manual reading group was higher than that of the manual reading group,but lower than that of the model reading group,indicating the superiority of the model diagnosis.Conclusion The diagnostic model established based on artificial intelligence and radiomics in this study has reached an ideal level of efficacy,with better diagnostic efficacy compared with manual reading,and can be used to assist X-ray in the early diagnosis of OVCF.

Objective To develop a clinical prediction model for predicting risk factors for prolonged hospital stay after anterior cervical discectomy and fusion(ACDF).Methods The clinical data of 914 patients underwent ACDF treatment for cervical spondylotic myelopathy(CSM)were retrospectively analyzed.According to the screening criteria,800 eligible patients were eventually included,and the patients were divided into the development cohort(n=560)and the validation cohort(n=240).LASSO regression was used to screen variables,and multivariate Logistic regression analysis was used to establish a prediction model.The prediction model was evaluated from three aspects:differentiation,calibration and clinical effectiveness.The performance of the model was evaluated by area under the curve(AUC)and Hosmer-Lemeshow test.Decision curve analysis(DCA)was used to evaluate the clinical effectiveness of the model.Results In this study,the five factors that were significantly associated with prolonged hospital stay were male,abnormal BMI,mild-to-moderate anemia,stage of surgery(morning,afternoon,evening),and alcohol consumption history.The AUC of the development cohort was 0.778(95%CI:0.740 to 0.816),with a cutoff value of 0.337,and that of the validation cohort was 0.748(95%CI:0.687 to 0.809),with a cutoff value of 0.169,indicating that the prediction model had good differentiation.At the same time,the Hosmer-Lemeshow test showed that the model had a good calibration degree,and the DCA proved that it was effective in clinical application.Conclusion The prediction model established in this study has excellent comprehensive performance,which can better predict the risk of prolonged hospital stay,and can guide clinical intervention as soon as possible,so as to minimize the postoperative hospital stay and reduce the cost of hospitalization.