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.

OBJECTIVE: The results of limited studies on the relationship between environmental pollution and dementia have been contradictory. We analyzed the combined effects of PM _2.5 and smoking on the prevalence of dementia and cognitive impairment in an elderly community-dwelling Chinese population. METHODS: We assessed 24,117 individuals along with the annual average PM _2.5 concentrations from 2012 to 2016. Dementia was confirmed in the baseline survey at a qualified clinical facility, and newly suspected dementia was assessed in 2017, after excluding cases of suspected dementia in 2015. National census data were used to weight the sample data to reflect the entire population in China, with multiple logistic regression performed to analyze the combined effects of PM _2.5 and smoking frequency on dementia and cognitive impairment. RESULTS: Individuals exposed to the highest PM _2.5 concentration and smoked daily were at higher risk of dementia than those in the lowest PM _2.5 concentration group ( OR, 1.603; 95% CI [1.626-1.635], P < 0.0001) and in the nonsmoking group ( OR, 1.248; 95% CI [1.244-1.252]; P < 0.0001). Moderate PM _2.5 exposure and occasional smoking together increased the short-term risk of cognitive impairment. High-level PM _2.5 exposure and smoking were associated with an increased risk of dementia, so more efforts are needed to reduce this risk through environmental protection and antismoking campaigns. CONCLUSION High-level PM _2.5 exposure and smoking were associated with an increased risk of dementia. Lowering the ambient PM _2.5, and smoking cessation are recommended to promote health.
Humans ; Dementia/etiology* ; Male ; Aged ; Female ; Cognitive Dysfunction/etiology* ; China/epidemiology* ; Particulate Matter/analysis* ; Smoking/epidemiology* ; Air Pollutants/analysis* ; Aged, 80 and over ; Environmental Exposure/adverse effects* ; Prevalence ; Middle Aged

Objective To establish an indirect enzyme-linked immunosorbent assay(ELISA)method for testing the concentration of a monoclonal antibody target tumor necrosis factor-α(TNF-α)in animal serum.Methods The critical parameters of the method including coating concentration of human TNF-α,source,concentration and stability of HRP-labeled goat anti-human immunoglobulin G(IgG)were investigated.The specificity,accuracy,precision,linearity and Limited of Determination of the method were investigated.Results The critical parameters of the method were confirmed as below:TNF-α was coated at 400 ng·mL-1;HRP labeled goat anti-human IgG antibody was diluted at 1:3.0 ×105;the diluted horseradish peroxidase labeled goat anti-human IgG antibody is well stored at 4 ℃ for 3 days.Meanwhile the method was confirmed to have good specificity,the recovery rate ranged from 84.00％to 106.82％,the coefficient of variation of different antibody concentration levels were no more than 10％;the method had a good linearity and the standard curve was y=(-8.37×103-2.37 × 106)/[1+(x/29.80)106]+2.37 × 106(R2=0.999);the limit of quantification was 1 ng·mL-1,all of which met the requirements.Conclusion A accurate and robust ELISA method was developed to test the concentration of anti-TNF-α monoclonal antibody in serum.

Objective To construct machine learning models that can be used to predict AUC fold change(FC)using a database of existing pharmacokinetic(PK)and drug-drug interaction(DDI)information,which can be used to explore the possibility of predicting existing drug interactions and to provide certain rational recommendations for clinical drug use.Methods PK data of DDIs and AUC fold change data were extracted from FDA-approved drug labels.Peptide and pharmacodynamic(PD)information related to drug interactions were retrieved through DrugBank,and PPDT identification of relevant peptide IDs was performed using Protein Resource(UniProt),and a matrix normalization code was used to generate multidimensional vector data that were easy to analysis.The effect of PPDT on the AUC,and the resulting multiplicity change was used as the dependent variable for machine learning model construction.The model with the smallest root mean square error(RMES)value was used for model construction to train a bagged decision tree(Bagged)prediction model.The models were tested using the trained models for some of the drug tests.The models were evaluated by reviewing the available literature findings on detection of drug interaction pairs and analyzing and comparing the predicted values.Results A total of 16 pairs of model drug pairs were tested for the effects of 16 drugs on tacrolimus,and it was found that the accuracy of the prediction of the presence or absence of drug interactions was 81.25％;the prediction results were classified according to the FDA standard classification of the strong and weak for the strength of drug interactions,and the results showed that the prediction of the strength of drug interactions,with a large deviation from the larger prediction was less.Conclusion The evaluation of the model to predict the presence or absence of drug interactions was general;however,after classifying the strengths and weaknesses of drug interactions,the prediction of drug interactions was better,and the prediction results indicated that the model prediction performance has a certain reference value for potential DDI assessment before clinical trials.

This article provides an in-depth analysis of the clinical development process of three non-hormonal drugs,desvenlafaxine extended-release tablets,esmirtazapine tablets and paroxetine capsules,for the treatment of menopausal hot flashes.Both desvenlafaxine extended-release tablets and esmirtazapine tablets were rejected by regulators for poor benefit-risk balance due to inadequate efficacy and safety concerns.In contrast,paroxetine capsules effectively reduced the risk of drug development failure by setting placebo lead-in periods,more conservative sample size estimates,and pre-specifying meta-analyses of key efficacy measures in the two phase Ⅲ trials,not only demonstrating efficacy in the frequency and degree of hot flashes,but also exploring the best-tolerated dose through multiple phase Ⅱ trials,which was finally approved by the U.S.Food and Drug Administration.The development of non-hormonal drugs for the treatment of menopausal hot flashes faces many challenges,including drug safety characteristics that are different from those in patients with psychiatric diseases,weak drug efficacy,weak correlation between preclinical efficacy models and clinical symptom remission,and the need for large sample sizes for dose-finding trials.This case study provides a strategic reference for optimizing trial design and reducing the risk of drug development for subsequent drug development with similar indications.

Highly sensitive multiple detection and accurate identification of gases are of great importance in the fields of public safety,environmental protection,health diagnosis and industrial production.However,the traditional means of gas detection have many shortcomings such as low sensitivity,long time-consuming,bulky equipment,cumbersome processes and expensive costs.In recent years,Raman spectroscopy has become a hotspot in the field of gas detection because of its fast,sensitive and non-destructive characteristics,and has been more and more closely combined with artificial intelligence.This paper reviews the progress of Raman spectroscopy in gas detection in recent years,including conventional Raman spectroscopy and enhanced Raman spectroscopy,and also introduces the integration of artificial intelligence algorithms in gas Raman detection technology,and discusses the future development of gas Raman detection.

The identification of the components absorbed in serum of platycosides in total saponins fraction of Platycodonis Radix is great significance, but there are still great challenges. In this study, 8 types of 44 primordial components from Platycodon saponins were firstly identified using the ultra-performance liquid chromatography-linear ion trap electrostatic field orbitrap high resolution mass spectrometry (UPLC-LTQ-Orbitrap-MS) equipped with the software of Compound Discoverer 3.2 and Trace finder 2.1. Then, the platycosides and their deglycosylated metabolites were used as the template molecules to construct the intestinal microbiota mediated method to identify the primary components and the prototypes in serum. As results, 57 components originating from 44 prototypes of platycosides were identified from drug-containing plasma. Those compounds consist of 12 prototypes of platycosides and 45 metabolites, while the prototypes in serum are also deglycosylated metabolites of other platycosides. The results showed that the intestinal microbiota mediated method could be applied to identify the metabolites of platycosides in total saponins fraction of Platycodonis Radix; and reveal the potential existing forms of prototypes of platycosides in plasma; In addition, it could clearly illustrate the one to many and many to one network of the prototypes and metabolites of platycosides. All animal protocols were approved by the Animal Ethics Committee of Jiangxi University of Traditional Chinese Medicine (No.JZLLSC-202100322).

Ichthyosis is a hereditary dyskeratotic skin disease with systemic skin dryness and roughness,mainly manifested by scaly skin,which may be accompanied by ocular abnormalities.At present,there are many studies on skin fungal infection caused by ichthyosis,but only few reports on cases with combined ocular fungal infection.This paper reports a case of X-linked recessive hereditary ichthyosis with recurrent fungal keratitis(FK),which is expec-ted to provide reference for clinical early diagnosis and treatment of this disease.

Objective We aimed to compare the efficacy and prognosis of percutaneous coronary intervention(PCI)in complex and high-risk patients with coronary heart disease(CHD)treated with extracorporeal membrane oxygenation(ECMO)combined with intra-aortic balloon pump(IABP)assistance,and explore the application value of combined use of mechanical circulatory support(MCS)devices in complex PCI.Methods A total of patients who met the inclusion criteria and underwent selective PCI supported by MCS at the Department of Cardiology,the First Affiliated Hospital of the Air Force Medical University from January 2018 to December 2022 were continuously enrolled.According to the mechanical circulatory support method,the patients were divided into ECMO+IABP group and IABP group.Clinical characteristics,angiographic features,in-hospital outcomes,and complications were collected.The intra-hospital outcomes and major adverse cardiovascular events(MACE)at one month and one year after the procedure were observed.The differences and independent risk factors between the two groups in the above indicators were analyzed.Results A total of 218 patients undergoing elective PCI were included,of which 66 patients were in the ECMO+IABP group and 152 patients were in the IABP group.The baseline characteristics of the two groups of patients were generally comparable,but the ECMO+IABP group had more complex lesion characteristics.The proportion of patients with atrial fibrillation(6.1％vs.0.7％,P=0.030),left main disease(43.9％vs.27.0％,P=0.018),triple vessel disease(90.9％vs.75.5％,P=0.009),and RCA chronic total occlusion disease(60.6％vs.35.5％,P＜0.001)was higher in the ECMO+IABP group compared to the IABP group.The proportion of patients with previous PCI history was higher in the IABP group(32.9％vs.16.7％,P=0.014).There was no statistically significant difference in the incidence of in-hospital complications between the two groups(P=0.176),but the incidence of hypotension after PCI was higher in the ECMO+IABP group(19.7％vs.9.2％,P=0.031).The rates of 1-month MACE(4.5％vs.2.6％,P=0.435)and 1-year MACE(7.6％vs.7.9％,P=0.936)were comparable between the two groups.Multivariate analysis showed that in-hospital cardiac arrest(OR 7.17,95％CI 1.27-40.38,P=0.025)and after procedure hypotension(OR 3.60,95％CI 1.10-11.83,P=0.035)were independent risk factors for the occurrence of 1-year MACE.Conclusions Combination use of ECMO+IABP support can provide complex and high-risk coronary heart disease patients with an opportunity to achieve coronary artery revascularization through PCI,and achieve satisfactory long-term prognosis.