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

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.

Hallux valgus is a common disorder of the forefeet,and its diagnosis and treatment have always drawn the attention of the practitioners.This article introduced the minimally-invasive diagnosis and treatment system for hallux valgus of integrated traditional Chinese and western medicine,which was established by Professor WEN Jian-Min after more than 40 years of in-depth clinical practice and research based on the theory of yin-yang balance and theory of tendons and bones in traditional Chinese medicine(TCM)and through the combination of modern surgical experience.The minimally-invasive diagnosis and treatment system for hallux valgus embodies the principles of balancing yin and yang,laying equal stress on tendons and bones,unifying the fixation and functional exercises,and treating the fractures and the whole body simultaneously,and includes the key technologies such as minimally-invasive osteotomy for the first metatarsal bone,curtain-wrapped external fixation,perioperative Chinese medicine therapy based on syndrome differentiation,and rehabilitation and nursing of TCM.The system will provide a systematic guide for the standardized minimally-invasive treatment of hallux valgus,and will supply an important approach to the treatment of other orthopedic diseases with integrated traditional Chinese and western medicine.The minimally-invasive diagnosis and treatment system for hallux valgus reflects the scientific research achievements and clinical experience of Professor WEN Jian-Min,which exerts high significance of reference and application value.

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.

Objective To analyze the correlation between the sagittal orientation of lumbar pedicle screws and screw loosening based on the craniocaudal cyclic load and artificial vertebra,and to provide a reference for the improvement of screw orientation during surgery,consequently reducing the incidence of screw loosening postoperatively.Methods Pedicle screws were inserted into artificial vertebrae in the cranial,parallel,and caudal orientations.After the artificial vertebra was installed in the material testing machine,the model was subjected to a 100 N preload and 166 N cyclic load for 10 000 cycles.During loading,displacements were recorded and cycle-displacement curves were drawn.After the test was completed and the screws were removed,the screw hole volume was measured and the screw hole volume ratio(the ratio of screw hole volume to the volume of the screw inserted into the vertebra)was calculated.Results The cyclic displacement curve showed that the cranial group had the largest displacement,followed by the parallel and caudal groups.The screw-hole volume ratio results showed that the cranial group had the highest ratio,followed by the parallel and caudal groups.Correlation analysis showed a negative relationship between screw sagittal orientation and terminal displacement(displacement corresponding to the 10 000th cycle)(ρ=-0.897,P=0.02)and screw hole volume ratio(ρ=-0.902,P=0.01).Conclusions There is a negative correlation between the sagittal orientation of the lumbar pedicle screw and screw loosening.Namely,when the screw sagittal orientation is adjusted from a cranial tilt to a parallel and even caudal tilt at a certain angle range,the incidence of screw loosening is reduced.

Objective To analyze the disposition of medical support equipment support units for medical service support of the synthetic battalion to carry out efficiently demand matching and replenishment for war loss,so as to enhance the efficiency of deployment and decision making of medical support equipment.Methods War loss of the medical support equipment was analyzed with queuing theory with the support service of the synthetic battalion as the basic research framework and desired equipment intact rate in the battlefield as the goal.A M/M/1/∞/∞ model and a M/M/c/∞/∞ model were established to investigate the disposition process of medical support equipment support units by simulating actual conditions of the battlefield.Multi synthetic battalions were assumed to be deployed to carry out 2 missions with different tasks and combat intensities,of which 40 synthetic battalions were involved in for mission 1 and 35 synthetic battalions for mission 2.The disposition of medical support equipment support units was calculated with the two models constructed.Results With the M/M/c/∞/∞ model it's suggested 5 medical support equipment support units be deployed to serve for 40 synthetic battalions;with the M/M/1/∞/∞ model only one unit was employed to support 35 battalions.The two models both met the requirements of 90％intact rate.Conclusion The research contributes to the decision making of medical support equipment support forces during synthetic battalion medical support and enhances the precision support of medical support equipment.[Chinese Medical Equipment Journal,2024,45(4):83-87]