Metabolomics in Childhood Asthma
As the most common chronic disease in children, bronchial asthma is highly underdiagnosed with complex pathogenesis. By qualitative and quantitative analyses of changes in low molecular weight molecules or metabolites in biological samples, metabolomics provides a new method to search biomarkers and pathogenesis. We reviewed the application of metabolomics in childhood asthma, which attempts to find the potential biomarkers and pathogenesis of childhood asthma by analyzing the samples of blood, exhaled breath, feces and urine of asthmatic children and healthy children using targeted or untargeted research approaches, providing help for clinical diagnosis and treatment of childhood asthma. Considerable progress has been made in metabolomics in childhood asthma, but due to factors such as individual differences, sample collection, data analysis, and genomic heterogeneity, metabolomics analysis of childhood asthma is still facing challenges.
Novel Developments in the Relationship of Gut Microbiota and Immune Regulation with Childhood Asthma
Bronchial asthma, commonly known as asthma, is a frequently seen chronic respiratory disease that seriously threatens human health. More than 300 million people have had asthma worldwide, and most of them are children. Epidemiological investigations have shown that the prevalence of asthma among Chinese children aged 0-14 is 2.32%, and it is increasing year by year. Children are more prone to gut flora imbalance due to underdeveloped immune system, and physiologically successional changing of gut flora, which leads to the destruction of the intestinal mucosal barrier and local immune imbalance, eventually causing the development of asthma. We reviewed the latest advances in the immune regulatory mechanism of childhood asthma, and its association with gut microbiota, as well as interventions targeting gut microbiota, providing new ideas for the treatment of childhood asthma.
Recent Advances in the Pathogenesis of Glucolipid Metabolism Disorder in Obstructive Sleep Apnea-hypopnea Syndrome
Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a disease marked by apnea, hypopnea, decreased oxygen saturation, and disordered sleep structure, which is a major risk for cardiovascular disease. Recent studies have found that OSAHS patients have an increased risk of hypertension, coronary atherosclerotic heart disease, insulin resistance, type 2 diabetes, metabolic syndrome, non-alcoholic fatty liver disease, etc. And these patients have a high prevalence of obvious glucolipid metabolism disorder (GMD) , which plays an important role in cardiovascular morbidity and mortality in OSAHS. We reviewed the latest advances in the association of GMD and OSAHS, and the potential pathogenesis of OSAHS-induced GMD and insulin resistance, aiming at providing new ideas for clinical treatment of GMD in OSAHS.
Role of Light Therapy in Circadian Rhythm Sleep-wake Disorders
Circadian rhythm sleep-wake disorder (CRSWD) affects people's health and well-being.Current treatments mainly include exogenous melatonin therapy and light therapy, among which light therapy plays an important role in the treatment of CRSWDas a non-drug treatment.We conducted a review on recent studies about CRSWD, covering the pathogenesis of CRSWD, principle and efficacy of light therapy in CRSWD, aiming to offer new ideas for clinical treatment of CRSWD.
Research Progress of Machine Learning in Clinical Drug Therapy
With the advancement and development of concepts such as real-world research and precision treatment, the demand of researchers for medical big data processing keeps increasing. Because machine learning technology has unique advantages in processing massive, high-dimensional data and conducting predictive research, it has been deeply applied in the medical field in recent years. In addition to the application in disease diagnosis, image recognition and risk prediction, more and more studies have proved that machine learning can be applied to the decision support related research of clinical drug treatment. This article reviews the research progress of machine learning in clinical drug therapy.
