Clinical eHealth最新文献

This research work presents a study on the relationship between stress & related events with meditation practice and other socio-demographic variables during COVID 19 pandemic among healthy adults. In this cross-sectional survey design, healthy adults with and without practice of Raja yoga meditation completed stress, anxiety & depression related questions (Depression Anxiety & stress Scale, DASS 21) and its impact (Impact of Event Scale-Revised, IES-R) along with other socio-demographic including COVID infection or contact related information. Data was assessed for difference in DASS 21 scores and IES-R scores between Raja yoga meditators (n = 802) & non-meditators (n = 357). An analysis was performed to study the predictors of DASS 21 and IES-R scores. We conclude that healthy Raja yoga meditation practitioners differ from non-meditators in terms of stress/anxiety/depression and its impact during COVID 19 pandemic and meditation practice predicts mental health better along with other sociodemographic variables.

Chronic respiratory diseases are staggering health burdens affecting the lives of hundreds of millions of people around the world, contributing substantially to mortality and morbidity globally, including in the United States, China and Europe. Chronic obstructive pulmonary disease (COPD), lung cancer, pneumonia, interstitial lung disease (ILD) and asthma are among the leading diseases that urges the development of effective prevention, diagnosis, treatment and management of these diseases. Medical Internet-of-Things (MIOT) is fast becoming one of the most promising approaches to achieve this largely due to its cost effectiveness, non-invasive deployments and automation capabilities. Coupled together with strategically leveraging artificial intelligence, continuous data collection and real time monitoring and response systems, MIOT shows potential to be an effective and efficient solution.

Lung cancer is the leading cause of cancer deaths. Although targeted therapies and programmed cell death protein 1 (PD-1) blockade have offered great advances, five-year survival rates remained low. Diagnosis of lung cancer at an earlier stage is the most efficient approach to improve survival. Tumor associated autoantibodies (TAAb) have been proven a promising innovative biomarker of lung cancer. We aimed to comprehensively evaluate the sensitivity, specificity, and accuracy of TAAbs in lung cancer detection, especially in early-stage patients, through a multicenter prospective observational clinical trial, and to screen out the novel combination of TAAbs with the best detection value for Chinese population. We aimed to enroll 1,400 participants from three clinical centers and divide into two cohorts. One cohort is participants with newly pathologically confirmed lung cancer as the case cohort, and the other is participants with benign nodules, matched healthy controls and other benign lung diseases as the control cohort. Cases and controls were randomly distributed into training or validation set. The level of 14 autoantibody candidates in their plasma were detected. A Monte-Carlo Simulated Annealing method was implemented to develop a composite panel of autoantibodies to distinguish between matched lung cancer cases and controls in the training set. The newly developed autoantibody panel was tested in the validation set for sensitivity, specificity and accuracy. This is the first and largest clinical trial designed to develop a novel autoantibody panel for lung cancer detection specifically for Chinese people. We performed a multicenter, prospective, observational study to find a novel panel of autoantibody markers that can help the diagnosis of lung cancer and the characterization of pulmonary nodules in Chinese population. The results may help to provide evidence-based recommendations to clinicians for lung cancer early detection and pulmonary nodule management. This study is registered in the ClinicalTrials.gov (NCT04216511).