Risk Factors Prediction, Clinical Outcomes, and Mortality of COVID-19 Patients

BackgroundPreventing communicable diseases requires understanding the spread, epidemiology, clinical features, progression, and prognosis of the disease. Early identification of risk factors and clinical outcomes might help to identify critically ill patients, provide proper treatment and prevent mortality. MethodsWe conducted a prospective study in patients with flu-like symptoms referred to the imaging department of a tertiary hospital in IRAN between 3 March 2020 and 8 April 2020. Patients with COVID- 19 were followed up to check their health condition after two months. The categorical data between groups were analyzed by Fishers exact test and continuous data by Wilcoxon Rank-Sum Test. Findings319 patients (mean age 45.48{+/-}18.50 years, 177 women) were enrolled. Fever, dyspnea, weakness, shivering, C-reactive protein (CRP), fatigue, dry cough, anorexia, anosmia, ageusia, dizziness, sweating and age were the most important symptoms of COVID-19 infection. Traveling in past three months, asthma, taking corticosteroids, liver disease, rheumatological disease, cough with sputum, eczema, conjunctivitis, tobacco use, and chest pain did not have any relationship with COVID-19. O_TEXTBOXResearch in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched Google scholar, PUBMED and Scopus for articles that investigated the recent epidemic of COVID-19, especially those that investigate effective risk factors. We found that there is not enough research in this field, especially the risk factor that is effective in finding the rate of mortality of this disease. Added value of this studyWe determined some of the most important effective risk factors on prediction, clinical outcome and mortality rate of COVID-19 infection. To the best of our knowledge, some of these risk factors are investigated in this work for the first time. Our findings could provide good insight into the early prediction of the disease, its clinical outcomes, and suggest a cost-effective method for mortality prediction. Implication of all the available evidenceCOVID-19 can transmit human-to-human and lead to severe symptoms and high mortality. Early prediction of this disease and the risk of mortality can help the physicians to better manage this worldwide health problem. C_TEXTBOX InterpretationFinding clinical symptoms for early diagnosis of COVID-19 is a critical part of prevention. These symptoms can help in the assessment of disease progression. To the best of our knowledge, some of the effective features on the mortality due to COVID-19 are investigated for the first time in this research. FundingNone

Towards precision medicine: from quantitative imaging to radiomics / 浙江大学学报（英文版）（B辑：生物医学和生物技术）

Radiology (imaging) and imaging-guided interventions, which provide multi-parametric morphologic and functional information, are playing an increasingly significant role in precision medicine. Radiologists are trained to understand the imaging phenotypes, transcribe those observations (phenotypes) to correlate with underlying diseases and to characterize the images. However, in order to understand and characterize the molecular phenotype (to obtain genomic information) of solid heterogeneous tumours, the advanced sequencing of those tissues using biopsy is required. Thus, radiologists image the tissues from various views and angles in order to have the complete image phenotypes, thereby acquiring a huge amount of data. Deriving meaningful details from all these radiological data becomes challenging and raises the big data issues. Therefore, interest in the application of radiomics has been growing in recent years as it has the potential to provide significant interpretive and predictive information for decision support. Radiomics is a combination of conventional computer-aided diagnosis, deep learning methods, and human skills, and thus can be used for quantitative characterization of tumour phenotypes. This paper discusses the overview of radiomics workflow, the results of various radiomics-based studies conducted using various radiological images such as computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET), the challenges we are facing, and the potential contribution of radiomics towards precision medicine.

The use of skin surface electropotentials for breast cancer detection--preliminary clinical trial results obtained using the biofield diagnostic system.

The purpose of this study is to evaluate the efficiency of the Biofield Diagnostic System (BDS) as an adjunct to established diagnostic techniques such as mammography and ultrasound in differentiating benign and malignant breast lesions. The clinical trial was conducted at the Tan Tock Seng hospital, Singapore. 103 women scheduled for mammography and/or ultrasound tests participated in the study. The BDS test recorded a sensitivity of 100%, specificity of 97.6%, and an accuracy of 98.1%. The area under the ROC curve was 0.988 which was slightly lower than that of ultrasound (0.994) and slightly higher than that of mammography (0.951). The BDS test has demonstrated high sensitivity and specificity values in the studied population. The accuracy is also comparable to that of diagnostic techniques like mammography and ultrasound. Thus, it is evident that BDS can be a fast and reliable adjunct tool for getting a secondary opinion on lesions with indeterminate mammographic and sonographic results.