Analysis of death risk factors among COVID-19 patients in Yazd, Iran: A case-cohort study.

Background: The COVID-19 epidemic control has become a global challenge and many contributing variables are still unknown to policymakers. This case-cohort study was conducted to investigate the risk factors of mortality in COVID-19 patients. Methods: This case-cohort study was conducted on 956 samples in Ardakan and Meybod counties, Yazd Province, between February 20 and May 20, 2020. The data collection tool was a researcher-made questionnaire. Data analysis was done using descriptive statistics and paired t-test, chi-square, and logistic regression analysis. Results: Of a total cohort population of 993 in Ardakan and Meybod counties, 435 were assigned to the control group and 521 were assigned to the case group. The results of outcome analysis showed that 14.4% of the patients in the case group and 11.5% of the patients in the control group died. According to the results of logistic regression analysis in COVID-19 patients, each one-year increase in age increased the risk of mortality by 6% (HR = 1.06, p < 0.001), each one-day increase in the hospital stay increased the risk of death by 8% (HR = 1.08, p < 0.001). Moreover, the presence of cardiovascular disease, chronic neurological disease, and chronic pulmonary disease increased the risk of death. The patients who underwent mechanical ventilation had 85% less chance of survival (HR = 0.15, p < 0.001). Conclusions: The results showed a higher mortality rate in the elderly patients as well as those with underlying diseases. Attention should be paid to at-risk and elderly patients in terms of ensuring a healthy diet, improving their self-care practices, and providing long-term medical and healthcare facilities.

Depression, anxiety, and quality of life in patients with systemic lupus erythematosus.

Introduction: Systemic lupus erythematosus (SLE) is a multisystem disorder that can affect multiple organs; psychiatric manifestations including depression and anxiety are commonly seen in SLE. The aim of this study is to explore the prevalence of depression, anxiety, and stress, and assess the quality of life (QOL) in patients with SLE and also evaluate associated risk factors. Material and methods: In this cross-sectional study, adult patients with SLE were identified through our institution's SLE data registry. Participants were evaluated with three questionnaires: Depression, Anxiety, and Stress Scale (DASS-42), General Health Ouestionnaire-28 (GHQ-28), and World Health Organization quality of life instrument short form (WHO-QOL BREF). Results: A total of 222 patients were included in the study, 203 (91%) of whom were female and 19 were male (9%). Participants had a mean age of 35.6 ±9.5 years. According to DASS-42 questionnaire, 22.1%, 28.7% and 20.3% of patients had varying degrees of depression, anxiety, and stress, respectively. Based on GHQ-28 questionnaire, 137 (62%) of patients reported some degree of distress. Quality of life score was 12.8, 13, 14.3, and 13.9 in physical health, psychological health, social relationships, and environmental health, respectively. Conclusions: We found that depression, anxiety, and stress are common in patients with SLE, and quality of life is significantly affected. A high percentage of patients with SLE deal with some degree of distress. Routine evaluation of the quality of life and psychological disturbances is recommended in patients with SLE. Non-pharmacological interventions as well as specialist referral should be considered in patients with anxiety, depression, or stress.

Neuroimaging and machine learning for studying the pathways from mild cognitive impairment to alzheimer's disease: a systematic review.

BACKGROUND: This systematic review synthesizes the most recent neuroimaging procedures and machine learning approaches for the prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia. METHODS: We systematically searched PubMed, SCOPUS, and Web of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review guidelines. RESULTS: Our search returned 2572 articles, 56 of which met the criteria for inclusion in the final selection. The multimodality framework and deep learning techniques showed potential for predicting the conversion of MCI to AD dementia. CONCLUSION: Findings of this systematic review identified that the possibility of using neuroimaging data processed by advanced learning algorithms is promising for the prediction of AD progression. We also provided a detailed description of the challenges that researchers are faced along with future research directions. The protocol has been registered in the International Prospective Register of Systematic Reviews- CRD42019133402 and published in the Systematic Reviews journal.

Aristotle: stratified causal discovery for omics data.

BACKGROUND: There has been a simultaneous increase in demand and accessibility across genomics, transcriptomics, proteomics and metabolomics data, known as omics data. This has encouraged widespread application of omics data in life sciences, from personalized medicine to the discovery of underlying pathophysiology of diseases. Causal analysis of omics data may provide important insight into the underlying biological mechanisms. Existing causal analysis methods yield promising results when identifying potential general causes of an observed outcome based on omics data. However, they may fail to discover the causes specific to a particular stratum of individuals and missing from others. METHODS: To fill this gap, we introduce the problem of stratified causal discovery and propose a method, Aristotle, for solving it. Aristotle addresses the two challenges intrinsic to omics data: high dimensionality and hidden stratification. It employs existing biological knowledge and a state-of-the-art patient stratification method to tackle the above challenges and applies a quasi-experimental design method to each stratum to find stratum-specific potential causes. RESULTS: Evaluation based on synthetic data shows better performance for Aristotle in discovering true causes under different conditions compared to existing causal discovery methods. Experiments on a real dataset on Anthracycline Cardiotoxicity indicate that Aristotle's predictions are consistent with the existing literature. Moreover, Aristotle makes additional predictions that suggest further investigations.

Moderate-Intensity Physical Activity, Music and Art Activities Preserved Cognitive Health in Older Adults: An Argument for Social Prescribing Solution.

Introduction: Rates of dementia are projected to increase over the coming years as global populations age. Without a treatment to slow the progression of dementia, many health policies are focusing on preventing dementia by slowing the rate of cognitive decline with age. However, it is unclear which lifestyle changes in old age meaningfully reduce the rate of cognitive decline associated with aging. Objectives: Use existing, multi-year longitudinal health data to determine if engagement in a variety of different lifestyle activities can slow the rate of cognitive decline as older adults age. Method: Data from the English Longitudinal Study of Aging was analyzed using a quasi-experimental, efficient matched-pair design inspired by the clinical trial methodology. Changes in short-term memory scores were assessed over a multi-year interval for groups who undertook one of 11 different lifestyle activities, compared to control groups matched across confounding socioeconomic and lifestyle factors. Results: Two factors, moderate-intensity physical activity and learning activities, resulted in significant positive impact on cognitive function. Conclusion: Our analysis brings cognitive benefit arguments in favor of two lifestyle activities, moderate-intensity physical activity and learning activities, while rejecting other factors advanced by the literature such as vigorous-intensity physical activity. Those findings justify and encourage the development of new lifestyle health programs by health authorities and bring forward the new health system solution, social prescribing.

HUME: large-scale detection of causal genetic factors of adverse drug reactions.

Motivation: Adverse drug reactions are one of the major factors that affect the wellbeing of patients and financial costs of healthcare systems. Genetic variations of patients have been shown to be a key factor in the occurrence and severity of many ADRs. However, the large number of confounding drugs and genetic biomarkers for each adverse reaction case demands a method that evaluates all potential genetic causes of ADRs simultaneously. Results: To address this challenge, we propose HUME, a multi-phase algorithm that recommends genetic factors for ADRs that are causally supported by the patient record data. HUME consists of the construction of a network from co-prevalence between significant genetic biomarkers and ADRs, a link score phase for predicting candidate relations based on the Adamic-Adar measure, and a causal refinement phase based on multiple hypothesis testing of quasi experimental designs for evaluating evidence and counter evidence of candidate relations in the patient records. Supplementary information: Supplementary data are available at Bioinformatics online.

Acute necrotizing pancreatitis following coronary artery angiography: A case report.

BACKGROUND: Acute pancreatitis has different etiologies from biliary stone to metabolic disturbances. Coronary angiography is one of the newly understood etiologies. CASE REPORT: This paper is about a women suffering from acute pancreatitis after coronary angiography. CONCLUSION: Embolization of cholesterol crystals due to vessel wall trauma during coronary angiography as well as contrast medium are responsible for such side effect.