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BackgroundThe risk of severe coronavirus disease 2019 (COVID-19) varies significantly among persons of similar age and is higher in males. Age-independent, sex-biased differences in susceptibility to severe COVID-19 may be ascribable to deficits in a sexually dimorphic protective attribute that we termed immunologic resilience (IR).ObjectiveWe sought to examine whether deficits in IR that antedate or are induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection independently predict COVID-19 mortality.MethodsIR levels were quantified with 2 novel metrics: immune health grades (IHG-I [best] to IHG-IV) to gauge CD8+ and CD4+ T-cell count equilibrium, and blood gene expression signatures. IR metrics were examined in a prospective COVID-19 cohort (n = 522); primary outcome was 30-day mortality. Associations of IR metrics with outcomes in non-COVID-19 cohorts (n = 13,461) provided the framework for linking pre-COVID-19 IR status to IR during COVID-19, as well as to COVID-19 outcomes.ResultsIHG-I, tracking high-grade equilibrium between CD8+ and CD4+ T-cell counts, was the most common grade (73%) among healthy adults, particularly in females. SARS-CoV-2 infection was associated with underrepresentation of IHG-I (21%) versus overrepresentation (77%) of IHG-II or IHG-IV, especially in males versus females (P < .01). Presentation with IHG-I was associated with 88% lower mortality, after controlling for age and sex; reduced risk of hospitalization and respiratory failure; lower plasma IL-6 levels; rapid clearance of nasopharyngeal SARS-CoV-2 burden; and gene expression signatures correlating with survival that signify immunocompetence and controlled inflammation. In non-COVID-19 cohorts, IR-preserving metrics were associated with resistance to progressive influenza or HIV infection, as well as lower 9-year mortality in the Framingham Heart Study, especially in females.ConclusionsPreservation of immunocompetence with controlled inflammation during antigenic challenges is a hallmark of IR and associates with longevity and AIDS resistance. Independent of age, a male-biased proclivity to degrade IR before and/or during SARS-CoV-2 infection predisposes to severe COVID-19.

Recently, the academic and non-academic staff turnover rate at a local university in Selangor, Malaysia, has increased, and the cause for resignation is unclear. This study investigates the employee perception of human resource management practices and their turnover intention. A thorough literature review suggests that performance appraisal, working conditions, equal employment opportunity, training & development, and rewards are the turnover determinants in the Malaysian context. This study considered the equity theory of motivation model, known as Adam's equity theory of motivation. The primary data was collected through a survey questionnaire involving 140 academic and non-academic staff. The 5-point Likert scale was used in constructing the items of the survey questionnaire. The majority of the respondents are females between the age of 26 and 35. They are Malays married, have an educational qualification of bachelor's degree, earn between the range of RM 3,500 and RM4,000, and work as lecturers and executives. The Cronbach's Alpha for turnover intention, performance appraisal, working conditions, training and development, and rewards is 0.600, 0.677, 0.774, 0.805, 0.680, and 0.772. Pearson correlation shows that turnover intention is significantly negatively correlated with performance appraisal(r=-0.632), working condition(r=-0,404), equal employment opportunity-0.454) training & development (-0.189), and rewards(-0.102). The multiple regression model’s R-square value is 35.8%. The hypotheses test results reveal a significant relationship between all the independent and dependent variables. This study supported the motivation theory model where better performance appraisal, working conditions, training and development opportunities, better reward system are important to reduce employees' intention to turnover.

We aimed to assess the effect of Vitamin E on hepatic pathology in terms of liver enzymes and ultrasonographic (USG) findings in Non-alcoholic Fatty liver disease (NAFLD) patients. Vitamin E was administered as α-tocopherol for 12 weeks for 60 patients. Baseline and after 12 weeks of Vitamin E therapy values of anthropometric measures, fasting blood glucose, lipid profile were estimated apart from liver enzymes and USG based assessment of liver size and Hamaguchi score. NAFLD fibrosis score (NFS), a predictor of hepatic fibrosis was evaluated pre and post treatment with Vitamin E. Significant statistical difference was noted in the reduction of Triglycerides, Cholesterol, VLDL and LDL both diabetic and non-diabetic population in our study. ALT and AST normalization was observed and the mean reduction were -38.11 (p

Conventional testing of novel contrast agents for magnetic resonance imaging (MRI) involves cell and animal studies. However, 2D cultures lack dynamic flow and in vivo MRI is limited by regulatory approval of long‐term anesthesia use. Microfluidic tumor models (MTMs) offer a cost‐effective, reproducible, and high throughput platform for bridging cell and animal models. Yet, MRI of microfluidic devices is challenging, due to small fluid volumes generating low sensitivity. For the first time, an MRI of MTMs was performed at low field strength (1 T) using conventional imaging equipment without microcoils. To enable longitudinal MRI, we developed (1) CHAMP‐3 (controlled heating apparatus for microfluidics and portability) which heats MTMs during MRI scans and (2) an MRI‐compatible temperature monitoring system. CHAMP‐3 maintained chip surface temperature at ~37°C and the media inside at ~35.5°C. Enhanced T1‐weighted MRI contrast was achieved in 3D MTMs with free manganese (Mn2+) solutions and Mn2+ labeled tumor cells. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is a broad network of actual, physical objects that can exchange data and communicate with other devices and systems over the Internet. Due to the scalability of the internet, the number of IoT users is increasing, and therefore it is vital to ensure the security of user info. Attacks such as DDoS (Distributed Denial of Service) and MitM (Man-in-the-Middle) can seriously interrupt operations and threaten the confidentiality and integrity of sensitive data. In this paper, a novel Iot based Security Assessment for intrusion using Deep Learning (ISAI-DL) technique has been proposed to identify IoT device vulnerabilities such as DDoS and MitM attacks. Initially, the features are extracted from the API documents using Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF) techniques. In proposed ISAI-DL technique, it triggers or activates the vulnerabilities deep within target devices by sending API documents. The vulnerability is detected via the GloVe-CNN-BiLSTM Model; if the attack occurs in an IoT device, the output is classified as either DDoS, MitM, or normal. Evaluation metrics such as accuracy, recall, precision, time efficiency, F1 score, false alarm rate, and detection rate have been used to measure how effective the proposed ISAI-DL technique is. Based on a comparative investigation, the detection rate of the proposed ISAI-DL technique is 17.23%, 18.53%, and 3.24% greater than that of the existing P2ADF, HDA-IDS, and BT-TPF techniques respectively. [ABSTRACT FROM AUTHOR]