This permits scientific community to be optimistic in the short term.In 2017, the Health Resources and Services Administration's HIV/AIDS Bureau funded an Evaluation Center (EC) and a Coordinating Center for Technical Assistance (CCTA) to oversee the rapid implementation of 11 evidence-informed interventions at 26 HIV care and treatment providers across the U.S. This initiative aims to address persistent gaps in HIV-related health outcomes emerging from social determinants of health that negatively impact access to and retention in care. The EC adapted the Conceptual Model of Implementation Research to develop a Hybrid Type III, multi-site mixed-methods evaluation, described in this paper. The results of the evaluation will describe strategies associated with uptake, implementation outcomes, as well as HIV-related health outcomes for clients engaged in the evidence-informed interventions. This approach will allow us to understand in detail the processes that sites undergo to implement these important intervention strategies for high priority populations.Coronavirus disease-2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread globally since its first report and become a worldwide pandemic. In response to the outbreak of COVID-19, Center for Medical Device Evaluation, NMPA (CMDE) initiated emergency review and approval procedures to accelerate the process of reviewing emergent medical products and issued the Key Points of Technical Review for the Registration of SARS-CoV-2 Antigen/Antibody Tests (Key Points) to provide the requirements on the technical review of the tests. selleck chemical With uncontrolled spread and evolution of COVID-19 in the world, continuous prevention and measurements are necessary for fighting this pandemic and SARS-CoV-2 antigen/antibody tests are still urgently needed. This article is an attempt to expand clarification of the Key Points to wider audiences based on current understanding of SARS-CoV-2 to facilitate the development and application of SARS-CoV-2 antigen/antibody tests.Naturally occurring plant-based gums and their engineered bio-nanostructures have gained an immense essence of excellence in several industrial, biotechnological, and biomedical sectors of the modern world. Gums derived from bio-renewable resources that follow green chemistry principles are considered green macromolecules with unique structural and functional attributes. For instance, gum mostly obtained as exudates are bio-renewable, bio-degradable, bio-compatible, sustainable, overall cost-effective, and nontoxic. Gum exudates also offer tunable attributes that play a crucial role in engineering bio-nanostructures of interest for several bio- and non-bio applications, e.g., food-related items, therapeutic molecules, sustained and controlled delivery cues, bio-sensing constructs, and so on. With particular reference to plant gum exudates, this review focuses on applied perspectives of various gums, i.e., gum Arabic, gum albizzia, gum karaya, gum tragacanth, and gum kondagogu. After a brief introduction with problem statement and opportunities, structural and physicochemical attributes of plant-based natural gums are presented. Following that, considerable stress is given to green synthesis and stabilization of gum-based bio-nanostructures. The final part of the review focuses on the bio- and non-bio related applications of various types of gums polysaccharides-oriented bio-nanostructures.There is a strong association between HIV-related stigma (HS) and health-related quality of life among people living with HIV/AIDS, yet few studies, to date, have examined mediators of this link. This study examined the mediating role of antiretroviral medication adherence (MA) on association between HS and HRQoL. Respondents were 969 people living with HIV/AIDS (PLWH) (628 males and 341 females, Mean age = 35.55, years), conveniently drawn from four hospital facilities in Enugu and Ebonyi States of Nigeria. Data were collected using HIV Stigma Scale, Morisky Medical Adherence Scale, and Patient Reported Outcome Quality of Life-HIV. Data were analysed using the Hayes PROCESS macro for SPSS which uses a regression-based, path-analytical framework. Results showed that HS across dimensions (personalised stigma, disclosure concern, concern about people's attitude and negative self-image) were negatively associated with MA (r = -.16; p  less then  .001, r = -.13; p  less then  .00, r = -.22; p  less then  .001 and r = -.21; p  less then  .001, respectively) HRQoL. MA was positively associated with HRQoL (r = . 24; p  less then  .001). MA mediated HS-HRQoL association. These findings suggest that stigma associated with HIV/AIDS is associated with poor adherence to active antiretroviral therapy (ART) treatment regimen, which can result in poor health and treatment outcome among PLWH. The findings may be helpful in improving HRQoL among PLWH.Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient's symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to "Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson's disease, and Alzheimer's disease", from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like "Deep Belief Network (DBN) and Recurrent Neural Network (RNN)". As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.