PubMed COVID-19 Clinical Care

CONCLUSION: A consensus-based model of collaboration between general practice teams, community pharmacy teams, and patients and their carers has been developed. It is practically focused, values the patient voice and incorporates general practice and

The newly emerging pandemic disease often poses unexpected troubles and hazards to the global health system, particularly in low and middle-income countries like Nepal. In this study, we developed mathematical models to estimate the risk of infection

CONCLUSIONS: According to the results of this study, it can be said that COVID-19 increases the incidence of periodontitis, and it can be interpreted that the pandemic may adversely affect the general oral health of all people.

CONCLUSIONS: The ACAFSS is highly sensitive, specific, and valid for detecting severity levels of FI among racially diverse households with children. The ACAFSS is recommended as a stand-alone scale or a follow-up scale after households with children

CONCLUSIONS: The findings of this study add to the limited literature regarding IoT in the health care supply chain by empirically identifying the most important drivers and barriers to IoT implementation. The ranking of drivers and barriers provides

CONCLUSIONS: A more precise understanding of how behaviors tend to co-occur would improve efficacy and efficiency in developing interventions and supporting the overall health of those who use drugs, and it would improve communication with, and

CONCLUSIONS: Findings suggest decreased intake for fruit, vegetable, and alcohol consumption and increased snack intake in response to the pandemic amongst individuals LWBC. These changes appear to differ across various characteristics, suggesting

Despite development of effective SARS-CoV-2 vaccines, a sub-group of vaccine non-responders depends on therapeutic antibodies or small-molecule drugs in cases of severe disease. However, perpetual viral evolution has required continuous efficacy

No abstract

CONCLUSIONS: Thus, DISCERN is a flexible tool for reconstructing missing single-cell gene expression using a reference dataset and can easily be applied to a variety of data sets yielding novel insights, e.g., into disease mechanisms.