Several Phase-III clinical studies investigating vaccine safety and effectiveness have been published a year following the first breakout of the COVID-19 pandemic. These vaccine candidates were produced using a variety of vaccination technologies, including mRNA, recombinant protein, adenoviral vector, and inactivated virus-based platforms, by various research organizations and pharmaceutical firms. Despite many successful clinical studies, participants are restricted by trial inclusion and exclusion criteria, geographic location, and the current state of the virus epidemic. Many concerns remain, particularly for specific populations such as the elderly, women who are pregnant or nursing, and teenagers. Vaccine effectiveness against asymptomatic infection and particular viral variations, on the other hand, is still largely unclear. This review will focus on vaccination candidates that have completed Phase-III clinical trials and will examine the scientific evidence that has been gathered so far for these vaccine candidates for various subgroups of individuals and virus variations.
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http://dx.doi.org/10.7759/cureus.28066 | DOI Listing |
CA Cancer J Clin
January 2025
Division of Medical Oncology, Department of Internal Medicine, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah, USA.
Poly(adenosine diphosphate ribose) polymerase (PARP) inhibitors, such as olaparib, talazoparib, rucaparib, and niraparib, comprise a therapeutic class that targets PARP proteins involved in DNA repair. Cancer cells with homologous recombination repair defects, particularly BRCA alterations, display enhanced sensitivity to these agents because of synthetic lethality induced by PARP inhibitors. These agents have significantly improved survival outcomes across various malignancies, initially gaining regulatory approval in ovarian cancer and subsequently in breast, pancreatic, and prostate cancers in different indications.
View Article and Find Full Text PDFClin Trials
January 2025
Liverpool Clinical Trials Centre, University of Liverpool, Liverpool, UK.
Background/aims: When conducting a randomised controlled trial in surgery, it is important to consider surgical learning, where surgeons' familiarity with one, or both, of the interventions increases during the trial. If present, learning may compromise trial validity. We demonstrate a statistical investigation into surgical learning within a trial of cleft palate repair.
View Article and Find Full Text PDFWorldviews Evid Based Nurs
February 2025
School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China.
Background: Coronary artery disease (CAD) is a major health problem of atherosclerotic cardiovascular (CV) disease and early intervention is regarded important. Given the proven effect of a lifestyle intervention with nursing telephone counselling and mHealth use in health care, yet the comparisons of both support are lacking, this study is proposed.
Objectives: This study aimed to compare the effects of a coronary artery disease (CAD) support program using a mobile application versus nurse phone advice on exercise amount and physical and psychological outcomes for clients at risk of CAD.
JMIR Public Health Surveill
January 2025
School of Public Health, Imperial College London, London, United Kingdom.
Background: High response rates are needed in population-based studies, as nonresponse reduces effective sample size and bias affects accuracy and decreases the generalizability of the study findings.
Objective: We tested different strategies to improve response rate and reduce nonresponse bias in a national population-based COVID-19 surveillance program in England, United Kingdom.
Methods: Over 19 rounds, a random sample of individuals aged 5 years and older from the general population in England were invited by mail to complete a web-based questionnaire and return a swab for SARS-CoV-2 testing.
J Alzheimers Dis
January 2025
Department of Neurology and the Franke Barrow Global Neuroscience Education Center, Barrow Neurological Institute, Phoenix, AZ, USA.
Background: The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) models, along with demographic features and Alzheimer's disease (AD) biomarkers, to predict decline or non-decline in global and domain-specific cognitive scores among community-dwelling older adults.
Objective: To evaluate the impact of adding NPS to AD biomarkers on ML model accuracy in predicting cognitive decline among older adults.
Methods: The study was conducted in the setting of the Mayo Clinic Study of Aging, including participants aged ≥ 50 years with information on demographics (i.
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