ABSTRACT
Introduction: During the current pandemic, Covid-19 vaccination has become an indispensable solution for what used to be a normal life. Several side effects commonly reported with Covid-19 vaccination are injection site reactions, fatigue, and fever. Although not common, there have been some reports of hormonal disturbances such as menstrual irregularities and hair loss. A reasonable explanation of menstrual changes includes the effect of immune response activation on menstrual cycle driving hormones. Similarly, hair loss could be explained by the interrelation of the immune response with vaccination (1,2). Objective: To determine the rate and seriousness of menstrual disturbance turbance and hair loss events following COVID-19 vaccine administration in Saudi Arabia using the Saudi Food and Drug Authoritys (SFDA) spontaneous reporting system. Methods: a cross-sectional retrospective study measured the rate of spontaneous reports of hormonal change, including menstrual disorder and hormonal hair loss events, after the COVID-19 vaccine was received on SFDA electronic system from January 2021 to December 2021. All reports associated with hormonal change other than menstrual disturbance and hair loss were excluded. The SFDA manages the Vigilance reporting System, which receives spontaneous reports from healthcare providers and the public. The study examines the rate and seriousness of the hormonal changes post-vaccination, specifically menstrual irregularities & hair loss (AEFI), for all covid-19 reports received from any stakeholders during 2021 and for all types of COVID-19 registered vaccines in Saudi Arabia including Pfizer (BNT162 vaccine), AstraZeneca (ChAdOx1-S) and Moderna (mRNA embedded in SM-102 lipid nanoparticles) vaccines. Results: During the study period, a total of 280,000 Adverse events Following Immunization (AEFI) reports with COVID-19 vaccines were received. Of all reports, 58 reports concerning menstrual disturbance and 20 reports concerning hair loss were identified. The majority of reported hair loss cases were female (95%). By vaccines, BioNTech BNT162 (14), AstraZeneca (5), Moderna (1). On the other hand, (76%) of menstrual disturbance cases occurred after administering BioNTech BNT162, followed by (17%) AstraZeneca, and a minor percentage was with Moderna at (7%). Regarding seriousness, from the total number of cases reported, 97% were non-serious. Conclusion: The association between hormonal disturbances and COVID-19 vaccines remains uncertain. Hence, further studies are warranted to investigate our observation. In conclusion, the key recommendation generated from the study is the implementation of awareness campaigns to promote an ADR reporting culture to correlate rare events occurrences with the use of COVID-19 vaccines.
ABSTRACT
BackgroundThe changing epidemiological profile of the COVID-19 pandemic and the uncertain clinical picture of patients characterise this ongoing and most challenging health event.ObjectivesTo report clinical features, laboratory characteristics, and mortality risk factors among COVID-19 patients admitted to a secondary hospital in Oman.MethodsA retrospective study for the first 455 patients admitted with COVID-19 to Rustaq hospital from 12th April, 2020 to 27th September, 2020. A predesigned questionnaire collected data from the hospital medical electronic system.ResultsThe mean age was 42.84 (SD = 19.86) years, and the majority of patients were aged 30 to 59 and 60 or above;207 (45.5%) and 189 (41.5%), respectively. Male patients constituted approximately two-thirds of the subjects. Fever, dyspnea and cough were the most common presenting symptoms (69%, 66%, and 62%, respectively), while comorbidities with diabetes mellitus and hypertension were 47% and 44%, respectively. Bacterial growth was identified at approximately 10%. Bivariate analysis turned out to be significant with a number of factors. However, multivariate analysis showed significance with patients aged over 60 (OR = 7.15, 95% CI 1.99–25.63), dyspnea (OR = 2.83, 95% CI 1.5–5.33), dyslipidemia (OR = 1.93, 95% CI 1.02–3.66) and being bed-ridden (OR = 5.01, 95% CI 1.73–14.44). Durations from onset of symptoms to admission and respiratory distress were lower among patients who died;p = 0.024 and p = 0.001, respectively. Urea, Troponin and LDH may act as potential diagnostic biomarkers for severity or mortality.ConclusionsThis study identified groups of patients with a higher risk of mortality, with severe disturbance in the laboratory markers while some could act as potential diagnostic biomarkers.
ABSTRACT
Wearable sensing electronic systems (WSES) are becoming a fundamental platform to construct smart and intelligent networks for broad applications. Various physiological data are readily collected by the WSES, including biochemical, biopotential, and biophysical signals from human bodies. However, understanding these sensing data, such as feature extractions, recognitions, and classifications, is largely restrained because of the insufficient capacity when using conventional data processing techniques. Recent advances in sensing performance and system‐level operation quality of the WSES are expedited with the assistance of machine learning (ML) algorithms. Here, the state‐of‐the‐art of the ML‐assisted WSES is summarized with emphasis on how the accurate perceptions on physiological signals under different algorithms paradigm augment the performance of the WSES for diverse applications. Concretely, ML algorithms that are frequently implemented in the WSES studies are first synopsized. Then broad applications of ML‐assisted WSES with strengthened functions are discussed in the following sections, including intelligent physiological signals monitoring, disease diagnosis, on‐demand treatments, assistive devices, human–machine interface, and multiple sensations‐based virtual and augmented reality. Finally, challenges confronted for the ML‐assisted WSES are addressed.