The spread of health misinformation through social media platforms has
transformed into a serious sociomedical threat in undermining public trust on
medicine, health professional’s scientific institutions and public health
authorities. This manuscript looks into how health-related false or misleading
narratives spread on Facebook, Twitter/X, TikTok, YouTube, WhatsApp, and
other similar platforms, especially during public health emergencies like the
COVID-19 outbreak. Health misinformation has the potential to change one’s
risk perceptions, lessening acceptance of vaccination, promoting unsafe
treatments, delaying appropriate care-seeking, and reducing adherence to
evidence-based recommendations. According to the review, cognitive
mechanism refers to the propagation of rumors as misinformation spreaders
are often incapable of disarming popular claims with evident truth. This paper
also describes specific monitoring approaches, including social listening, text
mining and natural language processing, coding frameworks, and citizenscience validation. It will require coordinated action by healthcare
professionals, public health institutions, governments, researchers, media
organizations and social media platforms. Strategies include open
communication about risks, rapid correction of false claims, promotion of
credible sources, digital health literacy, ethical content moderation, platform
accountability, and community-based engagement. To maintain public
confidence in medicine, it is essential to not only correct misinformation but
also enhance the credibility, accessibility and responsiveness of health
communication systems.
I. Alnaimi, Ibrahim Abdul Jaleel Yamani, A. Alkhatib· European Journal of Prosthod...· 0 citations
Artificial intelligence (AI) could transform health care, particularly in lowresource settings (1). The technology enables new capabilities, ranging from
data acquisition to decision support, that amplify ongoing investments in
digital technology to improve access to information, diagnostics, treatment,
and decision-making (2). As health gaps persist between and within countries,
a key question emerges: will deployable AI solutions widen or narrow existing
disparities? (3). In many countries, poverty, education, geography, and race
constitute risk factors that contribute both to health status and to access to
health services and other determinants of health (4).
Two competing hypotheses exist (5). The optimistic view holds that AI can
reduce these gaps by improving the accuracy of diagnostics and therapeutics
and by expanding access to services that would otherwise be out of reach
demonstrates that these capabilities can profitably influence health status and
survival in under-resourced settings where access to trained human capital,
diagnostic devices, and therapeutics is limited (6). The gloomy view maintains
that significant mechanisms exacerbate the very gaps that AI might alleviate:
1) the data on which AI training relies often reflects a chronic lack of
representation, 2) the post-deployment conditions under which models operate
may drift further from those on which models were trained, 3) the
infrastructure and knowledge necessary to deploy new capabilities may be
absent in the most vulnerable settings, and 4) oversights in governance and use could further endanger already vulnerable groups as the market for AI grows (7, 8).
I. Alnaimi, Ibrahim Abdul Jaleel Yamani, A. Alkhatib· European Journal of Prosthod...· 0 citations
Background: A foodborne mycotoxin, aflatoxin B1, is known to be hepatotoxic and hepatocarcinogenic. Experimental studies suggest renal toxicity due to oxidative stress and apoptosis, but human epidemiologic evidence is limited in humans. Objective: In the context of NHANES 1999-2000, this study evaluated whether measurable serum aflatoxin B1-lysine adducts were related to markers of renal function among adults.Methods: This 1999-2000 exploratory analysis of NHANES data investigates the relationship between aflatoxin B1 and kidney function, and other health and demographic factors. Study subjects consisted of adults aged 20 years or older with data on any variable pertaining to aflatoxin exposure. Detectable aflatoxin was defined in the laboratory comment code. The serum creatinine was standardized using the calibration equation for NHANES 1999–2000, estimated glomerular filtration rate was calculated using the CKD-EPI 2021 creatinine equation, and urine albumin-creatinine ratio was calculated from urine albumin–creatinine. The outcomes included creatinine, eGFR, blood urea nitrogen, natural-log UACR, albuminuria, lower eGFR, and any one-visit kidney abnormality. To estimate descriptive statistics from weighted data, we used non-parametric comparisons, weighted linear models with robust standard errors, exact tests for binary outcomes, and bootstrap median-difference intervals. Results: In an analysis of 1,258 adults, only sixteen were found to have measurable serum aflatoxin B1-lysine, which pertains to a weighted detectable prevalence of 1.10%. The presence of detectable aflatoxin wasn’t related to any of these tests after adjusting for age, sex, race, and poverty income ratio. Estimates of the fully adjusted beta were -0.022 mg/dL for creatinine, -0.219 mL/min/1.73 m2 (eGFR), 0.293 mg/dL for BUN, and -0.486 log-UACR, all with p values >0.05. Aflatoxin detectability did not significantly differ by binary kidney outcomes.Conclusion: In this adult U.S. sample, detectable serum aflatoxin B1-lysine was rare and not associated with markers of kidney dysfunction. The small size of the exposed group means that the findings are inconclusive rather than definitive evidence of no renal effect.
A. Alkhatib· Journal of Clinical Nephrolo...· 0 citations
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