Exosome Revolution or Marketing Mirage? AI-Based Multi-domain Evaluation of Claims, Scientific Evidence, Transparency, Public Sentiment, and Media Narratives.

Introduction: Exosome-based therapies are being hypothesised and promoted as a transformative innovation in regenerative aesthetics, offering promising benefits for skin rejuvenation, anti-ageing, and hair restoration. However, the field faces challenges related to scientific validation, transparency, and lack regulatory oversight, for use in humans. Furthermore, there is insufficient of clinical trials registered for use in aesthetic practise.

Influences of electrode density on intracranial seizure localisation: a single-blinded randomised crossover study.

Background: Successful seizure onset zone (SOZ) localisation for epilepsy surgery often relies upon intracranial recordings. Accurate delineation requires anatomical detail yet influences of intracranial electrode density on clinical variables have not been systematically studied.

Methods: In this experimental study we compared SOZ localisation between spontaneously captured seizures on higher-density depth and grid electrode arrays (4-5 mm inter-electrode spacing) vs.

Cost-effectiveness of screening, decolonisation and isolation strategies for carbapenem-resistant Enterobacterales and methicillin-resistant infections in hospitals: a sex-stratified mathematical modelling study.

Background: Methicillin-resistant (MRSA) and carbapenem-resistant Enterobacterales (CRE) impose the greatest burden among critical bacterial pathogens. Evidence for sex differences among antibiotic resistant bacterial infections is increasing but a focus on policy implications is needed. We assessed impact of CRE/MRSA on excess length of hospital stay, intensive care unit admission, and mortality by sex from a retrospective cohort study (n = 873) of patients in three Chilean hospitals, 2018-2021.

Using machine learning to predict deterioration of symptoms in COPD patients within a telemonitoring program.

COPD exacerbations have a profound clinical impact on patients. Accurately predicting these events could help healthcare professionals take proactive measures to mitigate their impact. For over a decade, telEPOC, a telehealthcare program, has collected data that can be utilized to train machine learning models to anticipate COPD exacerbations.