4 research outputs found
Promotion of new local sports media (Dunia Sukan) / Mohammad Haris Naim Adam
No full textThe promotion of Dunia Sukan Media & Event Company is the issue of the topic. The problem is, in Malaysia there’s not much local media that providing the full information and news about local sports development. Consequently, people in Malaysia were not interested anymore into getting news about local sports than the overseas. So, the purpose of this research is to get a best way to promote DS Media to the local sports lover as the best local sports media in Malaysia that providing the information that needed to develop the sports industry in Malaysia. The findings show that overall the result agrees that Malaysian still interested of local sports and the needed of the media to function as they should to develop the Malaysian sports industry
Author Correction: Federated learning enables big data for rare cancer boundary detection.
No full text10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14
From deterministic methods to a Bayesian approximation: towards reliable segmentation of multiple sclerosis Lesions in magnetic resonance imaging
No full textEn este trabajo, se presenta una metodología para la segmentación de lesiones de esclerosis múltiple (EM) en imágenes de resonancia magnética (IRM) que aborda las limitaciones de los modelos deterministas mediante la incorporación de la estimación de incertidumbre. Se compara una arquitectura U-Net 3D determinista con una versión modificada que emplea una aproximación bayesiana con Monte Carlo Dropout (MCD) para cuantificar la incertidumbre epistémica. Los resultados demuestran que, si bien ambos modelos alcanzan un rendimiento competitivo en términos de las métricas estándar de segmentación de imágenes médicas, la estimación de incertidumbre proporciona información valiosa sobre la fiabilidad de las predicciones, especialmente en regiones desafiantes como los bordes de las lesiones. Esto tiene el potencial de mejorar la aplicabilidad clínica de la segmentación automática al permitir a los usuarios médicos evaluar la confianza en los resultados y enfocar su revisión en áreas de mayor incertidumbre.In this work, we present a methodology for the segmentation of multiple sclerosis (MS) lesions in magnetic resonance imaging (MRI) that addresses the limitations of deterministic models by incorporating uncertainty estimation. We compare a deterministic 3D U-Net architecture with a modified version that employs a Bayesian approximation with Monte Carlo Dropout (MCD) to quantify epistemic uncertainty. The results demonstrate that while both models achieve competitive performance in terms of standard medical image segmentation metrics, the uncertainty estimation provides valuable information on the reliability of the predictions, especially in challenging regions such as lesion borders. This has the potential to improve the clinical applicability of automatic segmentation by allowing medical users to assess confidence in the results and focus their review on areas of higher uncertainty
Azithromycin in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial
No full textSummaryBackground Azithromycin has been proposed as a treatment for COVID-19 on the basis of its immunomodulatoryactions. We aimed to evaluate the safety and efficacy of azithromycin in patients admitted to hospital with COVID-19.Methods In this randomised, controlled, open-label, adaptive platform trial (Randomised Evaluation of COVID-19Therapy [RECOVERY]), several possible treatments were compared with usual care in patients admitted to hospitalwith COVID-19 in the UK. The trial is underway at 176 hospitals in the UK. Eligible and consenting patients wererandomly allocated to either usual standard of care alone or usual standard of care plus azithromycin 500 mg once perday by mouth or intravenously for 10 days or until discharge (or allocation to one of the other RECOVERY treatmentgroups). Patients were assigned via web-based simple (unstratified) randomisation with allocation concealment andwere twice as likely to be randomly assigned to usual care than to any of the active treatment groups. Participants andlocal study staff were not masked to the allocated treatment, but all others involved in the trial were masked to theoutcome data during the trial. The primary outcome was 28-day all-cause mortality, assessed in the intention-to-treatpopulation. The trial is registered with ISRCTN, 50189673, and ClinicalTrials.gov, NCT04381936.Findings Between April 7 and Nov 27, 2020, of 16 442 patients enrolled in the RECOVERY trial, 9433 (57%) wereeligible and 7763 were included in the assessment of azithromycin. The mean age of these study participants was65·3 years (SD 15·7) and approximately a third were women (2944 [38%] of 7763). 2582 patients were randomlyallocated to receive azithromycin and 5181 patients were randomly allocated to usual care alone. Overall,561 (22%) patients allocated to azithromycin and 1162 (22%) patients allocated to usual care died within 28 days(rate ratio 0·97, 95% CI 0·87–1·07; p=0·50). No significant difference was seen in duration of hospital stay (median10 days [IQR 5 to >28] vs 11 days [5 to >28]) or the proportion of patients discharged from hospital alive within 28 days(rate ratio 1·04, 95% CI 0·98–1·10; p=0·19). Among those not on invasive mechanical ventilation at baseline, nosignificant difference was seen in the proportion meeting the composite endpoint of invasive mechanical ventilationor death (risk ratio 0·95, 95% CI 0·87–1·03; p=0·24).Interpretation In patients admitted to hospital with COVID-19, azithromycin did not improve survival or otherprespecified clinical outcomes. Azithromycin use in patients admitted to hospital with COVID-19 should be restrictedto patients in whom there is a clear antimicrobial indication
