METHODS: Using open data repository with daily infected, recovered and death cases in the period between March 2020 and April 2021, a descriptive analysis was performed. The susceptible-exposed-infected-recovery model was used to estimate the effective productive number (Rt). The duration taken from Rt > 1 to Rt
METHODS: A multi-centre, retrospective observational study was performed among children aged ≤12 years with laboratory-proven COVID-19 between 1 February and 31 December 2020.
RESULTS: In total, 261 children (48.7% males, 51.3% females) were included in this study. The median age was 6 years [interquartile range (IQR) 3-10 years]. One hundred and fifty-one children (57.9%) were asymptomatic on presentation. Among the symptomatic cases, fever was the most common presenting symptom. Two hundred and forty-one (92.3%) cases were close contacts of infected household or extended family members. Twenty-one (8.4%) cases had abnormal radiological findings. All cases were discharged alive without requiring supplemental oxygen therapy or any specific treatment during hospitalization. The median duration of hospitalization was 7 days (IQR 6-10 days). One (2.1%) of the uninfected guardians accompanying a child in quarantine tested positive for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) upon discharge.
CONCLUSIONS: COVID-19 in children was associated with mild symptoms and a good prognosis. Familial clustering was an important epidemiologic feature in the outbreak in Negeri Sembilan. The risk of transmission of SARS-CoV-2 from children to guardians in hospital isolation was minimal despite close proximity.
METHOD: To overcome the limitation, the use of artificial intelligence along with technical tools has been extensively investigated for AD diagnosis. For developing a promising artificial intelligence strategy that can diagnose AD early, it is critical to supervise neuropsychological outcomes and imaging-based readouts with a proper clinical review.
CONCLUSION: Profound knowledge, a large data pool, and detailed investigations are required for the successful implementation of this tool. This review will enlighten various aspects of early diagnosis of AD using artificial intelligence.
AIMS: This study aimed to determine common combinations of medications used among women aged 77-96 years and to describe characteristics associated with these combinations.
METHODS: A cohort study of older women enroled in the Australian Longitudinal Study on Women's Health over a 15-year period was used to determine combinations of medications using latent class analysis. Multinomial logistic regression was used to determine characteristics associated with these combinations.
RESULTS: The highest medication users during the study were for the cardiovascular (2003: 80.28%; 2017: 85.63%) and nervous (2003: 66.03%; 2017: 75.41%) systems. A 3-class latent model described medication use combinations: class 1: 'Cardiovascular & neurology anatomical group' (27.25%) included participants using medications of the cardiovascular and nervous systems in their later years; class 2: 'Multiple anatomical group' (16.49%) and class 3: 'Antiinfectives & multiple anatomical group' (56.27%). When compared to the reference class (class 1), the risk of participants being in class 3 was slightly higher than being in class 2 if they had > 4 general practitioner visits (RRR 2.37; 95% CI 2.08, 2.71), Department of Veterans Affairs' coverage (RRR 1.59; 95% CI 1.36, 1.86), ≥ 4 chronic diseases (RRR 3.16; 95% CI 2.56, 3.90) and were frail (RRR 1.47; 95% CI 1.27, 1.69).
CONCLUSION: Identification of combinations of medication use may provide opportunities to develop multimorbidity guidelines and target medication reviews, and may help reduce medication load for older individuals.