METHODS: A web-based cross-sectional study was conducted among 1280 healthcare providers aged 18 years and older from 30 primary care clinics in Selangor, Malaysia. In this study, the Copenhagen Burnout Inventory was used to assess burnout. The results were analyzed using multiple logistic regression.
RESULTS: The prevalence of personal burnout was 41.7%, followed by work-related burnout (32.2%) and client-related burnout (14.5%). The determinants for personal burnout in this study were younger age, being a doctor, higher COVID-19 exposure risk, do not know where to seek help, inability to handle stress, poorer sleep quality score, higher total COVID-19 fear score, higher total stress score, and lower total BRS score. The determinants of work-related burnout were younger age, being a doctor, longer years of working, higher COVID-19 exposure risk, do not know where to seek help, lower altruistic score, poorer sleep quality score, higher total stress score, and lower total brief resilience score (BRS) score. The determinants of client-related burnout were doctor, single/divorced, more than one attachment site, and higher satisfaction toward the infection control, inability to handle stress, higher total depression score, and lower total BRS score.
CONCLUSION: Every fourth out of ten suffered from personal burnout, one-third from work-related burnout, and one-seventh from client-related burnout among healthcare providers during the COVID-19 pandemic. Healthcare systems must take care of healthcare workers' physical and emotional depletion, reducing the risk of burnout.
METHODS: A cross-sectional study on 284 epilepsy patients was performed in a local tertiary centre. The demographic and clinical epilepsy data were collected. The Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) questionnaires were utilised to determine the quality of life and daytime hypersomnolence of epilepsy patients, respectively.
RESULTS: Poor sleep quality was reported in 78 (27.5%) patients while daytime hypersomnolence was present in 17 (6%) patients. The predictors of poor sleep quality include structural causes (OR = 2.749; 95% CI: 1.436, 5.264, p = 0.002), generalised seizures (OR = 1.959, 95% CI: 1.04, 3.689, p = 0.037), and antiseizure medications such as Carbamazepine (OR = 2.34; 95% CI: 1.095, 5.001, p = 0.028) and Topiramate (OR 2.487; 95% CI: 1.028, 6.014, p = 0.043). Females are 3.797 times more likely score higher in ESS assessment (OR 3.797; 95% CI: 1.064, 13.555 p = 0.04).
DISCUSSION: Sleep disturbances frequently coexist with epilepsy. Patients should be actively evaluated using the PSQI and ESS questionnaires. It is imperative to identify the key factors that lead to reduced sleep quality and heightened daytime sleepiness in patients with epilepsy, as this is essential to properly manage their condition.
METHODS: In this analysis of 2-year retrospective cohort studies, we extracted data from the TriNetX electronic health records network, an international network of de-identified data from health-care records of approximately 89 million patients collected from hospital, primary care, and specialist providers (mostly from the USA, but also from Australia, the UK, Spain, Bulgaria, India, Malaysia, and Taiwan). A cohort of patients of any age with COVID-19 diagnosed between Jan 20, 2020, and April 13, 2022, was identified and propensity-score matched (1:1) to a contemporaneous cohort of patients with any other respiratory infection. Matching was done on the basis of demographic factors, risk factors for COVID-19 and severe COVID-19 illness, and vaccination status. Analyses were stratified by age group (age <18 years [children], 18-64 years [adults], and ≥65 years [older adults]) and date of diagnosis. We assessed the risks of 14 neurological and psychiatric diagnoses after SARS-CoV-2 infection and compared these risks with the matched comparator cohort. The 2-year risk trajectories were represented by time-varying hazard ratios (HRs) and summarised using the 6-month constant HRs (representing the risks in the earlier phase of follow-up, which have not yet been well characterised in children), the risk horizon for each outcome (ie, the time at which the HR returns to 1), and the time to equal incidence in the two cohorts. We also estimated how many people died after a neurological or psychiatric diagnosis during follow-up in each age group. Finally, we compared matched cohorts of patients diagnosed with COVID-19 directly before and after the emergence of the alpha (B.1.1.7), delta (B.1.617.2), and omicron (B.1.1.529) variants.
FINDINGS: We identified 1 487 712 patients with a recorded diagnosis of COVID-19 during the study period, of whom 1 284 437 (185 748 children, 856 588 adults, and 242 101 older adults; overall mean age 42·5 years [SD 21·9]; 741 806 [57·8%] were female and 542 192 [42·2%] were male) were adequately matched with an equal number of patients with another respiratory infection. The risk trajectories of outcomes after SARS-CoV-2 infection in the whole cohort differed substantially. While most outcomes had HRs significantly greater than 1 after 6 months (with the exception of encephalitis; Guillain-Barré syndrome; nerve, nerve root, and plexus disorder; and parkinsonism), their risk horizons and time to equal incidence varied greatly. Risks of the common psychiatric disorders returned to baseline after 1-2 months (mood disorders at 43 days, anxiety disorders at 58 days) and subsequently reached an equal overall incidence to the matched comparison group (mood disorders at 457 days, anxiety disorders at 417 days). By contrast, risks of cognitive deficit (known as brain fog), dementia, psychotic disorders, and epilepsy or seizures were still increased at the end of the 2-year follow-up period. Post-COVID-19 risk trajectories differed in children compared with adults: in the 6 months after SARS-CoV-2 infection, children were not at an increased risk of mood (HR 1·02 [95% CI 0·94-1·10) or anxiety (1·00 [0·94-1·06]) disorders, but did have an increased risk of cognitive deficit, insomnia, intracranial haemorrhage, ischaemic stroke, nerve, nerve root, and plexus disorders, psychotic disorders, and epilepsy or seizures (HRs ranging from 1·20 [1·09-1·33] to 2·16 [1·46-3·19]). Unlike adults, cognitive deficit in children had a finite risk horizon (75 days) and a finite time to equal incidence (491 days). A sizeable proportion of older adults who received a neurological or psychiatric diagnosis, in either cohort, subsequently died, especially those diagnosed with dementia or epilepsy or seizures. Risk profiles were similar just before versus just after the emergence of the alpha variant (n=47 675 in each cohort). Just after (vs just before) the emergence of the delta variant (n=44 835 in each cohort), increased risks of ischaemic stroke, epilepsy or seizures, cognitive deficit, insomnia, and anxiety disorders were observed, compounded by an increased death rate. With omicron (n=39 845 in each cohort), there was a lower death rate than just before emergence of the variant, but the risks of neurological and psychiatric outcomes remained similar.
INTERPRETATION: This analysis of 2-year retrospective cohort studies of individuals diagnosed with COVID-19 showed that the increased incidence of mood and anxiety disorders was transient, with no overall excess of these diagnoses compared with other respiratory infections. In contrast, the increased risk of psychotic disorder, cognitive deficit, dementia, and epilepsy or seizures persisted throughout. The differing trajectories suggest a different pathogenesis for these outcomes. Children have a more benign overall profile of psychiatric risk than do adults and older adults, but their sustained higher risk of some diagnoses is of concern. The fact that neurological and psychiatric outcomes were similar during the delta and omicron waves indicates that the burden on the health-care system might continue even with variants that are less severe in other respects. Our findings are relevant to understanding individual-level and population-level risks of neurological and psychiatric disorders after SARS-CoV-2 infection and can help inform our responses to them.
FUNDING: National Institute for Health and Care Research Oxford Health Biomedical Research Centre, The Wolfson Foundation, and MQ Mental Health Research.
Objective: To assess whether sleep timing and napping behavior are associated with increased obesity, independent of nocturnal sleep length.
Design, Setting, and Participants: This large, multinational, population-based cross-sectional study used data of participants from 60 study centers in 26 countries with varying income levels as part of the Prospective Urban Rural Epidemiology study. Participants were aged 35 to 70 years and were mainly recruited during 2005 and 2009. Data analysis occurred from October 2020 through March 2021.
Exposures: Sleep timing (ie, bedtime and wake-up time), nocturnal sleep duration, daytime napping.
Main Outcomes and Measures: The primary outcomes were prevalence of obesity, specified as general obesity, defined as body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) of 30 or greater, and abdominal obesity, defined as waist circumference greater than 102 cm for men or greater than 88 cm for women. Multilevel logistic regression models with random effects for study centers were performed to calculate adjusted odds ratios (AORs) and 95% CIs.
Results: Overall, 136 652 participants (81 652 [59.8%] women; mean [SD] age, 51.0 [9.8] years) were included in analysis. A total of 27 195 participants (19.9%) had general obesity, and 37 024 participants (27.1%) had abdominal obesity. The mean (SD) nocturnal sleep duration was 7.8 (1.4) hours, and the median (interquartile range) midsleep time was 2:15 am (1:30 am-3:00 am). A total of 19 660 participants (14.4%) had late bedtime behavior (ie, midnight or later). Compared with bedtime between 8 pm and 10 pm, late bedtime was associated with general obesity (AOR, 1.20; 95% CI, 1.12-1.29) and abdominal obesity (AOR, 1.20; 95% CI, 1.12-1.28), particularly among participants who went to bed between 2 am and 6 am (general obesity: AOR, 1.35; 95% CI, 1.18-1.54; abdominal obesity: AOR, 1.38; 95% CI, 1.21-1.58). Short nocturnal sleep of less than 6 hours was associated with general obesity (eg, <5 hours: AOR, 1.27; 95% CI, 1.13-1.43), but longer napping was associated with higher abdominal obesity prevalence (eg, ≥1 hours: AOR, 1.39; 95% CI, 1.31-1.47). Neither going to bed during the day (ie, before 8pm) nor wake-up time was associated with obesity.
Conclusions and Relevance: This cross-sectional study found that late nocturnal bedtime and short nocturnal sleep were associated with increased risk of obesity prevalence, while longer daytime napping did not reduce the risk but was associated with higher risk of abdominal obesity. Strategic weight control programs should also encourage earlier bedtime and avoid short nocturnal sleep to mitigate obesity epidemic.
METHOD: A multicenter, prospective, randomized, parallel-design, open label interventional study to estimate the effectiveness of zolpidem (10 mg) oral tablets versus acupressure on sleep quality and quality of life in patients with CKD-aP on hemodialysis. A total of 58 hemodialysis patients having sleep disturbance due to CKD-aP completed the entire 8-week follow-up. The patients were divided into a control (acupressure) group of 28 patients and an intervention (zolpidem) group of 30 patients.
RESULTS: A total of 58 patients having CKD-aP and sleep disturbance were recruited. In the control group there was a reduction in the PSQI score with a mean ± SD from 12.28 ± 3.59 to 9.25 ± 3.99, while in the intervention group the reduction in PSQI score with a mean ± SD was from 14.73 ± 4.14 to 10.03 ± 4.04 from baseline to endpoint. However, the EQ5D index score and EQ-visual analogue scale (VAS) at baseline for the control group with a mean ± SD was 0.49 ± 0.30 and 50.17 ± 8.65, respectively, while for the intervention group the values were 0.62 ± 0.26 and 47.17 ± 5.82, respectively. The mean EQ5D index score in the control group improved from 0.49 ± 0.30 to 0.53 ± 0.30, but in the intervention group there was no statistical improvement in mean EQ5D index score from 0.62 ± 0.26 to 0.62 ± 0.27 from baseline to week 8. The EQ 5D improved in both groups and the EQ-VAS score was 2.67 points higher at week 8 as compared to baseline in the control group, while in the intervention group the score was 3.33 points higher at week 8 as compared to baseline. Comparing with baseline, the PSQI scores were significantly reduced after week 4 and week 8 (P =
METHODS: In this study, we established persistently NDV-infected EJ28 bladder cancer cells, designated as EJ28P. Global transcriptomic analysis was subsequently carried out by microarray analysis. Differentially expressed genes (DEGs) between EJ28 and EJ28P cells identified by the edgeR program were further analysed by Gene Set Enrichment Analysis (GSEA) and Ingenuity Pathway Analysis (IPA) analyses. In addition, the microarray data were validated by RT-qPCR.
RESULTS: Persistently NDV-infected EJ28 bladder cancer cells were successfully established and confirmed by flow cytometry. Microarray analysis identified a total of 368 genes as differentially expressed in EJ28P cells when compared to the non-infected EJ28 cells. GSEA revealed that the Wnt/β-catenin and KRAS signalling pathways were upregulated while the TGF-β signalling pathway was downregulated. Findings from this study suggest that the upregulation of genes that are associated with cell growth, pro-survival, and anti-apoptosis may explain the survivability of EJ28P cells and the development of persistent infection of NDV.
CONCLUSIONS: This study provides insights into the transcriptomic changes that occur and the specific signalling pathways that are potentially involved in the development and maintenance of NDV persistency of infection in bladder cancer cells. These findings warrant further investigation and is crucial towards the development of effective NDV oncolytic therapy against cancer.
AIM: To equip faculty with tools to conduct TBL session online, synchronously, effectively and efficiently.
METHODS: We examined the published literature in the area of online teaching and combined it with our own experience of conducting TBL sessions online.
RESULTS: We created 12 tips to assist faculty to facilitate an effective and engaging TBL session online.
CONCLUSIONS: Applying these 12 tips while facilitating a TBL-online session will ensure the full engagement of students in the process of active learning.
DESIGN AND METHODS: This is a cross-sectional study conducted among 184 eligible hemodialysis patients at four dialysis units in Malaysia. Three days dietary recall were used in the analysis of dietary intake and behavior. Sleep quality was assessed through Pittsburgh Sleep Quality Index.
RESULTS: More than half of the patients were poor sleepers. Among the sleep components, sleep latency affected patients the most, with the use of sleep medications was relatively low. A majority of the patients had inadequate dietary intake of energy (88%) and protein (75%). Dietary protein, potassium adjusted for body weight, and sodium intake were significantly increased in poor sleepers. Lower percentage of energy from carbohydrates; higher percentage of energy from fats; higher intakes of dietary protein, fat, phosphorus, and sodium were correlated with poorer sleep quality and its components. Skipping dinner on non-dialysis days and having supper on dialysis days were associated with poor sleep quality.
CONCLUSION: Poor sleep is prevalent among hemodialysis patients. Sleep quality of hemodialysis patients was highly associated with certain dietary factors. Periodical assessment of sleep quality and dietary intake is necessary to identify poor sleepers with inappropriate dietary intake to allow effective clinical and nutritional interventions to improve the sleep quality and nutritional status of these patients.
METHODS: Nine AD experts from South and East Asia and one from Europe developed the algorithm based upon treatment guidelines, relevant literature and local treatment practices. The algorithm outlines current best practice for the use of emollients, topical corticosteroids (TCS) and topical calcineurin inhibitors (TCI), with the intention of simplifying the treatment regimen of mild-to-moderate AD in South and East Asia.
RESULTS: Patients with AD should bathe and cleanse affected skin to remove crusts and scales daily. Emollients should also be applied daily as a maintenance treatment. When selecting appropriate topical anti-inflammatory treatment for AD flares, several factors should be taken into consideration, including the patient's age, attitude to treatment options and site of AD lesions. Given the concerns regarding the risk of skin atrophy with use of TCS, a TCI should be used to treat AD lesions in sensitive skin areas: pimecrolimus is recommended for mild-to-moderate AD in these locations, while tacrolimus should be considered for moderate and severe cases. Either pimecrolimus or tacrolimus is recommended for flares in other, non-sensitive body locations. A proactive or intermittent maintenance treatment strategy involving regular emollient use and twice-weekly application of a TCI to previously affected areas is encouraged to reduce the risk of flares.
CONCLUSIONS: The algorithm proposed here is intended to simplify the topical treatment of mild-to-moderate AD in daily practice in South and East Asian countries.