METHODS: This is an open labelled interventional study of a virtual brief psychosocial intervention, called SANUBARI. The program was conducted among COVID-19 patients hospitalized in the COVID-19 wards of two centres from May 2020 until August 2020. Inclusion criteria include patients aged eighteen years and above, diagnosed with COVID-19, medically stable, speaking and reading Bahasa Melayu or English. All study subjects attended two sessions on OHP via telecommunication method and answered questionnaires (General Self-Efficacy (GSE) Scale, Patient Health Questionnaire and Generalized Anxiety Disorder Questionnaire) via computer-assisted self-interview. Data collection was done before the start of the intervention, at the end of the intervention and a month post-intervention.
RESULTS: A total of 37 patients were recruited and more than half of the subjects were males (62.2%), single (75.5%) and from the Malay ethnicity (78.4%). Seventy-three per cent of subjects had received tertiary education, and most of them were students reflecting a higher unemployment status (73%). Most subjects have no comorbid chronic medical illness (89.2%), and none has a comorbid psychiatric illness. Comparison of the GSE score across 3-time points (preintervention, immediate post-intervention and a month postintervention) showed statistically significant improvement in the mean total GSE score immediate and a month postintervention as compared to the pre-intervention; from mean total GSE score of 29.78 pre-intervention to 34.73 (mean difference 4.946, 95% Confidence Interval 95%CI: 3.361, 6.531) immediate post-intervention and 33.08 (mean difference 3.297, 95%CI: 1.211, 5.348) a month post intervention. There was no significant association between the socio-demographic or clinical data, depressive and anxiety symptoms, and changes in GSE scores over three time points.
CONCLUSION: COVID-19 patients improved their self-efficacy levels after the virtual brief OHP intervention, and it maintained a month post-intervention, protecting them from psychological stress and ultimately enhances wellbeing during this coronavirus pandemic.
METHODS: A modified susceptible-exposed-infectious-recovered compartmental model was developed that included two sequential incubation and infectious periods, with stratification by clinical state. The model was further stratified by age and incorporated population mobility to capture NPIs and micro-distancing (behaviour changes not captured through population mobility). Emerging variants of concern (VoC) were included as an additional strain competing with the existing wild-type strain. Several scenarios that included different vaccination strategies (i.e. vaccines that reduce disease severity and/or prevent infection, vaccination coverage) and mobility restrictions were implemented.
RESULTS: The national model and the regional models all fit well to notification data but underestimated ICU occupancy and deaths in recent weeks, which may be attributable to increased severity of VoC or saturation of case detection. However, the true case detection proportion showed wide credible intervals, highlighting incomplete understanding of the true epidemic size. The scenario projections suggested that under current vaccination rates complete relaxation of all NPIs would trigger a major epidemic. The results emphasise the importance of micro-distancing, maintaining mobility restrictions during vaccination roll-out and accelerating the pace of vaccination for future control. Malaysia is particularly susceptible to a major COVID-19 resurgence resulting from its limited population immunity due to the country's historical success in maintaining control throughout much of 2020.
CASE PRESENTATION: We present the case of a 37-year-old Malay gentleman with underlying type 2 diabetes mellitus on empagliflozin, who presented to our hospital with symptomatic coronavirus disease 2019 infection and diabetic ketoacidosis. He developed severe rebound euglycemic diabetic ketoacidosis due to the continuous usage of empagliflozin for glycemic control alongside intravenous insulin.
CONCLUSIONS: Physicians should have a high index of suspicion in diagnosing and managing euglycemic diabetic ketoacidosis, including withholding treatment of sodium-glucose cotransporter 2 inhibitors during the acute management of diabetic ketoacidosis.
DESIGN: Retrospective assessment using the Peer Assessment Rating (PAR) index.
SETTING: Consecutive patients treated by one consultant orthodontist at a tertiary care cleft center.
PARTICIPANTS: One hundred twenty-seven patients with either complete unilateral cleft lip and palate (UCLP) or bilateral cleft lip and palate (BCLP) consecutively treated with fixed appliances.
INTERVENTION: Fixed orthodontic appliance treatment and orthognathic surgery when required.
OUTCOMES: The PAR index assessment was carried out by a calibrated-independent assessor. Treatment duration, the number of patient visits, and data on dental anomalies were drawn from patient records and radiographs.
RESULTS: One hundred two patients' study models were assessed after exclusions. Mean start PAR score for UCLP (n = 71) was 43.9 (95% CI, 41.2-46.6, SD 11.5), with a mean score reduction of 84.3% (95% CI, 81.9-86.7, SD 10.1). The UCLP mean treatment time was 23.7 months with 20.1 appointments. Mean start PAR score for BCLP (n = 31) was 43.4 (95% CI, 39.2-47.6, SD 11.4), with a mean score reduction of 80.9% (95% CI, 76.3-85.5, SD 12.5). The BCLP mean treatment time was 27.8 months with 20.5 appointments.
CONCLUSION: These results compare well with other outcome reports, including those for patients without a cleft, and reflect the standard of care provided by an experienced cleft orthodontist. As with high-volume surgeons, orthodontic treatment for this high need group is favorable when provided by a high-volume orthodontist. These findings may be used for comparative audit with similar units providing cleft care.
Methods: We collected 3794 corneal images from 542 eyes of 280 subjects and developed seven deep learning models based on anterior and posterior eccentricity, anterior and posterior elevation, anterior and posterior sagittal curvature, and corneal thickness maps to extract deep corneal features. An independent subset with 1050 images collected from 150 eyes of 85 subjects from a separate center was used to validate models. We developed a hybrid deep learning model to detect KCN. We visualized deep features of corneal parameters to assess the quality of learning subjectively and computed area under the receiver operating characteristic curve (AUC), confusion matrices, accuracy, and F1 score to evaluate models objectively.
Results: In the development dataset, 204 eyes were normal, 123 eyes were suspected KCN, and 215 eyes had KCN. In the independent validation dataset, 50 eyes were normal, 50 eyes were suspected KCN, and 50 eyes were KCN. Images were annotated by three corneal specialists. The AUC of the models for the two-class and three-class problems based on the development set were 0.99 and 0.93, respectively.
Conclusions: The hybrid deep learning model achieved high accuracy in identifying KCN based on corneal maps and provided a time-efficient framework with low computational complexity.
Translational Relevance: Deep learning can detect KCN from non-invasive corneal images with high accuracy, suggesting potential application in research and clinical practice to identify KCN.