METHODS: Valproic acid-encapsulated nanoemulsions were formulated and physically characterised (osmolarity, viscosity, drug content, drug encapsulation efficiency). Further investigations were also conducted to estimate the drug release, cytotoxic profile, in-vitro blood-brain barrier (BBB) permeability, pharmacokinetic parameter and the concentration of VPA and VANE in blood and brain.
KEY FINDINGS: Physical characterisation confirmed that VANE was suitable for parenteral administration. Formulating VPA into nanoemulsion significantly reduced the cytotoxicity of VPA. In-vitro drug permeation suggested that VANEs crossed the BBB as freely as VPA. Pharmacokinetic parameters of VANE-treated rats in plasma and brain showed F3 VANE had a remarkable improvement in AUC, prolongation of half-life and reduction in clearance compared to VPA. Given the same extent of in-vitro BBB permeation of VPA and VANE, the higher bioavailability of VANE in brain was believed to have due to higher concentration of VANE in blood. The brain bioavailability of VPA was improved by prolonging the half-life of VPA by encapsulating it within the nanoemulsion-T80.
CONCLUSIONS: Nanoemulsion containing VPA has alleviated the cytotoxic effect of VPA and improved the plasma and brain bioavailability for parenteral delivery of VPA.
Patients and Methods: A cross-sectional study was conducted at a single rehabilitation outpatient clinic from June to December 2019. Inclusion criteria were stroke duration of over four weeks, aged 18 years and above. Exclusion criteria were presence of concurrent conditions other than stroke that could also lead to spasticity. Recruited patients were divided into "Spasticity" and "No spasticity" groups. Univariate analysis was deployed to identify significant predictive spasticity factors between the two groups followed by a two-step clustering approach for determining group of characteristics that collectively contributes to the risk of developing spasticity in the "Spasticity" group.
Results: A total of 216 post-stroke participants were recruited. The duration after stroke (p < 0.001) and the absence of hemisensory loss (p = 0.042) were two significant factors in the "Spasticity" group revealed by the univariate analysis. From a total of 98 participants with spasticity, the largest cluster of individuals (40 patients, 40.8%) was those within less than 20 months after stroke with moderate stroke and absence of hemisensory loss, while the smallest cluster was those within less than 20 months after severe stroke and absence of hemisensory loss (21 patients, 21.4%).
Conclusion: Analyzing collectively the significant factors of developing spasticity may have the potential to be more clinically relevant in a heterogeneous post-stroke population that may assist in the spasticity management and treatment.
METHODOLOGY: An online cross-sectional study was conducted via non-probabilistic convenience sampling. Data were collected on sociodemographic characteristics, lifestyle, COVID-19 related influences. Mental health status was assessed with depression, anxiety, and stress scale (DASS-21).
RESULTS: 388 students participated this study (72.4% female; 81.7% Bachelor's student). The prevalence of moderate to severe depression, anxiety and stress among university students are 53.9%, 66.2% and 44.6%, respectively. Multivariable logistic regression analysis found that the odds of depression were lower among students who exercise at least 3 times per week (OR: 0.380, 95% CI: 0.203-0.711). The odd ratio of student who had no personal history of depression to had depression, anxiety and stress during this pandemic was also lower in comparison (OR: 0.489, 95% CI: 0.249-0.962; OR: 0.482, 95% CI: 0.241-0.963; OR: 0.252, 95% CI: 0.111-0.576). Surprisingly, students whose are currently pursuing Master study was associated with lower stress levels (OR: 0.188, 95% CI: 0.053-0.663). However, student who had poorer satisfaction of current learning experience were more likely to experience stress (OR: 1.644, 95% CI: 1.010-2.675).
LIMITATIONS: It is impossible to establish causal relationships between variables on mental health outcomes, and there is a risk of information bias.
CONCLUSION: The prevalence of mental health issues among university students is high. These findings present essential pieces of predictive information when promoting related awareness among them.
MATERIALS AND METHODS: For women in the intervention arm (n = 130), they received one session of individualized health education at 36 gestational weeks, a booklet of diabetes prevention, five-session of postpartum booster educational program which were conducted including 1 session of dietary and exercise counseling by dietician and physiotherapist at 6 weeks postpartum. For women in the control group (n = 168), standard treatment whereby they had received group therapy on diet and physical activity modification by dietician and staff nurses during the antenatal period.
RESULTS: There were no significant differences in baseline characteristics between groups for most of the variables examined except for educational level which the control group had a higher education than the intervention group. The women assigned to system-based intervention have a significant difference to GDM women who were assigned to the control group for LDL and HDL but not in anthropometric measurements, blood pressure, glucose index, total cholesterol, and triglyceride. In addition, it was found that the incidence of Type 2 diabetes mellitus (T2DM) 2 years after delivery was 20% in the intervention arm compared to 17% in the control arm.
CONCLUSION: The system-based intervention was not statistically superior to the control intervention as there is no difference in terms of incidence of T2DM between the intervention and control group. We, therefore, suggested that more intensive interventions are needed to prevent GDM from developing into T2DM.
METHOD: This online-based cross-sectional study was conducted among 1280 healthcare providers aged ≥18 years from 30 primary care clinics in the state of Selangor, Malaysia. The Fear of COVID-19 Scale was used to assess the level of fear, and the results were analysed using multiple linear regression.
RESULTS: The mean age of the respondents was 36 years, and the mean working experience was 11 years. The majority of the respondents were women (82.4%) and Malays (82.3%). The factors that were significantly correlated with higher levels of fear were underlying chronic disease (ß=1.12, P=0.002, 95% confidence interval [CI]=0.08, 3.15), concern about mortality from COVID-19 (ß=3.3, P<0.001, 95% CI=0.19, 7.22), higher risk of exposure (ß=0.8, P<0.001, 95% CI=0.14, 5.91), concern for self at work (ß=2.8, P=0.002, 95% CI=0.08, 3.10) and work as a nurse (ß=3.6, P<0.001, 95% CI=0.30, 7.52), medical laboratory worker (ß=3.0, P<0.001, 95% CI=0.12, 4.27) and healthcare assistant (ß=3.9, P<0.001, 95% CI=0.17, 5.73). The level of fear was inversely correlated with a higher work-related stress management score (ß=-0.9, P<0.001, 95% CI=-0.14, -5.07) and a higher sleep quality score (ß=-1.8, P<0.001, 95% CI=-0.28, -10.41).
CONCLUSION: Family physicians should be vigilant and identify healthcare providers at risk of developing COVID-19-related fear to initiate early mental health intervention.
METHODS: We identified children ≤ 12 years old hospitalized for COVID-19 across five hospitals in Negeri Sembilan, Malaysia, from 1 January 2021 to 31 December 2021 from the state's pediatric COVID-19 case registration system. The primary outcome was the development of moderate/severe COVID-19 during hospitalization. Multivariate logistic regression was performed to identify independent risk factors for moderate/severe COVID-19. A nomogram was constructed to predict moderate/severe disease. The model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, and accuracy.
RESULTS: A total of 1,717 patients were included. After excluding the asymptomatic cases, 1,234 patients (1,023 mild cases and 211 moderate/severe cases) were used to develop the prediction model. Nine independent risk factors were identified, including the presence of at least one comorbidity, shortness of breath, vomiting, diarrhea, rash, seizures, temperature on arrival, chest recessions, and abnormal breath sounds. The nomogram's sensitivity, specificity, accuracy, and AUC for predicting moderate/severe COVID-19 were 58·1%, 80·5%, 76·8%, and 0·86 (95% CI, 0·79 - 0·92) respectively.
CONCLUSION: Our nomogram, which incorporated readily available clinical parameters, would be useful to facilitate individualized clinical decisions.
METHODS: A retrospective, multicentre, observational study was performed among children ≤15 years old who were hospitalized for MIS-C between January 18, 2021 and June 30, 2023. The incidence of MIS-C was estimated using reported SARS-CoV-2 cases and census population data. Descriptive analyses were used to summarize the clinical presentation and outcomes.
RESULTS: The study included 53 patients with a median age of 5.7 years (IQR 1.8-8.7 years); 75.5% were males. The overall incidence of MIS-C was approximately 5.9 cases per 1,000,000 person-months. Pediatric intensive care unit (PICU) admission was required for 22 (41.5%) patients. No mortalities were recorded. Children aged 6-12 years were more likely to present with cardiac dysfunction/shock (odds ratio [OR] 5.43, 95% confidence interval [CI] 1.67-17.66), whereas children below 6 years were more likely to present with a Kawasaki disease phenotype (OR 5.50, 95% CI 1.33-22.75). Twenty patients (37.7%) presented with involvement of at least four organ systems, but four patients (7.5%) demonstrated single-organ system involvement.
CONCLUSION: An age-based variation in the clinical presentation of MIS-C was demonstrated. Our findings suggest MIS-C could manifest in a spectrum, including single-organ involvement. Despite the high requirement for PICU admission, the prognosis of MIS-C was favorable, with no recorded mortalities.