METHODS: This was a cross-sectional study using self-administered questionnaire conducted in medical wards of fourteen Perak state public hospitals from September to October 2020. In-patients aged ≥18 years old were included. The validated questionnaire had four domains. The student's t-test, one-way analysis of variance (ANOVA) and multiple linear regression were was employed to evaluate the association between patients' demographic characteristics with their awareness, expectation and satisfaction towards ward pharmacy services.
RESULTS: 467 patients agreed to participate (response rate = 83.8%) but only 441 were analysed. The mean age of the patients was 54.9 years. Majority was male (56.2%), Malay (77.3%), with secondary education (62.9%), rural resident (57.1%) and reported good medication adherence (61.6%). The mean awareness score was 49.6 out of 60. Patients were least aware about drug-drug interaction (3.85 ± 1.15) and proper storage of medications (3.98 ± 1.06). Elderly patients (β = - 2.82, P
Objective: We summarize the re-emergence and response of the Nipah virus outbreaks (NiVD) in Kerala, India, about 1800 kms away, a decade later in 2018 and 2019. The paper recapitulates involvement of various stakeholders from the Ministry of Health and Family Welfare, Directorate of Health Research, Indian Council of Agricultural Research, State Health Department, State Animal Husbandry, District Administration, and multidisciplinary response mechanism during the NiVD outbreaks of 2018 and 2019.
Methods: Information was collected from the Press Information Bureau (PIB), media/weekly alerts from the Integrated Disease Surveillance Programme (IDSP), news articles from print and electronic media, newsletters, advisories from the National Centre for Disease Control (NCDC), Disease Outbreak News (DON), World Health Organization (WHO), and published papers from various stakeholders.
Findings & Conclusion: The evidence of NiV in humans and bats, with samples collected from the outbreak sites, was laboratory confirmed. The multidisciplinary response mechanisms during the 2018 outbreak helped in further understanding the importance of the One Health approach for systemic and streamlined response utilizing existing surveillance systems. This was of utmost help in the subsequent outbreak of the disease that occurred during 2019, wherein there was no documented spread of disease from the index case and no mortality was observed. This success reiterates the need for institutionalizing the involvement and cooperation of various departments and organizations during public health emergencies, especially of Zoonotic diseases, using the One Health approach.
OBJECTIVE: To evaluate the potential relationship between falls and diabetes in older persons and identify differences in risk factors of falls among older persons with and without diabetes using the first wave dataset of the Malaysian Elders Longitudinal Research (MELoR) study.
METHODOLOGY: Community dwelling adults aged ≥ 55 years were selected through stratified random sampling from three parliamentary constituencies in greater Kuala Lumpur. Baseline data was obtained through computer-assisted, home-based interviews. The presence of falls was established by enquiring about falls in the preceding 12 months. Diabetes was defined as self-reported, physician-diagnosed diabetes, diabetes medication use and an HbA1c of ≥ 6.3%.
RESULTS: Diabetes was present in 44.4% of the overall 1610 participants. The prevalence for fall among older diabetics was 25.6%. Recurrent falls (odds ratio (OR) 1.65; 95% confidence interval (CI) 1.06-2.57) was more common among diabetics. Following adjustment for potential confounders, osteoporosis (OR 2.58; 95% CI 1.31-5.08) and dizziness (OR 1.50; 95% CI 1.01-2.23) were independent risk factors for falls. Better instrumental activities of daily living scores were protective against falls (OR 0.75; 95% CI 0.58-0.97).
CONCLUSION: The presence of osteoporosis and dizziness was associated with an increased risk of falls among older diabetics. These findings will need to be confirmed in future prospective follow-up of this cohort.
METHODS: A cross-sectional online survey was conducted in four major cities in Yemen. The constructed questionnaire consisted of four main sections (sociodemographic data, misinformation, perceptions (perceived susceptibility, severity, and worry), and vaccination acceptance evaluation). Subject recruitment and data collection were conducted online utilizing social websites and using the snowball sampling technique. Descriptive and inferential analyses were performed using SPSS version 27.
RESULTS: The total number of respondents was 484. Over 60% of them were males and had a university education. More than half had less than 100$ monthly income and were khat chewers, while only 18% were smokers. Misinformation prevalence ranged from 8.9% to 38.9%, depending on the statement being asked. Men, university education, higher income, employment, and living in urban areas were associated with a lower misinformation level (p <0.05). Statistically significant association (p <0.05) between university education, living in urban areas, and being employed with perceived susceptibility were observed. The acceptance rate was 61.2% for free vaccines, but it decreased to 43% if they had to purchase it. Females, respondents with lower monthly income, and those who believed that pharmaceutical companies made the virus for financial gains were more likely to reject the vaccination (p <0.05).
CONCLUSION: The study revealed that the acceptance rate to take a vaccine was suboptimal and significantly affected by gender, misinformation, cost, and income. Furthermore, being female, non-university educated, low-income, and living in rural areas were associated with higher susceptibility to misinformation about COVID-19. These findings show a clear link between misinformation susceptibility and willingness to vaccinate. Focused awareness campaigns to decrease misinformation and emphasize the vaccination's safety and efficacy might be fundamental before initiating any mass vaccination in Yemen.
MAIN METHODS: A pull-down assay was performed to identify the binding partner of the L-SP40 peptide. Co-immunoprecipitation and co-localization assays with the L-SP40 peptide were employed to confirm the receptor partner in RD cells. The outcomes were validated using receptor knockdown and antibody blocking assays. The L-SP40 peptide was further evaluated for the protection of neonatal mice against lethal challenge by mouse-adapted EV-A71.
KEY FINDINGS: The L-SP40 peptide was found to interact and co-localize with nucleolin, the key attachment receptor of Enteroviruses A species, as demonstrated in the pull-down, co-immunoprecipitation and co-localization assays. Knockdown of nucleolin from RD cells led to a significant reduction of 3.5 logs of viral titer of EV-A71. The L-SP40 peptide demonstrated 80% protection of neonatal mice against lethal challenge by the mouse-adapted virus with a drastic reduction in the viral loads in the blood (~4.5 logs), skeletal muscles (1.5 logs) and brain stem (1.5 logs).
SIGNIFICANCE: L-SP40 peptide prevented severe hind limb paralysis and death in suckling mice and could serve as a potential broad-spectrum antiviral candidate to be further evaluated for safety and potency in future clinical trials against EV-A71.
METHOD: For designing and modeling the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials were used. Different Machine learning-based feature ranking techniques were investigated to identify the important MNSI features associated with DSPN diagnosis. A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading using the best performing top-ranked MNSI features.
RESULTS: Top-10 ranked features from MNSI features: Appearance of Feet (R), Ankle Reflexes (R), Vibration perception (L), Vibration perception (R), Appearance of Feet (L), 10-gm filament (L), Ankle Reflexes (L), 10-gm filament (R), Bed Cover Touch, and Ulceration (R) were identified as important features for identifying DSPN by Multi-Tree Extreme Gradient Boost model. The nomogram-based prediction model exhibited an accuracy of 97.95% and 98.84% for the EDIC test set and an independent test set, respectively. A DSPN severity score technique was generated for MNSI from the DSPN severity prediction model. DSPN patients were stratified into four severity levels: absent, mild, moderate, and severe using the cut-off values of 17.6, 19.1, 20.5 for the DSPN probability less than 50%, 75%-90%, and above 90%, respectively.
CONCLUSIONS: The findings of this work provide a machine learning-based MNSI severity grading system which has the potential to be used as a secondary decision support system by health professionals in clinical applications and large clinical trials to identify high-risk DSPN patients.