Point prevalence probing of antimicrobial prescription patterns from a developing country.

BACKGROUND: Irrational use of antibiotics intensifies resistance and jeopardizes advances made in modern medicine. We aimed to conduct a baseline gap analysis survey on antibiotic prescription practices across Pakistan. RESEARCH DESIGN AND METHODS: This multi-centered cross-sectional survey was conducted at six public sector tertiary care hospitals from February 2021 to March 2021. Data related to various variables including hospital infrastructure, policies and practices, monitoring and feedback, and epidemiological, clinical, and antibiotic prescription for surveyed patients was collected using World Health Organization (WHO) Point Prevalence Survey (PPS) methodology. RESULTS: In a survey of 837 inpatients, 78.5% were prescribed antibiotics. Most commonly prescribed antimicrobial was ceftriaxone (21.7%), followed by metronidazole (17.3%), cefoperazone-sulbactam (8.4%), amoxicillin-clavulanate (6.3%), and piperacillin/tazobactam (5.9%). Surgical prophylaxis (36.7%) and community-acquired infections (24.7%) were the main reasons for antibiotic prescriptions. Single antibiotics were given to 46.7% of patients, 39.9% received a combination of two antibiotics, and 12.5% were prescribed three or more antibiotics. Among six hospitals surveyed, two had drug and therapeutic committees, three had infection prevention and control committees, and one had an antibiotic formulary. CONCLUSION: Findings demonstrate high consumption of broad-spectrum antimicrobials and emphasize the importance of expanding antimicrobial stewardship programs among hospitals. Mentoring clinical teams could help rationalize antimicrobial use.

Potential of GJA8 gene variants in predicting age-related cataract: A comparison of supervised machine learning methods.

Cataracts are the problems associated with the crystallins proteins of the eye lens. Any perturbation in the conformity of these proteins results in a cataract. Age-related cataract is the most common type among all cataracts as it accounts for almost 80% of cases of senile blindness worldwide. This research study was performed to predict the role of single nucleotide polymorphisms (SNPs) of the GJA8 gene with age-related cataracts in 718 subjects (400 age-related cataract patients and 318 healthy individuals). A comparison of supervised machine learning classification algorithm including logistic regression (LR), random forest (RF) and Artificial Neural Network (ANN) were presented to predict the age-related cataracts. The results indicated that LR is the best for predicting age-related cataracts. This successfully developed model after accounting different genetic and demographic factors to predict cataracts will help in effective disease management and decision-making medical practitioner and experts.

Knowledge, Attitude, Practice, Behavior and Risk Perception of COVID-19 Pandemic among Medical and non-Medical University Students.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) pandemic has substantially affected students around the globe due to the closure of educational institutes. However, student involvements and contributions are important in combating the disease; for this reason, the current study was designed to assess the knowledge-attitude-practice (KAP), preventive behavior, and risk perception among university students. METHODS: A cross-sectional survey-based study was conducted among medical and non-medical university students, from April 1 to June 30, 2020. The 68-item questionnaire was used to evaluate responses using statistical approaches (Student's t-test, regression-analysis, and co-relation analysis) by considering a P-value <0.05 as statistically significant. RESULTS: A total of 503 university students (medical and nonmedical) were selected, where majority of participants were females (83%) and 64.5% were of age ranged from 16 to 21 years old. The participants (80%) reported good disease knowledge with a mean score of 12.06 ± 1.75, which substantially higher among medical students (P < 0.05). Most of the respondents (72%) believed that COVID-19 will be effectively controlled through precautionary measures. In correlation subgroup analysis, a significant relationship (P = 0.025) between knowledge and positive attitude were indicated. Fear and knowledge of COVID-19 emerged as strong predictors (P < 0.001) of preventive behaviors towards disease. CONCLUSION: This study demonstrated satisfactory knowledge, positive attitudes, and suitable practices among students toward COVID-19. University students can be involved in public education to aid the health authorities in achieving the targets of educational campaigns with maximum population coverage.