Trends in Traumatic Brain Injuries During the COVID-19 Pandemic: A Single-Center Review of Patient Charts From Pakistan.

Introduction A traumatic brain injury (TBI) is one of the leading causes of injury-related deaths, making it a public health concern of extreme importance. In a developing country such as Pakistan, TBIs are significantly underreported, with the treatment frequently being delayed and inadequate, especially in rural healthcare setups all across the country. This concern is further magnified by insufficient epidemiological data on TBIs available in Pakistan. The coronavirus disease 2019 (COVID-19) pandemic brought consequential changes to the healthcare system with the priority shifting toward COVID-19 patients, resulting in considerable changes to the workflow and management of TBIs. The primary objective of this study is to offer valuable insights into the epidemiology of TBIs in Pakistan and its relationship with the impact of the COVID-19 pandemic.  Methods A retrospective study was conducted at a tertiary care center in a metropolitan city in Pakistan. Patient charts were reviewed from January to August 2020, and data was extracted including demographics, clinical presentation, management, and outcomes for cases of TBI. Results The total number of patients is 2126, male 78% and female 21.4%. The mean age of the patients was 28.85. The state of admissions at the hospital is at 99.7% for EME admissions and 0.282% for OPD admissions. Participants presented with loss of consciousness (70.7%), nosebleeds, (53.2%), vomiting (69.0%), and seizures (11.5%). The majority (51.1%) were related to road traffic accidents, followed by falls (20.7%), and assaults (4%). While 1202 (58.5%) of these were managed conservatively, others underwent surgical treatment in the form of craniotomy (28.0%), Burr holes (3.20%), and fracture elevation and repair (10.5%). A decrease in the number of reported TBI cases was observed with lockdown implementation in Pakistan. Conclusion The transportation sector in Pakistan was severely affected by the COVID-19 pandemic, leading to a decline in road traffic injuries and TBIs. Stringent mobility constraints and changes in societal and cultural norms have contributed to this reduction.

Gender bias and 'doctor brides'. A social dilemma of medical students in Pakistan.

OBJECTIVE: To assess gender bias, discrimination and bullying at medical schools, and to explore the phenomenon of 'doctor brides'. METHODS: The multicentre survey was conducted at 14 medical education institutions across Pakistan from September 2020 to April 2021, and comprised medical students of either gender at both public-sector and private-sector institutions. The survey questions explored beliefs, experiences and knowledge related to common stereotypes and social issues in medical education, including female role models, work-life balance, gender roles, lack of support from family and faculty, and bullying. Association between gender with survey variables was explored. Data was analysed using SPSS 26. Thematic analysis was used to exploring knowledge around 'doctor-brides'. RESULTS: Of the 377 subjects, 245(65%) were females. The overall mean age was 21.4±1.8 years. There were 211(53.8%) subjects aged 21-23 years, and 368(97.6%) were Muslims. Significantly more women than men were of the opinion that men are encouraged and are more likely to assume leadership roles (p=0.002). More women than males agreed that household chores and work had an impact on speciality choice (p<0.001). Most sexual assault victims were women (p<0.0001), but men generally faced more bullying and hostile behaviour (p=0.014). With regard to women being forced to quit medicine after marriage/childbirth by their in-laws/husbands or change their careers from clinical medicine to preclinical teaching, 99(26.25%) subjects knew first-hand of such cases, while 238(63.12%) had no such experience to share. CONCLUSIONS: Gender bias, discriminatory behaviour and bullying were found to be widely prevalent in medical schools across Pakistan. The general perception of 'doctor brides' needs to be revisited.

Dysregulated Alanine as a Potential Predictive Marker of Glioma-An Insight from Untargeted HRMAS-NMR and Machine Learning Data.

Metabolic alterations play a crucial role in glioma development and progression and can be detected even before the appearance of the fatal phenotype. We have compared the circulating metabolic fingerprints of glioma patients versus healthy controls, for the first time, in a quest to identify a panel of small, dysregulated metabolites with potential to serve as a predictive and/or diagnostic marker in the clinical settings. High-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HRMAS-NMR) was used for untargeted metabolomics and data acquisition followed by a machine learning (ML) approach for the analyses of large metabolic datasets. Cross-validation of ML predicted NMR spectral features was done by statistical methods (Wilcoxon-test) using JMP-pro16 software. Alanine was identified as the most critical metabolite with potential to detect glioma with precision of 1.0, recall of 0.96, and F1 measure of 0.98. The top 10 metabolites identified for glioma detection included alanine, glutamine, valine, methionine, N-acetylaspartate (NAA), γ-aminobutyric acid (GABA), serine, α-glucose, lactate, and arginine. We achieved 100% accuracy for the detection of glioma using ML algorithms, extra tree classifier, and random forest, and 98% accuracy with logistic regression. Classification of glioma in low and high grades was done with 86% accuracy using logistic regression model, and with 83% and 79% accuracy using extra tree classifier and random forest, respectively. The predictive accuracy of our ML model is superior to any of the previously reported algorithms, used in tissue- or liquid biopsy-based metabolic studies. The identified top metabolites can be targeted to develop early diagnostic methods as well as to plan personalized treatment strategies.

Availability, Use, and Satisfaction of Personal Protective Equipment Among Healthcare Workers: A Cross-Sectional Assessment of Low- and Middle-Income Countries.

OBJECTIVE: To assess the discrepancy among and within low- and middle-income countries (LMICs) regarding PPE availability, use, and satisfaction. METHODS: The study population consisted of healthcare workers from LMICs who partook in the questionnaire survey from March 1, 2020, until April 15, 2020. RESULTS: In the bivariate analysis, gender (P = 0.05), HCWs (P < 0.01), and level of care (P < 0.01) were associated with the public or private sector (P < 0.05). Using multivariate analysis, PPE factors were associated with the health sector (p < 0.05). The multivariate logistic regression model determined a Pearson's χ value of 706.736 (df = 726, P = -0.689) and a c-statistic of 0.592, indicating a good model. CONCLUSION: In LMICs, huge discrepancies are present in PPE provision to HCWs, especially among the public healthcare sectors. Efforts at national and international levels ought to be addressed to protect frontline HCWs at higher risk of contracting COVID-19.