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Introduction Increasing evidence suggests Amyotrophic Lateral Sclerosis (ALS) as a widespread pathological process comprising nonmotor features like fatigue, mild sensory symptoms, cognitive decline, and visual impairment. Measurements of retinal nerve fiber layer (RNFL) thickness using Optical Coherence Tomography (OCT) may correlate with the neurodegeneration associated with ALS. In addition to RNFL thickness, other OCT parameters have been explored in the context of diagnosing ALS and predicting disease severity. In this study, we explore the possibility that OCT parameters of patients with ALS may differ significantly from those of healthy controls and thus serve as biomarkers for the disease and its progression. Materials and methods Between 2010 and 2021, the PubMed and EMBASE databases were examined for English language literature. ALS severity was assessed using the revised ALS functional rating scale (ALSFRS‐R). The pooled mean differences in RNFL thickness between ALS patients and controls were calculated using the Standard Mean Difference (Hedges's g) with a 95% confidence interval (CI) in STATA software version 16. Results Eleven studies were reviewed for data collection. RNFL thickness was not statistically significantly different between ALS patients (n = 412) and controls (n = 376) (Hedges's g = –0.22; 95% CI: –0.51 to 0.07, I2 = 73.04%, p = .14). However, the thickness of inner nuclear layer was significantly different between ALS patients and controls (Hedges's g = –0.38; 95% CI: –0.61 to 0.14, I2 = 14.85%, p = .00). Conclusion Our meta‐analysis found that RNFL thickness as a whole or by individual quadrants was not significantly different between ALS patients and controls while the inner nuclear layer (INL) was substantially thinner.

Coronavirus Disease 2019 (COVID-19) burden, often underestimated by case-based incidence reports, can be accurately estimated by measuring the population that has developed antibodies following an infection. Here, we report the prevalence of COVID-19 antibodies among health workers in Kathmandu, Nepal. This seroepidemiology of COVID-19 was a longitudinal survey of hospital-based health workers working in 20 hospitals in the Kathmandu Valley. A total of 800 participants were chosen in December 2020 by a two-stage cluster-stratified random sampling method and administered a questionnaire eliciting COVID-19 related history. A blood sample was also obtained from the participants and tested for COVID-19 IgG antibodies using a Chemiluminescence Immunoassay (CLIA). We then used a probabilistic multilevel regression model with post-stratification to correct for test accuracy, the effect of hospital-based clustering, and to ensure representativeness. The final analytic sample included 800 participants; 522 (65.2%) of them were female, 372 (46%) were between ages 18-29, 287 (36%) were nurses. Of the total 800, 321 (40.1%) individuals tested positive for COVID-19 antibodies. Adjusted for test accuracy and health-worker population, the seroprevalence was 38.2% (95% Credible Interval (CrI) 29.26%–47.82%). Posterior predictive hospital-wise seroprevalence ranged between 38.1% (95% CrI 30.7.0%–44.1%) and 40.5% (95% CrI 34.7%–47.0%). Our study suggested that about two in five health workers in the Kathmandu Valley were seropositive against SARS-CoV-2 by December 2020; a substantial proportion of them did not have a documented infection.