Inducible nitric oxide synthase deficiency promotes murine-ß-coronavirus induced demyelination.

BACKGROUND: Multiple sclerosis (MS) is characterized by neuroinflammation and demyelination orchestrated by activated neuroglial cells, CNS infiltrating leukocytes, and their reciprocal interactions through inflammatory signals. An inflammatory stimulus triggers inducible nitric oxide synthase (NOS2), a pro-inflammatory marker of microglia/macrophages (MG/Mφ) to catalyze sustained nitric oxide production. NOS2 during neuroinflammation, has been associated with MS disease pathology; however, studies dissecting its role in demyelination are limited. We studied the role of NOS2 in a recombinant ß-coronavirus-MHV-RSA59 induced neuroinflammation, an experimental animal model mimicking the pathological hallmarks of MS: neuroinflammatory demyelination and axonal degeneration. OBJECTIVE: Understanding the role of NOS2 in murine-ß-coronavirus-MHV-RSA59 demyelination. METHODS: Brain and spinal cords from mock and RSA59 infected 4-5-week-old MHV-free C57BL/6 mice (WT) and NOS2-/- mice were harvested at different disease phases post infection (p.i.) (day 5/6-acute, day 9/10-acute-adaptive and day 30-chronic phase) and compared for pathological outcomes. RESULTS: NOS2 was upregulated at the acute phase of RSA59-induced disease in WT mice and its deficiency resulted in severe disease and reduced survival at the acute-adaptive transition phase. Low survival in NOS2-/- mice was attributed to (i) high neuroinflammation resulting from increased accumulation of macrophages and neutrophils and (ii) Iba1 + phagocytic MG/Mφ mediated-early demyelination as observed at this phase. The phagocytic phenotype of CNS MG/Mφ was confirmed by significantly higher mRNA transcripts of phagocyte markers-CD206, TREM2, and Arg1 and double immunolabelling of Iba1 with MBP and PLP. Further, NOS2 deficiency led to exacerbated demyelination at the chronic phase as well. CONCLUSION: Taken together the results imply that the immune system failed to control the disease progression in the absence of NOS2. Thus, our observations highlight a protective role of NOS2 in murine-ß-coronavirus induced demyelination.

Towards smart surveillance as an aftereffect of COVID-19 outbreak for recognition of face masked individuals using YOLOv3 algorithm.

The eruption of COVID-19 pandemic has led to the blossoming usage of face masks among individuals in the communal settings. To prevent the transmission of the virus, a mandatory mask-wearing rule in public areas has been enforced. Owing to the use of face masks in communities at different workplaces, an effective surveillance seems essential because several security analyses indicate that face masks may be used as a tool to hide the identity. Therefore, this work proposes a framework for the development of a smart surveillance system as an aftereffect of COVID-19 for recognition of individuals behind the face mask. For this purpose, transfer learning approach has been employed to train the custom dataset by YOLOv3 algorithm in the Darknet neural network framework. Moreover, to demonstrate the competence of YOLOv3 algorithm, a comparative analysis with YOLOv3-tiny has been presented. The simulated results verify the robustness of YOLOv3 algorithm in the recognition of individuals behind the face mask. Also, YOLOv3 algorithm achieves a mAP of 98.73% on custom dataset, outperforming YOLOv3-tiny by approximately 62%. Moreover, YOLOv3 algorithm provides adequate speed and accuracy on small faces.

Impact of Covid Pandemic and Working Strategies on Private Practitioners

Background: The COVID-19 pandemic is badly affecting more than 2 million population of 213 countries and has caused 1,952,976 deaths till January 12, 2020. India has also suffered a great loss in terms of economy and people. Furthermore, owing to the complete lockdown enforced in India, restricting humanitarian movement also affected day-to-day life of Indians with a huge impact. Objectives: The present clinical trial was carried out to evaluate the impact of the COVID-19 pandemic on private practitioners. Furthermore, changes in their practice pattern were evaluated along with the perspective of their health, role, and response to the pandemic. Materials and Methods: The study includes a total of 120 health-care professionals of either gender. The stress and mental health of the participants was assessed using Depression Anxiety Stress Scale-21 in English format, also included the questions related to the demographic characteristics, use of technologies for providing virtual health care, changes in the working during the pandemic, and an opinion of their experiences during that period. The data collected were subjected to the analysis and were kept confidential and anonymous. Results: 16.6% of participants preferred virtual consultation before the pandemic in contrast to 83.3% during the pandemic. The most preferred method remained the phone call by 15.8% (n = 19) patients. Depression was seen severely in 35% of study participants (n = 42) and 13.3% of participants extremely severe depression was seen (n = 16). Severe and extremely severe anxiety was noticed in 31.66% (=38) and 15% (18) study participants respectively, whereas severe and extremely severe stress was found in 30% (n = 36), and 12.5% (n = 15) private practitioners. The most common factor that can be considered responsible for compromised mental health in private practitioners was the possibility of infecting the society seen in 59.1% (n = 71) participants. Conclusion: During the pandemic breakout, private practitioners suffered a great challenge both financially and mentally, especially during the lockdown. Furthermore, a significant increase was seen in the virtual consultation methods during the pandemic breakout.

Biomarkers-based assessment of triclosan toxicity in aquatic environment: A mechanistic review.

Triclosan (TCS), an emergent pollutant, is raising a global concern due to its toxic effects on organisms and aquatic ecosystems. The non-availability of proven treatment technologies for TCS remediation is the central issue stressing thorough research on understanding the underlying mechanisms of toxicity and assessing vital biomarkers in the aquatic organism for practical monitoring purposes. Given the unprecedented circumstances during COVID 19 pandemic, a several-fold higher discharge of TCS in the aquatic ecosystems cannot be considered a remote possibility. Therefore, identifying potential biomarkers for assessing chronic effects of TCS are prerequisites for addressing the issues related to its ecological impact and its monitoring in the future. It is the first holistic review on highlighting the biomarkers of TCS toxicity based on a comprehensive review of available literature about the biomarkers related to cytotoxicity, genotoxicity, hematological, alterations of gene expression, and metabolic profiling. This review establishes that biomarkers at the subcellular level such as oxidative stress, lipid peroxidation, neurotoxicity, and metabolic enzymes can be used to evaluate the cytotoxic effect of TCS in future investigations. Micronuclei frequency and % DNA damage proved to be reliable biomarkers for genotoxic effects of TCS in fishes and other aquatic organisms. Alteration of gene expression and metabolic profiling in different organs provides a better insight into mechanisms underlying the biocide's toxicity. In the concluding part of the review, the present status of knowledge about mechanisms of antimicrobial resistance of TCS and its relevance in understanding the toxicity is also discussed referring to the relevant reports on microorganisms.

Data Analytics and Mathematical Modeling for Simulating the Dynamics of COVID-19 Epidemic—A Case Study of India

The global explosion of the COVID-19 pandemic has created worldwide unprecedented health and economic challenges which stimulated one of the biggest annual migrations globally. In the Indian context, even after proactive decisions taken by the Government, the continual growth of COVID-19 raises questions regarding its extent and severity. The present work utilizes the susceptible-infected-recovered-death (SIRD) compartment model for parameter estimation and fruitful prediction of COVID-19. Further, various optimization techniques such as particle swarm optimization (PSO), gradient (G), pattern search (PS) and their hybrid are employed to solve the considered model. The simulation study endorse the efficiency of PSO (with or without G) and G+PS+G over other techniques for ongoing pandemic assessment. The key parametric values including characteristic time of infection and death and reproduction number have been estimated as 60 days, 67 days and 4.78 respectively by utilizing the optimum results. The model assessed that India has passed its peak duration of COVID-19 with more than 81% recovery and only a 1.59% death rate. The short duration analysis (15 days) of obtained results against reported data validates the effectiveness of the developed models for ongoing pandemic assessment.