COVID-19 Pandemic Lockdown: A Cloud with a Silver Lining for the Road Traffic Accidents Pandemic.

Context: Effects of coronavirus disease 2019 (COVID-19) pandemic lockdown on road traffic accidents (RTAs) in Chhattisgarh, India. Background: Most neurosurgical emergencies are from brain and spine trauma. RTA is the leading cause of such injuries. While the nationwide lockdown was an extreme measure to control the COVID pandemic, it influenced the overall road traffic dynamics and neurotrauma. Objective: This study aims to assess the impact of the lockdown on neurotrauma. Methods and Materials: This retrospective study included all patients with brain and spine injuries who were admitted between January 17th and May 31st, 2020. The study population was divided into prelockdown (PL) and lockdown (L) groups. Results: Of the 668 patients, 436 were placed in the PL and 232 in the L group. The mean ages were 36.34 (SD = 17.96) and 35.98 (SD = 16.93), respectively. Male to female ratios were 82.3:17.7 in the PL group and 79.7:20.3 for the L group. RTA-related injuries were significantly lower during the lockdown period (n = 335 PL vs. 162 L [P = 0.048]). During the lockdown, there were more mild injuries (25.91% PL vs. 36.63% L) and less severe injuries (33.25% PL vs. 18.96% L [P = 0.0002]). Mortality was significantly less (P = 0.029) during the lockdown (n = 48 L vs. 124 PL). The proportion of RTA-related neurotrauma cases increased (33.33% L1, 57.14% L2, 73.13% L3, and 80.39% L4) with each phase of lockdown (L1-L4). Conclusions: During the lockdown period, the number of trauma cases had decreased, with a significant decrease in RTA-related admissions, along with their severity and mortality. The number of trauma cases and their severity increased gradually with each phase of lockdown.

Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm.

The majority of people in the modern biosphere struggle with depression as a result of the coronavirus pandemic's impact, which has adversely impacted mental health without warning. Even though the majority of individuals are still protected, it is crucial to check for post-corona virus symptoms if someone is feeling a little lethargic. In order to identify the post-coronavirus symptoms and attacks that are present in the human body, the recommended approach is included. When a harmful virus spreads inside a human body, the post-diagnosis symptoms are considerably more dangerous, and if they are not recognised at an early stage, the risks will be increased. Additionally, if the post-symptoms are severe and go untreated, it might harm one's mental health. In order to prevent someone from succumbing to depression, the technology of audio prediction is employed to recognise all the symptoms and potentially dangerous signs. Different choral characters are used to combine machine-learning algorithms to determine each person's mental state. Design considerations are made for a separate device that detects audio attribute outputs in order to evaluate the effectiveness of the suggested technique; compared to the previous method, the performance metric is substantially better by roughly 67%.

Development of Advanced Artificial Intelligence and IoT Automation in the Crisis of COVID-19 Detection.

Internet of Things (IoT) is a successful area for many industries and academia domains, particularly healthcare is one of the application areas that uses IoT sensors and devices for monitoring. IoT transition replaces contemporary health services with scientific and socioeconomic viewpoints. Since the epidemic began, diverse scientific organizations have been making accelerated efforts to use a wide range of tools to tackle this global challenge and the founders of IoT analytics. Artificial intelligence (AI) plays a key role in measuring, assessing, and diagnosing the risk. It could be used to predict the number of alternate incidents, recovered instances, and casualties, also used for forecasting cases. Within the COVID-19 background, IoT technologies are used to minimize COVID-19 exposure to others by prenatal screening, patient monitoring, and postpatient incident response in specified procedures. In this study, the importance of IoT technology and artificial intelligence in COVID-19 is explored, and the 3 important steps discussed such as the evaluation of networks, implementations, and IoT industries to battle COVID-19, including early detection, quarantine times, and postrecovery activities, are reviewed. In this study, how IoT handles the COVID-19 pandemic at a new level of healthcare is investigated. In this research, the long short-term memory (LSTM) with recurrent neural network (RNN) is used for diagnosis purpose and in particular, its important architecture for the analysis of cough and breathing acoustic characteristics. In comparison with both coughing and respiratory samples, our findings indicate poor accuracy of the voice test.

A Comprehensive Biological and Synthetic Perspective on 2-Deoxy-d-Glucose (2-DG), A Sweet Molecule with Therapeutic and Diagnostic Potentials.

Glucose, the primary substrate for ATP synthesis, is catabolized during glycolysis to generate ATP and precursors for the synthesis of other vital biomolecules. Opportunistic viruses and cancer cells often hijack this metabolic machinery to obtain energy and components needed for their replication and proliferation. One way to halt such energy-dependent processes is by interfering with the glycolytic pathway. 2-Deoxy-d-glucose (2-DG) is a synthetic glucose analogue that can inhibit key enzymes in the glycolytic pathway. The efficacy of 2-DG has been reported across an array of diseases and disorders, thereby demonstrating its broad therapeutic potential. Recent approval of 2-DG in India as a therapeutic approach for the management of the COVID-19 pandemic has brought renewed attention to this molecule. The purpose of this perspective is to present updated therapeutic avenues as well as a variety of chemical synthetic strategies for this medically useful sugar derivative, 2-DG.

Optical Communication Engineering Simulation Laboratory for Effective Undergraduate Education during Era of Covid19

Corona virus pandemic COVID19 has greatly affected the education sector. All academics institutes have to shut off face to face classroom teaching and learning. Engineering education, where there is a need to understand more of the practical concepts using costly hardware is greatly affected. One such engineering course, which involves this type of hardware, it requires practical understanding, is optical communication engineering. This paper describes how effectively this course will be taught from home using simulation tool. OPTSIM 5.0TM simulation tool is used to simulate all basic and advanced optical systems. Various optical systems and experiments such as Optical receiver, Optical Transmitter, Optical fiber amplifiers, Optical MUX, Optical De-MUX etc. can be designed for various fiber parameters and performance is measured using BER analyzer, Eye diagram analyzer, Optical Monitor, spectrum analyzer etc. This paper also describes how effectively the benefits of optical system and its performance using OPTSIM 5.0TM software will be taught in the virtual environment of teaching and learning.

Effect of 12 mg vs 6 mg of Dexamethasone on the Number of Days Alive Without Life Support in Adults With COVID-19 and Severe Hypoxemia: The COVID STEROID 2 Randomized Trial.

Importance: A daily dose with 6 mg of dexamethasone is recommended for up to 10 days in patients with severe and critical COVID-19, but a higher dose may benefit those with more severe disease. Objective: To assess the effects of 12 mg/d vs 6 mg/d of dexamethasone in patients with COVID-19 and severe hypoxemia. Design, Setting, and Participants: A multicenter, randomized clinical trial was conducted between August 2020 and May 2021 at 26 hospitals in Europe and India and included 1000 adults with confirmed COVID-19 requiring at least 10 L/min of oxygen or mechanical ventilation. End of 90-day follow-up was on August 19, 2021. Interventions: Patients were randomized 1:1 to 12 mg/d of intravenous dexamethasone (n = 503) or 6 mg/d of intravenous dexamethasone (n = 497) for up to 10 days. Main Outcomes and Measures: The primary outcome was the number of days alive without life support (invasive mechanical ventilation, circulatory support, or kidney replacement therapy) at 28 days and was adjusted for stratification variables. Of the 8 prespecified secondary outcomes, 5 are included in this analysis (the number of days alive without life support at 90 days, the number of days alive out of the hospital at 90 days, mortality at 28 days and at 90 days, and ≥1 serious adverse reactions at 28 days). Results: Of the 1000 randomized patients, 982 were included (median age, 65 [IQR, 55-73] years; 305 [31%] women) and primary outcome data were available for 971 (491 in the 12 mg of dexamethasone group and 480 in the 6 mg of dexamethasone group). The median number of days alive without life support was 22.0 days (IQR, 6.0-28.0 days) in the 12 mg of dexamethasone group and 20.5 days (IQR, 4.0-28.0 days) in the 6 mg of dexamethasone group (adjusted mean difference, 1.3 days [95% CI, 0-2.6 days]; P = .07). Mortality at 28 days was 27.1% in the 12 mg of dexamethasone group vs 32.3% in the 6 mg of dexamethasone group (adjusted relative risk, 0.86 [99% CI, 0.68-1.08]). Mortality at 90 days was 32.0% in the 12 mg of dexamethasone group vs 37.7% in the 6 mg of dexamethasone group (adjusted relative risk, 0.87 [99% CI, 0.70-1.07]). Serious adverse reactions, including septic shock and invasive fungal infections, occurred in 11.3% in the 12 mg of dexamethasone group vs 13.4% in the 6 mg of dexamethasone group (adjusted relative risk, 0.83 [99% CI, 0.54-1.29]). Conclusions and Relevance: Among patients with COVID-19 and severe hypoxemia, 12 mg/d of dexamethasone compared with 6 mg/d of dexamethasone did not result in statistically significantly more days alive without life support at 28 days. However, the trial may have been underpowered to identify a significant difference. Trial Registration: ClinicalTrials.gov Identifier: NCT04509973 and ctri.nic.in Identifier: CTRI/2020/10/028731.