Repurposing Methylene Blue for the Management of COVID-19: Prospects, Paradox, and Perspective

COVID-19 onslaught has led to widespread morbidity and mortality globally. Another major concern, especially in developing countries like India, has been the development of fungal superinfection and colonization of other pathogens in hospitalized COVID-19 patients. Even though an armamentarium of repurposed, antiviral, anticytokine, and antifungal drugs is available to manage the disease progression, no single drug and/or therapy has provided positive clinical outcomes with efficacy and affordability. Therefore, it is imperative to explore innovative approaches for standalone treatment and/or adjunct therapeutic regimes based on our current understanding of disease prognosis. Low-income and emerging economies have less resources to protect themselves against the COVID-19-induced health and economic crisis. With the continuously evolving nature of coronavirus, a cost-effective strain independent mechanism that could be delivered easily even in a nonhealthcare setting is an urgent need of the hour. Methylene blue appears an apt candidate as it is an FDA-approved safe drug that is economically viable and easily available. Since MB has a long-standing history of being used in clinical setup for diverse medical applications and possesses intrinsic anti-inflammatory, anticytokine, and antifungal properties, this study analyzes prospects of its use in the management of COVID-19. Paradox and prospects of MB applications for the management of COVID-19, with or without fungal superinfections, are also discussed. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Electrospun Polymer Nanofibers for Technology Applications: A Short Review

Nanofibers are a type of nanomaterial with a diameter ranging from ten to a few hundred nanometers with a high surface-to-volume ratio and porosity. They can build a network of high-porosity material with excellent connectivity within the pores, making them a preferred option for numerous applications. This review explores nanofibers from the synthesis techniques to fabricate nanofibers, with an emphasis on the technological applications of nanofibers like water and air filtration, photovoltaics, batteries and fuel cells, gas sensing, photocatalysis, and biomedical applications like wound dressing and drug delivery. The nanofiber production market has an expected compound annual growth rate (CAGR) of 6% and should reach around 26 million US $ in 2026. The limitations and potential opportunities for large-scale applications of nano-fibrous membranes are also discussed. We expect this review could provide enriched information to better understand Electrospun Polymer Nanofiber Technology and recent advances in this field. © 2023 Bentham Science Publishers.

2-Deoxy-D-Glucose: A Repurposed Drug for COVID-19 Treatment

Coronaviruses is a broad group of viruses that has the potential to cause mild or severe respiratory infections. Currently, there is no specific treatment for the treatment of COVID-19. The symptomatic treatment is generally given on case-to-case basis along with basic life supportive measures for management of COVID-19. There is an acute urgency of evaluating the pre-existing drugs to develop a convincing treatment for COVID-19 or at least to reduce its severity. 2-DG being inhibitor of both glycolysis and glycosylation appears as a promising therapeutic option. In the present chapter, the rationale of repurposing of 2-DG as a potential treatment option for the management of COVID-19 has been discussed. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Rejuvenation and Restoration of Surface Water Quality Amid COVID-19 Lockdown: A Comprehensive Review in Indian Context

Rivers are our country's lifeline;however, we have done enough destruction to them which leads to deterioration in water quality. Fortunately, COVID-19 lockdown has brought new life to nature. This encouraged us to outline present review article which discusses pilot impacts of lockdown on six Indian rivers. Few rivers including Ganga showed major improvement at few sites in the assessed parameters such as pH, BOD, DO, FC, etc. The Ganga water at Haridwar and Rishikesh was investigated `fit for drinking' (Class A) while at Kanpur was found fit for `outdoor bathing' (Class B). These improvements can be attributed to strict restriction on human activities during lockdown as there were no or minimum industrial discharge, tourism activities, mass bathing and commercial events near rivers. However, after upliftment of lockdown, these activities will return to their previous state and most likely pollutants will eventually reappear in the water bodies. So, in this review we have reviewed government's existing water pollution control schemes, analysed their limitations and recommended several scopes for improvement. Further research directions in this area have also been highlighted. We believe that plans and actions described in the article, if implemented, will lead to fruitful outcomes in managing water resources.

Energy analysis of a phase change material embedded heat exchanger for air conditioning load reduction in different Indian climatic zones

Air conditioning (AC) systems for tropical countries like India account for sixty percent of the total energy needs of a building. With the onset of COVID-19, the increase of fresh air ventilation rate has been recommended by various guidelines for indoor spaces which increase the load on the AC system. The present study attempts to reduce this burden through retrofitting a phase change material (PCM) embedded pin fin heat exchanger into an air-conditioning system. The heat exchanger is designed to cater to the peak load fluctuations for cities in three hot climatic zones of India, viz., Jaisalmer, Kolkata, and Delhi. Dodecanol with a melting temperature of 24 degrees C, is chosen as the appropriate PCM material for these locations. The optimal pin fin diameters are estimated through an entropy generation minimization analysis for the three locations. A heat transfer analysis of the PCM embedded heat exchanger is further presented through an analytical approach to estimate the PCM mass requirement and energy savings potential. The masses of the PCM estimated for Jaisalmer, Kolkata, and Delhi are 11.36 kg, 22.42 kg, and 19.35 kg, respectively for their respective peak load fluctuations of 0.25 kW, 0.28 kW and 0.48 kW. Energy savings of up to 4.7 % for Delhi, 2 % for Kolkata, and 2.75 % for Jaisalmer are identified with the PCM embedded heat exchanger incorporation. The results show the potential of such PCM thermal storage in reducing the peak energy demands of buildings amidst various environmental and health concerns.

A multi-modal bone suppression, lung segmentation, and classification approach for accurate COVID-19 detection using chest radiographs

The high transmission rate of COVID-19 and the lack of quick, robust, and intelligent systems for its detection have become a point of concern for the public, Government, and health experts worldwide. The study of radiological images is one of the fastest ways to comprehend the infectious spread and diagnose a patient. However, it is difficult to differentiate COVID-19 from other pneumonic infections. The purpose of this research is to provide an automatic, precise, reliable, robust, and intelligent assisting system ‘Covid Scanner’ for mass screening of COVID-19, Non-COVID Viral Pneumonia, and Bacterial Pneumonia from healthy chest radiographs. To train the proposed system, the authors of this research prepared novel a dataset called, “COVID-Pneumonia CXR”. The system is a coherent integration of bone suppression, lung segmentation, and the proposed classifier, ‘EXP-Net’. The system reported an AUC of 96.58% on the validation dataset and 96.48% on the testing dataset comprising chest radiographs. The results from the ablation study prove the efficacy and generalizability of the proposed integrated pipeline of models. To prove the system's reliability, the feature heatmaps visualized in the lung region were validated by radiology experts. Moreover, a comparison with the state-of-the-art models and existing approaches shows that the proposed system finds clearer demarcation between the highly similar chest radiographs of COVID-19 and Non-COVID viral pneumonia. The copyright of “Covid Scanner” is protected with registration number SW-13625/2020. The code for the models used in this research is publicly available at: https://github.com/Ankit-Misra/multi_modal_covid_detection/.

Emerging real time analytics based health start-ups: opportunities during covid-19

Indian population has potential threat of communicable and non-communicable diseases. The low preventive health measure is a cause of major loss to the economy. Integration of the cloud platform with remote wearable sensors not only helps the health stakeholders to capture the patient vitals but also perform predictive analysis during COVID-19. Raising timely alarms through Internet of Medical Things and Artificial Intelligence has wide application in preventive care through real time analytics. However, Health Merchandise Startups using artificial intelligence and machine learning for timely device delivery face delay in making themselves available and affordable for Remote patients of Tier II and III. This study takes a health service provider perspective and seeks to study problem situation systemically by using a casual loop model. Finally, analysis of the feedback loops is done to be able to come out with suitable strategies for COVID-19 patients of Remote locations. © MEDIC SCIENTIFIC, All rights reserved.

Implementing a safe tracheostomy aftercare taskforce (STAT) during COVID-19

Introduction: At the height of the COVID-19 pandemic, our institution instituted a safe tracheostomy aftercare taskforce (STAT) team to care for the influx of patients undergoing tracheostomies. This review was undertaken to quantify and understand this team's benefits on the outcomes of tracheostomy care. Method: Retrospective data were collected from patients undergoing tracheostomies at our institution from February to June 2019, prior to creation of the STAT team, and was compared with prospectively collected data from tracheostomies performed from February to June 2020 while the STAT team was in place. The primary endpoint was decannulation before discharge. Secondary endpoints included downsizing and outpatient follow-up. Results: Prior to the STAT team, 92 patients underwent tracheostomy from February to June 2019, including 59 males (64%) and 33 females (36%). Following implementation of the STAT team, 170 patients underwent tracheostomy from February to June 2020, including 106 males (62%) and 64 females (38%). Mean time from tracheostomy to discharge was 43.7 days (range, 1-174;standard deviation [SD] 45.5) in 2019 and 39.7 days (range, 2-205;SD 30.3) in 2020. Twenty (22%) and 26 patients (15%) expired in the 2019 and 2020 cohorts, respectively. Of the surviving patients, 22% of patients in 2019 compared with 60% of patients in 2020 were decannulated before discharge (P < .00001). With the STAT team, decannulation rates before discharge increased absolutely by 40% and relatively by 178%. In the 2020 cohort, 59% of patients had documented downsizes during admission compared with just 20% of patients in 2019 (P < .0001). In 2020 the STAT team remained in contact to advise further tracheostomy care for 86% of discharged patients. Conclusion: The STAT team significantly increased decannulation rates, documentation of downsizing, and improved follow-up for tracheostomy care. It is possible that the STAT team's positive impact was related to improved documentation of significant tracheostomy care events. Nevertheless, this type of care team provides significant benefit to hospitals and improves the overall care of patients with tracheostomies.

Tracheostomy outcomes in COVID-19 patients at a New York City Hospital

Introduction: Tracheostomies have been performed in patients with prolonged intubation due to COVID-19, but the optimal timing, patient selection, and long-term outcomes largely remain unknown. Method: A prospectively collected database of patients with COVID-19 undergoing open tracheostomy at a major medical center in New York City between March 2020 and April 2020 was reviewed. Primary endpoints were weaning from the ventilator and sedation and time to decannulation. Secondary endpoints included both immediate and long-term complication rates as well as intensive care unit and hospital discharge. Results: In total, 61 patients underwent tracheostomy. There were 38 men (62.3%) and 28 women (37.7%) with an average age of 62 years (SD 13.7;range 23-91 years). Patients were intubated for a median time of 26 days prior to tracheostomy (interquartile range [IQR] 23-30 days). The median time to weaning from ventilatory support after tracheostomy was 18 days (IQR 10-27 days). Of those sedated at the time of tracheostomy, the median time to discontinuation of sedation was 5 days (IQR 3-9 days). Of patients who survived, 35 patients (60.3%) were decannulated. Of those decannulated (n = 33) before discharge, the median time to decannulation was 36 days following tracheostomy (IQR 27-48 days). Time from ventilator liberation to decannulation was 14 days (IQR 7-18 days). Fourteen patients (23.0%) had minor bleeding managed with packing. Two patients (3.3%) had bleeding requiring neck exploration. The all-use mortality rate was 9.4%. No patients died of procedural uses. No attending surgeons contracted COVID-19. Conclusion: Open tracheostomies were successfully and safely performed at our institution in the peak of the COVID- 19 pandemic. Most patients were successfully weaned from the ventilator and sedation. Approximately 60% of patients were decannulated prior to hospital discharge.

Thoracic Disease Detection using Deep Learning

Thoracic diseases are the most common radiological disorders worldwide especially in India. It is a life-threatening infectious disease affecting breathing organ like thorax and one or both lungs in human body commonly caused by bacteria. Physicians and radiologists are quiet using physical and visual graphical manners in order to diagnose the chest X-rays. Patient's diagnoses are entirely dependent on the consultant given by that chest expert. However, there might be emergency circumstances where radiology experts are too busy or may not be accessible. The timely and early diagnosis of thoracic diseases is very important. To resolve this situation, an algorithm that accept poster anterior (PA) chest X-rays images which classify whether the thorax is infected or not. If a thorax is infected, the proposed model will figure out which type of thoracic disorder is available on that PA view X-ray image. The proposed model can significantly improve the efficiency of doctors by early detection of the diseases using Computer aided diagnosis (CAD) wielding deep learning. Thus, an intelligent and automatic system is required to diagnose the chest radiograph to detect the various thorax related diseases. This research employ a web oriented identification system using deep learning based convolutional neural network algorithms for the detection, classification and early stage diagnosis of chest radiograph into healthy and thoracic disorders patients including COVID-19. The deep learning model is trained and tested on different radiographs which contain normal and numerous thorax disorders patient. Moreover, after developing the neural network model for the early diagnosis of the numerous thoracic disorders, a graphical user interface (web) based disease screening system also described for visualized the accurate diagnosis X-ray images in respective target disease classes. © 2021 IEEE.

Impact of COVID-19 Pandemic during the Lockdown on the Traumatic Brain Injury-An Institutional Experience

Background Traumatic brain injuries (TBIs) contribute to a significant socioeconomic impact, primarily affecting the lower-income sections of the society. The COVID-19 pandemic has resulted in a marked reduction in in-patient attendance. We are highlighting the impact of lockdown in neurotrauma cases in our institution compared to the prelockdown period. Methods We have done the retrospective review of the patients admitted due to TBI in prelockdown (January 14-March 21, 2020) and lockdown period (March 25-May 31, 2020) for the same duration of the 68 days at our tertiary institution. We have included demographic characteristics (age, sex), mode of injury, the severity of TBI, radiological diagnosis (computed tomography scan), and treatment obtained in our study. We compared the data for percentage (%) reduction of TBI cases and factors responsible for it during the lockdown period. Result A total of 166 patients were included in both groups. TBI's most common mechanism was road traffic accident, but we observed an increase in self-fall (16.9% vs. 38.1%) and assault (11.2% vs. 19%) during the lockdown period. We have noted that moderate TBI increased during the lockdown period by 17%. Overall, there is a reduction of 67% in TBI cases during the lockdown period. Conclusion The COVID pandemic has limited road traffic activity, and strict implementation of lockdown has restricted the infection and has reduced the neurotrauma emergencies. Simultaneously, moderate TBI cases have increased because of the lack of transportation facility and delay in the management of mild TBI cases.

Mathematical modelling of fighting current pandemic-using finite source retrial queues

In this paper, we prefer to implement a retrial queueing contraption with a finite number of homogeneous sources for covid-19 patients, orbital requests for the care, and unstable orbit, driven by the wish for overall output models suitable for modelling and study of covid-19 patients. Day by day pandemic situation in India is more critical, patients are facing the unavailable of treatment resources so they are automatically switched into the orbit mode. Data is taken from the ICMR website, Dated: Sep 21, 2020. It is believed that all random patients who are concerned with seeking care are impartial and exponentially distributed. Regular-state analysis of the underlying continuous-time Markov process is performed victimization Time NET package constant state performance measurements are computed by providing a generalized pandemic random Petri net model. The implementation of an unstable orbit and its use in a pandemic situation is the main novelty. Numerical derivation to explain the death/ recovery time effect. © 2021 the author(s). This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Phytoconstituents of zingiber officinale targeting host-viral protein interaction at entry point of sars-COV-2: A molecular docking study

Current COVID 19 outbreak is a critical issue in safeguarding public health worldwide. The lack of prophylactic drugs, vaccine and effective antiviral and other supporting therapies has prompted researchers to look for promising leads against the virus. Metabolic pathways and biochemicals involved in pathophysiology of SARS-CoV-2 can be targeted to find out effective inhibitor molecules acting at the entry point of infection. SARS-CoV-2 uses their Spike protein to dock at ACE2 and the serine protease, TMPRSS2 of host cell for Spike protein priming to get entry into the host cell. In the present study phytochemicals from Zingiber officinale were evaluated to find their binding with these proteins by conducting ligand-receptor binding docking study with AutoDockVina. The structures were observed by visualizing softwares Pymol to determine unique amino acids of receptor proteins. Physicochemical properties of phytochemicals and chemotherapeutic markers were assessed with Molinspiration tool. Docking study revealed that Gingerenone (-5.87 kcal/mol) and Zingiberene (-5.77 kcal/mol) have shown effective binding affinity towards ACE2. Shoagol (-5.72 kcal/mol), Zingerone (-5.79 kcal/mol) and Zingiberene (-5.52 kcal/mol) have shown higher binding with extracellular domain of serine protease TMPRSS2. Zingiberene scored significant binding energy of -6.23 kcal/mol with Spike protein of SARS-CoV-2. This study provides an evidence base to the experiential learning about use of Zingiber officinale in microbial infections. Once further validated, it may lead to development of herbal based anti-viral adjuvants. © 2020, DESIDOC.