Valorisation of hazardous medical waste using steam injected plasma gasifier: a parametric study on the modelling and multi-objective optimisation by integrating Aspen plus with RSM.

The COVID-19 Pandemic has a detrimental effect on the environment related to the exponential rise in medical waste (MW). Extraction of energy from the toxic MW with the latest gasification technology instead of conventional incineration is of utmost importance to promote sustainable development. This present study investigates the processing of MW for the generation of enriched hydrogen syngas using steam injected plasma gasifier. Modelling of Plasma gasifier was performed in Aspen Plus and Model validation was done with the experimental result and, a good agreement was attained. Sensitivity analysis was implemented on MW in which the influence of gasification temperature, equivalence ratio (ER), and Steam/Biomass (S/B) on the producer gas (PG) composition, gas yield, H2/CO ratio, cold gas efficiency (CGE), and the higher heating value (HHV) was calculated. Furthermore, Response surface methodology (RSM) has been incorporated for the multi-objective optimisation of the variable gasification parameters. R2 values obtained from ANOVA for H2, CGE, and HHV are 98.62%, 99.10%, and 98.9% respectively. Using the response optimiser, the optimum values of H2, CGE, and HHV were found to be 0.43 (mole frac), 89.95%, and 7.49 MJ/Nm3 for temperature at 1560.60°C, equivalence ratio 0.1, and S/B 0.99, respectively. The observed coefficient of desirability was about 0.97.

Hit or miss: The Dilemma of Specimen Selection for Microbiological Diagnosis of Rhino-Orbital-Cerebral- COVID-Associated Mucormycosis (ROCM-CAM).

BACKGROUND: We diagnosed various cases of rhino-orbital-cerebral- COVID-associated Mucormycosis (ROCM-CAM) during India's second wave of COVID-19. This helped formulate novel suggestions for improving laboratory output, applicable anywhere in the world. METHOD: To diagnose ROCM-CAM by microbiological methods, we used direct microscopy and conventional culture on various clinical samples within the shortest turn-around time. DESIGN: Prospective single-center observational study Participants: patients with ROCM-CAM Results: Of 113 suspected cases of ROCM-CAM during May 2021, direct microscopy and culture could confirm the disease in 87.61% and 44.25% of patients, respectively. The highest pathogen isolation was seen from maxillary bone fragments, FESS-guided biopsy from pterygopalatine fossae, nasal turbinates and nasal mucosal biopsy. Direct microscopy could diagnose the disease in almost 40% of patients within 24 hours and 60% within two days. Conventional cultures yielded Rhizopus spp. (86%) as the commonest fungal pathogen followed by Mucor spp. (12%) within 7 days. Deep tissue biopsies are more useful for rapid diagnosis than superficial specimens. Routine fungal cultures can supplement case detection and help prognosticate survivors. CONCLUSION: The management of ROCM is a surgical emergency. The diagnosis of the condition must therefore be prompt and precise. Despite ongoing antifungal therapy, nasal mucosal tissue, FESSguided, and intra-operative tissue biopsies showed the pathogen's highest diagnostic yield. The diagnostic index improved further when multiple (4-5) high-quality specimens were collected. Nasal swabs and crusts, among the most commonly requested specimens worldwide, were found to have an overall low diagnostic potential.

Ultrasound assisted Cu-catalyzed Ullmann-Goldberg type coupling-cyclization in a single pot: Synthesis and in silico evaluation of 11H-pyrido[2,1-b]quinazolin-11-ones against SARS-CoV-2 RdRp.

The in silico evaluation of 11H-pyrido[2,1-b]quinazolin-11-one derivatives against SARS-CoV-2 RdRp was undertaken based on the reports on antiviral activities of this class of compounds in addition to the promising interactions of the antiviral drug penciclovir as well as quinazoline derivatives with SARS-CoV-2 RdRp in silico. The target compounds were prepared via an Ullmann-Goldberg type coupling followed by the subsequent cyclization (involving amidation) in a single pot. The methodology involved a CuI-catalyzed reaction of 2-iodobenzoate ester with 2-aminopyridine or quinolin-2-amine or thiazol-2-amine under ultrasound to give the expected products in acceptable (51-93%) yields. The molecular interactions of the synthesized 11H-pyrido[2,1-b]quinazolin-11-one derivatives with the SARS-CoV-2 RdRp (PDB: 7AAP) were evaluated in silico. The study suggested that though none of these compounds showed interactions better than penciclovir but the compound 3a and 3n appeared to be comparable along with 3b seemed to be nearly comparable to favipiravir and remdesivir. The compound 3n with the best binding energy (-79.85 Kcal/mol) participated in the H-bond interactions through its OMe group with THR556 as well as ARG624 and via the N-5 atom with the residue SER682. The in silico studies further suggested that majority of the compounds interacted with the main cavity of active site pocket whereas 3h and 3o that showed relatively lower binding energies (-66.06 and -66.28 Kcal/mol) interacted with the shallow cavity underneath the active site of SARS CoV-2 RdRp. The study also revealed that a OMe group was favourable for interaction with respect to its position in the order C-8 > C-1 > C-2. Further, the presence of a fused quinoline ring was tolerated whereas a fused thiazole ring decreased the interaction significantly. The in silico predictions of pharmacokinetic properties of 3a, 3b and 3n indicated that besides the BBB (Blood Brain Barrier) penetration potential these molecules may show a good overall ADME. Overall, the regioisomers 3a, 3b and 3n have emerged as molecules of possible interest in the context of targeting COVID-19.

Disruptions in Accessing Women's Health Care Services: Evidence Using COVID-19 Health Services Disruption Survey.

INTRODUCTION: The world is experiencing the Coronavirus pandemic and the aftermath consequences leading to lockdown and travel restrictions. The pandemic has brought a far-reaching impact on various sectors, including the economic, and health sectors. Therefore, this study analyses the possible impact of the COVID-19 pandemic on the disruption - in accessing family planning measures i.e. contraception facilities; and in seeking help from women organizations for health services. METHODS: This study has used the COVID-19 health services disruption survey-2020 and data was obtained from the Institute of Health Metrics and Evaluation. This survey was conducted in 76 countries using the smartphone-based premise data collection platform. Respondents were individual members of the general population ages 15-49 years who were identified as women. Data were collected from 12,354 respondents. Our data analysis has been done on both aggregate samples as well as region-wise samples (i.e. Africa, Asia, Europe, and Latin America & the Caribbean) to assess the change in levels of service delivery in both pre-COVID and post-COVID periods. RESULTS: Descriptive statistics results shows that two particular reasons - unable to access due to lockdown restrictions (5.4%), and fear of being infected with COVID-19 (9.7%) were reported as mostly impacting the access to contraception facilities due to lockdown restriction. Further, the logit regression using socio-economic and demographic variables suggests that geographical location (rural), and poor financial status turned out negative and significant, showing higher odds of facing difficulty than the reference category for the aggregate sample. The region-wise analysis suggests that Europe and Asia are the regions with the highest percentage of respondents reporting unavailability of services during COVID-19. CONCLUSION: The study concludes that pandemic-related emergencies affect the health care system, especially women-related health care services. The implication of our study indicates the requirement of a supply chain strategy for managing health care demand during emerging situations. So, the disruptions and bottlenecks in health care facilities should be addressed by various governments through appropriate policies and interventions.

A Golden Age: Conspiracy Theories' Relationship with Misinformation Outlets, News Media, and the Wider Internet (preprint)

Do we live in a "Golden Age of Conspiracy Theories?" In the last few decades, conspiracy theories have proliferated on the Internet with some having dangerous real-world consequences. A large contingent of those who participated in the January 6th attack on the US Capitol believed fervently in the QAnon conspiracy theory. In this work, we study the relationships amongst five prominent conspiracy theories (QAnon, COVID, UFO/Aliens, 9-11, and Flat-Earth) and each of their respective relationships to the news media, both mainstream and fringe. Identifying and publishing a set of 755 different conspiracy theory websites dedicated to our five conspiracy theories, we find that each set often hyperlinks to the same external domains, with COVID and QAnon conspiracy theory websites largest amount of shared connections. Examining the role of news media, we further find that not only do outlets known for spreading misinformation hyperlink to our set of conspiracy theory websites more often than mainstream websites but this hyperlinking has increased dramatically between 2018 and 2021, with the advent of QAnon and the start of COVID-19 pandemic. Using partial Granger-causality, we uncover several positive correlative relationships between the hyperlinks from misinformation websites and the popularity of conspiracy theory websites, suggesting the prominent role that misinformation news outlets play in popularizing many conspiracy theories.

Parsonage-Turner Syndrome Following Covishield (AstraZeneca ChAdOx1 nCoV-19) Vaccination: A Case Report.

Parsonage-Turner Syndrome (PTS) is a rare neurological disorder involving brachial plexus and periscapular muscles following viral infection, surgery, and vaccination. We hereby describe the first case of PTS from India following Covishield (AstraZeneca ChAdOx1 nCoV-19) COVID-19 vaccination. A 21-year-old healthy male presented to us with complaints of pain and weakness in the right shoulder five weeks after Covishield vaccination on the contralateral deltoid. There was no history of injury or constitutional symptoms. On examination, hyperalgesia over the area innervated by the axillary nerve and wasting of the deltoid, supra, and infraspinatus muscles were noted. An MRI scan of the shoulder, cervical spine, and brachial plexus neurogram were normal. Decreased motor amplitude in right axillary and musculocutaneous nerve was recorded in the nerve conduction study (NCS). High titers of SARS-COV-2 IgG neutralizing antibodies were noted after a single dose of vaccination and SARS CoV-2 IgM antibodies were negative. Having been diagnosed with post-vaccination PTS, the right shoulder was splinted and an intravenous injection of 1g methylprednisolone was administered for three days followed by oral steroids for three weeks. NCS and electromyography at 10 weeks showed insignificant differences between the two sides suggesting early neurological recovery. Currently, the patient is being followed up regularly for complete neurological recovery. PTS is a known side effect of vaccination. We report the index case of PTS following the administration of Covishield vaccination from India to aid in early diagnosis and management, further evaluation, and public health safety.

Proinflammatory Innate Cytokines and Distinct Metabolomic Signatures Shape the T Cell Response in Active COVID-19.

The underlying factors contributing to the evolution of SARS-CoV-2-specific T cell responses during COVID-19 infection remain unidentified. To address this, we characterized innate and adaptive immune responses with metabolomic profiling longitudinally at three different time points (0-3, 7-9, and 14-16 days post-COVID-19 positivity) from young, mildly symptomatic, active COVID-19 patients infected during the first wave in mid-2020. We observed that anti-RBD IgG and viral neutralization are significantly reduced against the delta variant, compared to the ancestral strain. In contrast, compared to the ancestral strain, T cell responses remain preserved against the delta and omicron variants. We determined innate immune responses during the early stage of active infection, in response to TLR 3/7/8-mediated activation in PBMCs and serum metabolomic profiling. Correlation analysis indicated PBMCs-derived proinflammatory cytokines, IL-18, IL-1ß, and IL-23, and the abundance of plasma metabolites involved in arginine biosynthesis were predictive of a robust SARS-CoV-2-specific Th1 response at a later stage (two weeks after PCR positivity). These observations may contribute to designing effective vaccines and adjuvants that promote innate immune responses and metabolites to induce a long-lasting anti-SARS-CoV-2-specific T cell response.

Deep Learning-Aided Automated Pneumonia Detection and Classification Using CXR Scans.

The COVID-19 pandemic has caused a worldwide catastrophe and widespread devastation that reeled almost all countries. The pandemic has mounted pressure on the existing healthcare system and caused panic and desperation. The gold testing standard for COVID-19 detection, reverse transcription-polymerase chain reaction (RT-PCR), has shown its limitations with 70% accuracy, contributing to the incorrect diagnosis that exaggerated the complexities and increased the fatalities. The new variations further pose unseen challenges in terms of their diagnosis and subsequent treatment. The COVID-19 virus heavily impacts the lungs and fills the air sacs with fluid causing pneumonia. Thus, chest X-ray inspection is a viable option if the inspection detects COVID-19-induced pneumonia, hence confirming the exposure of COVID-19. Artificial intelligence and machine learning techniques are capable of examining chest X-rays in order to detect patterns that can confirm the presence of COVID-19-induced pneumonia. This research used CNN and deep learning techniques to detect COVID-19-induced pneumonia from chest X-rays. Transfer learning with fine-tuning ensures that the proposed work successfully classifies COVID-19-induced pneumonia, regular pneumonia, and normal conditions. Xception, Visual Geometry Group 16, and Visual Geometry Group 19 are used to realize transfer learning. The experimental results were promising in terms of precision, recall, F1 score, specificity, false omission rate, false negative rate, false positive rate, and false discovery rate with a COVID-19-induced pneumonia detection accuracy of 98%. Experimental results also revealed that the proposed work has not only correctly identified COVID-19 exposure but also made a distinction between COVID-19-induced pneumonia and regular pneumonia, as the latter is a very common disease, while COVID-19 is more lethal. These results mitigated the concern and overlap in the diagnosis of COVID-19-induced pneumonia and regular pneumonia. With further integrations, it can be employed as a potential standard model in differentiating the various lung-related infections, including COVID-19.