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The COVID-19 global pandemic, over the last year and a half, has managed to create massive disruptions in global supply chains and exposed their vulnerabilities, thereby reemphasizing the importance of resiliency. The current study aims to identify and prioritize, through the quantitative decision-making technique of Interpretive Structural Modelling (ISM), a set of barriers to resiliency for the pharmaceutical supply chain in India. The rationale behind choosing the Indian pharmaceutical supply chain was that the pharmaceutical sector in India supplies over half of the global demand for vaccines and generic drugs, and the trajectory of growth is indicated around US$100 billion by the year 2025, along with exporting pharmaceutical products to nearly 200 destination countries. The findings of the current study are expected to aid the decision-makers in evaluating the relative criticality and the interrelationship between the potential (and critical) barriers to supply chain resiliency, and in turn to develop strategic plans. This, in turn, can help to combat unforeseen supply chain disruptors such as COVID-19. This methodology and the findings of the study can be generalized for other supply chains. © 2023 K.J. Somaiya Institute of Management Studies and Research.
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Background COVID-19 as a trigger for A-beta+ ketosis-prone diabetes (KPD) [1,2] in previously normoglycemic individuals presenting with new-onset DKA, has been sparsely studied. Aim To study prospective changes in insulin secretion and insulin resistance in suspected A-beta+ KPD patients presenting with COVID-associated new-onset DKA. Method 22 previously non-diabetic, antibody-negative patients with new-onset DKA and RT-PCR positive COVID-19 (suspected A-beta + KPD), were followed up for one year. They were compared with 20 Type 1A and 18 Type 2 DM patients, with serial assessments (0,6 and 12 months) of insulin secretion rates (ISR) and multi-tissue insulin resistance (IR). 75-g OGTT with serial glucose, insulin and C-peptide estimation (0,15, 30,45, 60,90,120, 150 and 180 minutes) was used to derive IS, while hepatic and peripheral IR was calculated based on study by Ghani et al. [3]. Results At baseline, ISR in suspected KPD (n = 22) was significantly reduced but similar to Type 1A DM(p = 0.15). Serial ISR demonstrated complete recovery in 17 (77%) patients who became insulin independent at one-year follow-up (remission), while 5(23%) patients continued to require insulin (non-remission). KPD patients showed significant hepatic and peripheral IR at baseline compared to Type 1A DM (p < 0.05). The remission group (n = 17) showed significantly enhanced recovery of hepatic and peripheral insulin sensitivity at 6 and 12 months follow-up (all p < 0.01) compared to the non-remission (n = 5) group, with IR in the latter being comparable to Type 2 DM at follow-up (all p > 0.05). Younger age, lower BMI, initial severity of DKA and inflammation (IL-6 levels), along-with reduced 25-hydroxy-Vitamin-D levels were factors associated with poorer recovery of beta-cell secretion amongst the KPD patients. Conclusion This is the first prospective study to demonstrate progressive recovery of p-cell secretion in new-onset A-beta + KPD provoked by COVID-19 infection in Indian adults, with a distinctly different profile from Type 1A DM.Copyright © 2023 Elsevier B.V.
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In this COVID-19 pandemic era, students with Autism Spectrum Disorder (ASD) are struggling to adapt to classes in the online environment using Google Meet or Zoom. Failing to keep sustained attention in the class is a common problem for students with ASD. In face-to-face classes, teachers can track a student's behavior and activity to infer the student's attentiveness level and act accordingly. However, it becomes difficult for a teacher to monitor the attentiveness level of multiple students simultaneously on online platforms like Zoom. Detecting the attentiveness level of a student and notifying the teacher in an automated way can play a crucial role in improving the learning outcome. In this paper, we propose the first deep learning based attentiveness level prediction technique for students with ASD. Our model detects the behavior (e.g., unusual movement, gaze etc.) and activities from real-time videos and uses them as features to classify the attentiveness level as low, mid and high. Existing state-of-the-art techniques to detect the attentiveness level of typically developed students using gaze or facial expression cannot be trivially extended for students with ASD as they do not exhibit regular and consistent behavior. We collect video data belonging to different classes covering various types of activities over a long period, train our classifier, and run extensive experiments to validate the prediction performance. Our solution outperforms existing baselines by a large margin.
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The COVID-19 pandemic has shown that stock outs of essential items like hand sanitizers, tissue papers and other items of hygiene and daily use have been characteristic of a supply chain, especially immediately following a pandemic wave. Consequently, retailers have to indulge in substantial supplier management efforts to ensure product availability during a pandemic wave. Using a piecewise deterministic differential game, we model a scenario where, while anticipating a pandemic wave, a supplier decides on product availability efforts to ensure product availability under the impending threat of stock outs. A market leader coordinating retailer, on the other hand, decides on the proportion of the costs of the efforts to be shared with the supplier. Copyright (C) 2022 The Authors.
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"The development of Pico-metallic molecular dust as hair gel is supposed to be an innovative shielding formulation without involving the drugs moiety. The extraction and calcination of herbal parts unto the level of ""Bhasma"", where the extraction leads to inert through a series of processing acts as neutralizing and elimination of microbes, parasites and its toxins from physical intrusion through the hairs. The formulation of hair gel consisting Ketoconazole 1%, Methyl Paraben 1%, Glycerin 3% w/w along with reinforcement of Extracted Au, Ag & Fe Pico molecules. Most innovated here is the recovery of the Pico metallic dust extraction process from the collected micro metallic particular layer. Clinical investigation of the formulation among desirable health care 7 groups volunteers was representing remarkable results in cohort case-control study between its application, linear regression equation of y = 0.26175X-0.38108 and without, linear regression equation of y = 0.93168X-2.217 to estimate the incidence-prevalence ratio assessment. Thus, it exerts the P-Value is.000502. The result is significant at p <.05 r = 0.9628 and P-Value is.000402. The result is significant at p <.05, r = 0.966 respectively. Thus, its proved its clinical efficiency to minimize incidence and prevalence of microbial manifestation. Copyright © 2022, Anka Publishers. All rights reserved."
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During the SARS-Cov-2 pandemic, mask-wearing became an effective tool to prevent spreading and contracting the virus. The ability to monitor the mask-wearing rate in the population would be useful for determining public health strategies against the virus. In this paper, we present a two-step face mask detection approach consisting of two separate modules: 1) face detection and alignment and 2) face mask classification. This approach allows us to experiment with different combinations of face detection and face mask classification modules. More specifically, we experimented with PyramidKey and RetinaFace as face detectors while maintaining a lightweight backbone for the face mask classification module. Moreover, we also provide a relabeled annotation of the test set of the AIZOO dataset, where we rectified the incorrect labels for some face images. The evaluation results on the AIZOO and Moxa 3K datasets show that the proposed face mask detection pipeline surpassed the state-of-the-art methods. The proposed pipeline also yielded a higher mAP on the relabeled test set of the AIZOO dataset than the original test set. Since we trained the proposed model using in-the-wild face images, we can successfully deploy our model to monitor the mask-wearing rate using public CCTV images. © 2022 IEEE.
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Background: Recent evidence suggests a bidirectional relationship between COVID-infection and new-onset diabetes (NOD) presenting with DKA. Methodology: This one-year prospective study comprised of 29 COVID-negative DKA (controls) and 52 COVID-positive-DKA patients (18 NOD, 15 T1DM ,T2DM) . NOD were previously normoglycemic and negative for GAD/IA-2/ZnT8 autoantibodies. After 75g- OGTT with estimation of glucose, C-peptide, FFA and insulin at 0,15, 30,45, 60,90 ,120, 150 and 180minutes, Insulin secretion rate (ISR) [C-peptide-deconvolution] , Hepatic insulin sensitivity [AUC-glucose × AUC-insulin during first 30-minutes of OGTT ], Peripheral insulin sensitivity [ dG/dt ÷ mean plasma insulin concentration;dG/dt rate of decline in plasma glucose concentration]were calculated alongwith Metabolomics and Adipose tissue gene expression. All tests were performed at admission and 4, 8, and 12-months of followup. Results: At baseline, ISR in NOD was significantly reduced than controls (p=0.001) but similar to T1DM (p=0.15) . Nearly 83% (n=17) of NOD with DKA had near-complete recovery of ISR on follow-up compared to T1DM (all p<0.01) ,with non-remitters (n=3) having significantly worse admission Hba1c and IL-6 (all p<0.01) . NOD had significantly increased hepatic and peripheral insulin resistance compared to T1DM (all p<0.05) ,but similar to T2DM (all p>0.05) . Their Metabolomics revealed increased inflammatory phosphatidylcholines, that correlated with peripheral glucose uptake (p<0.01) ,while RNA sequencing showed significantly enhanced WNT5A , TLR4 (Toll-like Receptor-4) and RETN (resistin) than T1DM and T2DM (both p=0.001) . Conclusion: Our study provides novel insights into COVID-associated NOD with DKA. Majority have near-complete recovery of insulin secretion while simultaneous multi-tissue insulin resistance and inflammatory adipose tissue profiles persist as drivers of hyperglycemia.
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During the COVID pandemic with the employees in the different sectors working from home, there was perceptually spare time for self-development and enhancement of knowledge. The objective of the paper is to understand whether the same was perceived as an opportunity by the professionals and fruitfully utilized this spare time. The key purpose of this research paper is to study the professionals from the service sector to understand their view on the enhancement of their learning during the COVID pandemic. One hundred and twenty valid responses were collected from professionals in different industries. Statistical tools were used to find out the reliability statistics;factor analysis was done to identify the core factors and significant relationship of different cofactors and the satisfaction level was analyzed. The analysis indicated two main factors for online learning of the professionals: “upskilling or skill enhancement” and “utilization of spare time”, which explained 68.166% of the variance. The research highlighted the importance that professionals have placed on their upskilling and reskilling during the COVID pandemic through online modes and how industries can realign their learning and development programs for their employees using this medium. A framework has been proposed, using which organizations can be productive in upskilling their workforce amidst the new evolving working environment due to the pandemic. © 2022 selection and editorial matter, Arunava Dalal, Ajay Kumar Ganguly and Subrata Chattopadhyay.
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Zinc selenide microspheres were constructed using a simple hydrothermal technique at 180°C. It was ultrasonically treated with reduced graphene oxide modified with octadecylamine alkyl amine to form a hybrid nanocomposite. The optical, structural, and functional analysis by ultraviolet (UV) absorbance, X-ray diffraction, Fourier transform infrared spectroscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy revealed the crystal nature of the microspheres and the successful formation of the nanocomposite. Field emission scanning electron microscopy and transmission electron microscopy were done to study the morphological properties of the material. It was further used to fabricate a dual-modality sensor using both electrochemical and absorbance techniques for the detection of antimalarial drug chloroquine phosphate (CQP), which was used for the treatment of COVID-19 (SARS-CoV-2) virus. For electrochemical detection, the sensor showed a very low detection limit of 1.43 nM at a linear working range of 0.199–250.06 μM and a high sensitivity of 43.912 μA/μM/cm2. For UV-based detection, the sensor showed a very low detection limit of 6.88 nM at a linear working range of 0.045–7.324 μM. The sensor showed excellent analyte recovery rate for real-time analysis in biological as well as environmental samples. The results suggested that the sensor is effective for the detection of CQP with feasibility for future commercialization.
ABSTRACT
The novel coronavirus (SARS-CoV-2) is posing a serious threat to the mankind with its massive infection rate and potentially fatality. A total of 212 countries have been infected within the 112 days of first report causing 2 314 621 confirmed cases and 157 847 deaths worldwide. India, the country which is already battling with poverty, malnutrition and high population density is also at the second stage of coronavirus transmission. The situation is worsening and the attention has focused on the prevalence and preventive measures to be taken to protect 1.35 billion people of the largest democratic country of the world. In this review, a study has been designed to evaluate the prevalence, transmission, clinical symptoms, and preventive measures to control the community transmission of this fatal disease. The initial impact of coronavirus disease (COVID-19) outbreak on Indian economy has also been dealt with. This study reviews and summarizes the main points of the epidemic in India until the end of April 2020.