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1.
iScience ; 25(7): 104549, 2022 Jul 15.
Article in English | MEDLINE | ID: covidwho-1945338

ABSTRACT

We report robust SARS-CoV2 neutralizing sdAbs targeting the viral peptides encompassing the polybasic cleavage site (CSP) and in the receptor binding domain (RBD) of the spike (S) protein. Both the sdAbs inhibited infectivity of the CoV2 S protein expressing pseudoviruses (LV-CoV2S). Both anti-CSP and RBD intrabodies (IB) inhibited the output of LV(CoV2 S). Anti-CSP IB altered the proteolytic processing and targeted the viral S protein for degradation. Because of cross-reactive CSPs in the entry mediators, the anti-CSP sdAb neutralized in vitro and in vivo the infectivity of SARS-CoV2 unrelated viruses such as herpes simplex virus 1 (HSV1) and pestes des petits ruminants virus (PPRV). Conversely, anti-HSV1 and anti-PPRV sera neutralized LV(CoV2 S) owing to the presence of CSP reactive antibodies indicating that a prior infection with such pathogens could impact on the pattern of COVID-19.

2.
Eur J Radiol ; 152: 110341, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1821220

ABSTRACT

In the wake of the ongoing Coronavirus Disease 2019 (COVID-19) pandemic, a new epidemic of COVID associated mucormycosis (CAM) emerged in India. Early diagnosis and prompt treatment of this deadly disease are of paramount importance in improving patient survival. MRI is the cornerstone of diagnosis of early extrasinus disease, particularly intracranial complications which have traditionally been associated with a high mortality rate. In this review, we depict the sinonasal, perisinus, orbital and intracranial involvement in CAM. Special emphasis is laid on intracranial disease which is categorized into vascular, parenchymal, meningeal, bony involvement and perineural spread. Vascular complications are the most common form of intracranial involvement. Some unusual yet interesting imaging findings such as nerve abscesses involving the optic, trigeminal and mandibular nerves and long segment vasculitis of the internal carotid artery extending till its cervical segment are also illustrated. In our experience, patient outcome in CAM (survival rate of 88.5%) was better compared to the pre-pandemic era. Presence of intracranial disease also did not affect prognosis as poorly as traditionally expected (survival rate of 82.8%). Involvement of brain parenchyma was the only subset of intracranial involvement that was associated with higher mortality (p value 0.016). The aim of this review is to familiarise the reader with the MR imaging spectrum of CAM with special focus on intracranial complications and a brief account of their impact on patient prognosis in our experience.


Subject(s)
COVID-19 , Mucormycosis , Orbital Diseases , Humans , Magnetic Resonance Imaging , Mucormycosis/complications , Mucormycosis/diagnostic imaging , Orbital Diseases/diagnostic imaging , Prognosis , SARS-CoV-2
3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-321996

ABSTRACT

The importance of vaccination and the logistics involved in the procurement, storage and distribution of vaccines across their cold chain has come to the forefront during the COVID-19 pandemic. In this paper, we present a decision support framework for optimizing multiple aspects of vaccine distribution across a multi-tier cold chain network. We propose two multi-period optimization formulations within this framework: first to minimize inventory, ordering, transportation, personnel and shortage costs associated with a single vaccine;the second being an extension of the first for the case when multiple vaccines with differing efficacies and costs are available for the same disease. Vaccine transportation and administration lead times are also incorporated within the models. We use the case of the Indian state of Bihar and COVID-19 vaccines to illustrate the implementation of the framework. We present computational experiments to demonstrate: (a) the organization of the model outputs;(b) how the models can be used to assess the impact of storage capacities (at the cold chain points, transportation vehicle capacities) and manufacturer capacities on the optimal vaccine distribution pattern;and (c) the impact of vaccine efficacies and associated costs such as ordering and transportation costs on the vaccine selection decision informed by the model. We then consider the computational expense of the framework for realistic problem instances, and suggest multiple preprocessing techniques to reduce their computational burden. Our study presents public health authorities and other stakeholders with a vaccine distribution and capacity planning tool for multi-tier cold chain networks.

4.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-326417

ABSTRACT

The importance of vaccination and the logistics involved in the procurement, storage and distribution of vaccines across their cold chain has come to the forefront during the COVID-19 pandemic. In this paper, we present a decision support framework for optimizing multiple aspects of vaccine distribution across a multi-tier cold chain network. We propose two multi-period optimization formulations within this framework: first to minimize inventory, ordering, transportation, personnel and shortage costs associated with a single vaccine;the second being an extension of the first for the case when multiple vaccines with differing efficacies and costs are available for the same disease. Vaccine transportation and administration lead times are also incorporated within the models. We use the case of the Indian state of Bihar and COVID-19 vaccines to illustrate the implementation of the framework. We present computational experiments to demonstrate: (a) the organization of the model outputs;(b) how the models can be used to assess the impact of storage capacities at the cold chain points, transportation vehicle capacities, and manufacturer capacities on the optimal vaccine distribution pattern;and (c) the impact of vaccine efficacies and associated costs such as ordering and transportation costs on the vaccine selection decision informed by the model. We then consider the computational expense of the framework for realistic problem instances, and suggest multiple preprocessing techniques to reduce their computational burden. Our study presents public health authorities and other stakeholders with a vaccine distribution and capacity planning tool for multi-tier cold chain networks.

5.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-325836

ABSTRACT

The COVID-19 pandemic continues to have major impact to health and medical infrastructure, economy, and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes modelling attempts difficult and unreliable. Hence, we need to re-look at the situation with reliable data sources and innovative forecasting models. Deep learning models such as recurrent neural networks are well suited for modelling spatiotemporal sequences. In this paper, we apply recurrent neural networks such as long short term memory (LSTM), bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) COVID-19 infection forecasting. We select Indian states with COVID-19 hotpots and capture the first (2020) and second (2021) wave of infections and provide two months ahead forecast. Our model predicts that the likelihood of another wave of infections in October and November 2021 is low;however, the authorities need to be vigilant given emerging variants of the virus. The accuracy of the predictions motivate the application of the method in other countries and regions. Nevertheless, the challenges in modelling remain due to the reliability of data and difficulties in capturing factors such as population density, logistics, and social aspects such as culture and lifestyle.

6.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-308032

ABSTRACT

We have entered an era of a pandemic that has shaken the world with major impact to medical systems, economics and agriculture. Prominent computational and mathematical models have been unreliable due to the complexity of the spread of infections. Moreover, lack of data collection and reporting makes any such modelling attempts unreliable. Hence we need to re-look at the situation with the latest data sources and most comprehensive forecasting models. Deep learning models such as recurrent neural networks are well suited for modelling temporal sequences. In this paper, prominent recurrent neural networks, in particular \textit{long short term memory} (LSTMs) networks, bidirectional LSTM, and encoder-decoder LSTM models for multi-step (short-term) forecasting the spread of COVID-infections among selected states in India. We select states with COVID-19 hotpots in terms of the rate of infections and compare with states where infections have been contained or reached their peak and provide two months ahead forecast that shows that cases will slowly decline. Our results show that long-term forecasts are promising which motivates the application of the method in other countries or areas. We note that although we made some progress in forecasting, the challenges in modelling remain due to data and difficulty in capturing factors such as population density, travel logistics, and social aspects such culture and lifestyle.

7.
Viral Immunol ; 34(5): 300-306, 2021 06.
Article in English | MEDLINE | ID: covidwho-1343606

ABSTRACT

Coronavirus disease 2019 (COVID-19) has become a global pandemic in 2020. The pathogen responsible for the COVID-19 has been found to be coronavirus (2019-nCoV) with human transmission through droplets, airway secretions, and even direct contact with host. Currently multiple drugs and their combinations are being tried for the treatment of the COVID-19 disease, but none approved. In absence of definitive and approved treatment, it is imperative that prevention of COVID-19 infection is of utmost importance. For the same, face masks, hand hygiene, isolation, and quarantine are being practiced all over the world. However much successful these methods be, they cannot be used for a very long time. Thus, it becomes necessary that a vaccine be developed for the disease so that the further spread could be halted. Some reports suggest the use of Bacillus Calmette-Guerin (BCG) vaccine as the prophylaxis for coronavirus. BCG vaccine is a live attenuated vaccine, used for prophylaxis of Mycobacterium tuberculosis and is present in the essential list of the World Health Organization as well as immunization programs of many countries. Immunostimulatory antiviral effects of BCG vaccine are well known. At present, there are no published evidence available to support the use of BCG vaccine for the prevention of coronavirus infection. However, there have been speculations on enhanced immunity with BCG vaccine, which might be useful in prevention of coronavirus infection. Results from the clinical studies of BCG vaccine in vulnerable population are required to confirm this hypothesis.


Subject(s)
BCG Vaccine/administration & dosage , BCG Vaccine/immunology , COVID-19/immunology , COVID-19/prevention & control , Humans , Immunity, Innate , Vaccination
8.
Pol J Radiol ; 86: e4-e18, 2021.
Article in English | MEDLINE | ID: covidwho-1040153

ABSTRACT

Coronavirus disease (COVID-19), caused by a highly contagious novel coronavirus, has seen a rapid surge of cases over the past 6 months spreading to more than 215 countries and posing a global threat to mankind. Reverse transcriptase-polymerase chain reaction (RT-PCR) from pharyngeal swabs is considered the gold standard for diagnosis of this disease. Portable chest radiography (CXR), point of care ultrasound, and computed tomography (CT) are crucial modalities in diagnosis and follow-up. Portable CXR can help in patients who are clinically unstable, and also to prevent the cumbersome process of steriliastion after every CT scan. However, chest CT is useful as a problem-solving tool, to look for progression and complications associated with the disease. In a few cases, in our experience (as has also been documented by others), RT-PCR was negative in early disease, and CT chest was able to detect the radiologi-cal findings raising suspicion of COVID-19. With this pictorial review, we aim to describe and illustrate the typical, and a few atypical, radiological findings of this disease.

9.
Indian J Anaesth ; 64(9): 774-783, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-819031

ABSTRACT

BACKGROUND AND AIM: The anaesthesiologists are at the highest risk of contracting infection of coronavirus disease 2019 (COVID-19) in emergency room, operation theatres and intensive care units. This overwhelming situation can make them prone for psychological stress leading to anxiety and insomnia. MATERIALS AND METHODS: We did an online self-administered questionnaire-based observational cross-sectional study amongst anaesthesiologists across India. The objectives were to find out the main causes for anxiety and insomnia in COVID-19 pandemic. Generalised Anxiety Disorder-7 (GAD-7) scale and Insomnia Severity Index (ISI) were used for assessing anxiety and insomnia. RESULTS: Of 512 participants, 74.2% suffered from anxiety and 60.5% suffered from insomnia. The age <35 years, female sex, being married, resident doctors, fear of infection to self or family, fear of salary deductions, increase in working hours, loneliness due to isolation, food and accommodation issues and posting in COVID-19 duty were risk factors for anxiety. ISI scores ≥8 was observed in <35 years, unmarried, those with stress because of COVID-19, fear of loneliness, issues of food and accommodation, increased working hours and with GAD-7 score ≥5. Adjusted odd's ratio of insomnia in participants having GAD-7 score ≥5 was 10.499 (95% confidence interval 6.097-18.080; P < 0.001). CONCLUSION: The majority of anaesthesiologists on COVID-19 duty suffer from anxiety and insomnia. Addressing risk factors identified during this study with targeted interventions and psychosocial support will help them to cope better with the stress.

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