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1.
Neuroimmunology Reports ; 2:100089-100089, 2022.
Article in English | EuropePMC | ID: covidwho-1782186

ABSTRACT

Background Many central and peripheral nervous system complications, following COVID-19 vaccination, have been described. We report an unusual case of central demyelinating disorder, following the administration of the ChAdOx1 nCoV-19 SARS-CoV-2 (COVISHIELD™) vaccine. Case-report The 28-year female developed sudden onset headache followed by weakness of the left upper and lower limbs, and gait ataxia. Neurological symptoms developed two weeks after administration of the first dose of the ChAdOx1 nCoV-19 SARS-CoV-2 (COVISHIELD™) vaccine. Magnetic resonance imaging brain revealed T2/FLAIR hyperintense lesions involving bilateral subcortical white matter, splenium of the corpus callosum, and both cerebellar hemispheres. Few lesions showed blooming on gradient echo sequence suggestive of a hemorrhagic component. Post-contrast T1 images showed mild enhancement of demyelinating lesions. The patient was treated intravenously with methylprednisolone. After 12 weeks of follow-up, there was a substantial improvement in her symptoms. She became independent in all her activities of daily living. Conclusion In conclusion, this is an unusual case of acute hemorrhagic leukoencephalitis following ChAdOx1 nCoV-19 SARS-CoV-2 (COVISHIELD™) vaccination.

2.
Sci Rep ; 12(1): 4472, 2022 03 16.
Article in English | MEDLINE | ID: covidwho-1747178

ABSTRACT

Since it emerged in December of 2019, COVID-19 has placed a huge burden on medical care in countries throughout the world, as it led to a huge number of hospitalizations and mortalities. Many medical centers were overloaded, as their intensive care units and auxiliary protection resources proved insufficient, which made the effective allocation of medical resources an urgent matter. This study describes learned survival prediction models that could help medical professionals make effective decisions regarding patient triage and resource allocation. We created multiple data subsets from a publicly available COVID-19 epidemiological dataset to evaluate the effectiveness of various combinations of covariates-age, sex, geographic location, and chronic disease status-in learning survival models (here, "Individual Survival Distributions"; ISDs) for hospital discharge and also for death events. We then supplemented our datasets with demographic and economic information to obtain potentially more accurate survival models. Our extensive experiments compared several ISD models, using various measures. These results show that the "gradient boosting Cox machine" algorithm outperformed the competing techniques, in terms of these performance evaluation metrics, for predicting both an individual's likelihood of hospital discharge and COVID-19 mortality. Our curated datasets and code base are available at our Github repository for reproducing the results reported in this paper and for supporting future research.


Subject(s)
COVID-19 , Patient Discharge , COVID-19/epidemiology , Hospitals , Humans , Machine Learning , Triage/methods
3.
Neurol India ; 70(1): 409-411, 2022.
Article in English | MEDLINE | ID: covidwho-1726256

ABSTRACT

Background: Postmarketing surveillance of COVID-19 vaccination reveals that the COVID-19 vaccine administration is associated with several rare but serious neurological complications. Case Report: We report a case of new-onset tumefactive demyelinating brain lesion that developed after administration of an adenovector-based COVID-19 vaccine. A middle-aged female presented with recent right hemiparesis, which was noticed 2 days after she received the first dose of the vaccine. Magnetic resonance imaging (MRI) revealed a large subcortical T2/FLAIR hyperintensities involving corpus callosum as well. The patient responded to oral methylprednisolone. At 4 weeks, a follow-up MRI revealed a reduction in size of the lesion. Conclusion: To conclude, adenovector-based COVID-19 vaccination may be associated with a tumefactive demyelinating lesion.


Subject(s)
COVID-19 Vaccines , COVID-19 , Demyelinating Diseases/chemically induced , Adenoviridae , Brain/diagnostic imaging , Brain/pathology , COVID-19/prevention & control , COVID-19 Vaccines/adverse effects , Corpus Callosum/diagnostic imaging , Corpus Callosum/pathology , Female , Humans , Magnetic Resonance Imaging , Methylprednisolone/therapeutic use , Middle Aged , SARS-CoV-2
4.
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems ; 30(1), 2022.
Article in English | ProQuest Central | ID: covidwho-1691251

ABSTRACT

With the advancement in technology, the approach to learning has also been modified. “Standardization” and “One-size-fits-all” has become an outdated concept. To adjust to the changing learning approaches, e-learning came into being, but this was not as per the knowledge and intelligence of users. This created a hurdle in the achievement of better learning and acquisition of skills. This calls for the provision of personalization in e-learning. Successful implementation of personalized e-learning in the present education system will lead to better and faster learning by adapting as per the preferences and knowledge of students. The core idea behind this research is to make an application using Android, which provides a personalized and adaptable route of e-learning using Ant Colony Optimization and recommendations from similar peers. This research will cater to the needs of many students, and it will help in decreasing the time taken to complete any subject or course. It will also help in attaining better and efficient learning as the learning route is determined as per the user. Also, the collection of records of every user will help in improving efficiency and accuracy in the determination of the learning path. The developed app aiming for adaptative e-learning can act as a promising solution during the Covid-19 scenario.

5.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-326516

ABSTRACT

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure -- this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.

6.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-326103

ABSTRACT

Protein-ligand interactions (PLIs) are fundamental to biochemical research and their identification is crucial for estimating biophysical and biochemical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures, GNNF is the base implementation that employs distinct featurization to enhance domain-awareness, while GNNP is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and proteins 3D structure with 0.979 test accuracy for GNNF and 0.958 for GNNP for predicting activity of a protein-ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and pIC50 is crucial for drugs potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on pIC50 with GNNF and GNNP, respectively, outperforming similar 2D sequence-based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of GNNP on SARS-Cov-2 protein targets by screening a large compound library and comparing our prediction with the experimentally measured data.

7.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-325086

ABSTRACT

Existing deep learning technologies generally learn the features of chest X-ray data generated by Generative Adversarial Networks (GAN) to diagnose COVID-19 pneumonia. However, the above methods have a critical challenge: data privacy. GAN will leak the semantic information of the training data which can be used to reconstruct the training samples by attackers, thereby this method will leak the privacy of the patient. Furthermore, for this reason that is the limitation of the training data sample, different hospitals jointly train the model through data sharing, which will also cause the privacy leakage. To solve this problem, we adopt the Federated Learning (FL) frame-work which is a new technique being used to protect the data privacy. Under the FL framework and Differentially Private thinking, we propose a FederatedDifferentially Private Generative Adversarial Network (FedDPGAN) to detectCOVID-19 pneumonia for sustainable smart cities. Specifically, we use DP-GAN to privately generate diverse patient data in which differential privacy technology is introduced to make sure the privacy protection of the semantic information of training dataset. Furthermore, we leverage FL to allow hospitals to collaboratively train COVID-19 models without sharing the original data. Under Independent and Identically Distributed (IID) and non-IID settings, The evaluation of the proposed model is on three types of chest X-ray (CXR) images dataset (COVID-19, normal, and normal pneumonia). A large number of the truthful reports make the verification of our model can effectively diagnose COVID-19 without compromising privacy.

8.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-324171

ABSTRACT

We developed Distilled Graph Attention Policy Networks (DGAPNs), a curiosity-driven reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention Network (sGAT) that leverages self-attention over both node and edge attributes as well as encoding spatial structure -- this capability is of considerable interest in areas such as molecular and synthetic biology and drug discovery. An attentional policy network is then introduced to learn decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with enhanced stability. Exploration is efficiently encouraged by incorporating innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while increasing the diversity of proposed molecules and reducing the complexity of paths to chemical synthesis.

9.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-323822

ABSTRACT

Design of new drug compounds with target properties is a key area of research in generative modeling. We present a small drug molecule design pipeline based on graph-generative models and a comparison study of two state-of-the-art graph generative models for designing COVID-19 targeted drug candidates: 1) a variational autoencoder-based approach (VAE) that uses prior knowledge of molecules that have been shown to be effective for earlier coronavirus treatments and 2) a deep Q-learning method (DQN) that generates optimized molecules without any proximity constraints. We evaluate the novelty of the automated molecule generation approaches by validating the candidate molecules with drug-protein binding affinity models. The VAE method produced two novel molecules with similar structures to the antiretroviral protease inhibitor Indinavir that show potential binding affinity for the SARS-CoV-2 protein target 3-chymotrypsin-like protease (3CL-protease).

10.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-314655

ABSTRACT

Development of effective counteragents against the novel coronavirus disease caused by SARS CoV-2 strains requires clear insights for understanding immune responses associated with it. The succumbing of available therapeutics utterly warrants the development of a potential vaccine to contest the deadly situation. Herein, we report Cytotoxic T-cell Lymphocytes immunomodulator by advanced immunoinformatics avenues for spike-glycoprotein of SARS CoV-2, which can generate robust immune response with convincing immunological parameters (Antigenicity, TAP affinity, MHC-binder) engendering an efficient viral vaccine. Strong binding of the CTL construct with MHC-1 and membrane-specific TLR2 was conferred through molecular docking and molecular dynamics simulation in an explicit system. Steep magnitude RMSD variation and compelling residual fluctuations existed in terminal residues and various loops of the β linker segments of TLR2-epitope (residues 105-156 and 239-254) to about 0.4nm. The reduced R g value (3.3nm) and stagnant SASA analysis (275nm/S 2 /N after 8ns and 5ns) for protein surface and its orientation in exposed and buried regions suggests more compactness by strong binding of epitope. The CTL vaccine candidate establishes a high capability to elicit critical immune regulators, like T-cells and memory cells as proven by in silico immunization assays and can be further corroborated through in vitro and in vivo assays.

11.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-314642

ABSTRACT

Background: Arterial stiffness has been established as an independent and specific marker of various chronic cardiovascular diseases. Based on the detailed review of available research and case studies reported in reputed international journals, it can be concluded that Endothelial Damage (Endotheliitis) both in small and large arteries may be an important factor of morbidity and mortality in COVID-19 patients. Despite the pathological evidence of structural damage due to Endotheliitis in COVID-19 patients, the functional deterioration of the vasculature was not yet studied.Hyper activated inflammation of the arteries may lead to sudden rise in arterial stiffness, the functional indicator of severity of cardiovascular impairment, which develops into Multiple Organ Dysfunction Syndrome (MODS) in COVID-19. Supervising and controlling the arterial Stiffness may be a way to mitigate the morbidities and mortalities caused due to COVID-19. Objective: Our primary objective was to study functional arterial damage in COVID-19 disease and establish the non-invasive measurement of Arterial Stiffness as an independent marker of disease severity.Methods: We recorded the Arterial Stiffness of 23 Mild, 21 Moderate and 20 Severe COVID-19 patients grouped on latest NIH severity criteria. Patients with pre-existing Diabetes and Hypertension were excluded. We observed Arterial Stiffness of COVID-19 patients with standard parameters like non-invasive Carotid-Femoral Pulse Wave velocity (cfPWV), Age-Normalized increase in cfPWV (ANI_cfPWV), Age-Normalized increase in Aortic Augmentation Pressure (ANI_AugP) and Heart rate-normalized Augmentation Index (HRN_ AIx).Results: Moderate and Severe COVID-19 patients have extremely significantly elevated arterial stiffness than Mild patients. In Mild patients, cfPWV (829.1 ± 139.2 cm/s) was significantly lower than both Moderate (1067 ± 152.5 cm/s, P < 0.0001)and Severe (1416 ± 253.9 cm/s, P < 0.0001) patients. ANI_cfPWV in Moderate and Severe patients was significantly higher than Mild patients. (Mild: 101.2 ± 126.1 cm/s;Moderate: 279 ± 114.4 cm/s;Severe: 580.1 ± 216.4 cm/s;intergroup P <0.0001).Similarly, ANI_AugP also showed a significant difference in all three groups. (Mild: -1.891 ±2.817 mmHg;Moderate: 3.212 ± 3.124 mmHg;Severe: 7.246 ± 4.908 mmHg;with P <0.0001, P =0.0031, P <0.0001 respectively). HRN_ AIx also showed a significant increase in Moderate and Severe groups in comparison with the Mild Group. (Mild: 13.34 ±14.18;Moderate: 5.656±8.610;Severe: 24.80± 7.745;intergroup P <0.0001).Conclusion: This is the first study establishing the functional deterioration of vasculature in terms of abnormal increase in arterial stiffness in proportion with severity of COVID-19 disease. Our findings strongly suggest that arterial stiffness can be an independent and accurate marker for objective risk stratification and therapeutic alleviation of the acute cardiovascular complications like MODS in COVID-19.Trial Registration: The study design was registered with the Clinical Trials Registry of India (CTRI No. CTRI/2020/10/028489).Funding Statement: No external funding.Declaration of Interests: Authors declare no conflict of interest.Ethics Approval Statement: The study protocol, informed consents and other trial-related documents received the written approval of Institutional Ethics Committee (IEC No. AIIMS/Pat/IEC/2020/595).

12.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-313338

ABSTRACT

An outbreak of “Pneumonia of Unknown Etiology” occurred in Wuhan, China in late December 2019. Later the agent factor was identified and coined as SARS-COV-2 and the disease was named as coronavirus disease 2019 (COVID-19). In a shorter period, this newly emergent infection bought the world into a standstill. On 11th March 2020, WHO declared COVID-19 as a pandemic. The researchers across the globe have joined their hands to investigate about SARSCoV2 in terms of pathogenicity, transmissibility and deduce therapeutics to subjugate this infection. T The researchers and scholars practicing different art of medicine are into an extensive quest to come up with safer ways to curb the pathological implications of this viral infection. A huge number of clinical trials are underway from the branch of allopathy and naturopathy in this regard. Besides, a paradigm shift on cellular therapy and nano-medicine protocols have to be optimized for better clinical and functional outcome of COVID-19 affected individuals. This article unveils a comprehensive review of the pathogenesis, mode of spread and various treatment modalities to combat COVID-19 disease.Funding: National Natural Science Foundation of China.Conflict of Interest: We declare no competing interests.

13.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-306913

ABSTRACT

We examine a pair of graph generative models for the therapeutic design of novel drug candidates targeting SARS-CoV-2 viral proteins. Due to a sense of urgency, we chose well-validated models with unique strengths: an autoencoder that generates molecules with similar structures to a dataset of drugs with anti-SARS activity and a reinforcement learning algorithm that generates highly novel molecules. During generation, we explore optimization toward several design targets to balance druglikeness, synthetic accessability, and anti-SARS activity based on \icfifty. This generative framework\footnote{https://github.com/exalearn/covid-drug-design} will accelerate drug discovery in future pandemics through the high-throughput generation of targeted therapeutic candidates.

14.
Journal of Advances in Management Research ; 19(1):106-138, 2022.
Article in English | ProQuest Central | ID: covidwho-1612764

ABSTRACT

PurposeA poor performance of the cold supply chain (CSC) may increase the loss of quality and potency of perishables and temperature-sensitive products that deteriorate the financial and environmental aspects of the same. The purpose of the current research work is to identify the critical performance factors (criteria) and their co-factors (sub-criteria) that are responsible for the performance measurement of CSC and suggest the best possible solutions (alternatives) to improve the same.Design/methodology/approachTo achieve the objective of the research, a hierarchical model has been developed and analyzed using Analytic Hierarchy Process (AHP)-Fuzzy TOPSIS as a hybrid approach to obtain the severity weights of the identified criteria and prioritization toward their relative importance for the best possible alternatives.FindingsAnalysis reveals that the criteria “energy consumption” comes out to be the most critical criteria, and alternative “application of passive cold devices” is the most effective solution for improving the performance of CSC. Higher energy consumption leads to a higher rate of greenhouse gas (GHG) emissions increasing the global warming phenomenon, high operational cost and degradation of natural energy resources. The Application of Passive Cold Devices (PCDs) utilizes solar energy to operate the refrigeration units reducing the energy consumption, environmental and operating cost of CSC.Research limitations/implicationsThe research work provides several insights into the critical issues related to the CSC and suggests significant findings that enable the management and decision-makers to adopt these practices for performance evaluation and improvement of the same. The key findings of the work, such as “application of passive cold devices” and “application of IoT in cold chain logistics”, facilitate an improved platform to improve the CSC performance and proposed several directions that will enhance the merit of future research.Originality/valueThe presented study consolidates the various perspectives associated with CSC performance, explores the most critical criteria and proposes the best suitable cold chain practices for organizational growth. The work also provides an analytical analysis with the essence of practicalities and sensitivity analysis to support the robustness of the results. By enriching the literature and quantitative analysis of the new proposed model, this paper forms vast managerial and research implications in the field of CSC.

15.
Indian J Crit Care Med ; 25(2): 240-241, 2021 Feb.
Article in English | MEDLINE | ID: covidwho-1575149

ABSTRACT

How to cite this article: Kumar N, Kumar A, Pradhan S, Kumar A, Singh K. Painful Blisters of Left Hand Following Extravasation of Remdesivir Infusion in COVID-19. Indian J Crit Care Med 2021;25(2):240-241.

16.
2021.
Preprint in English | Other preprints | ID: ppcovidwho-295073

ABSTRACT

The prerequisite of therapeutic drug design is to identify novel molecules with desired biophysical and biochemical properties. Deep generative models have demonstrated their ability to find such molecules by exploring a huge chemical space efficiently. An effective way to obtain molecules with desired target properties is the preservation of critical scaffolds in the generation process. To this end, we propose a domain aware generative framework called 3D-Scaffold that takes 3D coordinates of the desired scaffold as an input and generates 3D coordinates of novel therapeutic candidates as an output while always preserving the desired scaffolds in generated structures. We show that our framework generates predominantly valid, unique, novel, and experimentally synthesizable molecules that have drug-like properties similar to the molecules in the training set. Using domain specific datasets, we generate covalent and non-covalent antiviral inhibitors. To measure the success of our framework in generating therapeutic candidates, generated structures were subjected to high throughput virtual screening via docking simulations, which shows favorable interaction against SARS-CoV-2 main protease and non-structural protein endoribonuclease (NSP15) targets. Most importantly, our model performs well with relatively small volumes of training data and generalizes to new scaffolds, making it applicable to other domains.

17.
Indian J Anaesth ; 65(9): 669-675, 2021 Sep.
Article in English | MEDLINE | ID: covidwho-1526905

ABSTRACT

BACKGROUND AND AIMS: The Nutrition Risk in Critically ill (NUTRIC) score is an appropriate nutritional assessment tool in mechanically ventilated patients. We retrospectively observed the applicability of the NUTRIC score for predicting outcomes in coronavirus disease (COVID)-19 acute respiratory distress syndrome (ARDS) patients. METHODS: All adult COVID-19 ARDS patients admitted to the intensive care unit and requiring various forms of oxygen therapy were included in the study. The demographic characteristics and clinical information about the patients were obtained from the hospital's medical records department. The nutritional risk for each patient was assessed using the NUTRIC score at 72 hours of ICU admission. The discriminating power and ability of NUTRIC score, Sequential Organ Failure Assessment (SOFA) score, age and Acute Physiology and Chronic Health Evaluation (APACHE) II to predict the 28-day mortality and need for mechanical ventilation (MV) was calculated using receiver operating characteristic curves and area under this curve. RESULTS: A total of 80 COVID-19 ARDS patients fitted into the inclusion criteria. Among non-survivors, the median Glasgow Coma Score, APACHE II score, NUTRIC score and SOFA score were 10, 16, 6 and 4, respectively. The cut-off values for NUTRIC score, SOFA, and APACHE II to predict 28-day mortality and need for MV was obtained as 3.5, 3.5 and 11.5, respectively. These cut-off values of NUTRIC score, SOFA score, and APACHE II have a sensitivity of 62%, 72.5% and 75.5%, respectively, and specificity of 95%, 72% and 83% for predicting mortality. CONCLUSIONS: Most COVID-19 ARDS patients requiring MV in the ICU are at nutritional risk, and a high NUTRIC score is associated with higher mortality.

18.
J Chem Inf Model ; 62(1): 116-128, 2022 01 10.
Article in English | MEDLINE | ID: covidwho-1521685

ABSTRACT

Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel noncovalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (Mpro) by employing a scalable high-throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this Mpro inhibitor with an inhibition constant (Ki) of 2.9 µM (95% CI 2.2, 4.0). Furthermore, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of Mpro forming stable hydrogen bond and hydrophobic interactions. We then used multiple µs-time scale molecular dynamics (MD) simulations and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by Mpro, involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits Mpro and offers a springboard for further therapeutic design.


Subject(s)
COVID-19 , Protease Inhibitors , Antiviral Agents , Coronavirus 3C Proteases , Humans , Molecular Docking Simulation , Molecular Dynamics Simulation , Orotic Acid/analogs & derivatives , Piperazines , SARS-CoV-2
19.
Indian J Crit Care Med ; 25(11): 1320-1321, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1515611

ABSTRACT

Pradhan S, Kumar N, Kumar S. Severe COVID-19 along with Cytokine Storm in Pemphigus Vulgaris Managed Successfully with Dexamethasone Pulse Therapy. Indian J Crit Care Med 2021;25(11):1320-1321.

20.
Indian J Crit Care Med ; 25(11): 1324-1325, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1512926

ABSTRACT

Kumar A, Kumar A, Kumar A, Sinha C, Kumar N, Singh PK. Acute Exacerbation of Cough as a Precipitating Cause of Hypoxia in COVID-19 Patients. Indian J Crit Care Med 2021;25(11):1324-1325.

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