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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.
Clinical Epidemiology and Global Health ; : 101044, 2022.
Article in English | ScienceDirect | ID: covidwho-1783224

ABSTRACT

Introduction Newer coexisting conditions should be identified in order to modify newer risk factors. Aim was to identify patients with non-classical or less common coexisting conditions in patients infected of COVID 19. Method Single centred study from June 2020 to May 2021 at a tertiary centre in North India. A preformed questionnaire was used to record clinical and laboratory parameters and to identify cases which are in addition to CDC list and Indian data. Results 0.67% (46) cases out of 6832 patients were identified to have non-classical coexisting illness. It was divided into 2 groups-infections A (60.1%) and non-infections B (39.9%). Group A included-tuberculosis- pulmonary (14.3%) & extra pulmonary (32.9%), bacterial (25.0%) viral infections [dengue, hepatitis B & C] (14.3%), HIV disease (10.7%) and malaria (3.6%). Group B included- organ transplant (27.8%), autoimmune [myasthenia gravis, polymyositis, psoriasis] (22.6%), haematologic [Haemophilia, ITP, Aplastic anaemia, APML, CML] (27.8%), uncommon malignancies [disseminated sacral chordoma and GTN] (11.1%) and snakebite (11.1%). Serum Procalcitonin was not helpful for diagnosis of bacterial infection in COVID-19 disease. Group A had significantly longer duration of illness, hepatitis and elevated CRP. The mortality in group A & B were 32.1% and 43.8% respectively. Death in non-severe COVID cases was in tetanus and snakebite. 30.7% death among tuberculosis patients. More than 70% of deaths were attributable to COVID 19 in both the groups. Conclusion In Indian settings, comorbidities like tuberculosis and bacterial infections can precipitate severe COVID 19 unlike other parts of the world where tuberculosis is relatively uncommon.

4.
J Oral Biol Craniofac Res ; 11(4): 569-580, 2021.
Article in English | MEDLINE | ID: covidwho-1492315

ABSTRACT

Even before the onslaught of COVID-19 pandemic could settle, the unprecedented rise in cases with COVID-19 associated mucormycosis pushed the medical health to the fringe. Hyperglycaemia and corticosteroids appear to be the most consistent associations leading to the commonest manifestation of mucormycosis, Rhino-Orbito-Cerebral Mucormycosis. To address challenges right from categorisation and staging of the disease to the management of relentless progression, a multi-disciplinary expert committee was formed to handle the task in an evidence-based format to enforce best practices. The report of the committee on one hand attempts to succinctly present the currently available evidence while at the other also attempts to bridge the evidence-deficient gaps with the specialty-specific virtuosity of experts.

5.
Int J Lab Hematol ; 43(6): 1291-1301, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1258939

ABSTRACT

INTRODUCTION: The clinical and laboratory features of severe COVID-19 infection overlap with those of hemophagocytic lymphohistiocytosis (HLH), a hyperinflammatory disorder often associated with several viral infections. The clinical syndrome of HLH encompasses fever, organomegaly, cytopenias, hyperferritinemia, hypertriglyceridemia, raised transaminases, hypofibrinogenemia, absent natural killer (NK) cell activity, increased soluble CD25 and hemophagocytic lymphohistiocytosis in bone marrow, spleen, and lymph nodes. METHODS: We analyzed clinicopathological and laboratory features of thirteen patients with severe COVID-19 infection suspected to have HLH and found to have hemophagocytic histiocytosis on bone marrow examination (BME). RESULTS: Five of thirteen (38.46%) patients fulfilled five of eight HLH 2004 criteria and/or had a H-score ≥169. Three (23.08%) satisfied four of eight and remainder five (38.46%) satisfied three of eight HLH 2004 criteria. Fever, raised serum ferritin (13/13, 100%), transaminases (9/13, 69.23%), triglycerides (4/13, 30.76%), cytopenias (5/13, 38.46%), hypofibrinogenemia (2/13, 15.38%), and organomegaly (1/13, 7.69%) were observed in our patients. BME showed hemophagocytic histiocytosis without lymphocytosis in all. Contrary to HLH, lymphocytopenia (11/13, 84.61%), leukocytosis (7/13, 53.84%), neutrophilia (7/13, 53.84%), and hyperfibrinogenemia (7/13, 53.84%) were observed. Serum CRP, LDH, and plasma D-dimer were elevated in all, while serum albumin was decreased in 12 of 13 (92.3%) patients. Five patients recovered with high-dose pulsed corticosteroid therapy. CONCLUSION: The immune response associated with severe COVID-19 infection is similar to HLH with few differences. HLH should be suspected in severe COVID-19 infection although all patients may not fulfill required HLH diagnostic criteria. BME should be done in suspected cases so that appropriate therapy may be initiated early.


Subject(s)
Bone Marrow/pathology , COVID-19/complications , Lymphohistiocytosis, Hemophagocytic/etiology , SARS-CoV-2 , Adrenal Cortex Hormones/therapeutic use , Adult , Aged , Biomarkers/blood , Blood Proteins/analysis , Bone Marrow Examination , COVID-19/immunology , Creatinine/blood , Diagnosis, Differential , Female , Humans , Leukocyte Count , Lymphohistiocytosis, Hemophagocytic/blood , Lymphohistiocytosis, Hemophagocytic/diagnosis , Lymphohistiocytosis, Hemophagocytic/pathology , Male , Middle Aged , Neutrophils , Severity of Illness Index , Symptom Assessment , Triglycerides/blood
7.
Biocybern Biomed Eng ; 41(1): 239-254, 2021.
Article in English | MEDLINE | ID: covidwho-1033562

ABSTRACT

The lethal novel coronavirus disease 2019 (COVID-19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID-19 as a quick alternative diagnostic option to control its spread. A robust deep learning-based system is proposed to detect the COVID-19 using chest X-ray images. Infected patient's chest X-ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi-class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID-19 vs. non-COVID). COVID-19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 ± 2.71% was achieved for multi-class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID-19 vs. non-COVID). The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real-time.

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