Impulse Noise Reduction using Isolated Vector Median Filtering using K- Means Clustering in Digital Color Images with Application to Medical Diagnosis of SARS-COV-2 Covid19 Disease and Visual Tracking (preprint)

This paper proposes a new approach for filtering out impulse noise in digital color images. This work is of critical interest in biomedical imaging, visual tracking, etc. The conventional filtering methods operate by applying a noise reduction scheme, generally the vector median filtering approach and its variants, for the center pixel of a suitably chosen window that iteratively slides along the entire image. These methods consider the window in its entirety in the filtering process. This consideration, however, comprehends the noise within the noisy pixels in the filtering process and could prove detrimental to the overall output. The method proposed in this paper operates by clustering the pixels in the chosen window into two groups, one that corresponds to the pixel intensities that lie in the signal space and the other to those that lie in the noise space. The motivating rationale for this clustering scheme is to marginalize those pixels that lie in the noise space that seemingly do not contribute to the information in the image. The median filter is then applied to the pixels that contribute to the signal, in isolation of the color components, to filter out the impulse noise. Simulation results show that the proposed method outperforms conventional filtering methods in terms of noise reduction and structural similarity and thus validates the proposed approach. The proposed method is applied to analyze CT scans for improved diagnosis of SARS-COV-2 Covid19 disease. The method is also applied to a visual tracking example as a preprocessor.

Spectrum of neurological manifestations among acute COVID-19 and long COVID-19 – A retrospective observational study

Objective. Preliminary clinical data indicate that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with neurological symptoms. To compare the clinical features, imaging and treatments in patients with and without COVID 19. To compare the mortality and in-hospital stay among patients with and without COVID 19 and negative patients. Materials and methods. In this retrospective, single-center study, we included all the patients who attended the department of neurology with neurologic symptoms with confirmed COVID-19 and long COVID-19 from June 2020 to January 2021. Data on clinical signs, diagnosis, laboratory findings were collected and analyzed from the records for positive patients and compared with neurologic patients without COVID-19 admitted in the same period. Statistical analysis: The mean values between study groups were compared using an independent sample t-test and Mann Whitney u test. Categorical outcomes were compared using the Chi square test. Data was analyzed using coGuide soft-ware. Results. Headache was the common neurologic manifestation present in COVID positive patients compared to COVID negative patients (39.13%). There was no statistically significant difference between the two groups in baseline parame-ters. Laboratory parameters like CRP, Serum Ferritin, LDH, D-dimer, ESR, and IL-6 showed a significant increase in COVID positive patients (P <0.05). In-hospital mortality was more in COVID positive patients than COVID negative patients (P <0.011). Conclusion. The study showed varied neurologic symptoms in COVID patients, with headache as the common symptom. Hospital stay, morbidity, mortality, and inflammatory parameters were more in COVID positive patients compared to COVID negative patients.

Evidence supporting safe diagnosis of coeliac disease in children with antitissue transglutaminase titre ≥5 times upper limit of normal.

OBJECTIVE: European Society for Paediatric Gastroenterology Hepatology and Nutrition (ESPGHAN) guidelines on coeliac disease (CD) recommend that children who have IgA-based antitissue transglutaminase (TGA-IgA) titre ≥10× upper limit of normal (ULN) and positive antiendomysial antibody, can be reliably diagnosed with CD via the no-biopsy pathway. The aim of this study was to examine the relationship between TGA-IgA ≥5×ULN and histologically confirmed diagnosis of CD. METHODS: Data including TGA-IgA levels at upper gastrointestinal endoscopy and histological findings from children diagnosed with CD following endoscopy from 2006 to 2021 were analysed. CD was confirmed by Marsh-Oberhuber histological grading 2 to 3 c. Statistical analysis was performed using χ² analysis (p<0.05= significant). RESULTS: 722 of 758 children had histological confirmation of CD. 457 children had TGA-IgA ≥5×ULN and 455 (99.5%) of these had histological confirmation for CD; the two that did not had eventual diagnosis of CD based on clinicopathological features. 114 of 457 had between TGA-IgA ≥5×ULN and <10×ULN, all had confirmed CD. The likelihood of a positive biopsy with TGA-IgA ≥5×ULN (455/457) compared with TGA-IgA <5×ULN (267/301) has strong statistical significance (p<0.00001). The optimal TGA-IgA cut-off from receiver operating characteristic curve analysis was determined to be below 5×ULN for the two assays used. CONCLUSION: 99.5% of children with TGA-IgA ≥5×ULN had histological confirmation of CD, suggesting that CD diagnosis can be made securely in children with TGA-IgA ≥5×ULN. If other studies confirm this finding, there is a case to be made to modify the ESPGHAN guidelines to a lower threshold of TGA-IgA for serological diagnosis of CD.

Facilitating Safe Discharge Through Predicting Disease Progression in Moderate Coronavirus Disease 2019 (COVID-19): A Prospective Cohort Study to Develop and Validate a Clinical Prediction Model in Resource-Limited Settings.

BACKGROUND: In locations where few people have received coronavirus disease 2019 (COVID-19) vaccines, health systems remain vulnerable to surges in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Tools to identify patients suitable for community-based management are urgently needed. METHODS: We prospectively recruited adults presenting to 2 hospitals in India with moderate symptoms of laboratory-confirmed COVID-19 to develop and validate a clinical prediction model to rule out progression to supplemental oxygen requirement. The primary outcome was defined as any of the following: SpO2 < 94%; respiratory rate > 30 BPM; SpO2/FiO2 < 400; or death. We specified a priori that each model would contain three clinical parameters (age, sex, and SpO2) and 1 of 7 shortlisted biochemical biomarkers measurable using commercially available rapid tests (C-reactive protein [CRP], D-dimer, interleukin 6 [IL-6], neutrophil-to-lymphocyte ratio [NLR], procalcitonin [PCT], soluble triggering receptor expressed on myeloid cell-1 [sTREM-1], or soluble urokinase plasminogen activator receptor [suPAR]), to ensure the models would be suitable for resource-limited settings. We evaluated discrimination, calibration, and clinical utility of the models in a held-out temporal external validation cohort. RESULTS: In total, 426 participants were recruited, of whom 89 (21.0%) met the primary outcome; 257 participants comprised the development cohort, and 166 comprised the validation cohort. The 3 models containing NLR, suPAR, or IL-6 demonstrated promising discrimination (c-statistics: 0.72-0.74) and calibration (calibration slopes: 1.01-1.05) in the validation cohort and provided greater utility than a model containing the clinical parameters alone. CONCLUSIONS: We present 3 clinical prediction models that could help clinicians identify patients with moderate COVID-19 suitable for community-based management. The models are readily implementable and of particular relevance for locations with limited resources.

Facilitating safe discharge through predicting disease progression in moderate COVID-19: a prospective cohort study to develop and validate a clinical prediction model in resource-limited settings (preprint)

BackgroundIn locations where few people have received COVID-19 vaccines, health systems remain vulnerable to surges in SARS-CoV-2 infections. Tools to identify patients suitable for community-based management are urgently needed. MethodsWe prospectively recruited adults presenting to two hospitals in India with moderate symptoms of laboratory-confirmed COVID-19 in order to develop and validate a clinical prediction model to rule-out progression to supplemental oxygen requirement. The primary outcome was defined as any of the following: SpO2 < 94%; respiratory rate > 30 bpm; SpO2/FiO2 < 400; or death. We specified a priori that each model would contain three clinical parameters (age, sex and SpO2) and one of seven shortlisted biochemical biomarkers measurable using near-patient tests (CRP, D-dimer, IL-6, NLR, PCT, sTREM-1 or suPAR), to ensure the models would be suitable for resource-limited settings. We evaluated discrimination, calibration and clinical utility of the models in a temporal external validation cohort. Findings426 participants were recruited, of whom 89 (21{middle dot}0%) met the primary outcome. 257 participants comprised the development cohort and 166 comprised the validation cohort. The three models containing NLR, suPAR or IL-6 demonstrated promising discrimination (c-statistics: 0{middle dot}72 to 0{middle dot}74) and calibration (calibration slopes: 1{middle dot}01 to 1{middle dot}05) in the validation cohort, and provided greater utility than a model containing the clinical parameters alone. InterpretationWe present three clinical prediction models that could help clinicians identify patients with moderate COVID-19 suitable for community-based management. The models are readily implementable and of particular relevance for locations with limited resources. FundingMedecins Sans Frontieres, India. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSA living systematic review by Wynants et al. identified 137 COVID-19 prediction models, 47 of which were derived to predict whether patients with COVID-19 will have an adverse outcome. Most lacked external validation, relied on retrospective data, did not focus on patients with moderate disease, were at high risk of bias, and were not practical for use in resource-limited settings. To identify promising biochemical biomarkers which may have been evaluated independently of a prediction model and therefore not captured by this review, we searched PubMed on 1 June 2020 using synonyms of "SARS-CoV-2" AND ["biomarker" OR "prognosis"]. We identified 1,214 studies evaluating biochemical biomarkers of potential value in the prognostication of COVID-19 illness. In consultation with FIND (Geneva, Switzerland) we shortlisted seven candidates for evaluation in this study, all of which are measurable using near-patient tests which are either currently available or in late-stage development. Added value of this studyWe followed the TRIPOD guidelines to develop and validate three promising clinical prediction models to help clinicians identify which patients presenting with moderate COVID-19 can be safely managed in the community. Each model contains three easily ascertained clinical parameters (age, sex, and SpO2) and one biochemical biomarker (NLR, suPAR or IL-6), and would be practical for implementation in high-patient-throughput low resource settings. The models showed promising discrimination and calibration in the validation cohort. The inclusion of a biomarker test improved prognostication compared to a model containing the clinical parameters alone, and extended the range of contexts in which such a tool might provide utility to include situations when bed pressures are less critical, for example at earlier points in a COVID-19 surge. Implications of all the available evidencePrognostic models should be developed for clearly-defined clinical use-cases. We report the development and temporal validation of three clinical prediction models to rule-out progression to supplemental oxygen requirement amongst patients presenting with moderate COVID-19. The models are readily implementable and should prove useful in triage and resource allocation. We provide our full models to enable independent validation.

Interlink between pollution and COVID-19 in India: compelling view and key attributes.

Pollution and pollution-related consequences have a historic reputation, being even considered as chief causative agents behind several tragedies linked to a huge impact on health and environment. Nonetheless, the unforeseen viral outburst has surprisingly led to the recovery of the atmospheric immaculacy, besides to the serious destruction. Thus, here some important aspects related to the impact of pollution on the viral epidemic and vice versa were attempted to be critically discussed.