A B S T R A C T COVID-19 infection has created a panic across the globe in recent times. Early detection of COVID-19 infection can save many lives in the prevailing situation. This virus affects the respiratory system of a person and creates white patchy shadows in the lungs. Deep learning is one of the most effective Artificial Intelligence techniques to analyse chest X-ray images for efficient and reliable COVID-19 screening. In this paper, we have proposed a Deep Convolutional Neural Network method for fast and dependable identification of COVID-19 infection cases from the patient chest X-ray images. To validate the performance of the proposed system, chest X-ray images of more than 150 confirmed COVID-19 patients from the Kaggle data repository are used in the experimentation. The results show that the proposed system identifies the cases with an accuracy of 93%.

No abstract

CONTEXT: Acute kidney injury (AKI) is an outcome of multiple etiologies and is mostly reversible. Data on its incidence and outcome, particularly from India, are limited. AIMS: To study the etiology, clinical profile, and short-term prognosis in AKI. SETTINGS AND DESIGN: A hospital-based prospective observational study on AKI. SUBJECTS AND METHODS: Seventy-five AKI patients diagnosed by Acute Kidney Injury network criteria were selected. Patients with preexisting chronic kidney disease were excluded. STATISTICAL ANALYSIS USED: Data were compiled using the Statistical Package for the Social Sciences version 17. Regression analysis was done for determining the association of various variables for mortality. P < 0.05 was considered statistically significant. RESULTS: The mean age of patients was 41.09 ± 16.17 years with a male:female ratio of 1.42:1. Comorbidities were present in 37.3%, with diabetes mellitus (10.6%) and chronic liver disease (10.6%) being the most common. Fever was the most common (40%) presenting symptom, followed by oliguria (25.8%). Infection was the most common cause of AKI (56%), with sepsis in 26.7% followed by acute gastroenteritis in 17.3%. Pneumonia was the primary focus in 50% of cases with sepsis. Mean serum creatinine and urea at admission were 2.37 ± 0.90 and 92.44 ± 39.67 mg/dl, respectively. Serum creatinine rose progressively to 2.96 ± 1.18 and 3.26 ± 1.56 mg/dl at 24 and 48 h, respectively, since hospitalization. Majority of the cases (73.3%) were nonoliguric. Hemodialysis was necessary in 24% of cases. Mean hospital stay was 8.16 days. In-hospital mortality was 24%. Among survivors, 92.9% had complete renal recovery on discharge. Sepsis, need for hemodialysis, urea >100 mg/dl, and peak serum creatinine >3 mg/dl were contributors to mortality (P < 0.01). CONCLUSIONS: Infections, especially sepsis, were the most common cause of AKI. Hemodialysis was required in one-quarter of the patients. Sepsis, need for hemodialysis, and high creatinine were associated with a significantly higher mortality.

Various dermatological manifestations have been observed in patients with Coronavirus disease (COVID-19) infection ranging from maculopapular rashes, urticaria, chickenpox-type lesions, chilblain-like, distal-limb ischemia, and livedo racemosa. While most of these cutaneous findings are self-resolving, they may aid in the timely diagnosis of this infection. We are reporting three patients presenting with dermatological features resembling (i) varicella zoster, (ii) herpes labialis, and (iii) Steven Johnson Syndrome (SJS) who were subsequently diagnosed with Covid-19 infection. The skin lesions disappeared after successful treatment of Covid-19.

Background and objective Since being declared a global pandemic, coronavirus disease 2019 (COVID-19) has led to millions of cases and deaths worldwide. Although severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to wreak havoc on individuals, healthcare systems, and economies, the intensive vaccination strategies adopted by several countries have significantly slowed the progress and the severity of the disease. In this study, we aimed to determine the COVID-19 vaccination status among healthcare workers (HCWs) and examine the effects of vaccination on disease manifestations. Materials and methods This cross-sectional study was conducted at a teaching hospital in Northeast India from April 2021 to September 2021, during the second phase of the COVID-19 pandemic. HCWs employed in the hospital who were laboratory-confirmed cases of COVID-19 based on semiquantitative real-time reverse transcriptase-polymerase chain reaction (RT-PCR) or cartridge-based nucleic acid amplification test (CBNAAT) on oropharyngeal samples were included in the study. Data analysis was performed using Microsoft Excel (Microsoft Office Professional Plus 2019, Microsoft Corp., Redmond, WA) Results A total of 178 HCWs reported positive for COVID-19 infection during the study period. Of these, 42 (23.59%) were males and 136 were females (76.40%). Among them, 86 (48.32%) HCWs were fully vaccinated, 58 (32.58%) were partially vaccinated, and 34 (19.10%) were not vaccinated. Most of the HCWs experienced mild disease (145, 81.46%), and only four (2.24%) reported moderate to severe disease. Compared with unvaccinated HCWs, individuals who have had either one or two doses of vaccines were less likely to have moderate to severe disease or seek treatment at the hospital. On symptoms analysis, shortness of breath was found to be more common in unvaccinated individuals than in vaccinated patients, and anosmia and loss of taste were more common in vaccinated than in unvaccinated individuals. No deaths were reported among the participants included in this study. Conclusions Following the first and second waves of the COVID-19 pandemic, a substantial proportion of HCWs were infected with SARS-CoV-2, likely as a result of the acquisition of the virus in the community during the early phase of local spread. Fully vaccinated individuals with COVID-19 were more likely to be completely asymptomatic or only mildly symptomatic compared to unvaccinated HCWs.