Assessment of SARS-CoV-2 virus in the tear secretion of conjunctivitis patients during COVID-19 pandemic.

Purpose: : To determine the presence of SARS-CoV-2 virus in the tear secretion of conjunctivitis patients during the COVID-19 pandemic. Methods: This observational, cross-sectional study was conducted in clinically diagnosed patients with conjunctivitis attending the outpatient services of our institute from July 2021 to December 2021. The tear samples were collected from patients using Schirmer's strips or capillary tubes to detect the presence of SARS-CoV-2 by real-time PCR assay. COVID-19 vaccination and infection status, visual acuity, and clinical features were documented in all cases. Results: A total of 111 patients with symptoms of conjunctivitis were included during the study period. The mean age was 41.1 ± 13.1 years, and the mean duration of symptoms was 7.1 ± 4.4 days, with 74% males. Conjunctival congestion was mild in 69 (62.1%) patients, moderate in 30 (27%) patients, and severe in 12 (10.8%) patients. All except four had superficial punctate keratitis (SPK). Five (4.3%) patients were positive for SARS-CoV-2 RNA in their tear samples. All had mild-moderate conjunctival congestions with variable papiliofollicular reaction and SPKs, superficial hemorrhages were seen in three and pseudomembrane in one patient. They were followed up with telemedicine and three of them developed mild COVID-19-related symptoms and recovered after in-home quarantine. None of them had a previous history of COVID-19 infection and all had received COVID-19 vaccination within 2 weeks to 2 months. Conclusion: SARS-CoV-2 transmission through ocular secretion of conjunctivitis patients cannot be ignored and appropriate COVID-19-preventive behavior should be followed in ocular settings.

Diagnostic Performance and Clinical Utility of Conventional PCR Assay in Early Diagnosis of COVID-19 Associated Rhino-Orbito-Cerebral Mucormycosis.

Early diagnosis and treatment of rhino-orbital-cerebral mucormycosis (ROCM) are crucial. Potassium hydroxide with Calcofluorwhite (KOH + CFW) smears can demonstrate the fungal hyphae, but mixed infections caused by both mucorales and non-mucorales pose a diagnostic challenge. Polymerase chain reaction (PCR) can detect mixed infections and differentiate mucorales from non-mucorales. This study aimed to evaluate the utility of a single reaction PCR in the diagnosis of ROCM and the efficacy of nasal biopsy and endonasal swab in the detection of fungus. Sixty-six clinical samples were collected from 33 patients and were subjected to KOH + CFW smear, culture and PCR. PCR was performed using pan-fungal primers targeting the 28S large subunit rRNA gene, and the amplified products were further sequenced to identify the fungi. KOH + CFW smear, culture and PCR detected mucorales in 54.6%, 27.3% and 63.6% patients, respectively. PCR detected mixed infection in 51.5% patients compared to 9.1% by KOH + CFW smear. PCR detected fungus in 90% of nasal biopsies and 77.8% of endonasal swabs. Rhizopus spp. was the most common fungi identified in 43.2% of PCR-positive samples. PCR is effective in detecting mixed infection and in the diagnosis of ROCM. Nasal biopsies had better fungal detection rates than endonasal swabs.

Fulminant fungal endogenous endophthalmitis following SARS-CoV-2 infection: A case report.

Systemic corticosteroids and immunocompromised state following SARS-CoV-2 infection can predispose individuals to endogenous endophthalmitis. A 66-year-old gentleman presented with complaints of diminution of vision and redness one week post discharge after hospitalization for COVID-19 infection. Clinical examination suggested fulminant endogenous endophthalmitis which responded poorly even after aggressive treatment requiring evisceration. Culture and gene sequenced analysis confirmed Aspergillus fumigatus to be the causative organism. A high degree of suspicion is warranted in the presence of recent onset of floaters in COVID-19-infected individuals to facilitate early diagnosis and outcomes.

Multiplicative Holts Winter Model for Trend Analysis and Forecasting of COVID-19 Spread in India.

The surge of the novel COVID-19 caused a tremendous effect on the health and life of the people resulting in more than 4.4 million confirmed cases in 213 countries of the world as of May 14, 2020. In India, the number of cases is constantly increasing since the first case reported on January 30, 2020, resulting in a total of 81,997 cases including 2649 deaths as of May 14, 2020. To assist the government and healthcare sector in preventing the transmission of disease, it is necessary to predict the future confirmed cases. To predict the dynamics of COVID-19 cases, in this paper, we project the forecast of COVID-19 for five most affected states of India such as Maharashtra, Tamil Nadu, Delhi, Gujarat, and Andhra Pradesh using the real-time data. Using Holt-Winters method, a forecast of the number of confirmed cases in these states has been generated. Further, the performance of the method has been determined using RMSE, MSE, MAPE, MAE and compared with other standard algorithms. The analysis shows that the proposed Holt-Winters model generates RMSE value of 76.0, 338.4, 141.5, 425.9, 1991.5 for Andhra Pradesh, Maharashtra, Gujarat, Delhi and Tamil Nadu, which results in more accurate predictions over Holt's Linear, Auto-regression (AR), Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA) model. These estimations may further assist the government in employing strong policies and strategies for enhancing healthcare support all over India.

Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review.

The World Health Organization (WHO) declared novel coronavirus 2019 (COVID-19), an infectious epidemic caused by SARS-CoV-2, as Pandemic in March 2020. It has affected more than 40 million people in 216 countries. Almost in all the affected countries, the number of infected and deceased patients has been enhancing at a distressing rate. As the early prediction can reduce the spread of the virus, it is highly desirable to have intelligent prediction and diagnosis tools. The inculcation of efficient forecasting and prediction models may assist the government in implementing better design strategies to prevent the spread of virus. In this paper, a state-of-the-art analysis of the ongoing machine learning (ML) and deep learning (DL) methods in the diagnosis and prediction of COVID-19 has been done. Moreover, a comparative analysis on the impact of machine learning and other competitive approaches like mathematical and statistical models on COVID-19 problem has been conducted. In this study, some factors such as type of methods(machine learning, deep learning, statistical & mathematical) and the impact of COVID research on the nature of data used for the forecasting and prediction of pandemic using computing approaches has been presented. Finally some important research directions for further research on COVID-19 are highlighted which may facilitate the researchers and technocrats to develop competent intelligent models for the prediction and forecasting of COVID-19 real time data.