A closer look at the link between cycle threshold, clinical features and biomarkers: An observational study in COVID-19 patients.

Background: Symptoms for severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) appear 2-3 days after exposure to the virus. Being a virus, detection is primarily by polymerase chain reaction as this offers superior sensitivity and specificity. There was a misconception that patients with low cycle threshold (Ct) have severe coronavirus disease (COVID), and for individuals with higher Ct, it is the other way around. The prognosis for COVID was derived from various biomarkers and physicians heavily relied on them. Materials and Methods: A cross-sectional study spanning a duration of 2 years was conducted at a tertiary care centre in western India. A total of 201 individuals were included and the correlation between Ct, clinical features and biomarkers was studied. Results: In the E-gene, 43.28% had lower Ct values and 40.79% had low Ct values in the RdRp gene. 50% of all patients had diabetes, with 60% being between the ages of 61 and 80. 54.1% of hypertension patients belonged to ages between 61 and 80. 90.54% of COVID-positive individuals had lactose dehydrogenase levels ranging from 440 to 760. 79% of patients had a procalcitonin value of more than one but less than six. 79.1% of patients had an erythrocyte sedimentation rate between 36 and 90. Conclusion: Ct value though has a research value; it is a poor prognostic marker when compared to the various biomarkers that have been studied earlier. We cannot conclusively state that all our findings are accurate due to a lack of data but further research into the prognostic value of Ct should be conducted which will help in the ongoing scenario.

Assessment of risk factors associated with drug-resistant tuberculosis in pulmonary tuberculosis patients.

INTRODUCTION: Tuberculosis remains a global health problem worldwide and the risk progression of Tuberculosis to Drug Resistant Tuberculosis is influenced by various factors. These include immunocompromised status, past history of tuberculosis, life style and nutritional level. Hence, identifying the population at risk of multidrug-resistant tuberculosis is essential and may help in developing appropriate case-finding strategies. Therefore, the present study was designed to study the contributing risk-factors associated with Drug resistant Tuberculosis. MATERIALS AND METHODS: In this prospective observational study, we assessed 189 Pulmonary tuberculosis diagnosed patients during the period of 2 years at government recognized tertiary care centers. Data was collected from all these patients checked to investigate risk factors associated with Drug resistant tuberculosis development by multivariant analysis. RESULTS: Of the 189 participants, 36 were diagnosed with drug resistant tuberculosis and 153 with drug sensitive tuberculosis. Factors associated with drug resistant tuberculosis include low-weight (OR 8.50; p = 0.0008430991), low-BMI (p = 0.0000527166), lower economic status (OR-2.1351; p = 0.048608696) and tobacco (OR-4.5192; p = 0.0023003189) were found clinically and statistically significant in development of drug resistant tuberculosis. Binary logistic regression was performed to ascertain the effects of various statistically significant factors. Drug resistant tuberculosis patients were 7.77 times more likely to be tobacco users than drug sensitive tuberculosis. CONCLUSIONS: Our study suggests that, there is a compelling and urgent need for increasing public awareness, initiating better nutrition and food programs, regular screening, and better management & control of MDR-TB.

Impact of COVID-19 outbreak on healthcare workers in a Tertiary Healthcare Center in India: a cross sectional study.

Numerous speculations have continually emerged, trying to explore the association between COVID-19 infection and a varied range of demographic and clinical factors. Frontline healthcare workers have been the primary group exposed to this infection, and there have been limited global research that examine this cohort. However, while there are a few large studies conducted on Indian healthcare professionals to investigate their potential risk and predisposing factors to COVID-19 infection, to our knowledge there are no studies evaluating the development of long COVID in this population. This cross-sectional study systematically utilized the demographic and clinical data of 3329 healthcare workers (HCW) from a tertiary hospital in India to gain significant insights into the associations between disease prevalence, severity of SARS-Cov-2 infection and long COVID. Most of the study population was found to be vaccinated (2,615, 78.5%), while 654 (19.65%) HCWs were found to be SARS-CoV-2 positive at least once. Of the infected HCWs, 75.1% (491) did not require hospitalization, whereas the rest were hospitalized for an average duration of 9 days. A total of 206 (6.19%) individuals were found to be suffering from long COVID. Persistent weakness/tiredness was the most experienced long-COVID symptom, while females (1.79, 1.25-2.57), individuals who consumed alcohol (1.85, 1.3-2.64) or had blood group B (1.9, 1.33-2.7) were at a significantly higher risk for developing long COVID.

Development of a machine learning model to predict risk of development of COVID-19-associated mucormycosis.

Aim: The study aimed to identify quantitative parameters that increase the risk of rhino-orbito-cerebral mucormycosis, and subsequently developed a machine learning model that can anticipate susceptibility to developing this condition. Methods: Clinicopathological data from 124 patients were used to quantify their association with COVID-19-associated mucormycosis (CAM) and subsequently develop a machine learning model to predict its likelihood. Results: Diabetes mellitus, noninvasive ventilation and hypertension were found to have statistically significant associations with radiologically confirmed CAM cases. Conclusion: Machine learning models can be used to accurately predict the likelihood of development of CAM, and this methodology can be used in creating prediction algorithms of a wide variety of infections and complications.

Fungal infections caused by the Mucorales order of fungi usually target patients with a weakened immune system. They are usually also associated with abnormal blood sugar states, such as in diabetic patients. Recent work during the COVID-19 outbreak suggested that excessive steroid use and diabetes may be behind the rise in fungal infections caused by Mucorales, known as mucormycosis, in India, but little work has been done to see whether we can predict the risk of mucormycosis. This study found that these fungal infections need not necessarily be caused by Mucorales' species, but by a wide variety of fungi that target patients with weak immune systems. Secondly, we found that diabetes, breathing-assisting devices and high blood pressure states had associations with COVID-19-associated fungal infections. Finally, we were able to develop a machine learning model that showed high accuracy when predicting the risk of development of these fungal infections.

A post-transplant infection by Nocardia cyriacigeorgica.

Nocardia are Gram-positive, acid-fast, filamentous bacteria that cause opportunistic infections in susceptible populations. We describe a case of post-transplant infection of pulmonary nocardiosis caused by the rare strain Nocardia cyriacigeorgica and the challenges faced in reaching a definitive diagnosis. This case report emphasizes on keeping nocardiosis as a differential diagnosis in transplant recipients, as this disease is largely underdiagnosed and underreported.