ABSTRACT
The effects of the coronavirus disease 2019 (COVID-19) pandemic on male fertility have received considerable attention because human testes contain high levels of angiotensin-converting enzyme-2 receptors, through which severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can enter. Early studies showed decreases in semen quality during and after recovery from COVID-19. However, no semen quality studies have examined the effects of widespread subclinical and mild disease, as well as changes in lifestyle, psychosocial behavior, intake of dietary supplements, and stress. This cross-sectional study compared semen quality parameters in male partners of infertile couples between men who underwent semen analysis before the COVID-19 pandemic (prepandemic group) and men who underwent semen analysis during the pandemic period (pandemic group); the analysis sought to clarify the overall effects of the pandemic. No participants in the pandemic group had experienced clinically overt disease. Among the 239 participants, mean body weight (P = 0.001), mean body mass index (P < 0.001), median sperm concentration (P = 0.014), total sperm count (P = 0.006), and total percentages of motile (P = 0.013) and abnormal cells (P < 0.001) were significantly greater in the pandemic group (n = 137) than those in the prepandemic group (n = 102). Among abnormal cells, the percentages of cells with excess residual cytoplasm (P < 0.001), head defects (P < 0.001), and tail defects (P = 0.015) were significantly greater in the pandemic group than those in the prepandemic group. With the exception of morphology, the overall semenogram results were better in the pandemic group than those in the prepandemic group.
ABSTRACT
Background: COVID-19 (SARS-CoV-2) has caused various clinical manifestations ranging from asymptomatic, minor flu-like symptoms to acute respiratory distress syndrome (ARDS), pneumonia, and even death. Early restriction of viruses is of utmost importance in controlling the spread of COVID-19. The present study aimed to evaluate the role of a common herbal extract combination of pomegranate (dantabija), turmeric (haridra), and zinger (DHZ) in mild to moderate covid cases. Methods: A hundred covid-positive subjects of mild to moderate severity have been randomized to control and study groups. The study population has been given the fixed-dose combination of DHZ as an adjuvant to standard treatment. Data have been analyzed using standard statistical tools. Finding: DHZ as an adjuvant helped in turning 83.33% of patients negative in the home quarantine group whereas 40% of patients in the hospitalized group turned negative with the addition of DHZ in the standard management. The percent negativity was lower in patients who received only standard management. Out of all patients, who did not receive DHZ, only 38% of patients in home quarantine and 32% in hospitalized patients became negative for COVID-19. Patients who received DHZ also showed improvement in blood pressure levels, oxygen levels as well as improvement in all symptoms associated with COVID-19 infections. Interpretation: DHZ has shown a promising effect in mild to moderate cases of COVID-19 as an adjuvant to the standard therapy. The study results indicated that the combination probably produces its effect by its immunomodulatory action.
ABSTRACT
Background: The COVID-19 pandemic has claimed millions of lives. A tool for early prediction of severity and mortality risk is desirable for better utilization of health care facilities. Several biomarkers like D-dimer, lactate dehydrogenase (LDH), C-reactive protein (CRP) and some recently explored biomarkers like serum cystatin C and serum calprotectin have been proposed as prognostic markers of COVID-19, but their role as prognostic markers is so far undefined. The present work attempted to investigate the possible role of serum cystatin C and serum calprotectin as prognostic tools to predict severity and outcome ahead of time. Material and Methods: This observational cohort study was carried out on 95 COVID-19 patients admitted to a dedicated COVID care facility from mid-October 2020 to January 2021. Serial estimations of serum cystatin C and serum calprotectin levels were done and assessed for significant difference between severe (NEWS 2 score ≥5) and non-severe (NEWS 2 score <5) groups, survivors and deceased and on the basis of comorbidities at each time points. Survival analysis was done based on the optimal thresholds for severity and mortality, calculated from the receiver operating characteristic (ROC). Result: The results showed that median cystatin C levels were significantly higher on the first day in the severe group (P < 0.001) and in patients with cardiovascular disease (P < 0.05), chronic lung disease (P = 0.009) and among patients who died (P < 0.05). It remained raised on day 3 in severe (P < 0.05) and deceased (P < 0.05) group. Serum calprotectin levels were significantly higher in patients with chronic lung disease (P = 0.008) and in those who died (P < 0.05). Conclusion: Serum cystatin C could be used as a tool for early prognosis and therapeutic decision-making for COVID-19 patients. Serum calprotectin seems to be a better marker of critical illness.
ABSTRACT
The year 2020 will certainly be remembered for the COVID-19 outbreak. First reported in Wuhan city of China back in December 2019, the number of people getting affected by this contagious virus has grown exponentially. Given the population density of India, the implementation of the mantra of the test, track, and isolate is not obtaining satisfactory results. A shortage of testing kits and an increasing number of fresh cases encouraged us to come up with a model that can aid radiologists in detecting COVID19 using chest Xray images. In the proposed framework the low level features from the Chest X-ray images are extracted using an ensemble of four pre-trained Deep Convolutional Neural Network (DCNN) architectures, namely VGGNet, GoogleNet, DenseNet, and NASNet and later on are fed to a fully connected layer for classification. The proposed multi model ensemble architecture is validated on two publicly available datasets and one private dataset. We have shown that our multi model ensemble architecture performs better than single classifier. On the publicly available dataset we have obtained an accuracy of 88.98% for three class classification and for binary class classification we report an accuracy of 98.58%. Validating the performance on private dataset we obtained an accuracy of 93.48%. The source code and the dataset are made available in the github linkhttps://github.com/sagardeepdeb/ensemble-model-for-COVID-detection.