Purpose The COVID-19 pandemic situation is increasing day by day and has affected the lifestyle and economy worldwide. Due to the absence of specific treatment, the only way to control a pandemic is by stopping its spread. Early identification of affected persons is urgently in demand. Diagnostic methods applied in hospitals are time-consuming, which delay the identification of positive patients. This study aims to develop machine learning-based diagnosis model which can predict positive cases and helps in decision-making. Design/methodology/approach In this research, the authors have developed a diagnosis model to check coronavirus positivity based on an artificial neural network. The authors have trained the model with clinically assessed symptoms, patient-reported symptoms, other medical histories and exposure data of the person. The authors have explored filter-based feature selection methods such as Chi2, ANOVA F-score and Mutual Information for improving performance of a classification model. Metrics used to evaluate performance of the model are accuracy, precision, sensitivity and F1-score. Findings The authors got highest classification performance with model trained with features ranked according to ANOVA FS method. Highest scores for accuracy, sensitivity, precision and F1-score of predictions are 0.93, 0.99, 0.94 and 0.93, respectively. The study reveals that most relevant predictors for COVID-19 diagnosis are sob severity, cough severity, sob presence, cough presence, fatigue and number of days since symptom onset. Originality/value Treatment for COVID-19 is not available to date. The best way to control this pandemic is the isolation of positive persons. It is very much necessary to identify positive persons at an early stage. RT-PCR test used to check COVID-19 positivity is the time-consuming, expensive and laborious method. Current diagnosis methods used in hospital demand more medical resources with increasing cases of coronavirus that introduce shortage of resources. The developed model provides solution to the problem cheaper and faster decreases the immediate need for medical resources and helps in decision-making.

Among the various biotic factors that disrupt crop yield, Xanthomonas oryzae pv oryzae (Xoo) is the most ruinous microbe of rice and causes bacterial leaf blight (BLB) disease. The present study focused on the utilization of copper nanoparticles (Cu-NPs) to control BLB. The copper nanosuspension (259.7 nm) prepared using Na-CMC, CuSO4·7H2O, and NaOH showed effectively inhibited Xoo (65.0 μg/ml). The performance of Cu-NPs in vivo showed enhanced plant attributes (127.9% root length and 53.9% shoot length) compared to the control and CuSO4 treated seedling. Furthermore, Cu-NPs treated seedlings showed 23.01% disease incidence (DI) compared to CuSO4 (85.71%) treated and control plants (91.83%). In addition to enhancing the growth parameters and reducing DI, seed priming with Cu-NPs improved the total chlorophyll content to 36.0% compared to the control. The assessment of antioxidant enzymes such as superoxide dismutase (1.9 U), polyphenol oxidase, peroxidase, and phenylalanine ammonia-lyase (two- to three-fold) in roots and shoots of rice plants revealed significant enhancement in Cu-NPs treated seedlings (P < 0.05). The present study suggests that Cu-NPs can be used to control Xoo and enhance rice growth.

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Organ transplantations save lives of patients with terminal organ failure and improve quality of life. However there is a huge gap between demand and supply of human organs. The only way to increase organ donations is to educate the health care professionals & public about the importance of organ donation and encourage them to become organ donor. As healthcare professionals are the most suitable person to carry the message to community, their knowledge and attitude towards organ donations should be studied. Aim: This study is determined to access the knowledge of medical students regarding organ donation. Methods: A Cross sectional study was conducted in a medical college of Ahmedabad. 100 medical students were included and they were given questionnaires designed to capture the knowledge and attitude toward organ donation. Responses were collected and analysed by Microsoft Excel and SSPS version 20. Results: 100% students were aware about the term organ donation. 90% students knew organization that work for organ donation awareness in city. 85% students were aware of the transplantation of human organ act 1994. 70% knew any hospital in city where organ transplantation is performed. Conclusion: The result of study revealed that there exists a knowledge gap among the medical students regarding organ donation & there is an urgent need for addressing this knowledge which will help in improving the organ donation rate in our country.