STUDY OF HRCT CHEST FINDINGS AND SEVERITY SCORE IN COVID-19 PATIENTS AND ITS CORRELATION WITH CLINICAL AND LABORATORY PARAMETERS

Objectives: High-resolution computed tomography (HRCT) refers to a CT scan that gives a more precise cross-section image of the lungs than a regular chest CT and chest X-ray. HRCT chest uses specific technologies for better image resolution with exquisite lung details ideal for assessment. This modality can be applied in diagnosing and grading severity in coronavirus disease 2019 (COVID-19) infection. HRCT is more sensitive and accurate in diagnosing diffuse lung disease. Since HRCT can detect even small nodules in the lungs, it can detect severe abnormalities at an early stage of the infection and help to plan appropriate treatment. The aim of the study was to study HRCT chest findings in patients with COVID-19 infection and correlation with clinical and laboratory parameters.   Methods: This was a prospective and retrospective observational study done for duration of 1 year, that is, from June 2020 to May 2021 in the Department of Radio-diagnosis at Shri Sathya Sai Medical College and Research Institute, Tiruporur-Guduvancherry, Main Road, Ammapettai, Nellikuppam, Kancheepuram district on 235 COVID-19 positive patients. Results: The typical findings were ground glass opacity + reticular pattern (GGO +crazy paving) noted 50.2% moderate cases and 13.2% severe cases. The mild group (CT-SS of 1–8) consisted of 56 patients (23.83%), moderate group (CT-SS of 9–12) consisted of 143 (60.85%) patients where as severe group (CT-SS of >13) was composed of 36 (15.32%). Conclusion: HRCT chest plays an important role in early identification of the COVID-19 infection. HRCT severity score helps to the patients in guiding the treatment and monitor disease progression

Three Dimensional Casson nanofluid Flow with Convective Boundary Layer via Stretching Sheet

The present work examined Casson nanofluid in a three-dimensional boundary layer motion via stretching sheet. The study focuses on analyzing the behavior of a Casson nanofluid, which is one type of non-Newtonian fluid. The study appears to involve solving partial differential equations related to fluid flow, heat transfer, and mass transfer. These PDEs are transformed into ordinary differential equations using standard similarity variables. To solve the ODEs, the researchers employ the Runge-Kutta-Fehlberg (R-K-F) 4th order iterative scheme. It appears that higher values of the Biot number can significantly affect the temperature and concentration profiles in the Casson liquid flow

Transgenic tobacco plants constitutively expressing Arabidopsis NPR1 show enhanced resistance to root-knot nematode, Meloidogyne incognita

In Arabidopsis, non-expressor of pathogenesis related genes-1, NPR1 has been shown to be a positive regulator of the salicylic acid controlled systemic acquired resistance pathway and modulates the cross talk between SA and JA signaling. Transgenic plants expressing AtNPR1 constitutively exhibited resistance against pathogens as well as herbivory. In the present study, tobacco transgenic plants expressing AtNPR1 were studied further for their response to infection by the sedentary endoparasitic root knot nematode, Meloidogyne incognita. Transgenic plants showed enhanced resistance against the root-knot nematode infection. Prominent differences in the shoot and root weights of wild type and transgenic plants were observed post-inoculation with M. incognita. This was associated with a decrease in the number of root galls and egg masses in transgenic plants compared to WT. The transgenic plants also showed constitutive and induced expression of some PR protein genes, when challenged with M. incognita

The novel big data algorithm for distributional instance learning

The presence of distributional data instances is the main problem in many big data applications. Some solutions were given by the researchers to handle multiple instance learning and distributional instance learning. Unfortunately, these are not applicable in many big data scenarios. The state-of-the- art techniques need to be updated in the big data perspective. The proposed DILBD algorithm solves the problem of distributional instances efficiently and it is also applicable when the number of instances or the number of features reaches the big data range. It reduces the execution time and provides scalability without affecting the overall result of a distributional classifier. The aggregation technique proposed in Lin et al. (2013) is enhanced and applied mapreduce. The results in section-IV prove that the algorithm is suitable for big data applications. Keywords: Big data, Distributional instance learning, Distributional data, Mapreduc

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Not AvailableThe compatibility of entomopathogenic nematode Heterorhabditis indica with 14 insecticides, 6 fungicides and 7 herbicides commonly used in rice ecosystems was investigated under laboratory conditions. The effect of these chemicals on nematode viability and virulence upon direct exposure to chemicals at recommended concentrations was studied. H. indica was tolerant to most of the insecticides tested with less than 10% nematode mortality observed in all insecticides except monocrotophos (19.5%) and cartap hydrochloride (100%) after 72 h of exposure. Less than 10% mortality was observed in all fungicides tested except tricyclazole (14.5%) and carbendazium +mancozeb (21.5%) after 72 h of exposure to fungicides. Nematode mortality in case of herbicides ranged from 3.5% to 18% after 72 h of exposure. Insecticides showed only a marginal effect on the virulence of H. indica. Mortality of Galleria mellonella larvae in all the treatments was more than 90% except in treatments with chlorpyrifos (80%) and monocrotophos (60%) treated infective juveniles after 48 h. These results show that H. indica is compatible with all the tested agrochemicals except monocrotophos and cartap hydrochloride among insecticides, tricyclazole and carbendazium+mancozeb among fungicides, and pendimethalin among herbicides.Not Availabl

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Not AvailableApplication of pendimethalin @ 1.0 kg a.i./ha as pre-emergence spray along with propaquizafop @ 62 g a.i/ha at 15-20 DAS as post emergent spray recorded significantly lower weed dry weight (53.5 g/m2 area), weed index (5.31) and higher weed control efficiency (89.1 %), seed yield (1728 kg/ha), head diameter (16.06 cm), test weight (6.06 g), gross returns (` 65,266), net returns (` 26,248) and B:C ratio (2.48).Not Availabl

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