Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease’s effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.

Approximately 35.0% of annual hospitals budget is spent on buying materials and supplies, including medicines. We can bring about substantial improvement in the hospital inventory and expenditures by the inventory control techniques. Objective: To identify the categories of drugs which need stringent management control. Material and Method: The ABC and VED analysis of the medical store of a Neuropsychiatry hospital at Delhi, India was conducted for the year 2008-2009 to identify the categories of items needing stringent management control. Results: The total number of the drugs at the medical store was 145 drugs. The total annual drug expenditure (ADE) on these drug items was Rs. 19219594.79. ABC analysis revealed 3.45%, 6.9% and 89.65% items as A, B and C category items, respectively, accounting for 70.5%, 19.68% and 9.83% of ADE of the medical store. VED analysis showed 32.41%, 61.38% and 6.2% items as V, E, and D category items, respectively, accounting for 70.9%, 28.72% and 0.38% of ADE of the medical store. On ABC-VED matrix analysis, 33.8%, 60% and 6.2% items were found to be category I, II and III items, respectively, accounting for 92.33%, 7.29% and 0.38% of ADE of the medical store. Conclusion: It is suggested by the study that the management of Category I drugs should be done by the top management resulting in stringent control on the annual expenses. The Category II should be managed by the middle management level and Category III at lower managerial level.

With only 33 cases reported so far, a purely extra-axial position of medulloblastoma at cerebellopontine (CP) angle is quite exceptional. We report a case of extra-axial medulloblastoma in a 15-year-old male child located in the CP angle that was surgically treated with a provisional diagnosis of schwannoma. Histopathological diagnosis of medulloblastoma was made with the routine hematoxylin and eosin stain and immunohistochemical markers. This case report highlights the fact that although extremely rare, the possibility of an extra-axial mass being a medulloblastoma does exist.

Extradural spinal meningiomas are extremely rare, more so in the cervical region. A purely extradural location as reported in this paper is quite exceptional. The authors report a case of extradural meningioma in a 50-year-old male located in the cervical spine that was surgically treated with the provisional diagnosis of a neurofibroma. Histopathological diagnosis of meningothelial meningioma was made with the routine hematoxylin and eosin (HE) stain. The origin, clinical course, radiological features, pathological findings with the differential diagnosis and surgical treatment are discussed based on a review of the literature. An extradural spinal meningioma, an extremely rare entity, is still a diagnostic dilemma on radiology as the radiologic findings overlap with many other common extradural spinal masses.

Background Coronavirus disease 2019 (Covid-19) has been declared global emergency with immediate safety, preventative and curative measures to control the spread of virus. Confirmed cases are treated with clinical management as they are diagnosed but so far, there is no effective treatment or vaccine yet for Covid-19. Ayurveda has been recommended by preventative and clinical management guidelines in India and several clinical trials are ongoing. But there is no study to assess impact of Ayurveda on Covid-19. Methods Objective of present study was to evaluate the clinical outcome in Covid-19 confirmed asymptomatic to mild symptomatic patients who had received Ayurveda and compare with control (who has not received Ayurveda or any support therapy). Patients having Ayurveda intervention (Guduchi Ghan Vati-extract of Tinospora cordifolia) were included from Jodhpur Covid Care Centre and non-recipients were taken from Jaipur Covid Care Centre between May 15 to June 15, 2020. Total 91 patients, who were asymptomatic at the time of hospital admission and between 18 -75 years of age, were included in the study to analyse retrospectively. Results In control group, 11.7% developed mild symptoms after average 1.8 days and none in Ayurveda group reported any symptoms. Significant difference was reported between the group of patients taking Guduchi Ghan Vati (n=40) and patients in standard care (n=51) in terms of virologic clearance at day-7 (97.5% vs 15.6% respectively; p=0.000), at day 14 (100% vs 82.3%) days to stay in hospital ( 6.4 vs 12.8 respectively; p< 0.0001) . Conclusion Results of the study suggest that Guduchi Ghan Vati, a common and widely used Ayurveda preparation, could benefit treating asymptomatic Covid-19 patients. Larger, randomised controlled Trials are required to confirm the findings. Keywords: Ayurveda, Guduchi Ghan