El doctor Velu

Nota bio-bibliográfica redactada para el ingreso en la Real Academia Frances

Ovoculture du virus de Carré (Quelques remarques)

Velu Henri. Ovoculture du Virus de Carré (Quelques remarques). In: Bulletin de l'Académie Vétérinaire de France tome 106 n°5, 1953. pp. 241-242

La pénicillinémie et son appréciation. Considérations générales sur son importance thérapeutique

Velu Henri. La Pénicillinémie et son appréciation, considérations générales sur sou importance thérapeutique. In: Bulletin de l'Académie Vétérinaire de France tome 102 n°7, 1949. pp. 265-268

Naissance d’un antibiotique (Film cinématographique)

Velu Henri. Naissance d’un antibiotique (Film cinématographique). In: Bulletin de l'Académie Vétérinaire de France tome 105 n°4, 1952. pp. 127-128

Les Vétérinaires et le Progrès médical au cours du XXe siècle

Velu Henri. Les Vétérinaires et le Progrès médical au cours du XXe siècle. In: Bulletin de l'Académie Vétérinaire de France tome 109 n°10, 1956. pp. 487-518

The Results of Treatment with Streptomycin Plus Pyrazinamide in Patients with Active Pulmonary Tuberculosis Despite Prolonged Treatment with Isoniazid Plus PAS

This report presents the findings during a year or more of observation of 20 South Indian patients who, after an initial course of isoniazid plus PAS, were treated with streptomycin plus pyrazinamide for active pulmonary tuberculosis. The combination of streptomycin plus pyrazinamide was chosen, first, because of its likely therapeutic effectiveness, since all the patients had streptomycin-sensitive strains of bacilli, secondly, because it presented an opportunity to study supervised drug administration in domiciliary patients in a community in which the selfadministration of antituberculosis drugs could not be depended on (Fox, 1958 ; Tuberculosis Chemotherapy Centre, 1959, 1960 ; Velu et al., 1960). The patients were either unsuitable for or unwilling to undergo surgery

Neural Networks based Smart e-Health Application for the Prediction of Tuberculosis using Serverless Computing.

The convergence of the Internet of Things (IoT) with e-health records is creating a new era of advancements in the diagnosis and treatment of disease, which is reshaping the modern landscape of healthcare. In this paper, we propose a neural networks-based smart e-health application for the prediction of Tuberculosis (TB) using serverless computing. The performance of various Convolution Neural Network (CNN) architectures using transfer learning is evaluated to prove that this technique holds promise for enhancing the capabilities of IoT and e-health systems in the future for predicting the manifestation of TB in the lungs. The work involves training, validating, and comparing Densenet-201, VGG-19, and Mobilenet-V3-Small architectures based on performance metrics such as test binary accuracy, test loss, intersection over union, precision, recall, and F1 score. The findings hint at the potential of integrating these advanced Machine Learning (ML) models within IoT and e-health frameworks, thereby paving the way for more comprehensive and data-driven approaches to enable smart healthcare. The best-performing model, VGG-19, is selected for different deployment strategies using server and serless-based environments. We used JMeter to measure the performance of the deployed model, including the average response rate, throughput, and error rate. This study provides valuable insights into the selection and deployment of ML models in healthcare, highlighting the advantages and challenges of different deployment options. Furthermore, it also allows future studies to integrate such models into IoT and e-health systems, which could enhance healthcare outcomes through more informed and timely treatments