Machine Learning for Online Automatic Prediction of Common Disease Attributes Using Never-Ending Image Learner.

The rapid increase in Internet technology and machine-learning devices has opened up new avenues for online healthcare systems. Sometimes, getting medical assistance or healthcare advice online is easier to understand than getting it in person. For mild symptoms, people frequently feel reluctant to visit the hospital or a doctor; instead, they express their questions on numerous healthcare forums. However, predictions may not always be accurate, and there is no assurance that users will always receive a reply to their posts. In addition, some posts are made up, which can misdirect the patient. To address these issues, automatic online prediction (OAP) is proposed. OAP clarifies the idea of employing machine learning to predict the common attributes of disease using Never-Ending Image Learner with an intelligent analysis of disease factors. Never-Ending Image Learner predicts disease factors by selecting from finite data images with minimum structural risk and efficiently predicting efficient real-time images via machine-learning-enabled M-theory. The proposed multi-access edge computing platform works with the machine-learning-assisted automatic prediction from multiple images using multiple-instance learning. Using a Never-Ending Image Learner based on Machine Learning, common disease attributes may be predicted online automatically. This method has deeper storage of images, and their data are stored per the isotropic positioning. The proposed method was compared with existing approaches, such as Multiple-Instance Learning for automated image indexing and hyper-spectrum image classification. Regarding the machine learning of multiple images with the application of isotropic positioning, the operating efficiency is improved, and the results are predicted with better accuracy. In this paper, machine-learning performance metrics for online automatic prediction tools are compiled and compared, and through this survey, the proposed method is shown to achieve higher accuracy, proving its efficiency compared to the existing methods.

In silico prediction of B- and T- cell epitope on Lassa virus proteins for peptide based subunit vaccine design.

BACKGROUND: Lassa fever is a severe, often-fatal and one of the most virulent disease in primates. However, the mechanism of escape of virus from the T-cell mediated immune response of the host cell is not explained in any studies yet. In our studies we had aimed to predict B- and T- cell epitope of Lassa virus protein, for impaling the futuristic approach of developing preventive measures against this disease, further we can also study its presumed viral- host mechanism. MATERIALS AND METHODS: Peptide based subunit vaccine was developed from all four protein against Lassa virus. We adopted sequence, 3D structure and fold level in silico analysis to predict B-cell and T-cell epitopes. The 3-D structure was determined for all protein by homology modeling and the modeled structure validated. RESULTS: One T-cell epitope from Glycoprotein (WDCIMTSYQ) and one from Nucleoprotein (WPYIASRTS) binds to maximum no of MHC class I and MHC class II alleles. They also specially bind to HLA alleles namely, A*0201, A*2705, DRB*0101 and DRB*0401. CONCLUSIONS: Taken together, the results indicate the Glycoprotein and nucleoprotein are most suitable vaccine candidates against Lassa virus.

Novel benzimidazole analogs as inhibitors of EGFR tyrosine kinase.

A series of new benzimidazole congeners were synthesized, and their structures were elucidated on the basis of elemental analyses and spectral studies (¹H NMR, FT-IR and EI-MS). Preliminary pharmacokinetic studies showed a promising outlook for further in vivo evaluation. The newly synthesized compounds were tested in vitro on human breast carcinoma cell line (MCF-7) in which EGFR is highly expressed. Most of the tested compounds exhibited antitumor activity with IC50 values in the micro to nano molar range.