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J Healthc Inform Res ; 6(1): 1-47, 2022 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-35419512


People of rural India often suffer from acute health conditions like diarrhea, flu, lung congestion, and anemia, but they are not receiving treatment even at primary level due to scarcity of doctors and health infrastructure in remote villages. Health workers are involved in diagnosing the patients based on the symptoms and physiological signs. However, due to inadequate domain knowledge, lack of expertise, and error in measuring the health data, uncertainty creeps in the decision space, resulting many false cases in predicting the diseases. The paper proposes an uncertainty management technique using fuzzy and rough set theory to diagnose the patients with minimum false-positive and false-negative cases. We use fuzzy variables with proper semantic to represent the vagueness of input data, appearing due to measurement error. We derive initial degree of belonging of each patient in two different disease class labels (YES/NO) using the fuzzified input data. Next, we apply rough set theory to manage uncertainty in diagnosing the diseases by learning approximations of the decision boundary between the two class labels. The optimum lower and upper approximation membership functions for each disease class label have been obtained using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Finally, using the proposed disease_similarity_factor, new patients are diagnosed precisely with 98% accuracy and minimum false cases.

Mol Neurobiol ; 56(9): 6551-6565, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-30868446


The amyloid cascade hypothesis dealing with the senile plaques is until date thought to be one of the causative pathways leading to the pathophysiology of Alzheimer's disease (AD). Though many aggregation inhibitors of misfolded amyloid beta (Aß42) peptide have failed in clinical trials, there are some positive aspects of the designed therapeutic peptides for diseases involving proteinaceous aggregation. Here, we evaluated a smart design of side chain tripeptide (Leu-Val-Phe)-based polymeric inhibitor addressing the fundamental hydrophobic amino acid stretch "Lys-Leu-Val-Phe-Phe-Ala" (KLVFFA) of the Aß42 peptide. The in vitro analyses performed through the thioflavin T (ThT) fluorescence assay, infrared spectroscopy, isothermal calorimetry, cytotoxicity experiments, and so on evinced a promising path towards the development of new age AD therapeutics targeting the inhibition of misfolded Aß42 peptide fibrillization. The in silico simulations done contoured the mechanism of drug action of the present block copolymer as the competitive inhibition of aggregate-prone hydrophobic stretch of Aß42. Graphical abstract The production of misfolded Aß42 peptide from amyloid precursor protein initiates amyloidosis pathway which ends with the deposition of fibrils via the oligomerization and aggregation of Aß42 monomers. The side chain tripeptide-based PEGylated polymer targets these Aß42 monomers and oligomers inhibiting their aggregation. This block copolymer also binds and helps degrading the preformed fibrils of Aß42.

Doença de Alzheimer/tratamento farmacológico , Polietilenoglicóis/química , Peptídeos beta-Amiloides/química , Peptídeos beta-Amiloides/metabolismo , Peptídeos beta-Amiloides/ultraestrutura , Morte Celular , Linhagem Celular Tumoral , Sobrevivência Celular , Humanos , Ligantes , Simulação de Dinâmica Molecular , Polietilenoglicóis/síntese química , Eletricidade Estática
Health Inf Sci Syst ; 7(1): 5, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-30863541


Purpose: In India, 67% of the total population live in remote area, where providing primary healthcare is a real challenge due to the scarcity of doctors. Health kiosks are deployed in remote villages and basic health data like blood pressure, pulse rate, height-weight, BMI, Oxygen saturation level (SpO2) etc. are collected. The acquired data is often imprecise due to measurement error and contains missing value. The paper proposes a comprehensive framework to impute missing symptom values by managing uncertainty present in the data set. Methods: The data sets are fuzzified to manage uncertainty and fuzzy c-means clustering algorithm has been applied to group the symptom feature vectors into different disease classes. The missing symptom values corresponding to each disease are imputed using multiple fuzzy based regression model. Relations between different symptoms are framed with the help of experts and medical literature. Blood pressure symptom has been dealt with using a novel approach due to its characteristics and different from other symptoms. Patients' records obtained from the kiosks are not adequate, so relevant data are simulated by the Monte Carlo method to avoid over-fitting problem while imputing missing values of the symptoms. The generated datasets are verified using Kulberk-Leiber (K-L) distance and distance correlation (dCor) techniques, showing that the simulated data sets are well correlated with the real data set. Results: Using the data sets, the proposed model is built and new patients are provisionally diagnosed using Softmax cost function. Multiple class labels as diseases are determined by achieving about 98% accuracy and verified with the ground truth provided by the experts. Conclusions: It is worth to mention that the system is for primary healthcare and in emergency cases, patients are referred to the experts.

IEEE/ACM Trans Comput Biol Bioinform ; 16(6): 1970-1985, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-29994718


The goal of the human genome project is to integrate genetic information into different clinical therapies. To achieve this goal, different computational algorithms are devised for identifying the biomarker genes, cause of complex diseases. However, most of the methods developed so far using DNA microarray data lack in interpreting biological findings and are less accurate in disease prediction. In the paper, we propose two parameters risk_factor and confusion_factor to identify the biologically significant genes for cancer development. First, we evaluate risk_factor of each gene and the genes with nonzero risk_factor result misclassification of data, therefore removed. Next, we calculate confusion_factor of the remaining genes which determines confusion of a gene in prediction due to closeness of the samples in the cancer and normal classes. We apply nondominated sorting genetic algorithm (NSGA-II) to select the maximally uncorrelated differentially expressed genes in the cancer class with minimum confusion_factor. The proposed Gene Selection Explore (GSE) algorithm is compared to well established feature selection algorithms using 10 microarray data with respect to sensitivity, specificity, and accuracy. The identified genes appear in KEGG pathway and have several biological importance.

Biomarcadores Tumorais/genética , Perfilação da Expressão Gênica , Regulação Neoplásica da Expressão Gênica , Neoplasias/diagnóstico , Neoplasias/genética , Algoritmos , Linhagem Celular Tumoral , Análise por Conglomerados , Neoplasias Colorretais/genética , Biologia Computacional/métodos , Feminino , Humanos , Masculino , Análise de Sequência com Séries de Oligonucleotídeos , Neoplasias Ovarianas/genética , Probabilidade , Neoplasias da Próstata/genética , Reprodutibilidade dos Testes , Fatores de Risco , Sensibilidade e Especificidade , Software