A Machine Learning based Drug Recommendation System for Health Care

In today’s digital era healthcare is one among the major core areas of the medical domain. People trying to find suitable health-related information that they are concerned with. The Internet could be a great resource for this kind of data, however you need to take care to avoid getting harmful information. Nowadays, a colossal quantity of clinical information dispersed totally across different websites on the Internet prevents users from finding useful information for their well-being improvement. Errors in medication are one of the foremost severe medical faults that would be a threat to patients’ lives. These problems increases the requirement to use recommendation systems within the domain of healthcare to assist users create additional economical and correct health-related decisions. During this paper, drug recommendation systems are developed to help end-users in distinctive correct medications for a particular wellness based on the reviews of other end-users provided on totally different medications for various specific diseases. The goal of this recommendation system is to examine the dataset using data mining concepts, visualization, sentiment analysis and recommend drugs based on the condition, ratings and reviews using Machine Learning approaches, Content and Collaborative filtering approach, for each health condition of a patient

Antiangiogenic Effects and Therapeutic Targets of Azadirachta indica Leaf Extract in Endothelial Cells

Azadirachta indica (common name: neem) leaves have been found to possess immunomodulatory, anti-inflammatory and anti-carcinogenic properties. The present study evaluates anti-angiogenic potential of ethanol extract of neem leaves (EENL) in human umbilical vein endothelial cells (HUVECs). Treatment of HUVECs with EENL inhibited VEGF induced angiogenic response in vitro and in vivo. The in vitro proliferation, invasion and migration of HUVECs were suppressed with EENL. Nuclear fragmentation and abnormally small mitochondria with dilated cristae were observed in EENL treated HUVECs by transmission electron microscopy. Genome-wide mRNA expression profiling after treatment with EENL revealed differentially regulated genes. Expression changes of the genes were validated by quantitative real-time polymerase chain reaction. Additionally, increase in the expression of HMOX1, ATF3 and EGR1 proteins were determined by immunoblotting. Analysis of the compounds in the EENL by mass spectrometry suggests the presence of nimbolide, 2′,3′-dehydrosalannol, 6-desacetyl nimbinene and nimolinone. We further confirmed antiproliferative activity of nimbolide and 2′,3′-dehydrosalannol in HUVECs. Our results suggest that EENL by regulating the genes involved in cellular development and cell death functions could control cell proliferation, attenuate the stimulatory effects of VEGF and exert antiangiogenic effects. EENL treatment could have a potential therapeutic role during cancer progression

Novel Molecular Targets of Azadirachta indica Associated with Inhibition of Tumor Growth in Prostate Cancer

Advanced prostate cancer has significant long-term morbidity, and there is a growing interest in alternative and complimentary forms of therapy that will improve the outcomes of patients. Azadirachta indica (common name: neem) contains multiple active compounds that have potent anti-inflammatory and anticancer properties. The present study investigates the novel targets of the anticancer activity of ethanol extract of neem leaves (EENL) in vitro and evaluates the in vivo efficacy in the prostate cancer models. Analysis of the components in the EENL by mass spectrometry suggests the presence of 2′,3′-dehydrosalannol, 6-desacetyl nimbinene, and nimolinone. Treatment of C4-2B and PC-3M-luc2 prostate cancer cells with EENL inhibited the cell proliferation. Genome-wide expression profiling, using oligonucleotide microarrays, revealed genes differentially expressed with EENL treatment in prostate cancer cells. Functional analysis unveiled that most of the up-regulated genes were associated with cell death, and drug metabolism, and the down-regulated genes were associated with cell cycle, DNA replication, recombination, and repair functions. Quantitative PCR confirmed significant up-regulation of 40 genes and immunoblotting revealed increase in the protein expression levels of HMOX1, AKR1C2, AKR1C3, and AKR1B10. EENL treatment inhibited the growth of C4-2B and PC-3M-luc2 prostate cancer xenografts in nude mice. The suppression of tumor growth is associated with the formation of hyalinized fibrous tumor tissue and the induction of cell death by apoptosis. These results suggest that EENL-containing natural bioactive compounds could have potent anticancer property and the regulation of multiple cellular pathways could exert pleiotrophic effects in prevention and treatment of prostate cancer

Validation of a clinical rotation evaluation for physician assistant students

Abstract Background We conducted a prospective validation study to develop a physician assistant (PA) clinical rotation evaluation (PACRE) instrument. The specific aims of this study were to 1) develop a tool to evaluate PA clinical rotations, and 2) explore associations between validated rotation evaluation scores and characteristics of the students and rotations. Methods The PACRE was administered to rotating PA students at our institution in 2016. Factor analysis, internal consistency reliability, and associations between PACRE scores and student or rotation characteristics were determined. Results Of 206 PACRE instruments sent, 124 were returned (60.2% response). Factor analysis supported a unidimensional model with a mean (SD) score of 4.31 (0.57) on a 5-point scale. Internal consistency reliability was excellent (Cronbach α=0.95). PACRE scores were associated with students’ gender (P = .01) and rotation specialty (P = .006) and correlated with students’ perception of being prepared (r = 0.32; P < .001) and value of the rotation (r = 0.57; P < .001). Conclusions This is the first validated instrument to evaluate PA rotation experiences. Application of the PACRE questionnaire could inform rotation directors about ways to improve clinical experiences. The findings of this study suggest that PA students must be adequately prepared to have a successful experience on their rotations. PA programs should consider offering transition courses like those offered in many medical schools to prepare their students for clinical experiences. Future research should explore whether additional rotation characteristics and educational outcomes are associated with PACRE scores

aiGeneR 1.0: An Artificial Intelligence Technique for the Revelation of Informative and Antibiotic Resistant Genes in Escherichia coli

Background: There are several antibiotic resistance genes (ARG) for the Escherichia coli (E. coli) bacteria that cause urinary tract infections (UTI), and it is therefore important to identify these ARG. Artificial Intelligence (AI) has been used previously in the field of gene expression data, but never adopted for the detection and classification of bacterial ARG. We hypothesize, if the data is correctly conferred, right features are selected, and Deep Learning (DL) classification models are optimized, then (i) non-linear DL models would perform better than Machine Learning (ML) models, (ii) leads to higher accuracy, (iii) can identify the hub genes, and, (iv) can identify gene pathways accurately. We have therefore designed aiGeneR, the first of its kind system that uses DL-based models to identify ARG in E. coli in gene expression data. Methodology: The aiGeneR consists of a tandem connection of quality control embedded with feature extraction and AI-based classification of ARG. We adopted a cross-validation approach to evaluate the performance of aiGeneR using accuracy, precision, recall, and F1-score. Further, we analyzed the effect of sample size ensuring generalization of models and compare against the power analysis. The aiGeneR was validated scientifically and biologically for hub genes and pathways. We benchmarked aiGeneR against two linear and two other non-linear AI models. Results: The aiGeneR identifies tetM (an ARG) and showed an accuracy of 93% with area under the curve (AUC) of 0.99 (p < 0.05). The mean accuracy of non-linear models was 22% higher compared to linear models. We scientifically and biologically validated the aiGeneR. Conclusions: aiGeneR successfully detected the E. coli genes validating our four hypotheses