Machine learning and statistical approaches for classification of risk of coronary artery disease using plasma cytokines.

BACKGROUND: As per the 2017 WHO fact sheet, Coronary Artery Disease (CAD) is the primary cause of death in the world, and accounts for 31% of total fatalities. The unprecedented 17.6 million deaths caused by CAD in 2016 underscores the urgent need to facilitate proactive and accelerated pre-emptive diagnosis. The innovative and emerging Machine Learning (ML) techniques can be leveraged to facilitate early detection of CAD which is a crucial factor in saving lives. The standard techniques like angiography, that provide reliable evidence are invasive and typically expensive and risky. In contrast, ML model generated diagnosis is non-invasive, fast, accurate and affordable. Therefore, ML algorithms can be used as a supplement or precursor to the conventional methods. This research demonstrates the implementation and comparative analysis of K Nearest Neighbor (k-NN) and Random Forest ML algorithms to achieve a targeted "At Risk" CAD classification using an emerging set of 35 cytokine biomarkers that are strongly indicative predictive variables that can be potential targets for therapy. To ensure better generalizability, mechanisms such as data balancing, repeated k-fold cross validation for hyperparameter tuning, were integrated within the models. To determine the separability efficacy of "At Risk" CAD versus Control achieved by the models, Area under Receiver Operating Characteristic (AUROC) metric is used which discriminates the classes by exhibiting tradeoff between the false positive and true positive rates. RESULTS: A total of 2 classifiers were developed, both built using 35 cytokine predictive features. The best AUROC score of .99 with a 95% Confidence Interval (CI) (.982,.999) was achieved by the Random Forest classifier using 35 cytokine biomarkers. The second-best AUROC score of .954 with a 95% Confidence Interval (.929,.979) was achieved by the k-NN model using 35 cytokines. A p-value of less than 7.481e-10 obtained by an independent t-test validated that Random Forest classifier was significantly better than the k-NN classifier with regards to the AUROC score. Presently, as large-scale efforts are gaining momentum to enable early, fast, reliable, affordable, and accessible detection of individuals at risk for CAD, the application of powerful ML algorithms can be leveraged as a supplement to conventional methods such as angiography. Early detection can be further improved by incorporating 65 novel and sensitive cytokine biomarkers. Investigation of the emerging role of cytokines in CAD can materially enhance the detection of risk and the discovery of mechanisms of disease that can lead to new therapeutic modalities.

Mutagenicity evaluation of industrial sludge from common effluent treatment plant.

Sludge from common effluent treatment plant (CETP) receiving effluents from textile industries at Mandia Road, Pali, was analyzed to assess the level of mutagenicity. Mutagenicity assay using Salmonella typhimurium tester strains TA 98 and TA 100 gave positive results, thus suggesting presence of genotoxic contaminants in the samples investigated. Further, mutagenic activity of chemical sludge was found to be lesser than that of biological sludge. This result is very surprising and unexpected as it is indicating that some mutagenic compounds are either being formed or certain promutagenic compounds are being converted into stable mutagenic metabolites during the biological treatment of the wastewater effluents. There have been no previous reports giving similar or contrary results. Most of the previous studies have reported effects of single combined sludge.

Mutagenicity assessment of effluents from textile/dye industries of Sanganer, Jaipur (India): a case study.

Sanganer town, district Jaipur (Rajasthan, India), is famous worldwide for its dyeing and printing industries. There are about 400 industries involved in textile printing processes, which discharge effluents into nearby ponds and drains, without any treatment. These effluents contain highly toxic dyes, bleaching agents, salts, acids, and alkalis. Heavy metals like cadmium, copper, zinc, chromium, and iron are also found in the dye effluents. Textile workers are exposed to such waters with no control over the length and frequency of exposure. Further, as the untreated effluents are discharged into the environment they can cause severe contamination of surface and underground water. Environmental pollution caused by such textile effluents results in adverse effects on flora, fauna, and the general health of not only the textile workers, but also the residents of Sanganer town. Therefore, to assess the possible genotoxic health risk and environmental genotoxicity due to the textile industry effluents, this study was carried out using the Ames Salmonella/microsome mutagenicity assay. The results clearly indicate that the effluents and the surface water of Amani Shah drainage have high mutagenic activity. Further, the drainage water and the dry bed of the drainage (during summer months) are not fit for agricultural or other recreational purposes. A low level of mutagenicity in the underground water of Sanganer again emphasizes the grave pollution problem existing in the area. Multiple post hoc comparison tests (LSD, Tukey's) were used for comparison of sample site, dose, and length of exposure. Quadratic Model was found to adequately fit the observed data.