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Prediction of coating thickness for polyelectrolyte multilayers via machine learning.

ABSTRACT: Layer-by-layer (LbL) deposition method of polyelectrolytes is a versatile way of developing functional nanoscale coatings. Even though the mechanisms of LbL film development are well-established, currently there are no predictive models that can link film components with their final properties. The current health crisis has shown the importance of accelerated development of biomedical solutions such as antiviral coatings, and the implementation of machine learning methodologies for coating development can enable achieving this. In this work, using literature data and newly generated experimental results, we first analyzed the relative impact of 23 coating parameters on the coating thickness. Next, a predictive model has been developed using aforementioned parameters and molecular descriptors of polymers from the DeepChem library. Model performance was limited because of insufficient number of data points in the training set, due to the scarce availability of data in the literature. Despite this limitation, we demonstrate, for the first time, utilization of machine learning for prediction of LbL coating properties. It can decrease the time necessary to obtain functional coating with desired properties, as well as decrease experimental costs and enable the fast first response to crisis situations (such as pandemics) where coatings can positively contribute. Besides coating thickness, which was selected as an output value in this study, machine learning approach can be potentially used to predict functional properties of multilayer coatings, e.g. biocompatibility, cell adhesive, antibacterial, antiviral or anti-inflammatory properties.

SUBMITTER: Gribova V

PROVIDER: S-EPMC8455527 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundWith the global increase of cesarean delivery rates, the long-term effects of cesarean delivery have started to become clearer. One of the most prominent complications of cesarean delivery in recurrent pregnancies is uterine rupture. Assessing the risk of uterine rupture by accurately predicting dehiscence is very important to prevent untimely operations and/or maternal and fetal complications.ObjectiveThis study aimed to assess whether machine learning can be used to predict uterine dehiscence by using patients' ultrasonographic findings, clinical findings, and demographic data as features. Hence, we investigated a potential method for preventing uterine rupture and its maternal and fetal complications.Study designThe study was conducted on 317 patients with term (>37 weeks' gestation) singleton pregnancies and no medical complications or medications that may affect uterine wound healing. Demographics, body mass indices, smoking and drinking habits, clinical features, past pregnancies, number and history of abortions, interdelivery period, gestational week, number of previous cesarean deliveries, fetal presentation, fetal weight, tocography data, transabdominal ultrasonographic measurement of lower uterine segment full thickness and myometrium thickness, and lower uterine segment findings during cesarean delivery were collected and analyzed using machine learning techniques. Logistic regression, multilayer perceptron, support vector machine, random forest, and naive Bayes algorithms were used for classification. The dataset was evaluated using 10-fold cross-validation. Correct classification rate, F-score, Matthews correlation coefficient, precision-recall curve area, and receiver operating characteristic area were used as performance metrics.ResultsAmong the machine learning techniques tested in this study, the naive Bayes algorithm showed the best predictive performance. Among the various combinations of features used for prediction, the essential features of parity, gravidity, tocographic contraction, cervical dilation, dilation and curettage, and sonographic thickness of lower uterine segment myometrium yielded the best results. The second-best performance was achieved with sonographic full thickness of lower uterine segment added to the base features. The base features alone could classify patients with 90.5% accuracy, whereas adding the myometrium measurement increased the classification performance by 5.1% to 95.6%. Adding the full thickness measurement to the base features raised the classification performance by 4.8% to 95.3% in terms of correct classification rate.ConclusionThe naive Bayes algorithm can correctly classify uterine dehiscence with a correct classification rate of 0.953, an F-score of 0.952, and a Matthews correlation coefficient value of 0.641. This result can be interpreted as indicating that by using clinical features and lower uterine segment ultrasonography findings, machine learning can be used to accurately predict uterine dehiscence.

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Prediction of coating thickness for polyelectrolyte multilayers via machine learning.

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