Most patients with diabetes mellitus are asymptomatic, which leads to delayed and more complex treatment. At the same time, most individuals are routinely subjected to standard clinical laboratory examinations, which create large health datasets over a lifetime. Computer processing has been used to search for health anomalies and predict diseases using clinical examinations. This work studied machine learning models to support the screening of diabetes through routine laboratory tests using data from laboratory tests of 62,496 patients. The classification and regression models used were the K-nearest neighbor, support vector machines, Bayes naïve, random forest models, and artificial neural networks. Glycated hemoglobin, a test used for diabetes diagnosis, was used as the target. Regression models calculated glycated hemoglobin directly and were later classified. The performance of classification computer models has been studied under various subdataset partitions and combinations (e.g., healthy, prediabetic, and diabetes, as well as no healthy and no diabetes). The best single performance was achieved with the artificial neural network model when detecting prediabetes or diabetes. The artificial neural network classification model scored 78.1%, 78.7%, and 78.4% for sensitivity, precision, and F1 scores, respectively, when identifying no healthy group. Other models also had good results, depending on what is desired. Machine learning-based models can predict glycated hemoglobin values from routine laboratory tests and can be used as a screening tool to refer a patient for further testing.
Electrochemotherapy is a selective electrical-based cancer treatment. A thriving treatment depends on the local electric field generated by pairs of electrodes. Electrode damage as deflection can directly affect this treatment pillar, the distribution of the electric field. Mechanical deformations such as tip misshaping and needle deflection are reported with needle electrode reusing in veterinary electrochemotherapy. We performed in vitro and in silico experiments to evaluate potential problems with ESOPE type II electrode deflection and potential treatment pitfalls. We also investigated the extent to which the electric currents of the electroporation model can describe deflection failure by comparing in vitro with the in silico model of potato tuber (Solanum tuberosum). The in silico model was also performed with the tumor electroporation model, which is more conductive than the vegetal model. We do not recommend using deflected electrodes. We have found that a deflection of ± 2 mm is unsafe for treatment. Inward deflection can cause dangerous electrical current levels when treating a tumor and cannot be described with the in silico vegetal model. Outward deflection can cause blind spots in the electric field.
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