Prediction of Antithrombin Activity and Bioavailability of a Synthetic Peptide Using a Decision Tree Algorithm.

This study is aimed at establishing the outcome of RSTI exposure to acetaminophen based on a decision tree algorithm for the first time. This study used the National Poison Data System (NPDS) to conduct a six-year retrospective cohort analysis, which included 4522 individuals. The patients had a mean age of 26.75 ± 16.3 years (1–89). 3160 patients (70%) were females. Most patients had intentional exposure to acetaminophen. Almost all the patients had acetaminophen exposure via ingestion. In addition, 400 (8.8%) experienced major outcomes, 1500 (33.2%) experienced moderate outcomes, and 2622 (58%) of the patients experienced mild ones. The decision tree model performed well in the training and test groups. In the test group, the accuracy was 0.813, precision of 0.827, recall being 0.798, specificity 0.898, and an F1 score 0.80. In the training group, accuracy was 0.831, recall was 0.825, precision was 0.837, specificity was 0.90, and F1 score was 0.829. Our results showed that serum liver enzymes being present at elevated levels (Alanine aminotransferase (ALT), Aspartate aminotransferase (AST) greater than 1000 U/L followed by ALT, AST between 100 and 1000 U/L) , prothrombin time (PT) prolongation, bilirubin increase, renal failure, confusion, age, hypotension, other coagulopathy (such as partial thromboplastin time (PTT) prolongation), acidosis, and electrolyte abnormality were the effective factors in determining the outcomes in these patients. The decision tree algorithm is a dependable method for establishing the prognosis of patients who have been exposed to RSTI acetaminophen and can be used throughout the patients’ hospitalization period.

In traditional decision (classification) tree algorithms, the label is assumed to be a categorical (class) variable. When the label is a continuous variable in the data, two possible approaches based on existing decision tree algorithms can be used to handle the situations. The first uses a data discretization method in the preprocessing stage to convert the continuous label into a class label defined by a finite set of nonoverlapping intervals and then applies a decision tree algorithm. The second simply applies a regression tree algorithm, using the continuous label directly. These approaches have their own drawbacks. We propose an algorithm that dynamically discretizes the continuous label at each node during the tree induction process. Extensive experiments show that the proposed method outperforms the preprocessing approach, the regression tree approach, and several nontree-based algorithms.

Studies have been conducted on the construction of a bioactive proteolysis-resistant peptide sequence of the CDF-11 protein followed by the synthesis of a plasmid encoding its expression in E.coli. The prediction of biological activity, resistance to proteolysis and bioavailability of the peptide sequence was carried out on the ADMET platform. The synthesis of plasmids of the peptide sequence of a new recombinant protein was carried out in the Russian biotechnology company “Eurogen” (Moscow) by cyclic assembly of oligonucleotides. The molecular weight distribution of the peptide sequence was evaluated by mass spectrometry. The peptide sequence was identified by the MALDI-TOF MS Ultraflex method. The peptide sequence of the GDF-11 LQIIYGKIPR protein was cyclized by introducing and crosslinking three cysteine residues and a new LQIIYGKIPR sequence was obtained. The predicted values of the bioavailability and clinical use of the new peptide indicate the effectiveness of its proposed modification and the possibility of oral administration. For the expression of a recombinant protein with increased stability to proteolysis and bioavailability containing a cyclic peptide sequence, a plasmid was obtained and synthesized, and the sequence of amino acids in it was determined. Further research will focus on the expression of the modified GDF 11 mut protein in E.coli.

Aim: Chronic kidney disease (CKD) is among the main 20 reasons for death worldwide and influences around 10% of the world grown-up populace. CKD is an issue that upsets typical kidney work. The main objective of this study aims to find the best-suited algorithm that will give us the most ideal prediction. We will be comparing Novel Decision Tree with Linear Regression to find out which of these can give us the best accuracy. Material and Methods: The study used 322 samples with Novel Decision Tree and Linear Regression is executed with varying training and testing splits for predicting the accuracy for kidney disease prediction with the G-power value of 80% and the kidney datasets were collected from various web sources with recent study findings and threshold 0.05%, confidence interval 95% mean and standard deviation. The performance of the classifiers are evaluated based on their accuracy rate using the chronic kidney disease dataset. Results: The accuracy of predicting kidney disease in Novel Decision Tree (96.66%) and Linear Regression (85.25%) is obtained. There is a statistical 2-tailed significant difference in accuracy for two algorithms is 0.000 (p<0.05) by performing independent samples t-tests. Conclusion: This study concludes that the Prediction of Kidney disease using the Novel Decision Tree (DT) algorithm appears to be significantly better than the Linear Regression(LR) with improved accuracy.

Results in heart disease classification that are inaccurate and have low accuracy can endanger the patient's life. Some parameters in the algorithm model also influence classification. This study compares the Decision Tree and Random Forest algorithms for heart disease. The influence of maximum depth on heart disease classification also has significant implications. If the maximum depth is not set correctly, the classification results can be inaccurate and lead to incorrect diagnoses. This study uses five data split schemes, namely 60%: 40%, 70%: 30%, 75%: 25%, 80%: 20%, 90%: 10% and tested with different max depth parameters, namely max depth = 3, 4, 5, 6, and 7. This research produces the best accuracy using the 90%:10% scheme and max depth = 7 with the best accuracy result using the Random Forest algorithm of 99.29% while the Decision Tree algorithm is 98.05%. Then the precision and recall value of the Random Forest algorithm is 99% while the Decision Tree is 98%. The results of computation time using Decision Tree are faster than using Random Forest with a computation time for training data of 0.0075 s, while the testing data are 0.009 s. In future research, research can be conducted on the effect of other parameters by testing using several data sets.

Mixture design optimization and machine learning-based prediction of Al-Mg alloy composite reinforced by Zn nanoparticles: A molecular dynamics study

Manufacturing industries facing problem in optimal selection of process parameters in machining process. Finding optimum process parameters for achieving maximum Material Removal Rate and minimum Surface Roughness is a challenging task and it requires lot of time and energy for experimentation trails or experience. It wastes lot of resources and money, sometimes ends up with negative results. To overcome the above issue, this paper presents an algorithm for prediction of Surface Roughness and Material Removal rate using Decision Tree Algorithm and Naive Bayes Algorithm without experimentation. Lot of resources and time can be saved using these machine learning algorithms. In this paper, Material removal rate and Surface roughness of EDM machining of Aluminum composites is predicted using Decision tree algorithm and Naive Bayes algorithm. Then the model can be used to predict the Material Removal Rate and Surface finish of any combination process parameters before machining process.

Species Specificity of the Anticoagulant Activity of Human Urinary Soluble Thrombomodulin

The virtualization of computing resources provided by containers has gained increasing attention and has been widely used in cloud computing. This new demand for container technology has been growing and the use of Docker and Kubernetes is considerable. According to recent technology surveys, containers are now mainstream. However, currently, one of the major challenges rises from the fact that multiple containers, with different owners, may cohabit on the same host. In container-based multi-tenant environments, security issues are of major concern. In this paper we investigate the performance of container-level anomaly-based intrusion detection systems for multi-tenant applications. We investigate the use of Bag of System Calls (BoSC) technique and the sliding window with the classifier and we consider eight machine learning algorithms for classification purposes. We show that among the eight machine learning algorithms, the best classification results are obtained with Decision Tree and Random Forest which lead to an F-Measure of 99.8%, using a sliding window with a size of 30 and the BoSC algorithm in both cases. We also show that, although both Decision Tree and Random Forest algorithms leads to the best classification results, the Decision Tree algorithm has a shorter execution time and consumes less CPU and memory than the Random Forest.

Energy spectra unfolding of fast neutron sources using the group method of data handling and decision tree algorithms

Gender prediction is among the most critical topics in forensic medicine and anthropology since it is the basis of identity (height, weight, ancestry, age). Today, osteometry which is a low-cost, easily accessible method that requires no expertise is preferred when compared to DNA technology, which has several disadvantages such as high cost, accessibility, laboratory facilities, and expert personnel requirements. The Computed Tomography (CT) method, which is little affected by orientation and provides reconstruction opportunities, was selected instead of traditional methods for osteometry. This study aims to predict high and accurate gender with the Decision Tree (DT) algorithms used in the field of health recently. In the present study, CT images of 300 individuals (150 females, 150 males) without a pathology on the pelvic skeleton and between the ages of 25 and 50 were transformed into orthogonal form, landmarks were placed on promontorium, sacroiliac joint, iliac crest, terminal line, anterior superior iliac spine, anterior inferior iliac spine, greater trochanter, obturator foramen, lesser trochanter, femoral head, femoral neck, the body of femur, ischial tuberosity, acetabulum, and pubic symphysis, and the coordinates of these landmarks were determined. Then, parameters such as angle and length were obtained with various combinations. These parameters were analyzed with the DT algorithm.The analysis conducted with the DT algorithm revealed that accuracy (Acc) was 0.93, sensitivity was 0.95, specificity was 0.90, and the Matthews correlation coefficient was 0.86 for the pelvic skeleton. It was observed that the accuracy was quite high and more realistic when determined with the DT algorithm. In conclusion, the DT algorithm with multiple parameters and samples on pelvic CT images could improve the Acc of gender prediction. [Med-Science 2021; 10(2.000): 356-61]

Energy management of hybrid energy system sources based on machine learning classification algorithms

Using decision tree algorithms for estimating ICU admission of COVID-19 patients

Aim: Prediction of coronary illness utilizing novel Novel k nearest neighbor (KNN) and contrasting its accuracy with decision tree algorithm. Materials and Methods: Two gatherings are proposed for foreseeing the accuracy (%) of coronary illness. To be specific, novel Novel k nearest neighbor and decision tree algorithm. Here we take 20 examples each for assessment and look at. The sample size was calculated using G power with pretest power at 80% and the alpha of 0.05 value. Result: The decision tree gives better accuracy (84.95%) contrasted with the Novel k nearest neighbor accuracy (76.29 %). Along these lines the factual meaning of the decision tree is superior to the novel k nearest neighbor algorithm with significance value of 0.115. Conclusion: From the outcome, it may very well be inferred that the decision tree helps in anticipating the coronary illness with more precision contrasted with the novel k nearest neighbor algorithm.

Platelet dysfunction contributes to blood loss after cardiopulmonary bypass. This study examined the antiplatelet effects of heparin, protamine, and varying heparin/protamine ratios in an in vitro physiologic model and further elucidated the mechanism of the antiplatelet and anticoagulant effects of protamine. We used the Clot Signature Analyzer (CSA(TM)), a system that analyzes coagulation in flowing whole blood, to test two aspects of platelet function, with different concentrations of heparin and protamine, under conditions simulating arterial flow: collagen-induced thrombus formation (CITF) under moderate shear and high shear platelet activation, platelet hemostasis time (PHT). In addition, platelet aggregometry, celite activated clotting time (Hepcon(TM) ACT), prothrombin time (PT), and partial thromboplastin time (PTT) were measured. Both PHT and the CITF were prolonged by heparin at 20 microg/mL, protamine at 20 and 40 microg/mL, and heparin/protamine ratios of 1:1 and 1:2, but not at 1:1.5. The Hepcon ACT was prolonged by heparin 20 microg/mL and protamine alone at 20 and 40 microg/mL, was normal at a ratio of 1:1, and was prolonged at 1:1.5 and 1:2. Protamine 80 microg/mL prolonged the PT and PTT. Dependency on thrombin, protein kinase C activation, and nonspecific charge effects were examined. The direct thrombin inhibitor D-phenylalanyl-L-prolyl-L-arginyl-chloromethyl ketone prolonged the PHT and ACT, but not the CITF, whereas the polycationic molecules polyarginine and polylysine prolonged the CITF, but not the PHT. The effect of protamine on the PTT, but not PT, could be shortened by the addition of excess phospholipid. Therefore, heparin inhibits both high shear collagen-independent and moderate shear collagen-dependent platelet activation; however, the latter is not mediated by its antithrombin activity. Protamine's antithrombin effect may explain its inhibition of platelet activation at high shear stress. Protamine's nonspecific charge effects are more important for inhibiting moderate shear collagen-induced platelet activation. This study suggests that protamine reversal of heparin's antiplatelet effect occurs within a narrow window because of the direct antiplatelet effects of protamine. Antithrombin effects may explain the inhibition of shear activation of platelets by both heparin and protamine. Nonspecific charge effects of protamine may explain the inhibition of collagen platelet activation in the presence of medium shear.