Classification Model of Pesticide Toxicity in Americamysis bahia Based on Quantum Chemical Descriptors.

A set of quantum chemical descriptors (molecular polarization, heat capacity, entropy, Mulliken net charge of the most positive hydrogen atom, APT charge of the most negative atom and APT charge of the most positive atom with hydrogen summed into heavy atoms) was successfully used to establish the classification models for the toxicity pLC50 of pesticides in Americamysis bahia. The optimal random forest model (Class Model A) yielded predictive accuracy of 100% (training set of 217 pesticides), 95.8% (test set of 72 pesticides) and 99.0% (total set of 289 pesticides), which were very satisfactory, compared with previous classification models reported for the toxicity of compounds in aquatic organisms. Therefore, it is reasonable to apply the quantum chemical descriptors associated with molecular structural information on molecular bulk, chemical reactivity and weak interactions, to develop classification models for the toxicity pLC50 of pesticides in A. bahia.

Feasibility and improvement of a three-dimensional printed navigation template for modified cortical bone trajectory screw placement in the lumbar spine.

Objectives: Compared with traditional pedicle screw trajectory, cortical bone trajectory (CBT) increases the contact surface between the screw and cortical bone where the screw is surrounded by dense cortical bone, which does not deform remarkably due to degeneration. We aimed to provide detailed information about the improvement of three-dimensional (3D)-printed navigation templates for modified CBT screw placement in the lumbar spine and evaluate the safety and accuracy thereof. Methods: Four human cadaveric lumbar spine specimens were selected. After CT scanning data were reconstructed to 3D models, either the left or right side of each specimen was randomly selected to establish a 3D-navigation template, mutually complemented with the surface anatomical structure of the lateral margin of the lumbar isthmus, vertebral plate, and spinous process. The corresponding 3D centrum was printed according to the CT scanning data, and a navigation template of supporting design was made according to modified cortical bone technique. The same template was used to insert CBT screws into 3D printed and cadaveric specimens. After the screws were inserted, the screw path of the 3D printed specimens was directly observed, and that of the anatomical specimens was scanned by CT, to determine the position and direction of the screws to analyze the success rate of screw placement. Results: Twenty cortical bone screws were placed in each of the 3D printed and anatomical specimens, with excellent rates of screw placement of 100% and 95%, respectively. Conclusions: We report the easy, safe, accurate, and reliable use of a 3D-printed navigation template to carry out screw placement by modified cortical bone technique in the lumbar spine.

Correlation between the structure and skin permeability of compounds.

A three-descriptor quantitative structure-activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

Machine learning-based prediction of toxicity of organic compounds towards fathead minnow.

Predicting the acute toxicity of a large dataset of diverse chemicals against fathead minnows (Pimephales promelas) is challenging. In this paper, 963 organic compounds with acute toxicity towards fathead minnows were split into a training set (482 compounds) and a test set (481 compounds) with an approximate ratio of 1 : 1. Only six molecular descriptors were used to establish the quantitative structure-activity/toxicity relationship (QSAR/QSTR) model for 96 hour pLC50 through a support vector machine (SVM) along with genetic algorithm. The optimal SVM model (R 2 = 0.756) was verified using both internal (leave-one-out cross-validation) and external validations. The validation results (q int 2 = 0.699 and q ext 2 = 0.744) were satisfactory in predicting acute toxicity in fathead minnows compared with other models reported in the literature, although our SVM model has only six molecular descriptors and a large data set for the test set consisting of 481 compounds.