Hydrodesulfurization of Dibenzothiophene: A Machine Learning Approach.

The hydrodesulfurization (HDS) process is widely used in the industry to eliminate sulfur compounds from fuels. However, removing dibenzothiophene (DBT) and its derivatives is a challenge. Here, the key aspects that affect the efficiency of catalysts in the HDS of DBT were investigated using machine learning (ML) algorithms.

Conceptual DFT, machine learning and molecular docking as tools for predicting LD toxicity of organothiophosphates.

Context: Several descriptors from conceptual density functional theory (cDFT) and the quantum theory of atoms in molecules (QTAIM) were utilized in Random Forest (RF), LASSO, Ridge, Elastic Net (EN), and Support Vector Machines (SVM) methods to predict the toxicity (LD) of sixty-two organothiophosphate compounds. The A-RF-G1 and A-RF-G2 models were obtained using the RF method, yielding statistically significant parameters with good performance, as indicated by R values for the training set (R) and R values for the test set (R), around 0.90.

QSTR Modeling to Find Relevant DFT Descriptors Related to the Toxicity of Carbamates.

Compounds containing carbamate moieties and their derivatives can generate serious public health threats and environmental problems due their high potential toxicity. In this study, a quantitative structure-toxicity relationship (QSTR) model has been developed by using one hundred seventy-eight carbamate derivatives whose toxicities in rats (oral administration) have been evaluated. The QSRT model was rigorously validated by using either tested or untested compounds falling within the applicability domain of the model.

Severity of SARS-CoV-2 infection is linked to double-negative (CD27 IgD) B cell subset numbers.

Objectives: The role of B cells in COVID-19, beyond the production of specific antibodies against SARS-CoV-2, is still not well understood. Here, we describe the novel landscape of circulating double-negative (DN) CD27 IgD B cells in COVID-19 patients, representing a group of atypical and neglected subpopulations of this cell lineage.

Methods: Using multiparametric flow cytometry, we determined DN B cell subset amounts from 91 COVID-19 patients, correlated those with cytokines, clinical and laboratory parameters, and segregated them by principal components analysis.