QSAR modeling for cytotoxicity of sulfur-containing Shikonin oxime derivatives targeting HCT-15, MGC-803, BEL-7402, and MCF-7 cell lines.

Targeting cancer cells through drug-based treatment or combination therapy protocols involving chemical compounds can be challenging due to multiple factors, including their resistance to bioactive compounds and the potential of drugs to damage healthy cells. This study aims to investigate the relationship between the structure of novel sulfur-containing shikonin oxime compounds and the corresponding cytotoxicity against four cancer types, namely colon, gastric, liver, and breast cancers, through computational chemistry tools. This investigation is suggested to help build insights into how the structure of the compounds influences their activity and understand the mechanisms behind it and subsequently might be used in multi-cancer drug design process to propose novel optimized compounds that potentially exhibit the desired activity.

Optimized variable selection and machine learning models for olive oil quality assessment using portable near infrared spectroscopy.

Olive oil is a key component of the Mediterranean diet, rich in antioxidants and beneficial monounsaturated fatty acids. As a result, high-quality olive oil is in great demand, with its price varying depending on its quality. Traditional chemical tests for assessing olive oil quality are expensive and time-consuming.

The synergic approach between machine learning, chemometrics, and NIR hyperspectral imagery for a real-time, reliable, and accurate prediction of mass loss in cement samples.

Alternative and non-destructive analytical methods that predict analyte concentration accurately and immediately in a specific matrix are becoming vital in the analytical chemistry domain. Here, a new innovative and rapid method of predicting mass loss of cement samples based on a combination of Machine Learning (ML) and the emerging technique called Hyperspectral Imaging (HSI) is presented. The method has proved its reliability and accuracy by providing a predictive ML model, with satisfactory best validation scores recorded using partial least squared regression, with a reported ratio of performance to inter-quartile distance and root mean squared error of 12,89 and 0.

Salt induced oxidative stress alters physiological, biochemical and metabolomic responses of green microalga .

Salinity is one of the most significant environmental factors limiting microalgal biomass productivity. In the present study, the model microalga () was exposed to 200 mM NaCl for eight days to explore the physiological, biochemical and metabolomic changes. exhibited a significant decrease in growth rate, and Chl a and Chl b levels.

Predicting soil phosphorus and studying the effect of texture on the prediction accuracy using machine learning combined with near-infrared spectroscopy.

The estimation of soil phosphorus is essential for agricultural activity. The laboratory chemical analysis techniques are expensive and labor-intensive. In the last decade, near-infrared spectroscopy has been become used as an alternative for soil attributes analysis.

Evaluation of Antibacterial Activity of Lavandulapedunculata subsp. atlantica (Braun-Blanq.) Romo Essential Oil and Selected Terpenoids against Resistant Bacteria Strains-Structure-Activity Relationships.

The genus Lavandula is known for its different uses in traditional medicine. This study is interested in the chemical composition of Lavandulapedunculata subsp.atlantica (Braun-Blanq.