Machine learning-enabled nanosafety assessment of multi-metallic alloy nanoparticles modified TiO2 system.

Establishing toxicological predictive modeling frameworks for heterogeneous nanomaterials is crucial for rapid environmental and health risk assessment. However, existing structure-toxicity correlation models for such nanomaterials are only based on simple linear regression algorithms that are prone to underfitting the training data. These models rely heavily on experimental and expensive computational quantum mechanical descriptors, which significantly limit their practical use. Herein, we present the application of empirical descriptors and complex machine learning algorithms to the development of high-performance quantitative structure-toxicity relationship (QSTR) models of TiO2 hybridized with multi-metallic (Ag, Au, Pt) alloy nanoparticles (multi-metallic NPs/TiO2). To confirm the viability of empirical descriptors as model input, we selected five distinct machine learning algorithms for predicting the toxicity of multi-metallic alloy NPs/TiO2 system in Chinese hamster ovary cell line. Notably, an empirical descriptor-based QSTR model (kernel ridge regression) revealed a predictive performance that is on par with density functional theory (DFT) descriptor-based counterparts. More specifically, the results indicated that model selection is influenced by descriptor choice, such that complex DFT descriptors worked best with a complex algorithm (random forest regression; RMSET = 0.0954, MAET = 0.0811, RT2 = 0.9411), whereas more straightforward empirical descriptors were most suitable with a simpler algorithm (kernel ridge regression; RMSET = 0.1244, MAET = 0.1106, RT2 = 0.8999). Moreover, our model outperforms existing QSAR models built on the same data set. This study offers a new perspective on using empirical features to develop accurate predictive computational models for the rapid discovery and profiling of safe-by-design nanomaterials.

Modeling Heterogeneous Brain Dynamics of Depression and Melancholia Using Energy Landscape Analysis.

Our current understanding of melancholic depression is shaped by its position in the depression spectrum. The lack of consensus on how it should be treated-whether as a subtype of depression, or as a distinct disorder altogethe-interferes with the recovery of suffering patients. In this study, we analyzed brain state energy landscape models of melancholic depression, in contrast to healthy and non-melancholic energy landscapes. Our analyses showed significant group differences on basin energy, basin frequency, and transition dynamics in several functional brain networks such as basal ganglia, dorsal default mode, and left executive control networks. Furthermore, we found evidences suggesting the connection between energy landscape characteristics (basin characteristics) and depressive symptom scores (BDI-II and SHAPS). These results indicate that melancholic depression is distinguishable from its non-melancholic counterpart, not only in terms of depression severity, but also in brain dynamics.