Prediction of Pt, Ir, Ru, and Rh complexes light absorption in the therapeutic window for phototherapy using machine learning.

Effective light-based cancer treatments, such as photodynamic therapy (PDT) and photoactivated chemotherapy (PACT), rely on compounds that are activated by light efficiently, and absorb within the therapeutic window (600-850 nm). Traditional prediction methods for these light absorption properties, including Time-Dependent Density Functional Theory (TDDFT), are often computationally intensive and time-consuming. In this study, we explore a machine learning (ML) approach to predict the light absorption in the region of the therapeutic window of platinum, iridium, ruthenium, and rhodium complexes, aiming at streamlining the screening of potential photoactivatable prodrugs.

Racial and Ethnic Disparities in Regulatory Air Quality Monitor Locations in the US.

Importance: Understanding exposure to air pollution is important to public health, and disparities in the spatial distribution of regulatory air quality monitors could lead to exposure misclassification bias.

Objective: To determine whether racial and ethnic disparities exist in Environmental Protection Agency (EPA) regulatory air quality monitor locations in the US.

Design, Setting, And Participants: This national cross-sectional study included air quality monitors in the EPA Air Quality System regulatory monitoring repository, as well as 2022 American Community Survey Census block group estimates for racial and ethnic composition and population size.

Charge-switchable cell-penetrating peptides for rerouting nanoparticles to glioblastoma treatment.

Glioblastoma (GB) is one of the most lethal types of neoplasms with unique anatomic, physiologic, and pathologic features that usually persist after exposure to standard therapeutic modalities. It is biologically aggressive, and the existence of the blood-brain barrier (BBB) limits the efficacy of standard therapies. In this work, we hypothesize the potential of surface-functionalized ultra-small nanostructured lipid carriers (usNLCs) with charge-switchable cell-penetrating peptides (CPPs) to overcome this biological barrier and improve targeted delivery to brain tumor tissues.

Machine Learning-Based Prediction of Reduction Potentials for Pt Complexes.

Some of the well-known drawbacks of clinically approved Pt complexes can be overcome using six-coordinate Pt complexes as inert prodrugs, which release the corresponding four-coordinate active Pt species upon reduction by cellular reducing agents. Therefore, the key factor of Pt prodrug mechanism of action is their tendency to be reduced which, when the involved mechanism is of outer-sphere type, is measured by the value of the reduction potential. Machine learning (ML) models can be used to effectively capture intricate relationships within Pt complex data, leading to highly accurate predictions of reduction potentials and other properties, and offering significant insights into their electrochemical behavior and potential applications.

Modeling protective action decision-making in earthquakes by using explainable machine learning and video data.

Earthquakes pose substantial threats to communities worldwide. Understanding how people respond to the fast-changing environment during earthquakes is crucial for reducing risks and saving lives. This study aims to study people's protective action decision-making in earthquakes by leveraging explainable machine learning and video data.