Machine learning coupled with causal inference to identify COVID-19 related chemicals that pose a high concern to drinking water.

Various synthetic substances were utilized in large quantities during the recent coronavirus pandemic, COVID-19. Some of these chemicals could potentially enter drinking water sources. Persistent, mobile, and toxic (PMT) substances have been recognized as a threat to drinking water resources. It has not yet been assessed how many COVID-19 related substances could be considered PMT substances. One reason is the lack of high-quality experimental data for the identification of PMT substances. To solve this problem, we applied a machine learning model to identify the PMT substances among COVID-19 related chemicals. The optimal model achieved an accuracy of 90.6% based on external test data. The model interpretation and causal inference indicated that our approach understood causation between PMT properties and molecular descriptors. Notably, the screening results showed that over 60% of the COVID-19 chemicals considered are candidate PMT substances, which should be prioritized to prevent undue pollution of water resources.

A novel CEST-contrast nanoagent for differentiating the malignant degree in breast cancer.

Different subtypes of breast cancer (BCC) have variable degrees of malignancy, which is closely related to their extracellular pH (pHe). Therefore, it is increasingly significant to monitor the extracellular pH sensitively to further determine the malignancy of different subtypes of BCC. Here, a l-arginine and Eu3+ assembled nanoparticle Eu3+@l-Arg was prepared to detect the pHe of two breast cancer models (TUBO is non-invasive and 4T1 is malignant) using a clinical chemical exchange saturation shift imaging technique. The experiments in vivo showed that Eu3+@l-Arg nanomaterials could respond sensitively to changes of pHe. In 4T1 models, the CEST signal enhanced about 5.42 times after Eu3+@l-Arg nanomaterials were used to detect the pHe. In contrast, few enhancements of the CEST signal were seen in the TUBO models. This significant difference had led to new ideas for identifying subtypes of BCC with different degrees of malignancy.

Magnetic resonance energy transfer for in vivo glutathione susceptibility weighted imaging.

Glutathione (GSH) plays a vital role in maintaining biological redox homeostasis. Accordingly, accurate imaging of glutathione in vivo is of great significance. Herein, we propose a magnetic resonance energy transfer (MRET) strategy based on a distance-dependent magnetic exchange coupling effect (MECE), which can realize GSH detection within tumors in vivo by susceptibility weighted imaging (SWI). Fe3O4 nanoparticles (NPs) and CoFe2O4 NPs linked with cystamine (Fe3O4-S-S-CoFe2O4) have been successfully designed as SWI nanoprobes. After the disulfide bonds are broken by excess GSH in the tumor, the increase in the distance between Fe3O4 NPs and CoFe2O4 NPs will induce a decrease of MECE and magnetic susceptibility. As a result, the changes in the SWI signals are used for tumor GSH detection in vivo. Experimental results in vitro and in vivo demonstrate that the Fe3O4-S-S-CoFe2O4 SWI nanoprobe can sensitively detect concentrations of GSH in tumors. Hence, this strategy not only improves the sensitivity of the GSH response in SWI but also provides a powerful basis for the design of other responsive functional MRI nanoprobes.