Nanoplastics are an increasing threat to the ecosystem; however, their mobility in the soil is still underexplored. Against this backdrop, researchers from Waseda University and the National Institute of Advanced Industrial Science and Technology investigated the adsorption and aggregation behavior of nanoplastics in different types of soil under different pH conditions. The study offers new perspectives on the migration and environmental interactions of nanoplastics, while broadening our knowledge of pollution dynamics and soil contamination processes.

COLUMBUS, Ohio – Researchers at The Ohio State University Wexner Medical Center and College of Medicine have discovered a new way that neurons act in neurodegeneration by using human neural organoids – also known as “mini-brain” models – from patients with frontotemporal lobar degeneration (FTLD). Understanding this new pathway could help researchers find better treatments for FTLD and Alzheimer’s, the two most common forms of dementia that lead to cognitive decline.

In recent years, chemotherapy-induced cardiovascular disease has emerged as a significant cause of mortality among cancer survivors. Anthracyclines, pivotal in treating cancers like breast cancer and leukemia, are indispensable in cancer therapy, yet their severe cardiotoxicity presents a formidable challenge. A recent review in Medicine Plus offers profound insights into anthracycline-induced cardiotoxicity (AIC), exploring its mechanisms and therapeutic strategies, contributing to safer oncological treatments in the future.

A recent paper published in National Science Review presents a multi-space alignment approach for cross-species and cross-modality electroencephalogram (EEG) based epileptic seizure detection. By employing deep learning techniques, including domain adaptation and knowledge distillation, it aligns cross-species and cross-modality EEG signals to enhance the detection capability beyond traditional within-species and within-modality models. Experiments on multiple scalp and intracranial EEG datasets of humans and canines demonstrated substantial improvements in the detection accuracy, achieving over 90% AUC scores for cross-species and cross-modality seizure detection with extremely limited labeled data from the target species/modality. This is the first study that demonstrates the effectiveness of integrating heterogeneous data from different species and modalities to improve EEG-based seizure detection performance.