Human adipose-derived stem cells genetically programmed to induce necroptosis for cancer immunotherapy.

Herein, we present human adipose-derived stem cells (ADSCs) inserted with the receptor-interacting protein kinase-3 (RIP3) gene (RP@ADSCs), which induces cell necroptosis, for tumor immunotherapy. Necroptosis has characteristics of both apoptosis, such as programmed cell death, and necrosis, such as swelling and plasma membrane rupture, during which damage-related molecular patterns are released, triggering an immune response. Therefore, necroptosis has the potential to be used as an effective anticancer immunotherapy. RP@ADSCs were programmed to necroptosis after a particular time after being injected in vivo, and various pro-inflammatory cytokines secreted during the stem cell death process stimulated the immune system, showing local and sustained anticancer effects. It was confirmed that RIP3 protein expression increased in ADSCs after RP transfection. RP@ADSCs continued to induce ADSCs death for 7 days, and various pro-inflammatory cytokines were secreted through ADSCs death. The efficacy of RP@ADSCs-mediated immunotherapy was evaluated in mouse models bearing GL-26 (glioblastoma) and K1735 (melanoma), and it was found that RP resulted in an increase in the population of long-term cytotoxic T cells and a decrease in the population of regulatory T cells. This shows that RP@ADSCs have potential and applicability as an excellent anticancer immunotherapy agent in clinical practice.

The effects of layer-wise relevance propagation-based feature selection for EEG classification: a comparative study on multiple datasets.

Introduction: The brain-computer interface (BCI) allows individuals to control external devices using their neural signals. One popular BCI paradigm is motor imagery (MI), which involves imagining movements to induce neural signals that can be decoded to control devices according to the user's intention. Electroencephalography (EEG) is frequently used for acquiring neural signals from the brain in the fields of MI-BCI due to its non-invasiveness and high temporal resolution. However, EEG signals can be affected by noise and artifacts, and patterns of EEG signals vary across different subjects. Therefore, selecting the most informative features is one of the essential processes to enhance classification performance in MI-BCI. Methods: In this study, we design a layer-wise relevance propagation (LRP)-based feature selection method which can be easily integrated into deep learning (DL)-based models. We assess its effectiveness for reliable class-discriminative EEG feature selection on two different publicly available EEG datasets with various DL-based backbone models in the subject-dependent scenario. Results and discussion: The results show that LRP-based feature selection enhances the performance for MI classification on both datasets for all DL-based backbone models. Based on our analysis, we believe that it can broad its capability to different research domains.