RESUMEN
Intestinal lavage fluid (IVF) containing the mucosa-associated microbiota instead of fecal samples was used to study the gut microbiota using different omics approaches. Focusing on the 63 IVF samples collected from healthy and hepatitis B virus-liver disease (HBV-LD), a question is prompted whether omics features could be extracted to distinguish these samples. The IVF-related microbiota derived from the omics data was classified into two enterotype sets, whereas the genomics-based enterotypes were poorly overlapped with the proteomics-based one in either distribution of microbiota or of IVFs. There is lack of molecular features in these enterotypes to specifically recognize healthy or HBV-LD. Running machine learning against the omics data sought the appropriate models to discriminate the healthy and HBV-LD IVFs based on selected genes or proteins. Although a single omics dataset is basically workable in such discrimination, integration of the two datasets enhances discrimination efficiency. The protein features with higher frequencies in the models are further compared between healthy and HBV-LD based on their abundance, bringing about three potential protein biomarkers. This study highlights that integration of metaomics data is beneficial for a molecular discriminator of healthy and HBV-LD, and reveals the IVF samples are valuable for microbiome in a small cohort.
RESUMEN
Working memory load (WML) is one of the widely applied signals in the areas of human-machine interaction. The precise evaluation of the WML is crucial for this kind of application. This study aims to propose a deep learning (DL) time series classification (TSC) model for inter-subject WML decoding. We used fNIRS to record the hemodynamic signals of 27 participants during visual working memory tasks. Traditional machine learning and deep time series classification algorithms were respectively used for intra-subject and inter-subject WML decoding from the collected blood oxygen signals. The intra-subject classification accuracy of LDA and SVM were 94.6% and 79.1%. Our proposed TAResnet-BiLSTM model had the highest inter-subject WML decoding accuracy, reaching 92.4%. This study provides a new idea and method for the brain-computer interface application of fNIRS in real-time WML detection.
RESUMEN
Chemotherapy and radiotherapy frequently lead to intestinal damage. The mechanisms governing the repair or regeneration of intestinal damage are still not fully elucidated. Intraepithelial lymphocytes (IELs) are the primary immune cells residing in the intestinal epithelial layer. However, whether IELs are involved in intestinal epithelial injury repair remains unclear. Here, we found that IELs rapidly infiltrated the intestinal crypt region and are crucial for the recovery of the intestinal epithelium post-chemotherapy. Interestingly, IELs predominantly promoted intestinal regeneration by modulating the proliferation of transit-amplifying (TA) cells. Mechanistically, the expression of CD160 on IELs allows for interaction with herpes virus entry mediator (HVEM) on the intestinal epithelium, thereby activating downstream nuclear factor kappa (NF-κB) signaling and further promoting intestinal regeneration. Deficiency in either CD160 or HVEM resulted in reduced proliferation of intestinal progenitor cells, impaired intestinal damage repair, and increased mortality following chemotherapy. Remarkably, the adoptive transfer of CD160-sufficient IELs rescued the Rag1 deficient mice from chemotherapy-induced intestinal inflammation. Overall, our study underscores the critical role of IELs in intestinal regeneration and highlights the potential applications of targeting the CD160-HVEM axis for managing intestinal adverse events post-chemotherapy and radiotherapy.