Robust stress classifier using adaptive neuro-fuzzy classifier-linguistic hedges

Recent studies show that chronic stress exposure can induce a long list of diseases that are prevalent in human body. In this paper, researchers work on measuring and analyzing stress level using human biosignal, electrocardiogram (ECG). First, a few preprocessing steps and different analysis domains is done onto the raw data signals to clean and extract any and every relevant features found in ECG signal. A Linguistic Hedges concept on fuzzy feature selection method is then proposed to select unique patterns from the listed heart rate variability features. From the extracted list of features, a neurofuzzy classifier (ANFC-LH) is used to classify the data points into 2 classes, high arousal and low arousal, high arousal indicating stress feature. Then a comparative study using different classification methods, including Multilayer Perceptron, kNearest Neighbor, and Linear Discriminant Analysis are used to determine the most relevant feature specifying high stress level. Comparing to MLP, kNN, and LDA, ANFC-LH achieved the highest recognition rate. This research paper also shows the effects of using dimension reduction methods on classification algorithms where the result of kNN and LDA improved about 20% when applied with dimension reduction method, however, MLP recognition rate deteriorates about 50% when classifying data point after dimension reduction

An AI-assisted integrated, scalable, single-cell phenomic-transcriptomic platform to elucidate intratumor heterogeneity against immune response

We present a novel framework combining single-cell phenotypic data with single-cell transcriptomic analysis to identify factors underpinning heterogeneity in antitumor immune response. We developed a pairwise, tumor-immune discretized interaction assay between natural killer (NK-92MI) cells and patient-derived head and neck squamous cell carcinoma (HNSCC) cell lines on a microfluidic cell-trapping platform. Furthermore we generated a deep-learning computer vision algorithm that is capable of automating the acquisition and analysis of a large, live-cell imaging data set (>1 million) of paired tumor-immune interactions spanning a time course of 24 h across multiple HNSCC lines (n = 10). Finally, we combined the response data measured by Kaplan–Meier survival analysis against NK-mediated killing with downstream single-cell transcriptomic analysis to interrogate molecular signatures associated with NK-effector response. As proof-of-concept for the proposed framework, we efficiently identified MHC class I-driven cytotoxic resistance as a key mechanism for immune evasion in nonresponders, while enhanced expression of cell adhesion molecules was found to be correlated with sensitivity against NK-mediated cytotoxicity. We conclude that this integrated, data-driven phenotypic approach holds tremendous promise in advancing the rapid identification of new mechanisms and therapeutic targets related to immune evasion and response.</p

Single-Cell RNA-seq Reveals Angiotensin-Converting Enzyme 2 and Transmembrane Serine Protease 2 Expression in TROP2+ Liver Progenitor Cells: Implications in Coronavirus Disease 2019-Associated Liver Dysfunction

10.3389/fmed.2021.603374Frontiers in Medicine860337

Onco-fetal Reprogramming of Endothelial Cells Drives Immunosuppressive Macrophages in Hepatocellular Carcinoma

10.1016/j.cell.2020.08.040CELL1832377-39

Trajectory of immune evasion and cancer progression in hepatocellular carcinoma

Immune evasion is key to cancer initiation and later at metastasis, but its dynamics at intermediate stages, where potential therapeutic interventions could be applied, is undefined. Here we show, using multi-dimensional analyses of resected tumours, their adjacent non-tumour tissues and peripheral blood, that extensive immune remodelling takes place in patients with stage I to III hepatocellular carcinoma (HCC). We demonstrate the depletion of anti-tumoural immune subsets and accumulation of immunosuppressive or exhausted subsets along with reduced tumour infiltration of CD8 T cells peaking at stage II tumours. Corresponding transcriptomic modification occur in the genes related to antigen presentation, immune responses, and chemotaxis. The progressive immune evasion is validated in a murine model of HCC. Our results show evidence of ongoing tumour-immune co-evolution during HCC progression and offer insights into potential interventions to reverse, prevent or limit the progression of the disease.National Medical Research Council (NMRC)National Research Foundation (NRF)Published versionThis work was supported by the National Medical Research Council (NMRC), Singapore (ref numbers: NMRC/TCR/015-NCC/2016, NMRC/CIRG/1460/2016, NMRC/ CSA-SI/0013/2017, NMRC/CSA-SI/0018/2017, NMRC/OFLCG/003/2018, NMRC/ STaR/020/2013, NMRC/CG/M003/2017, LCG17MAY004 and NMRC/OFIRG/0064/ 2017) and National Research Foundation, Singapore (ref number: NRF-NRFF2015-04)