Integrative analysis of H&E and IHC identifies prognostic immune subtypes in HPV related oropharyngeal cancer.

Background: Deep learning techniques excel at identifying tumor-infiltrating lymphocytes (TILs) and immune phenotypes in hematoxylin and eosin (H&E)-stained slides. However, their ability to elucidate detailed functional characteristics of diverse cellular phenotypes within tumor immune microenvironment (TME) is limited. We aimed to enhance our understanding of cellular composition and functional characteristics across TME regions and improve patient stratification by integrating H&E with adjacent immunohistochemistry (IHC) images.

Diagnosis of Alzheimer Disease and Tauopathies on Whole-Slide Histopathology Images Using a Weakly Supervised Deep Learning Algorithm.

Neuropathologic assessment during autopsy is the gold standard for diagnosing neurodegenerative disorders. Neurodegenerative conditions, such as Alzheimer disease (AD) neuropathological change, are a continuous process from normal aging rather than categorical; therefore, diagnosing neurodegenerative disorders is a complicated task. We aimed to develop a pipeline for diagnosing AD and other tauopathies, including corticobasal degeneration (CBD), globular glial tauopathy, Pick disease, and progressive supranuclear palsy.

Machine Learning Algorithm for Efficient Design of Separated Buffer Super-Junction IGBT.

An improved structure for an Insulated Gate Bipolar Transistor (IGBT) with a separated buffer layer is presented in order to improve the trade-off between the turn-off loss () and on-state voltage (). However, it is difficult to set efficient parameters due to the increase in the new buffer doping concentration variable. Therefore, a machine learning (ML) algorithm is proposed as a solution.

ACTA2 Expression Predicts Survival and Is Associated with Response to Immune Checkpoint Inhibitors in Gastric Cancer.

Purpose: We sought to identify biomarkers that predict overall survival (OS) and response to immune checkpoint inhibitors (ICI) for patients with gastric cancer.

Experimental Design: This was a retrospective study of multiple independent cohorts of patients with gastric cancer. The association between tumor ACTA2 expression and OS and ICI response were determined in patients whose tumors were analyzed with bulk mRNA sequencing.

Spatial heterogeneity and organization of tumor mutation burden with immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer.

Background: High tumor mutation burden (TMB-H) could result in an increased number of neoepitopes from somatic mutations expressed by a patient's own tumor cell which can be recognized and targeted by neighboring tumor-infiltrating lymphocytes (TILs). Deeper understanding of spatial heterogeneity and organization of tumor cells and their neighboring immune infiltrates within tumors could provide new insights into tumor progression and treatment response.

Methods: Here we first developed computational approaches using whole slide images (WSIs) to predict bladder cancer patients' TMB status and TILs across tumor regions, and then investigate spatial heterogeneity and organization of regions harboring TMB-H tumor cells and TILs within tumors, as well as their prognostic utility.

Sequential Single-Cell Transcriptional and Protein Marker Profiling Reveals TIGIT as a Marker of CD19 CAR-T Cell Dysfunction in Patients with Non-Hodgkin Lymphoma.

Unlabelled: Chimeric antigen receptor T-cell (CAR-T cell) therapy directed at CD19 produces durable remissions in the treatment of relapsed/refractory non-Hodgkin lymphoma (NHL). Nonetheless, many patients receiving CD19 CAR-T cells fail to respond for unknown reasons. To reveal changes in 4-1BB-based CD19 CAR-T cells and identify biomarkers of response, we used single-cell RNA sequencing and protein surface marker profiling of patient CAR-T cells pre- and postinfusion into patients with NHL.

Spatial analysis of tumor-infiltrating lymphocytes in histological sections using deep learning techniques predicts survival in colorectal carcinoma.

This study aimed to explore the prognostic impact of spatial distribution of tumor-infiltrating lymphocytes (TILs) quantified by deep learning (DL) approaches based on digitalized whole-slide images stained with hematoxylin and eosin in patients with colorectal cancer (CRC). The prognostic impact of spatial distributions of TILs in patients with CRC was explored in the Yonsei cohort (n = 180) and validated in The Cancer Genome Atlas (TCGA) cohort (n = 268). Two experienced pathologists manually measured TILs at the most invasive margin (IM) as 0-3 by the Klintrup-Mäkinen (KM) grading method and this was compared to DL approaches.

Multi-omics reveals microbiome, host gene expression, and immune landscape in gastric carcinogenesis.

To date, there has been no multi-omic analysis characterizing the intricate relationships between the intragastric microbiome and gastric mucosal gene expression in gastric carcinogenesis. Using multi-omic approaches, we provide a comprehensive view of the connections between the microbiome and host gene expression in distinct stages of gastric carcinogenesis (i.e.

Lenvatinib inhibits the growth of gastric cancer patient-derived xenografts generated from a heterogeneous population.

Background: Lenvatinib is a multitargeted tyrosine kinase inhibitor that is being tested in combination with immune checkpoint inhibitors to treat advanced gastric cancer; however, little data exists regarding the efficacy of lenvatinib monotherapy. Patient-derived xenografts (PDX) are established by engrafting human tumors into immunodeficient mice. The generation of PDXs may be hampered by growth of lymphomas.

Development and validation of a prognostic and predictive 32-gene signature for gastric cancer.

Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature.

Machine Learning and Artificial Intelligence-driven Spatial Analysis of the Tumor Immune Microenvironment in Pathology Slides.

A better understanding of the tumor immune microenvironment (TIME) could lead to accurate diagnosis, prognosis, and treatment stratification. Although molecular analyses at the tissue and/or single cell level could reveal the cellular status of the tumor microenvironment, these approaches lack information related to spatial-level cellular distribution, co-organization, and cell-cell interaction in the TIME. With the emergence of computational pathology coupled with machine learning (ML) and artificial intelligence (AI), ML- and AI-driven spatial TIME analyses of pathology images could revolutionize our understanding of the highly heterogeneous and complex molecular architecture of the TIME.

Associations between lipids in selected brain regions, plasma miRNA, and behavioral and cognitive measures following Si ion irradiation.

The space radiation environment consists of multiple species of charged particles, including Si ions, that may impact brain function during and following missions. To develop biomarkers of the space radiation response, BALB/c and C3H female and male mice and their F2 hybrid progeny were irradiated with Si ions (350 MeV/n, 0.2 Gy) and tested for behavioral and cognitive performance 1, 6, and 12 months following irradiation.

Single cell RNA sequencing of AML initiating cells reveals RNA-based evolution during disease progression.

The prognosis of most patients with AML is poor due to frequent disease relapse. The cause of relapse is thought to be from the persistence of leukemia initiating cells (LIC's) following treatment. Here we assessed RNA based changes in LICs from matched patient diagnosis and relapse samples using single-cell RNA sequencing.

A spatiotemporal analysis of opioid poisoning mortality in Ohio from 2010 to 2016.

Opioid-related deaths have severely increased since 2000 in the United States. This crisis has been declared a public health emergency, and among the most affected states is Ohio. We used statewide vital statistic data from the Ohio Department of Health (ODH) and demographics data from the U.

Radiomics Features of F-Fluorodeoxyglucose Positron-Emission Tomography as a Novel Prognostic Signature in Colorectal Cancer.

The aim of this study was to investigate the prognostic value of radiomics signatures derived from F-fluorodeoxyglucose (F-FDG) positron-emission tomography (PET) in patients with colorectal cancer (CRC). From April 2008 to Jan 2014, we identified CRC patients who underwent F-FDG-PET before starting any neoadjuvant treatments and surgery. Radiomics features were extracted from the primary lesions identified on F-FDG-PET.

Induced organoids derived from patients with ulcerative colitis recapitulate colitic reactivity.

The pathogenesis of ulcerative colitis (UC), a major type of inflammatory bowel disease, remains unknown. No model exists that adequately recapitulates the complexity of clinical UC. Here, we take advantage of induced pluripotent stem cells (iPSCs) to develop an induced human UC-derived organoid (iHUCO) model and compared it with the induced human normal organoid model (iHNO).

Network-based drug sensitivity prediction.

Background: Drug sensitivity prediction and drug responsive biomarker selection on high-throughput genomic data is a critical step in drug discovery. Many computational methods have been developed to serve this purpose including several deep neural network models. However, the modular relations among genomic features have been largely ignored in these methods.

DNA Methylation Regulates Alternative Polyadenylation via CTCF and the Cohesin Complex.

Dysregulation of DNA methylation and mRNA alternative cleavage and polyadenylation (APA) are both prevalent in cancer and have been studied as independent processes. We discovered a DNA methylation-regulated APA mechanism when we compared genome-wide DNA methylation and polyadenylation site usage between DNA methylation-competent and DNA methylation-deficient cells. Here, we show that removal of DNA methylation enables CTCF binding and recruitment of the cohesin complex, which, in turn, form chromatin loops that promote proximal polyadenylation site usage.

Myeloid-Derived Suppressor Cell Subsets Drive Glioblastoma Growth in a Sex-Specific Manner.

Myeloid-derived suppressor cells (MDSC) that block antitumor immunity are elevated in glioblastoma (GBM) patient blood and tumors. However, the distinct contributions of monocytic (mMDSC) versus granulocytic (gMDSC) subsets have yet to be determined. In mouse models of GBM, we observed that mMDSCs were enriched in the male tumors, whereas gMDSCs were elevated in the blood of females.

Hispanic/Latino Patients with Gastric Adenocarcinoma Have Distinct Molecular Profiles Including a High Rate of Germline Variants.

Hispanic/Latino patients have a higher incidence of gastric cancer and worse cancer-related outcomes compared with patients of other backgrounds. Whether there is a molecular basis for these disparities is unknown, as very few Hispanic/Latino patients have been included in previous studies. To determine the genomic landscape of gastric cancer in Hispanic/Latino patients, we performed whole-exome sequencing (WES) and RNA sequencing on tumor samples from 57 patients; germline analysis was conducted on 83 patients.

Permanent Pacemaker Implantations after Catheter Ablation in Patients with Atrial Fibrillation Associated with Underlying Sinus Node Dysfunction.

Background And Objectives: The objective of this study was to evaluate the long-term clinical outcomes and the incidence of permanent pacemaker implantation after catheter ablation in patients with of atrial fibrillation (AF) and sinus node dysfunction (SND).

Methods: Among 3,068 total consecutive patients who underwent AF catheter ablation (AFCA), this study included 222 (9.5%; men 53.