Digital profiling of gene expression from histology images with linearized attention.

Cancer is a heterogeneous disease requiring costly genetic profiling for better understanding and management. Recent advances in deep learning have enabled cost-effective predictions of genetic alterations from whole slide images (WSIs). While transformers have driven significant progress in non-medical domains, their application to WSIs lags behind due to high model complexity and limited dataset sizes.

Figure Correction: Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach.

[This corrects the article DOI: 10.2196/45767.].

Graph-based multi-modality integration for prediction of cancer subtype and severity.

Personalised cancer screening before therapy paves the way toward improving diagnostic accuracy and treatment outcomes. Most approaches are limited to a single data type and do not consider interactions between features, leaving aside the complementary insights that multimodality and systems biology can provide. In this project, we demonstrate the use of graph theory for data integration via individual networks where nodes and edges are individual-specific.

Digital profiling of cancer transcriptomes from histology images with grouped vision attention.

Cancer is a heterogeneous disease that demands precise molecular profiling for better understanding and management. Recently, deep learning has demonstrated potentials for cost-efficient prediction of molecular alterations from histology images. While transformer-based deep learning architectures have enabled significant progress in non-medical domains, their application to histology images remains limited due to small dataset sizes coupled with the explosion of trainable parameters.

Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach.

Background: While scientific knowledge of post-COVID-19 condition (PCC) is growing, there remains significant uncertainty in the definition of the disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most clinicians.

Objective: In this study, we aimed to determine the validity and effectiveness of advanced natural language processing approaches built to derive insight into PCC-related patient-reported health outcomes from social media platforms Twitter and Reddit.

Transcriptional profiling of recall responses to Francisella live vaccine strain.

Global gene expression profile changes were monitored in human peripheral blood mononuclear cells (PBMCs) after challenge with the live vaccine strain (LVS) of Francisella tularensis. Because these PBMCs were from individuals previously immunized with LVS, stimulating these cells with LVS should activate memory responses. The Ingenuity Pathway Analysis tool identified pathways, functions, and networks associated with this in vitro recall response, including novel pathways triggered by the memory response.

Actein activates stress- and statin-associated responses and is bioavailable in Sprague-Dawley rats.

The purpose of this study was to assess in rats the pharmacological parameters and effects on gene expression in the liver of the triterpene glycoside actein. Actein, an active component from the herb black cohosh, has been shown to inhibit the proliferation of human breast cancer cells. To conduct our assessment, we determined the molecular effects of actein on livers from Sprague-Dawley rats treated with actein at 35.

Pathway analysis tools and toxicogenomics reference databases for risk assessment.

The pharmaceutical industry has begun to leverage a range of new technologies (proteomics, pharmacogenomics, metabolomics and molecular toxicology [e.g., toxicogenomics]) and analysis tools that are becoming increasingly integrated in the area of drug discovery and development.

Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action.

Successful drug discovery requires accurate decision making in order to advance the best candidates from initial lead identification to final approval. Chemogenomics, the use of genomic tools in pharmacology and toxicology, offers a promising enhancement to traditional methods of target identification/validation, lead identification, efficacy evaluation, and toxicity assessment. To realize the value of chemogenomics information, a contextual database is needed to relate the physiological outcomes induced by diverse compounds to the gene expression patterns measured in the same animals.

Notch-induced E2A ubiquitination and degradation are controlled by MAP kinase activities.

Notch signals are important for lymphocyte development but downstream events that follow Notch signaling are not well understood. Here, we report that signaling through Notch modulates the turnover of E2A proteins including E12 and E47, which are basic helix-loop-helix proteins crucial for B and T lymphocyte development. Notch-induced degradation requires phosphorylation of E47 by p42/p44 MAP kinases.