Hematology

Lymphoma

Latest AI and machine learning research in lymphoma for healthcare professionals.

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Associations between calcium intake and T cell infiltration in colorectal tumours.

Higher T cell infiltration in colorectal tumours has been associated with better prognosis. Evidence...

Comparison of Manual Versus QuPath Software-based Immunohistochemical Scoring Using Oral Squamous Cell Carcinoma as a Model.

Gold standard for immunohistochemical analyses is the manual assessment by two specialist pathologis...

Automated generation of discharge summaries: leveraging large language models with clinical data.

This study explores the use of open-source large language models (LLMs) to automate generation of Ge...

Simultaneous determination of five alkaloids in extracted and non-extracted poppy APIs by HPLC.

BACKGROUND: Poppy Active Pharmaceutical Ingredients (APIs) are mainly used in the production of anti...

Arylacetamides exhibit antiproliferative effects on non-transformed mammalian and vegetal cells and toxicity on crustaceans and fish embryos.

Non-clinical steps for development, validation and biosafety of new medicines and products comprises...

Surgery scheduling based on large language models.

Large Language Models (LLMs) have shown remarkable potential in various fields. This study explores ...

Construction of exosome non-coding RNA feature for non-invasive, early detection of gastric cancer patients by machine learning: a multi-cohort study.

BACKGROUND AND OBJECTIVE: Gastric cancer (GC) remains a prevalent and preventable disease, yet accur...

Active Organic Salts Enabling Non-Intrusive Electrolyte Presodiation Strategy.

Na-ion batteries show great promise, but their practical utilization is hindered by irreversible Na-...

Automating Evaluation of AI Text Generation in Healthcare with a Large Language Model (LLM)-as-a-Judge.

Electronic Health Records (EHRs) store vast amounts of clinical information that are difficult for h...

A novel transfer learning framework for non-uniform conductivity estimation with limited data in personalized brain stimulation.

. Personalized transcranial magnetic stimulation (TMS) requires individualized head models that inco...

Frozen Large-Scale Pretrained Vision-Language Models are the Effective Foundational Backbone for Multimodal Breast Cancer Prediction.

Breast cancer is a pervasive global health concern among women. Leveraging multimodal data from ente...

Decoding Poultry Welfare from Sound-A Machine Learning Framework for Non-Invasive Acoustic Monitoring.

Acoustic monitoring presents a promising, non-invasive modality for assessing animal welfare in prec...

Optimizing non small cell lung cancer detection with convolutional neural networks and differential augmentation.

Lung cancer remains one of the leading causes of cancer-related deaths worldwide, with early detecti...

Replacing non-biomedical concepts improves embedding of biomedical concepts.

Embeddings are semantically meaningful representations of words in a vector space, commonly used to ...

Thorax-encompassing multi-modality PET/CT deep learning model for resected lung cancer prognostication: A retrospective, multicenter study.

BACKGROUND: Patients with early-stage non-small cell lung cancer (NSCLC) typically receive surgery a...

Machine learning approach to single cell transcriptomic analysis of Sjogren's disease reveals altered activation states of B and T lymphocytes.

Sjogren's Disease (SjD) is an autoimmune disorder characterized by salivary and lacrimal gland dysfu...

Forecasting optimal treatments in relapsed/refractory mature T- and NK-cell lymphomas: A global PETAL Consortium study.

There is no standard of care in relapsed/refractory T-cell/natural killer-cell lymphomas. Patients o...

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