Generative large language models are all-purpose text analytics engines: text-to-text learning is all your need.

Objective: To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via prompt tuning.

Methods: We formulated 7 key clinical NLP tasks as text-to-text learning and solved them using one unified generative clinical LLM, GatorTronGPT, developed using GPT-3 architecture and trained with up to 20 billion parameters. We adopted soft prompts (ie, trainable vectors) with frozen LLM, where the LLM parameters were not updated (ie, frozen) and only the vectors of soft prompts were updated, known as prompt tuning.

Model tuning or prompt Tuning? a study of large language models for clinical concept and relation extraction.

Objective: To develop soft prompt-based learning architecture for large language models (LLMs), examine prompt-tuning using frozen/unfrozen LLMs, and assess their abilities in transfer learning and few-shot learning.

Methods: We developed a soft prompt-based learning architecture and compared 4 strategies including (1) fine-tuning without prompts; (2) hard-prompting with unfrozen LLMs; (3) soft-prompting with unfrozen LLMs; and (4) soft-prompting with frozen LLMs. We evaluated GatorTron, a clinical LLM with up to 8.

A study of generative large language model for medical research and healthcare.

There are enormous enthusiasm and concerns in applying large language models (LLMs) to healthcare. Yet current assumptions are based on general-purpose LLMs such as ChatGPT, which are not developed for medical use. This study develops a generative clinical LLM, GatorTronGPT, using 277 billion words of text including (1) 82 billion words of clinical text from 126 clinical departments and approximately 2 million patients at the University of Florida Health and (2) 195 billion words of diverse general English text.

A large language model for electronic health records.

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain).

FOXM1 drives HPV+ HNSCC sensitivity to WEE1 inhibition.

Head and neck squamous cell carcinoma (HNSCC) associated with high-risk human papilloma virus (HPV) infection is a growing clinical problem. The WEE1 kinase inhibitor AZD1775 (WEE1i) overrides cell cycle checkpoints and is being studied in HNSCC regimens. We show that the HPV16 E6/E7 oncoproteins sensitize HNSCC cells to single-agent WEE1i treatment through activation of a FOXM1-CDK1 circuit that drives mitotic gene expression and DNA damage.

Perturbed maintenance of transcriptional repression on the inactive X-chromosome in the mouse brain after Xist deletion.

Background: The long noncoding RNA Xist is critical for initiation and establishment of X-chromosome inactivation during embryogenesis in mammals, but it is unclear whether its continued expression is required for maintaining X-inactivation in vivo.

Results: By using an inactive X-chromosome-linked MeCP2-GFP reporter, which allowed us to enumerate reactivation events in the mouse brain even when they occur in very few cells, we found that deletion of Xist in the brain after establishment of X-chromosome inactivation leads to reactivation in 2-5% of neurons and in a smaller fraction of astrocytes. In contrast to global loss of both H3 lysine 27 trimethylation (H3K27m3) and histone H2A lysine 119 monoubiquitylation (H2AK119ub1) we observed upon Xist deletion, alterations in CpG methylation were subtle, and this was mirrored by only minor alterations in X-chromosome-wide gene expression levels, with highly expressed genes more prone to both derepression and demethylation compared to genes with low expression level.

Transient rapamycin treatment can increase lifespan and healthspan in middle-aged mice.

The FDA approved drug rapamycin increases lifespan in rodents and delays age-related dysfunction in rodents and humans. Nevertheless, important questions remain regarding the optimal dose, duration, and mechanisms of action in the context of healthy aging. Here we show that 3 months of rapamycin treatment is sufficient to increase life expectancy by up to 60% and improve measures of healthspan in middle-aged mice.