Natural language processing to automatically extract the presence and severity of esophagitis in notes of patients undergoing radiotherapy

Radiotherapy (RT) toxicities can impair survival and quality-of-life, yet remain under-studied. Real-world evidence holds potential to improve our understanding of toxicities, but toxicity information is often only in clinical notes. We developed natural language processing (NLP) models to identify the presence and severity of esophagitis from notes of patients treated with thoracic RT. We fine-tuned statistical and pre-trained BERT-based models for three esophagitis classification tasks: Task 1) presence of esophagitis, Task 2) severe esophagitis or not, and Task 3) no esophagitis vs. grade 1 vs. grade 2-3. Transferability was tested on 345 notes from patients with esophageal cancer undergoing RT. Fine-tuning PubmedBERT yielded the best performance. The best macro-F1 was 0.92, 0.82, and 0.74 for Task 1, 2, and 3, respectively. Selecting the most informative note sections during fine-tuning improved macro-F1 by over 2% for all tasks. Silver-labeled data improved the macro-F1 by over 3% across all tasks. For the esophageal cancer notes, the best macro-F1 was 0.73, 0.74, and 0.65 for Task 1, 2, and 3, respectively, without additional fine-tuning. To our knowledge, this is the first effort to automatically extract esophagitis toxicity severity according to CTCAE guidelines from clinic notes. The promising performance provides proof-of-concept for NLP-based automated detailed toxicity monitoring in expanded domains.Comment: 17 pages, 6 tables, 1figure, submiting to JCO-CCI for revie

The impact of responding to patient messages with large language model assistance

Documentation burden is a major contributor to clinician burnout, which is rising nationally and is an urgent threat to our ability to care for patients. Artificial intelligence (AI) chatbots, such as ChatGPT, could reduce clinician burden by assisting with documentation. Although many hospitals are actively integrating such systems into electronic medical record systems, AI chatbots utility and impact on clinical decision-making have not been studied for this intended use. We are the first to examine the utility of large language models in assisting clinicians draft responses to patient questions. In our two-stage cross-sectional study, 6 oncologists responded to 100 realistic synthetic cancer patient scenarios and portal messages developed to reflect common medical situations, first manually, then with AI assistance. We find AI-assisted responses were longer, less readable, but provided acceptable drafts without edits 58% of time. AI assistance improved efficiency 77% of time, with low harm risk (82% safe). However, 7.7% unedited AI responses could severely harm. In 31% cases, physicians thought AI drafts were human-written. AI assistance led to more patient education recommendations, fewer clinical actions than manual responses. Results show promise for AI to improve clinician efficiency and patient care through assisting documentation, if used judiciously. Monitoring model outputs and human-AI interaction remains crucial for safe implementation.Comment: 4 figures and tables in main, submitted for revie

Telomerase and Telomere Length in Pulmonary Fibrosis

In addition to its expression in stem cells and many cancers,telomerase activity is transiently induced inmurine bleomycin (BLM)induced pulmonary fibrosis with increased levels of telomerase transcriptase (TERT) expression, which is essential for fibrosis. To extend these observations to human chronic fibrotic lung disease,we investigated the expression of telomerase activity in lung fibroblasts from patients with interstitial lung diseases (ILDs), including idiopathic pulmonaryfibrosis (IPF).The resultsshowedthat telomerase activity was induced in more than 66% of IPF lung fibroblast samples, in comparison with less than 29% from control samples,some of which were obtained from lung cancer resections. Less than 4%of the humanIPF lung fibroblast samples exhibited shortened telomeres,whereas less than 6% of peripheral blood leukocyte samples from patients with IPF or hypersensitivity pneumonitis demonstrated shortened telomeres. Moreover, shortened telomeres in lategeneration telomerase RNA component knockout mice did not exert a significant effect on BLM-induced pulmonary fibrosis. In contrast, TERT knockout mice exhibited deficient fibrosis that was independent of telomere length. Finally, TERT expression was up-regulated by a histone deacetylase inhibitor, while the induction of TERT in lung fibroblastswasassociatedwiththebindingofacetylatedhistoneH3K9to the TERT promoter region. These findings indicate that significant telomerase inductionwas evident in fibroblasts from fibroticmurine lungs and a majority of IPF lung samples, whereas telomere shortening was not a common finding in the human blood and lung fibroblast samples. Notably, the animal studies indicated that the pathogenesis of pulmonary fibrosis was independent of telomere length

Large Language Models to Identify Social Determinants of Health in Electronic Health Records

Social determinants of health (SDoH) have an important impact on patient outcomes but are incompletely collected from the electronic health records (EHR). This study researched the ability of large language models to extract SDoH from free text in EHRs, where they are most commonly documented, and explored the role of synthetic clinical text for improving the extraction of these scarcely documented, yet extremely valuable, clinical data. 800 patient notes were annotated for SDoH categories, and several transformer-based models were evaluated. The study also experimented with synthetic data generation and assessed for algorithmic bias. Our best-performing models were fine-tuned Flan-T5 XL (macro-F1 0.71) for any SDoH, and Flan-T5 XXL (macro-F1 0.70). The benefit of augmenting fine-tuning with synthetic data varied across model architecture and size, with smaller Flan-T5 models (base and large) showing the greatest improvements in performance (delta F1 +0.12 to +0.23). Model performance was similar on the in-hospital system dataset but worse on the MIMIC-III dataset. Our best-performing fine-tuned models outperformed zero- and few-shot performance of ChatGPT-family models for both tasks. These fine-tuned models were less likely than ChatGPT to change their prediction when race/ethnicity and gender descriptors were added to the text, suggesting less algorithmic bias (p<0.05). At the patient-level, our models identified 93.8% of patients with adverse SDoH, while ICD-10 codes captured 2.0%. Our method can effectively extracted SDoH information from clinic notes, performing better compare to GPT zero- and few-shot settings. These models could enhance real-world evidence on SDoH and aid in identifying patients needing social support.Comment: 38 pages, 5 figures, 5 tables in main, submitted for revie

The Effect of p38 Mitogen-Activated Protein Kinase Activation on Inflammatory Liver Damage following Hemorrhagic Shock in Rats

Hemorrhagic shock is a frequent cause of liver failure and often leads to a fatal outcome. Several studies have revealed that p38 MAPK is a key mediator in hemorrhagic damage of the primary organs through the activation of proinflammatory cytokines such as tumor necrosis factor (TNF)-α and interleukin (IL)-1β. However, the precise role of these factors in liver damage following hemorrhagic shock is unclear. In this study, we used FR167653, a specific inhibitor of p38 MAPK phosphorylation, to examine the role of p38 MAPK in liver damage occurring up to 5 hours after a hemorrhagic episode in a rat model. Activation of p38 MAPK in the liver as well as an increase in hepatic mRNA expression and serum concentrations of TNF-α and IL-1β occurred during the early phase after hemorrhage. Increased serum levels of hepatic enzymes, as well as histological damage and activated neutrophil accumulation in the liver, were observed in the late phase following hemorrhagic shock. FR167653 inhibited the inflammation-related hepatic injury following hemorrhagic shock. Bacterial lipopolysaccharide (LPS) derived from the gut appeared to have little effects on the hepatic damage. These results demonstrate that p38 MAPK activation is induced by hepatic ischemia during hemorrhagic shock and plays an important role both in the hepatic expression of proinflammatory cytokines and in the development of inflammation-related liver damage

Hyperammonemic Coma—Barking Up the Wrong Tree

Hepatic encephalopathy and myxedema coma share clinical features: coma, ascites, anemia, impaired liver functions, and a “metabolic” electroencephalogram (EEG). Hyperammonemia, a hallmark of hepatic encephalopathy, has also been described in hypothyroidism. Differentiation between the 2 conditions, recognition of their possible coexistence, and the consequent therapeutic implications are of utmost importance. We describe a case of an 82-year-old woman with a history of mild chronic liver disease who presented with hyperammonemic coma unresponsive to conventional therapy. Further investigation disclosed severe hypothyroidism. Thyroid hormone replacement resulted in gain of consciousness and normalization of hyperammonemia. In patients with an elevated ammonia level, altered mental status, and liver disease, who do not have a clear inciting event for liver disease decompensation, overwhelming evidence of hepatic decompensation, or who do not respond to appropriate therapy for hepatic encephalopathy, hypothyroidism should be considered and evaluated

Fibrotic Myofibroblasts Manifest Genome-Wide Derangements of Translational Control

Background: As a group, fibroproliferative disorders of the lung, liver, kidney, heart, vasculature and integument are common, progressive and refractory to therapy. They can emerge following toxic insults, but are frequently idiopathic. Their enigmatic propensity to resist therapy and progress to organ failure has focused attention on the myofibroblast–the primary effector of the fibroproliferative response. We have recently shown that aberrant beta 1 integrin signaling in fibrotic fibroblasts results in defective PTEN function, unrestrained Akt signaling and subsequent activation of the translation initiation machinery. How this pathological integrin signaling alters the gene expression pathway has not been elucidated. Results: Using a systems approach to study this question in a prototype fibrotic disease, Idiopathic Pulmonary Fibrosis (IPF); here we show organized changes in the gene expression pathway of primary lung myofibroblasts that persist for up to 9 sub-cultivations in vitro. When comparing IPF and control myofibroblasts in a 3-dimensional type I collagen matrix, more genes differed at the level of ribosome recruitment than at the level of transcript abundance, indicating pathological translational control as a major characteristic of IPF myofibroblasts. To determine the effect of matrix state on translational control, myofibroblasts were permitted to contract the matrix. Ribosome recruitment in control myofibroblasts was relatively stable. In contrast, IPF cells manifested large alterations in the ribosome recruitment pattern. Pathological studies suggest an epithelial origin for IPF myofibroblasts through the epithelial to mesenchymal transition (EMT). In accord wit

The Speed of Smell: Odor-Object Segregation within Milliseconds

Segregating objects from background, and determining which of many concurrent stimuli belong to the same object, remains one of the most challenging unsolved problems both in neuroscience and in technical applications. While this phenomenon has been investigated in depth in vision and audition it has hardly been investigated in olfaction. We found that for honeybees a 6-ms temporal difference in stimulus coherence is sufficient for odor-object segregation, showing that the temporal resolution of the olfactory system is much faster than previously thought