Squamous cell carcinoma predicts worse prognosis in stage IA (≤ 2 cm) non-small cell lung cancer patients following sublobectomy: a population-based study.

Recent studies recommend sublobectomy as a surgical approach for non-small cell lung cancer (NSCLC) tumors that are 2 cm or smaller. However, it remains unclear whether NSCLC patients with squamous cell carcinoma (SCC) have comparable outcomes to those with adenocarcinoma (ADC) following sublobectomy. To that end, this study aims to compare the survival outcomes between SCC and ADC in patients with stage IA NSCLC (≤ 2 cm) who have undergone sublobectomy.

Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing.

Delirium is an acute decline or fluctuation in attention, awareness, or other cognitive function that can lead to serious adverse outcomes. Despite the severe outcomes, delirium is frequently unrecognized and uncoded in patients' electronic health records (EHRs) due to its transient and diverse nature. Natural language processing (NLP), a key technology that extracts medical concepts from clinical narratives, has shown great potential in studies of delirium outcomes and symptoms.

Integrating genetic and clinical data to predict lung cancer in patients with chronic obstructive pulmonary disease.

Background: Chronic obstructive pulmonary disease (COPD) is closely linked to lung cancer (LC) development. The aim of this study is to identify the genetic and clinical risk factors for LC risk in COPD, according to which the prediction model for LC in COPD was constructed.

Methods: This is a case-control study in which patientis with COPD + LC as the case group, patientis with only COPD as the control group, and patientis with only LC as the second control group.

Design and development of a machine-learning-driven opioid overdose risk prediction tool integrated in electronic health records in primary care settings.

Background: Integrating advanced machine-learning (ML) algorithms into clinical practice is challenging and requires interdisciplinary collaboration to develop transparent, interpretable, and ethically sound clinical decision support (CDS) tools. We aimed to design a ML-driven CDS tool to predict opioid overdose risk and gather feedback for its integration into the University of Florida Health (UFHealth) electronic health record (EHR) system.

Methods: We used user-centered design methods to integrate the ML algorithm into the EHR system.

DNA methylation-regulated HK1 overexpression contributes to irradiation-resistance by promoting glycolysis in non-small cell lung cancer.

Irradiation-resistance presents a substantial challenge in the successful application of radiotherapy for non-small-cell lung cancer (NSCLC). However, the specific molecular mechanisms responsible for irradiation-resistance have yet to be completely understood. In this research, the DNA methylation and gene expression patterns resulting from irradiation treatment were produced using the DNA methylation BeadChip and RNA-Seq.

Impact of baseline steroids on the efficacy of neoadjuvant immunochemotherapy in locally advanced esophageal squamous cell carcinoma.

Background: Immunochemotherapy involving the combination of programmed cell death 1/programmed cell death ligand 1 inhibitors with chemotherapy has advanced the treatment of locally advanced esophageal squamous cell carcinoma (ESCC). The use of corticosteroids as pretreatment might reduce immunotherapy efficacy.

Aim: To investigate the impact of baseline corticosteroid use on neoadjuvant immunochemotherapy (nIC) outcomes in locally advanced ESCC patients.

Developing an automated algorithm for identification of children and adolescents with diabetes using electronic health records from the OneFlorida+ clinical research network.

Aim: To develop an automated computable phenotype (CP) algorithm for identifying diabetes cases in children and adolescents using electronic health records (EHRs) from the UF Health System.

Materials And Methods: The CP algorithm was iteratively derived based on structured data from EHRs (UF Health System 2012-2020). We randomly selected 536 presumed cases among individuals aged <18 years who had (1) glycated haemoglobin levels ≥ 6.

Use of Natural Language Processing to Extract and Classify Papillary Thyroid Cancer Features From Surgical Pathology Reports.

Background: We aim to use Natural Language Processing to automate the extraction and classification of thyroid cancer risk factors from pathology reports.

Methods: We analyzed 1410 surgical pathology reports from adult papillary thyroid cancer patients from 2010 to 2019. Structured and nonstructured reports were used to create a consensus-based ground truth dictionary and categorized them into modified recurrence risk levels.

Unveiling Fall Risk Factors: Nurse-Driven Corpus Development for Natural Language Processing.

Hospital-acquired falls are a continuing clinical concern. The emergence of advanced analytical methods, including NLP, has created opportunities to leverage nurse-generated data, such as clinical notes, to better address the problem of falls. In this nurse-driven study, we employed an iterative process for expert manual annotation of RNs clinical notes to enable the training and testing of an NLP pipeline to extract factors related to falls.

Triboelectric-electromagnetic hybrid nanogenerator for harvesting blue energy and creating an ocean wave warning system.

The abundant water wave energy on Earth stands as one of the most promising renewable blue energy sources, as it exhibits minimal dependence on weather, time and temperature. However, the low fluctuation frequency and extremely irregular nature of the wave energy restrict both the methods and efficiency of energy harvesting. In this study, a packed box-like hybrid nanogenerator was designed, comprising two single-electrode triboelectric nanogenerators (TENGs) and two electromagnetic generators (EMGs).

Purine metabolite inosine induced by transforming growth factor‑β promotes epithelial‑mesenchymal transition in colorectal cancer.

Transforming growth factor-β (TGF-β) signaling pathway serves a pivotal role in the pathogenesis of colorectal cancer (CRC). However, the specific molecular mechanisms by which the TGF-β signaling pathway regulates CRC are still not fully understood. In the present study, metabolomics and transcriptomics were used to screen for key metabolites and regulatory genes most related to the regulation of the TGF-β signaling pathway in CRC.

Combining Federated Machine Learning and Qualitative Methods to Investigate Novel Pediatric Asthma Subtypes: Protocol for a Mixed Methods Study.

Background: Pediatric asthma is a heterogeneous disease; however, current characterizations of its subtypes are limited. Machine learning (ML) methods are well-suited for identifying subtypes. In particular, deep neural networks can learn patient representations by leveraging longitudinal information captured in electronic health records (EHRs) while considering future outcomes.

Develop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data.

Introduction: Alzheimer's disease (AD) is often misclassified in electronic health records (EHRs) when relying solely on diagnosis codes. This study aimed to develop a more accurate, computable phenotype (CP) for identifying AD patients using structured and unstructured EHR data.

Methods: We used EHRs from the University of Florida Health (UFHealth) system and created rule-based CPs iteratively through manual chart reviews.

A Systematic Review of Natural Language Processing Methods and Applications in Thyroidology.

This study aimed to review the application of natural language processing (NLP) in thyroid-related conditions and to summarize current challenges and potential future directions. We performed a systematic search of databases for studies describing NLP applications in thyroid conditions published in English between January 1, 2012 and November 4, 2022. In addition, we used a snowballing technique to identify studies missed in the initial search or published after our search timeline until April 1, 2023.

Causal effects of circulating glutamine on colitis, IBD, and digestive system cancers: a Mendelian randomisation study.

There is growing evidence linking glutamine levels to the risk of gastrointestinal diseases, yet the presence of a causal relationship remains uncertain. In this study, we employed a Mendelian randomization (MR) approach to investigate potential causal associations between glutamine and colitis, inflammatory bowel disease (IBD), and digestive tumors. Genetic instrumental variables for glutamine exposure were identified from a genome-wide association study (GWAS) involving 114,751 participants.

Radiomics analysis based on CT for predicting lymph node metastasis and prognosis in duodenal papillary carcinoma.

Objectives: Radiomics has been demonstrated to be strongly associated with TNM stage and patient prognosis. We aimed to develop a model for predicting lymph node metastasis (LNM) and survival.

Methods: For radiomics texture selection, 3D Slicer 5.

Identification of subclusters and prognostic genes based on GLS-associated molecular signature in ulcerative colitis.

Ulcerative colitis (UC) is a chronic and recurrent inflammatory disease that affects the colon and rectum. The response to treatment varies among individuals with UC. Therefore, the aim of this study was to identify and explore potential biomarkers for different subtypes of UC and examine their association with immune cell infiltration.

Me-LLaMA: Foundation Large Language Models for Medical Applications.

Recent advancements in large language models (LLMs) such as ChatGPT and LLaMA have hinted at their potential to revolutionize medical applications, yet their application in clinical settings often reveals limitations due to a lack of specialized training on medical-specific data. In response to this challenge, this study introduces Me-LLaMA, a novel medical LLM family that includes foundation models - Me-LLaMA 13/70B, along with their chat-enhanced versions - Me-LLaMA 13/70B-chat, developed through continual pre-training and instruction tuning of LLaMA2 using large medical datasets. Our methodology leverages a comprehensive domain-specific data suite, including a large-scale, continual pre-training dataset with 129B tokens, an instruction tuning dataset with 214k samples, and a new medical evaluation benchmark (MIBE) across six critical medical tasks with 12 datasets.

Edaravone Alleviates Traumatic Brain Injury by Inhibition of Ferroptosis via FSP1 Pathway.

Traumatic brain injury (TBI) is a highly severe form of trauma with complex series of reactions in brain tissue which ultimately results in neuronal damage. Previous studies proved that neuronal ferroptosis, which was induced by intracranial haemorrhage and other reasons, was one of the most primary causes of neuronal damage following TBI. However, the association between neuronal mechanical injury and ferroptosis in TBI and relevant treatments remain unclear.