Exploring Multilingual Large Language Models for Enhanced TNM Classification of Radiology Report in Lung Cancer Staging.

Background/objectives: This study aimed to investigate the accuracy of Tumor, Node, Metastasis (TNM) classification based on radiology reports using GPT3.5-turbo (GPT3.5) and the utility of multilingual large language models (LLMs) in both Japanese and English.

Development and web deployment of prediction model for pulmonary arterial pressure in chronic thromboembolic pulmonary hypertension using machine learning.

Background And Purpose: Mean pulmonary artery pressure (mPAP) is a key index for chronic thromboembolic pulmonary hypertension (CTEPH). Using machine learning, we attempted to construct an accurate prediction model for mPAP in patients with CTEPH.

Methods: A total of 136 patients diagnosed with CTEPH were included, for whom mPAP was measured.

Development of Pericardial Fat Count Images Using a Combination of Three Different Deep-Learning Models: Image Translation Model From Chest Radiograph Image to Projection Image of Three-Dimensional Computed Tomography.

Rationale And Objectives: Pericardial fat (PF)-the thoracic visceral fat surrounding the heart-promotes the development of coronary artery disease by inducing inflammation of the coronary arteries. To evaluate PF, we generated pericardial fat count images (PFCIs) from chest radiographs (CXRs) using a dedicated deep-learning model.

Materials And Methods: We reviewed data of 269 consecutive patients who underwent coronary computed tomography (CT).

Comparison between pystan and numpyro in Bayesian item response theory: evaluation of agreement of estimated latent parameters and sampling performance.

Purpose: The purpose of this study is to compare two libraries dedicated to the Markov chain Monte Carlo method: pystan and numpyro. In the comparison, we mainly focused on the agreement of estimated latent parameters and the performance of sampling using the Markov chain Monte Carlo method in Bayesian item response theory (IRT).

Materials And Methods: Bayesian 1PL-IRT and 2PL-IRT were implemented with pystan and numpyro.

Computer-aided diagnosis of chest X-ray for COVID-19 diagnosis in external validation study by radiologists with and without deep learning system.

To evaluate the diagnostic performance of our deep learning (DL) model of COVID-19 and investigate whether the diagnostic performance of radiologists was improved by referring to our model. Our datasets contained chest X-rays (CXRs) for the following three categories: normal (NORMAL), non-COVID-19 pneumonia (PNEUMONIA), and COVID-19 pneumonia (COVID). We used two public datasets and private dataset collected from eight hospitals for the development and external validation of our DL model (26,393 CXRs).

Monocyte Chemoattractant Protein-1 (MCP-1) as a Potential Therapeutic Target and a Noninvasive Biomarker of Liver Fibrosis Associated With Transient Myeloproliferative Disorder in Down Syndrome.

Liver fibrosis is one of the common complications of transient myeloproliferative disorder (TMD) in Down syndrome (DS), but the exact molecular pathogenesis is largely unknown. We herein report a neonate of DS with liver fibrosis associated with TMD, in which we performed the serial profibrogenic cytokines analyses. We found the active monocyte chemoattractant protein-1 expression in the affected liver tissue and also found that both serum and urinary monocyte chemoattractant protein-1 concentrations are noninvasive biomarkers of liver fibrosis.

Nucleosome formation activity of human somatic nuclear autoantigenic sperm protein (sNASP).

NASP (nuclear autoantigenic sperm protein) is a member of the N1/N2 family, which is widely conserved among eukaryotes. Human NASP reportedly prefers to bind to histones H3.H4 and the linker histone H1, as compared with H2A.