The Automatic Context Measurement Tool (ACMT) to Compile Participant-Specific Built and Social Environment Measures for Health Research: Development and Usability Study.

Background: The environment shapes health behaviors and outcomes. Studies exploring this influence have been limited to research groups with the geographic information systems expertise required to develop built and social environment measures (eg, groups that include a researcher with geographic information system expertise).

Objective: The goal of this study was to develop an open-source, user-friendly, and privacy-preserving tool for conveniently linking built, social, and natural environmental variables to study participant addresses.

Generalizable clinical note section identification with large language models.

Objectives: Clinical note section identification helps locate relevant information and could be beneficial for downstream tasks such as named entity recognition. However, the traditional supervised methods suffer from transferability issues. This study proposes a new framework for using large language models (LLMs) for section identification to overcome the limitations.

Improving the Transferability of Clinical Note Section Classification Models with BERT and Large Language Model Ensembles.

Text in electronic health records is organized into sections, and classifying those sections into section categories is useful for downstream tasks. In this work, we attempt to improve the transferability of section classification models by combining the dataset-specific knowledge in supervised learning models with the world knowledge inside large language models (LLMs). Surprisingly, we find that zero-shot LLMs out-perform supervised BERT-based models applied to out-of-domain data.

Improving model transferability for clinical note section classification models using continued pretraining.

Objective: The classification of clinical note sections is a critical step before doing more fine-grained natural language processing tasks such as social determinants of health extraction and temporal information extraction. Often, clinical note section classification models that achieve high accuracy for 1 institution experience a large drop of accuracy when transferred to another institution. The objective of this study is to develop methods that classify clinical note sections under the SOAP ("Subjective," "Object," "Assessment," and "Plan") framework with improved transferability.

LeafAI: query generator for clinical cohort discovery rivaling a human programmer.

Objective: Identifying study-eligible patients within clinical databases is a critical step in clinical research. However, accurate query design typically requires extensive technical and biomedical expertise. We sought to create a system capable of generating data model-agnostic queries while also providing novel logical reasoning capabilities for complex clinical trial eligibility criteria.

Improving Model Transferability for Clinical Note Section Classification Models Using Continued Pretraining.

Objective: The classification of clinical note sections is a critical step before doing more fine-grained natural language processing tasks such as social determinants of health extraction and temporal information extraction. Often, clinical note section classification models that achieve high accuracy for one institution experience a large drop of accuracy when transferred to another institution. The objective of this study is to develop methods that classify clinical note sections under the SOAP ("Subjective", "Object", "Assessment" and "Plan") framework with improved transferability.

Parameter Sensitivity Analysis for the Progressive Sampling-Based Bayesian Optimization Method for Automated Machine Learning Model Selection.

As a key component of automating the entire process of applying machine learning to solve real-world problems, automated machine learning model selection is in great need. Many automated methods have been proposed for machine learning model selection, but their inefficiency poses a major problem for handling large data sets. To expedite automated machine learning model selection and lower its resource requirements, we developed a progressive sampling-based Bayesian optimization (PSBO) method to efficiently automate the selection of machine learning algorithms and hyper-parameter values.

Effect of Oleanolic Acid on Apoptosis and Autophagy of SMMC-7721 Hepatoma Cells.

BACKGROUND Liver cancer is a common cancer with high morbidity and mortality. Due to the large toxic side effects of chemotherapeutic drugs and the overexpression of multidrug resistance genes in liver cancer, no effective chemotherapeutic drug has yet been found. Therefore, the search for a highly effective, low-toxic, and safe natural anticancer therapy is a hot issue.

Role of JNK and ERK1/2 MAPK signaling pathway in testicular injury of rats induced by di-N-butyl-phthalate (DBP).

Background: Di-N-butyl-phthalate (DBP) is an endocrine disrupting substance. We investigated the adverse effect of DBP on testis of male rat and reveal its potential mechanism of MAPK signaling pathway involved this effect in vivo and in vitro. Gonadal hormone, sperm quality, morphological change and the activation status of JNK, ERK1/2 and p38 was determined in vivo.

Control of the Nucleation Density of Molybdenum Disulfide in Large-Scale Synthesis Using Chemical Vapor Deposition.

Atmospheric pressure chemical vapor deposition (CVD) is presently a promising approach for preparing two-dimensional (2D) MoS₂ crystals at high temperatures on SiO₂/Si substrates. In this work, we propose an improved CVD method without hydrogen, which can increase formula flexibility by controlling the heating temperature of MoO₃ powder and sulfur powder. The results show that the size and coverage of MoS₂ domains vary largely, from discrete triangles to continuous film, on substrate.