Confining the Node Accessibility of Zr-MOFs Along One-Dimensional Channels Toward High Water-Vapor Cycle-Stability.

Zirconium-based MOFs (Zr-MOFs) with high hydrolytic stability are promising materials for water-adsorption-related applications. However, besides hydrolytic stability, cycling stability is also a crucial feature that renders a MOF a good candidate for water adsorption. Through a series of Zr-MOFs with one-dimensional (1D) channels showing high water cycling stability, a principle of confining the accessibility of Zr nodes aligning along channel direction in Zr-MOFs was unveiled.

CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray.

The CXR-LT series is a community-driven initiative designed to enhance lung disease classification using chest X-rays (CXR). It tackles challenges in open long-tailed lung disease classification and enhances the measurability of state-of-the-art techniques. The first event, CXR-LT 2023, aimed to achieve these goals by providing high-quality benchmark CXR data for model development and conducting comprehensive evaluations to identify ongoing issues impacting lung disease classification performance.

One Year On: Assessing Progress of Multimodal Large Language Model Performance on RSNA 2024 Case of the Day Questions.

Background With the growing use of multimodal large language models (LLMs), numerous vision-enabled models have been developed and made available to the public. Purpose To assess and quantify the advancements of multimodal LLMs in interpreting radiologic quiz cases by examining both image and textual content over the course of 1 year, and to compare model performance with that of radiologists. Materials and Methods For this retrospective study, 95 questions from Case of the Day at the RSNA 2024 Annual Meeting were collected.

Melatonin biosynthesis and regulation in reproduction.

Melatonin, a neuroendocrine hormone widely present in animals, is a derivative of tryptophan secreted by the pineal gland. This hormone regulates animal circadian rhythms and can affect reproductive performance in many ways; for example, melatonin levels change in response to sunshine duration changes, which can inhibit or promote reproductive performance. In juvenile animals, melatonin inhibits estrus, whereas in mature animals, it promotes estrus.

Accelerating clinical evidence synthesis with large language models.

Clinical evidence synthesis largely relies on systematic reviews (SR) of clinical studies from medical literature. Here, we propose a generative artificial intelligence (AI) pipeline named TrialMind to streamline study search, study screening, and data extraction tasks in SR. We chose published SRs to build TrialReviewBench, which contains 100 SRs and 2,220 clinical studies.

A foundation model for human-AI collaboration in medical literature mining.

Systematic literature review is essential for evidence-based medicine, requiring comprehensive analysis of clinical trial publications. However, the application of artificial intelligence (AI) models for medical literature mining has been limited by insufficient training and evaluation across broad therapeutic areas and diverse tasks. Here, we present LEADS, an AI foundation model for study search, screening, and data extraction from medical literature.

CXR-LT 2024: A MICCAI challenge on long-tailed, multi-label, and zero-shot disease classification from chest X-ray.

The CXR-LT series is a community-driven initiative designed to enhance lung disease classification using chest X-rays (CXR). It tackles challenges in open long-tailed lung disease classification and enhances the measurability of state-of-the-art techniques. The first event, CXR-LT 2023, aimed to achieve these goals by providing high-quality benchmark CXR data for model development and conducting comprehensive evaluations to identify ongoing issues impacting lung disease classification performance.

Recommending Clinical Trials for Online Patient Cases using Artificial Intelligence.

Clinical trials are crucial for assessing new treatments; however, recruitment challenges-such as limited awareness, complex eligibility criteria, and referral barriers-hinder their success. With the growth of online platforms, patients, caregivers, and family members increasingly post medical cases on social media and health communities, while physicians publish case reports accessible on platforms like PubMed-collectively expanding recruitment pools beyond traditional clinical trial pathways. Recognizing this potential, we utilized TrialGPT, a framework that leverages a large language model, to match 50 online patient cases (collected from case reports and social media) to clinical trials and evaluate performance against traditional keyword-based searches.

Universal Lymph Node Detection in Multiparametric MRI with Selective Augmentation.

Robust localization of lymph nodes (LNs) in multiparametric MRI (mpMRI) is critical for the assessment of lymphadenopathy. Radiologists routinely measure the size of LN to distinguish benign from malignant nodes, which would require subsequent cancer staging. Sizing is a cumbersome task compounded by the diverse appearances of LNs in mpMRI, which renders their measurement difficult.

Enhancing Biomedical Relation Extraction with Directionality.

Biological relation networks contain rich information for understanding the biological mechanisms behind the relationship of entities such as genes, proteins, diseases, and chemicals. The vast growth of biomedical literature poses significant challenges updating the network knowledge. The recent Biomedical Relation Extraction Dataset (BioRED) provides valuable manual annotations, facilitating the develop-ment of machine-learning and pre-trained language model approaches for automatically identifying novel document-level (inter-sentence context) relationships.

Segment-and-Classify: ROI-Guided Generalizable Contrast Phase Classification in CT Using XGBoost.

Purpose: To automate contrast phase classification in CT using organ-specific features extracted from a widely used segmentation tool with a lightweight decision tree classifier.

Materials And Methods: This retrospective study utilized three public CT datasets from separate institutions. The phase prediction model was trained on the WAW-TACE (median age: 66 [60,73]; 185 males) dataset, and externally validated on the VinDr-Multiphase (146 males; 63 females; 56 unk) and C4KC-KiTS (median age: 61 [50.

GeneAgent: self-verification language agent for gene-set analysis using domain databases.

Gene-set analysis seeks to identify the biological mechanisms underlying groups of genes with shared functions. Large language models (LLMs) have recently shown promise in generating functional descriptions for input gene sets but may produce factually incorrect statements, commonly referred to as hallucinations in LLMs. Here we present GeneAgent, an LLM-based AI agent for gene-set analysis that reduces hallucinations by autonomously interacting with biological databases to verify its own output.

Using the Electronic Grab-Transport Mechanism to Construct Metal-Organic Frameworks with Type I/II Dual Sonodynamic Therapy Reinforcement.

Sonodynamic therapy (SDT) has demonstrated promising potential in the treatment of tumors and has attracted widespread attention. The majority of sound-sensitive materials developed to date have been categorized as oxygen-dependent type II sonosensitizers (SSs), which are susceptible to tumor hypoxia and significantly limit their efficacy. In this study, highly active porphyrin-based metal-organic frameworks (Yb-TCPP PMOF) with type I/II SDT dual actions were constructed by regulating the electron transfer process between metal nodes and ligands, which can produce multiple reactive oxygen species (ROS) such as O, O, and •OH.

AI Workflow, External Validation, and Development in Eye Disease Diagnosis.

Importance: Timely disease diagnosis is challenging due to limited clinical availability and growing burdens. Although artificial intelligence (AI) has shown expert-level diagnostic accuracy, a lack of downstream accountability, including workflow integration, external validation, and further development, continues to hinder its clinical adoption.

Objective: To address gaps in the downstream accountability of medical AI through a case study on age-related macular degeneration (AMD) diagnosis and severity classification.

Enhancing biomedical relation extraction with directionality.

Summary: Biological relation networks contain rich information for understanding the biological mechanisms behind the relationship of entities such as genes, proteins, diseases, and chemicals. The vast growth of biomedical literature poses significant challenges in updating the network knowledge. The recent Biomedical Relation Extraction Dataset (BioRED) provides valuable manual annotations, facilitating the development of machine learning and pre-trained language model approaches for automatically identifying novel document-level (inter-sentence context) relationships.

Critical Pathways for Transforming the Energy Future: A Review of Innovations and Challenges in Spent Lithium Battery Recycling Technologies.

In the wake of global energy transition and the "dual-carbon" goal, the rapid growth of electric vehicles has posed challenges for large-scale lithium-ion battery decommissioning. Retired batteries exhibit dual attributes of strategic resources (cobalt/lithium concentrations several times higher than natural ores) and environmental risks (heavy metal pollution, electrolyte toxicity). This paper systematically reviews pyrometallurgical and hydrometallurgical recovery technologies, identifying bottlenecks: high energy/lithium loss in pyrometallurgy, and corrosion/cost/solvent regeneration issues in hydrometallurgy.

Resting-state EEG alterations and cognitive impairment in atrial fibrillation: insights into neural biomarkers and functional connectivity.

Aims: Atrial fibrillation (AF) is associated with cognitive decline, but the role of electroencephalography (EEG) in assessing cognitive dysfunction in AF patients is underexplored.

Objective: This study investigated the relationship between resting-state EEG patterns and cognitive impairment in AF patients.

Methods: We recruited 120 participants from the Affiliated Xuancheng Hospital, China (January 2023 to January 2024), categorizing them into healthy controls and AF patients.

How well do multimodal LLMs interpret CT scans? An auto-evaluation framework for analyses.

Objective: This study introduces a novel evaluation framework, GPTRadScore, to systematically assess the performance of multimodal large language models (MLLMs) in generating clinically accurate findings from CT imaging. Specifically, GPTRadScore leverages LLMs as an evaluation metric, aiming to provide a more accurate and clinically informed assessment than traditional language-specific methods. Using this framework, we evaluate the capability of several MLLMs, including GPT-4 with Vision (GPT-4V), Gemini Pro Vision, LLaVA-Med, and RadFM, to interpret findings in CT scans.

Knowledge-guided Contextual Gene Set Analysis Using Large Language Models.

Gene set analysis (GSA) is a foundational approach for interpreting genomic data of diseases by linking genes to biological processes. However, conventional GSA methods overlook clinical context of the analyses, often generating long lists of enriched pathways with redundant, nonspecific, or irrelevant results. Interpreting these requires extensive, ad-hoc manual effort, reducing both reliability and reproducibility.

LabQAR: A Manually Curated Dataset for Question Answering on Laboratory Test Reference Ranges and Interpretation.

Laboratory tests are crucial for diagnosing and managing health conditions, providing essential reference ranges for result interpretation. The diversity of lab tests, influenced by variables like the specimen type (e.g.

LitSense 2.0: AI-powered biomedical information retrieval with sentence and passage level knowledge discovery.

LitSense 2.0 (https://www.ncbi.

Oocytes with aggregates of smooth endoplasmic reticulum may not affect reproductive outcomes in split IVF-ICSI insemination: a retrospective study.

Objective: To investigate the impact of smooth endoplasmic reticulum aggregates (SERa) in oocytes on embryological outcomes and clinical and neonatal outcomes during split IVF-ICSI cycles.

Methods: A retrospective analysis was conducted using clinical data from January 2020 to December 2023 at the Reproductive Medicine Center of Hainan Women and Children's Medical Center. Patients were divided into SERa+ and SERa- cycles based on the visibility of SERa after the removal of cumulus cells.

A Compact High-Precision Cascade PID-Control Laser Driver for Airborne Coherent LiDAR Applications.

This paper solves the challenge of precise dual-frequency laser control in Airborne Coherent Doppler LiDAR systems by implementing an innovative laser driver architecture, which integrates compact hardware design with cascade Proportional-Integral-Derivative (PID) control and a frequency-temperature compensation mechanism. The experimental results demonstrate eminent performance with long-term temperature fluctuation below 0.007 °C, temperature stabilizing time under 4 s and long-term power fluctuation of the linear constant current source being <1%.

Allelopathic effects and composition of aqueous extracts from different parts of Cav. on L. and L.

Background: Cav. is a high-risk invasive plant that seriously threatens the development of grasslands in southern China. However, the allelopathic effects on L.

Leveraging long context in retrieval augmented language models for medical question answering.

While holding great promise for improving and facilitating healthcare through applications of medical literature summarization, large language models (LLMs) struggle to produce up-to-date responses on evolving topics due to outdated knowledge or hallucination. Retrieval-augmented generation (RAG) is a pivotal innovation that improves the accuracy and relevance of LLM responses by integrating LLMs with a search engine and external sources of knowledge. However, the quality of RAG responses can be largely impacted by the rank and density of key information in the retrieval results, such as the "lost-in-the-middle" problem.