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A large language model for clinical outcome adjudication from telephone follow-up interviews: a secondary analysis of a multicenter randomized clinical trial
Abstract Automated adjudication of clinical outcomes from telephone follow-ups is crucial for reducing workload and increasing data quality in large-scale trials. Here, we show that a domain-specific large language model (Fu-LLM) effectively automates the preadjudication of key clinical events—including death, hospitalization, and medication use—based on 1,046 vignettes of follow-up telephone interviews conducted across three centers in a randomized clinical trial (China CT-FFR Study 3). Fu-LLM outperforms not only state-of-the-art general-purpose LLMs (e.g. GPT-3.5-turbo, GPT-4o, DeepSeek-v3, Claude 3.5-Sonnet, and Gemini-2.0-Pro) and conventional machine learning models (Support Vector Machine), but also human adjudicators in a silico human−model comparative study. It also shows greater robustness than different versions of GPT-4 do in temporal drift tests. Our findings demonstrate that Fu-LLM can significantly streamline outcome identification in clinical trials, offering a scalable and accurate tool for automating labour-intensive adjudication processes.
Simulation and optimal control of stochastic delay differential models for hepatitis C virus epidemics
Conservative behavior of dissolved black carbon in the northwestern Pacific marginal seas
Modeling method for the digital twin of a discrete workshop based on the transfer of multi-dimensional model information
Circadian regulation of homologous recombination by cryptochrome1-mediated dampening of DNA end resection
Interpretable multiclassification machine learning models in predicting the extent of small cell lung cancer metastasis
Nature of 2D XY antiferromagnetism in a van der Waals monolayer
Abstract Two-dimensional antiferromagnetism has long attracted significant interest in many areas of condensed matter physics, but only recently has experimental exploration become feasible due to the isolation of van der Waals antiferromagnetic monolayers. Probing the magnetic phase diagram of these monolayers remains however challenging because established experimental techniques often lack the required sensitivity. Here, we investigate antiferromagnetism in atomically thin van der Waals magnet NiPS₃ using magnetotransport measurements in field-effect transistor devices. Temperature-dependent conductance and magnetoresistance data reveal a distinct magnetic behavior in monolayers as compared to thicker samples. While bilayer and multilayer NiPS₃ exhibit a single magnetic phase transition into a zig-zag antiferromagnetic state driven by uniaxial anisotropy, monolayer NiPS₃ undergoes two magnetic transitions, with a low-temperature phase governed by in-plane hexagonal magnetic anisotropy. The experimentally constructed phase diagram for monolayer NiPS₃ matches theoretical predictions from the six-state clock and 2D-XY models incorporating hexagonal anisotropy.
Investigation of drivers’ visual attributes in highway entrance zones utilizing Self-Organizing Mapping neural network
Ei24 deficiency in brown adipocytes induces severe hypothermia under cold stress independent of UCP1 activity
Aqualogging tool for web based mapping and mitigation of soil erosion and water pollution in sprinkler irrigation systems
Abstract This study presents a web-based tool based on the Green-Ampt model, designed to mitigate soil erosion and water pollution in sprinkler irrigation areas in Artajona, Spain. Called AquaLogging tool, its main objective is to determine the duration of waterlogging in these areas, with emphasis on soil texture to prevent surface runoff. The tool uses soil texture as a key variable to calculate water infiltration, also considering other parameters such as saturated hydraulic conductivity and initial moisture content. The methodology used simulates different irrigation scenarios to obtain different infiltration and runoff curves, which allow us to calculate the time of waterlogging for different scenarios. Through an interactive web map, AquaLogging tool allows quick access to the spatial distribution of irrigation units and soil textural classes, and with a single click we obtain detailed simulation results. In this study, three irrigation scenarios (daily, every 2 days, and every 5 days) are simulated, in which soil texture significantly influences infiltration capacity and surface runoff generation. Clay soils, characteristic of the study area, showed insufficient infiltration, resulting in runoff after short irrigation periods, while loam soils showed a higher infiltration capacity. AquaLogging tool is offered as a practical support tool for efficient irrigation management, helping farmers to make informed decisions on irrigation timing and frequency to optimise water use in irrigation.
The evolution of the obesity drug market
Hierarchical triple-channel architectures unlock scalable and high-efficient uranium extraction from seawater
RETRACTED ARTICLE: Fusion of transfer learning models for detection of alzheimer’s disease using bidirectional long short-term memory with equilibrium optimization algorithm
Water-resistant redox-active metal–organic framework
Abstract Metal–organic frameworks (MOFs) comprise coordination bonds and have attracted attention for electrochemical applications. However, MOFs are usually structurally weak in aqueous solutions, especially in acidic aqueous solutions, owing to their coordination bonds, making their application in charge-storage devices challenging. In the current work, we demonstrate a redox-active MOF (RAMOF) that is structurally stable and achieves reversible charge storage with almost the theoretical capacity even in acidic aqueous electrolytes owing to its strong Zr–O bonds and the large coordination number. In addition, the RAMOF exhibits high durability ( > 98% after 100 cycles) and high Coulombic efficiency (99.9%) owing to its high crystallinity and proton conductivity. An aqueous MOF–air rechargeable battery is fabricated and exhibits high durability and high Coulombic efficiency. Furthermore, the material recycling of the RAMOF based on its coordination bonds is demonstrated. Therefore, we conceptually prove the application and advantages of RAMOFs in aqueous environments.
Managing ecosystem service deficits through ecological-social prioritization
Identification of a Proteolysis‐Targeting‐Chimera that Addresses Activated Checkpoint Kinase‐1 Reveals its Non‐Catalytic Functions in Tumor Cells
Abstract Checkpoint kinase‐1 (CHK1) controls DNA replication and repair. Tumor cells depend on CHK1, whose high levels are associated with worse patient prognosis. We define a bona fide proteolysis‐targeting‐chimera (PROTAC) for CHK1. PROTAC MA203 contains the type I kinase inhibitor rabusertib, which preferentially inhibits activated CHK1, and the cereblon (CRBN) ligand pomalidomide. MA203 accelerates CRBN‐dependent proteasomal degradation of CHK1 in solid tumor‐derived cells and acute leukemia cells. Chemotherapy‐induced DNA replication stress and a consequent activation of CHK1 accelerate this event‐driven process which promotes DNA damage and tumor cell apoptosis. Biochemical and cellular target engagement studies confirm the potency and selectivity of MA203. MA203 does not damage healthy differentiated and primitive hematopoietic cells, stromal cells, and retinal epithelial cells. MA203 is superior to its corresponding kinase inhibitor concerning DNA damage, dysregulation of BCL2 proteins, and apoptosis induction. These processes occur independently of the tumor‐suppressive transcription factor p53. Elimination of CHK1 protein as structural element, but not its inhibition per se, triggers a proteasomal degradation of key DNA replication and repair proteins. Genetic CHK1 elimination confirms that such newly recognized functions of CHK1 rely on functions beyond its well‐known catalytic activity. Thus, kinase‐independent functions of CHK1 can be exploited with innovative pharmacological agents.