Assessing and Optimizing Large Language Models on Spondyloarthritis Multi-Choice Question Answering: Protocol for Enhancement and Assessment.

BACKGROUND: Spondyloarthritis (SpA), a chronic inflammatory disorder, predominantly impacts the sacroiliac joints and spine, significantly escalating the risk of disability. SpA's complexity, as evidenced by its diverse clinical presentations and symptoms that often mimic other diseases, presents substantial challenges in its accurate diagnosis and differentiation. This complexity becomes even more pronounced in nonspecialist health care environments due to limited resources, resulting in delayed referrals, increased misdiagnosis rates, and exacerbated disability outcomes for patients with SpA. The emergence of large language models (LLMs) in medical diagnostics introduces a revolutionary potential to overcome these diagnostic hurdles. Despite recent advancements in artificial intelligence and LLMs demonstrating effectiveness in diagnosing and treating various diseases, their application in SpA remains underdeveloped. Currently, there is a notable absence of SpA-specific LLMs and an established benchmark for assessing the performance of such models in this particular field. OBJECTIVE: Our objective is to develop a foundational medical model, creating a comprehensive evaluation benchmark tailored to the essential medical knowledge of SpA and its unique diagnostic and treatment protocols. The model, post-pretraining, will be subject to further enhancement through supervised fine-tuning. It is projected to significantly aid physicians in SpA diagnosis and treatment, especially in settings with limited access to specialized care. Furthermore, this initiative is poised to promote early and accurate SpA detection at the primary care level, thereby diminishing the risks associated with delayed or incorrect diagnoses. METHODS: A rigorous benchmark, comprising 222 meticulously formulated multiple-choice questions on SpA, will be established and developed. These questions will be extensively revised to ensure their suitability for accurately evaluating LLMs' performance in real-world diagnostic and therapeutic scenarios. Our methodology involves selecting and refining top foundational models using public data sets. The best-performing model in our benchmark will undergo further training. Subsequently, more than 80,000 real-world inpatient and outpatient cases from hospitals will enhance LLM training, incorporating techniques such as supervised fine-tuning and low-rank adaptation. We will rigorously assess the models' generated responses for accuracy and evaluate their reasoning processes using the metrics of fluency, relevance, completeness, and medical proficiency. RESULTS: Development of the model is progressing, with significant enhancements anticipated by early 2024. The benchmark, along with the results of evaluations, is expected to be released in the second quarter of 2024. CONCLUSIONS: Our trained model aims to capitalize on the capabilities of LLMs in analyzing complex clinical data, thereby enabling precise detection, diagnosis, and treatment of SpA. This innovation is anticipated to play a vital role in diminishing the disabilities arising from delayed or incorrect SpA diagnoses. By promoting this model across diverse health care settings, we anticipate a significant improvement in SpA management, culminating in enhanced patient outcomes and a reduced overall burden of the disease. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/57001.

Encapsulation and thermal properties of composite phase change materials based on cobalt/nitrogen double-doped ZIF-67 derived carbon.

To solve the problems of easy leakage and weak thermal conductivity of single-phase change material, in this experiment, cobalt/nitrogen-doped ZIF-67 derived carbon (CoN-ZIF-Cx) was constructed as the carrier material, and paraffin was used as the phase change core material to construct thermally enhanced shaped composite phase change materials (P0.6@CoN-ZIF-Cx). The composite PCMs were characterized using scanning electron microscopy, isothermal nitrogen adsorption-desorption, X-ray diffraction, and Fourier infrared spectroscopy, and their performance was evaluated using transient planar heat source techniques, differential scanning calorimetry, and thermal cycling tests. The results indicated that the impurities of the acid-washed porous carbon material were reduced and the loading of the paraffin was 60%, and the prepared P0.6@CoN-ZIF-Cx had an excellent thermal performance. Among them, P0.6@CoN-ZIF-C3 has the melting and crystallization enthalpy of 71.03 J g-1 and 68.81 J g-1. The thermal conductivity is 0.4127 W m-1 K-1, a 46.19% thermal conductivity improvement compared with pure paraffin. It still has favourable thermal storage capacity after 50 cycles without paraffin leakage during the phase transition.

Research progress on MOFs and their derivatives as promising and efficient electrode materials for electrocatalytic hydrogen production from water.

Hydrogen energy is considered to be the most potential "ultimate energy source" due to its high combustion calorific value, cleanliness, and pollution-free characteristics. Furthermore, the production of hydrogen via the electrolysis of water has the advantages of simplicity, high efficiency, environmentally safe, and high-purity hydrogen. However, it is also associated with issues such as high-power consumption for the reaction and limited large-scale application of noble metal catalysts. Metal-organic frameworks (MOFs) are porous composite materials composed of metal ions and organic functional groups through orderly coordination with large specific surface areas and large porosity. Herein, we focus on the research status of MOFs and their transition metal derivatives for electrocatalytic water splitting to produce hydrogen and briefly describe the reaction mechanism and evaluation parameters of the electrocatalytic hydrogen evolution and oxygen evolution reactions. Furthermore, the relationship between the catalytic behavior and catalytic activity of different MOF-based catalysts and their morphology, elemental composition, and synthetic strategy is analyzed and discussed. The reasons for the excellent activity and poor stability of the original MOF materials for the electrolysis of water reaction are shown through analysis, and using various means to improve the catalytic activity by changing the electronic structure, active sites, and charge transfer rate, MOF-based catalysts were obtained. Finally, we present perspectives on the future development of MOFs for the electrocatalytic decomposition of water.

Two-year online measurements of volatile organic compounds (VOCs) at four sites in a Chinese city: Significant impact of petrochemical industry.

Volatile organic compounds (VOCs) management has been recently given a high priority in China to mitigate ozone (O3) air pollution. However, there is a relatively poor understanding of VOCs due to their complexity and fewer observations. To better understand the pollution characteristics of VOCs and their impact on O3 pollution, two-year continuous measurements were conducted at four representative sites in Ji'nan, eastern China. These four sites cover urban, background, and industrial areas (within a petroleum refinery). Ambient VOCs showed higher concentrations at industrial site than at urban and background sites, owing to intensive emissions from petrochemical industry. The VOCs compositions present spatial heterogeneity with alkenes dominated in total reactivity at urban and background sites, while alkenes and aromatics together dominated at industrial site. The VOCs emission profile from petrochemical industry was calculated based on observational data, which revealed a huge impact on light alkanes (C2-C5), light alkenes (ethene), and aromatics (toluene and m/p-xylene). The positive matrix factorization (PMF) model analysis further refined the impact of different petrochemical industrial processes. Alkanes and alkenes dominated the emissions during refining process, while aromatics dominated during solvent usage process. Analysis by an observation-based model indicated stronger in-situ O3 production and higher sensitivity to nitrogen oxides at industrial site compared to urban and background sites. The reduction of VOCs emissions from petrochemical industry would significantly reduce the O3 concentrations. The analyses underline the significant impact of petrochemical industry on VOCs and O3 pollution, and provide important reference for the formulation of refined and effective control strategies.

Synergistic Catalytic Performance of Toluene Degradation Based on Non-Thermal Plasma and Mn/Ce-Based Bimetal-Organic Frameworks.

A series of Mn/Ce-based bimetal-organic frameworks, recorded as MCDx (x = 1, 2, 4, 6), were prepared by a solvothermal synthesis method to explore their effects and performance in the synergistic catalysis of toluene under the irradiation of non-thermal plasma. The catalytic properties of different manganese loadings in MCDx for degradation of toluene were investigated. The microphysical structures of the material were analyzed by powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA). The results showed that a MCDx coupling with non-thermal plasma can greatly improve the degradation efficiency, the energy efficiency and the CO2 selectivity, and could also significantly reduce the generation of O3 in the by-products. Among the test samples, MCD6 with Mn:Ce = 6:1 (molar ratio) showed the best catalytic performance and stability, exhibited toluene catalytic efficiency 95.2%, CO2 selectivity 84.2% and energy efficiency 5.99 g/kWh, and reduced O3 emission concentration 81.6%. This research provides a reference for the development and application of synergistic catalysis based on bimetal-organic frameworks and non-thermal plasma in the reduction of industrial volatile organic compounds.

Biomethylation and Volatilization of Arsenic by Model Protozoan Tetrahymena pyriformis under Different Phosphate Regimes.

Tetrahymena pyriformis, a freshwater protozoan, is common in aquatic systems. Arsenic detoxification through biotransformation by T. pyriformis is important but poorly understood. Arsenic metabolic pathways (including cellular accumulation, effluxion, biomethylation, and volatilization) of T. pyriformis were investigated at various phosphate concentrations. The total intracellular As concentration increased markedly as the external phosphate concentration decreased. The highest concentration was 168.8 mg·kg-1 dry weight, after exposure to As(V) for 20 h. Inorganic As was dominant at low phosphate concentrations (3, 6, and 15 mg·L-1), but the concentration was much lower at 30 mg·L-1 phosphate, and As(V) contributed only ~7% of total cellular As. Methylated As contributed 84% of total As at 30 mg·L-1 phosphate, and dimethylarsenate (DMAs(V)) was dominant, contributing up to 48% of total As. Cellular As effluxion was detected, including inorganic As(III), methylarsenate (MAs(V)) and DMAs(V). Volatile As was determined at various phosphate concentrations in the medium. All methylated As concentrations (intracellular, extracellular, and volatilized) had significant linear positive relationships with the initial phosphate concentration. To the best of our knowledge, this is the first study of As biotransformation by protozoa at different phosphate concentrations.