Large language model enhanced corpus of CO2 reduction electrocatalysts and synthesis procedures.

CO2 electroreduction has garnered significant attention from both the academic and industrial communities. Extracting crucial information related to catalysts from domain literature can help scientists find new and effective electrocatalysts. Herein, we used various advanced machine learning, natural language processing techniques and large language models (LLMs) approaches to extract relevant information about the CO2 electrocatalytic reduction process from scientific literature. By applying the extraction pipeline, we present an open-source corpus for electrocatalytic CO2 reduction. The database contains two types of corpus: (1) the benchmark corpus, which is a collection of 6,985 records extracted from 1,081 publications by catalysis postgraduates; and (2) the extended corpus, which consists of content extracted from 5,941 documents using traditional NLP techniques and LLMs techniques. The Extended Corpus I and II contain 77,016 and 30,283 records, respectively. Furthermore, several domain literature fine-tuned LLMs were developed. Overall, this work will contribute to the exploration of new and effective electrocatalysts by leveraging information from domain literature using cutting-edge computer techniques.

Dexmedetomidine suppressed the biological behavior of RAW264.7 cells treated with LPS by down-regulating HOTAIR.

Background: Previous studies have revealed dexmedetomidine have potential protective effects on vital organs by inhibiting the release of inflammatory cytokines. To investigate the effects of dexmedetomidine on sepsis, especially in the initial inflammatory stage of sepsis. RAW264.7 cells were used as the cell model in this study to elucidate the underlying mechanisms. Methods: In this study, we conducted several assays to investigate the mechanisms of dexmedetomidine and HOTAIR in sepsis. Cell viability was assessed using the CCK-8 kit, while inflammation responses were measured using ELISA for IL-1ß, IL-6, and TNF-α. Additionally, we employed qPCR, MeRIP, and RIP to further explore the underlying mechanisms. Results: Our findings indicate that dexmedetomidine treatment enhanced cell viability and reduced the production of inflammatory cytokines in LPS-treated RAW264.7 cells. Furthermore, we observed that the expression of HOTAIR was increased in LPS-treated RAW264.7 cells, which was then decreased upon dexmedetomidine pre-treatment. Further investigation demonstrated that HOTAIR could counteract the beneficial effects of dexmedetomidine on cell viability and cytokine production. Interestingly, we discovered that YTHDF1 targeted HOTAIR and was upregulated in LPS-treated RAW264.7 cells, but reduced in dexmedetomidine treatment. We also found that YTHDF1 increased HOTAIR and HOTAIR m6A levels. Conclusions: Collectively, our results suggest that dexmedetomidine downregulates HOTAIR and YTHDF1 expression, which in turn inhibits the biological behavior of LPS-treated RAW264.7 cells. This finding has potential implications for the prevention and treatment of sepsis-induced kidney injury.

Construction and enhancement of government capacity for international communication and promotion of Chinese Wushu.

BACKGROUND: In the process of international communication in Chinese Wushu (ICCW), the government controls the orientation, scale, pace. However, the ICCW currently lacks a standardised government capacity structural system, and a detailed study of framework construction may be required to ensure the smooth development of the ICCW. OBJECTIVES: This study aims to clarify these elements and construct a framework for a governmental capacity system for ICCW. METHODS: For this purpose, an expert interview outline was designed, and in-depth interviews were conducted with 61 experts. Using grounded theory in the qualitative research method, NVivo 12 software was used to conduct a three-level coding analysis of the interview text for data processing and analysis. RESULTS: We extracted 58 opening codes and 11 tree nodes and categorised them into three core categories: supply side government capacity, environment-side government capacity, and demand-side government capacity, accounting for 62.36 %, 24.76 %, and 12.86 % of the total, respectively, which jointly constructed the framework structure system of the governmental capacity system for the ICCW. CONCLUSIONS: This study found that these three-dimensional government capacities have synergistic effects and that multiple measures work together. The government should ensure the supply side's direct promotion effect; the environmental side's indirect influencing effect; and the demand side's internal driving effect to promote ICCW. Meanwhile, a closed-loop systematic study of communication processes should be conducted in combination with communication organisations and individuals.

Assessing bioartificial organ function: the 3P model framework and its validation.

The rapid advancement in the fabrication and culture of in vitro organs has marked a new era in biomedical research. While strides have been made in creating structurally diverse bioartificial organs, such as the liver, which serves as the focal organ in our study, the field lacks a uniform approach for the predictive assessment of liver function. Our research bridges this gap with the introduction of a novel, machine-learning-based "3P model" framework. This model draws on a decade of experimental data across diverse culture platform studies, aiming to identify critical fabrication parameters affecting liver function, particularly in terms of albumin and urea secretion. Through meticulous statistical analysis, we evaluated the functional sustainability of the in vitro liver models. Despite the diversity of research methodologies and the consequent scarcity of standardized data, our regression model effectively captures the patterns observed in experimental findings. The insights gleaned from our study shed light on optimizing culture conditions and advance the evaluation of the functional maintenance capacity of bioartificial livers. This sets a precedent for future functional evaluations of bioartificial organs using machine learning.

Serum Cholinesterase, C-reactive Protein, Interleukin 6, and Procalcitonin Levels as Predictors of Mortality in Patients in the Intensive Care Unit.

Objective: The prognostic utility of inflammatory markers in survival has been suggested in patients with cancer; however, evidence on their prognostic value in severely ill patients is very limited. We aimed to explore the prognostic value of cholinesterase (ChE), C-reactive protein (CRP), interleukin-6 (IL-6), and procalcitonin (PCT) in predicting mortality in patients from the intensive care unit (ICU). Methods: Serum levels of ChE, CRP, IL-6 and PCT were measured in ICU patients from December 13th, 2019 to June 28th, 2022. We assessed the predictive power of ChE, CRP, IL-6, and PCT using the receiver operating characteristic (ROC) curves. Furthermore, we evaluated their diagnostic accuracy by comparing the areas under the ROC curve (AUCs) along with their corresponding 95% confidence intervals (CIs). The cut-off values were determined to dichotomise these biomarkers, which were then included in multivariable logistic regression models to examine their relationship with ICU mortality. Results: Among 253 ICU patients included in the study, 66 (26%) died during the ICU stay. The AUCs to predict ICU mortality were 0.643 (95% CI, 0.566-0.719), 0.648 (95% CI, 0.633-0.735), 0.643 (95% CI, 0.563-0.723) and 0.735 (95% CI, 0.664-0.807) for ChE, CRP, IL-6 and PCT, respectively. After adjusting for age, sex and disease severity, lower ChE level (<3.668 × 103 U L-1) and higher levels of CRP (>10.546 mg dL-1), IL-6 (>986.245 pg mL-1) and PCT (>0.505 µg L-1) were associated with higher mortality risk, with odd ratios of 2.70 (95% CI, 1.32-5.54), 4.99 (95% CI, 2.41-10.38), 3.24 (95% CI, 1.54-6.78) and 3.67 (95% CI, 1.45-9.95), respectively. Conclusion: ChE, CRP, IL-6 and PCT were independent ICU mortality risk factors in severely ill patients. Elevated PCT levels exhibited better predictive value than the other three biomarkers that were evaluated.

HTCL-DDI: a hierarchical triple-view contrastive learning framework for drug-drug interaction prediction.

Drug-drug interaction (DDI) prediction can discover potential risks of drug combinations in advance by detecting drug pairs that are likely to interact with each other, sparking an increasing demand for computational methods of DDI prediction. However, existing computational DDI methods mostly rely on the single-view paradigm, failing to handle the complex features and intricate patterns of DDIs due to the limited expressiveness of the single view. To this end, we propose a Hierarchical Triple-view Contrastive Learning framework for Drug-Drug Interaction prediction (HTCL-DDI), leveraging the molecular, structural and semantic views to model the complicated information involved in DDI prediction. To aggregate the intra-molecular compositional and structural information, we present a dual attention-aware network in the molecular view. Based on the molecular view, to further capture inter-molecular information, we utilize the one-hop neighboring information and high-order semantic relations in the structural view and semantic view, respectively. Then, we introduce contrastive learning to enhance drug representation learning from multifaceted aspects and improve the robustness of HTCL-DDI. Finally, we conduct extensive experiments on three real-world datasets. All the experimental results show the significant improvement of HTCL-DDI over the state-of-the-art methods, which also demonstrates that HTCL-DDI opens new avenues for ensuring medication safety and identifying synergistic drug combinations.

A corpus of CO2 electrocatalytic reduction process extracted from the scientific literature.

The electrocatalytic CO2 reduction process has gained enormous attention for both environmental protection and chemicals production. Thereinto, the design of new electrocatalysts with high activity and selectivity can draw inspiration from the abundant scientific literature. An annotated and verified corpus made from massive literature can assist the development of natural language processing (NLP) models, which can offer insight to help guide the understanding of these underlying mechanisms. To facilitate data mining in this direction, we present a benchmark corpus of 6,086 records manually extracted from 835 electrocatalytic publications, along with an extended corpus with 145,179 records in this article. In this corpus, nine types of knowledge such as material, regulation method, product, faradaic efficiency, cell setup, electrolyte, synthesis method, current density, and voltage are provided by either annotating or extracting. Machine learning algorithms can be applied to the corpus to help scientists find new and effective electrocatalysts. Furthermore, researchers familiar with NLP can use this corpus to design domain-specific named entity recognition (NER) models.