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.

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.

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.

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.

Through-drop imaging of moving contact lines and contact areas on opaque water-repellent surfaces.

A myriad of natural surfaces such as plant leaves and insect wings can repel water and remain unwetted inspiring scientists and engineers to develop water-repellent surfaces for various applications. Those natural and artificial water-repellent surfaces are typically opaque, containing micro- and nano-roughness, and their wetting properties are determined by the details at the actual liquid-solid interface. However, a generally applicable way to directly observe moving contact lines on opaque water-repellent surfaces is missing. Here, we show that the advancing and receding contact lines and corresponding contact area on micro- and nano-rough water-repellent surfaces can be readily and reproducibly quantified using a transparent droplet probe. Combined with a conventional optical microscope, we quantify the progression of the apparent contact area and apparent contact line irregularity in different types of superhydrophobic silicon nanograss surfaces. Contact angles near 180° can be determined with an uncertainty as low as 0.2°, that a conventional contact angle goniometer cannot distinguish. We also identify the pinning/depinning sequences of a pillared model surface with excellent repeatability and quantify the progression of the apparent contact interface and contact angle of natural plant leaves with irregular surface topography.

MHTAN-DTI: Metapath-based hierarchical transformer and attention network for drug-target interaction prediction.

Drug-target interaction (DTI) prediction can identify novel ligands for specific protein targets, and facilitate the rapid screening of effective new drug candidates to speed up the drug discovery process. However, the current methods are not sensitive enough to complex topological structures, and complicated relations between multiple node types are not fully captured yet. To address the above challenges, we construct a metapath-based heterogeneous bioinformatics network, and then propose a DTI prediction method with metapath-based hierarchical transformer and attention network for drug-target interaction prediction (MHTAN-DTI), applying metapath instance-level transformer, single-semantic attention and multi-semantic attention to generate low-dimensional vector representations of drugs and proteins. Metapath instance-level transformer performs internal aggregation on the metapath instances, and models global context information to capture long-range dependencies. Single-semantic attention learns the semantics of a certain metapath type, introduces the central node weight and assigns different weights to different metapath instances to obtain the semantic-specific node embedding. Multi-semantic attention captures the importance of different metapath types and performs weighted fusion to attain the final node embedding. The hierarchical transformer and attention network weakens the influence of noise data on the DTI prediction results, and enhances the robustness and generalization ability of MHTAN-DTI. Compared with the state-of-the-art DTI prediction methods, MHTAN-DTI achieves significant performance improvements. In addition, we also conduct sufficient ablation studies and visualize the experimental results. All the results demonstrate that MHTAN-DTI can offer a powerful and interpretable tool for integrating heterogeneous information to predict DTIs and provide new insights into drug discovery.

A novel multimodal needs assessment to inform the longitudinal education program for an international interprofessional critical care team.

BACKGROUND: The current global pandemic has caused unprecedented strain on critical care resources, creating an urgency for global critical care education programs. Learning needs assessment is a core element of designing effective, targeted educational interventions. In theory, multimodal methods are preferred to assess both perceived and unperceived learning needs in diverse, interprofessional groups, but a robust design has rarely been reported. Little is known about the best approach to determine the learning needs of international critical care professionals. METHOD: We conducted multimodal learning needs assessment in a pilot group of critical care professionals in China using combined quantitative and qualitative methods. The assessments consisted of three phases: 1) Twenty statements describing essential entrustable professional activities (EPAs) were generated by a panel of critical care education experts using a Delphi method. 2) Eleven Chinese critical care professionals participating in a planned education program were asked to rank-order the statements according to their perceived learning priority using Q methodology. By-person factor analysis was used to study the typology of the opinions, and post-ranking focus group interviews were employed to qualitatively explore participants' reasoning of their rankings. 3) To identify additional unperceived learning needs, daily practice habits were audited using information from medical and nursing records for 3 months. RESULTS: Factor analysis of the rank-ordered statements revealed three learning need patterns with consensual and divergent opinions. All participants expressed significant interest in further education on organ support and disease management, moderate interest in quality improvement topics, and relatively low interest in communication skills. Interest in learning procedure/resuscitation skills varied. The chart audit revealed suboptimal adherence to several evidence-based practices and under-perceived practice gaps in patient-centered communication, daily assessment of antimicrobial therapy discontinuation, spontaneous breathing trial, and device discontinuation. CONCLUSIONS: We described an effective mixed-methods assessment to determine the learning needs of an international, interprofessional critical care team. The Q survey and focus group interviews prioritized and categorized perceived learning needs. The chart audit identified additional practice gaps that were not identified by the learners. Multimodal methods can be employed in cross-cultural scenarios to customize and better target medical education curricula.

3D Printing of Superhydrophobic Objects with Bulk Nanostructure.

The rapid development of 3D printing (or additive manufacturing) technologies demands new materials with novel properties and functionalities. Superhydrophobic materials, owing to their ultralow water adhesion, self-cleaning, anti-biofouling, or superoleophilic properties are useful for myriad applications involving liquids. However, the majority of the methods for making superhydrophobic surfaces have been based on surface functionalization and coatings, which are challenging to apply to 3D objects. Additionally, these coatings are vulnerable to abrasion due to low mechanical stability and limited thickness. Here, a new materials concept and methodology for 3D printing of superhydrophobic macroscopic objects with bulk nanostructure and almost unlimited geometrical freedom is presented. The method is based on a specific ink composed of hydrophobic (meth)acrylate monomers and porogen solvents, which undergoes phase separation upon photopolymerization to generate inherently nanoporous and superhydrophobic structures. Using a desktop Digital Light Processing printer, superhydrophobic 3D objects with complex shapes are demonstrated, with ultralow and uniform water adhesion measured with scanning droplet adhesion microscopy. It is shown that the 3D-printed objects, owing to their nanoporous structure throughout the entire volume, preserve their superhydrophobicity upon wear damage. Finally, a superhydrophobic 3D-printed gas-permeable and water-repellent microfluidic device and a hierarchically structured 3D-printed super-oil-absorbent are demonstrated.

Foodborne Disease Risk Prediction Using Multigraph Structural Long Short-term Memory Networks: Algorithm Design and Validation Study.

BACKGROUND: Foodborne disease is a common threat to human health worldwide, leading to millions of deaths every year. Thus, the accurate prediction foodborne disease risk is very urgent and of great importance for public health management. OBJECTIVE: We aimed to design a spatial-temporal risk prediction model suitable for predicting foodborne disease risks in various regions, to provide guidance for the prevention and control of foodborne diseases. METHODS: We designed a novel end-to-end framework to predict foodborne disease risk by using a multigraph structural long short-term memory neural network, which can utilize an encoder-decoder to achieve multistep prediction. In particular, to capture multiple spatial correlations, we divided regions by administrative area and constructed adjacent graphs with metrics that included region proximity, historical data similarity, regional function similarity, and exposure food similarity. We also integrated an attention mechanism in both spatial and temporal dimensions, as well as external factors, to refine prediction accuracy. We validated our model with a long-term real-world foodborne disease data set, comprising data from 2015 to 2019 from multiple provinces in China. RESULTS: Our model can achieve F1 scores of 0.822, 0.679, 0.709, and 0.720 for single-month forecasts for the provinces of Beijing, Zhejiang, Shanxi and Hebei, respectively, and the highest F1 score was 20% higher than the best results of the other models. The experimental results clearly demonstrated that our approach can outperform other state-of-the-art models, with a margin. CONCLUSIONS: The spatial-temporal risk prediction model can take into account the spatial-temporal characteristics of foodborne disease data and accurately determine future disease spatial-temporal risks, thereby providing support for the prevention and risk assessment of foodborne disease.

High-Efficiency Machine Learning Method for Identifying Foodborne Disease Outbreaks and Confounding Factors.

The China National Center for Food Safety Risk Assessment (CFSA) uses the Foodborne Disease Monitoring and Reporting System (FDMRS) to monitor outbreaks of foodborne diseases across the country. However, there are problems of underreporting or erroneous reporting in FDMRS, which significantly increase the cost of related epidemic investigations. To solve this problem, we designed a model to identify suspected outbreaks from the data generated by the FDMRS of CFSA. In this study, machine learning models were used to fit the data. The recall rate and F1-score were used as evaluation metrics to compare the classification performance of each model. Feature importance and pathogenic factors were identified and analyzed using tree-based and gradient boosting models. Three real foodborne disease outbreaks were then used to evaluate the best performing model. Furthermore, the SHapley Additive exPlanation value was used to identify the effect of features. Among all machine learning classification models, the eXtreme Gradient Boosting (XGBoost) model achieved the best performance, with the highest recall rate and F1-score of 0.9699 and 0.9582, respectively. In terms of model validation, the model provides a correct judgment of real outbreaks. In the feature importance analysis with the XGBoost model, the health status of the other people with the same exposure has the highest weight, reaching 0.65. The machine learning model built in this study exhibits high accuracy in recognizing foodborne disease outbreaks, thus reducing the manual burden for medical staff. The model helped us identify the confounding factors of foodborne disease outbreaks. Attention should be paid not only to the health status of those with the same exposure but also to the similarity of the cases in time and space.

Machine Learning Prediction of Foodborne Disease Pathogens: Algorithm Development and Validation Study.

BACKGROUND: Foodborne diseases, as a type of disease with a high global incidence, place a heavy burden on public health and social economy. Foodborne pathogens, as the main factor of foodborne diseases, play an important role in the treatment and prevention of foodborne diseases; however, foodborne diseases caused by different pathogens lack specificity in clinical features, and there is a low proportion of clinically actual pathogen detection in real life. OBJECTIVE: We aimed to analyze foodborne disease case data, select appropriate features based on analysis results, and use machine learning methods to classify foodborne disease pathogens to predict foodborne disease pathogens that have not been tested. METHODS: We extracted features such as space, time, and exposed food from foodborne disease case data and analyzed the relationship between these features and the foodborne disease pathogens using a variety of machine learning methods to classify foodborne disease pathogens. We compared the results of 4 models to obtain the pathogen prediction model with the highest accuracy. RESULTS: The gradient boost decision tree model obtained the highest accuracy, with accuracy approaching 69% in identifying 4 pathogens including Salmonella, Norovirus, Escherichia coli, and Vibrio parahaemolyticus. By evaluating the importance of features such as time of illness, geographical longitude and latitude, and diarrhea frequency, we found that they play important roles in classifying the foodborne disease pathogens. CONCLUSIONS: Data analysis can reflect the distribution of some features of foodborne diseases and the relationship among the features. The classification of pathogens based on the analysis results and machine learning methods can provide beneficial support for clinical auxiliary diagnosis and treatment of foodborne diseases.

Overexpression of miR-451a in sepsis and septic shock patients is involved in the regulation of sepsis-associated cardiac dysfunction and inflammation.

The purpose of this study was to investigate the expression and clinical value of microRNA-451a (miR-451a) in septic patients and analyze its effect on sepsis-associated cardiac dysfunction and inflammation response. A rat model of sepsis was constructed by cecal ligation and puncture. The expression of miR-451a was measured by quantitative real-time PCR. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic value of serum miR-451a. The cardiac function and inflammatory responses in septic rats were measured to explore the functional role of miR-451a. Serum expression of miR-451a was increased in septic patients compared with healthy controls, and had the ability to distinguish septic patients from healthy volunteers with a sensitivity and specificity of 87.8% and 81.5%, respectively. Elevated serum miR-451a was associated with sepsis severity, as evidenced by the increased expression of miR-451a in septic shock patients and its correlation with key clinical indicators. Significantly upregulated expression of miR-451a was found in septic patients with cardiac dysfunction, and the knockdown of miR-451a in sepsis rats improved cardiac function and inhibited inflammatory responses. All the data revealed that serum miR-451a serves as a candidate diagnostic biomarker of sepsis and a potential parameter to indicate disease severity. The reduction of miR-451a may mitigate sepsis-induced cardiac dysfunction and inflammatory responses.

Comparison of HAS-BLED with other risk models for predicting the bleeding risk in anticoagulated patients with atrial fibrillation: A PRISMA-compliant article.

BACKGROUND: The HAS-BLED, HEMORR2HAGES, ATRIA, and ORBIT scores are used to predict bleeding risk in anticoagulated patients with atrial fibrillation (AF). Recently, these scores have been validated in various studies. Therefore, we aimed to compare the occurrence of major bleeding across different risk categories between HAS-BLED and any of HEMORR2HAGES, ATRIA, or ORBIT scores. METHODS: A systemic literature search of PubMed and Embase databases was conducted to screen the relevant studies. We calculated and pooled the odds ratios (ORs) and 95% confidence intervals (CIs) for a comparative analysis of the occurrence of major bleeding. RESULTS: Nine studies fulfilled the inclusion criteria in this meta-analysis. Compared with HEMORR2HAGES, there were 87% and 39% reduced rates of major bleeding in the HAS-BLED "low-risk" and "moderate-risk" groups, respectively. Compared with ATRIA, there was an 89% decreased rate of major bleeding in the HAS-BLED "low-risk" group. Compared with ORBIT, there were 84% and 44% reduced rates of major bleeding in the HAS-BLED "low-risk" and "moderate-risk" groups, respectively. Patients with HAS-BLED scores ≥3 showed an approximately 3-fold greater risk of major bleeding compared with patients with scores <3 (OR=3.00, CI: 1.21-7.43). CONCLUSIONS: Compared with any of HEMORR2HAGES, ATRIA, or ORBIT scores, the HAS-BLED score distributed more major bleeding events into the "low" or "moderate" risk categories.

Development and full validation of an LC-MS/MS methodology to quantify capmatinib (INC280) following intragastric administration to rats.

A highly sensitive, specific and simple LC-MS/MS method for quantification of capmatinib (INC280) in rat plasma was presented. The LC-MS/MS method was validated in terms of specificity and selectivity, linearity, accuracy and precision, matrix effect, extraction recovery, dilution integrity, carryover and stability as per the US Food and Drug Administration's bioanalytical method validation guideline. The validated assay was applied for quantification of capmatinib from a pharmacokinetic study in rats following oral administration at the doses of 1.0, 3.0 and 9.0 mg/kg. The calibration curve ranges from 1 to 2000 ng/ml with desirable linearity and r2 > 0.99. The intra- and inter-batch accuracies were within 99.24-103.59 and 97.76-102.83% with coefficients of variation 5.08-7.36 and 3.18-4.99%, respectively. No significant interference was observed by endogenous peak at the retention time of capmatinib and IS. The assay was free from any matrix effect and showed precise recovery across the calibration curve range, and samples were stable under all experimental conditions. The validated assay was successfully applied to analyze plasma samples of pharmacokinetic study in rat to determine the concentration of capmatinib. In summary, a novel method for analyzing capmatinib in rat plasma has been successfully validated and is now being utilized for quantification of capmatinib from pre-clinical studies.