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Abstract Aim The aim of this review is to evaluate and summarize the evidence for preoperative visit-care of transcatheter aortic valve replacement (TAVR) and to provide evidence-based support for clinical intervention. Design The review presents an evidence summary report, following the standard of the Fudan University Center for Evidence-based Nursing. Methods Literature related to preoperative visit-care for transcatheter aortic valve replacement have been retrieved based on the “6S” pyramid model of evidence. The types of literature included systematic reviews, expert consensus, evidence summary, and guidelines. Data sources Up To Date, Cochrane Library, Joanna Briggs Institute, National Institute for Health and Care Excellence, Medlive, American Heart Association, Registered Nurses Association of Ontario, Scottish Intercollegiate Guidelines Network, European Society of Cardiology, American College of Cardiology, PubMed, CINAHL, Wanfang database, VIP database, Chinese biomedical literature database, CNKI. The search period is limited to the time when each database was established until February 2024. Results A total of 18 articles were included in this review, consisting of 1 systematic review, 1 evidence summary, 3 guidelines and 13 expert consensuses. This review summarized 30 pieces of the best evidence for preoperative visit-care for transcatheter aortic valve replacement, encompassing three evidence themes: multidisciplinary team collaboration, preoperative assessment, and preoperative education. Conclusion The study has established an ideal foundation of evidence for preoperative visit-care in TAVR. However, throughout the particular application process, it was crucial to assess the feasibility and relevance of the evidence in clinical practice by taking into account elements such as the application setting, recommendations from experts with diverse expertise, and the preferences of the patients.

Abstract Background Preoperative visit-care for transcatheter aortic valve replacement (TAVR) plays a crucial role in improving the quality of care and patient safety. However, preoperative care for TAVR patients is still in its early stages in China, with the care often being experience-based. The application of relevant evidence in nursing practice is necessary. Little is known regarding the facilitators and barriers to apply and compliance to the evidences about preoperative visit-care for TAVR in nursing. Methods The Nurse’s Compliance Checklist was used to investigate the evidence-based compliance of nurses (n = 21) who worked in the TAVR team in the evidence-based implementation setting. Meanwhile, an Evidence-Based Practice Beliefs Scale, and Influencing Factors Checklist were used to investigate all nurses (n = 66) who work in the same setting. Stakeholders (Middle and senior-level nursing administrators, frontline clinical nurses, and patients) interview was carried out to further disclose the barriers and facilitators in the process of evidence-based practice. Results The results of this study showed that only 1 evidence implemented fully (100%) by nurses, 3 evidences with 0% implementation rate, and implementation rate of the other evidences were 9.5∼71.4%. The overall score of nurses’ evidence-based nursing belief level was (3.52 ± 0.82). Three domains of barriers were identified: the Context Domain included lack of nursing procedures, inadequate health education materials, insufficient training; the Practitioner Domain included insufficient attention, lack of relevant knowledge, high work pressure and uncertainty of expected results, and Patient Domain included lack of relational knowledge. Facilitating factors included leadership support, nurse’ high evidence-based nursing belief, high executive ability and enthusiasm for learning. Conclusion The study indicated that the nurses’ compliance of evidence-based practice in preoperative visit-care for TAVR was in lower level. There were some factors influencing the application of the evidences. The study revealed potential modifiable barriers to the successful implementation of evidence-based preoperative visit-care, including a lack of preoperative visit- care routine, related knowledge and training. Leadership support and nurse training should be considered to improve nurses’ compliance with evidence-based practice.

Abstract In current land surface models or satellite remote sensing retrievals, clear‐sky surface albedo (α) is usually assumed to be symmetrical and relies only on the solar elevation angle (SEA). Based on 1‐min high‐resolution measurements of surface radiation fluxes, this study demonstrated that the diurnal variations of clear‐sky surface albedo exhibited a significant asymmetrical pattern in both summer and winter seasons over a semi‐arid grassland of the China's Loess Plateau. The results indicated that α values in the morning were generally larger than those in the afternoon at the same SEA, and diurnal asymmetry of surface albedo was distinctly prominent with SEA

Hybrid-dimensional simulations can balance simulation accuracy and computational costs. However, applying hybrid-dimensional simulations to flight status remains challenging due to weak convergence. In this study, an adaptive integration method for hybrid-dimensional simulations was developed by introducing both adaptive incorporation schemes and polynomial transformations into the existing direct integration method. Experimental validations and T-MATS analysis show that errors in the thrust and specific fuel consumption of throttle characteristics are within 2% and 3%, respectively. Hybrid-dimensional simulations using both adaptive and existing direct integration methods were then applied to predict flight speed and altitude characteristics. Overall, the adaptive integration method demonstrates a broad convergence range for altitude (0-10 km) and speed (0-0.9 Ma) characteristics, requiring 25 iteration steps with co-working errors of less than 10−03. However, the direct integration method covers smaller ranges for altitude (0-1 km) and speed (0-0.4 Ma) characteristics, requiring 50 iteration steps with co-working errors of larger than 10−03. The results indicate that hybrid-dimensional simulations using the adaptive integration method exhibit good stability and convergence in the flight status compared to the direct integration method. Moreover, both the speed and altitude characteristics using the adaptive integration method cost approximately 24 hours on a computer workstation.

Named entity recognition (NER) is a crucial step in extracting medical information from Chinese text, and fine-tuning large language models (LLMs) for this task is an effective approach. However, full parameter fine-tuning can potentially damage the model’s original parameters, resulting in catastrophic forgetting. To overcome this challenge, we introduce a novel adapter-based fine-tuning approach. Our adapter is integrated into the first and last transformers of the LLM, operating in parallel to the feed-forward network (FFN), following multi-head attention. It mirrors the FFN’s structure and uses the FFN’s weights for initializing. Additionally, to further enhance performance, we incorporate prefix embeddings into the first and last transformers. Our experiments on the Chinese medical NER benchmark demonstrate that our adapter, combined with prefix embeddings, achieves the highest F1-score of 65.90%, surpassing prompt templates (21.99%), in-context learning (18.65%), P-tuning (63.03%), and the benchmark for the Chinese medical NER task (62.40%). These results indicate that our adapter effectively fine-tunes the LLM for Chinese medical NER while preserving the original parameters.

BackgroundCardioembolic Stroke (CS) and Atrial Fibrillation (AF) are prevalent diseases that significantly impact the quality of life and impose considerable financial burdens on society. Despite increasing evidence of a significant association between the two diseases, their complex interactions remain inadequately understood. We conducted bioinformatics analysis and employed machine learning techniques to investigate potential shared biomarkers between CS and AF.MethodsWe retrieved the CS and AF datasets from the Gene Expression Omnibus (GEO) database and applied Weighted Gene Co-Expression Network Analysis (WGCNA) to develop co-expression networks aimed at identifying pivotal modules. Next, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the shared genes within the modules related to CS and AF. The STRING database was used to build a protein-protein interaction (PPI) network, facilitating the discovery of hub genes within the network. Finally, several common used machine learning approaches were applied to construct the clinical predictive model of CS and AF. ROC curve analysis to evaluate the diagnostic value of the identified biomarkers for AF and CS.ResultsFunctional enrichment analysis indicated that pathways intrinsic to the immune response may be significantly involved in CS and AF. PPI network analysis identified a potential association of 4 key genes with both CS and AF, specifically PIK3R1, ITGAM, FOS, and TLR4.ConclusionIn our study, we utilized WGCNA, PPI network analysis, and machine learning to identify four hub genes significantly associated with CS and AF. Functional annotation outcomes revealed that inherent pathways related to the immune response connected to the recognized genes might could pave the way for further research on the etiological mechanisms and therapeutic targets for CS and AF.

Recent trends suggest that Chinese herbal medicine formulas (CHM formulas) are promising treatments for complex diseases. To characterize the precise syndromes, precise diseases and precise targets of the precise targets between complex diseases and CHM formulas, we developed an artificial intelligence-based quantitative predictive algorithm (DeepTCM). DeepTCM has gone through multilevel model calibration and validation against a comprehensive set of herb and disease data so that it accurately captures the complex cellular signaling, molecular and theoretical levels of traditional Chinese medicine (TCM). As an example, our model simulated the optimal CHM formulas for the treatment of coronary heart disease (CHD) with depression, and through model sensitivity analysis, we calculated the balanced scoring of the formulas. Furthermore, we constructed a biological knowledge graph representing interactions by associating herb-target and gene-disease interactions. Finally, we experimentally confirmed the therapeutic effect and pharmacological mechanism of a novel model-predicted intervention in humans and mice. This novel multiscale model opened up a new avenue to combine “disease syndrome” and “macro micro” system modeling to facilitate translational research in CHM formulas.

The vertical profiles of aerosol optical properties are vital to clarify their transboundary transport, climate forcing and environmental health influences. Based on synergistic measurements of multiple advanced detection techniques, this study investigated aerosol vertical structure and optical characteristics during two dust and haze events in Lanzhou of northwest China. Dust particles originated from remote deserts traveled eastward at different altitudes and reached Lanzhou on 10 April 2020. The trans-regional aloft (~4.0 km) dust particles were entrained into the ground, and significantly modified aerosol optical properties over Lanzhou. The maximum aerosol extinction coefficient (σ), volumetric depolarization ratio (VDR), optical depth at 500 nm (AOD500), and surface PM10 and PM2.5 concentrations were 0.4~1.5 km−1, 0.15~0.30, 0.5~3.0, 200~590 μg/m3 and 134 μg/m3, respectively, under the heavy dust event, which were 3 to 11 times greater than those at the background level. The corresponding Ångström exponent (AE440–870), fine-mode fraction (FMF) and PM2.5/PM10 values consistently persisted within the ranges of 0.10 to 0.50, 0.20 to 0.50, and 0.20 to 0.50, respectively. These findings implied a prevailing dominance of coarse-mode and irregular non-spherical particles. A severe haze episode stemming from local emissions appeared at Lanzhou from 30 December 2020 to 2 January 2021. The low-altitude transboundary transport aerosols seriously deteriorated the air quality level in Lanzhou, and aerosol loading, surface air pollutants and fine-mode particles strikingly increased during the gradual strengthening of haze process. The maximum AOD500, AE440–870nm, FMF, PM2.5 and PM10 concentrations, and PM2.5/PM10 were 0.65, 1.50, 0.85, 110 μg/m3, 180 μg/m3 and 0.68 on 2 January 2021, respectively, while the corresponding σ and VDR at 0.20–0.80 km height were maintained at 0.68 km−1 and 0.03~0.12, implying that fine-mode and spherical small particles were predominant. The profile of ozone concentration exhibited a prominent two-layer structure (0.60–1.40 km and 0.10–0.30 km), and both concentrations at two heights always remained at high levels (60~72 μg/m3) during the entire haze event. Conversely, surface ozone concentration showed a significant decrease during severe haze period, with the peak value of 20~30 μg/m3, which was much smaller than that before haze pollution (~80 μg/m3 on 30 December). Our results also highlighted that the vertical profile of aerosol extinction coefficient was a good proxy for evaluating mass concentrations of surface particulate matters under uniform mixing layers, which was of great scientific significance for retrieving surface air pollutants in remote desert or ocean regions. These statistics of the aerosol vertical profiles and optical properties under heavy dust and haze events in Lanzhou would contribute to investigate and validate the transboundary transport and radiative forcing of aloft aerosols in the application of climate models or satellite remote sensing.

Scalable fabrication of Low-cost hydrogen evolution reaction (HER) catalysts that can operate at industry-relevant conditions is highly needed for efficient water electrolyzers. Here the authors show a scalable synthesis of a MoC-Mo2C heterojunction electrode with efficient HER activity and high stability at industrial conditions.

IntroductionIn China, the increasing number of people with Alzheimer's disease (AD) poses a great challenge to families and the country. Economic and cultural differences cause a urban-rural gap in medical resources. This multicenter survey aimed to investigate the real-world practice of disease treatment among people with AD.MethodsPeople with AD and their caregivers from 30 provincial regions in mainland China were enrolled from October 2020 to December 2020 to be surveyed for their treatment experience. Logistic regression was used to explore the factors that influence medication adherence in all areas, urban areas, and rural areas.ResultsIn this survey, 1,427 participants came from urban areas, and 539 participants came from rural areas. Patients in urban areas were older (mean age 74 vs. 70, p = 0.001), less frequently had mild AD (36.0 vs. 52.1%, p < 0.001), and more often were cared for at professional institutions (8.8 vs. 3.2%, p < 0.001). In terms of pharmacotherapy, 77.8% of people accepted taking lifelong medication, whereas 61.3% of patients insisted on taking medications. Although 72.0% of rural people believed in taking lifelong medication, only 30.0% adhered to drug use. The major factors that influenced medication adherence for all patients with AD were regional distribution (p < 0.001, OR = 6.18, 95% CI: 4.93–7.74) and family earnings (p = 0.003, OR = 1.22, 95% CI: 1.07–1.38). In rural areas, family earnings (p = 0.008, OR = 1.44, 95% CI: 1.10–1.89) and severity of AD (p = 0.033, OR = 1.31, 95% CI: 1.02–1.68) were the main factors. Family earnings (p = 0.038, OR = 1.16, 95% CI: 1.01–1.34) was the only factor among urban areas. Among all non-pharmaceutical activities except for cognitive intervention, the participation rates of rural patients were significantly higher than those of urban patients (p < 0.05).ConclusionAlthough national progress has been made in the public awareness of disease treatment, adequate diagnosis and medication adherence need to be prompted, especially in rural areas. Furthermore, lifelong treatment should be improved based on regional characteristics through the joint efforts of the government, health workers, and social volunteers.

Summary: Molybdenum and tungsten carbides (Mo2C and WC) are promising catalysts for hydrogen evolution reaction (HER), but their electrocatalytic activities are still inferior to theoretical predictions. Herein, density functional theory (DFT) calculations imply that WC-Mo2C heterostructure may exert a high HER performance since the hybridization of WC and Mo2C can optimize the proton adsorption energy and energy barrier for water dissociation. To test this predication, we prepare the WC-Mo2C heterostructure on a flexible carbon cloth (CC). The obtained WC-Mo2C@CC exhibits notable HER activity (η10 = 122 mV; η500 = 309 mV) and stability (continuous 100 h at 500 mA cm−2) in alkaline media. Furthermore, a solar-panel-driven electrolyzer, including the WC-Mo2C@CC cathode and a NiFe-LDH@Nif anode, operates at 10 mA cm−2 with a cell voltage of 1.51 V in 10 M KOH at 70°C, demonstrating its potential for practical applications. Overall, this work offers an atomic-level insight into designing advanced carbide HER electrocatalysts.

Objective: Overexpression of microRNA-21 (miR-21) has been well-recognized during fibrosis in a wide range of tissue types through regulation of fibrogenesis via diverse pathways. Curcumin exhibits anti-fibrotic effects in various organs. We and others have previously demonstrated that curcumin has positive effects on the protection against fibrosis. However, the effect of curcumin on miR-21 expression has not been reported. Methods: In this study, transforming growth factor-β1 (TGF-β1)-stimulated rat renal fibroblast cells (NRK-49F) were transfected with miR-21 inhibitors, then the expression of Smad3, α-smooth muscle actin (α-SMA), type I collagen (COL1A1), and type Ⅲ collagen (COL3A1) was determined using quantitative real-time-PCR (qRT-PCR) and western blot analysis. The effect of curcumin on production of the extracellular matrix was evaluated using immunofluorescence, qRT-PCR, and western blot analysis. Results: TGF-β1 stimulation upregulated the expression of miR-21 and induced fibrogenesis in NRK-49F cells. Transfection with miR-21 inhibitors selectively decreased smad3 activity, and noticeably reduced the expression of α-SMA, COL1A1, and COL3A1. Curcumin treatment significantly inhibited the expressions of smad3, α-SMA, COL1A1, COL3A1, as well as miR-21 expression in NRK-49F cells in a dose-dependent manner. Conclusion: Curcumin exerted its anti-fibrotic effects by targeting the TGF-β1/smad3 signaling pathway and suppressing miR-21 expression, thereby provides novel insight in the protective effects of curcumin against fibrosis in various organs. Keywords: Curcumin, miR-21, NRK-49F, Renal fibrosis, TGF-β1/Smad3

The miniaturization of transistors and the high density of integrated circuits makes conventional transistors reach a size limit, causing leakage currents, unstable performance, and increasing production cost. Spin field effect transistors (SFETs) based on spintronics use electron spin as an information carrier and regulate electron spin degree of freedom for storing and processing information. As a class of spintronics, Rashba spin–orbit coupling (SOC) effect attracts increasing attention because of its electric tunability. SFETs based on Rashba SOC and two-dimensional materials will make a qualitative leap in future semiconductor devices. Based on first-principles calculations, it is found that the applied strains along zigzag (ZZ), armchair (AC), and biaxial direction make the bandgaps of SnS and SnSe monolayers (ML) have an indirect-direct-zero transition. SnS and SnSe ML have a more stable electron spin and stronger Rashba spin splitting than InGaAs/InAlAs heterojunction and Au(111) surface. For SnS ML, 6% tensile strain along ZZ direction not only stabilizes the electron spin but also strengthens Rashba spin splitting to a maximum of 0.76 eV Å. For SnSe ML, 2% tensile strain along biaxial direction makes Rashba spin splitting strength reach a maximum of 1.33 eV Å.

Interactive model analysis, the process of understanding, diagnosing, and refining a machine learning model with the help of interactive visualization, is very important for users to efficiently solve real-world artificial intelligence and data mining problems. Dramatic advances in big data analytics have led to a wide variety of interactive model analysis tasks. In this paper, we present a comprehensive analysis and interpretation of this rapidly developing area. Specifically, we classify the relevant work into three categories: understanding, diagnosis, and refinement. Each category is exemplified by recent influential work. Possible future research opportunities are also explored and discussed. Keywords: Interactive model analysis, Interactive visualization, Machine learning, Understanding, Diagnosis, Refinement