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Surface Plasmon Resonance imaging (SPRi) is a powerful label-free technique for high-throughput biochemical analysis. Wavelength modulation is particularly suitable for SPRi due to its wide dynamic range and robustness to fabrication tolerances. However, conventional systems relying on tunable filters (e.g., AOTF, LCTF) suffer from high cost, complexity, and limited temporal resolution. To overcome these drawbacks, we developed a rapid wavelength-modulation SPRi system using a multi-LED source and an adaptive second-order fitting (ASF) algorithm. The system covers the 730–805 nm spectrum with five LEDs. The ASF algorithm first performs a coarse full-spectrum scan to locate the resonance wavelength, then dynamically selects an optimal three-LED subset for fast second-order fitting, enabling accurate reconstruction of resonance wavelength without mechanical scanning. This approach significantly reduces cost and complexity while achieving a scanning cycle of 105 ms, RI resolution of 5.54 × 10−6 RIU, dynamic range of 0.0241 RIU, and excellent multi-channel consistency. The system has been successfully applied to monitor multi-channel antibody–antigen interactions in real time. Furthermore, it was used to detect cartilage oligomeric matrix protein (COMP) in synovial fluid, where an elevated concentration in an osteoarthritis sample versus a control aligned with its role as a cartilage catabolism marker. This work validates a practical and reliable platform for early diagnosis of osteoarthritis.

Wave energy provides a renewable and clear power for the future energy mix and fights against climate change. Currently, there are many different wave energy converters, but their costs of extracting wave energy are still much higher than other matured renewables. One of the best indicators of calculating the generating cost of wave energy is the ‘levelized cost of energy’ (LCOE), which is the combined capital expenditure (CAPEX), operational expenditure (OPEX), and decommissioning cost with the inclusion of the annual energy production, discount factor, and project’s lifespan. However, the results of the LCOE are in disagreement. Hence, it is important to explore the cost breakdown of wave energy by the wave energy converter (WEC), so for finding potential ways to decrease the cost, and finally compare it with other renewable energies. Different WECs have been installed in the same place; the Wave Dragon LCOE platform is the best one, with an energy conversion of EUR 316.90/MWh, followed by Pelamis with EUR 735.94/MWh and AquaBuOY with EUR 2967.85/MWh. Even when using different locations to test, the rank of the LCOE would remain unchanged with the different value. As the CAPEX and OPEX dramatically drop, the availability and capacity factors slowly increase, and the LCOE decreases from a maximum of USD 470/MWh to a minimum of USD 120/MWh. When the discount rate is down from 11% to 6%, the LCOE reduces from USD 160/MWh to USD 102/MWh. Under the ideal condition of the optimal combination of multiple factors, in theory, the LCOE can be less than USD 0.3/KWh. To better explore the LCOE for WECs, the detailed cost elements found in the CAPEX and OPEX have been examined for the scenarios of the undiscounted, half-discounted, and discounted cost models. When the AEP is discounted, the lowest LCOE is equal to USD 1.171/kWh in scene 2 when using a five-step investment, which is below the LCOE value of USD 1.211/kWh in scene 1 when using a two-step investment. Meanwhile, the highest LCOE amounts to USD 2.416/kWh using the five-step investment, whose value is below the LCOE of a two-step investment. When using a one-step investment in scene 3, the lowest LCOE is equal to USD 0.296/kWh, which accounts for 25% of the lowest value in the five-step investment. Meanwhile, the highest LCOE amounts to USD 0.616/kWh, which accounts for 24% of the highest value in the two-step investment. The results of the case study show that a one-step investment program in the half-discounted model is superior to the multi-step investment in the discounted model. This paper examines the viability of wave energy technologies, which is a critical factor for the LCOE of wave energy; furthermore, the form of investment in the wave energy project is also important when calculating the LCOE.

Gastric cancer is one of the most common malignant tumors, and chemotherapy is the main treatment for advanced gastric cancer. However, chemotherapy resistance leads to treatment failure and poor prognosis in patients with gastric cancer. Multidrug resistance (MDR) is a major challenge that needs to be overcome in chemotherapy. According to recent research, ferroptosis activation is crucial for tumor therapeutic strategies. In this work, we explored the solution to chemoresistance in gastric cancer by investigating the effects of the Chinese medicine monomer baicalin on ferroptosis. Baicalin with different concentrations was used to treat the parent HGC27 and drug-resistant HGC27/L cells of gastric cancer. Cell viability was measured by CCK8, and synergistic effects of baicalin combined with oxaliplatin were evaluated using Synergy Finder software. The effects of baicalin on organelles and cell morphology were investigated using projective electron microscopy. Iron concentration, MDA production and GSH inhibition rate were measured by colorimetry. ROS accumulation was detected by flow cytometry. The ferroptosis-related genes (IREB2, TfR, GPX4, FTH1), P53, and SLC7A11 were analysed by Western blot, and the expression differences of the above proteins between pretreatment and pretreatment of different concentrations of baicalin, were assayed in both parental HGC27 cells and Oxaliplatin-resistant HGC27/L cells. Mechanically, Baicalin disrupted iron homeostasis and inhibits antioxidant defense, resulting in iron accumulation, lipid peroxide aggregation, and specifically targeted and activated ferroptosis by upregulating the expression of tumor suppressor gene p53, thereby activating the SLC7A11/GPX4/ROS pathway mediated by it. Baicalin activates ferroptosis through multiple pathways and targets, thereby inhibiting the viability of oxaliplatin-resistant gastric cancer HGC27/L cells and enhancing the sensitivity to oxaliplatin chemotherapy.

To rapidly obtain the complete characterization information of pulse signals and to verify the sensitivity and validity of pulse signals in the clinical diagnosis of related diseases. In this paper, an improved PNCC method is proposed as a supplementary feature to enable the complete characterization of pulse signals. In this paper, the wavelet scattering method is used to extract time-domain features from impulse signals, and EEMD-based improved PNCC (EPNCC) is used to extract frequency-domain features. The time–frequency features are mixed into a convolutional neural network for final classification and recognition. The data for this study were obtained from the MIT-BIH-mimic database, which was used to verify the effectiveness of the proposed method. The experimental analysis of three types of clinical symptom pulse signals showed an accuracy of 98.3% for pulse classification and recognition. The method is effective in complete pulse characterization and improves pulse classification accuracy under the processing of the three clinical pulse signals used in the paper.

Background Heart failure (HF) after acute myocardial infarction (AMI) is a leading cause of mortality and morbidity worldwide. Accurate prediction and early identification of HF severity are crucial for initiating preventive measures and optimizing treatment strategies. This study aimed to develop an interpretable artificial intelligence (AI) model for HF severity prediction using multidimensional clinical data. Methods This study included data from 1574 AMI patients, including medical history, clinical features, physiological parameters, laboratory test, coronary angiography and echocardiography results. Both deep learning (TabNet, Multi-Layer Perceptron) and machine learning (Random Forest, XGboost) models were employed in constructing model. Additionally, the Shapley Additive Explanation (SHAP) method was used to elucidate clinical factors importance and enhance model interpretability. A web platform ( https://prediction-killip-gby.streamlit.app/ ) was also developed to facilitate clinical application. Results Among the models, TabNet demonstrated the best performance, achieving an AUROC of 0.827 for KILLIP four-class classification and 0.831 for KILLIP binary classification. Key clinical factors such as GRACE score, NT-pro BNP, and TIMI score were highly correlated with KILLIP classification, aligning with established clinical knowledge. Conclusions By leveraging easily accessible multidimensional data, this model enables accurate early prediction and personalized diagnosis of HF risk and severity following AMI. It supports early clinical intervention and improves patient outcomes, offering significant clinical application value. Clinical trial number Not applicable.

This paper presents a general theoretical framework for understanding the Belt‐and‐Road Initiative (BRI). We begin with an introduction to the connotations of the BRI and a review of the initiative's main achievements since its inception. From these achievements, we identify the existence of a potential match between supply and demand in jointly building the BRI to be the foremost necessary condition for win–win cooperation for participating countries. Several features of China's contemporary economic structure are shown to provide the huge potential of supply that matches the massive demand of many Belt‐and‐Road countries for infrastructure development. To unleash the full potential of the BRI, a series of sufficient conditions must be met so that supply and demand interact in a virtuous manner. In the first stage of jointly building the BRI, the key to turn the initiative's potential into reality is to develop the “Five Links” of policy coordination, infrastructure connectivity, unimpeded trade, financial integration, and people‐to‐people bond. Now that the BRI has entered the high‐quality development stage, we demonstrate that China has taken or needs to work on policy measures in 10 areas to ensure the continuing and sustainable success of the BRI construction.

With the increasing air traffic flow, the workload of air traffic controllers is also growing, and their proficiency directly impacts civil aviation safety and efficiency. To address the lack of clear training objectives and inconsistent evaluation methods in the initial controller training at the Southwest Air Traffic Management Bureau, this study aimed to develop and validate a core competency model for initial air traffic controllers. Referencing ICAO Document 10056, the study first defined core competencies. Subsequently, using job analysis, the behavioral event interview (BEI) method, and expert panels, a core competency model tailored to the training objectives of the Southwest ATMB was constructed. The key findings of this research include: first, the defined structure of the developed model, comprising seven competency dimensions, 21 elements, and 26 observable behaviors (OBs); second, the determination of combined weights for each dimension and indicator using questionnaire surveys, the Analytic Hierarchy Process (AHP), and the Entropy Weight Method; and third, the successful application and validation of the model. Specifically, in its application, the weighted TOPSIS method was employed to evaluate trainees in a specific group. This not only provided a ranking of trainee abilities but also facilitated in-depth analysis through radar charts of competency dimensions and box plots of OB items. These application results demonstrate the model’s effectiveness and practicality.

Inflammation is a key driver of adverse outcomes in acute myocardial infarction (AMI), yet current western anti-inflammatory therapies are limited by their single-target nature and side effects. Traditional Chinese medicine (TCM), such as Tanhuo Decoction (THD), offers a multi-target, low-toxicity alternative. In a randomized controlled trial, AMI patients with high inflammatory responses received either standard Western medicine (WM) alone or combined with THD for 3 days. Clinical outcomes and inflammatory markers were assessed, and proteomic and network pharmacology analyses were performed. The THD + WM group showed significant reductions in neutrophil counts and hs-CRP levels, along with improved creatinine clearance rate (CCR), compared to WM alone. Proteomic analysis revealed downregulation of pro-inflammatory proteins (PTX3, IL-18, TNFRSF11A) and upregulation of the anti-inflammatory IL1RL2. THD also modulated lipid metabolism and insulin sensitivity pathways. THD enhances the anti-inflammatory and metabolic benefits of standard AMI therapy through multi-target pathway regulation. These findings support its integration into modern cardiovascular care, particularly for patients with high inflammatory and metabolic risk.

Negative effects of long‐term overgrazing have been seriously, grasslands temporal stability is an important ecological concern we need to research. Here, we performed a 12‐year‐long (2007–2018) two‐factor controlled experiment on Kobresia humilis meadow on the Tibetan Plateau. The manipulations included three clipping levels (no clipping, NC; moderate clipping, MC; heavy clipping, HC) and two fertilization levels (no fertilization, NF; fertilization, F). Our results revealed that the two clipping manipulations significantly increased the temporal stability of alpine meadow communities, whose significant increase was more pronounced under the MC than HC treatment. Species asynchrony had a significant positive correlation with species abundance along with compound community gradient. Moreover, asynchrony effects, portfolio effects, and facilitation interactions were all present in the communities under the six types of experimental treatment combinations. Additionally, a selection effect was detected in the compound communities, demonstrating characteristics that are common to different mechanisms. There were no significant differences in the effects of these mechanisms on community temporal stability between the NC–NF and MC–NF interactive communities. The portfolio effects predominated when clipping intensity was moderate under both fertilization and nonfertilization conditions. By contrast, in the compound communities, the selection effect predominated. In summary, we conclude that in meadow communities that undergo clipping and fertilization disturbances, facilitation interactions and weak interactions make a greater contribution toward maintaining their temporal stability. Under six experimental conditions of clipping and fertilization levels, we found that fertilization, as well as heavy and especially moderate clipping intensity, significantly increased the temporal stability of K. humilis. Asynchrony effects, portfolio effects, and facilitation interactions were all present under the six experimental treatment conditions in this study. Clipping mainly increased community temporal stability via the asynchrony effect, whereas this increase under fertilization treatment was mainly owing to facilitation and weak interactions.

Activated carbons (ACs) derived from biomass have become one of the most promising electrode materials for supercapacitors due to their reproducibility and low cost. In this study, peanut shell is used as the precursor to prepare AC via one-step synthesis method activated by ZnCl2, FeCl3 and their mixture in N2 atmosphere at 700 °C. The characteristics and structure of the obtained ACs were studied by SEM, HR-TEM, BET, FT-IR, XRD and Raman spectroscopy. The prepared AC materials showed high specific surface area and large amount of micropores, and the maximum specific surface area reached 1481.59 m2/g. The etching effect of iron oxide and zinc chloride on the carbon skeleton facilitated the formation of micropores. The XRD pattern and Raman spectra indicated that all samples were amorphous carbons with some graphitic crystalline structures. In addition, FT-IR analysis illustrated that the surface of AC materials possessed a large number of oxygen-containing functional groups, which were beneficial to their electrochemical performance. From the electrochemical performance of the AC materials, it was observed that better electrochemical properties were achieved at a weight ratio of biomass to activator of 2:1 in comparison with 0.8:1 and 4:1 for all the activators. The obtained AC showed a high specific capacitance of 239.88 F/g at the current density of 0.5 A/g in 1 M Na2SO4 electrolyte and exhibited excellent cycling performance with 94.55% capacity retention after 5000 cycles.