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Kai-Yu Wang,1 Yi-Chuan Chen,1,2 Jau-Yuan Chen,1,2 Song-Seng Loke,3 Wei-Chung Yeh,4 Wen-Cheng Li1,2,5 1Department of Family Medicine, Chang-Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; 2College of Medicine, Chang Gung University, Taoyuan, Taiwan; 3Department of Family Medicine, Chang-Gung Memorial Hospital at Kaohsiung, Kaohsiung, Taiwan; 4Department of Family Medicine, Chang-Gung Memorial Hospital at Keelung, Keelung, Taiwan; 5Department of Health Management, Xiamen Chang-Gung Hospital, Xiamen, People’s Republic of ChinaCorrespondence: Wen-Cheng Li, Email win3135@hotmail.comBackground: Lipoprotein-associated phospholipase A2 (Lp-PLA2) has been recognized as a valuable biomarker for identifying the risk of cardiovascular diseases and inflammation. Furthermore, there is strong evidence to suggest that metabolic syndrome is closely associated with chronic inflammation. Accordingly, the present study endeavors to examine the potential correlation between metabolic syndrome and the levels of Lp-PLA2.Methods: To explore the relationship between Lp-PLA2 levels and metabolic syndrome, and to establish the predictive cut-off value of Lp-PLA2, a retrospective analysis was conducted using medical data from a sample of 3549 Chinese adults (comprising 2182 men and 1367 women) aged between 18 and 50 years, who had undergone health check-ups. In addition, the study also sought to investigate any potential differences in Lp-PLA2 levels based on sex and age.Results: The analysis of the data indicated that participants had a mean age of 44.2 years, a mean Lp-PLA2 level of 589 IU/L, and a metabolic syndrome prevalence of 22%. Lp-PLA2 levels were significantly different between males and females, and a significant correlation was observed between Lp-PLA2 levels and clinical and metabolic characteristics, including BMI, cholesterol, and triglycerides. Interestingly, Lp-PLA2 demonstrated potential as an indicator of metabolic syndrome, particularly in females, despite other biomarkers, such as TG/HDL-C and WHR, exhibiting better area under the curve.Conclusion: Our findings suggest that Lp-PLA2 may serve as a useful biomarker for identifying individuals at risk of developing metabolic syndrome, particularly in females. Further research is needed to explore the potential of Lp-PLA2 as a diagnostic and therapeutic target for metabolic syndrome.Keywords: Lp-PLA2, lipoprotein, metabolic syndrome, cardiovascular risk, and lipid profile

Dual orexin receptor antagonists (DORAs) are indicated for the treatment of insomnia disorder. However, DORAs may change sleep parameters, thus having adverse effects on patients with obstructive sleep apnea (OSA). This meta-analysis clarified the impact of DORAs in OSA treatment on sleep architecture and respiratory parameters. We systematically searched PubMed, Embase, and Cochrane Central databases for randomised control trials published up to May 2024. The search focussed on studies discussing the effects of DORAs on sleep architecture in patients with OSA. Nonrandomised studies were excluded. A meta-analysis using a random-effects model was performed. The patients were categorised into subgroups based on the treatment protocol (single or multiple dosages). The Cochrane risk of bias tool for randomised trials assessed the risk of bias. Our meta-analysis included four randomised placebo-controlled trials, encompassing 126 patients with a mean age of 49.1 years. The effects of DORAs on sleep architecture and respiratory parameters were examined. The main findings were as follows: DORAs significantly increased the total sleep time and improved sleep efficiency. However, they did not affect rapid eye movement sleep. DORAs also showed a trend towards decreased wake after sleep onset and did not increase the apnea-hypopnea index. DORAs did not increase the percentage of total sleep time with oxygen saturation lower than 90% and 85% compared with placebo, respectively. Furthermore, DORAs were not associated with significantly higher adverse effects compared with placebo. This meta-analysis demonstrated that DORAs improve sleep and do not impair nighttime respiratory function in patients with OSA., (© 2024 European Sleep Research Society.)

Optimization problems are common across various fields, and one effective solution is the swarm intelligence algorithm.It is essential for the algorithm to deliver high-quality solutions for problems with varying characteristics. However, most existing swarm intelligence rely on fixed and monotonic search strategies, which limits their ability to handle the diverse and complex situations encountered when solving real-world optimization problems with unknown fitness landscapes. To extend the applicability of swarm intelligence and thus offer users an efficient black-box optimizer for various applications, a novel self-learning mechanism is proposed and applied to the Salp Swarm Algorithm (SSA) to develop the self-learning salp swarm algorithm (SLSSA) in this paper. In SLSSA, four distinct search strategies, including a novel multiple food sources search strategy, are adopted to strengthen the search agents' abilities to conquer various difficulties in the search space. To improve the efficiency of the search process, the self-learning strategy dynamically determines the execution probability of each search strategy according to the quality of solutions it produced previously. Moreover, a parameter setting method is proposed in this paper, which eliminates the need for a trial-and-error approach and allows for straightforward configuration of the parameters that optimize the performance of SLSSA. In comparison with several highly regarded state-of-the-art peer algorithms, the performance of SLSSA in solving the CEC2014 benchmark functions was thoroughly examined. Subsequently, SLSSA was applied to train multi-layer perceptron classifiers and test on the UCI machine-learning datasets. The experimental results and analysis on benchmark functions and multi-layer perceptron classifier training problems demonstrate that SLSSA outperforms the competing algorithms in terms of solution accuracy, stability, and overall convergence speed. Moreover, computational time comparisons reveal that SLSSA achieves significant performance improvement with only a marginal increase in time cost compared to the original SSA., (© 2024. The Author(s).)

This study primarily investigated the impacts of unnatural in-house deaths on nearby property prices. Propensity score matching was used to assess the homogeneity of experimental and control groups, and the difference-in-differences (DID) method was used for their comparison. Data for real estate transactions in Taipei City from 2012 to 2022 were retrieved from the Ministry of the Interior's real estate actual transaction price registration query system. The DID interaction variable coefficient of 0.066 was significant at the 10% level. A comparison of the experimental and control groups showed a 6.8% property price difference following an unnatural in-house death. This suggests that an unnatural in-house death has a positive impact on property prices nearby. There were four unnatural deaths at the Xining Public Housing from 2003 to 2012 and four at the Jinxing Building from 1984 to 2012. The high incidence of events and the disclosure of information may have depreciated nearby property prices. The study results show that the stigmatization of unnatural in-house deaths reduces nearby property prices. The depreciation of implicit prices reverted and followed a U-shape pattern after several years., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Introduction: In this study, we harnessed three cutting-edge algorithms' capabilities to refine the elbow fracture prediction process through X-ray image analysis. Employing the YOLOv8 (You only look once) algorithm, we first identified Regions of Interest (ROI) within the X-ray images, significantly augmenting fracture prediction accuracy., Methods: Subsequently, we integrated and compared the ResNet, the SeResNet (Squeeze-and-Excitation Residual Network) ViT (Vision Transformer) algorithms to refine our predictive capabilities. Furthermore, to ensure optimal precision, we implemented a series of meticulous refinements. This included recalibrating ROI regions to enable finer-grained identification of diagnostically significant areas within the X-ray images. Additionally, advanced image enhancement techniques were applied to optimize the X-ray images' visual quality and structural clarity., Results: These methodological enhancements synergistically contributed to a substantial improvement in the overall accuracy of our fracture predictions. The dataset utilized for training, testing & validation, and comprehensive evaluation exclusively comprised elbow X-ray images, where predicting the fracture with three algorithms: Resnet50; accuracy 0.97, precision 1, recall 0.95, SeResnet50; accuracy 0.97, precision 1, recall 0.95 & ViTB- 16 with high accuracy of 0.99, precision same as the other two algorithms, with a recall of 0.95., Conclusion: This approach has the potential to increase the precision of diagnoses, lessen the burden of radiologists, easily integrate into current medical imaging systems, and assist clinical decision-making, all of which could lead to better patient care and health outcomes overall., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Artificial intelligence has garnered significant attention in recent years as a rapidly advancing field of computer technology. With the continual advancement of computer hardware, deep learning has made breakthrough developments within the realm of artificial intelligence. Over the past few years, applying deep learning architecture in medicine and industrial anomaly inspection has significantly contributed to solving numerous challenges related to efficiency and accuracy. For excellent results in radiological, pathological, endoscopic, ultrasonic, and biochemical examinations, this paper utilizes deep learning combined with image processing to identify spinal canal and vertebral foramen dimensions. In existing research, technologies such as corrosion and expansion in magnetic resonance image (MRI) processing have also strengthened the accuracy of results. Indicators such as area and Intersection over Union (IoU) are also provided for assessment. Among them, the mean Average Precision (mAP) for identifying intervertebral foramen (IVF) and intervertebral disc (IVD) through YOLOv4 is 95.6%. Resnet50 mixing U-Net was employed to identify the spinal canal and intervertebral foramen and achieved IoU scores of 79.11% and 80.89%.

Pedestrian trajectory prediction is crucial for developing collision avoidance algorithms in autonomous driving systems, aiming to predict the future movement of the detected pedestrians based on their past trajectories. The traditional methods for pedestrian trajectory prediction involve a sequence of tasks, including detection and tracking to gather the historical movement of the observed pedestrians. Consequently, the accuracy of trajectory prediction heavily relies on the accuracy of the detection and tracking models, making it susceptible to their performance. The prior research in trajectory prediction has mainly assessed the model performance using public datasets, which often overlook the errors originating from detection and tracking models. This oversight fails to capture the real-world scenario of inevitable detection and tracking inaccuracies. In this study, we investigate the cumulative effect of errors within integrated detection, tracking, and trajectory prediction pipelines. Through empirical analysis, we examine the errors introduced at each stage of the pipeline and assess their collective impact on the trajectory prediction accuracy. We evaluate these models across various custom datasets collected in Taiwan to provide a comprehensive assessment. Our analysis of the results derived from these integrated pipelines illuminates the significant influence of detection and tracking errors on downstream tasks, such as trajectory prediction and distance estimation.

Half of NSCLC patients harbor epidermal growth factor receptor (EGFR) mutations, and their therapeutic responses are remarkably different from patients with wild-type EGFR (EGFR-WT) NSCLC. We previously demonstrated that the hedgehog inhibitor vismodegib (Vis) potentiates paclitaxel (PTX)-induced cytotoxicity via suppression of Bax phosphorylation, which promotes accumulation of mitochondrial damage and apoptosis in EGFR-WT NSCLC cells. In this study, we further delineated the anticancer activity and underlying mechanisms of this combination treatment in EGFR-mutant NSCLC cells. MTS/PMS activity and trypan blue exclusion assays were used to assess cell viability. Apoptosis was monitored by chromosome condensation, annexin V staining, and cleavage of PARP and caspase-3. Western blots were conducted to track proteins of interest after treatment. Reactive oxygen species (ROS) level was monitored by 2',7'-dichlorodihydrofluorescein diacetate. Mitochondrial status was analyzed by tetramethylrhodamine, ethyl ester. Hedgehog signaling was induced by PTX, which rendered H1975 and PC9 cells insensitive to PTX-induced mitochondrial apoptosis via suppression of Bak. However, Vis enhanced PTX-induced Bak activation, leading to mitochondrial damage, ROS accumulation, and subsequent apoptosis. Our findings suggest that the combination of Vis and PTX could be a potential therapeutic strategy to increase PTX sensitivity of EGFR-mutant NSCLC., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Dopamine agonists (DAs) constitute the standard therapeutic scheme for restless leg syndrome (RLS) because they have been proven to be effective. However, DAs may change sleep parameters, thus having adverse effects on patient condition. This meta-analysis clarified the effects of DAs used in RLS treatment on the sleep architecture., Methods: PubMed, Embase, and Cochrane Central databases were searched for randomized control trials (RCT) (up to October 2023) that discussed the effects of DAs on sleep architecture in patients with RLS. A meta-analysis employing a random-effects model was conducted. The patients were divided into subgroups according to individual DAs and treatment duration (1 day or ≥4 weeks)., Results: Thirteen eligible randomized placebo-controlled trials were included in the assessment. The effects of three DAs (i.e., pramipexole, ropinirole, and rotigotine) on rapid eye movement (REM) sleep, slow-wave sleep (SWS), and sleep efficiency (SE) were analyzed. Overall, pramipexole significantly improved SE but decreased the percentage of REM sleep among treated patients. Ropinirole also enhanced SE compared with the placebo group. Rotigotine did not affect SE and REM sleep. Subgroup analysis found that pramipexole used for 1 day and ≥4 weeks significantly diminished the percentage of REM sleep. Ropinirole used for 1 day showed similar REM sleep patterns. Finally, none of the three DAs affected SWS., Conclusions: This meta-analysis demonstrated that DAs significantly affect sleep parameters., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Study Objectives: Restless legs syndrome (RLS) is a circadian rhythm related sensorimotor disorder due to brain iron deficiency, with lesion sites at the putamen and substantia nigra. However, epilepsy is a disease with abnormal electric discharge from the cortex and can be triggered with iron disequilibrium. We designed a case-control study to discover the association between epilepsy and RLS., Methods: A total of 24 patients with epilepsy and RLS and 72 patients with epilepsy without RLS were included. Most of the patients underwent polysomnography and video electroencephalogram tests and took sleep questionnaires. We collected information on seizure characteristics, including general or focal onset, epileptogenic focus, current antiseizure medications, medically responsive epilepsy or refractory epilepsy, and nocturnal attacks. The sleep architectures of the two groups were compared. We analyzed the risk factors for RLS using multivariate logistic regression., Results: Among the patients with epilepsy, the occurrence of RLS was associated with refractory epilepsy (OR 6.422, p = 0.002) and nocturnal seizures (OR 4.960, p = 0.005). Sleep parameters were not significantly associated with RLS status. Quality of life was significantly impaired in the group with RLS in both the physical and mental domains., Conclusions: Refractory epilepsy and nocturnal seizures were strongly correlated with RLS in patients with epilepsy. RLS should be considered a predictable comorbidity in patients with epilepsy. The management of RLS not only led to better control of the patient's epilepsy but also improved their quality of life., (© The Author(s) 2023. Published by Oxford University Press on behalf of Sleep Research Society. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Molecules that facilitate targeted protein degradation (TPD) offer great promise as novel therapeutics. The human hepatic lectin asialoglycoprotein receptor (ASGR) is selectively expressed on hepatocytes. We have previously engineered an anti-ASGR1 antibody-mutant RSPO2 (RSPO2RA) fusion protein (called SWEETS) to drive tissue-specific degradation of ZNRF3/RNF43 E3 ubiquitin ligases, which achieved hepatocyte-specific enhanced Wnt signaling, proliferation, and restored liver function in mouse models, and an antibody-RSPO2RA fusion molecule is currently in human clinical trials. In the current study, we identified two new ASGR1- and ASGR1/2-specific antibodies, 8M24 and 8G8. High-resolution crystal structures of ASGR1:8M24 and ASGR2:8G8 complexes revealed that these antibodies bind to distinct epitopes on opposing sides of ASGR, away from the substrate-binding site. Both antibodies enhanced Wnt activity when assembled as SWEETS molecules with RSPO2RA through specific effects sequestering E3 ligases. In addition, 8M24-RSPO2RA and 8G8-RSPO2RA efficiently downregulate ASGR1 through TPD mechanisms. These results demonstrate the possibility of combining different therapeutic effects and degradation mechanisms in a single molecule., Competing Interests: PS, HJ, ZZ, ZH, TL, RB, WY former full-time employee and shareholder of Surrozen, Inc, HC, NS, YY, SL, JY current full-time employee and shareholder of Surrozen, Inc, YL current full-time employee and shareholder of Surrozen, Inc; is Executive Vice President of Research at Surrozen, Inc, (© 2024, Sampathkumar, Jung et al.)

Traditional Chinese medicine (TCM) has relied on pulse diagnosis as a cornerstone of healthcare assessment for thousands of years. Despite its long history and widespread use, TCM pulse diagnosis has faced challenges in terms of diagnostic accuracy and consistency due to its dependence on subjective interpretation and theoretical analysis. This study introduces an approach to enhance the accuracy of TCM pulse diagnosis for diabetes by leveraging the power of deep learning algorithms, specifically LeNet and ResNet models, for pulse waveform analysis. LeNet and ResNet models were applied to analyze TCM pulse waveforms using a diverse dataset comprising both healthy individuals and patients with diabetes. The integration of these advanced algorithms with modern TCM pulse measurement instruments shows great promise in reducing practitioner-dependent variability and improving the reliability of diagnoses. This research bridges the gap between ancient wisdom and cutting-edge technology in healthcare. LeNet-F, incorporating special feature extraction of a pulse based on TMC, showed improved training and test accuracies (73% and 67%, respectively, compared with LeNet's 70% and 65%). Moreover, ResNet models consistently outperformed LeNet, with ResNet18-F achieving the highest accuracy (82%) in training and 74% in testing. The advanced preprocessing techniques and additional features contribute significantly to ResNet18-F's superior performance, indicating the importance of feature engineering strategies. Furthermore, the study identifies potential avenues for future research, including optimizing preprocessing techniques to handle pulse waveform variations and noise levels, integrating additional time-frequency domain features, developing domain-specific feature selection algorithms, and expanding the scope to other diseases. These advancements aim to refine traditional Chinese medicine pulse diagnosis, enhancing its accuracy and reliability while integrating it into modern technology for more effective healthcare approaches.

Background: The assessment information of tumor margins is extremely important for the success of the breast cancer surgery and whether the patient undergoes a second operation. However, conducting surgical margin assessments is a time-consuming task that requires pathology-related skills and equipment, and often cannot be provided in a timely manner. To address this challenge, digital breast tomosynthesis technology was utilized to generate detailed cross-sectional images of the breast tissue and integrate deep learning algorithms for image segmentation, achieving an assessment of tumor margins during surgery., Methods: this study utilized post-operative tissue samples from 46 patients who underwent breast-conserving treatment, and generated image sets using digital breast tomosynthesis for the training and evaluation of deep learning models., Results: Deep learning algorithms effectively identifying the tumor area. They achieved a Mean Intersection over Union (MIoU) of 0.91, global accuracy of 99%, weighted IoU of 44%, precision of 98%, recall of 83%, F1 score of 89%, and dice coefficient of 93% on the training dataset; for the testing dataset, MIoU was at 83%, global accuracy at 97%, weighted IoU at 38%, precision at 87%, recall rate at 69%, F1 score at 76%, dice coefficient at 86%., Conclusions: The initial evaluation suggests that the deep learning-based image segmentation method is highly accurate in measuring breast tumor margins. This helps provide information related to tumor margins during surgery, and by using different datasets, this research method can also be applied to the surgical margin assessment of various types of tumors.

Non-small cell lung cancer (NSCLC) is a common cancer with several accepted treatments, such as chemotherapy, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors, and immune checkpoint inhibitors. Nevertheless, NSCLC cells often become insensitive to these treatments, and therapeutic resistance is a major reason NSCLC still has a high mortality rate. The induction of therapeutic resistance in NSCLC often involves hedgehog, and suppression of hedgehog can increase NSCLC cell sensitivity to several conventional therapies. In our previous work, we demonstrated that the marine antimicrobial peptide tilapia piscidin 4 (TP4) exhibits potent anti-NSCLC activity in both EGFR-WT and EGFR-mutant NSCLC cells. Here, we sought to further explore whether hedgehog might influence the sensitivity of NSCLC cells to TP4. Our results showed that hedgehog was activated by TP4 in both WT and EGFR-mutant NSCLC cells and that pharmacological inhibition of hedgehog by vismodegib, a Food and Drug Administration-approved hedgehog inhibitor, potentiated TP4-induced cytotoxicity. Mechanistically, vismodegib acted by enhancing TP4-mediated increases in mitochondrial membrane potential and intracellular reactive oxygen species (ROS). MitoTempo, a specific mitochondrial ROS scavenger, abolished vismodegib/TP4 cytotoxicity. The combination of vismodegib with TP4 also reduced the levels of the antioxidant proteins catalase and superoxide dismutase, and it diminished the levels of chemoresistance-related proteins, Bcl-2 and p21. Thus, we conclude that hedgehog regulates the cytotoxic sensitivity of NSCLC cells to TP4 by protecting against mitochondrial dysfunction and suppressing oxidative stress. These findings suggest that combined treatment of vismodegib and TP4 may be a promising therapeutic strategy for NSCLC., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Wingless-related integration site or Wingless and Int-1 or Wingless-Int (WNT) signaling is crucial for embryonic development, and adult tissue homeostasis and regeneration, through its essential roles in cell fate, patterning, and stem cell regulation. The biophysical characteristics of WNT ligands have hindered efforts to interrogate ligand activity in vivo and prevented their development as therapeutics. Recent breakthroughs have enabled the generation of synthetic WNT signaling molecules that possess characteristics of natural ligands and potently activate the pathway, while also providing distinct advantages for therapeutic development and manufacturing. This review provides a detailed discussion of the protein engineering of these molecular platforms for WNT signaling agonism. We discuss the importance of WNT signaling in several organs and share insights from the initial application of these new classes of molecules in vitro and in vivo . These molecules offer a unique opportunity to enhance our understanding of how WNT signaling agonism promotes tissue repair, enabling targeted development of tailored therapeutics., Competing Interests: All authors are current or former full-time employees and shareholders of Surrozen, Inc. Y.L. is Executive Vice President of Research at Surrozen, Inc. Patent applications are pending for the work described in this manuscript., (© 2024 The Author(s).)

Background: Wnt/β-catenin signaling is critical for lung development and AT2 stem cell maintenance in adults, but excessive pathway activation has been associated with pulmonary fibrosis, both in animal models and human diseases such as idiopathic pulmonary fibrosis (IPF). IPF is a detrimental interstitial lung disease, and although two approved drugs limit functional decline, transplantation is the only treatment that extends survival, highlighting the need for regenerative therapies., Methods: Using our antibody-based platform of Wnt/β-catenin modulators, we investigated the ability of a pathway antagonist and pathway activators to reduce pulmonary fibrosis in the acute bleomycin model, and we tested the ability of a WNT mimetic to affect alveolar organoid cultures., Results: A WNT mimetic agonist with broad FZD-binding specificity (FZD1,2,5,7,8) potently expanded alveolar organoids. Upon therapeutic dosing, a broad FZD-binding specific Wnt mimetic decreased pulmonary inflammation and fibrosis and increased lung function in the bleomycin model, and it impacted multiple lung cell types in vivo., Conclusions: Our results highlight the unexpected capacity of a WNT mimetic to effect tissue repair after lung damage and support the continued development of Wnt/β-catenin pathway modulation for the treatment of pulmonary fibrosis., (© 2024. The Author(s).)

Hydrogen-based energy systems hold promise for sustainable development and carbon neutrality, minimizing environmental impact with electrolysis as the preferred fossil-fuel-free hydrogen generation method. Effective electrocatalysts are required to reduce energy consumption and improve kinetics, given the need for additional voltage (overpotential, η) despite the theoretical water splitting potential of 1.23 V. To date, platinum has been acknowledged as the most effective but expensive hydrogen evolution reaction (HER) catalyst. Hence, we introduce a cost-effective (∼2-fold cheaper) ruthenium-modified tungsten diphosphide (Ru/WP 2 ) catalyst on carbon fiber for HER in ∼0.5 M H 2 SO 4 , with η ≈ 34 mV at -10 mA cm -2 which can be comparable (only ∼2-fold higher) to benchmark Pt/C (η ≈ 17 mV). The HER performance of WP 2 can be enhanced through the modification of ruthenium, as indicated by the electrochemical characterizations. Considering the Tafel value of ∼40 ± 0.2 mV dec -1 , it can be inferred that Ru/WP 2 follows the Volmer-Heyrovsky reaction pathway for hydrogen generation. Furthermore, the Faradaic efficiency estimation indicates that Ru/WP 2 demonstrates a minimal loss of electrons during the electrochemical reaction with an estimated value of ∼98.7 ± 1.4%. Therefore, this study could emphasize the potential of the Ru/WP 2 electrode in advancing sustainable hydrogen production through water splitting.