Alkaline phosphatase-activated prodrug system based on a bifunctional CuO NP tandem nanoenzyme for on-demand bacterial inactivation and wound disinfection.

Nanozymes are promising antimicrobials, as they produce reactive oxygen species (ROS). However, the intrinsic lack of selectivity of ROS in distinguishing normal flora from pathogenic bacteria deprives nanozymes of the necessary selectivities of ideal antimicrobials. Herein, we exploit the physiological conditions of bacteria (high alkaline phosphatase (ALP) expression) using a novel CuO nanoparticle (NP) nanoenzyme system to initiate an ALP-activated ROS prodrug system for use in the on-demand precision killing of bacteria. The prodrug strategy involves using 2-phospho-L-ascorbic acid trisodium salt (AAP) that catalyzes the ALP in pathogenic bacteria to generate ascorbic acid (AA), which is converted by the CuO NPs, with intrinsic ascorbate oxidase- and peroxidase-like activities, to produce ROS. Notably, the prodrug system selectively kills Escherichia coli (pathogenic bacteria), with minimal influence on Staphylococcus hominis (non-pathogenic bacteria) due to their different levels of ALP expression. Compared to the CuO NPs/AA system, which generally depletes ROS during storage, CuO NPs/AAP exhibits a significantly higher stability without affecting its antibacterial activity. Furthermore, a rat model is used to indicate the applicability of the CuO NPs/AAP fibrin gel in wound disinfection in vivo with negligible side effects. This study reveals the therapeutic precision of this bifunctional tandem nanozyme platform against pathogenic bacteria in ALP-activated conditions.

Effect of microwave combined with ethanol pretreatment on the quality of potato CO2 explosion puffing drying.

To enhance the drying quality of potato slices, this investigation employed a microwave heating (MH) combined with ethanol osmotic dehydration (EOD) pretreatment strategy to improve the quality of explosion puffing drying (EPD). This paper systematically investigated the effects of different pretreatment methods (no treatment, HAD, MH, EOD, MH+EOD) on the quality and physicochemical properties of potato slices subjected to CO2-EPD. The results showed that after MH and EOD pretreatments, the internal pores of the potato slices exhibited a uniform porous structure. The MH+EOD+CO2-EPD treatment demonstrated superior expansion, crispness, hardness, and color, with higher retention rates of vitamin C and protein. The measurements were an expansion ratio of 2.15, hardness of 1290.01 g, crispness of 745.94 g, ΔE of 6.54, protein content of 1.99 g/100 g, and VC content of 17.33 mg/100 g. Additionally, the study explored the effects of microwave power, microwave drying time, ethanol concentration, and ethanol soaking time on the expansion ratio, hardness, crispness, protein content, VC content, and color. MH+EOD+CO2-EPD is an environmentally sustainable and efficient solution with potential for widespread industrial application to enhance processing quality and economic benefits.

Insomnia mediates the relation between empathy and anxiety among nursing students: a latent moderated mediation model of self-compassion.

BACKGROUND: Nursing students are faced with multiple challenges and have a higher probability of suffering from anxiety. The current study aims to explore the relation between empathy and anxiety, examining the mediation and moderation effects of insomnia and self-compassion, respectively. METHODS: This study employed a convenient sampling method, recruiting 1,161 nursing students (female = 923, male = 238, Meanage = 18.37, SDage = 2.38) from three universities in China. These students completed the questionnaires online, including General Anxiety Disorder -7 (GAD-7), Jefferson Scale of Physician Empathy-Nursing student (JSPE-NS), Youth Self-rating Insomnia Scale -8 (YSIS-8), and Self-Compassion Scale (SCS). The study employed latent variable structural equation models to analyze the relation and mechanisms between empathy and anxiety. Then, the mediated role of insomnia and the moderated role of self-compassion were examined. RESULTS: The prevalence rates of anxiety and insomnia in the current sample are 18.24% and 26.76%, respectively. The results showed that empathy could negatively predict anxiety, with a significant mediating effect of insomnia between them (B = -0.081, p < 0.05, 95% CI [-0.197, -0.063]). Additionally, it was proven that self-compassion moderated the positive relation between insomnia and anxiety. With a higher level of self-compassion, the indirect effect of empathy on anxiety through insomnia was weaker (B = -0.053, p < 0.01, 95% CI [-0.095, -0.019]). When individuals showed a lower level of self-compassion, the indirect effect of empathy on anxiety through insomnia was stronger (B = -0.144, p < 0.01, 95% CI [-0.255, -0.059]). CONCLUSION: The analysis of this research proved that empathy was negatively related to anxiety, and insomnia served as a mediator between empathy and anxiety. Besides, the protective role of self-compassion on individuals' mental health was identified. The findings of the study suggest that the education of nursing students should highlight the significance of fostering empathy and self-compassion. The intervention on insomnia may be helpful in reducing the levels of anxiety since insomnia is a risky factor for anxiety.

Liraglutide ameliorates high glucose-induced vascular endothelial injury through TRIB3/NF-κB signaling pathway.

As one of the most commonly used antidiabetic medications clinically, liraglutide is involved in the protection of vascular endothelium, and whether it can relieve high glucose-induced vascular endothelial damage was unknown. This study aims to address the response of liraglutide (LIRA) on human umbilical vein endothelial cells, as well as to elucidate its possible underlying mechanism. We established a vascular endothelial cell injury model by exposing human umbilical vein endothelial cells (HUVECs) to high glucose, and used LIRA pretreatment before HG treatment to address the endothelial protective effect of LIRA. Our results suggest that LIRA prevented HG-induced HUVEC apoptosis, oxidative stress, inflammasome activation, and pyroptosis. Furthermore, silencing of tribbles homolog 3 (TRIB3) could markedly reduce HG-induced HUVEC apoptosis, ROS level, the expressions of TXNIP, cleaved caspase3, NLRP3, and caspase1, indicating TRIB3 inhibition protected HUVECs against HG-induced vascular endothelial injury. In addition, LIRA restrained NF-κB/IκB-α signaling pathway activation in HUVECs. Thus, LIRA appears to mitigate HG-induced apoptosis, oxidative stress, inflammasome activation, and pyroptosis in HUVECs via regulating the TRIB3/NF-κB/IκB-α signaling pathway. Our study provides new insight into the mechanisms underlying the protective activity of LIRA against the vascular endothelial injury in diabetic vascular complication.

ACSL3 regulates breast cancer progression via lipid metabolism reprogramming and the YES1/YAP axis.

OBJECTIVE: Mitochondrial fatty acid oxidation is a metabolic pathway whose dysregulation is recognized as a critical factor in various cancers, because it sustains cancer cell survival, proliferation, and metastasis. The acyl-CoA synthetase long-chain (ACSL) family is known to activate long-chain fatty acids, yet the specific role of ACSL3 in breast cancer has not been determined. METHODS: We assessed the prognostic value of ACSL3 in breast cancer by using data from tumor samples. Gain-of-function and loss-of-function assays were also conducted to determine the roles and downstream regulatory mechanisms of ACSL3 in vitro and in vivo. RESULTS: ACSL3 expression was notably downregulated in breast cancer tissues compared with normal tissues, and this phenotype correlated with improved survival outcomes. Functional experiments revealed that ACSL3 knockdown in breast cancer cells promoted cell proliferation, migration, and epithelial-mesenchymal transition. Mechanistically, ACSL3 was found to inhibit ß-oxidation and the formation of associated byproducts, thereby suppressing malignant behavior in breast cancer. Importantly, ACSL3 was found to interact with YES proto-oncogene 1, a member of the Src family of tyrosine kinases, and to suppress its activation through phosphorylation at Tyr419. The decrease in activated YES1 consequently inhibited YAP1 nuclear colocalization and transcriptional complex formation, and the expression of its downstream genes in breast cancer cell nuclei. CONCLUSIONS: ACSL3 suppresses breast cancer progression by impeding lipid metabolism reprogramming, and inhibiting malignant behaviors through phospho-YES1 mediated inhibition of YAP1 and its downstream pathways. These findings suggest that ACSL3 may serve as a potential biomarker and target for comprehensive therapeutic strategies for breast cancer.

Phase I/II Clinical Study of PRaG Regimen Combined With Intraperitoneal Infusion of PD-1 Inhibitor for Advanced Refractory Solid Tumors With Cancerous Ascites (PRaG4.0P Study).

Objective: The prognosis of malignant tumors with peritoneal metastases and cancerous ascites has generally been poor, with limited treatment options. The PRaG regimen, which comprised of hypofractionated radiotherapy, programmed cell death-1 (PD-1) inhibitor, and granulocyte-macrophage colony-stimulating factor (GM-CSF), showed a survival advantage in patients with advanced solid tumors who failed at least the first line of standard systemic treatment. Intraperitoneal infusion of PD-1 inhibitors may be a novel therapeutic strategy for managing malignant ascites. Integrating the PRaG regimen with intraperitoneal perfusion of a PD-1 inhibitor might control malignant ascites and provide further survival benefits in these patients. This proposed study aims to investigate the safety and efficacy of intraperitoneal infusion of serplulimab in combination with the PRaG regimen in patients with simultaneous advanced solid tumors and cancerous ascites who fail at least the first-line treatment. Methods: This proposed study is a prospective, single-arm, open-label, multicenter clinical trial. All eligible patients will receive 2 cycles of intensive treatment, a combination of PRaG regimen with an intraperitoneal infusion of PD-1 inhibitor. The patients who are beneficially treated with intensive treatment will receive consolidation treatment every 2 weeks until ascites disappear, disease progression occurs, intolerable toxicity occurs, or for up to 1 year. Phase I of this study will be conducted using a modified 3 + 3 design. The dose of intraperitoneal infusion of PD-1 inhibitor for phase II will be determined according to dose-limiting toxicity evaluation in the phase I study. Conclusion: This prospective, open-label, multicenter study will potentially lead to intraperitoneal perfusion of a PD-1 inhibitor being a new strategy for malignant ascites patients and provide a meaningful efficacy and safety of the combination of PRaG regimen with an intraperitoneal infusion of PD-1 inhibitor for these patients.

Operando-reconstructed polyatomic ion layers boost the activity and stability of industrial current-density water splitting.

Metal-organic frameworks have garnered attention as highly efficient pre-electrocatalysts for the oxygen evolution reaction (OER). Current structure-activity relationships primarily rely on the assumption that the complete dissolution of organic ligands occurs during electrocatalysis. Herein, modeling based on NiFe Prussian blue analogs (NiFe-PBAs) show that cyanide ligands leach from the matrix and subsequently oxidize to corresponding inorganic ions (ammonium and carbonate) that re-adsorb onto the surface of NiFe OOH during the OER process. Interestingly, the surface-adsorbed inorganic ions induce the OER reaction of NiFe OOH to switch from the adsorbate evolution to the lattice-oxygen-mediated mechanism, thus contributing to the high activity. In addition, this reconstructed inorganic ion layer acting as a versatile protective layer can prevent the dissolution of metal sites to maintain contact between catalytic sites and reactive ions, thus breaking the activity-stability trade-off. Consequently, our constructed NiFe-PBAs exhibit excellent durability for 1250 h with an ultralow overpotential of 253 mV at 100 mA cm-2. The scale-up NiFe-PBAs operated with a low energy consumption of ∼4.18 kWh m-3 H2 in industrial water electrolysis equipment. The economic analysis of the entire life cycle demonstrates that this green hydrogen production is priced at US$2.59/ [Formula: see text] , meeting global targets (

Contrastive voxel clustering for multiscale modeling of brain network.

Resting-state functional magnetic resonance imaging (fMRI) provides an efficient way to analyze the functional connectivity between brain regions. A comprehensive understanding of brain functionality requires a unified description of multi-scale layers of neural structure. However, existing brain network modeling methods often simplify this property by averaging Blood oxygen level dependent (BOLD) signals at the brain region level for fMRI-based analysis with the assumption that BOLD signals are homogeneous within each brain region, which ignores the heterogeneity of voxels within each Region of Interest (ROI). This study introduces a novel multi-stage self-supervised learning framework for multiscale brain network analysis, which effectively delineates brain functionality from voxel to ROIs and up to sample level. A Contrastive Voxel Clustering (CVC) module is proposed to simultaneously learn the voxel-level features and clustering assignments, which ensures the retention of informative clustering features at the finest voxel-level and concurrently preserves functional connectivity characteristics. Additionally, based on the extracted features and clustering assignments at the voxel level by CVC, a Brain ROI-based Graph Neural Network (BR-GNN) is built to extract functional connectivity features at the brain ROI-level and used for sample-level prediction, which integrates the functional clustering maps with the pre-established structural ROI maps and creates a more comprehensive and effective analytical tool. Experiments are performed on two datasets, which illustrate the effectiveness and generalization ability of the proposed method by analyzing voxel-level clustering results and brain ROIs-level functional characteristics. The proposed method provides a multiscale modeling framework for brain functional connectivity analysis, which will be further used for other brain disease identification. Code is available at https://github.com/yanliugroup/fmri-cvc.

Improving quantitative prediction of protein subcellular locations in fluorescence images through deep generative models.

Machine learning has been employed in recognizing protein localization at the subcellular level, which highly facilitates the protein function studies, especially for those multi-label proteins that localize in more than one organelle. However, existing works mostly study the qualitative classification of protein subcellular locations, ignoring fraction of one multi-label protein in different locations. Actually, about 50 % proteins are multi-label proteins, and the ignorance of quantitative information highly restricts the understanding of their spatial distribution and functional mechanism. One reason of the lack of quantitative study is the insufficiency of quantitative annotations. To address the data shortage problem, here we proposed a generative model, PLocGAN, which could generate cell images with conditional quantitative annotation of the fluorescence distribution. The model was a conditional generative adversarial network, in which the condition learning utilized partial label learning to overcome the lack of training labels and allowed training with only qualitative labels. Meanwhile, it used contrastive learning to enhance diversity of the generated images. We assessed the PLocGAN on four pixel-fused synthetic datasets and one real dataset, and demonstrated that the model could generate images with good fidelity and diversity, outperforming existing state-of-the-art generative methods. To verify the utility of PLocGAN in the quantitative prediction of protein subcellular locations, we replaced the training images with generated quantitative images and built prediction models, and found that they had a boosting effect on the quantitative estimation. This work demonstrates the effectiveness of deep generative models in bioimage analysis, and provides a new solution for quantitative subcellular proteomics.

Changes of phonon modes and electron transfer induced by interface interactions of Pd/MoS2 heterostructures.

As functional materials and nano-catalysts, Pd nanoparticles (NPs) are often used to modify two-dimensional (2D) materials. In the heterostructures of metal NPs and 2D transition metal dichalcogenides, the interface atomic configuration and interface effect greatly affect material properties and stability. Therefore, the rational design of interface structures and in-depth analysis of interface interactions are of vital importance for the preparation of specific functional devices. In this work, Pd NPs were deposited on mechanically exfoliated MoS2 flakes and the epitaxial relationship between Pd and MoS2 was observed, accompanied by distinct moiré patterns. Raman spectra of the Pd NPs/MoS2 heterostructure showed an E12g' vibration mode indicative of the local strain in MoS2. A new vibration mode A'1g appeared in the higher-frequency direction compared with the pristine A1g peak. Combined with X-ray photoelectron spectra and density functional theory calculations, the new vibration mode can be attributed to the bonding between Pd and MoS2. Besides, graphene was inserted between Pd NPs and MoS2, and the decoupling of the interfacial effect by graphene was investigated. This study will help deepen our understanding on the interaction mechanism between metals and MoS2, thereby enabling the modulation of optoelectronic properties and the performance of these hybrid materials.

Inflammatory microenvironment regulation and osteogenesis promotion by bone-targeting calcium and magnesium repletion nanoplatform for osteoporosis therapy.

Osteoporosis is the most common bone metabolic disease that affects the health of middle-aged and elderly people, which is hallmarked by imbalanced bone remodeling and a deteriorating immune microenvironment. Magnesium and calcium are pivotal matrix components that participate in the bone formation process, especially in the immune microenvironment regulation and bone remodeling stages. Nevertheless, how to potently deliver magnesium and calcium to bone tissue remains a challenge. Here, we have constructed a multifunctional nanoplatform composed of calcium-based upconversion nanoparticles and magnesium organic frameworks (CM-NH2-PAA-Ald, denoted as CMPA), which features bone-targeting and pH-responsive properties, effectively regulating the inflammatory microenvironment and promoting the coordination of osteogenic functions for treating osteoporosis. The nanoplatform can efficaciously target bone tissue and gradually degrade in response to the acidic microenvironment of osteoporosis to release magnesium and calcium ions. This study validates that CMPA possessing favorable biocompatibility can suppress inflammation and facilitate osteogenesis to treat osteoporosis. Importantly, high-throughput sequencing results demonstrate that the nanoplatform exerts a good inflammatory regulation effect through inhibition of the nuclear factor kappa-B signaling pathway, thereby normalizing the osteoporotic microenvironment. This collaborative therapeutic strategy that focuses on improving bone microenvironment and promoting osteogenesis provides new insight for the treatment of metabolic diseases such as osteoporosis.

The role of autophagy regulated by the PI3K/AKT/mTOR pathway and innate lymphoid cells in eosinophilic chronic sinusitis with nasal polyps.

BACKGROUND: The PI3K/Akt/mTOR pathway and autophagy are important physiological processes. But their roles in eCRSwNP remains controversial. METHODS: In this study, we used the eCRSwNP mouse model, PI3K/Akt/mTOR pathway inhibitors, and autophagy inhibitors and activators to investigate the regulatory effects of the PI3K/Akt/mTOR pathway on autophagy, and their effects on eosinophilic inflammation, and tissue remodeling. The role of ILC2s in eCRSwNP was also studied, and the relationship between ILC2s and autophagy was preliminarily determined. RESULTS: Our results show that eosinophilic inflammation in eCRSwNP mice could be inhibited by promoting the autophagy; otherwise, eosinophilic inflammation could be promoted. Meanwhile, inhibition of the PI3K/Akt/mTOR pathway can further promote autophagy and inhibit eosinophilic inflammation. Meanwhile, inhibiting the PI3K/Akt/mTOR pathway and promoting autophagy can reduce the number of ILC2s and the severity of tissue remodeling in the nasal polyps of eCRSwNP mice. CONCLUSIONS: We conclude that the PI3K/Akt/mTOR pathway plays roles in eosinophilic inflammation and tissue remodeling of eCRSwNP, in part by regulating the level of autophagy. The downregulation of autophagy is a pathogenesis of eCRSwNP; therefore, the recovery of normal autophagy levels might be a new target for eCRSwNP therapy. Furthermore, autophagy might inhibit eosinophilic inflammation and tissue remodeling, in part by reducing the number of ILC2s.

Few-shot bearing fault detection based on multi-dimensional convolution and attention mechanism.

Bearings are critical components of industrial equipment and have a significant impact on the safety of industrial physical systems. Their failure may lead to equipment shutdown and accidents, posing a significant risk to production safety. However, it is difficult to obtain a large amount of bearing fault data in practice, which makes the problem of small sample size a major challenge for bearing fault detection. In addition, some methods may overlook important features in bearing vibration signals, leading to insufficient detection capabilities. To address the challenges in bearing fault detection, this paper proposed a few sample learning methods based on the multidimensional convolution and attention mechanism. First, a multichannel preprocessing method was designed to more effectively utilize the information in the bearing vibration signal. Second, by extracting multidimensional features and enhancing the attention to important features through multidimensional convolution operations and attention mechanisms, the feature extraction ability of the network was improved. Furthermore, nonlinear mapping of feature vectors into the metric space to calculate distance can better measure the similarity between samples, thereby improving the accuracy of bearing fault detection and providing important guarantees for the safe operation of industrial systems. Extensive experiments have shown that the proposed method has good fault detection performance under small sample conditions, which is beneficial for reducing machine downtime and economic losses.

Smart Microneedle Arrays Integrating Cell-Free Therapy and Nanocatalysis to Treat Liver Fibrosis.

Liver fibrosis is a chronic pathological condition lacking specific clinical treatments. Stem cells, with notable potential in regenerative medicine, offer promise in treating liver fibrosis. However, stem cell therapy is hindered by potential immunological rejection, carcinogenesis risk, efficacy variation, and high cost. Stem cell secretome-based cell-free therapy offers potential solutions to address these challenges, but it is limited by low delivery efficiency and rapid clearance. Herein, an innovative approach for in situ implantation of smart microneedle (MN) arrays enabling precisely controlled delivery of multiple therapeutic agents directly into fibrotic liver tissues is developed. By integrating cell-free and platinum-based nanocatalytic combination therapy, the MN arrays can deactivate hepatic stellate cells. Moreover, they promote excessive extracellular matrix degradation by more than 75%, approaching normal levels. Additionally, the smart MN arrays can provide hepatocyte protection while reducing inflammation levels by ≈70-90%. They can also exhibit remarkable capability in scavenging almost 100% of reactive oxygen species and alleviating hypoxia. Ultimately, this treatment strategy can effectively restrain fibrosis progression. The comprehensive in vitro and in vivo experiments, supplemented by proteome and transcriptome analyses, substantiate the effectiveness of the approach in treating liver fibrosis, holding immense promise for clinical applications.

A comprehensive analysis of GAS2 family members identifies that GAS2L1 is a novel biomarker and promotes the proliferation of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common primary liver cancer with a high incidence and mortality. Members of the growth-arresting-specific 2 (GAS2) family are involved in various biological processes in human malignancies. To date, there is only a limited amount of information available about the expression profile and clinical importance of GAS2 family in HCC. In this study, we found that GAS2L1 and GAS2L3 were distinctly upregulated in HCC specimens compared to non-tumor specimens. Pan-cancer assays indicated that GAS2L1 and GAS2L3 were highly expressed in most cancers. The Pearson's correlation revealed that the expressions of GAS2, GAS2L1 and GAS2L2 were negatively associated with methylation levels. Survival assays indicated that GAS2L1 and GAS2L3 were independent prognostic factors for HCC patients. Immune cell infiltration analysis revealed that GAS2, GAS2L1 and GAS2L3 were associated with several immune cells. Finally, we confirmed that GAS2L1 was highly expressed in HCC cells and its knockdown suppressed the proliferation of HCC cells. Taken together, our findings suggested the expression patterns and prognostic values of GAS2 members in HCC, providing insights for further study of the GAS2 family as sensitive diagnostic and prognostic markers for HCC.

The effect of mango aroma in low-sugar beverage: A sensory study on odor induced sweetness enhancement.

Excessive intake of sugar has become a public concern. However, it is challenging for food industries to decrease sugar level without sacrificing safety and sensory profile. Odor-induced sweetness enhancement (OISE) is believed to be a novel and promising strategy for sugar reduction. In order to investigate the OISE effect of mango aroma and evaluate its degree of sugar reduction in low-sugar beverages, a mathematical model was constructed through sensory evaluation in this study. The results showed that the maximum liking of low-sugar model beverages was 4.28 % sucrose and 0.57 % mango flavor. The most synergistic of OISE was at the concentration level of 2.24 % sucrose + 0.25 % mango flavor, which was equivalent to 2.96 % pure sucrose solution. With 32.14 % sugar reduction, the mango aroma was suggested to generate the OISE effect. However, the same level of garlic aroma was not able to enhance sweetness perception, suggesting that the congruency of aroma and taste is a prerequisite for the OISE effect to occur. This study demonstrated that the cross-modal interaction of mango aroma on sweetness enhancement in low-sugar model beverages could provide practical guidance for developing sugar-reduced beverages without applying sweeteners.

Discovery of a class of glycosaminoglycan lyases with ultrabroad substrate spectrum and their substrate structure preferences.

Glycosaminoglycan (GAG) lyases are often strictly substrate specific, and it is especially difficult to simultaneously degrade GAGs with different types of glycosidic bonds. Herein, we found a new class of GAG lyases (GAGases) from different bacteria. These GAGases belong to polysaccharide lyase 35 family and share quite low homology with the identified GAG lyases. The most surprising thing is that GAGases can not only degrade three types of GAGs: hyaluronan, chondroitin sulfate, and heparan sulfate but also even one of them can also degrade alginate. Further investigation of structural preferences revealed that GAGases selectively act on GAG domains composed of non/6-O-/N-sulfated hexosamines and d-glucoronic acids as well as on alginate domains composed of d-mannuronic acids. In addition, GAG lyases were once speculated to have evolved from alginate lyases, but no transitional enzymes have been found. The discovery of GAGases not only broadens the category of GAG lyases, provides new enzymatic tools for the structural and functional studies of GAGs with specific structures, but also provides candidates for the evolution of GAG lyases.

Application of a hybrid algorithm of LSTM and Transformer based on random search optimization for improving rainfall-runoff simulation.

Flood forecasting using traditional physical hydrology models requires consideration of multiple complex physical processes including the spatio-temporal distribution of rainfall, the spatial heterogeneity of watershed sub-surface characteristics, and runoff generation and routing behaviours. Data-driven models offer novel solutions to these challenges, though they are hindered by difficulties in hyperparameter selection and a decline in prediction stability as the lead time extends. This study introduces a hybrid model, the RS-LSTM-Transformer, which combines Random Search (RS), Long Short-Term Memory networks (LSTM), and the Transformer architecture. Applied to the typical Jingle watershed in the middle reaches of the Yellow River, this model utilises rainfall and runoff data from basin sites to simulate flood processes, and its outcomes are compared against those from RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models. It was evaluated against RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models using the Nash-Sutcliffe Efficiency Coefficient (NSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Bias percentage as metrics. At a 1-h lead time during calibration and validation, the RS-LSTM-Transformer model achieved NSE, RMSE, MAE, and Bias values of 0.970, 14.001m3/s, 5.304m3/s, 0.501% and 0.953, 14.124m3/s, 6.365m3/s, 0.523%, respectively. These results demonstrate the model's superior simulation capabilities and robustness, providing more accurate peak flow forecasts as the lead time increases. The study highlights the RS-LSTM-Transformer model's potential in flood forecasting and the advantages of integrating various data-driven approaches for innovative modelling.

Analysis of prognostic value of lactate metabolism-related genes in ovarian cancer based on bioinformatics.

BACKGROUND: Recent studies have provided evidence supporting the functional role and mechanism of lactate in suppressing anticancer immunity. However, there is no systematic analysis of lactate metabolism-related genes (LMRGs) and ovarian cancer (OV) prognosis. RESULTS: Six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) were selected as prognostic genes and a prognostic model was utilized. Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) analyses were further performed and indicated that the prognostic model was effective. Subsequently, the neoplasm_cancer_status and RiskScore were determined as independent prognostic factors, and a nomogram was established with relatively accurate forecasting ability. Additionally, 2 types of immune cells (Central memory CD8 T cell and Immature B cell), 4 types of immune functions (APC co inhibition, DCs, Tfh and Th1 cells), 9 immune checkpoints (BTLA, CTLA4, IDO1, LAG3, VTCN1, CXCL10, CXCL9, IFNG, CD27) and tumor immune dysfunction and exclusion (TIDE) scores were significantly different between risk groups. The expression of 6 genes were verified by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and the expression of 6 genes were higher in the high-grade serous carcinoma (HGSC) samples. CONCLUSION: A prognostic model related to lactate metabolism was established for OV based on six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) that could provide new insights into therapy.