Statistical machine learning models for prediction of China's maritime emergency patients in dynamic: ARIMA model, SARIMA model, and dynamic Bayesian network model.

Introduction: Rescuing individuals at sea is a pressing global public health issue, garnering substantial attention from emergency medicine researchers with a focus on improving prevention and control strategies. This study aims to develop a Dynamic Bayesian Networks (DBN) model utilizing maritime emergency incident data and compare its forecasting accuracy to Auto-regressive Integrated Moving Average (ARIMA) and Seasonal Auto-regressive Integrated Moving Average (SARIMA) models. Methods: In this research, we analyzed the count of cases managed by five hospitals in Hainan Province from January 2016 to December 2020 in the context of maritime emergency care. We employed diverse approaches to construct and calibrate ARIMA, SARIMA, and DBN models. These models were subsequently utilized to forecast the number of emergency responders from January 2021 to December 2021. The study indicated that the ARIMA, SARIMA, and DBN models effectively modeled and forecasted Maritime Emergency Medical Service (EMS) patient data, accounting for seasonal variations. The predictive accuracy was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R 2) as performance metrics. Results: In this study, the ARIMA, SARIMA, and DBN models reported RMSE of 5.75, 4.43, and 5.45; MAE of 4.13, 2.81, and 3.85; and R 2 values of 0.21, 0.54, and 0.44, respectively. MAE and RMSE assess the level of difference between the actual and predicted values. A smaller value indicates a more accurate model prediction. R 2 can compare the performance of models across different aspects, with a range of values from 0 to 1. A value closer to 1 signifies better model quality. As errors increase, R 2 moves further from the maximum value. The SARIMA model outperformed the others, demonstrating the lowest RMSE and MAE, alongside the highest R 2, during both modeling and forecasting. Analysis of predicted values and fitting plots reveals that, in most instances, SARIMA's predictions closely align with the actual number of rescues. Thus, SARIMA is superior in both fitting and forecasting, followed by the DBN model, with ARIMA showing the least accurate predictions. Discussion: While the DBN model adeptly captures variable correlations, the SARIMA model excels in forecasting maritime emergency cases. By comparing these models, we glean valuable insights into maritime emergency trends, facilitating the development of effective prevention and control strategies.

A Robust Induced Fit Docking Approach with the Combination of the Hybrid All-Atom/United-Atom/Coarse-Grained Model and Simulated Annealing.

Molecular docking remains an indispensable tool in computational biology and structure-based drug discovery. However, the correct prediction of binding poses remains a major challenge for molecular docking, especially for target proteins where a substrate binding induces significant reorganization of the active site. Here, we introduce an Induced Fit Docking (IFD) approach named AA/UA/CG-SA-IFD, which combines a hybrid All-Atom/United-Atom/Coarse-Grained model with Simulated Annealing. In this approach, the core region is represented by the All-Atom(AA) model, while the protein environment beyond the core region and the solvent are treated with either the United-Atom (UA) or the Coarse-Grained (CG) model. By combining the Elastic Network Model (ENM) for the CG region, the hybrid model ensures a reasonable description of ligand binding and the environmental effects of the protein, facilitating highly efficient and reliable sampling of ligand binding through Simulated Annealing (SA) at a high temperature. Upon validation with two testing sets, the AA/UA/CG-SA-IFD approach demonstrates remarkable accuracy and efficiency in induced fit docking, even for challenging cases where the docked poses significantly deviate from crystal structures.

Tuning Vertical Electrodeposition for Dendrites-Free Zinc-Ion Batteries.

Manipulating the crystallographic orientation of zinc deposition is recognized as an effective approach to address zinc dendrites and side reactions for aqueous zinc-ion batteries (ZIBs). We introduce 2-methylimidazole (Mlz) additive in zinc sulfate (ZSO) electrolyte to achieve vertical electrodeposition with preferential orientation of the (100) and (110) crystal planes. Significantly, the zinc anode exhibited long lifespan with 1500 h endurance at 1 mA cm-2 and an excellent 400 h capability at a depth of discharge (DOD) of 34% in Zn||Zn battery configurations, while in Zn||MnO2 battery assemblies, a capacity retention of 68.8% over 800 cycles is attained. Theoretical calculation reveals that the strong interactions between Mlz and (002) plane impeding its growth, while Zn atoms exhibit lower migration energy barrier and superior mobility on (100) and (110) crystal planes guaranteed the heightened mobility of zinc atoms on the (100) and (110) crystal planes, thus ensuring their superior ZIB performance than that with only ZSO electrolyte, which offers a route for designing next-generation high energy density ZIB devices.

Identification of Long Noncoding RNAs Expression Profiles Between Gallstone and Gallbladder Cancer Using Next-Generation Sequencing Analysis.

Background: Gallstone disease (GS) is an important risk factor for Gallbladder cancer (GBC). However, the mechanisms of the progression of GS to GBC remain unclear. Long non-coding RNA (lncRNA), modulates DNA/RNA/proteins at epigenetic, pre-transcriptional, transcriptional and posttranscriptional levels, and plays a potential therapeutic role in various diseases. This study aims to identify lncRNAs that have a potential impact on GS-promoted GBC progression. Methods and Results: Six GBC patients without GS, six normal gallbladder tissues, nine gallstones and nine GBC patients with GS were admitted to our hospital. The next-generation RNA-sequencing was performed to analyze differentially expressed (DE) lncRNA and messenger RNA (mRNA) in four groups. Then overlapping and specific molecular signatures were analyzed. We identified 29 co-DEGs and 500 co-DElncRNAs related to gallstone or GBC. The intersection and concatenation of co-DEGs and co-DElncRNA functionally involved in focal adhesion, Transcriptional misregulation in cancers, Protein digestion and absorption, and ECM-receptor interaction signaling pathways may contribute to the development of gallbladder cancer. Further exploration is necessary for early diagnosis and the potential treatment of GBC. FXYD2, MPZL1 and PAH were observed in both co-DEGs and co-DElncRNA and validated by qRT-PCR. Conclusion: Our data identified a series of DEGs and DElncRNAs, which were involved in the progression of GBC and GS-related metabolism pathways. Compared to GBC, the GS profile was more similar to para-tumor tissues in transcriptome level and lower risk of cancer. Further exploration is necessary from GBC patients with different periods of follow-up gallstone.

In-Situ Ultrafast Construction of Zinc Tungstate Interface Layer for Highly Reversible Zinc Anodes.

Constructing artificial solid electrolyte interface on the Zn anode surface is recognized as an appealing method to inhibit zinc dendrites and side reactions, whereas the current techniques are complex and time-consuming. Here, a robust and zincophilic zinc tungstate (ZnWO4) layer has been in situ constructed on the Zn anode surface (denoted as ZWO@Zn) by an ultrafast chemical solution reaction. Comprehensive characterizations and theoretical calculations demonstrate that the ZWO layer can effectively modulate the interfacial electric field distribution and promote the Zn2+ uniform diffusion, thus facilitating the uniform Zn2+ nucleation and suppressing zinc dendrites. Besides, ZWO layer can prevent direct contact between the Zn/water and increase the hydrogen evolution reaction overpotential to eliminate side reactions. Consequently, the in situ constructed ZWO layer facilitates remarkable reversibility in the ZWO@Zn||Ti battery, achieving an impressive Coulombic efficiency of 99.36 % under 1.0 mA cm-2, unprecedented cycling lifespan exceeding 1800 h under 1.0 mA cm-2 in ZWO@Zn||ZWO@Zn battery, and a steady and reliable operation of the overall ZWO@Zn||VS2 battery. The work provides a simple, low cost, and ultrafast pathway to crafting protective layers for driving advancements in aqueous zinc-metal batteries.

DTNPD: A comprehensive database of drugs and targets for neurological and psychiatric disorders.

In response to the shortcomings in data quality and coverage for neurological and psychiatric disorders (NPDs) in existing comprehensive databases, this paper introduces the DTNPD database, specifically designed for NPDs. DTNPD contains detailed information on 30 NPDs types, 1847 drugs, 514 drug targets, 64 drug combinations, and 61 potential target combinations, forming a network with 2389 drug-target associations. The database is user-friendly, offering open access and downloadable data, which is crucial for network pharmacology studies. The key strength of DTNPD lies in its robust networks of drug and target combinations, as well as drug-target networks, facilitating research and development in the field of NPDs. The development of the DTNPD database marks a significant milestone in understanding and treating NPDs. For accessing the DTNPD database, the primary URL is http://dtnpd.cnsdrug.com, complemented by a mirror site available at http://dtnpd.lyhbio.com.

Deep learning assists detection of esophageal cancer and precursor lesions in a prospective, randomized controlled study.

Endoscopy is the primary modality for detecting asymptomatic esophageal squamous cell carcinoma (ESCC) and precancerous lesions. Improving detection rate remains challenging. We developed a system based on deep convolutional neural networks (CNNs) for detecting esophageal cancer and precancerous lesions [high-risk esophageal lesions (HrELs)] and validated its efficacy in improving HrEL detection rate in clinical practice (trial registration ChiCTR2100044126 at www.chictr.org.cn). Between April 2021 and March 2022, 3117 patients ≥50 years old were consecutively recruited from Taizhou Hospital, Zhejiang Province, and randomly assigned 1:1 to an experimental group (CNN-assisted endoscopy) or a control group (unassisted endoscopy) based on block randomization. The primary endpoint was the HrEL detection rate. In the intention-to-treat population, the HrEL detection rate [28 of 1556 (1.8%)] was significantly higher in the experimental group than in the control group [14 of 1561 (0.9%), P = 0.029], and the experimental group detection rate was twice that of the control group. Similar findings were observed between the experimental and control groups [28 of 1524 (1.9%) versus 13 of 1534 (0.9%), respectively; P = 0.021]. The system's sensitivity, specificity, and accuracy for detecting HrELs were 89.7, 98.5, and 98.2%, respectively. No adverse events occurred. The proposed system thus improved HrEL detection rate during endoscopy and was safe. Deep learning assistance may enhance early diagnosis and treatment of esophageal cancer and may become a useful tool for esophageal cancer screening.

Identification of IGF2 promotes skin wound healing by co-expression analysis.

Oral mucosa is an ideal model for studying scarless wound healing. Researchers have shown that the key factors which promote scarless wound healing already exist in basal state of oral mucosa. Thus, to identify the other potential factors in basal state of oral mucosa will benefit to skin wound healing. In this study, we identified eight gene modules enriched in wound healing stages of human skin and oral mucosa through co-expression analysis, among which the module M8 was only module enriched in basal state of oral mucosa, indicating that the genes in module M8 may have key factors mediating scarless wound healing. Through bioinformatic analysis of genes in module M8, we found IGF2 may be the key factor mediating scarless wound healing of oral mucosa. Then, we purified IGF2 protein by prokaryotic expression, and we found that IGF2 could promote the proliferation and migration of HaCaT cells. Moreover, IGF2 promoted wound re-epithelialization and accelerated wound healing in a full-thickness skin wound model. Our findings identified IGF2 as a factor to promote skin wound healing which provide a potential target for wound healing therapy in clinic.

Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning.

Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learning methods are limited by their reliance on single-modal representations, hindering a comprehensive understanding of protein-ligand interactions. We introduce a graph-neural-network-based scoring function that utilizes a triplet contrastive learning loss to improve protein-ligand representations. In this model, three-dimensional complex representations and the fusion of two-dimensional ligand and coarse-grained pocket representations converge while distancing from decoy representations in latent space. After rigorous validation on multiple external data sets, our model exhibits commendable generalization capabilities compared to those of other deep learning-based scoring functions, marking it as a promising tool in the realm of drug discovery. In the future, our training framework can be extended to other biophysical- and biochemical-related problems such as protein-protein interaction and protein mutation prediction.

High-Rate and Ultra-Stable aqueous Zinc-Ion batteries enabled by Potassium-Infused ammonium vanadate nanosheets.

Aqueous zinc-ion batteries (AZIBs), defined by low expenses, superior safety, and plentiful reserves, demonstrate tremendous development potential in energy storage systems at the grid scale. Whereas the cathode instability and the limited diffusion of Zn2+ have impeded the development of AZIBs. Herein, a high-performance K-NH4V4O10 (K-NVO) cathode with K+ doping synthesized successfully through one-step hydrothermal approach. Experiments and density functional theory (DFT) calculations indicate that K-NVO has Zn2+ diffusion pathways with lower barriers for smoother transport, and lower formation energy. The combination of the rapid Zn2+ diffusion and the stable structure results in outstanding electrochemical performance of K-NVO as demonstrated in tests. K-NVO cathode achieves a specific capacity of 406 mAh g-1 at 0.2 A g-1, maintains satisfactory cyclic stability with 81.6 % capacity retention after 1000 cycles at 5 A g-1, and possesses a high energy density of 350.9 Wh kg-1. Furthermore, confirmation of the zinc storage mechanism in K-NVO was carried out through Ex situ tests, such as XRD and XPS. This research contributes a unique perspective to the formulation of high-performance cathode materials for AZIBs.

Electro-acupuncture inhibits HDAC2 via modulating gut microbiota to ameliorate SNI-induced pain and depression-like behavior in rats.

BACKGROUND: Depression and chronic pain frequent co-occur, exacerbating each other's symptoms and hindering treatment. Emerging studies have highlighted abnormal gut microbiota in both conditions. Previous studies have demonstrated the clinical effectiveness of electro-acupuncture (EA) in managing these conditions, yet the underlying mechanisms remain elusive. METHODS: Spared nerve injury (SNI) was employed to induce chronic pain and depression-like behavior. Rats were randomly assigned to sham SNI (SS), SNI, and EA groups. SNI surgery was performed on all rats, except those in SS group, which underwent sham SNI surgery. Then EA group received 5 weeks of EA treatment. Pain and depression-like behavior were assessed through paw withdrawal threshold, sucrose-preference test, and forced swim test. Gut microbiota composition was analyzed via 16S rDNA sequencing. Brain-Derived Neurotrophic Factor (BDNF) and acetylation-related proteins in the medial prefrontal cortex (mPFC) were evaluated through enzyme-linked immunosorbent assay and western blot. RESULTS: EA treatment significantly ameliorated pain and depression-like behavior. The 16S rDNA sequencing showed EA modulated gut microbiota composition, increased short-chain fatty acids (SCFAs)-producing bacteria, including Akkermansi, Ruminococcaceae and Lachnospiraceae family, particularly Akkermansia. Furthermore, EA increased BDNF, AcH3 and decreased HDAC2 in mPFC. Notably, SCFAs-producing bacteria exhibited a negative correlation with HDAC2 levels. LIMITATIONS: This study exclusively investigated microbiota differences resulting from EA stimulation, without delving into the functional variations brought about by these microbial distinctions. CONCLUSIONS: The therapeutic effects of EA on the comorbidity of chronic pain and depression may involve the modulation of the gut microbiota, resulting in histone acetylation changes and upregulation of BDNF.

A novel maximum power point tracking with hybrid control algorithm for automotive thermoelectric generator system.

To dynamically track the maximum power of an automotive thermoelectric generator (ATEG) system in real-time, this study introduces a novel maximum power point tracking (MPPT) algorithm that integrates Kalman filtering and fuzzy control. Employing a two-phase interleaved parallel DC-DC boost converter in the MPPT controller effectively reduces current ripple and switch loss. Results demonstrated a significant improvement in tracking time compared to the traditional incremental conductance algorithm, attributed to the elimination of high-frequency components in output power by the Kalman filter. The novel algorithm exhibits enhanced tracking stability through the application of fuzzy control. Ultimately, the tracking accuracy of the novel algorithm surpasses that of the incremental conductance algorithm by 5.2%, achieving an impressive 94.9%. This study, therefore, presents a valuable contribution to a novel MPPT algorithm for precisely and rapidly tracking the global maximum power points of the ATEG system throughout the entire vehicle driving cycle.

Global research trends in tongue cancer from 2000 to 2022: bibliometric and visualized analysis.

OBJECTIVES: This study conducts a systematic bibliometric analysis of tongue cancer publications to identify key topics, hotspots, and research distribution. METHODS: We analyzed tongue cancer publications in the Web of Science core collection database, assessing their quantity and quality. We investigated contributors, including countries, affiliations, journals, authors, and categories, within collaborative networks. Additionally, we synthesized key research findings using various analytical techniques, such as alluvial flow, burstness analysis, cluster analysis, co-occurrence network of associations, and network layer overlay. RESULTS: From 2000 to 2022, this bibliometric study covers 2205 articles and reviews across 617 journals, involving 72 countries, 2233 institutions, and 11,266 authors. It shows consistent growth, particularly in 2016. Key contributors include China (499 publications), Karolinska Institute (84 publications), Oral Oncology (144 publications), and Tuula Salo (47 publications). Other notable contributors are the USA (16,747 citations), the National Cancer Institute (NCI) (2597 citations), and the Memorial Sloan-Kettering Cancer Center (MSK) (2231 citations). Additionally, there are significant teams led by Tuula Salo and Dalianis. We have identified six primary clusters: #0 apoptosis, #1 depth of invasion, #2 radiotherapy, #3 hpv, #4 tongue cancer, #5 oral cancer. The top ten highly cited documents primarily pertain to epidemiology, prognostic indicators in early-stage oral tongue cancer, and HPV. Additionally, we observed 16 reference clusters, with depth of invasion (#3), young patients (#4), and tumor budding (#6) gaining prominence since 2012, indicating sustained research interests. CONCLUSIONS: This analysis emphasizes the increasing scholarly interest in tongue cancer research. The bibliometric evaluation highlights pivotal recent research themes such as HPV, depth of invasion, tumor budding, and surgical margins. CLINICAL RELEVANCE: The bibliometric analysis highlights the key topics and studies which have shaped the understanding and management of tongue cancer.

Microscopic extrathyroidal extension does not affect the prognosis of patients with papillary thyroid carcinoma: A propensity score matching analysis.

Background: Extrathyroidal extension (ETE) in papillary thyroid carcinoma (PTC) can be divided into two categories based on different degrees of invasion: microscopic ETE (micro-ETE) and macroscopic ETE (macro-ETE). At present, there is a consensus that macro-ETE significantly affects PTC prognosis, while the prognostic significance of micro-ETE remains controversial. Methods: The clinicopathological and follow-up data for PTC patients who underwent surgical treatment at the Hangzhou First People's Hospital between 2015 and 2018 were retrospectively analyzed. According to the degree of ETE, patients were divided into three groups: non-ETE, micro-ETE and macro-ETE. Cox regression analysis was performed to evaluate the effect of ETE on recurrence-free survival (RFS). The propensity score matching (PSM) method was used to reduce the interference of confounding factors, and Kaplan-Meier curves were utilized to compare the RFS. Results: Both micro- and macro-ETE were associated with some aggressive tumor features, including tumor size, multifocality, and lymph node metastasis. Univariate and multivariate Cox regression analyses showed that macro-ETE was an independent risk factor for recurrence, while micro-ETE was not associated with recurrence. The K-M curves showed that RFS for micro-ETE and non-ETE were not statistically different before and after PSM, while RFS for macro-ETE was significantly shorter than that for non-ETE. Conclusion: The presence of micro-ETE in PTC did not affect prognosis of patients, suggesting that its treatment should be consistent with the treatment for intrathyroidal tumors. The surgical method and the necessity for radioiodine therapy should be carefully evaluated to reduce overtreatment.

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives.

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

Association between metabolites in tryptophan-kynurenine pathway and inflammatory bowel disease: a two-sample Mendelian randomization.

Previous observational studies have suggested an association between tryptophan (TRP)-kynurenine (KYN) pathway and inflammatory bowel disease (IBD). However, whether there is a causal relationship among them remains unclear. Therefore, a two-sample Mendelian randomization (MR) study was conducted to explore the potential causal effects of crucial metabolites in TRP-KYN pathway on IBD and its subtypes. Using summary data from genome-wide association studies, a two-sample MR was employed to evaluate the genetic associations between TRP and KYN as exposures and IBD as an outcome. The inverse variance weighted method was used as the primary MR analysis, with MR-Egger, weighted mode, simple mode, and weighted median methods as complementary analyses. The odds ratios (OR) and 95% confidence intervals (CI) were determined for TRP-IBD (OR 0.739, 95% CI [0.697; 0.783]), TRP-UC (OR 0.875, 95% CI [0.814; 0.942]), TRP-CD (OR 0.685, 95% CI [0.613; 0.765]), KYN-IBD (OR 4.406, 95% CI [2.247; 8.641]), KYN-UC (OR 2.578, 95% CI [1.368; 4.858], and KYN-CD (OR 13.516, 95% CI [4.919; 37.134]). Collectively, the MR analysis demonstrated a significant protective association between TRP and IBD, whereas KYN was identified as a risk factor for IBD.

Water Network-Augmented Two-State Model for Protein-Ligand Binding Affinity Prediction.

Water network rearrangement from the ligand-unbound state to the ligand-bound state is known to have significant effects on the protein-ligand binding interactions, but most of the current machine learning-based scoring functions overlook these effects. In this study, we endeavor to construct a comprehensive and realistic deep learning model by incorporating water network information into both ligand-unbound and -bound states. In particular, extended connectivity interaction features were integrated into graph representation, and graph transformer operator was employed to extract features of the ligand-unbound and -bound states. Through these efforts, we developed a water network-augmented two-state model called ECIFGraph::HM-Holo-Apo. Our new model exhibits satisfactory performance in terms of scoring, ranking, docking, screening, and reverse screening power tests on the CASF-2016 benchmark. In addition, it can achieve superior performance in large-scale docking-based virtual screening tests on the DEKOIS2.0 data set. Our study highlights that the use of a water network-augmented two-state model can be an effective strategy to bolster the robustness and applicability of machine learning-based scoring functions, particularly for targets with hydrophilic or solvent-exposed binding pockets.

Undescribed matrine-type alkaloids from Sophora alopecuroides with anti-inflammatory activity.

A phytochemical investigation on the alkaloid fractions of Sophora alopecuroides L. led to the production of 11 undescribed matrine-type alkaloids, sophaloseedlines I-S (1-11), 12 known analogs (12-23), and an unexpected artificial matrine-derived Al(III) complex (24). The corresponding structures were elucidated by the interpretation of spectroscopic analyses, quantum chemical calculation, and six instances (1-4, 18, and 24), verified by X-ray crystallography. The biological activities screening demonstrated that none of the isolates exhibited cytotoxicity against four human cancer cell lines (HepG2, A549, THP-1, and MCF-7) and respiratory syncytial virus (RSV) at 50 µM, while moderate anti-inflammatory activity with IC50 value from 15.6 to 47.8 µM was observed. The key structure-activity relationships of those matrine-type alkaloids for anti-inflammatory effects have been summarized. In addition, the most potent 7-epi-sophoramine (19) and aluminum sophaloseedline T (24) could effectively inhibit the release of pro-inflammatory factors (TNF-α, IL-6, and IL-1ß), as well as the expression of iNOS and COX-2 proteins.

Constructing stable V2O5/V6O13 heterostructure interface with fast Zn2+ diffusion kinetics for ultralong lifespan zinc-ion batteries.

Given their plentiful reserves, impressive safety features, and economical pricing, aqueous zinc - ion batteries (ZIBs) have positioned themselves as strong competitors to lithium - ion batteries. Yet, the scarcity of available cathode materials poses a challenge to their continued development. In this study, a V2O5/V6O13 heterostructure has been synthesized using a one - pot hydrothermal approach and employed as the cathode material for ZIBs. As evidenced by both experimental and theoretical findings, V2O5/V6O13 heterostructure delivers a rapid electrons and ions diffusion kinetics promoted by the stable interface and strong electronic coupling with significant charge transfer between V2O5 and V6O13, as well as a stable interface achieved by adjusting V - O bond length. Consequently, the optimized V2O5/V6O13 heterostructure cathode of ZIBs demonstrates exceptional capacity (338 mAh g-1 at 0.1 A g-1), remarkable cycling stability (92.96 % retained after 1400 cycles at 1 A g-1). Through comprehensive theoretical calculations and ex situ characterization, the kinetic analysis and storage mechanism of Zn2+ are thoroughly investigated, providing a solid theoretical foundation for the advancement of novel V - based cathode materials aimed at enhancing the performance of ZIBs.