Preparation and characterization of liquefied eggplant branch bio-based controlled-release fertilizer.

Bio-based coating materials have received increased attention because of their low-cost, environmentally friendly, and sustainable properties. In this paper, a novel coating material was developed to coat ureas using bio-based coating material derived from liquefied eggplant branches to form controlled-release ureas (CRUs). Also, the optimum proportion of liquefier was studied. Furthermore, dimethyl siloxane was used to modify liquified eggplant branches to make them hydrophobic, resulting in hydrophobic controlled-release ureas (SCRUs). This hydrophobic-enabled coating is environmentally friendly and highly efficient. The products were characterized by specific scanning electron microscopy, energy-dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, thermogravimetric analysis, and differential scanning calorimetry, and the water contact angles of CRUs and SCRUs were determined. The nutrient-release characteristics of the SCRUs in water were determined at 25 °C and compared with those of CRUs. The results showed that the modification with dimethyl siloxane reduced the N release rate and increased the longevity of the fertilizer coated with hydrophobic bio-based coating material. In addition, organosilicon atoms on the SCRU surface also block the micro-holes on the coating and thus reduce the entry of water onto the coating. The results suggest that the new coating technology can create a hydrophobic surface on bio-based coating material and thus improve their controlled-release characteristics.

CollaPPI: A Collaborative Learning Framework for Predicting Protein-Protein Interactions.

Exploring protein-protein interaction (PPI) is of paramount importance for elucidating the intrinsic mechanism of various biological processes. Nevertheless, experimental determination of PPI can be both time-consuming and expensive, motivating the exploration of data-driven deep learning technologies as a viable, efficient, and accurate alternative. Nonetheless, most current deep learning-based methods regarded a pair of proteins to be predicted for possible interaction as two separate entities when extracting PPI features, thus neglecting the knowledge sharing among the collaborative protein and the target protein. Aiming at the above issue, a collaborative learning framework CollaPPI was proposed in this study, where two kinds of collaboration, i.e., protein-level collaboration and task-level collaboration, were incorporated to achieve not only the knowledge-sharing between a pair of proteins, but also the complementation of such shared knowledge between biological domains closely related to PPI (i.e., protein function, and subcellular location). Evaluation results demonstrated that CollaPPI obtained superior performance compared to state-of-the-art methods on two PPI benchmarks. Besides, evaluation results of CollaPPI on the additional PPI type prediction task further proved its excellent generalization ability.

Complexes of glycated casein and carboxymethyl cellulose enhance stability and control release of anthocyanins.

To improve the stability and sustained-release property of anthocyanins (ACNs), casein (CA) - dextran (DEX) glycated conjugates (UGCA) and carboxymethyl cellulose (CMC) were used to prepare ACNs-loaded binary and ternary complexes. The ACNs-loaded binary complexes (ACNs-UGCA) and ternary complexes (ACNs-UGCA-CMC) achieved by 8 min' ultrasonic treatment with 40 % amplitude. The binary and ternary complexes showed spherical structure and good dispersibility, with the average size of 121.2 nm and 132.4 nm respectively. The anthocyanins encapsulation efficiency of ACNs-UGCA-CMC increased almost 20 % than ACNs-UGCA. ACNs-UGCA-CMC had better colloidal stabilities than ACNs-UGCA, such as thermal stability and dilution stability. Simultaneously, both of the binary and ternary complexes significantly prevented anthocyanins from being degraded by heat treatment, ascorbic acid, sucrose and simulated gastrointestinal environment. The protective effect of ACNs-UGCA-CMC was more significant. Furthermore, ACNs-UGCA-CMC showed slower anthocyanins release in simulated releasing environment in vitro and a long retention time in vivo. Our current study provides a potential delivery for improving the stability and controlling release of anthocyanins.

Structural and functional changes in the default mode network in drug-resistant epilepsy.

PURPOSE: To investigate brain network properties and connectivity abnormalities of the default mode network (DMN) in drug-resistant epilepsy (DRE). The study was based on probabilistic fiber tracking and functional connectivity (FC) analysis, to explore the structural and functional connectivity patterns change between frontal lobe epilepsy (FLE) and temporal lobe epilepsy (TLE). METHODS: A total of 33 DRE patients (18 TLE and 15 FLE) and 30 healthy controls (HCs) were recruited. The volume fraction of the septal brain region of the DMN in DRE was calculated using FreeSurfer. The FC analysis was performed using Data Processing and Analysis for Brain Imaging in MATLAB. The structural connections between brain regions of the DMN were calculated based on probabilistic fiber tracking. RESULTS: The left precuneus (PCUN) volumes in epilepsy groups were lower than that in HCs. Compared with FLE, TLE showed reduced FC between the left hippocampus (HIP) and PCUN/medial frontal gyrus, and between the right inferior parietal lobule (IPL) and right superior temporal gyrus. Compared with HCs, FLE showed increased FCs between the right IPL and occipital lobe, and between the left superior frontal gyrus (SFG) and bilateral superior temporal gyrus. In terms of structural connectivity, TLE exhibited increased connectivity strength between the left SFG and left PCUN, and showed reduced connection strength between the left HIP and left posterior cingulate gyrus/left PCUN, when compared with the FLE. CONCLUSIONS: TLE and FLE patients showed structural and functional changes in the DMN. Compared with FLE patients, the TLE patients showed reduced structural and functional connection strengths between the left HIP and PCUN. These alterations in connection strengths holds promise for the identification of TLE and FLE.

HiSIF-DTA: A Hierarchical Semantic Information Fusion Framework for Drug-Target Affinity Prediction.

Accurately identifying drug-target affinity (DTA) plays a significant role in promoting drug discovery and has attracted increasing attention in recent years. Exploring appropriate protein representation methods and increasing the abundance of protein information is critical in enhancing the accuracy of DTA prediction. Recently, numerous deep learning-based models have been proposed to utilize the sequential or structural features of target proteins. However, these models capture only the low-order semantics that exist in a single protein, while the high-order semantics abundant in biological networks are largely ignored. In this article, we propose HiSIF-DTA'a hierarchical semantic information fusion framework for DTA prediction. In this framework, a hierarchical protein graph is constructed that includes not only contact maps as low-order structural semantics but also protein-rotein interaction (PPI) networks as high-order functional semantics. Particularly, two distinct hierarchical fusion strategies (i.e., Top-down and Bottom-Up) are designed to integrate the different protein semantics, therefore contributing to a richer protein representation. Comprehensive experimental results demonstrate that HiSIF-DTA outperforms current state-of-the-art methods for prediction on the benchmark datasets of the DTA task. Further validation on binary tasks and visualization analysis demonstrates the generalization and interpretation abilities of the proposed method.

Ammonium Sulfite Slurry from Ammonia-Based Desulfurization Activates Water-Soluble Humic Substances from Lignite: Performance, Application, and Mechanism.

Both ammonium sulfite slurry (ASS) from ammonia-based desulfurization and lignite are waste materials with low value. In this work, an innovative method was developed by applying ASS in lignite activation to produce water-soluble humic substances (WHSs) with a high bioactivity and economic value. The optimal activation method was to mix lignite and ASS at a 4:1-liquid-solid ratio by vortex blender and then oscillate it for 30 min at 25 °C. Compared with that of the unactivated lignite (UAL), the yield of WHSs from activated lignite (AL) increased by 42.72%. WHSs from AL consisted of a large number of aliphatic carbons with low molecular weight and functional groups such as amides, amines, sulfonic acid groups, C-O, and so forth. Moreover, WHSs from AL at lower concentrations (2 mg/L) has a more obvious root-elongation-promoting effect than WHSs from UAL (10 mg/L). Activation experiment with the lignite-related model compounds revealed that ASS caused the breakage of Caliph-O, Caliph-Caliph, and Carom-Caliph linkages between aromatic rings. These findings provide a theoretical basis for the development of green and sustainable technologies for the beneficial reuse of ASS and lignite in agriculture.

Gene Expression, Enzyme Activity, Nitrogen Use Efficiency, and Yield of Rice Affected by Controlled-Release Nitrogen.

Controlled- or slow-release urea can improve crop nitrogen use efficiencies and yields in many agricultural production systems. The effect of controlled-release urea on the relationships between levels of gene expression and yields has not been adequately researched. We conducted a 2 year field study with direct-seeded rice, which included treatments of controlled-release urea at four rates (120, 180, 240, and 360 kg N ha-1), a standard urea treatment (360 kg N ha-1), and a control treatment without applied nitrogen. Controlled-release urea improved the inorganic nitrogen concentrations of root-zone soil and water, functional enzyme activities, protein contents, grain yields, and nitrogen use efficiencies. Controlled-release urea also improved the gene expressions of nitrate reductase [NAD(P)H] (EC 1.7.1.2), glutamine synthetase (EC 6.3.1.2), and glutamate synthase (EC 1.4.1.14). With the exception of glutamate synthase activity, there were significant correlations among these indices. The results showed that controlled-release urea improved the content of inorganic nitrogen within the rice root zone. Compared with urea, the average enzyme activity of controlled-release urea increased by 50-200%, and the relative gene expression was increased by 3-4 times on average. The added soil nitrogen increased the level of gene expression, allowing enhanced synthesis of enzymes and proteins for nitrogen absorption and use. Hence, controlled-release urea improved the nitrogen use efficiency and the grain yield of rice. Controlled-release urea is an ideal nitrogen fertilizer showing great potential for improving rice production.

Structural and functional differences of the thalamus between drug-naïve Parkinson's disease motor subtypes.

Objective: The thalamus is an integrative hub of motor circuits in Parkinson's disease (PD). This study aimed to investigate the alterations of structure and functional connectivity (FC) of the thalamic subregions in the tremor-dominant (TD) subtype and the postural instability and gait difficulty (PIGD) subtype in PD. Methods: A total of 59 drug-naïve patients (24 TD and 35 PIGD) and 37 healthy controls were recruited. The volumes of the thalamus and the thalamic subregions were calculated using FreeSurfer. Functional connectivity (FC) analysis of the resting-state functional MRI (rsfMRI) was conducted on the thalamic subregions. Finally, the altered structure and FC were used for correlation analysis with clinical motor scores and for further motor subtypes differentiation. Results: The volumes of the left posterior parietal thalamus (PPtha) in TD patients were significantly lower than those of PIGD patients. Compared with PIGD patients, TD patients exhibited higher FC between the thalamic subregions, the left middle temporal gyrus (MTG), the right dorsolateral superior frontal gyrus (SFGdl), the left middle occipital gyrus (MOG), and the right superior temporal gyrus (STG). Compared with HCs, TD patients showed higher FC between the thalamic subregions and the right SFGdl, as well as the left MOG. Compared with HCs, PIGD patients showed lower FC between the thalamic subregions and the left MTG. In addition, the altered FC was closely related to clinical symptoms and performed high-discriminative power in differentiating the motor subtypes. Conclusion: Increased FC between the thalamic subregions and the sensory cortices in TD patients may indicate a better compensatory capacity for impairment of sensory information integration than that in PIGD patients. The altered FC between the thalamus and the MTG was a potential biomarker for the distinction of the PD motor subtypes.

Cardiac biophysical detailed synergetic modality rendering and visible correlation.

The heart is a vital organ in the human body. Research and treatment for the heart have made remarkable progress, and the functional mechanisms of the heart have been simulated and rendered through the construction of relevant models. The current methods for rendering cardiac functional mechanisms only consider one type of modality, which means they cannot show how different types of modality, such as physical and physiological, work together. To realistically represent the three-dimensional synergetic biological modality of the heart, this paper proposes a WebGL-based cardiac synergetic modality rendering framework to visualize the cardiac physical volume data and present synergetic correspondence rendering of the cardiac electrophysiological modality. By constructing the biological detailed interactive histogram, users can implement local details rendering for the heart, which could reveal the cardiac biology details more clearly. We also present cardiac physical-physiological correlation visualization to explore cardiac biological association characteristics. Experimental results show that the proposed framework can provide favorable cardiac biological detailed synergetic modality rendering results in terms of both effectiveness and efficiency. Compared with existing methods, the framework can facilitate the study of the internal mechanism of the heart and subsequently deduce the process of initiation, development, and transformation from a healthy heart to an ill one, and thereby improve the diagnosis and treatment of cardiac disorders.

Predicting Drug-Target Affinity by Learning Protein Knowledge From Biological Networks.

Predicting drug-target affinity (DTA) is a crucial step in the process of drug discovery. Efficient and accurate prediction of DTA would greatly reduce the time and economic cost of new drug development, which has encouraged the emergence of a large number of deep learning-based DTA prediction methods. In terms of the representation of target proteins, current methods can be classified into 1D sequence- and 2D-protein graph-based methods. However, both two approaches focused only on the inherent properties of the target protein, but neglected the broad prior knowledge regarding protein interactions that have been clearly elucidated in past decades. Aiming at the above issue, this work presents an end-to-end DTA prediction method named MSF-DTA (Multi-Source Feature Fusion-based Drug-Target Affinity). The contributions can be summarized as follows. First, MSF-DTA adopts a novel "neighboring feature"-based protein representation. Instead of utilizing only the inherent features of a target protein, MSF-DTA gathers additional information for the target protein from its biologically related "neighboring" proteins in PPI (i.e., protein-protein interaction) and SSN (i.e., sequence similarity) networks to get prior knowledge. Second, the representation was learned using an advanced graph pre-training framework, VGAE, which could not only gather node features but also learn topological connections, therefore contributing to a richer protein representation and benefiting the downstream DTA prediction task. This study provides new perspective for the DTA prediction task, and evaluation results demonstrated that MSF-DTA obtained superior performances compared to current state-of-the-art methods.

Preparation and Nutrient Release Characteristics of Liquefied Apple Tree Branche-Based Biocoated Large Tablet Urea.

Controlled-release fertilizers (CRFs) could improve crop yield and fertilizer use efficiency. However, the coating materials of conventional CRFs are mainly derived from petrochemical products, which are expensive and nondegradable, bringing potential environmental pollution. Therefore, using sustainable bio-based materials is the development direction. In this study, large tablet urea (LTU) was prepared using physical extrusion technology. The economical and biodegradable liquefied apple tree branch bio-based coating material was used to coat LTU, obtaining large tablet CRFs (LTCRUs). Also, the optimum proportion of liquefaction of apple tree branches modified by castor oil was studied. The specific surface area, surface morphology, and FTIR of LTCRU were characterized. The results showed that the surface of the LTCRU was the most smooth and the LTCRU modified with 30% castor oil presented the best controlled-release characteristics. The specific surface area of LTCRU was one-third of that of traditional small-particle fertilizers, which indicated that reducing the using dosage of coating materials is economical. Overall, this work provided theoretical and technical supports for the industrialization of biocoated superlarge tablet urea, which is conducive to the green development of agriculture.

Development of multifunctional copper alginate and bio-polyurethane bilayer coated fertilizer: Controlled-release, selenium supply and antifungal.

Bio-based controlled release fertilizers (BCRFs) are cost-effective and renewable thus gradually replacing petroleum-based controlled release fertilizers (CRFs). However, most of the study mainly focused on modifying BCRFs to improve controlled-release performance. It is necessary to further increase the functionality of BCRF for expanding the application. A multifunctional double layered bio-based CRF (DCRF) was prepared. Urea was used as the core of fertilizer, bio-based polyurethane was used as the inner coating, and sodium alginate and copper ions formed the hydrogel as the outer coating. In addition, mesoporous silica nanoparticles loaded with sodium selenate was used to modify the sodium alginate hydrogel (MSN@Se hydrogel). The results showed that the nitrogen longevity of the DCRF was much better than that of urea and BCRF. The selenium nutrient longevity of the DCRF was 40 h, much longer than that of sodium selenate. The DCRF improved the yield and nutritive value of cherry radish (Raphanus sativus L. var.radculus pers) with the elevated contents of selenium, an essential trace element. Moreover, the DCRF showed inhibitory effect on Fusarium oxysporum Schltdl. and could resist soil-borne fungal diseases continuously. Overall, this multifunctional fertilizer has great potential for expanding the use of BCRFs for sustainable development of agriculture.

In silico assessment of pharmacotherapy for carbon monoxide induced arrhythmias in healthy and failing human hearts.

Background: Carbon monoxide (CO) is gaining increased attention in air pollution-induced arrhythmias. The severe cardiotoxic consequences of CO urgently require effective pharmacotherapy to treat it. However, existing evidence demonstrates that CO can induce arrhythmias by directly affecting multiple ion channels, which is a pathway distinct from heart ischemia and has received less concern in clinical treatment. Objective: To evaluate the efficacy of some common clinical antiarrhythmic drugs for CO-induced arrhythmias, and to propose a potential pharmacotherapy for CO-induced arrhythmias through the virtual pathological cell and tissue models. Methods: Two pathological models describing CO effects on healthy and failing hearts were constructed as control baseline models. After this, we first assessed the efficacy of some common antiarrhythmic drugs like ranolazine, amiodarone, nifedipine, etc., by incorporating their ion channel-level effects into the cell model. Cellular biomarkers like action potential duration and tissue-level biomarkers such as the QT interval from pseudo-ECGs were obtained to assess the drug efficacy. In addition, we also evaluated multiple specific I Kr activators in a similar way to multi-channel blocking drugs, as the I Kr activator showed great potency in dealing with CO-induced pathological changes. Results: Simulation results showed that the tested seven antiarrhythmic drugs failed to rescue the heart from CO-induced arrhythmias in terms of the action potential and the ECG manifestation. Some of them even worsened the condition of arrhythmogenesis. In contrast, I Kr activators like HW-0168 effectively alleviated the proarrhythmic effects of CO. Conclusion: Current antiarrhythmic drugs including the ranolazine suggested in previous studies did not achieve therapeutic effects for the cardiotoxicity of CO, and we showed that the specific I Kr activator is a promising pharmacotherapy for the treatment of CO-induced arrhythmias.

Bio-based Polyurethane Based on a Dynamic Covalent Network with Damage Tolerance for Controlled Release of Fertilizers.

Bio-based polyurethanes are promising for the controlled release of nutrients and fertilizers, but their toughness and plasticity need to be improved. We developed a smooth, dense, elastic, and indestructible bio-based polyurethane (BPU) coating with a nutrient controlled release ∼150% superior, a tensile strength ∼300% higher, and a toughness ∼1200% higher than those for the original BPU coating. Through a one-step reaction of soybean oil polyols (accounting for more than 60%), isocyanate, and benzil dioxime, the dynamic covalent network based on oxime-carbamate replaces part of irreversible covalent cross-linking. The dynamic fracture-bonding reaction in the modified coating BPU can effectively promote the hydrogen bond recombination and oxime-carbamate chain migration in the coating process, which avoids the structural defects caused by coating tear and fertilizer collision. This work provides a simple and versatile strategy for building controlled-release fertilizer coatings.

Computational analysis of arrhythmogenesis in KCNH2 T618I mutation-associated short QT syndrome and the pharmacological effects of quinidine and sotalol.

Short QT syndrome (SQTS) is a rare but dangerous genetic disease. In this research, we conducted a comprehensive in silico investigation into the arrhythmogenesis in KCNH2 T618I-associated SQTS using a multi-scale human ventricle model. A Markov chain model of IKr was developed firstly to reproduce the experimental observations. It was then incorporated into cell, tissue, and organ models to explore how the mutation provided substrates for ventricular arrhythmias. Using this T618I Markov model, we explicitly revealed the subcellular level functional alterations by T618I mutation, particularly the changes of ion channel states that are difficult to demonstrate in wet experiments. The following tissue and organ models also successfully reproduced the changed dynamics of reentrant spiral waves and impaired rate adaptions in hearts of T618I mutation. In terms of pharmacotherapy, we replicated the different effects of a drug under various conditions using identical mathematical descriptions for drugs. This study not only simulated the actions of an effective drug (quinidine) at various physiological levels, but also elucidated why the IKr inhibitor sotalol failed in SQT1 patients through profoundly analyzing its mutation-dependent actions.

Deep learning methods for molecular representation and property prediction.

With advances in artificial intelligence (AI) methods, computer-aided drug design (CADD) has developed rapidly in recent years. Effective molecular representation and accurate property prediction are crucial tasks in CADD workflows. In this review, we summarize contemporary applications of deep learning (DL) methods for molecular representation and property prediction. We categorize DL methods according to the format of molecular data (1D, 2D, and 3D). In addition, we discuss some common DL models, such as ensemble learning and transfer learning, and analyze the interpretability methods for these models. We also highlight the challenges and opportunities of DL methods for molecular representation and property prediction.

Clinical features of NOTCH2NLC-related neuronal intranuclear inclusion disease.

BACKGROUND: Abnormal expanded GGC repeats within the NOTCH2HLC gene has been confirmed as the genetic mechanism for most Asian patients with neuronal intranuclear inclusion disease (NIID). This cross-sectional observational study aimed to characterise the clinical features of NOTCH2NLC-related NIID in China. METHODS: Patients with NOTCH2NLC-related NIID underwent an evaluation of clinical symptoms, a neuropsychological assessment, electrophysiological examination, MRI and skin biopsy. RESULTS: In the 247 patients with NOTCH2NLC-related NIID, 149 cases were sporadic, while 98 had a positive family history. The most common manifestations were paroxysmal symptoms (66.8%), autonomic dysfunction (64.0%), movement disorders (50.2%), cognitive impairment (49.4%) and muscle weakness (30.8%). Based on the initial presentation and main symptomology, NIID was divided into four subgroups: dementia dominant (n=94), movement disorder dominant (n=63), paroxysmal symptom dominant (n=61) and muscle weakness dominant (n=29). Clinical (42.7%) and subclinical (49.1%) peripheral neuropathies were common in all types. Typical diffusion-weighted imaging subcortical lace signs were more frequent in patients with dementia (93.9%) and paroxysmal symptoms types (94.9%) than in those with muscle weakness (50.0%) and movement disorders types (86.4%). GGC repeat sizes were negatively correlated with age of onset (r=-0.196, p<0.05), and in the muscle weakness-dominant type (median 155.00), the number of repeats was much higher than in the other three groups (p<0.05). In NIID pedigrees, significant genetic anticipation was observed (p<0.05) without repeat instability (p=0.454) during transmission. CONCLUSIONS: NIID is not rare; however, it is usually misdiagnosed as other diseases. Our results help to extend the known clinical spectrum of NOTCH2NLC-related NIID.

Enhancing Protein Function Prediction Performance by Utilizing AlphaFold-Predicted Protein Structures.

The structure of a protein is of great importance in determining its functionality, and this characteristic can be leveraged to train data-driven prediction models. However, the limited number of available protein structures severely limits the performance of these models. AlphaFold2 and its open-source data set of predicted protein structures have provided a promising solution to this problem, and these predicted structures are expected to benefit the model performance by increasing the number of training samples. In this work, we constructed a new data set that acted as a benchmark and implemented a state-of-the-art structure-based approach for determining whether the performance of the function prediction model can be improved by putting additional AlphaFold-predicted structures into the training set and further compared the performance differences between two models separately trained with real structures only and AlphaFold-predicted structures only. Experimental results indicated that structure-based protein function prediction models could benefit from virtual training data consisting of AlphaFold-predicted structures. First, model performances were improved in all three categories of Gene Ontology terms (GO terms) after adding predicted structures as training samples. Second, the model trained only on AlphaFold-predicted virtual samples achieved comparable performances to the model based on experimentally solved real structures, suggesting that predicted structures were almost equally effective in predicting protein functionality.

Sequence-based drug-target affinity prediction using weighted graph neural networks.

BACKGROUND: Affinity prediction between molecule and protein is an important step of virtual screening, which is usually called drug-target affinity (DTA) prediction. Its accuracy directly influences the progress of drug development. Sequence-based drug-target affinity prediction can predict the affinity according to protein sequence, which is fast and can be applied to large datasets. However, due to the lack of protein structure information, the accuracy needs to be improved. RESULTS: The proposed model which is called WGNN-DTA can be competent in drug-target affinity (DTA) and compound-protein interaction (CPI) prediction tasks. Various experiments are designed to verify the performance of the proposed method in different scenarios, which proves that WGNN-DTA has the advantages of simplicity and high accuracy. Moreover, because it does not need complex steps such as multiple sequence alignment (MSA), it has fast execution speed, and can be suitable for the screening of large databases. CONCLUSION: We construct protein and molecular graphs through sequence and SMILES that can effectively reflect their structures. To utilize the detail contact information of protein, graph neural network is used to extract features and predict the binding affinity based on the graphs, which is called weighted graph neural networks drug-target affinity predictor (WGNN-DTA). The proposed method has the advantages of simplicity and high accuracy.

Mechanistic Insights Into Inflammation-Induced Arrhythmias: A Simulation Study.

Cardiovascular diseases are the primary cause of death of humans, and among these, ventricular arrhythmias are the most common cause of death. There is plausible evidence implicating inflammation in the etiology of ventricular fibrillation (VF). In the case of systemic inflammation caused by an overactive immune response, the induced inflammatory cytokines directly affect the function of ion channels in cardiomyocytes, leading to a prolonged action potential duration (APD). However, the mechanistic links between inflammatory cytokine-induced molecular and cellular influences and inflammation-associated ventricular arrhythmias need to be elucidated. The present study aimed to determine the potential impact of systemic inflammation on ventricular electrophysiology by means of multiscale virtual heart models. The experimental data on the ionic current of three major cytokines [i.e., tumor necrosis factor-α (TNF-α), interleukin-1 (IL-1ß), and interleukin-6 (IL-6)] were incorporated into the cell model, and the effects of each cytokine and their combined effect on the cell action potential (AP) were evaluated. Moreover, the integral effect of these cytokines on the conduction of excitation waves was also investigated in a tissue model. The simulation results suggested that inflammatory cytokines significantly prolonged APD, enhanced the transmural and regional repolarization heterogeneities that predispose to arrhythmias, and reduced the adaptability of ventricular tissue to fast heart rates. In addition, simulated pseudo-ECGs showed a prolonged QT interval-a manifestation consistent with clinical observations. In summary, the present study provides new insights into ventricular arrhythmias associated with inflammation.