A mathematical model simulating the adaptive immune response in various vaccines and vaccination strategies.

Vaccination has been widely recognized as an effective measure for preventing infectious diseases. To facilitate quantitative research into the activation of adaptive immune responses in the human body by vaccines, it is important to develop an appropriate mathematical model, which can provide valuable guidance for vaccine development. In this study, we constructed a novel mathematical model to simulate the dynamics of antibody levels following vaccination, based on principles from immunology. Our model offers a concise and accurate representation of the kinetics of antibody response. We conducted a comparative analysis of antibody dynamics within the body after administering several common vaccines, including traditional inactivated vaccines, mRNA vaccines, and future attenuated vaccines based on defective interfering viral particles (DVG). Our findings suggest that booster shots play a crucial role in enhancing Immunoglobulin G (IgG) antibody levels, and we provide a detailed discussion on the advantages and disadvantages of different vaccine types. From a mathematical standpoint, our model proposes four essential approaches to guide vaccine design: enhancing antigenic T-cell immunogenicity, directing the production of high-affinity antibodies, reducing the rate of IgG decay, and lowering the peak level of vaccine antigen-antibody complexes. Our study contributes to the understanding of vaccine design and its application by explaining various phenomena and providing guidance in comprehending the interactions between antibodies and antigens during the immune process.

AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network.

The structural stability of proteins is an important topic in various fields such as biotechnology, pharmaceuticals, and enzymology. Specifically, understanding the structural stability of protein is crucial for protein design. Artificial design, while pursuing high thermodynamic stability and rigidity of proteins, inevitably sacrifices biological functions closely related to protein flexibility. The thermodynamic stability of proteins is not always optimal when they are highest to perfectly perform their biological functions. Extensive theoretical and experimental screening is often required to obtain stable protein structures. Thus, it becomes critically important to develop a stability prediction model based on the balance between protein stability and bioactivity. To design protein drugs with better functionality in a broader structural space, a novel protein structural stability predictor called PSSP has been developed in this study. PSSP is a mean pooled dual graph convolutional network (GCN) model based on sequence characteristics and secondary structure, distance matrix, graph, and residue properties of a nanoprotein to provide rapid prediction and judgment. This model exhibits excellent robustness in predicting the structural stability of nanoproteins. Comparing with previous artificial intelligence algorithms, the results indicate this model can provide a rapid and accurate assessment of the structural stability of artificially designed proteins, which shows the great promises for promoting the robust development of protein design.

Ceramic Microsphere Aerogels by Curled Precursor Molecular Chain: Antishrinkage, Thermal Insulation and Multiscale Simulations.

Ceramic aerogels prepared by the route of polymer-derived ceramics (PDCs) have been of significant attention in high-temperature insulation. However, the application of ceramic aerogels was seriously confined due to the structural damage and shrinkage cracking of ceramic precursors during pyrolysis. In this investigation, precursor ceramic microsphere aerogels with outstanding antishrinkage properties were prepared by storing strain in curled molecular chains and using polysilazane (PSZ) as the precursor. Meanwhile, precursor ceramic microsphere aerogels with different curled molecular chain structures were prepared by modulating solvent interactions and cross-linked structures. Different curled molecular chain structures were formed, and the impact on the antishrinkage properties of precursor aerogels was analyzed. The shrinkage resistance, thermal insulation, and mechanical properties of the prepared aerogels were tested and compared. Furthermore, the mechanism of the impact of different curled molecular chain structures on thermal insulation and mechanical properties was investigated through multiscale simulations combined with fractal theory. The thermal and stress transfer at the interfaces of different microsphere skeleton structures and the mechanisms were investigated. An idea for solving the problem of pyrolytic shrinkage in the preparation of ceramic aerogels was provided in this investigation. In addition, insights into the influence of the microsphere skeleton structure on the thermal and mechanical properties of ceramic aerogels were provided.

Increasing trends of non-tuberculous mycobacteria clinical isolates in Guangzhou, China.

Non-tuberculous mycobacteria (NTM) are one of major public health concern. The current study aimed to find the prevalence trends of NTM in Guangzhou, China from January 2018 to December 2023. A total of 26,716 positive mycobacterial cultures were collected. Thirty-six specimens with incomplete personal information were excluded. The remaining 26,680 specimens were identified using a gene chip method. 16,709 isolates were Mycobacterium tuberculosis (MTB) (62.63 %), and 9,971 were NTM (37.37 %). 43.43 % (4,330/9,971) of NTM isolates were male, and 56.57 % (5,641/9,971) were female (χ2 = 24.36, P < 0.05), a male to female ratio of approximately 1:1.30. Infections in individuals with aged 40 years and above was higher (77.63 %) than below 40 years (22.37 %) (χ2 = 4.94, P = 0.026). The annual NTM isolation rates from 2018 to 2023 were 32.03 %, 34.00 %, 36.27 %, 38.58 %, 38.99 %, and 43.24 %, respectively, showing an increasing trend (χ2 for trend = 0.097, P < 0.05) (R = 0.097, P < 0.05). Out of 9,971 NTM isolates, 8,881 cases include only five common NTM species (MAC, M. abscessus/M. chelonae, M. kansasii, M. fortuitum, and M. gordonae). The overall NTM isolation rate was 37.37 %. The NTM isolation rate was significantly higher than the national average, showing an increasing trend over the last six years.

Unveiling the Therapeutic Potential of Paclitaxel Combinations Against Breast Carcinoma and Identification of In Vivo Biomarkers.

Breast cancer (BC) is one of the leading causes of high mortality rates in women worldwide. Although advancements have been made in the design of therapeutic strategies and drug discovery, drug resistance remains one of the key challenges. One of the ways to overcome drug resistance is finding potential drug combinations since the efficacy of combined drugs is higher than their individual efficacies if the combination is a synergistic pair. Therefore, the current study uses a BC patient-derived xenograft (PDX) dataset to evaluate the effects of various cancer drugs on breast cancer in vivo models. The drug effects are further validated by four machine learning models, namely Elastic Net, Least Absolute Shrinkage and Selection (LASSO), Support Vector Machine (SVM), Random Forests (RF), as well as exploring the shortlisted drugs in combination with paclitaxel, a baseline drug for enhanced efficacy on tumor volume reduction. Additionally, the study also shortlists the top 50 in vivo biomarkers correlated with the effects of the drugs. The outcomes could be significantly important for the design of an effective anti-breast cancer therapy.

Integrated structural proteomics and machine learning-guided mapping of a highly protective precision vaccine against mycoplasma pulmonis.

Mycoplasma pulmonis (M. pulmonis) is an emerging respiratory infection commonly linked to prostate cancer, and it is classified under the group of mycoplasmas. Improved management of mycoplasma infections is essential due to the frequent ineffectiveness of current antibiotic treatments in completely eliminating these pathogens from the host. The objective of this study is to design and construct effective and protective vaccines guided by structural proteomics and machine learning algorithms to provide protection against the M. pulmonis infection. Through a thorough examination of the entire proteome of M. pulmonis, four specific targets Membrane protein P80, Lipoprotein, Uncharacterized protein and GGDEF domain-containing protein have been identified as appropriate for designing a vaccine. The proteins underwent mapping of cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL) (IFN)-γ ±, and B-cell epitopes using artificial and recurrent neural networks. The design involved the creation of mRNA and peptide-based vaccine, which consisted of 8 CTL epitopes associated by GGS linkers, 7 HTL (IFN-positive) epitopes, and 8 B-cell epitopes joined by GPGPG linkers. The vaccine designed exhibit antigenic behavior, non-allergenic qualities, and exceptional physicochemical attributes. Structural modeling revealed that correct folding is crucial for optimal functioning. The coupling of the MEVC and Toll-like Receptors (TLR)1, TLR2, and TLR6 was examined through molecular docking experiments. This was followed by molecular simulation investigations, which included binding free energy estimations. The results indicated that the dynamics of the interaction were stable, and the binding was strong. In silico cloning and optimization analysis revealed an optimized sequence with a GC content of 49.776 % and a CAI of 0.982. The immunological simulation results showed strong immune responses, with elevated levels of active and plasma B-cells, regulatory T-cells, HTL, and CTL in both IgM+IgG and secondary immune responses. The antigen was completely cleared by the 50th day. This study lays the foundation for creating a potent and secure vaccine candidate to combat the newly identified M. pulmonis infection in people.

DDSBC: A Stacking Ensemble Classifier-Based Approach for Breast Cancer Drug-Pair Cell Synergy Prediction.

Breast cancer (BC) ranks as a leading cause of mortality among women worldwide, with incidence rates continuing to rise. The quest for effective treatments has led to the adoption of drug combination therapy, aiming to enhance drug efficacy. However, identifying synergistic drug combinations remains a daunting challenge due to the myriad of potential drug pairs. Current research leverages machine learning (ML) and deep learning (DL) models for drug-pair synergy prediction and classification. Nevertheless, these models often underperform on specific cancer types, including BC, as they are trained on data spanning various cancers without any specialization. Here, we introduce a stacking ensemble classifier, the drug-drug synergy for breast cancer (DDSBC), tailored explicitly for BC drug-pair cell synergy classification. Unlike existing models that generalize across cancer types, DDSBC is exclusively developed for BC, offering a more focused approach. Our comparative analysis against classical ML methods as well as DL models developed for drug synergy prediction highlights DDSBC's superior performance across test and independent datasets on BC data. Despite certain metrics where other methods narrowly surpass DDSBC by 1-2%, DDSBC consistently emerges as the top-ranked model, showcasing significant differences in scoring metrics and robust performance in ablation studies. DDSBC's performance and practicality position it as a preferred choice or an adjunctive validation tool for identifying synergistic or antagonistic drug pairs in BC, providing valuable insights for treatment strategies.

Correction: Suleman et al. Immunoinformatics and Immunogenetics-Based Design of Immunogenic Peptides Vaccine against the Emerging Tick-Borne Encephalitis Virus (TBEV) and Its Validation through In Silico Cloning and Immune Simulation. Vaccines 2021, 9, 1210.

The authors would like to make the following correction to this published paper [...].

CELA-MFP: a contrast-enhanced and label-adaptive framework for multi-functional therapeutic peptides prediction.

Functional peptides play crucial roles in various biological processes and hold significant potential in many fields such as drug discovery and biotechnology. Accurately predicting the functions of peptides is essential for understanding their diverse effects and designing peptide-based therapeutics. Here, we propose CELA-MFP, a deep learning framework that incorporates feature Contrastive Enhancement and Label Adaptation for predicting Multi-Functional therapeutic Peptides. CELA-MFP utilizes a protein language model (pLM) to extract features from peptide sequences, which are then fed into a Transformer decoder for function prediction, effectively modeling correlations between different functions. To enhance the representation of each peptide sequence, contrastive learning is employed during training. Experimental results demonstrate that CELA-MFP outperforms state-of-the-art methods on most evaluation metrics for two widely used datasets, MFBP and MFTP. The interpretability of CELA-MFP is demonstrated by visualizing attention patterns in pLM and Transformer decoder. Finally, a user-friendly online server for predicting multi-functional peptides is established as the implementation of the proposed CELA-MFP and can be freely accessed at http://dreamai.cmii.online/CELA-MFP.

Sources, transmission and hospital-associated outbreaks of nontuberculous mycobacteria: a review.

Nontuberculous mycobacteria (NTM) are widespread environmental organisms found in both natural and man-made settings, such as building plumbing, water distribution networks and hospital water systems. Their ubiquitous presence increases the risk of transmission, leading to a wide range of human infections, particularly in immunocompromised individuals. NTM primarily spreads through environmental exposures, such as inhaling aerosolized particles, ingesting contaminated food and introducing it into wounds. Hospital-associated outbreaks have been linked to contaminated medical devices and water systems. Furthermore, the rising global incidence, prevalence and isolation rates highlight the urgency of addressing NTM infections. Gaining a thorough insight into the sources and epidemiology of NTM infection is crucial for devising novel strategies to prevent and manage NTM transmission and infections.

Non-tuberculous mycobacteria (NTM) are environmental pathogens affecting humans and animals, with a substantial public health impact. These bacteria have been frequently identified in various natural and human-engineered settings, contributing to their potential transmission.

An end-to-end method for predicting compound-protein interactions based on simplified homogeneous graph convolutional network and pre-trained language model.

Identification of interactions between chemical compounds and proteins is crucial for various applications, including drug discovery, target identification, network pharmacology, and elucidation of protein functions. Deep neural network-based approaches are becoming increasingly popular in efficiently identifying compound-protein interactions with high-throughput capabilities, narrowing down the scope of candidates for traditional labor-intensive, time-consuming and expensive experimental techniques. In this study, we proposed an end-to-end approach termed SPVec-SGCN-CPI, which utilized simplified graph convolutional network (SGCN) model with low-dimensional and continuous features generated from our previously developed model SPVec and graph topology information to predict compound-protein interactions. The SGCN technique, dividing the local neighborhood aggregation and nonlinearity layer-wise propagation steps, effectively aggregates K-order neighbor information while avoiding neighbor explosion and expediting training. The performance of the SPVec-SGCN-CPI method was assessed across three datasets and compared against four machine learning- and deep learning-based methods, as well as six state-of-the-art methods. Experimental results revealed that SPVec-SGCN-CPI outperformed all these competing methods, particularly excelling in unbalanced data scenarios. By propagating node features and topological information to the feature space, SPVec-SGCN-CPI effectively incorporates interactions between compounds and proteins, enabling the fusion of heterogeneity. Furthermore, our method scored all unlabeled data in ChEMBL, confirming the top five ranked compound-protein interactions through molecular docking and existing evidence. These findings suggest that our model can reliably uncover compound-protein interactions within unlabeled compound-protein pairs, carrying substantial implications for drug re-profiling and discovery. In summary, SPVec-SGCN demonstrates its efficacy in accurately predicting compound-protein interactions, showcasing potential to enhance target identification and streamline drug discovery processes.Scientific contributionsThe methodology presented in this work not only enables the comparatively accurate prediction of compound-protein interactions but also, for the first time, take sample imbalance which is very common in real world and computation efficiency into consideration simultaneously, accelerating the target identification and drug discovery process.

Abrogation of ORF8-IRF3 binding interface with Carbon nanotube derivatives to rescue the host immune system against SARS-CoV-2 by using molecular screening and simulation approaches.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to over six million deaths worldwide. In human immune system, the type 1 interferon (IFN) pathway plays a crucial role in fighting viral infections. However, the ORF8 protein of the virus evade the immune system by interacting with IRF3, hindering its nuclear translocation and consequently downregulate the type I IFN signaling pathway. To block the binding of ORF8-IRF3 and inhibit viral pathogenesis a quick discovery of an inhibitor molecule is needed. Therefore, in the present study, the interface between the ORF8 and IRF3 was targeted on a high-affinity carbon nanotube by using computational tools. After analysis of 62 carbon nanotubes by multiple docking with the induced fit model, the top five compounds with high docking scores of - 7.94 kcal/mol, - 7.92 kcal/mol, - 7.28 kcal/mol, - 7.19 kcal/mol and - 7.09 kcal/mol (top hit1-5) were found to have inhibitory activity against the ORF8-IRF3 complex. Molecular dynamics analysis of the complexes revealed the high compactness of residues, stable binding, and strong hydrogen binding network among the ORF8-nanotubes complexes. Moreover, the total binding free energy for top hit1-5 was calculated to be - 43.21 ± 0.90 kcal/mol, - 41.17 ± 0.99 kcal/mol, - 48.85 ± 0.62 kcal/mol, - 43.49 ± 0.77 kcal/mol, and - 31.18 ± 0.78 kcal/mol respectively. These results strongly suggest that the identified top five nanotubes (hit1-5) possess significant potential for advancing and exploring innovative drug therapies. This underscores their suitability for subsequent in vivo and in vitro experiments, marking them as promising candidates worthy of further investigation.

In Silico Prediction of New Inhibitors for Kirsten Rat Sarcoma G12D Cancer Drug Target Using Machine Learning-Based Virtual Screening, Molecular Docking, and Molecular Dynamic Simulation Approaches.

Single-point mutations in the Kirsten rat sarcoma (KRAS) viral proto-oncogene are the most common cause of human cancer. In humans, oncogenic KRAS mutations are responsible for about 30% of lung, pancreatic, and colon cancers. One of the predominant mutant KRAS G12D variants is responsible for pancreatic cancer and is an attractive drug target. At the time of writing, no Food and Drug Administration (FDA) approved drugs are available for the KRAS G12D mutant. So, there is a need to develop an effective drug for KRAS G12D. The process of finding new drugs is expensive and time-consuming. On the other hand, in silico drug designing methodologies are cost-effective and less time-consuming. Herein, we employed machine learning algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF) for the identification of new inhibitors against the KRAS G12D mutant. A total of 82 hits were predicted as active against the KRAS G12D mutant. The active hits were docked into the active site of the KRAS G12D mutant. Furthermore, to evaluate the stability of the compounds with a good docking score, the top two complexes and the standard complex (MRTX-1133) were subjected to 200 ns MD simulation. The top two hits revealed high stability as compared to the standard compound. The binding energy of the top two hits was good as compared to the standard compound. Our identified hits have the potential to inhibit the KRAS G12D mutation and can help combat cancer. To the best of our knowledge, this is the first study in which machine-learning-based virtual screening, molecular docking, and molecular dynamics simulation were carried out for the identification of new promising inhibitors for the KRAS G12D mutant.

MF-MNER: Multi-models Fusion for MNER in Chinese Clinical Electronic Medical Records.

To address the problem of poor entity recognition performance caused by the lack of Chinese annotation in clinical electronic medical records, this paper proposes a multi-medical entity recognition method F-MNER using a fusion technique combining BART, Bi-LSTM, and CRF. First, after cleaning, encoding, and segmenting the electronic medical records, the obtained semantic representations are dynamically fused using a bidirectional autoregressive transformer (BART) model. Then, sequential information is captured using a bidirectional long short-term memory (Bi-LSTM) network. Finally, the conditional random field (CRF) is used to decode and output multi-task entity recognition. Experiments are performed on the CCKS2019 dataset, with micro avg Precision, macro avg Recall, weighted avg Precision reaching 0.880, 0.887, and 0.883, and micro avg F1-score, macro avg F1-score, weighted avg F1-score reaching 0.875, 0.876, and 0.876 respectively. Compared with existing models, our method outperforms the existing literature in three evaluation metrics (micro average, macro average, weighted average) under the same dataset conditions. In the case of weighted average, the Precision, Recall, and F1-score are 19.64%, 15.67%, and 17.58% higher than the existing BERT-BiLSTM-CRF model respectively. Experiments are performed on the actual clinical dataset with our MF-MNER, the Precision, Recall, and F1-score are 0.638, 0.825, and 0.719 under the micro-avg evaluation mechanism. The Precision, Recall, and F1-score are 0.685, 0.800, and 0.733 under the macro-avg evaluation mechanism. The Precision, Recall, and F1-score are 0.647, 0.825, and 0.722 under the weighted avg evaluation mechanism. The above results show that our method MF-MNER can integrate the advantages of BART, Bi-LSTM, and CRF layers, significantly improving the performance of downstream named entity recognition tasks with a small amount of annotation, and achieving excellent performance in terms of recall score, which has certain practical significance. Source code and datasets to reproduce the results in this paper are available at https://github.com/xfwang1969/MF-MNER .

Identification of an immune-related gene signature for predicting prognosis and immunotherapy efficacy in liver cancer via cell-cell communication.

BACKGROUND: Liver cancer is one of the deadliest malignant tumors worldwide. Immunotherapy has provided hope to patients with advanced liver cancer, but only a small fraction of patients benefit from this treatment due to individual differences. Identifying immune-related gene signatures in liver cancer patients not only aids physicians in cancer diagnosis but also offers personalized treatment strategies, thereby improving patient survival rates. Although several methods have been developed to predict the prognosis and immunotherapeutic efficacy in patients with liver cancer, the impact of cell-cell interactions in the tumor microenvironment has not been adequately considered. AIM: To identify immune-related gene signals for predicting liver cancer prognosis and immunotherapy efficacy. METHODS: Cell grouping and cell-cell communication analysis were performed on single-cell RNA-sequencing data to identify highly active cell groups in immune-related pathways. Highly active immune cells were identified by intersecting the highly active cell groups with B cells and T cells. The significantly differentially expressed genes between highly active immune cells and other cells were subsequently selected as features, and a least absolute shrinkage and selection operator (LASSO) regression model was constructed to screen for diagnostic-related features. Fourteen genes that were selected more than 5 times in 10 LASSO regression experiments were included in a multivariable Cox regression model. Finally, 3 genes (stathmin 1, cofilin 1, and C-C chemokine ligand 5) significantly associated with survival were identified and used to construct an immune-related gene signature. RESULTS: The immune-related gene signature composed of stathmin 1, cofilin 1, and C-C chemokine ligand 5 was identified through cell-cell communication. The effectiveness of the identified gene signature was validated based on experimental results of predictive immunotherapy response, tumor mutation burden analysis, immune cell infiltration analysis, survival analysis, and expression analysis. CONCLUSION: The findings suggest that the identified gene signature may contribute to a deeper understanding of the activity patterns of immune cells in the liver tumor microenvironment, providing insights for personalized treatment strategies.

Fully Bio-Based 2,5-Furandicarboxylic Acid Polyester toward Plastics with Mechanically Robust, Excellent Gas Barrier and Fast Degradation.

Aliphatic-aromatic copolyesters offer a promising solution to mitigate plastic pollution, but high content of aliphatic units (>40 %) often suffer from diminished comprehensive performances. Poly(butylene oxalate-co-furandicarboxylate) (PBOF) copolyesters were synthesized by precisely controlling the oxalic acid content from 10 % to 60 %. Compared with commercial PBAT, the barrier properties of PBOF for H2O and O2 increased by more than 6 and 26 times, respectively. The introduction of the oxalic acid units allowed the water contact angle to be reduced from 82.5° to 62.9°. Superior hydrophilicity gave PBOF an excellent degradation performance within a 35-day hydrolysis. Interestingly, PBO20F and PBO30F also displayed obvious decrease of molecular weight during hydrolysis, with elastic modulus >1 GPa and tensile strength between 35-54 MPa. PBOF achieved the highest hydrolysis rates among the reported PBF-based copolyesters. The hydrolytic mechanism was further explored based on Fukui function analysis and density functional theory (DFT) calculation. Noncovalent analysis indicated that the water molecules formed hydrogen bonding interaction with adjacent ester groups and thus improved the reactivity of carbonyl carbon. PBOF not only meet the requirements of the high-performance packaging market but can quickly degrade after the end of their usage cycles, providing a new choice for green and environmental protection.

ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler.

Numerous investigations increasingly indicate the significance of microRNA (miRNA) in human diseases. Hence, unearthing associations between miRNA and diseases can contribute to precise diagnosis and efficacious remediation of medical conditions. The detection of miRNA-disease linkages via computational techniques utilizing biological information has emerged as a cost-effective and highly efficient approach. Here, we introduced a computational framework named ReHoGCNES, designed for prospective miRNA-disease association prediction (ReHoGCNES-MDA). This method constructs homogenous graph convolutional network with regular graph structure (ReHoGCN) encompassing disease similarity network, miRNA similarity network and known MDA network and then was tested on four experimental tasks. A random edge sampler strategy was utilized to expedite processes and diminish training complexity. Experimental results demonstrate that the proposed ReHoGCNES-MDA method outperforms both homogenous graph convolutional network and heterogeneous graph convolutional network with non-regular graph structure in all four tasks, which implicitly reveals steadily degree distribution of a graph does play an important role in enhancement of model performance. Besides, ReHoGCNES-MDA is superior to several machine learning algorithms and state-of-the-art methods on the MDA prediction. Furthermore, three case studies were conducted to further demonstrate the predictive ability of ReHoGCNES. Consequently, 93.3% (breast neoplasms), 90% (prostate neoplasms) and 93.3% (prostate neoplasms) of the top 30 forecasted miRNAs were validated by public databases. Hence, ReHoGCNES-MDA might serve as a dependable and beneficial model for predicting possible MDAs.

The Mathematical Modeling of the Host-Virus Interaction in Dengue Virus Infection: A Quantitative Study.

Infectious diseases, such as Dengue fever, pose a significant public health threat. Developing a reliable mathematical model plays a crucial role in quantitatively elucidating the kinetic characteristics of antibody-virus interactions. By integrating previous models and incorporating the antibody dynamic theory, we have constructed a novel and robust model that can accurately simulate the dynamics of antibodies and viruses based on a comprehensive understanding of immunology principles. It explicitly formulates the viral clearance effect of antibodies, along with the positive feedback stimulation of virus-antibody complexes on antibody regeneration. In addition to providing quantitative insights into the dynamics of antibodies and viruses, the model exhibits a high degree of accuracy in capturing the kinetics of viruses and antibodies in Dengue fever patients. This model offers a valuable solution to modeling the differences between primary and secondary Dengue infections concerning IgM/IgG antibodies. Furthermore, it demonstrates that a faster removal rate of antibody-virus complexes might lead to a higher peak viral loading and worse clinical symptom. Moreover, it provides a reasonable explanation for the antibody-dependent enhancement of heterogeneous Dengue infections. Ultimately, this model serves as a foundation for constructing an optimal mathematical model to combat various infectious diseases in the future.