Bioinspired Nanoparticles Mediated from Bioactive Plants and Their Therapeutic Application in Liver Cancer

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: May 21, 2025

Language: Английский

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Could Mathematics be the Key to Unlocking the Mysteries of Multiple Sclerosis?

Bulletin of Mathematical Biology, Journal Year: 2023, Volume and Issue: 85(8)

Published: June 29, 2023

Multiple sclerosis (MS) is an autoimmune, neurodegenerative disease that driven by immune system-mediated demyelination of nerve axons. While diseases such as cancer, HIV, malaria and even COVID have realised notable benefits from the attention mathematical community, MS has received significantly less despite increasing incidence rates, lack curative treatment, long-term impact on patient well-being. In this review, we highlight existing, MS-specific research discuss outstanding challenges open problems remain for mathematicians. We focus how both non-spatial spatial deterministic models been used to successfully further our understanding T cell responses treatment in MS. also review agent-based other stochastic modelling techniques begun shed light highly oscillatory nature disease. Reviewing current work MS, alongside biology specific immunology, it clear dedicated immunotherapies cancer or viral infections could be readily translatable might hold key unlocking some its mysteries.

Language: Английский

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Agent-Based and Continuum Models for Spatial Dynamics of Infection by Oncolytic Viruses

Bulletin of Mathematical Biology, Journal Year: 2023, Volume and Issue: 85(10)

Published: Aug. 31, 2023

Language: Английский

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Identifying cell-to-cell variability in internalization using flow cytometry

Journal of The Royal Society Interface, Journal Year: 2022, Volume and Issue: 19(190)

Published: May 1, 2022

Biological heterogeneity is a primary contributor to the variation observed in experiments that probe dynamical processes, such as internalization of material by cells. Given critical process which many therapeutics and viruses reach their intracellular site action, quantifying cell-to-cell variability high biological interest. Yet, it common for studies neglect variability. We develop simple mathematical model captures behaviour, variation, extrinsic noise introduced flow cytometry. calibrate our through novel distribution-matching approximate Bayesian computation algorithm cytometry data anti-transferrin receptor antibody human B-cell lymphoblastoid cell line. This approach provides information relating region parameter space, consequentially nature variability, produces realizations consistent with experimental data. agnostic sample size signal-to-noise ratio, modelling framework broadly applicable identify single-cell from assays similar cellular processes.

Language: Английский

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Tumor treatment with chemo-virotherapy and MEK inhibitor: A mathematical model of Caputo fractional differential operator

Alexandria Engineering Journal, Journal Year: 2023, Volume and Issue: 71, P. 173 - 183

Published: March 25, 2023

Mitogen-activated protein kinase (MEK) inhibitors and oncolytic virotherapy are identified as promising cancer therapies that can enhance the efficacy of other treatments. A few studies demonstrate cells proliferate when exposed to with MEK in an integer order model or without them a fractional model. None intended investigate tumor cell growth under combined treatment strategy chemo-virotherapy inhibitor In this paper, mathematical based on ordinary differential equations (ODEs) is developed for mutual interactions among cells, well therapeutic combination chemotherapy, viruses functional consequence inhibitor, how could chemotherapy action inhibitor. The numerical results show virus burst size have noticeable impact regulating trend proliferation. While responses proliferation undoubtedly quicker than chemotherapeutic responses, intensity clearly affects success regimen. study contribute development combines monitoring control.

Language: Английский

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Leveraging high-resolution omics data for predicting responses and adverse events to immune checkpoint inhibitors

Computational and Structural Biotechnology Journal, Journal Year: 2023, Volume and Issue: 21, P. 3912 - 3919

Published: Jan. 1, 2023

A long-standing goal of personalized and precision medicine is to enable accurate prediction the outcomes a given treatment regimen for patients harboring disease. Currently, many clinical trials fail meet their endpoints due underlying factors in patient population that contribute either poor responses drug interest or treatment-related adverse events. Identifying these beforehand correcting them can lead an increased success trials. Comprehensive large-scale data gathering efforts biomedicine by omics profiling healthy diseased individuals has led treasure-trove host, disease environmental effectiveness drugs aiming treat With increasing data, artificial intelligence allows in-depth analysis big offers wide range applications real-world use, including improved selection identification actionable targets companion therapeutics translatability across more patients. As blueprint complex drug-disease-host interactions, we here discuss challenges utilizing predicting events cancer immunotherapy with immune checkpoint inhibitors (ICIs). The omics-based methodologies improving as ICI case have also been applied wide-range settings, exemplifying use use.

Language: Английский

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Viability control of chemo-immunotherapy and radiotherapy by set-valued analysis

International Journal of Informatics and Applied Mathematics, Journal Year: 2023, Volume and Issue: unknown

Published: April 23, 2023

In this paper we set-valued analyze the problem of asymptotic stabilizing tumor size. A mathematical model exponential growing caused by carcinogenic substance is considered, with chemotherapy, immunotherapy, and radiotherapy effects. We control to be viable in therapeutic domains, reverse The obtained controls derive from derivative cone domains as solution minimizing problem.

Language: Английский

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Examining the efficacy of localised gemcitabine therapy for the treatment of pancreatic cancer using a hybrid agent-based model

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2022, Volume and Issue: unknown

Published: April 19, 2022

Abstract The prognosis for pancreatic ductal adenocarcinoma (PDAC) patients has not significantly improved in the past 3 decades, highlighting need more effective treatment approaches. Poor patient outcomes and lack of response to therapy can be attributed, part, dense, fibrotic nature PDAC tumours, which impedes uptake systemically administered drugs. Wet-spun alginate fibres loaded with chemotherapeutic agent gemcitabine have been developed as a potential tool overcoming physical biological barriers presented by tumour microenvironment deliver high concentrations drug directly over an extended period time. While exciting, practicality, safety, effectiveness these devices clinical setting requires further investigation. Furthermore, in-depth assessment drug-release rate from needs undertaken determine whether optimal release profile exists. Using hybrid computational model (agent-based partial differential equation system), we simulation growth fibres. was calibrated using vitro vivo data simulated finite volume method discretization. We then used compare different intratumoural implantation protocols gemcitabine-release rates. In our model, primary driver cell division degree extracellular matrix deposition. were able demonstrate that placement than peritumoural placement. Additionally, found exponential would improve placed peritumourally. Altogether, here is investigate other delivery arsenal treatments available difficult-to-treat cancers future. Author Summary Pancreatic cancer dismal median survival 3-5 months untreated disease. challenging due dense tumours retention at site. As such, systemic administration chemotherapies, such gemcitabine, limited efficacy. To overcome this, sustained-release proposed. These are injected locally slowly time, providing concentrated local, sustained concentration. possible efficacy devices, mathematical allow us probe perturbations silico . modelled individual cells their death into devices. Our platform allows future investigations run so may better understand forms release-profile necessary treatment.

Language: Английский

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Personalized Plasma Medicine for Cancer: Transforming Treatment Strategies with Mathematical Modeling and AI Algorithms

Published: Aug. 22, 2023

Plasma technology shows tremendous potential for revolutionizing oncology research and treatment. Reactive oxygen nitrogen species, electromagnetic emissions generated through gas plasma jets, have attracted significant attention due to their selective cytotoxicity towards cancer cells. To leverage the full of medicine, researchers explored use mathematical models various subsets machine learning, such as reinforcement deep learning. This review emphasizes application AI algorithms in adaptive system, paving way precision dynamic Realizing requires efforts, data sharing interdisciplinary collaborations. Unravelling complex mechanisms, developing real-time diagnostics, optimizing will be crucial harness true power oncology. The integration personalized therapies, alongside diagnostic sensors, presents a transformative approach treatment with improve outcomes globally.

Language: Английский

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Spatial computational modelling illuminates the role of the tumour microenvironment for treating glioblastoma with immunotherapies

npj Systems Biology and Applications, Journal Year: 2024, Volume and Issue: 10(1)

Published: Aug. 18, 2024

Glioblastoma is the most common and deadliest brain tumour in adults, with a median survival of 15 months under current standard care. Immunotherapies like immune checkpoint inhibitors oncolytic viruses have been extensively studied to improve this endpoint. However, thus far failed. To efficacy immunotherapies treat glioblastoma, new single-cell imaging modalities mass cytometry can be leveraged integrated computational models. This enables better understanding microenvironment its role treatment success or failure hard-to-treat tumour. Here, we implemented an agent-based model that allows for spatial predictions combination chemotherapy, virus, against glioblastoma. We initialised our patient data predict patient-specific responses found drive determined by intratumoral cell density. tumours higher density responded treatment. When fixing number cancer cells, was shown function CD4 + T and, lesser extent, macrophage counts. Critically, simulations show care must put into integration models effectively capture dynamics. Together, study emphasizes use predictive modelling understand immunotherapy dynamics, while highlighting key factors consider during design implementation.

Language: Английский

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