Mathematical models applied to thyroid cancer

Silva, Jairo Gomes da; Morais, Rafael Martins de; Silva, Izabel Cristina Rodrigues da; Mancera, Paulo Fernando de Arruda

doi:10.1007/s12551-019-00504-7

Cited by 10 publications

(6 citation statements)

References 40 publications

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“…PDE, unlike ODE, investigates spatial patterns of inherently heterogeneous systems with regionally varying fields. A literature study has revealed the application of these models in various fields, including bone remodeling [ 142 ], dengue [ 143 ] and cancer [ 144 ]. Studies complimented with PDE and ML approaches have also opened a new window for disease treatment [ 145 , 146 ].…”

Section: Future Directionmentioning

confidence: 99%

Tracing the footsteps of autophagy in computational biology

Sarmah

Bairagi

Chatterjee

2020

Briefings in Bioinformatics

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Autophagy plays a crucial role in maintaining cellular homeostasis through the degradation of unwanted materials like damaged mitochondria and misfolded proteins. However, the contribution of autophagy toward a healthy cell environment is not only limited to the cleaning process. It also assists in protein synthesis when the system lacks the amino acids’ inflow from the extracellular environment due to diet consumptions. Reduction in the autophagy process is associated with diseases like cancer, diabetes, non-alcoholic steatohepatitis, etc., while uncontrolled autophagy may facilitate cell death. We need a better understanding of the autophagy processes and their regulatory mechanisms at various levels (molecules, cells, tissues). This demands a thorough understanding of the system with the help of mathematical and computational tools. The present review illuminates how systems biology approaches are being used for the study of the autophagy process. A comprehensive insight is provided on the application of computational methods involving mathematical modeling and network analysis in the autophagy process. Various mathematical models based on the system of differential equations for studying autophagy are covered here. We have also highlighted the significance of network analysis and machine learning in capturing the core regulatory machinery governing the autophagy process. We explored the available autophagic databases and related resources along with their attributes that are useful in investigating autophagy through computational methods. We conclude the article addressing the potential future perspective in this area, which might provide a more in-depth insight into the dynamics of autophagy.

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Section: Future Directionmentioning

confidence: 99%

Tracing the footsteps of autophagy in computational biology

Sarmah

Bairagi

Chatterjee

2020

Briefings in Bioinformatics

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“… 32 In addition, compartmental computer modeling has been used to analyze complex clinical chemistry data, namely, predicting the tumor size based on the analysis of circulating tumor biomarker levels in the plasma of cancer patients. 33 , 34 Another example is the use of a computational pipeline for the diagnosis of common variable immunodeficiency (CVID). This pipeline is based on an automated machine learning approach for diagnosing CVID using flowcytometry to sort the cells.…”

Section: Computational Approaches Used In Analysis Of Clinical Chemismentioning

confidence: 99%

The emergence of new trends in clinical laboratory diagnosis

Alaidarous

2020

SMJ

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Diagnostic processes typically rely on traditional and laborious methods, that are prone to human error, resulting in frequent misdiagnosis of diseases. Computational approaches are being increasingly used for more precise diagnosis of the clinical pathology, diagnosis of genetic and microbial diseases, and analysis of clinical chemistry data. These approaches are progressively used for improving the reliability of testing, resulting in reduced diagnostic errors. Artificial intelligence (AI)-based computational approaches mostly rely on training sets obtained from patient data stored in clinical databases. However, the use of AI is associated with several ethical issues, including patient privacy and data ownership. The capacity of AI-based mathematical models to interpret complex clinical data frequently leads to data bias and reporting of erroneous results based on patient data. In order to improve the reliability of computational approaches in clinical diagnostics, strategies to reduce data bias and analyzing real-life patient data need to be further refined.

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“…Ref. 22 present a review about mathematical models of thyroid cancer, one that discusses different models used and aspects investigated to date. In Ref.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Lenvatinib and Pembrolizumab on Thyroid Cancer Refractory to Iodine 131i Simulated by Mathematical Modeling

et al. 2022

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Immunotherapy and targeted therapy are alternative treatments to differentiated thyroid cancer (DTC), which is usually treated with surgery and radioactive iodine. However, in advanced thyroid carcinomas, molecular alterations can cause a progressive loss of iodine sensitivity, thereby making cancer resistant to radioactive iodine-refractory (RAIR). In the treatment of cancer, tyrosine kinase inhibitors are administered to prevent the growth of cancer cells. One such inhibitor, lenvatinib, forms a targeted therapy for RAIR-DTC, while the immunotherapeutic pembrolizumab, a humanized antibody, prevents the binding of programmed cell death ligand 1 (PD-L1) to the PD-1 receptor. As one of the first studies on treatments for thyroid cancer with mathematical model involving immunotherapy and targeted therapy, we developed an ordinary differential system and tested variables such as concentration of lenvatinib and pembrolizumab, total cancer cells, and number of immune cells (i.e., T cells and natural killer cells). Analyzing local and global stability and the simulated action of drugs in patients with RAIR-DTC, revealed the combined effect of the targeted therapy with pembrolizumab. The scenarios obtained favor the combined therapy as the best treatment option, given its unrivaled ability to boost the immune system’s rate of eliminating tumor cells.

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Mathematical models applied to thyroid cancer

Cited by 10 publications

References 40 publications

Tracing the footsteps of autophagy in computational biology

Tracing the footsteps of autophagy in computational biology

The emergence of new trends in clinical laboratory diagnosis

The Effect of Lenvatinib and Pembrolizumab on Thyroid Cancer Refractory to Iodine 131i Simulated by Mathematical Modeling

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