{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:25:50Z","timestamp":1787030750886,"version":"build-2736575974"},"reference-count":131,"publisher":"American Institute of Mathematical Sciences (AIMS)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MBE"],"published-print":{"date-parts":[[2024]]},"abstract":"<jats:p xml:lang=\"fr\">&lt;p&gt;In the pursuit of personalized medicine, there is a growing demand for computational models with parameters that are easily obtainable to accelerate the development of potential solutions. Blood tests, owing to their affordability, accessibility, and routine use in healthcare, offer valuable biomarkers for assessing hemostatic balance in thrombotic and bleeding disorders. Incorporating these biomarkers into computational models of blood coagulation is crucial for creating patient-specific models, which allow for the analysis of the influence of these biomarkers on clot formation. This systematic review aims to examine how clinically relevant biomarkers are integrated into computational models of blood clot formation, thereby advancing discussions on integration methodologies, identifying current gaps, and recommending future research directions. A systematic review was conducted following the PRISMA protocol, focusing on ten clinically significant biomarkers associated with hemostatic disorders: D-dimer, fibrinogen, Von Willebrand factor, factor \u2167, P-selectin, prothrombin time (PT), activated partial thromboplastin time (APTT), antithrombin \u2162, protein C, and protein S. By utilizing this set of biomarkers, this review underscores their integration into computational models and emphasizes their integration in the context of venous thromboembolism and hemophilia. Eligibility criteria included mathematical models of thrombin generation, blood clotting, or fibrin formation under flow, incorporating at least one of these biomarkers. A total of 53 articles were included in this review. Results indicate that commonly used biomarkers such as D-dimer, PT, and APTT are rarely and superficially integrated into computational blood coagulation models. Additionally, the kinetic parameters governing the dynamics of blood clot formation demonstrated significant variability across studies, with discrepancies of up to 1, 000-fold. This review highlights a critical gap in the availability of computational models based on phenomenological or first-principles approaches that effectively incorporate affordable and routinely used clinical test results for predicting blood coagulation. This hinders the development of practical tools for clinical application, as current mathematical models often fail to consider precise, patient-specific values. This limitation is especially pronounced in patients with conditions such as hemophilia, protein C and S deficiencies, or antithrombin deficiency. Addressing these challenges by developing patient-specific models that account for kinetic variability is crucial for advancing personalized medicine in the field of hemostasis.&lt;\/p&gt;<\/jats:p>","DOI":"10.3934\/mbe.2024339","type":"journal-article","created":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T01:45:14Z","timestamp":1733363114000},"page":"7707-7739","source":"Crossref","is-referenced-by-count":8,"title":["Integrating biomarkers for hemostatic disorders into computational models of blood clot formation: A systematic review"],"prefix":"10.3934","volume":"21","author":[{"given":"Mohamad","family":"Al Bannoud","sequence":"first","affiliation":[{"name":"Laboratory of Optimization, Design, and Advanced Control, School of Chemical Engineering, Universidade Estadual de Campinas, Campinas, S\u00e3o Paulo, Brazil"},{"name":"Centro de Doen\u00e7as Tromboemb\u00f3licas, Centro de Hematologia e Hemoterapia, Universidade Estadual de Campinas, Campinas, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiago Dias","family":"Martins","sequence":"additional","affiliation":[{"name":"Departamento de Engenharia Qu\u00edmica, Universidade Federal de S\u00e3o Paulo, Diadema, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silmara Aparecida","family":"de Lima Montalv\u00e3o","sequence":"additional","affiliation":[{"name":"Hematology and Hemotherapy Center, Instituto Nacional de Ci\u00eancia e Tecnologia do Sangue, Universidade Estadual de Campinas, Campinas, S\u00e3o Paulo, Brazil"},{"name":"Centro de Doen\u00e7as Tromboemb\u00f3licas, Centro de Hematologia e Hemoterapia, Universidade Estadual de Campinas, Campinas, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joyce Maria","family":"Annichino-Bizzacchi","sequence":"additional","affiliation":[{"name":"Hematology and Hemotherapy Center, Instituto Nacional de Ci\u00eancia e Tecnologia do Sangue, Universidade Estadual de Campinas, Campinas, S\u00e3o Paulo, Brazil"},{"name":"Centro de Doen\u00e7as Tromboemb\u00f3licas, Centro de Hematologia e Hemoterapia, Universidade Estadual de Campinas, Campinas, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rubens Maciel","family":"Filho","sequence":"additional","affiliation":[{"name":"Laboratory of Optimization, Design, and Advanced Control, School of Chemical Engineering, Universidade Estadual de Campinas, Campinas, S\u00e3o Paulo, Brazil"},{"name":"Centro de Doen\u00e7as Tromboemb\u00f3licas, Centro de Hematologia e Hemoterapia, Universidade Estadual de Campinas, Campinas, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Regina Wolf","family":"Maciel","sequence":"additional","affiliation":[{"name":"Laboratory of Optimization, Design, and Advanced Control, School of Chemical Engineering, Universidade Estadual de Campinas, Campinas, S\u00e3o Paulo, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2321","reference":[{"key":"key-10.3934\/mbe.2024339-1","doi-asserted-by":"publisher","unstructured":"S. Z. Goldhaber, H. Bounameaux, Pulmonary embolism and deep vein thrombosis, <i>Lancet (London, England)<\/i>, <b>379<\/b> (2012), 1835\u20131846. https:\/\/doi.org\/10.1016\/S0140-6736(11)61904-1","DOI":"10.1016\/S0140-6736(11)61904-1"},{"key":"key-10.3934\/mbe.2024339-2","doi-asserted-by":"publisher","unstructured":"G. E. Raskob, P. Angchaisuksiri, A. N. Blanco, H. Buller, A. Gallus, B. J. Hunt, et al., Thrombosis: a major contributor to global disease burden, <i>Arterioscler. Thromb. Vasc. Biol.<\/i>, <b>34<\/b> (2014), 2363\u20132371. https:\/\/doi.org\/10.1161\/ATVBAHA.114.304488","DOI":"10.1161\/ATVBAHA.114.304488"},{"key":"key-10.3934\/mbe.2024339-3","doi-asserted-by":"publisher","unstructured":"D. Voci, U. Fedeli, I. T. Farmakis, L. Hobohm, K. Keller, L. Valerio, et al., Deaths related to pulmonary embolism and cardiovascular events before and during the 2020 COVID-19 pandemic: An epidemiological analysis of data from an Italian high-risk area, <i>Thromb. Res.<\/i>, <b>212<\/b> (2022), 44\u201350. https:\/\/doi.org\/10.1016\/j.thromres.2022.02.008","DOI":"10.1016\/j.thromres.2022.02.008"},{"key":"key-10.3934\/mbe.2024339-4","doi-asserted-by":"publisher","unstructured":"I. Katsoularis, O. Fonseca-Rodr\u00edguez, P. Farrington, H. Jerndal, E. H. Lundevaller, M. Sund, et al., Risks of deep vein thrombosis, pulmonary embolism, and bleeding after covid-19: nationwide self-controlled cases series and matched cohort study, <i>BMJ<\/i>, <b>377<\/b> (2022). https:\/\/doi.org\/10.1136\/bmj-2021-069590","DOI":"10.1136\/bmj-2021-069590"},{"key":"key-10.3934\/mbe.2024339-5","doi-asserted-by":"publisher","unstructured":"T. N. Nguyen, M. M. Qureshi, P. Klein, H. Yamagami, M. Abdalkader, R. Mikulik, et al., Global impact of the COVID-19 pandemic on cerebral venous thrombosis and mortality, <i>J. Stroke<\/i>, <b>24<\/b> (2022), 256\u2013265. https:\/\/doi.org\/10.5853\/jos.2022.00752","DOI":"10.5853\/jos.2022.00752"},{"key":"key-10.3934\/mbe.2024339-6","doi-asserted-by":"publisher","unstructured":"E. Berntorp, K. Fischer, D. P. Hart, M. E. Mancuso, D. Stephensen, A. D. Shapiro, et al., Haemophilia, <i>Nat. Rev. Dis. Prim.<\/i>, <b>7<\/b> (2021), 45. https:\/\/doi.org\/10.1038\/s41572-021-00278-x","DOI":"10.1038\/s41572-021-00278-x"},{"key":"key-10.3934\/mbe.2024339-7","doi-asserted-by":"publisher","unstructured":"K. G. Link, M. T. Stobb, M. G. Sorrells, M. Bortot, K. Ruegg, M. J. Manco\u2010Johnson, et al., A mathematical model of coagulation under flow identifies factor V as a modifier of thrombin generation in hemophilia A, <i>J. Thromb. Haemost.<\/i>, <b>18<\/b> (2020), 306\u2013317. https:\/\/doi.org\/10.1111\/jth.14653","DOI":"10.1111\/jth.14653"},{"key":"key-10.3934\/mbe.2024339-8","doi-asserted-by":"publisher","unstructured":"F. W. G. Leebeek, W. Miesbach, Gene therapy for hemophilia: a review on clinical benefit, limitations, and remaining issues, <i>Blood<\/i>, <b>138<\/b> (2021), 923\u2013931. https:\/\/doi.org\/10.1182\/blood.2019003777","DOI":"10.1182\/blood.2019003777"},{"key":"key-10.3934\/mbe.2024339-9","doi-asserted-by":"publisher","unstructured":"S. S. G. Halfmann, N. Evangelatos, P. Schr\u00f6der-B\u00e4ck, A. Brand, European healthcare systems readiness to shift from 'One-Size Fits All' to personalized medicine, <i>Per. Med.<\/i>, <b>14<\/b> (2017), 63\u201374. https:\/\/doi.org\/10.2217\/pme-2016-0061","DOI":"10.2217\/pme-2016-0061"},{"key":"key-10.3934\/mbe.2024339-10","doi-asserted-by":"publisher","unstructured":"T. Behl, I. Kaur, A. Sehgal, S. Singh, A. Albarrati, M. Albratty, et al., The road to precision medicine: Eliminating the \"One Size Fits All\" approach in Alzheimer's disease, <i>Biomed. Pharmacother.<\/i>, <b>153<\/b> (2022), 113337. https:\/\/doi.org\/10.1016\/j.biopha.2022.113337","DOI":"10.1016\/j.biopha.2022.113337"},{"key":"key-10.3934\/mbe.2024339-11","doi-asserted-by":"publisher","unstructured":"N. M. Hamdy, E. B. Basalious, M. G. El-Sisi, M. Nasr, A. M. Kabel, E. S. Nossier, et al., Advancements in current one-size-fits-all therapies compared to future treatment innovations for better improved chemotherapeutic outcomes: a step-toward personalized medicine, <i>Curr. Med. Res. Opin.<\/i>, <b>40<\/b> (2024), 1\u201319. https:\/\/doi.org\/10.1080\/03007995.2024.2416985","DOI":"10.1080\/03007995.2024.2416985"},{"key":"key-10.3934\/mbe.2024339-12","doi-asserted-by":"publisher","unstructured":"G. Di Minno, E. Tremoli, Tailoring of medical treatment: hemostasis and thrombosis towards precision medicine, <i>Haematologica<\/i>, <b>102<\/b> (2017), 411\u2013418. https:\/\/doi.org\/10.3324\/haematol.2016.156000","DOI":"10.3324\/haematol.2016.156000"},{"key":"key-10.3934\/mbe.2024339-13","unstructured":"D. L. Ornstein, Chapter 41 - Personalized medicine for disorders of hemostasis and thrombosis, in Diagnostic Molecular Pathology (eds. W. B. Coleman and G. J. Tsongalis), Academic Press, (2024), 643\u2013653. <ext-link ext-link-type=\"uri\" xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.1016\/B978-0-12-822824-1.00006-7\">https:\/\/doi.org\/10.1016\/B978-0-12-822824-1.00006-7<\/ext-link>"},{"key":"key-10.3934\/mbe.2024339-14","doi-asserted-by":"publisher","unstructured":"S. Nagalla, P. F. Bray, Personalized medicine in thrombosis: back to the future, <i>Blood<\/i>, <b>127<\/b> (2016), 2665\u20132671. https:\/\/doi.org\/10.1182\/blood-2015-11-634832","DOI":"10.1182\/blood-2015-11-634832"},{"key":"key-10.3934\/mbe.2024339-15","doi-asserted-by":"publisher","unstructured":"R. J. S. Preston, J. M. O'Sullivan, Personalized approaches to the treatment of hemostatic disorders, <i>Semin. Thromb. Hemostasis<\/i>, <b>47<\/b> (2021), 117\u2013119. https:\/\/doi.org\/10.1055\/s-0041-1723800","DOI":"10.1055\/s-0041-1723800"},{"key":"key-10.3934\/mbe.2024339-16","doi-asserted-by":"publisher","unstructured":"H. Al\u2010Samkari, W. Eng, A precision medicine approach to hereditary hemorrhagic telangiectasia and complex vascular anomalies, <i>J. Thromb. Haemost.<\/i>, <b>20<\/b> (2022), 1077\u20131088. https:\/\/doi.org\/10.1111\/jth.15715","DOI":"10.1111\/jth.15715"},{"key":"key-10.3934\/mbe.2024339-17","doi-asserted-by":"publisher","unstructured":"X. Delavenne, E. Ollier, A. Lienhart, Y. Dargaud, A new paradigm for personalized prophylaxis for patients with severe haemophilia A, <i>Haemophilia<\/i>, <b>26<\/b> (2020), 228\u2013235. https:\/\/doi.org\/10.1111\/hae.13935","DOI":"10.1111\/hae.13935"},{"key":"key-10.3934\/mbe.2024339-18","doi-asserted-by":"publisher","unstructured":"L. H. Bukkems, L. L. F. G. Valke, W. Barteling, B. A. P. Laros-van Gorkom, N. M. A. Blijlevens, M. H. Cnossen, et al., Combining factor \u2167 levels and thrombin\/plasmin generation: A population pharmacokinetic-pharmacodynamic model for patients with haemophilia A, <i>Br. J. Clin. Pharmacol.<\/i>, <b>88<\/b> (2022), 2757\u20132768. https:\/\/doi.org\/10.1111\/bcp.15185","DOI":"10.1111\/bcp.15185"},{"key":"key-10.3934\/mbe.2024339-19","doi-asserted-by":"publisher","unstructured":"N. Mackman, W. Bergmeier, G. A. Stouffer, J. I. Weitz, Therapeutic strategies for thrombosis: new targets and approaches, <i>Nat. Rev. Drug. Discov.<\/i>, <b>19<\/b> (2020), 333\u2013352. https:\/\/doi.org\/10.1038\/s41573-020-0061-0","DOI":"10.1038\/s41573-020-0061-0"},{"key":"key-10.3934\/mbe.2024339-20","doi-asserted-by":"publisher","unstructured":"P. S. Wells, R. Ihaddadene, A. Reilly, M. A. Forgie, Diagnosis of venous thromboembolism: 20 years of progress, <i>Ann. Intern. Med.<\/i>, <b>168<\/b> (2018), 131\u2013140. https:\/\/doi.org\/10.7326\/M17-0291","DOI":"10.7326\/M17-0291"},{"key":"key-10.3934\/mbe.2024339-21","doi-asserted-by":"publisher","unstructured":"F. Khan, T. Tritschler, S. R. Kahn, M. A. Rodger, Venous thromboembolism, <i>Lancet (London, England)<\/i>, <b>398<\/b> (2021), 64\u201377. https:\/\/doi.org\/10.1016\/S0140-6736(20)32658-1","DOI":"10.1016\/S0140-6736(20)32658-1"},{"key":"key-10.3934\/mbe.2024339-22","doi-asserted-by":"publisher","unstructured":"T. D. Martins, S. D. Martins, S. Montalv\u00e3o, M. Al Bannoud, G. Y. Ottaiano, L. Q. Silva, et al., Combining artificial neural networks and hematological data to diagnose Covid-19 infection in Brazilian population, <i>Neural Comput. Appl.<\/i>, <b>36<\/b> (2024), 4387\u20134399. https:\/\/doi.org\/10.1007\/s00521-023-09312-3","DOI":"10.1007\/s00521-023-09312-3"},{"key":"key-10.3934\/mbe.2024339-23","doi-asserted-by":"publisher","unstructured":"T. D. Martins, R. Maciel-Filho, S. A. L. Montalv\u00e3o, G. S. S. Gois, M. Al Bannoud, G. Y. Ottaiano, et al., Predicting mortality of cancer patients using artificial intelligence, patient data and blood tests, <i>Neural Comput. Appl<\/i>., <b>36<\/b> (2024), 15599\u201315616. https:\/\/doi.org\/10.1007\/s00521-024-09915-4","DOI":"10.1007\/s00521-024-09915-4"},{"key":"key-10.3934\/mbe.2024339-24","doi-asserted-by":"publisher","unstructured":"F. W. G. Leebeek, New developments in diagnosis and management of acquired hemophilia and acquired von willebrand syndrome, <i>HemaSphere<\/i>, <b>5<\/b> (2021). https:\/\/doi.org\/10.1097\/HS9.0000000000000586","DOI":"10.1097\/HS9.0000000000000586"},{"key":"key-10.3934\/mbe.2024339-25","doi-asserted-by":"publisher","unstructured":"F. Peyvandi, G. Kenet, I. Pekrul, R. K. Pruthi, P. Ramge, M. Spannagl, Laboratory testing in hemophilia: Impact of factor and non\u2010factor replacement therapy on coagulation assays, <i>J. Thromb. Haemost.<\/i>, <b>18<\/b> (2020), 1242\u20131255. https:\/\/doi.org\/10.1111\/jth.14784","DOI":"10.1111\/jth.14784"},{"key":"key-10.3934\/mbe.2024339-26","doi-asserted-by":"publisher","unstructured":"B. Pezeshkpoor, J. Oldenburg, A. Pavlova, Insights into the molecular genetic of hemophilia A and hemophilia B: The relevance of genetic testing in routine clinical practice, <i>Hamostaseologie<\/i>, <b>42<\/b> (2022), 390\u2013399. https:\/\/doi.org\/10.1055\/a-1945-9429","DOI":"10.1055\/a-1945-9429"},{"key":"key-10.3934\/mbe.2024339-27","doi-asserted-by":"publisher","unstructured":"A. H. Kristoffersen, E. Ajzner, D. Rogic, E. Y. Sozmen, P. Carraro, A. P. Faria, et al., Is D-dimer used according to clinical algorithms in the diagnostic work-up of patients with suspicion of venous thromboembolism? A study in six European countries, <i>Thromb. Res.<\/i>, <b>142<\/b> (2016), 1\u20137. https:\/\/doi.org\/10.1016\/j.thromres.2016.04.001","DOI":"10.1016\/j.thromres.2016.04.001"},{"key":"key-10.3934\/mbe.2024339-28","doi-asserted-by":"publisher","unstructured":"M. Kafeza, J. Shalhoub, N. Salooja, L. Bingham, K. Spagou, A. H. Davies, A systematic review of clinical prediction scores for deep vein thrombosis, <i>Phlebology<\/i>, <b>32<\/b> (2017), 516\u2013531. https:\/\/doi.org\/10.1177\/0268355516678729","DOI":"10.1177\/0268355516678729"},{"key":"key-10.3934\/mbe.2024339-29","doi-asserted-by":"publisher","unstructured":"M. T. Greene, A. C. Spyropoulos, V. Chopra, P. J. Grant, S. Kaatz, S. J. Bernstein, et al., Validation of risk assessment models of venous thromboembolism in hospitalized medical patients, <i>Am. J. Med.<\/i>, <b>129<\/b> (2016), 1001.e9\u20131001.e18. https:\/\/doi.org\/10.1016\/j.amjmed.2016.03.031","DOI":"10.1016\/j.amjmed.2016.03.031"},{"key":"key-10.3934\/mbe.2024339-30","doi-asserted-by":"publisher","unstructured":"P. C. Silveira, I. K. Ip, S. Z. Goldhaber, G. Piazza, C. B. Benson, R. Khorasani, Performance of wells score for deep vein thrombosis in the inpatient setting, <i>JAMA Intern. Med<\/i>., <b>175<\/b> (2015), 1112\u20131117. https:\/\/doi.org\/10.1001\/jamainternmed.2015.1687","DOI":"10.1001\/jamainternmed.2015.1687"},{"key":"key-10.3934\/mbe.2024339-31","doi-asserted-by":"publisher","unstructured":"M. Kafeza, J. Shalhoub, N. Salooja, L. Bingham, K. Spagou, A. H. Davies, A systematic review of clinical prediction scores for deep vein thrombosis, <i>Phlebology<\/i>, <b>32<\/b> (2016), 516\u2013531. https:\/\/doi.org\/10.1177\/0268355516678729","DOI":"10.1177\/0268355516678729"},{"key":"key-10.3934\/mbe.2024339-32","doi-asserted-by":"publisher","unstructured":"I. Nichele, A. Tosetto, Scoring Systems for Estimating the Risk of Recurrent Venous Thromboembolism, <i>Semin. Thromb. Hemost.<\/i>, <b>43<\/b> (2017), 493\u2013499. https:\/\/doi.org\/10.1055\/s-0037-1602662","DOI":"10.1055\/s-0037-1602662"},{"key":"key-10.3934\/mbe.2024339-33","doi-asserted-by":"publisher","unstructured":"A. Mu\u00f1oz, C. Ay, E. Grilz, S. L\u00f3pez, C. Font, V. Pach\u00f3n, et al., A clinical-genetic risk score for predicting cancer-associated venous thromboembolism: A development and validation study involving two independent prospective cohorts, <i>J. Clin. Oncol.<\/i>, <b>41<\/b> (2023), 2911\u20132925. https:\/\/doi.org\/10.1200\/JCO.22.00255","DOI":"10.1200\/JCO.22.00255"},{"key":"key-10.3934\/mbe.2024339-34","doi-asserted-by":"publisher","unstructured":"F. Rodeghiero, A. Tosetto, T. Abshire, D. M. Arnold, B. Coller, P. James, et al., ISTH\/SSC bleeding assessment tool: a standardized questionnaire and a proposal for a new bleeding score for inherited bleeding disorders, <i>J. Thromb. Haemost.<\/i>, <b>8<\/b> (2010), 2063\u20132065. https:\/\/doi.org\/10.1111\/j.1538-7836.2010.03975.x","DOI":"10.1111\/j.1538-7836.2010.03975.x"},{"key":"key-10.3934\/mbe.2024339-35","doi-asserted-by":"publisher","unstructured":"M. Borhany, N. Fatima, M. Abid, T. Shamsi, M. Othman, Application of the ISTH bleeding score in hemophilia, <i>Transfus. Apher. Sci.<\/i>, <b>57<\/b> (2018), 556\u2013560. https:\/\/doi.org\/10.1016\/j.transci.2018.06.003","DOI":"10.1016\/j.transci.2018.06.003"},{"key":"key-10.3934\/mbe.2024339-36","doi-asserted-by":"publisher","unstructured":"M. Khalifa, M. Albadawy, Artificial intelligence for clinical prediction: Exploring key domains and essential functions, <i>Comput. Methods Programs Biomed. Updat<\/i>., <b>5<\/b> (2024), 100148. https:\/\/doi.org\/10.1016\/j.cmpbup.2024.100148","DOI":"10.1016\/j.cmpbup.2024.100148"},{"key":"key-10.3934\/mbe.2024339-37","doi-asserted-by":"publisher","unstructured":"T. H. Tan, C. C. Hsu, C. J. Chen, S. L. Hsu, T. L. Liu, H. J. Lin, et al., Predicting outcomes in older ED patients with influenza in real time using a big data-driven and machine learning approach to the hospital information system, <i>BMC Geriatr<\/i>., <b>21<\/b> (2021), 280. https:\/\/doi.org\/10.1186\/s12877-021-02229-3","DOI":"10.1186\/s12877-021-02229-3"},{"key":"key-10.3934\/mbe.2024339-38","doi-asserted-by":"publisher","unstructured":"C. Guan, F. Ma, S. Chang, J. Zhang, Interpretable machine learning models for predicting venous thromboembolism in the intensive care unit: an analysis based on data from 207 centers, <i>Crit. Care<\/i>, <b>27<\/b> (2023), 406. https:\/\/doi.org\/10.1186\/s13054-023-04683-4","DOI":"10.1186\/s13054-023-04683-4"},{"key":"key-10.3934\/mbe.2024339-39","doi-asserted-by":"publisher","unstructured":"T. D. Martins, J. M. Annichino-Bizzacchi, A. V. C. Romano, R. Maciel Filho, Artificial neural networks for prediction of recurrent venous thromboembolism, <i>Int. J. Med. Inform.<\/i>, <b>141<\/b> (2020), 104221. https:\/\/doi.org\/10.1016\/j.ijmedinf.2020.104221","DOI":"10.1016\/j.ijmedinf.2020.104221"},{"key":"key-10.3934\/mbe.2024339-40","doi-asserted-by":"publisher","unstructured":"I. Pabinger, C. Ay, Biomarkers and venous thromboembolism, <i>Arter. Thromb. Vasc. Biol.<\/i>, <b>29<\/b> (2009), 332\u2013336. https:\/\/doi.org\/10.1161\/ATVBAHA.108.182188","DOI":"10.1161\/ATVBAHA.108.182188"},{"key":"key-10.3934\/mbe.2024339-41","doi-asserted-by":"publisher","unstructured":"I. Pabinger, J. Thaler, C. Ay, Biomarkers for prediction of venous thromboembolism in cancer, <i>Blood<\/i>, <b>122<\/b> (2013), 2011\u20132018. https:\/\/doi.org\/10.1182\/blood-2013-04-460147","DOI":"10.1182\/blood-2013-04-460147"},{"key":"key-10.3934\/mbe.2024339-42","doi-asserted-by":"publisher","unstructured":"F. Galeano-Valle, L. Ordieres-Ortega, C. M. Oblitas, J. del-Toro-Cervera, L. Alvarez-Sala-Walther, P. Demelo-Rodr\u00edguez, Inflammatory biomarkers in the short-term prognosis of venous thromboembolism: A narrative review, <i>Int. J. Mol. Sci.<\/i>, <b>22<\/b> (2021), 2627. https:\/\/doi.org\/10.3390\/ijms22052627","DOI":"10.3390\/ijms22052627"},{"key":"key-10.3934\/mbe.2024339-43","doi-asserted-by":"publisher","unstructured":"B. Jacobs, A. Obi, T. Wakefield, Diagnostic biomarkers in venous thromboembolic disease, <i>J. Vasc. Surg. Venous Lymphat. Disord.<\/i>, <b>4<\/b> (2016), 508\u2013517. https:\/\/doi.org\/10.1016\/j.jvsv.2016.02.005","DOI":"10.1016\/j.jvsv.2016.02.005"},{"key":"key-10.3934\/mbe.2024339-44","doi-asserted-by":"publisher","unstructured":"M. A. Bannoud, B. P. Gomes, M. C. de S. P. Abdalla, M. V Freire, K. Andreola, T. D. Martins, et al., Mathematical modeling of drying kinetics of ground A\u00e7a\u00ed (Euterpe oleracea) kernel using artificial neural networks, <i>Chem. Pap.<\/i>, <b>78<\/b> (2024), 1033\u20131054. https:\/\/doi.org\/10.1007\/s11696-023-03142-2","DOI":"10.1007\/s11696-023-03142-2"},{"key":"key-10.3934\/mbe.2024339-45","doi-asserted-by":"publisher","unstructured":"M. A. Bannoud, T. D. Martins, B. F. dos Santos, Control of a closed dry grinding circuit with ball mills using predictive control based on neural networks, <i>Digit. Chem. Eng.<\/i>, <b>5<\/b> (2022), 100064. https:\/\/doi.org\/10.1016\/j.dche.2022.100064","DOI":"10.1016\/j.dche.2022.100064"},{"key":"key-10.3934\/mbe.2024339-46","doi-asserted-by":"publisher","unstructured":"J. Berg, K. Nystr\u00f6m, Data-driven discovery of PDEs in complex datasets, <i>J. Comput. Phys.<\/i>, <b>384<\/b> (2019), 239\u2013252. https:\/\/doi.org\/10.1016\/j.jcp.2019.01.036","DOI":"10.1016\/j.jcp.2019.01.036"},{"key":"key-10.3934\/mbe.2024339-47","doi-asserted-by":"publisher","unstructured":"L. Burzawa, L. Li, X. Wang, A. Buganza-Tepole, D. M. Umulis, Acceleration of PDE-based biological simulation through the development of neural network metamodels, <i>Curr. Pathobiol. Rep.<\/i>, <b>8<\/b> (2020), 121\u2013131. https:\/\/doi.org\/10.1007\/s40139-020-00216-8","DOI":"10.1007\/s40139-020-00216-8"},{"key":"key-10.3934\/mbe.2024339-48","doi-asserted-by":"publisher","unstructured":"M. A. Bannoud, C. A. M. da Silva, T. D. Martins, Applications of metaheuristic optimization algorithms in model predictive control for chemical engineering processes: A systematic review, <i>Annu. Rev. Control.<\/i>, <b>58<\/b> (2024), 100973. https:\/\/doi.org\/10.1016\/j.arcontrol.2024.100973","DOI":"10.1016\/j.arcontrol.2024.100973"},{"key":"key-10.3934\/mbe.2024339-49","doi-asserted-by":"publisher","unstructured":"M. A. Bannoud, P. H. N. Ferreira, R. R. de Andrade, C. A. M. da Silva, Control of an integrated first and second-generation continuous alcoholic fermentation process with cell recycling using model predictive control, <i>Chem. Eng. Commun.<\/i>, (2024), 1\u201324. https:\/\/doi.org\/10.1080\/00986445.2024.2417901","DOI":"10.1080\/00986445.2024.2417901"},{"key":"key-10.3934\/mbe.2024339-50","doi-asserted-by":"publisher","unstructured":"Y. Zhao, C. Li, X. Liu, R. Qian, R. Song, X. Chen, Patient-specific seizure prediction via adder network and supervised contrastive learning, <i>IEEE Trans. Neural Syst. Rehabil. Eng.<\/i>, <b>30<\/b> (2022), 1536\u20131547. https:\/\/doi.org\/10.1109\/TNSRE.2022.3180155","DOI":"10.1109\/TNSRE.2022.3180155"},{"key":"key-10.3934\/mbe.2024339-51","doi-asserted-by":"publisher","unstructured":"K. Leiderman, S. S. Sindi, D. M. Monroe, A. L. Fogelson, K. B. Neeves, The art and science of building a computational model to understand hemostasis, <i>Semin. Thromb. Hemost.<\/i>, <b>47<\/b> (2021), 129\u2013138. https:\/\/doi.org\/10.1055\/s-0041-1722861","DOI":"10.1055\/s-0041-1722861"},{"key":"key-10.3934\/mbe.2024339-52","doi-asserted-by":"publisher","unstructured":"N. Ratto, A. Bouchnita, P. Chelle, M. Marion, M. Panteleev, D. Nechipurenko, et al., Patient-specific modelling of blood coagulation, <i>Bull. Math. Biol<\/i>., <b>83<\/b> (2021), 50. https:\/\/doi.org\/10.1007\/s11538-021-00890-8","DOI":"10.1007\/s11538-021-00890-8"},{"key":"key-10.3934\/mbe.2024339-53","doi-asserted-by":"publisher","unstructured":"R. Burghaus, K. Coboeken, T. Gaub, L. Kuepfer, A. Sensse, H.-U. Siegmund, et al., Evaluation of the efficacy and safety of rivaroxaban using a computer model for blood coagulation, <i>PLoS One<\/i>, <b>6<\/b> (2011), e17626. https:\/\/doi.org\/10.1371\/journal.pone.0017626","DOI":"10.1371\/journal.pone.0017626"},{"key":"key-10.3934\/mbe.2024339-54","doi-asserted-by":"publisher","unstructured":"C. Watson, H. Saaid, V. Vedula, J. C. Cardenas, P. K. Henke, F. Nicoud, et al., Venous thromboembolism: Review of clinical challenges, biology, assessment, treatment, and modeling, <i>Ann. Biomed. Eng.<\/i>, <b>52<\/b> (2024), 467\u2013486. https:\/\/doi.org\/10.1007\/s10439-023-03390-z","DOI":"10.1007\/s10439-023-03390-z"},{"key":"key-10.3934\/mbe.2024339-55","doi-asserted-by":"publisher","unstructured":"N. N. Ramli, S. Iberahim, N. H. M. Noor, Z. Zulkafli, T. M. T. M. Shihabuddin, M. H. Din, et al., Haemostasis and inflammatory parameters as potential diagnostic biomarkers for VTE in trauma-immobilized patients, <i>Diagnostics (Basel)<\/i>, <b>13<\/b> (2023), 150. https:\/\/doi.org\/10.3390\/diagnostics13010150","DOI":"10.3390\/diagnostics13010150"},{"key":"key-10.3934\/mbe.2024339-56","doi-asserted-by":"publisher","unstructured":"L. G. R. Ferreira, R. C. Figueiredo, M. das Gra\u00e7as Carvalho, D. R. A. Rios, Thrombin generation assay as a biomarker of cardiovascular outcomes and mortality: A narrative review, <i>Thromb. Res.<\/i>, <b>220<\/b> (2022), 107\u2013115. https:\/\/doi.org\/10.1016\/j.thromres.2022.10.007","DOI":"10.1016\/j.thromres.2022.10.007"},{"key":"key-10.3934\/mbe.2024339-57","doi-asserted-by":"publisher","unstructured":"M. S. Edvardsen, K. Hindberg, E. S. Hansen, V. M. Morelli, T. Ueland, P. Aukrust, et al., Plasma levels of von Willebrand factor and future risk of incident venous thromboembolism, <i>Blood Adv<\/i>., <b>5<\/b> (2021), 224\u2013232. https:\/\/doi.org\/10.1182\/bloodadvances.2020003135","DOI":"10.1182\/bloodadvances.2020003135"},{"key":"key-10.3934\/mbe.2024339-58","doi-asserted-by":"publisher","unstructured":"L. Anghel, R. Sasc\u0103u, R. Radu, C. St\u0103tescu, From classical laboratory parameters to novel biomarkers for the diagnosis of venous thrombosis, <i>Int. J. Mol. Sci.<\/i>, <b>21<\/b> (2020), 1920. https:\/\/doi.org\/10.3390\/ijms21061920","DOI":"10.3390\/ijms21061920"},{"key":"key-10.3934\/mbe.2024339-59","doi-asserted-by":"publisher","unstructured":"H. Y. Lim, C. O'Malley, G. Donnan, H. Nandurkar, P. Ho, A review of global coagulation assays \u2014 Is there a role in thrombosis risk prediction?, <i>Thromb. Res.<\/i>, <b>179<\/b> (2019), 45\u201355. https:\/\/doi.org\/10.1016\/j.thromres.2019.04.033","DOI":"10.1016\/j.thromres.2019.04.033"},{"key":"key-10.3934\/mbe.2024339-60","doi-asserted-by":"publisher","unstructured":"H. Hou, Z. Ge, P. Ying, J. Dai, D. Shi, Z. Xu, et al., Biomarkers of deep venous thrombosis, <i>J. Thromb. Thrombolysis.<\/i>, <b>34<\/b> (2012), 335\u2013346. https:\/\/doi.org\/10.1007\/s11239-012-0721-y","DOI":"10.1007\/s11239-012-0721-y"},{"key":"key-10.3934\/mbe.2024339-61","doi-asserted-by":"publisher","unstructured":"D. Moher, A. Liberati, J. Tetzlaff, D. G. Altman, Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement, <i>PLoS Med.<\/i>, <b>6<\/b> (2009), e1000097. https:\/\/doi.org\/10.1371\/journal.pmed.1000097","DOI":"10.1371\/journal.pmed.1000097"},{"key":"key-10.3934\/mbe.2024339-62","doi-asserted-by":"publisher","unstructured":"M. J. Page, J. E. McKenzie, P. M. Bossuyt, I. Boutron, T. C. Hoffmann, C. D. Mulrow, et al., The PRISMA 2020 statement: an updated guideline for reporting systematic reviews, <i>BMJ<\/i>, <b>372<\/b> (2021), n71. https:\/\/doi.org\/10.1136\/bmj.n71","DOI":"10.1136\/bmj.n71"},{"key":"key-10.3934\/mbe.2024339-63","doi-asserted-by":"publisher","unstructured":"E. N. Sorensen, G. W. Burgreen, W. R. Wagner, J. F. Antaki, Computational simulation of platelet deposition and activation: I. Model development and properties, <i>Ann. Biomed. Eng<\/i>., <b>27<\/b> (1999), 436\u2013448. https:\/\/doi.org\/10.1114\/1.200","DOI":"10.1114\/1.200"},{"key":"key-10.3934\/mbe.2024339-64","doi-asserted-by":"publisher","unstructured":"K. Boryczko, W. Dzwinel, D. A. Yuen, Modeling fibrin aggregation in blood flow with discrete-particles, <i>Comput. Methods Programs Biomed.<\/i>, <b>75<\/b> (2004), 181\u2013194. https:\/\/doi.org\/10.1016\/j.cmpb.2004.02.001","DOI":"10.1016\/j.cmpb.2004.02.001"},{"key":"key-10.3934\/mbe.2024339-65","doi-asserted-by":"publisher","unstructured":"I. V. Pivkin, P. D. Richardson, G. Karniadakis, Blood flow velocity effects and role of activation delay time on growth and form of platelet thrombi, <i>Proc. Natl. Acad. Sci. USA<\/i>, <b>103<\/b> (2006), 17164\u201317169. https:\/\/doi.org\/10.1073\/pnas.0608546103","DOI":"10.1073\/pnas.0608546103"},{"key":"key-10.3934\/mbe.2024339-66","doi-asserted-by":"publisher","unstructured":"Z. Xu, N. Chen, M. M. Kamocka, E. D. Rosen, M. Alber, A multiscale model of thrombus development, <i>J. R. Soc. Interface<\/i>, <b>5<\/b> (2008), 705\u2013722. https:\/\/doi.org\/10.1098\/rsif.2007.1202","DOI":"10.1098\/rsif.2007.1202"},{"key":"key-10.3934\/mbe.2024339-67","doi-asserted-by":"publisher","unstructured":"Z. Xu, N. Chen, S. C. Shadden, J. E. Marsden, M. M. Kamocka, E. D. Rosen, et al., Study of blood flow impact on growth of thrombi using a multiscale model, <i>Soft. Matter<\/i>, <b>5<\/b> (2009), 769\u2013779. https:\/\/doi.org\/10.1039\/B812429A","DOI":"10.1039\/B812429A"},{"key":"key-10.3934\/mbe.2024339-68","doi-asserted-by":"publisher","unstructured":"Z. Xu, J. Lioi, J. Mu, M. M. Kamocka, X. Liu, D. Z. Chen, et al., A multiscale model of venous thrombus formation with surface-mediated control of blood coagulation cascade, <i>Biophys. J.<\/i>, <b>98<\/b> (2010), 1723\u20131732. https:\/\/doi.org\/10.1016\/j.bpj.2009.12.4331","DOI":"10.1016\/j.bpj.2009.12.4331"},{"key":"key-10.3934\/mbe.2024339-69","doi-asserted-by":"publisher","unstructured":"A. M. Shibeko, E. S. Lobanova, M. A. Panteleev, F. I. Ataullakhanov, Blood flow controls coagulation onset via the positive feedback of factor \u2166 activation by factor Xa, <i>BMC Syst. Biol.<\/i>, <b>4<\/b> (2010), 5. https:\/\/doi.org\/10.1186\/1752-0509-4-5","DOI":"10.1186\/1752-0509-4-5"},{"key":"key-10.3934\/mbe.2024339-70","doi-asserted-by":"publisher","unstructured":"S. W. Jordan, E. L. Chaikof, Simulated surface-induced thrombin generation in a flow field, <i>Biophys. J.<\/i>, <b>101<\/b> (2011), 276\u2013286. https:\/\/doi.org\/10.1016\/j.bpj.2011.05.056","DOI":"10.1016\/j.bpj.2011.05.056"},{"key":"key-10.3934\/mbe.2024339-71","doi-asserted-by":"publisher","unstructured":"K. Leiderman, A. L. Fogelson, Grow with the flow: a spatial-temporal model of platelet deposition and blood coagulation under flow, <i>Math. Med. Biol.<\/i>, <b>28<\/b> (2011), 47\u201384. https:\/\/doi.org\/10.1093\/imammb\/dqq005","DOI":"10.1093\/imammb\/dqq005"},{"key":"key-10.3934\/mbe.2024339-72","doi-asserted-by":"publisher","unstructured":"A. L. Fogelson, Y. H. Hussain, K. Leiderman, Blood clot formation under flow: The importance of factor XI depends strongly on platelet count, <i>Biophys. J.<\/i>, <b>102<\/b> (2012), 10\u201318. https:\/\/doi.org\/10.1016\/j.bpj.2011.10.048","DOI":"10.1016\/j.bpj.2011.10.048"},{"key":"key-10.3934\/mbe.2024339-73","doi-asserted-by":"publisher","unstructured":"K. Leiderman, A. L. Fogelson, The influence of hindered transport on the development of platelet thrombi under flow, <i>Bull. Math. Biol.<\/i>, <b>75<\/b> (2013), 1255\u20131283. https:\/\/doi.org\/10.1007\/s11538-012-9784-3","DOI":"10.1007\/s11538-012-9784-3"},{"key":"key-10.3934\/mbe.2024339-74","doi-asserted-by":"publisher","unstructured":"A. Tosenberger, F. Ataullakhanov, N. Bessonov, M. Panteleev, A. Tokarev, V. Volpert, Modelling of thrombus growth in flow with a DPD-PDE method, <i>J. Theor. Biol.<\/i>, <b>337<\/b> (2013), 30\u201341. https:\/\/doi.org\/10.1016\/j.jtbi.2013.07.023","DOI":"10.1016\/j.jtbi.2013.07.023"},{"key":"key-10.3934\/mbe.2024339-75","doi-asserted-by":"publisher","unstructured":"A. Sequeira, T. Bodn\u00e1r, Blood coagulation simulations using a viscoelastic model, <i>Math. Model. Nat. Phenom.<\/i>, <b>9<\/b> (2014), 34\u201345. https:\/\/doi.org\/10.1051\/mmnp\/20149604","DOI":"10.1051\/mmnp\/20149604"},{"key":"key-10.3934\/mbe.2024339-76","doi-asserted-by":"publisher","unstructured":"A. Tosenberger, N. Bessonov, V. Volpert, Influence of fibrinogen deficiency on clot formation in flow by hybrid model, <i>Math. Model. Nat. Phenom<\/i>., <b>10<\/b> (2015), 36\u201347. https:\/\/doi.org\/10.1051\/mmnp\/201510102","DOI":"10.1051\/mmnp\/201510102"},{"key":"key-10.3934\/mbe.2024339-77","doi-asserted-by":"publisher","unstructured":"O. S. Rukhlenko, O. A. Dudchenko, K. E. Zlobina, G. T. Guria, Mathematical modeling of intravascular blood coagulation under wall shear stress, <i>PLoS One<\/i>, <b>10<\/b> (2015), e0134028. https:\/\/doi.org\/10.1371\/journal.pone.0134028","DOI":"10.1371\/journal.pone.0134028"},{"key":"key-10.3934\/mbe.2024339-78","doi-asserted-by":"publisher","unstructured":"J. Pavlova, A. Fasano, J. Janela, A. Sequeira, Numerical validation of a synthetic cell-based model of blood coagulation, <i>J. Theor. Biol.<\/i>, <b>380<\/b> (2015), 367\u2013379. https:\/\/doi.org\/10.1016\/j.jtbi.2015.06.004","DOI":"10.1016\/j.jtbi.2015.06.004"},{"key":"key-10.3934\/mbe.2024339-79","doi-asserted-by":"publisher","unstructured":"A. Piebalgs, X. Y. Xu, Towards a multi-physics modelling framework for thrombolysis under the influence of blood flow, <i>J. R. Soc. Interface<\/i>, <b>12<\/b> (2015), 20150949. https:\/\/doi.org\/10.1098\/rsif.2015.0949","DOI":"10.1098\/rsif.2015.0949"},{"key":"key-10.3934\/mbe.2024339-80","doi-asserted-by":"publisher","unstructured":"Z. Li, A. Yazdani, A. Tartakovsky, G. E. Karniadakis, Transport dissipative particle dynamics model for mesoscopic advection-diffusion-reaction problems, <i>J. Chem. Phys.<\/i>, <b>143<\/b> (2015), 14101. https:\/\/doi.org\/10.1063\/1.4923254","DOI":"10.1063\/1.4923254"},{"key":"key-10.3934\/mbe.2024339-81","doi-asserted-by":"publisher","unstructured":"A. Bouchnita, K. Bouzaachane, T. Galochkina, P. Kurbatova, P. Nony, V. Volpert, An individualized blood coagulation model to predict INR therapeutic range during warfarin treatment, <i>Math. Model. Nat. Phenom.<\/i>, <b>11<\/b> (2016), 28\u201344. https:\/\/doi.org\/10.1051\/mmnp\/201611603","DOI":"10.1051\/mmnp\/201611603"},{"key":"key-10.3934\/mbe.2024339-82","doi-asserted-by":"publisher","unstructured":"J. H. Seo, T. Abd, R. T. George, R. Mittal, A coupled chemo-fluidic computational model for thrombogenesis in infarcted left ventricles, <i>Am. J. Physiol. Heart Circ. Physiol.<\/i>, <b>310<\/b> (2016), H1567\u201382. https:\/\/doi.org\/10.1152\/ajpheart.00855.2015","DOI":"10.1152\/ajpheart.00855.2015"},{"key":"key-10.3934\/mbe.2024339-83","doi-asserted-by":"publisher","unstructured":"M. N. Ngoepe, Y. Ventikos, Computational modelling of clot development in patient-specific cerebral aneurysm cases, <i>J. Thromb. Haemost.<\/i>, <b>14<\/b> (2016), 262\u2013272. https:\/\/doi.org\/10.1111\/jth.13220","DOI":"10.1111\/jth.13220"},{"key":"key-10.3934\/mbe.2024339-84","doi-asserted-by":"publisher","unstructured":"A. Bouchnita, T. Galochkina, V. Volpert, Influence of antithrombin on the regimes of blood coagulation: Insights from the mathematical model, <i>Acta Biotheor.<\/i>, <b>64<\/b> (2016), 327\u2013342. https:\/\/doi.org\/10.1007\/s10441-016-9291-2","DOI":"10.1007\/s10441-016-9291-2"},{"key":"key-10.3934\/mbe.2024339-85","doi-asserted-by":"publisher","unstructured":"E. V. Dydek, E. L. Chaikof, Simulated thrombin generation in the presence of surface-bound heparin and circulating tissue factor, <i>Ann. Biomed. Eng.<\/i>, <b>44<\/b> (2016), 1072\u20131084. https:\/\/doi.org\/10.1007\/s10439-015-1377-5","DOI":"10.1007\/s10439-015-1377-5"},{"key":"key-10.3934\/mbe.2024339-86","doi-asserted-by":"publisher","unstructured":"J. Pavlova, A. Fasano, A. Sequeira, Numerical simulations of a reduced model for blood coagulation, <i>Zeitschrift f\u00fcr Angew. Math. und Phys.<\/i>, <b>67<\/b> (2016), 28. https:\/\/doi.org\/10.1007\/s00033-015-0610-2","DOI":"10.1007\/s00033-015-0610-2"},{"key":"key-10.3934\/mbe.2024339-87","doi-asserted-by":"publisher","unstructured":"A. Tosenberger, F. Ataullakhanov, N. Bessonov, M. Panteleev, A. Tokarev, V. Volpert, Modelling of platelet-fibrin clot formation in flow with a DPD-PDE method, <i>J. Math. Biol.<\/i>, <b>72<\/b> (2016), 649\u2013681. https:\/\/doi.org\/10.1007\/s00285-015-0891-2","DOI":"10.1007\/s00285-015-0891-2"},{"key":"key-10.3934\/mbe.2024339-88","doi-asserted-by":"publisher","unstructured":"V. Govindarajan, V. Rakesh, J. Reifman, A. Y. Mitrophanov, Computational study of thrombus formation and clotting factor effects under venous flow conditions, <i>Biophys. J.<\/i>, <b>110<\/b> (2016), 1869\u20131885. https:\/\/doi.org\/10.1016\/j.bpj.2016.03.010","DOI":"10.1016\/j.bpj.2016.03.010"},{"key":"key-10.3934\/mbe.2024339-89","doi-asserted-by":"publisher","unstructured":"C. Ou, W. Huang, M. M.-F. Yuen, A computational model based on fibrin accumulation for the prediction of stasis thrombosis following flow-diverting treatment in cerebral aneurysms, <i>Med. Biol. Eng. Comput.<\/i>, <b>55<\/b> (2017), 89\u201399. https:\/\/doi.org\/10.1007\/s11517-016-1501-1","DOI":"10.1007\/s11517-016-1501-1"},{"key":"key-10.3934\/mbe.2024339-90","doi-asserted-by":"publisher","unstructured":"A. Yazdani, H. Li, J. D. Humphrey, G. E. Karniadakis, A general shear-dependent model for thrombus formation, <i>PLoS Comput. Biol.<\/i>, <b>13<\/b> (2017), e1005291. https:\/\/doi.org\/10.1371\/journal.pcbi.1005291","DOI":"10.1371\/journal.pcbi.1005291"},{"key":"key-10.3934\/mbe.2024339-91","doi-asserted-by":"publisher","unstructured":"H. Hosseinzadegan, D. K. Tafti, Prediction of thrombus growth: Effect of stenosis and reynolds number, <i>Cardiovasc. Eng. Technol.<\/i>, <b>8<\/b> (2017), 164\u2013181. https:\/\/doi.org\/10.1007\/s13239-017-0304-3","DOI":"10.1007\/s13239-017-0304-3"},{"key":"key-10.3934\/mbe.2024339-92","doi-asserted-by":"publisher","unstructured":"L. M. Haynes, T. Orfeo, K. G. Mann, S. J. Everse, K. E. Brummel-Ziedins, Probing the dynamics of clot-bound thrombin at venous shear rates, <i>Biophys. J.<\/i>, <b>112<\/b> (2017), 1634\u20131644. https:\/\/doi.org\/10.1016\/j.bpj.2017.03.002","DOI":"10.1016\/j.bpj.2017.03.002"},{"key":"key-10.3934\/mbe.2024339-93","doi-asserted-by":"publisher","unstructured":"H. Kamada, Y. Imai, M. Nakamura, T. Ishikawa, T. Yamaguchi, Shear-induced platelet aggregation and distribution of thrombogenesis at stenotic vessels, <i>Microcirculation<\/i>, <b>24<\/b> (2017). https:\/\/doi.org\/10.1111\/micc.12355","DOI":"10.1111\/micc.12355"},{"key":"key-10.3934\/mbe.2024339-94","doi-asserted-by":"publisher","unstructured":"J. D. Horn, D. J. Maitland, J. Hartman, J. M. Ortega, A computational thrombus formation model: application to an idealized two-dimensional aneurysm treated with bare metal coils, <i>Biomech. Model. Mechanobiol.<\/i>, <b>17<\/b> (2018), 1821\u20131838. https:\/\/doi.org\/10.1007\/s10237-018-1059-y","DOI":"10.1007\/s10237-018-1059-y"},{"key":"key-10.3934\/mbe.2024339-95","doi-asserted-by":"publisher","unstructured":"R. M\u00e9ndez Rojano, S. Mendez, F. Nicoud, Introducing the pro-coagulant contact system in the numerical assessment of device-related thrombosis, <i>Biomech. Model. Mechanobiol.<\/i>, <b>17<\/b> (2018), 815\u2013826. https:\/\/doi.org\/10.1007\/s10237-017-0994-3","DOI":"10.1007\/s10237-017-0994-3"},{"key":"key-10.3934\/mbe.2024339-96","doi-asserted-by":"publisher","unstructured":"B. Gu, A. Piebalgs, Y. Huang, C. Longstaff, A. D. Hughes, R. Chen, et al., Mathematical modelling of intravenous thrombolysis in acute ischaemic stroke: Effects of dose regimens on levels of fibrinolytic proteins and clot lysis time, <i>Pharmaceutics<\/i>, <b>11<\/b> (2019), 111. https:\/\/doi.org\/10.3390\/pharmaceutics11030111","DOI":"10.3390\/pharmaceutics11030111"},{"key":"key-10.3934\/mbe.2024339-97","doi-asserted-by":"publisher","unstructured":"K. Ayabe, S. Goto, H. Oka, H. Yabushita, M. Nakayama, A. Tomita, et al., Potential different impact of inhibition of thrombin function and thrombin generation rate for the growth of thrombi formed at site of endothelial injury under blood flow condition, <i>Thromb. Res.<\/i>, <b>179<\/b> (2019), 121\u2013127. https:\/\/doi.org\/10.1016\/j.thromres.2019.05.007","DOI":"10.1016\/j.thromres.2019.05.007"},{"key":"key-10.3934\/mbe.2024339-98","doi-asserted-by":"publisher","unstructured":"J. Chen, S. L. Diamond, Reduced model to predict thrombin and fibrin during thrombosis on collagen\/tissue factor under venous flow: Roles of \u03b3'-fibrin and factor XIa, <i>PLoS Comput. Biol.<\/i>, <b>15<\/b> (2019), e1007266. https:\/\/doi.org\/10.1371\/journal.pcbi.1007266","DOI":"10.1371\/journal.pcbi.1007266"},{"key":"key-10.3934\/mbe.2024339-99","doi-asserted-by":"publisher","unstructured":"A. Bouchnita, V. Volpert, A multiscale model of platelet-fibrin thrombus growth in the flow, <i>Comput. Fluids<\/i>, <b>184<\/b> (2019), 10\u201320. https:\/\/doi.org\/10.1016\/j.compfluid.2019.03.021","DOI":"10.1016\/j.compfluid.2019.03.021"},{"key":"key-10.3934\/mbe.2024339-100","doi-asserted-by":"publisher","unstructured":"H. Hosseinzadegan, D. K. Tafti, A predictive model of thrombus growth in stenosed vessels with dynamic geometries, <i>J. Med. Biol. Eng.<\/i>, <b>39<\/b> (2019), 605\u2013621. https:\/\/doi.org\/10.1007\/s40846-018-0443-5","DOI":"10.1007\/s40846-018-0443-5"},{"key":"key-10.3934\/mbe.2024339-101","doi-asserted-by":"publisher","unstructured":"O. E. Kadri, V. D. Chandran, M. Surblyte, R. S. Voronov, In vivo measurement of blood clot mechanics from computational fluid dynamics based on intravital microscopy images, <i>Comput. Biol. Med.<\/i>, <b>106<\/b> (2019), 1\u201311. https:\/\/doi.org\/10.1016\/j.compbiomed.2019.01.001","DOI":"10.1016\/j.compbiomed.2019.01.001"},{"key":"key-10.3934\/mbe.2024339-102","doi-asserted-by":"publisher","unstructured":"J. Du, D. Kim, G. Alhawael, D. N. Ku, A. L. Fogelson, Clot permeability, agonist transport, and platelet binding kinetics in arterial thrombosis, <i>Biophys. J.<\/i>, <b>119<\/b> (2020), 2102\u20132115. https:\/\/doi.org\/10.1016\/j.bpj.2020.08.041","DOI":"10.1016\/j.bpj.2020.08.041"},{"key":"key-10.3934\/mbe.2024339-103","doi-asserted-by":"publisher","unstructured":"W. T. Wu, M. Zhussupbekov, N. Aubry, J. F. Antaki, M. Massoudi, Simulation of thrombosis in a stenotic microchannel: The effects of vWF-enhanced shear activation of platelets, <i>Int. J. Eng. Sci.<\/i>, <b>147<\/b> (2020), 103206. https:\/\/doi.org\/10.1016\/j.ijengsci.2019.103206","DOI":"10.1016\/j.ijengsci.2019.103206"},{"key":"key-10.3934\/mbe.2024339-104","doi-asserted-by":"publisher","unstructured":"A. Bouchnita, K. Terekhov, P. Nony, Y. Vassilevski, V. Volpert, A mathematical model to quantify the effects of platelet count, shear rate, and injury size on the initiation of blood coagulation under venous flow conditions, <i>PLoS One<\/i>, <b>15<\/b> (2020), e0235392. https:\/\/doi.org\/10.1371\/journal.pone.0235392","DOI":"10.1371\/journal.pone.0235392"},{"key":"key-10.3934\/mbe.2024339-105","doi-asserted-by":"publisher","unstructured":"Z. L. Liu, D. N. Ku, C. K. Aidun, Mechanobiology of shear-induced platelet aggregation leading to occlusive arterial thrombosis: A multiscale in silico analysis, <i>J. Biomech.<\/i>, <b>120<\/b> (2021), 110349. https:\/\/doi.org\/10.1016\/j.jbiomech.2021.110349","DOI":"10.1016\/j.jbiomech.2021.110349"},{"key":"key-10.3934\/mbe.2024339-106","doi-asserted-by":"publisher","unstructured":"V. N. Kaneva, J. L. Dunster, V. Volpert, F. Ataullahanov, M. A. Panteleev, D. Y. Nechipurenko, Modeling thrombus shell: Linking adhesion receptor properties and macroscopic dynamics, <i>Biophys. J.<\/i>, <b>120<\/b> (2021), 334\u2013351. https:\/\/doi.org\/10.1016\/j.bpj.2020.10.049","DOI":"10.1016\/j.bpj.2020.10.049"},{"key":"key-10.3934\/mbe.2024339-107","doi-asserted-by":"publisher","unstructured":"A. Yazdani, Y. Deng, H. Li, E. Javadi, Z. Li, S. Jamali, et al., Integrating blood cell mechanics, platelet adhesive dynamics and coagulation cascade for modelling thrombus formation in normal and diabetic blood, <i>J. R. Soc. Interface.<\/i>, <b>18<\/b> (2021), 20200834. https:\/\/doi.org\/10.1098\/rsif.2020.0834","DOI":"10.1098\/rsif.2020.0834"},{"key":"key-10.3934\/mbe.2024339-108","doi-asserted-by":"publisher","unstructured":"C. Ma, Y. Ren, Q. Zheng, J. Wang, A computational model on cartesian adaptive grid for thrombosis simulation, <i>IEEE Access<\/i>, <b>10<\/b> (2022), 67694\u201367702. https:\/\/doi.org\/10.1109\/ACCESS.2022.3184123","DOI":"10.1109\/ACCESS.2022.3184123"},{"key":"key-10.3934\/mbe.2024339-109","doi-asserted-by":"publisher","unstructured":"R. M\u00e9ndez Rojano, A. Lai, M. Zhussupbekov, G. W. Burgreen, K. Cook, J. F. Antaki, A fibrin enhanced thrombosis model for medical devices operating at low shear regimes or large surface areas, <i>PLoS Comput. Biol.<\/i>, <b>18<\/b> (2022), e1010277. https:\/\/doi.org\/10.1371\/journal.pcbi.1010277","DOI":"10.1371\/journal.pcbi.1010277"},{"key":"key-10.3934\/mbe.2024339-110","doi-asserted-by":"publisher","unstructured":"M. Rezaeimoghaddam, F. N. van de Vosse, Continuum modeling of thrombus formation and growth under different shear rates, <i>J. Biomech.<\/i>, <b>132<\/b> (2022), 110915. https:\/\/doi.org\/10.1016\/j.jbiomech.2021.110915","DOI":"10.1016\/j.jbiomech.2021.110915"},{"key":"key-10.3934\/mbe.2024339-111","doi-asserted-by":"crossref","unstructured":"A. S. Pisaryuk, N. M. Povalyaev, A. V Poletaev, A. M. Shibeko, Systems biology approach for personalized hemostasis correction, <i>J. Pers. Med.<\/i>, <b>12<\/b> (2022). <ext-link ext-link-type=\"uri\" xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.3390\/jpm12111903\">https:\/\/doi.org\/10.3390\/jpm12111903<\/ext-link>","DOI":"10.3390\/jpm12111903"},{"key":"key-10.3934\/mbe.2024339-112","doi-asserted-by":"publisher","unstructured":"M. Zhussupbekov, R. M\u00e9ndez Rojano, W. T. Wu, J. F. Antaki, von Willebrand factor unfolding mediates platelet deposition in a model of high-shear thrombosis, <i>Biophys. J.<\/i>, <b>121<\/b> (2022), 4033\u20134047. https:\/\/doi.org\/10.1016\/j.bpj.2022.09.040","DOI":"10.1016\/j.bpj.2022.09.040"},{"key":"key-10.3934\/mbe.2024339-113","doi-asserted-by":"publisher","unstructured":"Y. Wang, J. Luan, K. Luo, T. Zhu, J. Fan, Multi-constituent simulation of thrombosis in aortic dissection, <i>Int. J. Eng. Sci.<\/i>, <b>184<\/b> (2023), 103817. http:\/\/dx.doi.org\/10.1016\/j.ijengsci.2023.103817","DOI":"10.1016\/j.ijengsci.2023.103817"},{"key":"key-10.3934\/mbe.2024339-114","doi-asserted-by":"publisher","unstructured":"R. Petkantchin, A. Rousseau, O. Eker, K. Zouaoui Boudjeltia, F. Raynaud, B. Chopard, A simplified mesoscale 3D model for characterizing fibrinolysis under flow conditions, <i>Sci. Rep.<\/i>, <b>13<\/b> (2023), 13681. https:\/\/doi.org\/10.1038\/s41598-023-40973-1","DOI":"10.1038\/s41598-023-40973-1"},{"key":"key-10.3934\/mbe.2024339-115","doi-asserted-by":"publisher","unstructured":"K. Miyazawa, A. L. Fogelson, K. Leiderman, Inhibition of platelet-surface-bound proteins during coagulation under flow \u2161: Antithrombin and heparin, <i>Biophys. J.<\/i>, <b>122<\/b> (2023), 230\u2013240. https:\/\/doi.org\/10.1016\/j.bpj.2022.10.038","DOI":"10.1016\/j.bpj.2022.10.038"},{"key":"key-10.3934\/mbe.2024339-116","doi-asserted-by":"publisher","unstructured":"A. R. Rezaie, S. T. Olson, Calcium enhances heparin catalysis of the antithrombin-factor Xa reaction by promoting the assembly of an intermediate heparin-antithrombin-factor Xa bridging complex: Demonstration by rapid kinetics studies, <i>Biochemistry<\/i>, <b>39<\/b> (2000), 12083\u201312090. https:\/\/doi.org\/10.1021\/bi0011126","DOI":"10.1021\/bi0011126"},{"key":"key-10.3934\/mbe.2024339-117","doi-asserted-by":"publisher","unstructured":"M. Anand, K. Rajagopal, K. R. Rajagopal, A model for the formation, growth, and lysis of clots in quiescent plasma: A comparison between the effects of antithrombin \u2162 deficiency and protein C deficiency, <i>J. Theor. Biol<\/i>., <b>253<\/b> (2008), 725\u2013738. https:\/\/doi.org\/10.1016\/j.jtbi.2008.04.015","DOI":"10.1016\/j.jtbi.2008.04.015"},{"key":"key-10.3934\/mbe.2024339-118","doi-asserted-by":"crossref","unstructured":"M. J. Griffith, The heparin-enhanced antithrombin \u2162\/thrombin reaction is saturable with respect to both thrombin and antithrombin \u2162, <i>J. Biol. Chem.<\/i>, <b>257<\/b> (1982), 13302\u201313899.","DOI":"10.1016\/S0021-9258(19)45315-5"},{"key":"key-10.3934\/mbe.2024339-119","doi-asserted-by":"crossref","unstructured":"M. J. Griffith, Kinetics of the heparin-enhanced antithrombin \u2162\/thrombin reaction. Evidence for a template model for the mechanism of action of heparin, <i>J. Biol. Chem.<\/i>, <b>257<\/b> (1982), 7360\u20137365.","DOI":"10.1016\/S0021-9258(18)34385-0"},{"key":"key-10.3934\/mbe.2024339-120","doi-asserted-by":"publisher","unstructured":"C. M. Danforth, T. Orfeo, K. G. Mann, K. E. Brummel-Ziedins, S. J. Everse, The impact of uncertainty in a blood coagulation model, <i>Math. Med. Biol.<\/i>, <b>26<\/b> (2009), 323\u2013336. https:\/\/doi.org\/10.1093\/imammb\/dqp011","DOI":"10.1093\/imammb\/dqp011"},{"key":"key-10.3934\/mbe.2024339-121","doi-asserted-by":"publisher","unstructured":"P. P. Naidu, M. Anand, Importance of \u2167a inactivation in a mathematical model for the formation, growth, and lysis of clots, <i>Math. Model. Nat. Phenom.<\/i>, <b>9<\/b> (2014), 17\u201333. https:\/\/doi.org\/10.1051\/mmnp\/20149603","DOI":"10.1051\/mmnp\/20149603"},{"key":"key-10.3934\/mbe.2024339-122","doi-asserted-by":"publisher","unstructured":"F. Saitta, J. Masuri, M. Signorelli, S. Bertini, A. Bisio, D. Fessas, Thermodynamic insights on the effects of low-molecular-weight heparins on antithrombin \u2162, <i>Thermochim. Acta<\/i>, <b>713<\/b> (2022), 179248. https:\/\/doi.org\/10.1016\/j.tca.2022.179248","DOI":"10.1016\/j.tca.2022.179248"},{"key":"key-10.3934\/mbe.2024339-123","doi-asserted-by":"publisher","unstructured":"H. Minakami, M. Morikawa, T. Yamada, T. Yamada, Candidates for the determination of antithrombin activity in pregnant women, <i>J. Perinat. Med.<\/i>, <b>39<\/b> (2011), 369\u2013374. https:\/\/doi.org\/10.1515\/jpm.2011.026","DOI":"10.1515\/jpm.2011.026"},{"key":"key-10.3934\/mbe.2024339-124","doi-asserted-by":"crossref","unstructured":"K. C. Jones, K. G. Mann, A model for the tissue factor pathway to thrombin. \u2161: A mathematical simulation, <i>J. Biol. Chem.<\/i>, <b>269<\/b> (1994), 23367\u201323373.","DOI":"10.1016\/S0021-9258(17)31662-9"},{"key":"key-10.3934\/mbe.2024339-125","doi-asserted-by":"publisher","unstructured":"M. F. Hockin, K. C. Jones, S. J. Everse, K. G. Mann, A model for the stoichiometric regulation of blood coagulation, <i>J. Biol. Chem.<\/i>, <b>277<\/b> (2002), 18322\u201318333. https:\/\/doi.org\/10.1074\/jbc.m201173200","DOI":"10.1074\/jbc.m201173200"},{"key":"key-10.3934\/mbe.2024339-126","doi-asserted-by":"publisher","unstructured":"R. M\u00e9ndez Rojano, S. Mendez, D. Lucor, A. Ranc, M. Giansily-Blaizot, J. F. Schved, et al., Kinetics of the coagulation cascade including the contact activation system: sensitivity analysis and model reduction, <i>Biomech. Model. Mechanobiol.<\/i>, <b>18<\/b> (2019), 1139\u20131153. https:\/\/doi.org\/10.1007\/s10237-019-01134-4","DOI":"10.1007\/s10237-019-01134-4"},{"key":"key-10.3934\/mbe.2024339-127","doi-asserted-by":"publisher","unstructured":"M. S. Chatterjee, W. S. Denney, H. Jing, S. L. Diamond, Systems biology of coagulation initiation: Kinetics of thrombin generation in resting and activated human blood, <i>PLOS Comput. Biol.<\/i>, <b>6<\/b> (2010), e1000950. https:\/\/doi.org\/10.1371\/journal.pcbi.1000950","DOI":"10.1371\/journal.pcbi.1000950"},{"key":"key-10.3934\/mbe.2024339-128","doi-asserted-by":"publisher","unstructured":"A. Ranc, S. Bru, S. Mendez, M. Giansily-Blaizot, F. Nicoud, R. M\u00e9ndez Rojano, Critical evaluation of kinetic schemes for coagulation, <i>PLoS One<\/i>, <b>18<\/b> (2023), e0290531. https:\/\/doi.org\/10.1371\/journal.pone.0290531","DOI":"10.1371\/journal.pone.0290531"},{"key":"key-10.3934\/mbe.2024339-129","doi-asserted-by":"publisher","unstructured":"M. F. Hockin, K. C. Jones, S. J. Everse, K. G. Mann, A model for the stoichiometric regulation of blood coagulation, <i>J. Biol. Chem.<\/i>, <b>277<\/b> (2002), 18322\u201318333. https:\/\/doi.org\/10.1074\/jbc.m201173200","DOI":"10.1074\/jbc.m201173200"},{"key":"key-10.3934\/mbe.2024339-130","doi-asserted-by":"publisher","unstructured":"D. Luan, M. Zai, J. D. Varner, Computationally derived points of fragility of a human cascade are consistent with current therapeutic strategies, <i>PLoS Comput. Biol.<\/i>, <b>3<\/b> (2007), 1347\u20131359. https:\/\/doi.org\/10.1371\/journal.pcbi.0030142","DOI":"10.1371\/journal.pcbi.0030142"},{"key":"key-10.3934\/mbe.2024339-131","doi-asserted-by":"publisher","unstructured":"K. G. Link, M. T. Stobb, J. Di Paola, K. B. Neeves, A. L. Fogelson, S. S. Sindi, et al., A local and global sensitivity analysis of a mathematical model of coagulation and platelet deposition under flow, <i>PLoS One<\/i>, <b>13<\/b> (2018), e0200917. https:\/\/doi.org\/10.1371\/journal.pone.0200917","DOI":"10.1371\/journal.pone.0200917"}],"container-title":["Mathematical Biosciences and Engineering"],"original-title":[],"link":[{"URL":"http:\/\/www.aimspress.com\/article\/doi\/10.3934\/mbe.2024339?viewType=html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T06:55:41Z","timestamp":1734072941000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.aimspress.com\/article\/doi\/10.3934\/mbe.2024339"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":131,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.3934\/mbe.2024339","relation":{},"ISSN":["1551-0018"],"issn-type":[{"value":"1551-0018","type":"print"}],"subject":[],"published":{"date-parts":[[2024]]}}}