数学模拟炎症作为肿瘤生长的促进剂。

IF 0.8 4区 数学 Q4 BIOLOGY Mathematical Medicine and Biology-A Journal of the Ima Pub Date : 2020-12-15 DOI:10.1093/imammb/dqaa005
Kathleen P Wilkie, Farjana Aktar
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引用次数: 2

摘要

现在已知炎症在肿瘤生长和发展中起着重要作用。也很难充分量化全身性炎症及其对癌症的局部影响。在这里,我们使用实验数据来推断炎症在小鼠癌症模型中的可能贡献。通过预测抗炎治疗下的肿瘤生长,验证了该模型,并探索了联合治疗癌症的方法。然后,我们将模型扩展到考虑同时在两个不同的位置植入肿瘤,实验表明,这将导致一个大的和一个小的肿瘤。我们使用这个模型来检验炎症在生长速率分离中可能起的作用。最后,我们使用这种预测双肿瘤模型来探讨炎症对转移、手术切除和辅助抗炎治疗的影响。这项工作表明,通过抗炎治疗,包括减少原发肿瘤手术切除后的转移性负担,可以同时靶向癌症和宿主,从而改善肿瘤控制。
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Mathematically modelling inflammation as a promoter of tumour growth.

Inflammation is now known to play a significant role in tumour growth and progression. It is also difficult to adequately quantify systemic inflammation and the resulting localized effects in cancer. Here, we use experimental data to infer the possible contributions of inflammation in a mouse model of cancer. The model is validated by predicting tumour growth under anti-inflammatory treatments, and combination cancer therapies are explored. We then extend the model to consider simultaneous tumour implants at two distinct sites, which experimentally was shown to result in one large and one small tumour. We use this model to examine the role inflammation may play in the growth rate separation. Finally, we use this predictive two-tumour model to explore implications of inflammation on metastases, surgical removal of the primary and adjuvant anti-inflammatory treatments. This work suggests that improved tumour control can be obtained by targeting both the cancer and host, through anti-inflammatory treatments, including reduced metastatic burden post-surgical removal of primary tumours.

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来源期刊
CiteScore
2.20
自引率
0.00%
发文量
15
审稿时长
>12 weeks
期刊介绍: Formerly the IMA Journal of Mathematics Applied in Medicine and Biology. Mathematical Medicine and Biology publishes original articles with a significant mathematical content addressing topics in medicine and biology. Papers exploiting modern developments in applied mathematics are particularly welcome. The biomedical relevance of mathematical models should be demonstrated clearly and validation by comparison against experiment is strongly encouraged. The journal welcomes contributions relevant to any area of the life sciences including: -biomechanics- biophysics- cell biology- developmental biology- ecology and the environment- epidemiology- immunology- infectious diseases- neuroscience- pharmacology- physiology- population biology
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