Computational Modeling Intervertebral Disc Pathophysiology: A Review

Lower back pain is a medical condition of epidemic proportion, and the degeneration of the intervertebral disc has been identified as a major contributor. The etiology of intervertebral disc (IVD) degeneration is multifactorial, depending on age, cell-mediated molecular degradation processes and genetics, which is accelerated by traumatic or gradual mechanical factors. The complexity of such intertwined biochemical and mechanical processes leading to degeneration makes it difficult to quantitatively identify cause–effect relationships through experiments. Computational modeling of the IVD is a powerful investigative tool since it offers the opportunity to vary, observe and isolate the effects of a wide range of phenomena involved in the degenerative process of discs. This review aims at discussing the main findings of finite element models of IVD pathophysiology with a special focus on the different factors contributing to physical changes typical of degenerative phenomena. Models presented are subdivided into those addressing role of nutritional supply, progressive biochemical alterations stemming from an imbalance between anabolic and catabolic processes, aging and those considering mechanical factors as the primary source that induces morphological change within the disc. Limitations of the current models, as well as opportunities for future computational modeling work are also discussed.

Recent Advances in Computational Modeling of Biomechanics and Biorheology of Red Blood Cells in Diabetes

Diabetes mellitus, a metabolic disease characterized by chronically elevated blood glucose levels, affects about 29 million Americans and more than 422 million adults all over the world. Particularly, type 2 diabetes mellitus (T2DM) accounts for 90–95% of the cases of vascular disease and its prevalence is increasing due to the rising obesity rates in modern societies. Although multiple factors associated with diabetes, such as reduced red blood cell (RBC) deformability, enhanced RBC aggregation and adhesion to the endothelium, as well as elevated blood viscosity are thought to contribute to the hemodynamic impairment and vascular occlusion, clinical or experimental studies cannot directly quantify the contributions of these factors to the abnormal hematology in T2DM. Recently, computational modeling has been employed to dissect the impacts of the aberrant biomechanics of diabetic RBCs and their adverse effects on microcirculation. In this review, we summarize the recent advances in the developments and applications of computational models in investigating the abnormal properties of diabetic blood from the cellular level to the vascular level. We expect that this review will motivate and steer the development of new models in this area and shift the attention of the community from conventional laboratory studies to combined experimental and computational investigations, aiming to provide new inspirations for the development of advanced tools to improve our understanding of the pathogenesis and pathology of T2DM.

A computational modeling approach for dosing endoscopic intratumoral chemotherapy for advanced non-small cell lung cancer

We recently developed a computational model of cisplatin pharmacodynamics in an endobronchial lung tumor following ultrasound-guided transbronchial needle injection (EBUS-TBNI). The model suggests that it is more efficacious to apportion the cisplatin dose between injections at different sites rather than giving it all in a single central injection, but the model was calibrated only on blood cisplatin data from a single patient. Accordingly, we applied a modified version of our original model in a set of 32 patients undergoing EBUS-TBNI for non-small cell lung cancer (NSCLC). We used the model to predict clinical responses and compared them retrospectively to actual patient outcomes. The model correctly predicted the clinical response in 72% of cases, with 80% accuracy for adenocarcinomas and 62.5% accuracy for squamous-cell lung cancer. We also found a power-law relationship between tumor volume and the minimal dose needed to induce a response, with the power-law exponent depending on the number of injections administered. Our results suggest that current injection strategies may be significantly over- or under-dosing the agent depending on tumor size, and that computational modeling can be a useful planning tool for EBUS-TBNI of cisplatin in lung cancer.

Stronger Prejudices Are Associated With Decreased Model-Based Control

Background: Prejudices against minorities can be understood as habitually negative evaluations that are kept in spite of evidence to the contrary. Therefore, individuals with strong prejudices might be dominated by habitual or “automatic” reactions at the expense of more controlled reactions. Computational theories suggest individual differences in the balance between habitual/model-free and deliberative/model-based decision-making.Methods: 127 subjects performed the two Step task and completed the blatant and subtle prejudice scale.Results: By using analyses of choices and reaction times in combination with computational modeling, subjects with stronger blatant prejudices showed a shift away from model-based control. There was no association between these decision-making processes and subtle prejudices.Conclusion: These results support the idea that blatant prejudices toward minorities are related to a relative dominance of habitual decision-making. This finding has important implications for developing interventions that target to change prejudices across societies.