In this paper we consider chemotherapy in a spatial model of tumor growth. The model, which is of reaction-diffusion type, takes into account the complex interactions between the tumor and surrounding stromal cells by including densities of endothelial cells and the extra-cellular matrix. When no treatment is applied the model reproduces the typical dynamics of early tumor growth. The initially avascular tumor reaches a diffusion limited size of the order of millimeters and initiates angiogenesis through the release of vascular endothelial growth factor (VEGF) secreted by hypoxic cells in the core of the tumor. This stimulates endothelial cells to migrate towards the tumor and establishes a nutrient supply sufficient for sustained invasion. To this model we apply cytostatic treatment in the form of a VEGF-inhibitor, which reduces the proliferation and chemotaxis of endothelial cells. This treatment has the capability to reduce tumor mass, but more importantly, we were able to determine that inhibition of endothelial cell proliferation is the more important of the two cellular functions targeted by the drug. Further, we considered the application of a cytotoxic drug that targets proliferating tumor cells. The drug was treated as a diffusible substance entering the tissue from the blood vessels. Our results show that depending on the characteristics of the drug it can either reduce the tumor mass significantly or in fact accelerate the growth rate of the tumor. This result seems to be due to complicated interplay between the stromal and tumor cell types and highlights the importance of considering chemotherapy in a spatial context.

Background: Although the peptidyl-prolyl isomerase, cyclophilin-A (peptidyl-prolyl isomerase, PPIA), has been studied for decades in the context of its intracellular functions, its extracellular roles as a major contributor to both inflammation and multiple cancers have more recently emerged. A wide range of activities have been ascribed to extracellular PPIA that include induction of cytokine and matrix metalloproteinase (MMP) secretion, which potentially underlie its roles in inflammation and tumorigenesis. However, there have been conflicting reports as to which particular signaling events are under extracellular PPIA regulation, which may be due to either cell-dependent responses and/or the use of commercial preparations recently shown to be highly impure.Methods: We have produced and validated the purity of recombinant PPIA in order to subject it to a comparative analysis between different cell types. Specifically, we have used a combination of multiple methods such as luciferase reporter screens, translocation assays, phosphorylation assays, and nuclear magnetic resonance to compare extracellular PPIA activities in several different cell lines that included epithelial and monocytic cells.Results: Our findings have revealed that extracellular PPIA activity is cell type-dependent and that PPIA signals via multiple cellular receptors beyond the single transmembrane receptor previously identified, Extracellular Matrix MetalloPRoteinase Inducer (EMMPRIN). Finally, while our studies provide important insight into the cell-specific responses, they also indicate that there are consistent responses such as nuclear factor kappa B (NFκB) signaling induced in all cell lines tested.Conclusions: We conclude that although extracellular PPIA activates several common pathways, it also targets different receptors in different cell types, resulting in a complex, integrated signaling network that is cell type-specific.

Mild cognitive impairment (MCI) is cognitive decline that can indicate future risk of Alzheimer’s disease (AD). We developed and validated a machine learning algorithm (MLA), based on a gradient-boosted tree ensemble method, to analyze phenotypic data for individuals 55–88 years old (n = 493) diagnosed with MCI. Data were analyzed within multiple prediction windows and averaged to predict progression to AD within 24–48 months. The MLA outperformed the mini-mental state examination (MMSE) and three comparison models at all prediction windows on most metrics. Exceptions include sensitivity at 18 months (MLA and MMSE each achieved 0.600); and sensitivity at 30 and 42 months (MMSE marginally better). For all prediction windows, the MLA achieved AUROC ≥ 0.857 and NPV ≥ 0.800. With averaged data for the 24–48-month lookahead timeframe, the MLA outperformed MMSE on all metrics. This study demonstrates that machine learning may provide a more accurate risk assessment than the standard of care. This may facilitate care coordination, decrease healthcare expenditures, and maintain quality of life for patients at risk of progressing from MCI to AD.

Background/Objective: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by lifelong impacts on functional social and daily living skills, and restricted, repetitive behaviors (RRBs). Applied behavior analysis (ABA), the gold-standard treatment for ASD, has been extensively validated. ABA access is hindered by limited availability of qualified professionals and logistical and financial barriers. Scientifically validated, parent-led ABA can fill the accessibility gap by overcoming treatment barriers. This retrospective cohort study examines how our ABA treatment model, utilizing parent behavior technicians (pBTs) to deliver ABA, impacts adaptive behaviors and interfering behaviors (IBs) in a cohort of children on the autism spectrum with varying ASD severity levels, and with or without clinically significant IBs. Methods: Clinical outcomes of 36 patients ages 3–15 years were assessed using longitudinal changes in Vineland-3 after 3+ months of pBT-delivered ABA treatment. Results: Within the pBT model, our patients demonstrated clinically significant improvements in Vineland-3 Composite, domain, and subdomain scores, and utilization was higher in severe ASD. pBTs utilized more prescribed ABA when children initiated treatment with clinically significant IBs, and these children also showed greater gains in their Composite scores. Study limitations include sample size, inter-rater reliability, potential assessment metric bias and schedule variability, and confounding intrinsic or extrinsic factors. Conclusion: Overall, our pBT model facilitated high treatment utilization and showed robust effectiveness, achieving improved adaptive behaviors and reduced IBs when compared to conventional ABA delivery. The pBT model is a strong contender to fill the widening treatment accessibility gap and represents a powerful tool for addressing systemic problems in ABA treatment delivery.

Scanning electrochemical microscopy (SECM), a non-invasive variant of scanning probe microscopy, can be used to measure oxygen uptake or insulin secretion of isolated murine pancreatic islets in the presence of low and high physiological glucose concentrations levels. SECM imaging of islet topography and islet oxygen uptake is accomplished by the use of a redox mediator that cannot penetrate into the cell membranes of the islet coupled to direct reduction of oxygen at the SECM tip. An insulin sensing ultramicroelectrode (UME), adapted from a macro-scale insulin sensor incorporating a multiwalled carbon nanotube/dihydropyran film developed in our lab previously, was used for the direct electrochemical detection of insulin levels within extracellular media in real-time. We were able to stimulate and record increased insulin production in a single pancreatic islet by raising the concentration of extracellular glucose to the physiological ‘high level.’ These experiments generated probe scan curves (PSCs) for insulin production in the extracellular media surrounding single pancreatic islets extracted from male mice. This research is a part of an ongoing attempt to increase the scope of application for Bio-SECM, both through the adaptation of specialized biosensors and application of existing techniques for biological analysis of single cells and micro-organs.