Chronic Kidney Disease Increases Cerebral Microbleeds in Mouse and Man.

Brain microbleeds are increased in chronic kidney disease (CKD) and their presence increases risk of cognitive decline and stroke. We examined the interaction between CKD and brain microhemorrhages (the neuropathological substrate of microbleeds) in mouse and cell culture models and studied progression of microbleed burden on serial brain imaging from humans. Mouse studies: Two CKD models were investigated: adenine-induced tubulointerstitial nephritis and surgical 5/6 nephrectomy. Cell culture studies: bEnd.3 mouse brain endothelial cells were grown to confluence, and monolayer integrity was measured after exposure to 5-15% human uremic serum or increasing concentrations of urea. Human studies: Progression of brain microbleeds was evaluated on serial MRI from control, pre-dialysis CKD, and dialysis patients. Microhemorrhages were increased 2-2.5-fold in mice with CKD independent of higher blood pressure in the 5/6 nephrectomy model. IgG staining was increased in CKD animals, consistent with increased blood-brain barrier permeability. Incubation of bEnd.3 cells with uremic serum or elevated urea produced a dose-dependent drop in trans-endothelial electrical resistance. Elevated urea induced actin cytoskeleton derangements and decreased claudin-5 expression. In human subjects, prevalence of microbleeds was 50% in both CKD cohorts compared with 10% in age-matched controls. More patients in the dialysis cohort had increased microbleeds on follow-up MRI after 1.5 years. CKD disrupts the blood-brain barrier and increases brain microhemorrhages in mice and microbleeds in humans. Elevated urea alters the actin cytoskeleton and tight junction proteins in cultured endothelial cells, suggesting that these mechanisms explain (at least in part) the microhemorrhages and microbleeds observed in the animal and human studies

Protocol for Mathematical Prediction of Patient Response and Survival to Immune Checkpoint Inhibitor Immunotherapy

This protocol describes the application of a mechanistic mathematical model of immune checkpoint inhibitor (ICI) immunotherapy to patient tumor imaging data for predicting solid tumor response and patient survival under ICI intervention. We describe steps for data collection and processing, data pipelines, and approaches to increase precision. The protocol is highly predictive as early as the first restaging after treatment start and can be used with standard-of-care imaging measures. For complete details on the use and execution of this protocol, please refer to Butner et al. (2020

Mathematical Modeling of Cancer Immunotherapy for Personalized Clinical Translation

Encouraging advances are being made in cancer immunotherapy modeling, especially in the key areas of developing personalized treatment strategies based on individual patient parameters, predicting treatment outcomes and optimizing immunotherapy synergy when used in combination with other treatment approaches. Here we present a focused review of the most recent mathematical modeling work on cancer immunotherapy with a focus on clinical translatability. It can be seen that this field is transitioning from pure basic science to applications that can make impactful differences in patients\u27 lives. We discuss how researchers are integrating experimental and clinical data to fully inform models so that they can be applied for clinical predictions, and present the challenges that remain to be overcome if widespread clinical adaptation is to be realized. Lastly, we discuss the most promising future applications and areas that are expected to be the focus of extensive upcoming modeling studies

Windage Torque Reduction in Low-Pressure Turbine Cavities Part 2:Experimental and Numerical Results

Minimizing the losses within a low-pressure turbine (LPT) system is critical for the design of next-generation ultra-high bypass ratio aero-engines. The stator-well cavity windage torque can be a significant source of loss within the system, influenced by the ingestion of mainstream annulus air with a tangential velocity opposite to that of the rotor. This paper presents experimental and numerical results of three carefully designed Flow Control Concepts (FCCs) – additional geometric features on the stator surfaces, which were optimized to minimize the windage torque within a scaled, engine-representative stator-well cavity. FCC1 and FCC2 featured rows of guide vanes at the inlet to the downstream and upstream wheel-spaces, respectively. FCC3 combined FCC1 and FCC2. Superposed flows were introduced to the upstream section of the cavity, which modelled the low radius coolant and higher radius leakage between the rotor blades. In addition to torque measurements, total and static pressures were collected, from which the cavity swirl ratio was derived. Additional swirl measurements were collected using a five-hole aerodynamic probe, which traversed radially at the entrance and exit of the cavity. A cavity windage torque reduction of 55% on the baseline (which has no flow control) was measured for FCC3, at the design condition with superposed flow. For this concept, an increase in the cavity swirl in both the upstream and downstream wheel-spaces was demonstrated experimentally and numerically. With increasing superposed flow, the contribution of FCC1 surpassed FCC2, due to more mass flow enterin

The S1P receptor 1 antagonist Ponesimod reduces TLR4-induced neuroinflammation and increases Aβ clearance in 5XFAD mice

Background Previously, we showed that the sphingosine-1-phosphate (S1P) transporter spinster 2 (Spns2) mediates activation of microglia in response to amyloid β peptide (Aβ). Here, we investigated if Ponesimod, a functional S1P receptor 1 (S1PR1) antagonist, prevents Aβ-induced activation of glial cells and Alzheimer’s disease (AD) pathology. Methods We used primary cultures of glial cells and the 5XFAD mouse model to determine the effect of Aβ and Ponesimod on glial activation, Aβ phagocytosis, cytokine levels and pro-inflammatory signaling pathways, AD pathology, and cognitive performance. Findings Aβ42 increased the levels of TLR4 and S1PR1, leading to their complex formation. Ponesimod prevented the increase in TLR4 and S1PR1 levels, as well as the formation of their complex. It also reduced the activation of the pro-inflammatory Stat1 and p38 MAPK signaling pathways, while activating the anti-inflammatory Stat6 pathway. This was consistent with increased phagocytosis of Aβ42 in primary cultured microglia. In 5XFAD mice, Ponesimod decreased the levels of TNF-α and CXCL10, which activate TLR4 and Stat1. It also increased the level of IL-33, an anti-inflammatory cytokine that promotes Aβ42 phagocytosis by microglia. As a result of these changes, Ponesimod decreased the number of Iba-1+ microglia and GFAP+ astrocytes, and the size and number of amyloid plaques, while improving spatial memory as measured in a Y-maze test. Interpretation Ponesimod targeting S1PR1 is a promising therapeutic approach to reprogram microglia, reduce neuroinflammation, and increase Aβ clearance in AD

Windage Torque Reduction in Low-Pressure Turbine Cavities Part 1:Concept Design and Numerical Investigations

The windage torque on rotational walls has negative effect on the performance of the low pressure turbine. In this paper, three novel flow control concepts (FCCs) were proposed to reduce the windage torque within a turbine stator well, with upstream and downstream cavities connected by an interstage labyrinth seal. The swirl and flow pattern inside a reference turbine cavity was first investigated and the potential locations for the FCCs were identified using numerical simulations. FCC1 was a circumferential row of leaned deflectors downstream of the labyrinth seal. FCC2 was a set of deflector vanes and platform to optimize the ingress swirl at high radius in the upstream cavity. FCC3 combined the two flow concepts and the superposition resulted in a stator well windage torque reduction of 70% when compared to the baseline design. The FCCs also showed performance benefits at off-design conditions and over a range of secondary flow rates to the cavity. In Part 2 [1], the numerical analysis and performance of the FCCs are validated in an experimental rig, using additively-manufactured components

Hybridizing Mechanistic Modeling and Deep Learning for Personalized Survival Prediction After Immune Checkpoint Inhibitor Immunotherapy

We present a study where predictive mechanistic modeling is combined with deep learning methods to predict individual patient survival probabilities under immune checkpoint inhibitor (ICI) immunotherapy. This hybrid approach enables prediction based on both measures that are calculable from mechanistic models of key mechanisms underlying ICI therapy that may not be directly measurable in the clinic and easily measurable quantities or patient characteristics that are not always readily incorporated into predictive mechanistic models. A deep learning time-to-event predictive model trained on a hybrid mechanistic + clinical data set from 93 patients achieved higher per-patient predictive accuracy based on event-time concordance, Brier score, and negative binomial log-likelihood-based criteria than when trained on only mechanistic model-derived values or only clinical data. Feature importance analysis revealed that both clinical and model-derived parameters play prominent roles in increasing prediction accuracy, further supporting the advantage of our hybrid approach