The conducted vascular response as a mediator of hypercapnic cerebrovascular reactivity: A modelling study.

It is well established that the cerebral blood flow (CBF) shows exquisite sensitivity to changes in the arterial blood partial pressure of CO2 ( [Formula: see text] ), which is reflected by an index termed cerebrovascular reactivity. In response to elevations in [Formula: see text] (hypercapnia), the vessels of the cerebral microvasculature dilate, thereby decreasing the vascular resistance and increasing CBF. Due to the challenges of access, scale and complexity encountered when studying the microvasculature, however, the mechanisms behind cerebrovascular reactivity are not fully understood. Experiments have previously established that the cholinergic release of the Acetylcholine (ACh) neurotransmitter in the cortex is a prerequisite for the hypercapnic response. It is also known that ACh functions as an endothelial-dependent agonist, in which the local administration of ACh elicits local hyperpolarization in the vascular wall; this hyperpolarization signal is then propagated upstream the vascular network through the endothelial layer and is coupled to a vasodilatory response in the vascular smooth muscle (VSM) layer in what is known as the conducted vascular response (CVR). Finally, experimental data indicate that the hypercapnic response is more strongly correlated with the CO2 levels in the tissue than in the arterioles. Accordingly, we hypothesize that the CVR, evoked by increases in local tissue CO2 levels and a subsequent local release of ACh, is responsible for the CBF increase observed in response to elevations in [Formula: see text] . By constructing physiologically grounded dynamic models of CBF and control in the cerebral vasculature, ones that integrate the available knowledge and experimental data, we build a new model of the series of signalling events and pathways underpinning the hypercapnic response, and use the model to provide compelling evidence that corroborates the aforementioned hypothesis. If the CVR indeed acts as a mediator of the hypercapnic response, the proposed mechanism would provide an important addition to our understanding of the repertoire of metabolic feedback mechanisms possessed by the brain and would motivate further in-vivo investigation. We also model the interaction of the hypercapnic response with dynamic cerebral autoregulation (dCA), the collection of mechanisms that the brain possesses to maintain near constant CBF despite perturbations in pressure, and show how the dCA mechanisms, which otherwise tend to be overlooked when analysing experimental results of cerebrovascular reactivity, could play a significant role in shaping the CBF response to elevations in [Formula: see text] . Such in-silico models can be used in tandem with in-vivo experiments to expand our understanding of cerebrovascular diseases, which continue to be among the leading causes of morbidity and mortality in humans.

A network-based model of dynamic cerebral autoregulation.

Cerebrovascular diseases continue to be one of the leading causes of morbidity and mortality in humans. Abnormalities in dynamic cerebral autoregulation (dCA) have been implicated in many of these disease conditions. Accurate models are therefore needed to better understand the complex pathophysiology behind impaired dCA. We thus present here a simple framework for modelling a vessel-driven network model of dCA in the microvasculature, as opposed to the conventional compartmental modelling approach. Network models incorporate the actual connectivity and anatomy of the vasculature, thereby allowing us to include and trace changes in the calibre and morphology of individual vessels, investigate the spatial specificity and heterogeneity of the various control mechanisms to help disentangle their contributions, and link the model parameters to the actual network physiology. The proposed control feedback mechanisms are incorporated at the level of the individual vessel, and the dynamic pressure and flow fields are solved for here within a simple vessel network. In response to an upstream pressure drop, the network is found to be able to recover cerebral blood flow (CBF) while exhibiting the characteristic autoregulatory behaviour in terms of changes in vessel calibre and the biphasic flow response. We assess the feasibility of our formulation in larger networks by comparing the simulation results to those obtained using a one-dimensional (1D) model of CBF applied to the same microvasculature network and find that our model results are in very good agreement with the 1D solution, while significantly reducing the computational cost, thus enabling more detailed models of network behaviour to be adopted in the future. Accurate and computationally feasible models of dCA that are more representative of the vasculature can help increase the translatability of haemodynamic models into the clinical environment, which would help develop more informed treatment guidelines for patients with cerebrovascular diseases.

Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge.

Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in virtually every industrial or preliminary system. Consequently, the Raspberry Pi is adopted, which is a low-cost computing platform that is profitably applied in the field of IoT. As for the software part, among the plethora of Machine Learning (ML) paradigms reported in the literature, we identified Rulex, as a good ML platform, suitable to be implemented on the Raspberry Pi. In this paper, we present the porting of the Rulex ML platform on the board to perform ML forecasts in an IoT setup. Specifically, we explain the porting Rulex's libraries on Windows 32 Bits, Ubuntu 64 Bits, and Raspbian 32 Bits. Therefore, with the aim of carrying out an in-depth verification of the application possibilities, we propose to perform forecasts on five unrelated datasets from five different applications, having varying sizes in terms of the number of records, skewness, and dimensionality. These include a small Urban Classification dataset, three larger datasets concerning Human Activity detection, a Biomedical dataset related to mental state, and a Vehicle Activity Recognition dataset. The overall accuracies for the forecasts performed are: 84.13%, 99.29% (for SVM), 95.47% (for SVM), and 95.27% (For KNN) respectively. Finally, an image-based gender classification dataset is employed to perform image classification on the Edge. Moreover, a novel image pre-processing Algorithm was developed that converts images into Time-series by relying on statistical contour-based detection techniques. Even though the dataset contains inconsistent and random images, in terms of subjects and settings, Rulex achieves an overall accuracy of 96.47% while competing with the literature which is dominated by forward-facing and mugshot images. Additionally, power consumption for the Raspberry Pi in a Client/Server setup was compared with an HP laptop, where the board takes more time, but consumes less energy for the same ML task.

Effect of Shear Flow on Nanoparticles Migration near Liquid Interfaces.

The effect of shear flow on spherical nanoparticles (NPs) migration near a liquid-liquid interface is studied by numerical simulation. We have implemented a compact model through which we use the diffuse interface method for modeling the two fluids and the molecular dynamics method for the simulation of the motion of NPs. Two different cases regarding the state of the two fluids when introducing the NPs are investigated. First, we introduce the NPs randomly into the medium of the two immiscible liquids that are already separated, and the interface is formed between them. For this case, it is shown that before applying any shear flow, 30% of NPs are driven to the interface under the effect of the drag force resulting from the composition gradient between the two fluids at the interface. However, this percentage is increased to reach 66% under the effect of shear defined by a Péclet number Pe = 0.316. In this study, different shear rates are investigated in addition to different shearing times, and we show that both factors have a crucial effect regarding the migration of the NPs toward the interfacial region. In particular, a small shear rate applied for a long time will have approximately the same effect as a greater shear rate applied for a shorter time. In the second studied case, we introduce the NPs into the mixture of two fluids that are already mixed and before phase separation so that the NPs are introduced into the homogenous medium of the two fluids. For this case, we show that in the absence of shear, almost all NPs migrate to the interface during phase separation, whereas shearing has a negative result, mainly because it affects the phase separation.

The lack of selection criteria for surgery in patients with non-colorectal non-neuroendocrine liver metastases.

BACKGROUND: The benefit of surgery in patients with non-colorectal non-neuroendocrine liver metastases (NCRNNELM) remains controversial. At the population level, several statistical prognostic factors and scores have been proposed but inconsistently verified. At the patient level, no selection criteria have been demonstrated to guide individual therapeutic decision making. We aimed to evaluate potential individual selection criteria to predict the benefit of surgery in patients undergoing treatment for NCRNNELM. METHODS: Data for 114 patients undergoing surgery for NCRNNELM were reviewed. In this population, we identified an early relapse group (ER), defined as patients with unresectable recurrence < 1 year postoperatively who did not benefit from surgery (N = 28), and a long-term survival group (LTS), defined as patients who were recurrence-free ≥ 5 years postoperatively and benefited from surgery (N = 20). Clinicopathologic parameters, the Association Française de Chirurgie (AFC) score, and a modified 4-point Clinical Risk Score (mCRS) (excluding CEA level) were analyzed and compared between LTS and ER groups. RESULTS: The majority of patients were female and a majority had an ASA score ≤ 2 at the time of liver surgery. The median age was 55 years. Almost half of the patients (46%) presented with a single-liver metastasis. Intermediate- and low-risk AFC scores represented 40% and 60% of the population, respectively. Five- and 10-year overall survival (OS) and disease-free survival (DFS) rates were 56% and 27%, and 30% and 12%, respectively. Negative prognostic factors were the size of liver metastases > 50 mm and delay between primary and NCRNNELM <24 months for OS and DFS, respectively. AFC score was not prognostic while high-risk mCRS (scores 3-4) was predictive for the poorer OS. The clinicopathologic parameters were similar in the ER and LTS groups, except the presence of N+ primary tumor, and the size of liver metastases was significantly higher in the ER group. CONCLUSION: In patients with resectable NCRNNELM, no predictive factors or scores were found to accurately preoperatively differentiate individual cases in whom surgery would be futile from those in whom surgery could be associated with a significant oncological benefit.

MIT-Skywalker: Evaluating comfort of bicycle/saddle seat.

The MIT-Skywalker is a robotic device developed for the rehabilitation of gait and balance after a neurological injury. This device has been designed based on the concept of a passive walker and provides three distinct training modes: discrete movement, rhythmic movement, and balance training. In this paper, we present our efforts to evaluate the comfort of a bicycle/saddle seat design for the system's novel actuated body weight support device. We employed different bicycle and saddle seats and evaluated comfort using objective and subjective measures. Here we will summarize the results obtained from a study of fifteen healthy subjects and one stroke patient that led to the selection of a saddle seat design for the MIT-Skywalker.