Altered cerebral perfusion in response to chronic mild hypercapnia and head-down tilt Bed rest as an analog for Spaceflight.

PURPOSE: Following prolonged stays on the International Space Station (ISS), some astronauts exhibit visual acuity changes, ophthalmological findings, and mildly elevated intracranial pressures as part of a novel process called spaceflight-associated neuro-ocular syndrome (SANS). To determine the pathophysiology of SANS, NASA conducted a multi-investigator study in which 11 healthy participants underwent head-down tilt bed rest, mimicking microgravity-induced cephalad fluid shifts, combined with elevated ambient CO2 levels similar to those on the ISS (HDT+CO2). As part of that study, we examined the effects of HDT+CO2 on cerebral perfusion. METHODS: Using arterial spin labeling, we compared cerebral perfusion before, during, and after HDT+CO2 in participants who developed SANS (n = 5) with those who did not (n = 6). RESULTS: All participants demonstrated a decrease in perfusion during HDT+CO2 (mean decrease of 25.1% at HDT7 and 16.2% at HDT29); however, the timing and degree of change varied between the groups. At day 7 of HDT+CO2, the SANS group experienced a greater reduction in perfusion than the non-SANS group (p =.05, 95% CI:-0.19 to 16.11, d=.94, large effect). Conversely, by day 29 of HDT+CO2, the SANS group had significantly higher perfusion (approaching their baseline) than the non-SANS group (p = .04, 95% CI:0.33 to 13.07, d=1.01, large effect). CONCLUSION: Compared with baseline and recovery, HDT+CO2 resulted in reduced cerebral perfusion which varied based on SANS status. Further studies are needed to unravel the relative role of HDT vs hypercapnia, to determine if these perfusion changes are clinically relevant, and whether perfusion changes contribute to the development of SANS during spaceflight.

Longitudinal change in ventricular volume is accelerated in astronauts undergoing long-duration spaceflight.

An 11-25% increase in total ventricular volume has been documented in astronauts following spaceflight on the ISS. Given the approximately 2-year time interval between pre- and post-flight MRI, it is unknown if ventricular enlargement simply reflects normal aging or is unique to spaceflight exposure. Therefore, we compared percent ventricular volume change per year (PVVC/yr) documented on pre- to post-flight MRI in a group of NASA ISS astronauts (n = 18, 16.7% women, mean age (SD) 48.43 (4.35) years) with two groups who underwent longitudinal MRI: (1.) healthy age- and sex-matched adults (n = 18, 16.7% women, mean age (SD) 51.26 (3.88) years), and (2.) healthy older adults (n = 79, 16.5% women, mean age (SD) 73.26 (5.34) years). The astronauts, who underwent a mean (SD) 173.4 (51.3) days in spaceflight, showed a greater increase in PVVC/yr than the control (6.86 vs 2.23%, respectively, p < .001) and older adult (4.18%, p = 0.04) groups. These results highlight that on top of physiologically ventricular volume changes due to normal aging, NASA astronauts undergoing ISS missions experience an additional 4.63% PVVC/yr and underscore the need to perform post-flight follow-up scans to determine the time course of PVVC in astronauts over time back on Earth along with monitoring to determine if the PVVC is ultimately clinically relevant. One sentence summary: NASA astronauts who were exposed to prolonged spaceflight experienced an annual rate of ventricular expansion more than three times that expected from normal aging.

Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions.

To estimate dynamic functional connectivity (dFC), the conventional method of sliding window correlation (SWC) suffers from poor performance of dynamic connection detection. This stems from the equal weighting of observations, suboptimal time scale, nonsparse output, and the fact that it is bivariate. To overcome these limitations, we exploited the kernel-reweighted logistic regression (KELLER) algorithm, a method that is common in genetic studies, to estimate dFC in resting state functional magnetic resonance imaging (rs-fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. This paper compares the performance of the proposed KELLER method with current methods (SWC and tapered-SWC (T-SWC) with different window lengths) based on both simulated and real rs-fMRI data. Estimated dFC networks were assessed for detecting dynamically connected brain region pairs with hypothesis testing. Simulation results revealed that KELLER can detect dynamic connections with a statistical power of 87.35% compared with 70.17% and 58.54% associated with T-SWC (p-value = .001) and SWC (p-value <.001), respectively. Results of these different methods applied on real rs-fMRI data were investigated for two aspects: calculating the similarity between identified mean dynamic pattern and identifying dynamic pattern in default mode network (DMN). In 68% of subjects, the results of T-SWC with window length of 100 s, among different window lengths, demonstrated the highest similarity to those of KELLER. With regards to DMN, KELLER estimated previously reported dynamic connection pairs between dorsal and ventral DMN while SWC-based method was unable to detect these dynamic connections.

Optimized Dose of Dendritic Cell-based Vaccination in Experimental Model of Tumor Using Artificial Neural Network.

Previous studies have demonstrated that maturation of dendritic cells (DCs) by pathogenic components through pathogen-associated molecular patterns (PAMPs) such as Listeria monocytogenes lysate (LML) or CpG DNA can improve cancer vaccination in experimental models. In this study, a mathematical model based on an artificial neural network (ANN) was used to predict several patterns and dosage of matured DC administration for improved vaccination. The ANN model predicted that repeated co-injection of tumor antigen (TA)-loaded DCs matured with CpG (CpG-DC) and LML (List-DC) results in improved antitumor immune response as well as a reduction of immunosuppression in the tumor microenvironment. In the present study, we evaluated the ANN prediction accuracy about DC-based cancer vaccines pattern in the treatment of Wehi164 fibrosarcoma cancer-bearing mice. Our results showed that the administration of the DC vaccine according to ANN predicted pattern, leads to a decrease in the rate of tumor growth and size and augments CTL effector function. Furthermore, gene expression analysis confirmed an augmented immune response in the tumor microenvironment. Experimentations justified the validity of the ANN model forecast in the tumor growth and novel optimal dosage that led to more effective treatment.

Detection of Colorectal Carcinoma Based on Microbiota Analysis Using Generalized Regression Neural Networks and Nonlinear Feature Selection.

To obtain a screening tool for colorectal cancer (CRC) based on gut microbiota, we seek here to identify an optimal classifier for CRC detection as well as a novel nonlinear feature selection method for determining the most discriminative microbial species. In this study, the intestinal microflora in feces of 141 patients were modeled using general regression neural networks (GRNNs) combined with the proposed feature selection method. The proposed model led to slightly higher accuracy (AUC = 0.911) than previous studies . The results show that the Clostridium scindens and Bifidobacterium angulatum are indicators of healthy gut flora and CRC happens to reduce these bacterial species. In addition, Fusobacterium gonidiaformans was found to be closely correlated with the CRC. The occurrence of colorectal adenoma was not sufficiently discriminatory based on fecal microbiota implicating that the change of colonic flora happens in the advanced phase of CRC development rather than initial adenoma. Integrating the proposed model with fecal occult blood test (FOBT), the CRC detection accuracy remained nearly unchanged (AUC = 0.915). The performance of the proposed method is validated using independent cohorts from America and Austria. Our results suggest that the proposed feature selection method combined with GRNN is potentially an accurate method for CRC detection.

In vitro Measurement of Release Kinetics of Temperature Sensitive Liposomes with a Fluorescence Imaging System.

Temperature sensitive liposomes (TSL) are a promising type of nanoparticles for localized drug delivery. TSL typically release the contained drug at mild hyperthermic temperatures (40-42 °C). Combined with localized hyperthermia, this allows for local drug delivery. In vitro characterization of TSL involves measurements of drug release at varying temperatures, but current methods are inadequate due to low temporal resolution of ~8 - 10 seconds. We present a novel method for measuring the drug release with sub-second temporal resolution. In the proposed system, the TSL entrapping the fluorescent drug (Doxorubicin) are pumped through a capillary tube. The tube is rapidly heated to a desired temperature via Peltier element. Since fluorescence increases as drug is released from TSL, drug release kinetics can be measured via fluorescent imaging. By fitting exponential models, we calculated the time constants of drug release at temperatures of 39.5, 40.5 and 41.5.C were about 6.09, 2.06 and 1.03 seconds, respectively. Our initial tests show that the developed system can measure TSL release at subsecond resolution, and thus allow adequate in vitro evaluation of TSL formulations.

A structural methodology for modeling immune-tumor interactions including pro- and anti-tumor factors for clinical applications.

The immune system turns out to have both stimulatory and inhibitory factors influencing on tumor growth. In recent years, the pro-tumor role of immunity factors such as regulatory T cells and TGF-ß cytokines has specially been considered in mathematical modeling of tumor-immune interactions. This paper presents a novel structural methodology for reviewing these models and classifies them into five subgroups on the basis of immune factors included. By using our experimental data due to immunotherapy experimentation in mice, these five modeling groups are evaluated and scored. The results show that a model with a small number of variables and coefficients performs efficiently in predicting the tumor-immune system interactions. Though immunology theorems suggest to employ a larger number of variables and coefficients, more complicated models are here shown to be inefficient due to redundant parallel pathways. So, these models are trapped in local minima and restricted in prediction capability. This paper investigates the mathematical models that were previously developed and proposes variables and pathways that are essential for modeling tumor-immune. Using these variables and pathways, a minimal structure for modeling tumor-immune interactions is proposed for future studies.

A Unified Mathematical Model for Nano-Liposomal Drug Delivery to Solid Tumors.

Nanoparticles, such as liposomes, allow more targeted drug delivery for improved efficacy and/or reduced toxicity in both passive (e.g., Doxil) or active [e.g., thermo-sensitive liposomes (TSL)] release forms compared with unencapsulated drugs (i.e., conventional chemotherapy). Optimization and evaluation of these different drug delivery systems are experimentally challenging because of varying tissue parameters as well as limited avaiability of experimental data. Here, we present a novel unified mathematical model that can simulate various liposomal drug delivery systems and unencapsulated drugs with a single set of equations. We use this model to evaluate the chemotherapy performance of free Doxorubicin (as drug), as well as various liposomal drug delivery systems: 1) passive liposomes (Doxil) and 2) active-triggered TSL with either intravascular (TSLi) or extravascular (TSLe)-triggered release. Furthermore, we implemented a more accurate expression to consider incomplete liposomal drug release. The proposed model matches experimental in vivo results in terms of maximum drug concentration in tumor. The simulations predict better overall performance for all liposomal delivery systems than free Dox. TSLe is shown to be more efficient for less permeable and perfused tumors than other systems. The optimal release rate is lower for TSLe and Doxil than TSLi. The performance of free DOX changes a little for varying tumor characteristics such as perfusion and permeability.

Effects of Spaceflight on Astronaut Brain Structure as Indicated on MRI.

BACKGROUND: There is limited information regarding the effects of spaceflight on the anatomical configuration of the brain and on cerebrospinal fluid (CSF) spaces. METHODS: We used magnetic resonance imaging (MRI) to compare images of 18 astronauts' brains before and after missions of long duration, involving stays on the International Space Station, and of 16 astronauts' brains before and after missions of short duration, involving participation in the Space Shuttle Program. Images were interpreted by readers who were unaware of the flight duration. We also generated paired preflight and postflight MRI cine clips derived from high-resolution, three-dimensional imaging of 12 astronauts after long-duration flights and from 6 astronauts after short-duration flights in order to assess the extent of narrowing of CSF spaces and the displacement of brain structures. We also compared preflight ventricular volumes with postflight ventricular volumes by means of an automated analysis of T1-weighted MRIs. The main prespecified analyses focused on the change in the volume of the central sulcus, the change in the volume of CSF spaces at the vertex, and vertical displacement of the brain. RESULTS: Narrowing of the central sulcus occurred in 17 of 18 astronauts after long-duration flights (mean flight time, 164.8 days) and in 3 of 16 astronauts after short-duration flights (mean flight time, 13.6 days) (P<0.001). Cine clips from a subgroup of astronauts showed an upward shift of the brain after all long-duration flights (12 astronauts) but not after short-duration flights (6 astronauts) and narrowing of CSF spaces at the vertex after all long-duration flights (12 astronauts) and in 1 of 6 astronauts after short-duration flights. Three astronauts in the long-duration group had optic-disk edema, and all 3 had narrowing of the central sulcus. A cine clip was available for 1 of these 3 astronauts, and the cine clip showed upward shift of the brain. CONCLUSIONS: Narrowing of the central sulcus, upward shift of the brain, and narrowing of CSF spaces at the vertex occurred frequently and predominantly in astronauts after long-duration flights. Further investigation, including repeated postflight imaging conducted after some time on Earth, is required to determine the duration and clinical significance of these changes. (Funded by the National Aeronautics and Space Administration.).

Effects of ageing and Alzheimer disease on haemodynamic response function: a challenge for event-related fMRI.

Functional magnetic resonance imaging (fMRI) can generate brain images that show neuronal activity due to sensory, cognitive or motor tasks. Haemodynamic response function (HRF) may be considered as a biomarker to discriminate the Alzheimer disease (AD) from healthy ageing. As blood-oxygenation-level-dependent fMRI signal is much weak and noisy, particularly for the elderly subjects, a robust method is necessary for HRF estimation to efficiently differentiate the AD. After applying minimum description length wavelet as an extra denoising step, deconvolution algorithm is here employed for HRF estimation, substituting the averaging method used in the previous works. The HRF amplitude peaks are compared for three groups HRF of young, non-demented and demented elderly groups for both vision and motor regions. Prior works often reported significant differences in the HRF peak amplitude between the young and the elderly. The authors' experimentations show that the HRF peaks are not significantly different comparing the young adults with the elderly (either demented or non-demented). It is here demonstrated that the contradictory findings of the previous studies on the HRF peaks for the elderly compared with the young are originated from the noise contribution in fMRI data.

Evaluation of Hemodynamic Response Function in Vision and Motor Brain Regions for the Young and Elderly Adults.

INTRODUCTION: Prior studies comparing Hemodynamic Response Function (HRF) in the young and elderly adults based on fMRI data have reported inconsistent findings for brain vision and motor regions in healthy aging. It is shown that the averaging method employed in all previous works has caused this inconsistency. The averaging is so sensitive to outliers and noise. However, fMRI data are obscured with a major contribution of noise particularly in the elderly case. METHODS: Deconvolution algorithm is here proposed for HRF extraction to achieve more robustness against noise. In spite of earlier works, proposed deconvolution algorithm yields compatible HRF results using either original or denoised fMRI data, though a large percentage of selected active voxels change in the latter case. In the current study, event-related fMRI data have been used for 18 subjects (8 young and 10 elderly adults) with a simple visual and motor task of pressing a key with index in response to the visual presentation of the word tap. Considering anatomically-defined vision and motor regions and preprocessing steps in FSL and SPM, the activated voxels have been selected according to t-test for which HRF is estimated using deconvolution method. RESULTS: Experimental results demonstrate that HRF peak amplitudes do not differ significantly (P=0.8) in the vision region for the young and the elderly. In motor region, the HRF peak significantly increases for the young compared to the elderly (P<0.03). Repeating the procedure on the denoised fMRI data using MDL algorithm, the same results have been obtained. DISCUSSION: In this study, a comparative study has been realized on the hemodynamic response properties associated with the young and the elderly adults on a simple visual and motor task.

Surface defect detection in tiling Industries using digital image processing methods: analysis and evaluation.

Ceramic and tile industries should indispensably include a grading stage to quantify the quality of products. Actually, human control systems are often used for grading purposes. An automatic grading system is essential to enhance the quality control and marketing of the products. Since there generally exist six different types of defects originating from various stages of tile manufacturing lines with distinct textures and morphologies, many image processing techniques have been proposed for defect detection. In this paper, a survey has been made on the pattern recognition and image processing algorithms which have been used to detect surface defects. Each method appears to be limited for detecting some subgroup of defects. The detection techniques may be divided into three main groups: statistical pattern recognition, feature vector extraction and texture/image classification. The methods such as wavelet transform, filtering, morphology and contourlet transform are more effective for pre-processing tasks. Others including statistical methods, neural networks and model-based algorithms can be applied to extract the surface defects. Although, statistical methods are often appropriate for identification of large defects such as Spots, but techniques such as wavelet processing provide an acceptable response for detection of small defects such as Pinhole. A thorough survey is made in this paper on the existing algorithms in each subgroup. Also, the evaluation parameters are discussed including supervised and unsupervised parameters. Using various performance parameters, different defect detection algorithms are compared and evaluated.

Modeling of tumor growth in dendritic cell-based immunotherapy using artificial neural networks.

Exposure-response modeling and simulation is especially useful in oncology as it permits to predict and design un-experimented clinical trials as well as dose selection. Dendritic cells (DC) are the most effective immune cells in the regulation of immune system. To activate immune system, DCs may be matured by many factors like bacterial CpG-DNA, Lipopolysaccharaide (LPS) and other microbial products. In this paper, a model based on artificial neural network (ANN) is presented for analyzing the dynamics of antitumor vaccines using empirical data obtained from the experimentations of different groups of mice treated with DCs matured by bacterial CpG-DNA, LPS and whole lysate of a Gram-positive bacteria Listeria monocytogenes. Also, tumor lysate was added to DCs followed by addition of maturation factors. Simulations show that the proposed model can interpret the important features of empirical data. Owing to the nonlinearity properties, the proposed ANN model has been able not only to describe the contradictory empirical results, but also to predict new vaccination patterns for controlling the tumor growth. For example, the proposed model predicts an exponentially increasing pattern of CpG-matured DC to be effective in suppressing the tumor growth.