Current-induced spin-orbit torques (SOTs) in structurally asymmetric multilayers have been used to efficiently manipulate magnetization. In a structure with vertical symmetry breaking, a damping-like SOT can deterministically switch a perpendicular magnet, provided an in-plane magnetic field is applied. Recently, it has been further demonstrated that the in-plane magnetic field can be eliminated by introducing a new type of perpendicular field-like SOT via incorporating a lateral structural asymmetry into the device. Typically, however, when a current is applied to such devices with combined vertical and lateral asymmetries, both the perpendicular field-like torque and the damping-like torque coexist, hence jointly affecting the magnetization switching behavior. Here, we study perpendicular magnetization switching driven by the combination of the perpendicular field-like and the damping-like SOTs, which exhibits deterministic switching mediated through domain wall propagation. It is demonstrated that the role of the damping-like SOT in the deterministic switching is highly dependent on the magnetization direction in the domain wall. By contrast, the perpendicular field-like SOT is solely determined by the relative orientation between the lateral structural asymmetry and the current direction, regardless of the magnetization direction in the domain wall. The experimental results further the understanding of SOTs-induced switching, with implications for spintronic devices.
Noncontact diffuse correlation tomography (ncDCT) is an emerging technology for 3D imaging of deep tissue blood flow distribution without distorting hemodynamic properties. To adapt the ncDCT for imaging in vivo breast tumors, we designed a motorized ncDCT probe to scan over the breast surface. A computer-aided design (CAD)-based approach was proposed to create solid volume mesh from arbitrary breast surface obtained by a commercial 3D camera. The sources and detectors of ncDCT were aligned on the breast surface through ray tracing to mimic the ncDCT scanning with CAD software. The generated breast volume mesh along with the boundary data of ncDCT at the aligned source and detector pairs were used for finite-element-method-based flow image reconstruction. We evaluated the accuracy of source alignments on mannequin and human breasts; largest alignment errors were less than 10% in both tangential and radial directions of scanning. The impact of alignment errors (assigned 10%) on the tumor reconstruction was estimated using computer simulations. The deviations of simulated tumor location and blood flow contrast resulted from the alignment errors were 0.77 mm (less than the node distance of 1 mm) and 1%, respectively, which result in minor impact on flow image reconstruction. Finally, a case study on a human breast tumor was conducted and a tumor-to-normal flow contrast was reconstructed, demonstrating the feasibility of ncDCT in clinical application.
Tissue heterogeneity is both a major confounding factor and an underexploited information source. While a handful of reports have demonstrated the potential of supervised computational methods to deconvolute tissue heterogeneity, these approaches require a priori information on the marker genes or composition of known subpopulations. To address the critical problem of the absence of validated marker genes for many (including novel) subpopulations, we describe convex analysis of mixtures (CAM), a fully unsupervised in silico method, for identifying subpopulation marker genes directly from the original mixed gene expressions in scatter space that can improve molecular analyses in many biological contexts. Validated with predesigned mixtures, CAM on the gene expression data from peripheral leukocytes, brain tissue, and yeast cell cycle, revealed novel marker genes that were otherwise undetectable using existing methods. Importantly, CAM requires no a priori information on the number, identity, or composition of the subpopulations present in mixed samples, and does not require the presence of pure subpopulations in sample space. This advantage is significant in that CAM can achieve all of its goals using only a small number of heterogeneous samples, and is more powerful to distinguish between phenotypically similar subpopulations.
Manipulating magnetism by electric current is of great interest for both fundamental and technological reasons. Much effort has been dedicated to spin–orbit torques (SOTs) in metallic structures, while quantitative investigation of analogous phenomena in magnetic insulators remains challenging due to their low electrical conductivity. Here we address this challenge by exploiting the interaction of light with magnetic order, to directly measure SOTs in both metallic and insulating structures. The equivalency of optical and transport measurements is established by investigating a heavy-metal/ferromagnetic-metal device (Ta/CoFeB/MgO). Subsequently, SOTs are measured optically in the contrasting case of a magnetic-insulator/heavy-metal (YIG/Pt) heterostructure, where analogous transport measurements are not viable. We observe a large anti-damping torque in the YIG/Pt system, revealing its promise for spintronic device applications. Moreover, our results demonstrate that SOT physics is directly accessible by optical means in a range of materials, where transport measurements may not be possible.
The study of spin orbit torques in insulating materials via conventional transport methods is restricted due to low electrical conductivity. Here, the authors use magneto-optical methods to measure spin orbit torques in ferromagnetic-insulator/heavy-metal heterostructures.
Biological network inference is a major challenge in systems biology. Traditional correlation-based network analysis results in too many spurious edges since correlation cannot distinguish between direct and indirect associations. To address this issue, Gaussian graphical models (GGM) were proposed and have been widely used. Though they can significantly reduce the number of spurious edges, GGM are insufficient to uncover a network structure faithfully due to the fact that they only consider the full order partial correlation. Moreover, when the number of samples is smaller than the number of variables, further technique based on sparse regularization needs to be incorporated into GGM to solve the singular covariance inversion problem. In this paper, we propose an efficient and mathematically solid algorithm that infers biological networks by computing low order partial correlation (LOPC) up to the second order. The bias introduced by the low order constraint is minimal compared to the more reliable approximation of the network structure achieved. In addition, the algorithm is suitable for a dataset with small sample size but large number of variables. Simulation results show that LOPC yields far less spurious edges and works well under various conditions commonly seen in practice. The application to a real metabolomics dataset further validates the performance of LOPC and suggests its potential power in detecting novel biomarkers for complex disease.
Systems biology; undirected network inference; correlation; Gaussian graphical models; low order partial correlation; biomarker discovery
Most published copy number datasets on solid tumors were obtained from specimens comprised of mixed cell populations, for which the varying tumor-stroma proportions are unknown or unreported. The inability to correct for signal mixing represents a major limitation on the use of these datasets for subsequent analyses, such as discerning deletion types or detecting driver aberrations. We describe the BACOM2.0 method with enhanced accuracy and functionality to normalize copy number signals, detect deletion types, estimate tumor purity, quantify true copy numbers, and calculate average-ploidy value. While BACOM has been validated and used with promising results, subsequent BACOM analysis of the TCGA ovarian cancer dataset found that the estimated average tumor purity was lower than expected. In this report, we first show that this lowered estimate of tumor purity is the combined result of imprecise signal normalization and parameter estimation. Then, we describe effective allele-specific absolute normalization and quantification methods that can enhance BACOM applications in many biological contexts while in the presence of various confounders. Finally, we discuss the advantages of BACOM in relation to alternative approaches. Here we detail this revised computational approach, BACOM2.0, and validate its performance in real and simulated datasets.
Clinical monitoring of shock mainly depends on blood-oxygen-indices obtained from invasive blood sample tests. The central internal jugular central vein oxygenation level (ScvO2) has been considered as a gold standard indicator for shock prediction. We developed a noninvasive spatially-resolved near-infrared spectroscopy (SR-NIRS) to measure tissue blood oxygen saturation (StO2) surrounding the region of taking blood sample for the ScvO2 test in 25 patients with shock. StO2 values were found to be highly correlated (r = 0.84, p < 0.001) with ScvO2 levels and the concordance coefficient of 0.80 is high. The results suggest the potential of noninvasive SR-NIRS for bedside shock monitoring.
(170.1470) Blood or tissue constituent monitoring; (300.0300) Spectroscopy
Ever growing “omics” data and continuously accumulated biological knowledge provide an unprecedented opportunity to identify molecular biomarkers and their interactions that are responsible for cancer phenotypes that can be accurately defined by clinical measurements such as in vivo imaging. Since signaling or regulatory networks are dynamic and context-specific, systematic efforts to characterize such structural alterations must effectively distinguish significant network rewiring from random background fluctuations. Here we introduced a novel integration of network biology and imaging to study cancer phenotypes and responses to treatments at the molecular systems level. Specifically, Differential Dependence Network (DDN) analysis was used to detect statistically significant topological rewiring in molecular networks between two phenotypic conditions, and in vivo Magnetic Resonance Imaging (MRI) was used to more accurately define phenotypic sample groups for such differential analysis. We applied DDN to analyze two distinct phenotypic groups of breast cancer and study how genomic instability affects the molecular network topologies in high-grade ovarian cancer. Further, FDA-approved arsenic trioxide (ATO) and the ND2-SmoA1 mouse model of Medulloblastoma (MB) were used to extend our analyses of combined MRI and Reverse Phase Protein Microarray (RPMA) data to assess tumor responses to ATO and to uncover the complexity of therapeutic molecular biology.
Network biology; MRI; differential network; cancer biology
Near-infrared diffuse correlation spectroscopy (DCS) has recently been employed for noninvasive acquisition of blood flow information in deep tissues. Based on the established correlation diffusion equation, the light intensity autocorrelation function detected by DCS is determined by a blood flow index αDB, tissue absorption coefficient μa, reduced scattering coefficient μs’, and a coherence factor β. The present study is designed to investigate the possibility of extracting multiple parameters such as μa, μs’, β, and αDB through fitting one single autocorrelation function curve and evaluate the performance of different fitting methods. For this purpose, computer simulations, tissue-like phantom experiments and in-vivo tissue measurements were utilized. The results suggest that it is impractical to simultaneously fit αDB and μa or αDB and μs’ from one single autocorrelation function curve due to the large crosstalk between these paired parameters. However, simultaneously fitting β and αDB is feasible and generates more accurate estimation with smaller standard deviation compared to the conventional two-step fitting method (i.e., first calculating β and then fitting αDB). The outcomes from this study provide a crucial guidance for DCS data analysis.
Near-infrared spectroscopy; diffuse correlation spectroscopy; blood flow; noise model; autocorrelation function
Near-infrared diffuse correlation spectroscopy (DCS) is an emerging technology for monitoring blood flow in various tissues. This article reviews the recent progress of DCS for the assessment of skeletal muscle blood flow, including the developments in technology allowing use during dynamic exercise and muscular electrical stimulation, the utilization for diagnosis of muscle vascular diseases, and the applications for evaluating treatment effects. The limitations of current DCS studies and future perspective are finally discussed.
Diffuse Correlation Spectroscopy (DCS); Skeletal muscle; Blood flow; Motion artifact; Gating algorithm; Peripheral Arterial Disease (PAD)
To identify muscle physiological properties that may contribute to post-exertional fatigue and malaise in women with fibromyalgia (FM).
Healthy postmenopausal women with (n=11) and without (n=11) fibromyalgia, age 51–70 years, participated in this study. Physical characteristics along with self-reported questionnaires were evaluated. Strength loss and tissue oxygenation in response to a fatiguing exercise protocol were used to quantify fatigability and the local muscle hemodynamic profile. Muscle biopsies were obtained to assess between-group differences in baseline muscle properties using histochemical, immunohistochemical and electron microscopic analyses.
No significant difference in muscle fatigue in response to exercise was apparent between healthy controls and subjects with FM. However, self-reported fatigue and pain were correlated to prolonged loss of strength following 12-min of recovery in subjects with FM. Although there was no difference in percent SDH positive (type I) and SDH negative (type II) fibers or in mean fiber cross-sectional area between groups, subjects with FM showed greater size variability and altered fiber size distribution. Only in healthy controls, fatigue-resistance was strongly correlated with the size of SDH positive fibers and hemoglobin oxygenation. By contrast, subjects with FM with the highest percentage of SDH positive fibers recovered strength most effectively, which was correlated to capillary density. However, overall, capillary density was lower in subjects with FM.
Peripheral mechanisms i.e. altered muscle fiber size distribution and decreased capillary density may contribute to post-exertional fatigue in subjects with FM. Understanding these defects in fibromyalgic muscle may provide valuable insight for treatment.
The efficacy of photodynamic therapy (PDT) depends upon the delivery of both photosensitizing drug and oxygen. In this study, we hypothesized that local vascular microenvironment is a determinant of tumor response to PDT. Tumor vascularization and its basement membrane (collagen) were studied as a function of supplementation with basement membrane matrix (Matrigel) at the time of tumor cell inoculation. Effects on vascular composition with consequences to tumor hypoxia, photosensitizer uptake and PDT response were measured. Matrigel-supplemented tumors developed more normalized vasculature, composed of smaller and more uniformly-spaced blood vessels than their unsupplemented counterparts, but these changes did not affect tumor oxygenation or PDT-mediated direct cytotoxicity. However, PDT-induced vascular damage increased in Matrigel-supplemented tumors, following an affinity of the photosensitizer Photofrin for collagen-containing vascular basement membrane coupled with increased collagen content in these tumors. The more highly-collagenated tumors demonstrated more vascular congestion and ischemia after PDT, along with a higher probability of curative outcome that was collagen dependent. In the presence of photosensitizer-collagen localization, PDT effects on collagen were evidenced by a decrease in its association with vessels. Together, our findings demonstrate that photosensitizer localization to collagen increases vascular damage and improves treatment efficacy in tumors with greater collagen content. The vascular basement membrane is thus identified to be a determinant of therapeutic outcome in PDT of tumors.
collagen; photodynamic therapy; microenvironment; normalization; vasculature
We report a novel noncontact diffuse correlation spectroscopy flow-oximeter for simultaneous quantification of relative changes in tissue blood flow (rBF) and oxygenation (Δ[oxygenation]). The noncontact probe was compared against a contact probe in tissue-like phantoms and forearm muscles (n = 10), and the dynamic trends in both rBF and Δ[oxygenation] were found to be highly correlated. However, the magnitudes of Δ[oxygenation] measured by the two probes were significantly different. Monte Carlo simulations and phantom experiments revealed that the arm curvature resulted in a significant underestimation (~−20%) for the noncontact measurements in Δ[oxygenation], but not in rBF. Other factors that may cause the residual discrepancies between the contact and noncontact measurements were discussed, and further comparisons with other established technologies are needed to identify/quantify these factors. Our research paves the way for noncontact and simultaneous monitoring of blood flow and oxygenation in soft and vulnerable tissues without distorting tissue hemodynamics.
A noncontact diffuse correlation spectroscopy (DCS) probe has been developed using two separated optical paths for the source and detector. This unique design avoids the interference between the source and detector and allows large source-detector separations for deep tissue blood flow measurements. The noncontact probe has been calibrated against a contact probe in a tissue-like phantom solution and human muscle tissues; flow changes concurrently measured by the two probes are highly correlated in both phantom (R2=0.89, p<10−5) and real-tissue (R2=0.77, p<10−5, n=9) tests. The noncontact DCS holds promise for measuring blood flow in vulnerable (e.g., pressure ulcer) and soft (e.g., breast) tissues without distorting tissue hemodynamic properties.
noncontact; diffuse; correlation; spectroscopy; deep tissue; blood flow
Recurrent copy number alterations (CNAs) play an important role in cancer genesis. While a number of computational methods have been proposed for identifying such CNAs, their relative merits remain largely unknown in practice since very few efforts have been focused on comparative analysis of the methods. To facilitate studies of recurrent CNA identification in cancer genome, it is imperative to conduct a comprehensive comparison of performance and limitations among existing methods. In this paper, six representative methods proposed in the latest six years are compared. These include one-stage and two-stage approaches, working with raw intensity ratio data and discretized data respectively. They are based on various techniques such as kernel regression, correlation matrix diagonal segmentation, semi-parametric permutation and cyclic permutation schemes. We explore multiple criteria including type I error rate, detection power, Receiver Operating Characteristics (ROC) curve and the area under curve (AUC), and computational complexity, to evaluate performance of the methods under multiple simulation scenarios. We also characterize their abilities on applications to two real datasets obtained from cancers with lung adenocarcinoma and glioblastoma. This comparison study reveals general characteristics of the existing methods for identifying recurrent CNAs, and further provides new insights into their strengths and weaknesses. It is believed helpful to accelerate the development of novel and improved methods.
Women with fibromyalgia (FM) have symptoms of increased muscular fatigue and reduced exercise tolerance, which may be associated with alterations in muscle microcirculation and oxygen metabolism. This study used near-infrared diffuse optical spectroscopies to noninvasively evaluate muscle blood flow, blood oxygenation and oxygen metabolism during leg fatiguing exercise and during arm arterial cuff occlusion in post-menopausal women with and without FM.
Fourteen women with FM and twenty-three well-matched healthy controls participated in this study. For the fatiguing exercise protocol, the subject was instructed to perform 6 sets of 12 isometric contractions of knee extensor muscles with intensity steadily increasing from 20 to 70% maximal voluntary isometric contraction (MVIC). For the cuff occlusion protocol, forearm arterial blood flow was occluded via a tourniquet on the upper arm for 3 minutes. Leg or arm muscle hemodynamics, including relative blood flow (rBF), oxy- and deoxy-hemoglobin concentration ([HbO2] and [Hb]), total hemoglobin concentration (THC) and blood oxygen saturation (StO2), were continuously monitored throughout protocols using a custom-built hybrid diffuse optical instrument that combined a commercial near-infrared oximeter for tissue oxygenation measurements and a custom-designed diffuse correlation spectroscopy (DCS) flowmeter for tissue blood flow measurements. Relative oxygen extraction fraction (rOEF) and oxygen consumption rate (rVO2) were calculated from the measured blood flow and oxygenation data. Post-manipulation (fatiguing exercise or cuff occlusion) recovery in muscle hemodynamics was characterized by the recovery half-time, a time interval from the end of manipulation to the time that tissue hemodynamics reached a half-maximal value.
Subjects with FM had similar hemodynamic and metabolic response/recovery patterns as healthy controls during exercise and during arterial occlusion. However, tissue rOEF during exercise in subjects with FM was significantly lower than in healthy controls, and the half-times of oxygenation recovery (Δ[HbO2] and Δ[Hb]) were significantly longer following fatiguing exercise and cuff occlusion.
Our results suggest an alteration of muscle oxygen utilization in the FM population. This study demonstrates the potential of using combined diffuse optical spectroscopies (i.e., NIRS/DCS) to comprehensively evaluate tissue oxygen and flow kinetics in skeletal muscle.
Occlusions of bilateral common carotid arteries (bi-CCA) in mice are popular models for the investigation of transient forebrain ischemia. Currently available technologies for assessing cerebral blood flow (CBF) and oxygenation in ischemic mice have limitations. This study tests a novel near-infrared diffuse correlation spectroscopy (DCS) flow-oximeter for monitoring both CBF and cerebral oxygenation in mice undergoing repeated transient forebrain ischemia. Concurrent flow measurements in a mouse brain were first conducted for validation purposes; DCS measurement was found highly correlated with laser Doppler measurement (R2 = 0.94) and less susceptible to motion artifacts. With unique designs in experimental protocols and fiber-optic probes, we have demonstrated high sensitivities of DCS flow-oximeter in detecting the regional heterogeneity of CBF responses in different hemispheres and global changes of both CBF and cerebral oxygenation across two hemispheres in mice undergoing repeated 2-minute bi-CCA occlusions over 5 days. More than 75% CBF reductions were found during bi-CCA occlusions in mice, which may be considered as a threshold to determine a successful bi-CCA occlusion. With the progress of repeated 2-minute bi-CCA occlusions over days, a longitudinal decline in the magnitudes of CBF reduction was observed, indicating the brain adaptation to cerebral ischemia through the repeated preconditioning.
(170.0170) Medical optics and biotechnology; (170.3660) Light propagation in tissues; (170.3880) Medical and biological imaging; (170.6480) Spectroscopy, speckle
Somatic Copy Number Alterations (CNAs) in human genomes are present in almost all human cancers. Systematic efforts to characterize such structural variants must effectively distinguish significant consensus events from random background aberrations. Here we introduce Significant Aberration in Cancer (SAIC), a new method for characterizing and assessing the statistical significance of recurrent CNA units. Three main features of SAIC include: (1) exploiting the intrinsic correlation among consecutive probes to assign a score to each CNA unit instead of single probes; (2) performing permutations on CNA units that preserve correlations inherent in the copy number data; and (3) iteratively detecting Significant Copy Number Aberrations (SCAs) and estimating an unbiased null distribution by applying an SCA-exclusive permutation scheme.
We test and compare the performance of SAIC against four peer methods (GISTIC, STAC, KC-SMART, CMDS) on a large number of simulation datasets. Experimental results show that SAIC outperforms peer methods in terms of larger area under the Receiver Operating Characteristics curve and increased detection power. We then apply SAIC to analyze structural genomic aberrations acquired in four real cancer genome-wide copy number data sets (ovarian cancer, metastatic prostate cancer, lung adenocarcinoma, glioblastoma). When compared with previously reported results, SAIC successfully identifies most SCAs known to be of biological significance and associated with oncogenes (e.g., KRAS, CCNE1, and MYC) or tumor suppressor genes (e.g., CDKN2A/B). Furthermore, SAIC identifies a number of novel SCAs in these copy number data that encompass tumor related genes and may warrant further studies.
Supported by a well-grounded theoretical framework, SAIC has been developed and used to identify SCAs in various cancer copy number data sets, providing useful information to study the landscape of cancer genomes. Open–source and platform-independent SAIC software is implemented using C++, together with R scripts for data formatting and Perl scripts for user interfacing, and it is easy to install and efficient to use. The source code and documentation are freely available at http://www.cbil.ece.vt.edu/software.htm.
Motivation: Identification of somatic DNA copy number alterations (CNAs) and significant consensus events (SCEs) in cancer genomes is a main task in discovering potential cancer-driving genes such as oncogenes and tumor suppressors. The recent development of SNP array technology has facilitated studies on copy number changes at a genome-wide scale with high resolution. However, existing copy number analysis methods are oblivious to normal cell contamination and cannot distinguish between contributions of cancerous and normal cells to the measured copy number signals. This contamination could significantly confound downstream analysis of CNAs and affect the power to detect SCEs in clinical samples.
Results: We report here a statistically principled in silico approach, Bayesian Analysis of COpy number Mixtures (BACOM), to accurately estimate genomic deletion type and normal tissue contamination, and accordingly recover the true copy number profile in cancer cells. We tested the proposed method on two simulated datasets, two prostate cancer datasets and The Cancer Genome Atlas high-grade ovarian dataset, and obtained very promising results supported by the ground truth and biological plausibility. Moreover, based on a large number of comparative simulation studies, the proposed method gives significantly improved power to detect SCEs after in silico correction of normal tissue contamination. We develop a cross-platform open-source Java application that implements the whole pipeline of copy number analysis of heterogeneous cancer tissues including relevant processing steps. We also provide an R interface, bacomR, for running BACOM within the R environment, making it straightforward to include in existing data pipelines.
Availability: The cross-platform, stand-alone Java application, BACOM, the R interface, bacomR, all source code and the simulation data used in this article are freely available at authors' web site: http://www.cbil.ece.vt.edu/software.htm.
Supplementary Information: Supplementary data are available at Bioinformatics online.
Summary: Phenotypic Up-regulated Gene Support Vector Machine (PUGSVM) is a cancer Biomedical Informatics Grid (caBIG™) analytical tool for multiclass gene selection and classification. PUGSVM addresses the problem of imbalanced class separability, small sample size and high gene space dimensionality, where multiclass gene markers are defined by the union of one-versus-everyone phenotypic upregulated genes, and used by a well-matched one-versus-rest support vector machine. PUGSVM provides a simple yet more accurate strategy to identify statistically reproducible mechanistic marker genes for characterization of heterogeneous diseases.
Supplementary information: Supplementary data are available at Bioinformatics online.
This study explored using a novel diffuse correlation spectroscopy (DCS) flow-oximeter to noninvasively monitor blood flow and oxygenation changes in head and neck tumors during radiation delivery. A fiber-optic probe connected to the DCS flow-oximeter was placed on the surface of the radiologically/clinically involved cervical lymph node. The DCS flow-oximeter in the treatment room was remotely operated by a computer in the control room. From the early measurements, abnormal signals were observed when the optical device was placed in close proximity to the radiation beams. Through phantom tests, the artifacts were shown to be caused by scattered x rays and consequentially avoided by moving the optical device away from the x-ray beams. Eleven patients with head and neck tumors were continually measured once a week over a treatment period of seven weeks, although there were some missing data due to the patient related events. Large inter-patient variations in tumor hemodynamic responses were observed during radiation delivery. A significant increase in tumor blood flow was observed at the first week of treatment, which may be a physiologic response to hypoxia created by radiation oxygen consumption. Only small and insignificant changes were found in tumor blood oxygenation, suggesting that oxygen utilizations in tumors during the short period of fractional radiation deliveries were either minimal or balanced by other effects such as blood flow regulation. Further investigations in a large patient population are needed to correlate the individual hemodynamic responses with the clinical outcomes for determining the prognostic value of optical measurements.
(170.0170) Medical optics and biotechnology; (170.3660) Light propagation in tissues; (170.3880) Medical and biological imaging; (170.6480) Spectroscopy, speckle
Diffuse optics has proven useful for quantitative assessment of tissue oxy- and deoxyhaemoglobin concentrations and, more recently, for measurement of microvascular blood flow. In this paper, we focus on the flow monitoring technique: diffuse correlation spectroscopy (DCS). Representative clinical and pre-clinical studies from our laboratory illustrate the potential of DCS. Validation of DCS blood flow indices in human brain and muscle is presented. Comparison of DCS with arterial spin-labelled MRI, xenon-CT and Doppler ultrasound shows good agreement (0.50
diffuse correlation spectroscopy; blood flow; cerebral blood flow; oxygen metabolism; brain; cancer
We used a nonimpact inertial rotational model of a closed head injury in neonatal piglets to simulate the conditions following traumatic brain injury in infants. Diffuse optical techniques, including diffuse reflectance spectroscopy and diffuse correlation spectroscopy (DCS), were used to measure cerebral blood oxygenation and blood flow continuously and noninvasively before injury and up to 6 h after the injury. The DCS measurements of relative cerebral blood flow were validated against the fluorescent microsphere method. A strong linear correlation was observed between the two techniques (R = 0.89, p < 0.00001). Injury-induced cerebral hemodynamic changes were quantified, and significant changes were found in oxy- and deoxy-hemoglobin concentrations, total hemoglobin concentration, blood oxygen saturation, and cerebral blood flow after the injury. The diffuse optical measurements were robust and also correlated well with recordings of vital physiological parameters over the 6-h monitoring period, such as mean arterial blood pressure, arterial oxygen saturation, and heart rate. Finally, the diffuse optical techniques demonstrated sensitivity to dynamic physiological events, such as apnea, cardiac arrest, and hypertonic saline infusion. In total, the investigation corraborates potential of the optical methods for bedside monitoring of pediatric and adult human patients in the neurointensive care unit.
diffuse correlation spectroscopy (DCS); diffuse reflectance spectroscopy (DRS); cerebral hemodynamics; cerebral blood flow; traumatic brain injury; near—infrared spectroscopy (NIRS)
Photodynamic therapy (PDT) with low light fluence rate has rarely been studied in protocols that use short drug–light intervals and thus deliver illumination while plasma concentrations of photosensitizer are high, creating a prominent vascular response. In this study, the effects of light fluence rate on PDT response were investigated using motexafin lutetium (10 mg/kg) in combination with 730 nm light and a 180-min drug–light interval. At 180 min, the plasma level of photosensitizer was 5.7 ng/μl compared to 3.1 ng/mg in RIF tumor, and PDT-mediated vascular effects were confirmed by a spasmodic decrease in blood flow during illumination. Light delivery at 25 mW/cm2 significantly improved long-term tumor responses over that at 75 mW/cm2. This effect could not be attributed to oxygen conservation at low fluence rate, because 25 mW/cm2 PDT provided little benefit to tumor hemoglobin oxygen saturation. However, 25 mW/cm2 PDT did prolong the duration of ischemic insult during illumination and was correspondingly associated with greater decreases in perfusion immediately after PDT, followed by smaller increases in total hemoglobin concentration in the hours after PDT. Increases in blood volume suggest blood pooling from suboptimal vascular damage; thus the smaller increases after 25 mW/cm2 PDT provide evidence of more widespread vascular damage, which was accompanied by greater decreases in clonogenic survival. Further study of low fluence rate as a means to improve responses to PDT under conditions designed to predominantly damage vasculature is warranted.
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