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Quantitative computed tomography has identified a cluster of women (33%) cooking with biomass fuels (wood) with image-based markers of functional small airways disease and associated alterations in regional lung mechanics. Texture and image registration-based metrics of lung function may allow for early detection of potential inflammatory processes that may arise in response to inhaled biomass smoke, and help identify phenotypes of chronic lung disease prevalent in nonsmoking women in the developing world.

Introduction Dry eye is a multifactorial condition of the eye caused by insufficient tear production and imbalance in tear composition leading to faster evaporation of tear fluid. It is also associated with inflammation that often leads to ocular surface damage. Symptoms of dry eyes include itchiness, soreness, red eyes, a burning sensation, eye fatigue and blurred vision. The objective of this study was to evaluate the efficacy and safety of our multi-ingredient supplement in subjects with dry eye syndrome (DES). Methods We recruited 60 subjects with mild to moderate DES who were randomized in a 1:1 ratio in a single-center study to receive LCD (lutein 20 mg, zeaxanthin 4 mg, curcumin 200 mg curcuminoids, vitamin D3 600 IU) or placebo (soybean oil) capsules for 8 weeks. The primary outcomes evaluated were changes in tear volume by Schirmer’s test and ocular symptoms by the Ocular Surface Disease Index (OSDI); secondary outcomes included evaluation of changes in Standard Patient Evaluation of Eye Dryness (SPEED) questionnaire, tear film break-up time (TBUT), corneal and conjunctival staining, tear osmolarity, matrix metalloproteinase-9 (MMP-9), artificial tear use and safety assessments. The outcomes were compared between the LCD and placebo groups at baseline and day 56 of supplementation. Results Fifty-nine subjects, 30 from LCD and 29 from placebo group, completed the study. The LCD group showed significant improvements (P

We sought to utilize quantitative computed tomography (QCT) to characterize lungs of women cooking with biomass and poor ventilation compared to those using liquified petroleum gas (LPG) in Thanjavur, India. We recruited 18 biomass cooks and 5 LPG cooks. Spirometry was performed pre-/post-bronchodilation. QCT (GE Optima128) was performed at coached total lung capacity (TLC) and residual volume (RV). RV air trapping was measured using a threshold (voxels QCT demonstrated a predominance of diffuse increase in lung density. Individuals from both groups presented with air trapping visually. Using the -856HU threshold, a mean of 6.98 ± 8.38% of the lungs were classified as air-trapped. DPM-fSAD) estimates in biomass vs LPG users were 36.3±28.3% vs. 20.8±7.3% respectively (difference: p=0.05). In subjects with respiratory obstruction (post FEV1/FVC In the presence of diffuse inflammation increase in regional density can blunt the sensitivity of a purely threshold-based air trapping QCT metric. Image registration-based method incorporating regional volume change provides a greater sensitivity required to adequately quantify and characterize these changes.

BACKGROUND. Cooks exposed to biomass fuel experience increased risk of respiratory disease and mortality. We sought to characterize lung function and environmental exposures of primary cooking women using two fuel-types in southeastern India, as well as to investigate the effect of particulate matter (PM) from kitchens on human airway epithelial (HAE) cells in vitro. METHODS. We assessed pre-and post-bronchodilator lung function on 25 primary female cooks using wood biomass or liquified petroleum gas (LPG), and quantified exposures from 34 kitchens (PM(2.5), PM 90). The remaining 40% in the LPG group had normal spirometry (post FEV(1)/FVC=80–90), while only 10% of participants in the biomass group had normal spirometry, and the remaining biomass cooks (30%) had respiratory obstruction (post FEV(1)/FVC

The present study investigated the antitumor effect and antioxidant role of the methanol extract of Oxystelma esculentum R. Br. (Asclepiadaceae) (MEOE) on tumor growth and the host survival time with mice. The antitumor and antioxidant potential of Oxystelma esculentum were studied against Ehrlich’s ascites carcinoma cell line (EAC) treated mice. MEOE was administered at doses of 200 and 400 mg/kg body weight (bw) once a day for 9 days after 24 h of tumor inoculation. Among the treated animals, six animals were sacrificed for biochemical and tumor analysis, and the remaining four groups were kept to study lifespan. On day 10, the parameters of tumor volume, packed cell volume, viable, and non-viable cell count were studied. Hematological and liver biochemical parameters, and antioxidant enzymes such as lipid peroxidation (LPO), glutathione (GSH), superoxide dismutase (SOD), catalase (CAT), etc. were estimated. Decreases in tumor volume, packed cell volume, and viable cell count were observed in MEOE-treat...

In this paper, we present a Born-type approximation method for bioluminescence tomography (BLT), which is to reconstruct an internal bioluminescent source from the measured bioluminescent signal on the external surface of a small animal. Based on the diffusion approximation for the photon propagation in biological tissue, this BLT method utilizes the Green function to establish a linear relationship between the measured bioluminescent signal and the internal bioluminescent source distribution. The Green function can be modified to describe a heterogeneous medium with an arbitrary boundary using the Born approximation. The BLT reconstruction is formulated in a linear least-squares optimization framework with simple bounds constraint. The performance of this method is evaluated in numerical simulation and phantom experiments.

Bioluminescence tomography (BLT) is a new molecular imaging mode, which is being actively developed to reveal molecular and cellular signatures as labeled by bioluminescent probes in a living small animal. This technology can help diagnose diseases, evaluate therapies, and facilitate drug development with mouse models. In this paper, we describe in vivo mouse experiments with BLT, and propose the reconstruction procedure of bioluminescent sources from optical data measured on the body surface of the mouse using a modality fusion approach. The results show the feasibility of our methodology for localization and quantification of the bioluminescent activities in vivo.

In this paper, we present a Born-type approximation method for bioluminescence tomography (BLT), which is to reconstruct an internal bioluminescent source from the measured bioluminescent signal on the external surface of a small animal. Based on the diffusion approximation for the photon propagation in biological tissue, this BLT method utilizes the Green function to establish a linear relationship between the measured bioluminescent signal and the internal bioluminescent source distribution. The Green function can be modified to describe a heterogeneous medium with an arbitrary boundary using the Born approximation. The BLT reconstruction is formulated in a linear least-squares optimization framework with simple bounds constraint. The performance of this method is evaluated in numerical simulation and phantom experiments.

We describe the system design of the first bioluminescence tomography (BLT) system for parallel acquisition of multiple bioluminescent views around a mouse in a number of spectral channels simultaneously. The primary component of this BLT system is a novel mirror module and a unique mouse holder. The mirror module consists of a mounting plate and four mirrors with stages. These mirror stages are right triangular blocks symmetrically arranged and attached to the mounting plate such that the hypotenuse surfaces of the triangular blocks all make45∘to the plate surface. The cylindrical/polygonal mouse holder has semitransparent rainbow bands on its side surface for the acquisition of spectrally resolved data. Numerical studies and experiments are performed to demonstrate the feasibility of this system. It is shown that bioluminescent signals collected using our system can produce a similar BLT reconstruction quality while reducing the data acquisition time, as compared to the sequential data acquisition mode.

Biomedical imaging is a rapidly growing field to provide a state-of-the-art tool for preclinical biomedical research and clinical applications, in view of its ability to noninvasively reveal subtle structural variations of biological tissues and visualize in vivo physiological and pathological processes at the cellular and molecular levels. Mathematical methods are involved with imaging theories, models, and reconstruction algorithms in biomedical imaging. X-ray computed tomography (CT) was a successful application of mathematical method in medical imaging. The CT mathematical model can be reduced to a Radon transform. The inverse transform of Radon transform is invented by Radon in 1917. Magnetic resonance imaging (MRI) is a versatile medical imaging modality. MRI can provide more diagnostic information than any of the existing imaging techniques. It does not involve the use of ionizing radiation, hence free from associated harmful effects. Inverse Fast Fourier Transform (IFFT) is a standard method of image reconstruction in MRI from uniformly sampled K-space data. From nonuniform K-space data, iterative algorithms can improve image quality of image reconstruction for MRI. In the optical molecular imaging, the radiative transport equation (RTE) is the fundamental equation to describe photon propagation in biological tissues. The forward solution predicts photon propagation in the optical molecular imaging. The inverse solution can reconstruct molecular probe distribution in a small animal for providing unique insights into disease pathogenesis, drug development, and responses of therapy. The solutions for RTE usually involve analytical methods, Monte Carlo (MC) method, diffusion approximation (DA) method, simplified spherical harmonics method, and some numerical methods. In this special issue, each paper was reviewed by at least two reviewers and revised according to review comments. This special issue covered most of common biomedical imaging models and various image processing methods, such as registration, segmentation, and so forth, were involved. For Positron Emission Tomography (PET) imaging model, two attenuation correction methods based on X-rays CT (CTAC method) and segmentation of emission images (SE-AC method) were simulated with Monte Carlo method and compared. For synchrotron Micro-CT imaging model, a semiautomatic segmentation algorithm for extracting the complete structure of acini has been proposed. For ultrasound imaging model, a common carotid artery segmentation scheme based on active shape model can get better result and promote the translation of carotid 3D US to clinical care for the monitoring of the atherosclerotic disease progression and regression. For MRI model, a mesh-deformation constraints based image registration algorithm was carefully investigated. Also the development of image segmentation for intracranial aneurysms and rotation covariant image processing method for biomedical applications are summarized. Furthermore, in order to accelerate the parallel imaging, a sparse constrained reconstruction approach with variable splitting methods was proposed and verified: total variation (TV)-minimization interior tomography algorithm, dynamic and robust blind watermarking scheme to resist against common distortions, modified global and modified linear contrast stretching techniques for identification of various stages and species of malaria, a 3D surface-based deformable model as guidance for nonrigid 3D medical image registration and fusion, mathematical or computational modeling for oncogene inactivation, a group factor analysis model for neuroimaging applications by assigning separate factor patterns to control and patient groups yielding more reasonable factor scores and patterns, different MISO Volterra methods to model simulated ultrasound contrast agents signals, an automated process that determines whether an aortic object in a slice is a candidate for aortic dissection or PAU based on contrast enhanced CT data, image-based computational techniques to quantify the severity and directionality of individual scratches and scrapes, combination of genetic algorithm and closed loop to obtain optimal ternary command which maximized the contrast to tissue ratio, and magnetoacoustic tomography with magnetic induction (MAT-MI) for generating electrical conductivity images of biological tissues with high spatial resolution. An acceleration strategy for fluorescence molecular tomography (FMT) with early photons is proposed using graphics processing units (GPUs). The fluorescence molecular tomography (FMT) with early photons can efficiently improve the spatial resolution and fidelity of the reconstructed results. An efficient compressed sensing-based algorithm is proposed for CT image reconstruction from few-view data to suppress the streak artifact. The compressed sensing (CS) algorithm shows the potential to accurately recover images from highly undersampled data. The discriminant analysis techniques are discussed using MRI to identify the correlative pattern of brain changes for differentiating parkinsonian syndromes. A hybrid multiscale and multilevel image fusion algorithm for green fluorescent protein (GFP) image and phase contrast image of Arabidopsis cell is proposed. This algorithm uses different fusion strategies for different detailed subbands, which include neighborhood consistency measurement (NCM) that can adaptively find balance between color background and gray structure. A detrended fluctuation analysis (DFA) method is applied to image analysis to investigate the characteristic of different type of simulated and lymphoma image. A method is presented to estimate the tilt and decentration of intraocular lens (IOL) more accurately. The Bayesian hierarchical model for the analysis of categorical longitudinal data is investigated from sedation measurement for MRI and CT. In vivo MRI of local drug delivery is discussed to visualize and quantify the time resolved distribution of MRI contrast agents. These papers represent an insightful observation into the state of the art, as well as future topics in this biomedical imaging field. We hope that this special issue would attract a wide attention of the peers. Wenxiang Cong Kumar Durairaj Peng Feng

Currently, we are developing a computational optical biopsy technology for molecular sensing. We use the diffusion equation to model photon propagation but have a concern about the accuracy of diffusion approximation when the optical sensor is close to a bioluminescent source. We derive formulas to describe photon fluence for point and ball sources and measurement formulas for an idealized optical biopsy probe. Then, we numerically compare the diffusion approximation and the radiative transport as implemented by Monte Carlo simulation in the cases of point and ball sources. Our simulation results show that the diffusion approximation can be accurately applied if mu's>>mu(a) even if the sensor is very close to the source (>1mm). Furthermore, an approximate formula is given to describe the measurement of a cut-end fiber probe for a ball source.