Predicted and Experimental Bending Behaviour of Glulam Bonded by RPF Adhesive.

In this study, alder, spruce, and beech woods were used for homogeneous symmetric, inhomogeneous symmetric (combined) and inhomogeneous non-symmetric glued laminated timber (glulam) beams glued with resorcinol phenol formaldehyde (RPF) adhesive. The aim of this paper is to determine and compare the modulus of elasticity of glulam beams using three methods, i.e., analytical calculation, numerical model (FEM) and experimental testing. As an additional characteristic, the bending strength (MOR) of the beams was determined during experimental testing. Analytical calculation was used to calculate the modulus of elasticity (MOE) of glued laminated timber based on the knowledge of the modulus of elasticity of solid wood and to estimate the location of the neutral axis during bending. According to calculations, for symmetrical combinations, the deviation from the real neutral axis does not exceed 5%. In the case of the modulus of elasticity, the deviation is an average of 4.1% from that of the actual measured beams. The numerical model includes finite element modelling, where the deflection of the modelled beams can be calculated with a deviation of up to 10%. The last method was experimental testing of glued beams using four-point bending, in which, among homogeneous beams, beech glulam beams achieved the highest MOE and MOR, while alder glulam beams achieved the lowest. The combination of wood species resulted in an increase in both MOE and MOR compared to homogeneous spruce and alder beams.

In-vivo lung biomechanical modeling for effective tumor motion tracking in external beam radiation therapy.

Lung cancer is the most common cause of cancer-related death in both men and women. Radiation therapy is widely used for lung cancer treatment; however, respiratory motion presents challenges that can compromise the accuracy and/or effectiveness of radiation treatment. Respiratory motion compensation using biomechanical modeling is a common approach used to address this challenge. This study focuses on the development and validation of a lung biomechanical model that can accurately estimate the motion and deformation of lung tumor. Towards this goal, treatment planning 4D-CT images of lung cancer patients were processed to develop patient-specific finite element (FE) models of the lung to predict the patients' tumor motion/deformation. The tumor motion/deformation was modeled for a full respiration cycle, as captured by the 4D-CT scans. Parameters driving the lung and tumor deformation model were found through an inverse problem formulation. The CT datasets pertaining to the inhalation phases of respiration were used for validating the model's accuracy. The volumetric Dice similarity coefficient between the actual and simulated gross tumor volumes (GTVs) of the patients calculated across respiration phases was found to range between 0.80 ± 0.03 and 0.92 ± 0.01. The average error in estimating tumor's center of mass calculated across respiration phases ranged between 0.50 ± 0.10 (mm) and 1.04 ± 0.57 (mm), indicating a reasonably good accuracy of the proposed model. The proposed model demonstrates favorable accuracy for estimating the lung tumor motion/deformation, and therefore can potentially be used in radiation therapy applications for respiratory motion compensation.

Postendodontic Pain after Pulpotomy or Root Canal Treatment in Mature Teeth with Carious Pulp Exposure: A Multicenter Randomized Controlled Trial.

This equivalence, randomized, clinical trial aimed to compare the postoperative pain of root canal therapy (RCT) with pulpotomy with mineral trioxide aggregate (PMTA) or calcium-enriched mixture (PCEM) in permanent mature teeth. In seven academic centers, 550 cariously exposed pulps were included and randomly allocated into PMTA (n = 188), PCEM (n = 194), or RCT (n = 168) arms. Preoperative "Pain Intensity" (PI) on Numerical Rating Scale and postoperative PIs until day 7 were recorded. Patients' demographic and pre-/intra-/postoperative factors/conditions were recorded/analysed. The arms were homogeneous in terms of demographics. The mean preoperative PIs were similar (P=0.998), the mean sum PIs recorded during 10 postoperative intervals were comparable (P=0.939), and the trend/changes in pain relief were parallel (P=0.821) in all study arms. The incidences of preoperative moderate-severe pain in RCT, PMTA, and PCEM arms were 56.5%, 55.7%, and 56.7%, which after 24 hours considerably decreased to 13.1%, 10.6%, and 12.9%, respectively (P=0.578). The time span of endodontic procedures was statistically different; RCT = 69.73, PMTA = 35.37, and PCEM = 33.62 minutes (P < 0.001). Patients with greater preoperative pain, symptomatic apical periodontitis, or presence of PDL widening suffered more pain (P=0.002, 0.035, and 0.023, resp.); however, other pre-/intra-/postoperative factors/conditions were comparable. Pulpotomy with MTA/CEM and RCT demonstrate comparable and effective postoperative pain relief.

An Automated Segmentation Pipeline for Intratumoural Regions in Animal Xenografts Using Machine Learning and Saturation Transfer MRI.

Saturation transfer MRI can be useful in the characterization of different tumour types. It is sensitive to tumour metabolism, microstructure, and microenvironment. This study aimed to use saturation transfer to differentiate between intratumoural regions, demarcate tumour boundaries, and reduce data acquisition times by identifying the imaging scheme with the most impact on segmentation accuracy. Saturation transfer-weighted images were acquired over a wide range of saturation amplitudes and frequency offsets along with T1 and T2 maps for 34 tumour xenografts in mice. Independent component analysis and Gaussian mixture modelling were used to segment the images and identify intratumoural regions. Comparison between the segmented regions and histopathology indicated five distinct clusters: three corresponding to intratumoural regions (active tumour, necrosis/apoptosis, and blood/edema) and two extratumoural (muscle and a mix of muscle and connective tissue). The fraction of tumour voxels segmented as necrosis/apoptosis quantitatively matched those calculated from TUNEL histopathological assays. An optimal protocol was identified providing reasonable qualitative agreement between MRI and histopathology and consisting of T1 and T2 maps and 22 magnetization transfer (MT)-weighted images. A three-image subset was identified that resulted in a greater than 90% match in positive and negative predictive value of tumour voxels compared to those found using the entire 24-image dataset. The proposed algorithm can potentially be used to develop a robust intratumoural segmentation method.

Quantitative MRI Biomarkers of Stereotactic Radiotherapy Outcome in Brain Metastasis.

About 20-40% of cancer patients develop brain metastases, causing significant morbidity and mortality. Stereotactic radiation treatment is an established option that delivers high dose radiation to the target while sparing the surrounding normal tissue. However, up to 20% of metastatic brain tumours progress despite stereotactic treatment, and it can take months before it is evident on follow-up imaging. An early predictor of radiation therapy outcome in terms of tumour local failure (LF) is crucial, and can facilitate treatment adjustments or allow for early salvage treatment. In this study, an MR-based radiomics framework was proposed to derive and investigate quantitative MRI (qMRI) biomarkers for the outcome of LF in brain metastasis patients treated with hypo-fractionated stereotactic radiation therapy (SRT). The qMRI biomarkers were constructed through a multi-step feature extraction/reduction/selection framework using the conventional MR imaging data acquired from 100 patients (133 lesions), and were applied in conjunction with machine learning techniques for outcome prediction and risk assessment. The results indicated that the majority of the features in the optimal qMRI biomarkers characterize the heterogeneity in the surrounding regions of tumour including edema and tumour/lesion margins. The optimal qMRI biomarker consisted of five features that predict the outcome of LF with an area under the curve (AUC) of 0.79, and a cross-validated sensitivity and specificity of 81% and 79%, respectively. The Kaplan-Meier analyses showed a statistically significant difference in local control (p-value < 0.0001) and overall survival (p = 0.01). Findings from this study are a step towards using qMRI for early prediction of local failure in brain metastasis patients treated with SRT. This may facilitate early adjustments in treatment, such as surgical resection or salvage radiation, that can potentially improve treatment outcomes. Investigations on larger cohorts of patients are, however, required for further validation of the technique.

An Automatic Framework for Segmentation of Brain Tumours at Follow-up Scans after Radiation Therapy.

Brain metastasis is the most common intracranial malignancy with a poor overall survival (OS) after treatment. The standard stereotactic radiation therapy (SRT) planning procedure for brain metastasis requires delineating the tumour volume on magnetic resonance (MR) images. MR images are also acquired at multiple follow-up scans after SRT to monitor the treatment outcome through measuring changes in the physical dimensions of the tumour. Such measurements require manual segmentation of the tumour volume on multiple slices of several follow-up images which is tedious and impedes the SRT evaluation work flow considerably. In this study, an automatic framework was proposed to segment the tumour volume on longitudinal MR images acquired at standard follow-up scans after SRT. The multi-step segmentation framework was based on region growing and morphological snakes models that applied the standard SRT planning tumour contour as a basis to approximate the tumour shape and location at each follow-up scan for an accurate automatic segmentation of tumour volume. The framework was evaluated using the MR imaging data acquired from five patients prior to and at three follow-up scans after SRT. The preliminary results indicated that the Dice similarity coefficient between the ground truth tumour masks and their automatically segmented counterparts ranged between 0.84 and 0.90, while the average Dice coefficient for all the follow-up scans was 0.88. The results obtained implied a good potential of the proposed framework for being incorporated into the SRT treatment planning and evaluation systems as well as outcome prediction models.

An MR Radiomics Framework for Predicting the Outcome of Stereotactic Radiation Therapy in Brain Metastasis.

Despite recent advances in cancer treatment, patients with brain metastasis still suffer from poor overall survival (OS) after standard treatment. Predicting the treatment outcome before or early after the treatment can potentially assist the physicians in improving the therapy outcome by adjusting a standard treatment on an individual patient basis. In this study, a data-driven computational framework was proposed and investigated to predict the local control/failure (LC/LF) outcome in patients with brain metastasis treated with hypo-fractionated stereotactic radiation therapy (SRT). The framework extracted several geometrical and textural features from the magnetic resonance (MR) images of the tumour and edema regions acquired for 38 patients. Subsequent to a multi-step feature reduction/selection, a quantitative MR biomarker consisting of two features was constructed. A support vector machine classifier was used for outcome prediction using the constructed MR biomarker. The bootstrap .632+ and leave-one-patient-out cross-validation methods were used to assess the model's performance. The results indicated that the outcome of LF after SRT could be predicted with an area under the curve of 0.80 and a cross-validated accuracy of 82%. The results obtained implied a good potential of the proposed framework for local outcome prediction in patients with brain metastasis treated with SRT and encourage further investigations on a larger cohort of patients.

Effect of vitamin D supplementation as adjunctive therapy to methylphenidate on ADHD symptoms: A randomized, double blind, placebo-controlled trial.

OBJECTIVES: Previous studies have shown that serum levels of vitamin D were lower in attention deficit hyperactivity disorder (ADHD) children compared to healthy controls. The aim of the study was to determine the effect of vitamin D supplementation as adjunctive therapy to methylphenidate on symptoms of children with ADHD. METHODS: Sixty-two children aged 5-12 years with a diagnosis of ADHD based on DSM-IV criteria were randomly assigned into two groups to receive either 2000IU vitamin D or placebo in addition to methylphenidate for 8 weeks. Symptoms severity was assessed by Conner's Parent Rating Scale-Revised[S] (CPRS), ADHD rating scale-IV (ADHD-RS), and Weekly Parent Ratings of Evening and Morning Behavior (WPREMB) at weeks 0, 4, and 8. Serum levels of 25(OH)D were measured at baseline and after 8 weeks. Anthropometric variables, dietary intake, physical activity, sun exposure, and side effects were assessed. RESULTS: Fifty-four participants completed the trial. After 8 weeks of supplementation, serum levels of 25(OH)D significantly increased in the vitamin D group. ADHD symptoms decreased significantly in both groups (P < 0.05). Evening symptoms and total score of WPREMB scale were significantly different at weeks 4 and 8 between the two groups (P = 0.013, 0.016, respectively), but no differences were found in symptoms by CPRS and ADHD-RS scales. DISCUSSION: Vitamin D supplementation as adjunctive therapy to methylphenidate improved ADHD evening symptoms. Future research is needed to clarify vitamin D effects as monotherapy in ADHD and its mechanism. The trial was registered in www.irct.ir is (IRCT201404222394N10).

Plasma concentration, genetic variation, and gene expression levels of matrix metalloproteinase 9 in Iranian patients with coronary artery disease.

BACKGROUND: Matrix metalloproteinase 9 (MMP9) -1562C>T (rs3918242) polymorphism has been proposed as a risk factor for coronary artery disease (CAD) with conflicting results. The aim of the present study was to investigate the association of -1562C>T genetic polymorphism, gene expression and circulating levels of MMP9 with CAD risk in an Iranian subpopulation in in Zanjan City. MATERIALS AND METHODS: This was a retrospective case-control study we investigated retrospectively 100 patients with angiographically verified CAD and 100 matched controls. Genotyping of -1562C>T polymorphism was done by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Gene expression levels and circulating levels of MMP9 was determined by real-time reverse transcription-PCR and enzyme immunoassay method, respectively. Statistical analysis was done using Student's t-test or Chi-square test by SPSS 16 software. RESULTS: The mean circulating levels of MMP9 were significantly higher in CAD Group than control group (P = 0.002). Mean plasma levels of MMP9 were also significantly higher in triple vessel stenosis patients than double vessel or single vessel stenosis patients (P < 0.001). Moreover, mean plasma levels and gene expression levels of MMP9 were significantly higher in T allele carrier than C allele carrier of MMP9 -1562C>T polymorphism (P = 0.002, P = 0.01, respectively). However, genotype and allele frequencies of MMP9 -1562C>T polymorphism were similar between CAD patients and controls (P > 0.05). Additionally, the -1562C>T polymorphism of MMP9 gene didn't increase the risk of CAD in dominant (P = 0.537) or recessive (P = 0.249) genetic models. CONCLUSION: Our study demonstrated that circulating levels of MMP9 but not -1562C>T polymorphism of MMP9 gene may be a risk factor for development and severity of CAD in an Iranian subpopulation in Zanjan.

Anatomy-based algorithm for automatic segmentation of human diaphragm in noncontrast computed tomography images.

In-depth understanding of the diaphragm's anatomy and physiology has been of great interest to the medical community, as it is the most important muscle of the respiratory system. While noncontrast four-dimensional (4-D) computed tomography (CT) imaging provides an interesting opportunity for effective acquisition of anatomical and/or functional information from a single modality, segmenting the diaphragm in such images is very challenging not only because of the diaphragm's lack of image contrast with its surrounding organs but also because of respiration-induced motion artifacts in 4-D CT images. To account for such limitations, we present an automatic segmentation algorithm, which is based on a priori knowledge of diaphragm anatomy. The novelty of the algorithm lies in using the diaphragm's easy-to-segment contacting organs-including the lungs, heart, aorta, and ribcage-to guide the diaphragm's segmentation. Obtained results indicate that average mean distance to the closest point between diaphragms segmented using the proposed technique and corresponding manual segmentation is [Formula: see text], which is favorable. An important feature of the proposed technique is that it is the first algorithm to delineate the entire diaphragm. Such delineation facilitates applications, where the diaphragm boundary conditions are required such as biomechanical modeling for in-depth understanding of the diaphragm physiology.

Association of Nitric Oxide Levels and Endothelial Nitric Oxide Synthase G894T Polymorphism with Coronary Artery Disease in the Iranian Population.

PURPOSE: The endothelial nitric oxide synthase (eNOS) G894T polymorphism has been reported to cause endothelial dysfunction and may have a role in the development of coronary artery disease (CAD). The aim of the present study was to investigate the association of eNOS G894T genetic polymorphism and plasma levels of nitric oxide (NO) with CAD risk in an Iranian population. MATERIALS AND METHODS: We studied 200 patients with angiographically documented CAD and 100 matched controls. Analysis of G894T genetic polymorphism of eNOS was performed by polymerase chain reaction-restriction fragment length polymorphism method. Plasma levels of NO were determined using Griess method. Biochemical analysis was conducted by routine colorimetric methods. RESULTS: Plasma levels of NO were significantly lower in CAD patients than control subjects (41.60±12.70 vs. 55.48±16.57, P=0.001). Also, the mean plasma levels of NO were significantly lower in T allele carriers of eNOS G894T polymorphism than G allele carriers (P<0.001). The genotype distribution and minor T allele frequency of eNOS G894T polymorphism significantly differed between CAD patients and control subjects (P<0.05). However, no significant association was found between the eNOS G894T polymorphism and the severity of CAD (number of diseased vessel) or the lipid profile of CAD patients (P>0.05). CONCLUSION: Reduced plasma level of NO is associated with increased risk of CAD in our population. Moreover, eNOS G894T polymorphism is a significant risk factor for CAD development via reducing the plasma levels of NO. However, eNOS G894T polymorphism is not a contributing factor for the severity of CAD.