Indomethacin and Prostaglandin Effects On Absorption, Retention And Urinary Excretion Of 51Chromium

Nutritional Science

Detection of Breast Thermograms using Ensemble Classifiers

Mortality rate of breast cancer can be reduced by detecting breast cancer in its early stage. Breast thermography plays an important role in early detection of breast cancer, as it can detect tumors when the physiological changes start in the breast prior to structural changes. Computer Aided Detection (CAD) systems improve the diagnostic accuracy by providing a detailed analysis of images, which are not visible to the naked eye. The performance of CAD systems depends on many factors. One of the important factors is the classifier used for classification of breast thermograms. In this paper, we made a comparison of classifier performances using two ensemble classifiers namely Ensemble Bagged Trees and AdaBoost. Spatial and spectral features are used for classification. Ensemble Bagged Trees classifier performed better than AdaBoost in terms of accuracy of classification, but training time required is higher than AdaBoost classifier. An accuracy of 87%, sensitivity of 83% and specificity of 90.6% is obtained using Ensemble Bagged Trees classifier

Semantic segmentation and PSO based method for segmenting liver and lesion from CT images

The liver is a vital organ of the human body andhepatic cancer is one of the major causes of cancer deaths. Earlyand rapid diagnosis can reduce the mortality rate. It can beachieved through computerized cancer diagnosis and surgeryplanning systems. Segmentation plays a major role in thesesystems. This work evaluated the efficacy of the SegNet model inliver and particle swarm optimization-based clustering techniquein liver lesion segmentation. The method was evaluated on portalvenous phase CT images obtained from ten patients at KasturbaHospital, Manipal. The segmentation results were satisfactory.The values for Dice Coefficient and volumetric overlap errorachieved were 0.940 ± 0.022 and 0.112 ± 0.038, respectively forliver and the results for lesion delineation were 0.4629 ± 0.287and 0.6986 ± 0.203, respectively. The proposed method is effectivefor liver segmentation. However, lesion segmentation needs to befurther improved for better accuracy

BHPR research: qualitative1. Complex reasoning determines patients' perception of outcome following foot surgery in rheumatoid arhtritis

Background: Foot surgery is common in patients with RA but research into surgical outcomes is limited and conceptually flawed as current outcome measures lack face validity: to date no one has asked patients what is important to them. This study aimed to determine which factors are important to patients when evaluating the success of foot surgery in RA Methods: Semi structured interviews of RA patients who had undergone foot surgery were conducted and transcribed verbatim. Thematic analysis of interviews was conducted to explore issues that were important to patients. Results: 11 RA patients (9 ♂, mean age 59, dis dur = 22yrs, mean of 3 yrs post op) with mixed experiences of foot surgery were interviewed. Patients interpreted outcome in respect to a multitude of factors, frequently positive change in one aspect contrasted with negative opinions about another. Overall, four major themes emerged. Function: Functional ability & participation in valued activities were very important to patients. Walking ability was a key concern but patients interpreted levels of activity in light of other aspects of their disease, reflecting on change in functional ability more than overall level. Positive feelings of improved mobility were often moderated by negative self perception ("I mean, I still walk like a waddling duck”). Appearance: Appearance was important to almost all patients but perhaps the most complex theme of all. Physical appearance, foot shape, and footwear were closely interlinked, yet patients saw these as distinct separate concepts. Patients need to legitimize these feelings was clear and they frequently entered into a defensive repertoire ("it's not cosmetic surgery; it's something that's more important than that, you know?”). Clinician opinion: Surgeons' post operative evaluation of the procedure was very influential. The impact of this appraisal continued to affect patients' lasting impression irrespective of how the outcome compared to their initial goals ("when he'd done it ... he said that hasn't worked as good as he'd wanted to ... but the pain has gone”). Pain: Whilst pain was important to almost all patients, it appeared to be less important than the other themes. Pain was predominately raised when it influenced other themes, such as function; many still felt the need to legitimize their foot pain in order for health professionals to take it seriously ("in the end I went to my GP because it had happened a few times and I went to an orthopaedic surgeon who was quite dismissive of it, it was like what are you complaining about”). Conclusions: Patients interpret the outcome of foot surgery using a multitude of interrelated factors, particularly functional ability, appearance and surgeons' appraisal of the procedure. While pain was often noted, this appeared less important than other factors in the overall outcome of the surgery. Future research into foot surgery should incorporate the complexity of how patients determine their outcome Disclosure statement: All authors have declared no conflicts of interes

A Liver Segmentation Algorithm with Interactive Error Correction for Abdominal CT Images: A Preliminary Study

Part 1: Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT)International audienceAn automatic method for segmenting the liver from the portal venous phase of abdominal CT images using the K-Means clustering method is described in this paper. We have incorporated an interactive technique for correcting the errors in the liver segmentation results using power law transformation. The proposed method was validated on abdominal CT volumes of fifteen patients obtained from Kasturba Medical College, Manipal. The average values of the various standard evaluation metrics obtained are as follows: Dice coefficient = 0.9361, Jaccard index = 0.8805, volumetric overlap error = 0.1195, absolute volume difference = 4.048%, average symmetric surface distance = 1.7282 mm and maximum symmetric surface distance = 38.039 mm. The quantitative and qualitative results obtained in our preliminary work show that the K-Means clustering technique along with power law transformation is effective in producing good liver segmentation outputs. As future work, we will attempt to automate the power law transformation technique

Enhanced wound contraction and epithelization period in steroid treated rats: Role of pyramid environment

902-904Contribution and role of a pyramid/square box on the wound healing suppressant effect of dexamethasone was studied in rats of either sex using excision wound model to record the wound contraction rate and epithelization period. The results showed enhanced wound contraction rate and decreased epithelization period in the pyramid-exposed rats as compared to controls. Thus, it appears that pyramid environment facilitates the process of wound healing. Also, the wound healing suppressant effects of dexamethasone were significantly reduced

Semantic segmentation and PSO based method for segmenting liver and lesion from CT images

The liver is a vital organ of the human body and hepatic cancer is one of the major causes of cancer deaths. Early and rapid diagnosis can reduce the mortality rate. It can be achieved through computerized cancer diagnosis and surgery planning systems. Segmentation plays a major role in these systems. This work evaluated the efficacy of the SegNet model in liver and particle swarm optimization-based clustering technique in liver lesion segmentation. Over 2400 CT images were used for training the deep learning network and ten CT datasets for validating the algorithm. The segmentation results were satisfactory. The values for Dice Coefficient and volumetric overlap error achieved were 0.940 ± 0.022 and 0.112 ± 0.038, respectively for liver and the results for lesion delineation were 0.4629 ± 0.287 and 0.6986 ± 0.203, respectively. The proposed method is effective for liver segmentation. However, lesion segmentation needs to be further improved for better accuracy