Histomorphometric and sympathetic innervation of the human internal thoracic artery

INTRODUCTION: Internal thoracic artery (ITA) is an established arterial graft for the coronary artery by-pass surgery. Special micro-anatomical features of the ITA wall may protect it from age related pathological changes. One of the complications seen after coronary artery bypass grafting is vasospasm. Sympathetic nerves may be involved in vasospasm. OBJECTIVE: To ascertain the sympathetic innervation of the internal thoracic artery and to assess the effect of aging on this artery by histomorphometry. METHOD: Fifty-four human internal thoracic artery samples were collected from 27 cadavers (19 male and 8 female) with ages of 19 to 83 years. Samples were divided into three age groups: G1, 19-40 years; G2, 41-60 years; G3, >61 years. Sections (thickness 5 mm) of each sample were taken and stained with hematoxylin-eosin and Verhoeff-Van Gieson stains. Five of fifty-four samples were processed for tyrosine hydroxylase immunostaining. RESULTS: The thickness of the tunica intima was found to be constant in all age groups, whereas the thickness of the tunica media decreased in proportion to age. Verhoeff-Van Gieson staining showed numerous elastic laminae in the tunica media. Significant differences (p<0.0001) in the number of elastic laminae were found between G1 with G2 cadavers, between G2 and G3 cadavers and between G3 and G1 cadavers. Tyrosine hydroxylase immunostaining demonstrated sympathetic fibers, located mainly in the tunica adventitia and the adventitia-media border. The sympathetic nerve fiber area and sympathetic index were found to be 0.0016 mm² and 0.012, respectively. DISCUSSION: Histology of the ITA showed features of the elastic artery. This may be associated with lower incidence of Atherosclerosis or intimal hyperplasia in ITA samples even in elderly cases. Low sympathetic index (0.012) of ITA may be associated with fewer incidences of sympathetic nervous systems problems (vasospasm) of the ITA. CONCLUSION: Sympathetic nerve fibers are present in the adventitia of the internal thoracic artery. This is an elastic artery, although anatomically it is considered to be medium-sized. The sympathetic index may be used for analysis of sympathetic nerve fiber-related problems of the internal thoracic arter

Alleviation of Lower Anterior Crowding with Super-Elastic and Heat-Activated NiTi Wires: A Prospective Clinical Trial

Objective: To compare the amount of alleviation of lower anterior crowding and changes in intercanine width (ICW), intermolar width (IMW), and arch depth (AD) dimensions using 2 different types of nickel-titanium (NiTi) archwires.Methods: Thirty participants were randomly allocated to 2 treatment groups, using heat-activated NiTi (HANT) or super-elastic (SE-NiTi) round (0.014”) archwires. The inclusion criteria were a Little’s Irregularity Index (LII) greater than 4, malocclusion requiring non-extraction therapy, all teeth erupted to the second molars in the lower arch, and Angle’s Class I malocclusion. The primary aim was to measure alleviation in mandibular crowding over 12 weeks; the secondary aim was to measure changes in ICW, IMW, and AD during those 12 weeks. Simple randomization was performed. The measurements were made on dental stone casts using a coordinate measuring machine at 4-week intervals.Results: LII at 0, 4, 8, and 12 weeks was 8.59 ± 1.44, 6.17 ± 1.65, 4.65 ± 1.63, and 3.28 ± 1.57 mm in the HANT; 8.87 ± 1.29, 6.92 ± 1.49, 5.25 ± 1.32, and 3.63 ± 1.32 mm in the SE-NiTi group, respectively. ICW increased from 25.43 ± 1.39 to 27.41 ± 1.29 mm in the HANT and from 25.81 ± 1.78 to 27.27 ± 1.83 mm in the SE-NiTi groups over a period of 12 weeks, at P .05).Conclusions: The amount of alleviation of lower anterior crowding was similar with both archwires. ICW, IMW, and AD increased with HANT archwires

Datamining Approach for Automation of Diagnosis of Breast Cancer in Immunohistochemically Stained Tissue Microarray Images

Cancer of the breast is the second most common human neoplasm, accounting for approximately one quarter of all cancers in females after cervical carcinoma. Estrogen receptor (ER), Progesteron receptor and human epidermal growth factor receptor (HER-2/neu) expressions play an important role in diagnosis and prognosis of breast carcinoma. Tissue microarray (TMA) technique is a high throughput technique which provides a standardized set of images which are uniformly stained, facilitating effective automation of the evaluation of the specimen images. TMA technique is widely used to evaluate hormone expression for diagnosis of breast cancer. If one considers the time taken for each of the steps in the tissue microarray process workflow, it can be observed that the maximum amount of time is taken by the analysis step. Hence, automated analysis will significantly reduce the overall time required to complete the study. Many tools are available for automated digital acquisition of images of the spots from the microarray slide. Each of these images needs to be evaluated by a pathologist to assign a score based on the staining intensity to represent the hormone expression, to classify them into negative or positive cases. Our work aims to develop a system for automated evaluation of sets of images generated through tissue microarray technique, representing the ER expression images and HER-2/neu expression images. Our study is based on the Tissue Microarray Database portal of Stanford university at http://tma.stanford.edu/cgi-bin/cx?n=her1, which has made huge number of images available to researchers. We used 171 images corresponding to ER expression and 214 images corresponding to HER-2/neu expression of breast carcinoma. Out of the 171 images corresponding to ER expression, 104 were negative and 67 were representing positive cases. Out of the 214 images corresponding to HER-2/neu expression, 112 were negative and 102 were representing positive cases. Our method has 92.31% sensitivity and 93.18% specificity for ER expression image classification and 96.67% sensitivity and 88.24% specificity for HER-2/neu expression image classification

Optimization of Deep CNN Techniques to Classify Breast Cancer and Predict Relapse

Breast cancer is a fatal disease that has a high rate of morbidity and mortality. Finding the right diagnosis is one of the most crucial steps in breast cancer treatment. Doctors can use machine learning (ML) and deep learning techniques to aid with diagnosis. This work makes an effort to devise a methodology for the classification of Breast cancer into its molecular subtypes and prediction of relapse. The objective is to compare the performance of Deep CNN, Tuned CNN and Hypercomplex-Valued CNN, and infer the results, thus automating the classification process. The traditional method used by doctors to detect is tedious and time consuming. It employs multiple methods, including MRI, CT scanning, aspiration, and blood tests as well as image testing. The proposed approach uses image processing techniques to detect irregular breast tissues in the MRI. The survivors of Breast Cancer are still at risk for relapse after remission, and once the disease relapses, the survival rate is much lower. A thorough analysis of data can potentially identify risk factors and reduce the risk of relapse in the first place. A SVM (Support Vector Machine) module with GridSearchCV for hyperparameter tuning is used to identify patterns in those patients who experience a relapse, so that these patterns can be used to predict the relapse before it occurs. The traditional deep learning CNN model achieved an accuracy of 27%, the tuned CNN model achieved an accuracy of 92% and the hypercomplex-valued CNN achieved an accuracy of 98%. The SVM model achieved an accuracy of 89% and on tuning the hyperparameters by using GridSearchCV it achieved and accuracy of 98%

BIOMECHANICAL, BIOCHEMICAL AND HISTOLOGICAL EVIDENCES FOR WOUND HEALING PROPERTIES OF INDIAN TRADITIONAL MEDICINES

Objective: Ayurveda, India's traditional medicinal system is a rich source of natural remedies, frequently used as home and folk medicine in wound healing due to easy availability and affordability. Honey, Ghee and roots of Glycyrrhiza glabra are effectively used in Ayurveda for treating wounds of various types. Nerium indicum (a folk medicine) is also a known healing agent. Even though the known end result of these medications is faster wound healing, the mechanism of actions at tissue level, changes in the micro-environment of the wound and quantification of the rate of healing is not explored and documented using modern scientific methods.Methods: Healthy Wistar rats were used for incision wound model. Wounds were inflicted and the treatment plan was followed with regular topical application of test materials. The nature of healing was observed regularly and photographed. At different interval of the treatment plan-biomechanical, biochemical and histological studies were carried out. An attempt was also made to quantify the microscopic changes at the wound site.Results: Faster healing was observed in all the animals treated with test materials. This was indicated by alterations in the nature of epithelisation, inflammatory changes, fibroblast recruitment and activity, fibrous composition and arrangement at the wound site in comparison with untreated group.Conclusion: The present study is useful in exploring the mechanism of action of these traditional Indian medicinal systems–Ayurveda and folk medicine and thereby provides scientific evidences for the same.Â

Erratum to “Quantitative Fraction Evaluation of Dermal Collagen and Elastic Fibres in the Skin Samples Obtained in Two Orientations from the Trunk Region”

Background. Histomorphic evaluation of dermal collagen and elastic fibres was analysed by image analysis technique. The quantification of dermal elements was performed in skin tissues, collected in horizontal and vertical directions from trunk region and discussed under the perspective of consequences of scar related complications. Materials and Method. Total number of 200 skin samples collected from 5 areas of trunk region were processed histologically and subjected to tissue-quant image analysis. Statistical analysis involving mean with SEM and paired t test by SPSS were employed to the percentage values obtained from image analysis. Result. Among the chosen 5 areas of trunk region, abdomen showed the statistically significant difference for both collagen and elastic content between horizontal and vertical orientations (P< 0.05), whereas upper back, presternal, and lateral chest areas showed significant difference (P< 0.05) only for collagen and groin only for elastic content. Conclusion. The differences in the distribution of dermal collagen and elastic fibres in 2 directions of the samples from the same areas might be attributed to final outcome of wound healing process by influencing the appearance and behaviour of scar related complications in the region of trunk

Estimation of Channel Performance of Satellite Communication and Frequency Reader

ABSTRACT: National Remote Sensing Center (NRSC) receives data from different remote satellites like IRS-P6, IRS-P5, Cartosat-2, Cartosat-2a, etc., and processes it depending on the user requirements. The satellite data received in X band is in a particular data format. This data has to be frame synchronized using a special hardware. The receiver hardware setup must be ready at any time to make it ready it&quot;s performance is to be tested continuously.The frequency with which satellite data is coming is also continuously tested . In the proposed project VHDL code has been developed for BER reader with differential encoding and decoding and frequency reader. The external frequency and number of errors in satellite data will be displayed on HP display devices. This project has been implemented and tested using the ALTERA EPLDs. This needs crystal oscillators, thumb wheel switches,7 segment display etc., must be programmed as per the requirement. The hardware required for this has been implemented on the wire-warp board

Artificial intelligence and visual inspection in cervical cancer screening

INTRODUCTION: Visual inspection with acetic acid is limited by subjectivity and a lack of skilled human resource. A decision support system based on artificial intelligence could address these limitations. We conducted a diagnostic study to assess the diagnostic performance using visual inspection with acetic acid under magnification of healthcare workers, experts, and an artificial intelligence algorithm.METHODS: A total of 22 healthcare workers, 9 gynecologists/experts in visual inspection with acetic acid, and the algorithm assessed a set of 83 images from existing datasets with expert consensus as the reference. Their diagnostic performance was determined by analyzing sensitivity, specificity, and area under the curve, and intra- and inter-observer agreement was measured using Fleiss kappa values.RESULTS: Sensitivity, specificity, and area under the curve were, respectively, 80.4%, 80.5%, and 0.80 (95% CI 0.70 to 0.90) for the healthcare workers, 81.6%, 93.5%, and 0.93 (95% CI 0.87 to 1.00) for the experts, and 80.0%, 83.3%, and 0.84 (95% CI 0.75 to 0.93) for the algorithm. Kappa values for the healthcare workers, experts, and algorithm were 0.45, 0.68, and 0.63, respectively.CONCLUSION: This study enabled simultaneous assessment and demonstrated that expert consensus can be an alternative to histopathology to establish a reference standard for further training of healthcare workers and the artificial intelligence algorithm to improve diagnostic accuracy.</p