Chronic Lactiferous Fistula: A Case Report

Lactiferous or milk fistula is a tract between the skin and lactiferous duct. Chronic lactiferous duct is uncommon and generally formed during lactating period. It commonly result from complication of the surgical intervention, such as excision biopsy of breast mass or incision and drainage (I&D) for breast abscess. We reported a case of chronic lactiferous fistula secondary to previous I&D for breast abscess in 31 year old lactating woman

Seasonal Influence on Botanical Composition of Plantain, Chicory, White- and Red-Clover Based Herbage Mixes

Use of herb based sward mixes that are productive in different weather conditions could be an effective option to provide feed requirements for finishing lambs year round compared to ryegrass (Lolium perenne L.)/white clover (Trifolium repens L.) sward in New Zealand (Kemp et al., 2010). Herbs such as plantain (Plantago lanceolata L.) and chicory (Cichorium intybus L.) and the legumes such as white clover and red clover (Trifolium pratense L.) as pure swards are known to improve lamb performance compared to ryegrass/white clover. Therefore, there is scope to have herb-clover mixes to enhance post-weaning lamb performance. However, one significant limitation in the use of these herbages is the seasonality of their production. A further issue could be their persistence and poor winter growth (Moloney and Milne, 1993). Botanical composition of a sward mix changes depending on the grazing management system. Herbs such as plantain and chicory are susceptible to winter grazing and treading damage affecting their proportion in a sward mix. A sward’s botanical composition (legumes or grasses, leaf or stem materials, dead matter or weeds) and morphological composition (growth in different seasons), would likely affect the nutrient composition and lamb production (Lambert and Litherland, 2000). Therefore, it is essential to observe potential changes within and across seasons of the botanical composition in herb clover sward mixes

Net Herbage Accumulation Rate (NHAR) of Plantain and Chicory Based Sward Mixes

The managed grasslands of New Zealand have a range of forage species comprising grasses and legumes that can be grown and utilised in a wide range of conditions (Saggar et al., 2007). Perennial ryegrass (Lolium perenne) and white clover (Trifolium repens) are the dominant grass and legume species found in these grasslands (Hodgson et al., 2005; Waghorn and Clark, 2004). Annual pasture production is affected by the soil moisture status, nutrient levels and temperatures (Valantine and Kemp, 2007). Therefore, monthly pasture production can vary from year to year. Net herbage accumulation rate (NHAR) is a measurement of pasture production (Valantine and Kemp, 2007). Net herbage accumulation rate can be used to help with livestock management by determining the carrying capacity of the land. Alternative forages such as plantain (Plantago lanceolata) and chicory (Cichorium intybus) are becoming popular in New Zealand. Kemp et al., (2010) suggested that farmers could achieve „marketable target weight‟ of lambs sooner by feeding a herb and legume mix consisted of plantain, chicory, white clover (Trifolium repens) and red clover (Trifolium pratense) compared to a ryegrass/white clover pasture. However, a significant potential limitation in the use of these herbages is the seasonality of their production. There is a dearth of knowledge on NHAR of these herb-clover mixes. Therefore, the aim of this study was to determine NHAR and NHAR curves for plantain and chicory based sward mixes

Herb and Clover Mixes Increase Average Daily Gain (ADG) of Finishing Lambs in Different Seasons

Approximately half of the usable land area in New Zealand is under grasslands (Saggar 2001). Production of lamb meat is seasonal in New Zealand (Fisher 2004) with the majority of lambs born in the spring and slaughtered in late summer and autumn depending on the international demand (Clemens and Babcock 2004) and pasture growth pattern. Finishing lambs outside this window using high quality pastures would help to facilitate a continuous supply of meat to the domestic and international markets. Charlton and Belgrave (1992) and Kemp et al. (2010) reported that the use of herb-clover mixes instead of perennial ryegrass/white clover swards would facilitate finishing lambs to a high carcass weight or in a shorter time period. Therefore, a research was undertaken in four different seasons: early spring, late spring, summer and autumn during 2011/2012 with the hypothesis that the average daily gain (ADG) and average live weight per ha per day of finishing lambs would be greater in herb-clover mixes than on a perennial ryegrass/white clover sward

Chronic Lactiferous Fistula: A Case Report

Lactiferous or milk fistula is a tract between the skin and lactiferous duct. Chronic lactiferous duct is uncommon and generally formed during lactating period. It commonly result from complication of the surgical intervention, such as excision biopsy of breast mass or incision and drainage (I&amp;D) for breast abscess. We reported a case of chronic lactiferous fistula secondary to previous I&amp;D for breast abscess in 31 year old lactating woman.</jats:p

A Novel Classification Method using Effective Neural Network and Quantitative Magnetization Transfer Imaging of Brain White Matter in Relapsing Remitting Multiple Sclerosis

Background: Quantitative Magnetization Transfer Imaging (QMTI) is often used to quantify the myelin content in multiple sclerosis (MS) lesions and normal appearing brain tissues. Also, automated classifiers such as artificial neural networks (ANNs) can significantly improve the identification and classification processes of MS clinical datasets. Objective: We classified patients with relapsing-remitting multiple sclerosis (RRMS) from healthy subjects using QMTI and T1 longitudinal relaxation time data of brain white matter and the performance of three ANN-based classifiers have been investigated. Materials and Methods: Conventional magnetic resonance imaging (MRI) and quantitative magnetization transfer scans were obtained from RRMS patients (n=30) and age-matched healthy subjects (n=30). After image pre-processing and brain tissue segmentation, QMTI parameters including magnetization transfer ratio (MTR), magnetization transfer rate (Ksat), T1 relaxation time under MT saturation pulse (T1sat) and T1 longitudinal relaxation time were calculated as parametric maps. Three ANN algorithms, including multilayer perceptron (MLP), radial basis function (RBF) and ensemble neural network based on Akaike information criterion (ENN-AIC) input features were extracted in the form of QMTI and T1 mean values. The ANNs quantitative performance is measured by the standard evaluation of confusion matrix criteria. Results: The results indicate that ENN-AIC-based classification method has achieved 90% accuracy, 92% sensitivity and 86% precision compared to other ANN classification models such as RBF and MLP. NPV, FPR and FDR values of the proposed ENN-AIC model were found to be 0.933, 0.125 and 0.133, respectively. A graphical representation of how to track actual data by the predictive values derived from each of the three algorithms, was also presented. It has been demonstrated that ENN-AIC as an effective neural network improves the quality of classification results compared to MLP and RBF. Conclusion: The efficiency and robustness of ENN classifier will greatly enhance with the use of AIC-based combination weights assignment. In addition, this research provides a new direction to classify a large amount of quantitative MRI data that can help the physician in a correct MS diagnosis

A Novel Classification Method using Effective Neural Network and Quantitative Magnetization Transfer Imaging of Brain White Matter in Relapsing Remitting Multiple Sclerosis

Background: Quantitative Magnetization Transfer Imaging (QMTI) is often used to quantify the myelin content in multiple sclerosis (MS) lesions and normal appearing brain tissues. Also, automated classifiers such as artificial neural networks (ANNs) can significantly improve the identification and classification processes of MS clinical datasets.Objective: We classified patients with relapsing-remitting multiple sclerosis (RRMS) from healthy subjects using QMTI and T1 longitudinal relaxation time data of brain white matter and the performance of three ANN-based classifiers have been investigated.   Materials and Methods: Conventional magnetic resonance imaging (MRI) and quantitative magnetization transfer scans were obtained from RRMS patients (n=30) and age-matched healthy subjects (n=30). After image pre-processing and brain tissue segmentation, QMTI parameters including magnetization transfer ratio (MTR), magnetization transfer rate (Ksat), T1 relaxation time under MT saturation pulse (T1sat) and T1 longitudinal relaxation time were calculated as parametric maps. Three ANN algorithms, including multilayer perceptron (MLP), radial basis function (RBF) and ensemble neural network based on Akaike information criterion (ENN-AIC) input features were extracted in the form of QMTI and T1 mean values. The ANNs quantitative performance is measured by the standard evaluation of confusion matrix criteria.Results: The results indicate that ENN-AIC-based classification method has achieved 90% accuracy, 92% sensitivity and 86% precision compared to other ANN classification models such as RBF and MLP. NPV, FPR and FDR values of the proposed ENN-AIC model were found to be 0.933, 0.125 and 0.133, respectively. A graphical representation of how to track actual data by the predictive values derived from each of the three algorithms, was also presented. It has been demonstrated that ENN-AIC as an effective neural network improves the quality of classification results compared to MLP and RBF.Conclusion: The efficiency and robustness of ENN classifier will greatly enhance with the use of AIC-based combination weights assignment. In addition, this research  provides a new direction to classify a large amount of quantitative MRI data that can help the physician in a correct MS diagnosis.</jats:p

Brain functional mechanisms in attentional processing following modified conflict stroop task

Background: Cognitive control of brain regions can be determined by the tasks involving the cognitive control such as the color word Stroop task. Stroop task define the reduction in function in incongruent condition, which requires more attention and control of competitive responses. Objective: The purpose of this study was to evaluate the activity of brain using the Modified Conflict Stroop Task in Military Personnel. Material and Methods: In this applied experimental study, to specify the activity of different regions of brain in response to conflict Persian color-word Stroop task, 20 healthy persons participated in this study. To evaluate selective attention, the traditional color-word Stroop Task Model was modified, and the Stroop test was designed in high-and low-threat zones. We used functional magnetic resonance imaging (fMRI) to evaluate the brain activation during the Stroop task performance. The color-word Stroop task consists of incongruent, congruent, and neutral condi-tions, and the subjects were requested to carefully choose the correct answer. Results: The mean response time was longer in incongruent condition (867.6±193.5ms) compared to congruent and neutral conditions. Analysis of neu-roimaging data revealed that the brain conflict-related regions are activated by the Stroop interference. In incongruent trial, the superior frontal gyrus (SFG) and inferior frontal gyrus (IFG) showed the most active and stronger BOLD responses. In congruent trials, the activation in the brain was less and had difference compared with incongruent trials. Conclusion: Our result offers that the frontal cortex and the anterior cingulate cortex are sensitive to different trials of Persian Stroop task. Using modified Stroop task, we determined the brain responses to the selective attention test. © 2020, Shiraz University of Medical Sciences. All rights reserved