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Mammography imaging is one of the most successful techniques for breast cancer screening and detecting breast lesions. Detection of the Region of Interest (ROI) (where the possible abnormalities could be present) is the backbone for the success of any Computer-Aided Detection or Diagnosis (CADx) system. In this paper, to assist the CADx system, one computational model is proposed to detect breast mass lesions from mammogram images. At the beginning of the process, pectoral muscles from the mammograms are removed as a pre-processing step. Then by applying an automatic thresholding scheme with the required image processing techniques, different regions of breast tissues are ranked to detect the possible suspected region to refine the further segmentation task. One seeded region growing approach is proposed with an automatic seed selection criterion to detect the suspected region to segment the ROI. The proposed model has very less user intervention as maximum of the parameters are computed automatically. To evaluate the performance of the proposed model, it is compared with four different methods with six different evaluation metrics viz., Jaccard & Dice co-efficient, relative error, segmentation accuracy, error and Fowlkes–Mallows index (FMI). On the proposed model, 57 mammogram images are tested, consisting of four different cases that are collected from the publicly available benchmark database. The qualitative and quantitative analyses are performed to evaluate the proposed model. The best dice co-efficient, Jaccard co-efficient, accuracy, error and FMI values observed are 0.9506, 0.9471, 95.62%, 4.38% and 0.932, respectively. The superiority of the model over six state-of-the-art compared methods is well evident from the experimental results. [ABSTRACT FROM AUTHOR]

Mammography images are the most commonly used tool for breast cancer screening. The presence of pectoral muscle in images for the mediolateral oblique view makes designing a robust automated breast cancer detection system more challenging. Most of the current methods for removing the pectoral muscle are based on traditional machine learning approaches. This is partly due to the lack of segmentation masks of pectoral muscle in available datasets. In this paper, we provide the segmentation masks of the pectoral muscle for the INbreast, MIAS, and CBIS-DDSM datasets, which will enable the development of supervised methods and the utilization of deep learning. Training deep learning-based models using segmentation masks will also be a powerful tool for removing pectoral muscle for unseen data. To test the validity of this idea, we trained AU-Net separately on the INbreast and CBIS-DDSM for the segmentation of the pectoral muscle. We used cross-dataset testing to evaluate the performance of the models on an unseen dataset. In addition, the models were tested on all of the images in the MIAS dataset. The experimental results show that cross-dataset testing achieves a comparable performance to the same-dataset experiments. [ABSTRACT FROM AUTHOR]

The objective of this study was to validate a novel computerized method of ultrasound image analysis to determine chemical composition of pectoralis major muscles in broiler chickens. Ultrasonograms of pectoral muscles in the longitudinal and transverse planes were obtained from 40 birds just before slaughter. All chemical constituents of muscle samples were determined with the validated laboratory techniques, and the results served as a benchmark for developing the present algorithmic estimates of chicken meat composition. An inhouse developed algorithm (r-Algo) was used to normalize the ultrasonograms and to identify pixel intensity ranges for which linear correlations between mean numerical pixel values and the content of various chemical constituents were the strongest (based on the values of correlation coefficients), using a stepwise sequestration of ultrasound bitmaps. Percentages of chemical constituents were the dependent (accepted) variables and the results of echotextural analyses (luminance or pixel intensity), carried out with a commercially available image analysis software (ImageProPlus®), were the explanatory variables. The predictive regression equations were determined in 30 randomly selected algorithm-training experimental units, and their accuracy was tested in a subset of 10 birds allocated to the algorithm-validation group. Significant determination coefficients were found for all chemical constituents studied, with the accuracy ranging from 62.70% (linoleic acid, transverse plane, pixel range of 141–142) to 96.65% (total hypocholesterolemic acids, longitudinal plane, pixel range of 136–150). The present validation results indicate that accurate prediction of muscle chemical composition using echotextural image analyses is feasible after identifying specific pixel intensity ranges. [ABSTRACT FROM AUTHOR]

ABSTRACT: Fatty acids (FAs) can serve as energy for poultry, maintain normal cell structure and function, and support a healthy immune system. Although the addition of polyunsaturated fatty acids (PUFAs) to the diet has been extensively studied and reported, the mechanism of action of saturated fatty acids (SFAs) remains to be elucidated. We investigated the effect of 0.04% dietary myristic acid (MA) on slaughter performance, lipid components, tissue FAs, and the transcriptome profile in chickens. The results showed that dietary MA had no effect on slaughter performance (body weight, carcass weight, eviscerated weight, and pectoral muscle weight) (P > 0.05). Dietary MA enrichment increased MA (P

Abstract Objectives This retrospective single-center analysis aimed to evaluate whether artificial intelligence can detect type 2 diabetes mellitus by evaluating the pectoral muscle on digital breast tomosynthesis (DBT). Material method An analysis of 11,594 DBT images of 287 consecutive female patients (mean age 60, range 40–77 years) was conducted using convolutional neural networks (EfficientNetB5). The inclusion criterion was left-sided screening images with unsuspicious interpretation who also had a current glycosylated hemoglobin A1c (HBA1c) % value. The exclusion criteria were inadequate imaging, history of breast cancer, and/or diabetes mellitus. HbA1c values between 5.6 and 6.4% were categorized as prediabetic, and those with values ≥ 6.5% were categorized as diabetic. A recorded HbA1c ≤ 5.5% served as the control group. Each group was divided into 3 subgroups according to age. Images were subjected to pattern analysis parameters then cropped and resized in a format to contain only pectoral muscle. The dataset was split into 85% for training and 15% for testing the model’s performance. The accuracy rate and F1-score were selected as performance indicators. Results The training process was concluded in the 15th epoch, each comprising 1000 steps, with an accuracy rate of 92% and a loss of only 0.22. The average specificity and sensitivity for all 3 groups were 95%. The F1-score was 0.95. AUC-ROC was 0.995. PPV was 94%, and NPV was 98%. Conclusion Our study presented a pioneering approach, applying deep learning for the detection of diabetes mellitus status in women using pectoral muscle images and was found to function with an accuracy rate of 92%. Critical relevance statement AI can differentiate pathological changes within pectoral muscle tissue by assessing radiological images and maybe a potential diagnostic tool for detecting diabetes mellitus and other diseases that affect muscle tissues. Key points • AI may have an opportunistic use as a screening exam for diabetes during digital breast tomosynthesis. • This technique allows for early and non-invasive detection of diabetes mellitus by AI. • AI may have broad applications in detecting pathological changes within muscle tissue. Graphical Abstract

Mammography images are the most commonly used tool for breast cancer screening. The presence of pectoral muscle in images for the mediolateral oblique view makes designing a robust automated breast cancer detection system more challenging. Most of the current methods for removing the pectoral muscle are based on traditional machine learning approaches. This is partly due to the lack of segmentation masks of pectoral muscle in available datasets. In this paper, we provide the segmentation masks of the pectoral muscle for the INbreast, MIAS, and CBIS-DDSM datasets, which will enable the development of supervised methods and the utilization of deep learning. Training deep learning-based models using segmentation masks will also be a powerful tool for removing pectoral muscle for unseen data. To test the validity of this idea, we trained AU-Net separately on the INbreast and CBIS-DDSM for the segmentation of the pectoral muscle. We used cross-dataset testing to evaluate the performance of the models on an unseen dataset. In addition, the models were tested on all of the images in the MIAS dataset. The experimental results show that cross-dataset testing achieves a comparable performance to the same-dataset experiments.

Objective: Breast cancer is the most common and deadly female cancer. In breast cancer cases, survival is closely related to muscle mass, which is one of the components of body composition. Our aim was to investigate the usefulness of computed-tomography (CT)-based pectoral muscle measurements in detecting sarcopenia in patients with non-metastatic breast cancer and the relationship of these measurements with survival. Patients and Methods: Our study included 62 adult female breast cancer cases diagnosed with breast cancer between January 2012 and January 2018 and without metastasis in positron emission tomography/CT (PET/CT) examination obtained for pre-treatment staging. To evaluate sarcopenia, skeletal muscle index (SMI) and pectoral muscle index (PMI) were calculated by measuring pectoral muscle area and skeletal muscle area at L3 vertebra level on PET/CT images. Results: Deceased patients were significantly older (Median=73.90, IQR=27.04) than surviving patients (Median=54.60, IQR=13.37, p=0.025) and were diagnosed with cancer later in life (Median=63.92 IQR=30.16' vs. Median=47.51 IQR=15.0, p=0.030). When the threshold of 31 cm2/m2 was selected, there was a statistically significant difference in survival between sarcopenic and non-sarcopenic groups (p=0.031). Conclusion: In conclusion, the presence of sarcopenia in female breast cancer cases is a parameter that affects survival and can be measured using radiological imaging methods. In addition to the measurements accepted in the literature regarding sarcopenia, pectoral muscle measurements can be chosen as an alternative method in the diagnosis of sarcopenia. [ABSTRACT FROM AUTHOR]

Objectives: This retrospective single-center analysis aimed to evaluate whether artificial intelligence can detect type 2 diabetes mellitus by evaluating the pectoral muscle on digital breast tomosynthesis (DBT). Material method: An analysis of 11,594 DBT images of 287 consecutive female patients (mean age 60, range 40–77 years) was conducted using convolutional neural networks (EfficientNetB5). The inclusion criterion was left-sided screening images with unsuspicious interpretation who also had a current glycosylated hemoglobin A1c (HBA1c) % value. The exclusion criteria were inadequate imaging, history of breast cancer, and/or diabetes mellitus. HbA1c values between 5.6 and 6.4% were categorized as prediabetic, and those with values ≥ 6.5% were categorized as diabetic. A recorded HbA1c ≤ 5.5% served as the control group. Each group was divided into 3 subgroups according to age. Images were subjected to pattern analysis parameters then cropped and resized in a format to contain only pectoral muscle. The dataset was split into 85% for training and 15% for testing the model's performance. The accuracy rate and F1-score were selected as performance indicators. Results: The training process was concluded in the 15th epoch, each comprising 1000 steps, with an accuracy rate of 92% and a loss of only 0.22. The average specificity and sensitivity for all 3 groups were 95%. The F1-score was 0.95. AUC-ROC was 0.995. PPV was 94%, and NPV was 98%. Conclusion: Our study presented a pioneering approach, applying deep learning for the detection of diabetes mellitus status in women using pectoral muscle images and was found to function with an accuracy rate of 92%. Critical relevance statement: AI can differentiate pathological changes within pectoral muscle tissue by assessing radiological images and maybe a potential diagnostic tool for detecting diabetes mellitus and other diseases that affect muscle tissues. Key points: • AI may have an opportunistic use as a screening exam for diabetes during digital breast tomosynthesis. • This technique allows for early and non-invasive detection of diabetes mellitus by AI. • AI may have broad applications in detecting pathological changes within muscle tissue. [ABSTRACT FROM AUTHOR]

Background and Aim: Over the last decades, the poultry industry has experienced steady growth. Although the industry is gradually expanding in Indonesia, poultry feed production has always been expensive. There is a need to study alternative ingredients to obtain affordable feed from natural resources. Chaetomorpha linum (CL) is an abundant macroalgae available throughout the year in Indonesia. This study aimed to determine the effect of CL on the histological structure of the small intestine, pectoralis muscle, growth performance, and meat quality of broilers. Materials and Methods: This study used 300-day-old chick (DOC) male broilers that were reared until they were 21 days old. This study used a completely randomized design with four treatment groups and five replications, and each replication group contained 15 DOC individuals. The treatment groups consisted of Control (CON), CON basal feed (BF), CL1 (0.75%/kg BF), CL2 (1.5%/kg BF), and CL3 (3%/kg BF) groups. The histological structure of the small intestine, pectoralis muscle, growth performance, and meat quality of the broiler was examined. Results: Small intestine and pectoral muscle histomorphology, growth performance, and meat quality were significantly improved in the CL2 (1.5%) and CL3 (3%) groups compared with the CL1 (0.75%) and CON groups. Conclusion: Dietary CL supplementation ameliorates small intestine and pectoral muscle histomorphology, growth performance, and meat quality of broilers.

Simple Summary: The meat production performance of pigeons is largely determined by the growth and development of skeletal muscle, and the pectoral muscle accounts for a large proportion of skeletal muscle. The incubation period of pigeons is around 18 days. In order to better investigate the developmental phenotypic differences and hub differentially expressed genes (DEGs) in the pectoral muscles between the European meat pigeon Mimas strain and the Shiqi pigeon during the whole embryonic stages, we selected embryonic day 6 (E6), day 10 (E10), day 14 (E14) and day 1 after birth (P1), representing early embryonic development, mid-embryonic development, late-embryonic development, and the first day after hatching. H&E staining and RNA-seq at different embryonic stages were analyzed. It was found that myofiber density was significantly higher in the Shiqi pigeon than that of the European meat pigeon on E6, and myofibers with a diameter in the range of 1~50 μm in the Shiqi pigeon were significantly higher than those in the European meat pigeon on P1. In addition, we found several myogenic DEGs (CLU, PTGS1, NXK6-1, NR1H4, HNF1A, ABCB11, PLA2G12B and BPHL) which may play roles in regulating distinct embryonic pectoral muscle development between two pigeon breeds. This study provides basic data for revealing the distinct embryonic developmental mechanisms of pectoral muscle between European meat pigeons and Shiqi pigeons. In avian muscle development, embryonic muscle development determines the number of myofibers after birth. Therefore, in this study, we investigated the phenotypic differences and the molecular mechanism of pectoral muscle development of the European meat pigeon Mimas strain (later called European meat pigeon) and Shiqi pigeon on embryonic day 6 (E6), day 10 (E10), day 14 (E14) and day 1 after birth (P1). The results showed that the myofiber density of the Shiqi pigeon was significantly higher than that of the European meat pigeon on E6, and myofibers with a diameter in the range of 50~100 μm of the Shiqi pigeon on P1 were significantly higher than those of European meat pigeon. A total of 204 differential expressed genes (DEGs) were obtained from RNA-seq analysis in comparison between pigeon breeds at the same stage. DEGs related to muscle development were found to significantly enrich the cellular amino acid catabolism, carboxylic acid catabolism, extracellular matrix receptor interaction, REDOX enzyme activity, calcium signaling pathway, ECM receptor interaction, PPAR signaling pathway and other pathways. Using Cytoscape software to create mutual mapping, we identified 33 candidate genes. RT-qPCR was performed to verify the 8 DEGs selected—DES, MYOD, MYF6, PTGS1, MYF5, MYH1, MSTN and PPARG—and the results were consistent with RNA-seq. This study provides basic data for revealing the distinct embryonic development mechanism of pectoral muscle between European meat pigeons and Shiqi pigeons. [ABSTRACT FROM AUTHOR]

In order to investigate the adaptation mechanism of the pectoral and myocardial muscle tissues of white-rumped snowfinches (Onychostruthus taczanowskii) to the plateau environment, white-rumped snowfinches from two different high-altitudes were selected as treatment groups and tree sparrows (Passer montanus) from low-altitude area were referred to as the controls. We applied microscopy and stereology to analyze the numerical density on area (NA), volume density (VV), surface density (SV), mean section area (MS) and specific surface (δ) of mitochondria. The activity of mitochondrial respiratory chain complex Ⅰ-Ⅳ were measured by spectrophotometer. Results showed that the NA, VV, SV and δ of the mitochondria in the pectoral and myocardial muscle tissues of the white-rumped snowfinches habiting at high altitudes were greater than the counterparts at lower altitudes, and all parameters reached at a significant level (p<0.05) but not the NA of the cardiac myocyte mitochondria. For the enzymatic activity of the four complexes in the mitochondria of the pectoral and myocardial tissues of white-rumped snowfinches, it was greater at high altitudes than at low altitudes, and reached significant levels (p<0.01) except for complexes III and IV in the pectoral muscle tissue. Compared with the control, the NA and δ of mitochondria in pectoral and myocardial muscle tissues were significantly higher (p<0.05), while MS was significantly lower (p<0.001) than that of tree sparrow. The enzyme activities of all four complexes in the mitochondrial respiratory chain of white-rumped snowfinches were greater than those of tree sparrows (p<0.01), except for complex Ⅰ in pectoral muscle tissue. The above results suggested that the pectoral and myocardial muscle tissues of white-rumped snowfinches could better adapt to the plateau by reducing the volume of mitochondria, increasing the number of intracellular mitochondria, enhancing the utilization rate of mitochondrial membrane, and improving the enzyme activities of the four complexes in the mitochondrial respiratory chain under low temperatures and low oxygen level in plateau. [ABSTRACT FROM AUTHOR]

ABSTRACT The objective of this study was to evaluate the effects of replacing choline chloride with a plant source of choline on the locomotor system, liver health, and development of breast myopathies in broilers aged 1-42 days of age. We allocated 1,120 one-day-old Cobb broilers to four treatment groups and fed them commercial diets based on corn and soybean meal. The treatments included choline in the form of 1,800.00 mg/kg choline chloride; 1,350.00 mg/kg choline chloride + 450.00 mg/kg herbal choline; 900.00 mg/kg choline chloride + 900.00 mg/kg herbal choline; and 1,000.00 mg/kg herbal choline. Each treatment group had eight replications. Throughout the experiment, gait score, footpad dermatitis, hock burn, and leg deformities (valgus and varus) were evaluated in the birds at 28 and 35 days of age. After slaughter, parameters such as breast myopathies, tibial dyschondroplasia score, and histological slides of the pectoral muscle, liver, and proximal tibial epiphysis were assessed. The results demonstrated good hepatic and locomotor health in the broilers, as no classical signs of choline deficiency were observed. Statistical analyses indicated no significant differences between treatments in terms of liver and locomotor health, suggesting that broilers fed diets supplemented with the plant source did not experience choline deficiency. Additionally, no statistically significant differences were found between treatments regarding breast myopathies. Overall, the tested choline plant source can effectively replace choline chloride in broiler diets.

Aged chickens are often a secondary dietary choice, owing to the poor organoleptic qualities of their meat. With regard to the meat quality of chickens, the metabolic profiles of pectoral muscle in Guangyuan grey chickens (group G) and Hy-Line grey hens (group H) aged 55 weeks were compared via ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS). A total of 74 metabolites were identified with differential changes in the ion model. Lipids and lipid-like molecules comprised the largest proportion among the different metabolites. The content of myristic acid and palmitic acid were found to be higher in the pectoral muscle of group G, while group H showed significantly higher levels of glycerophospholipid molecules, such as LPC(18:2/0:0), Pi(38:5), Pc(16:0/16:0), and Pe(16:1e/14-hdohe). KEGG pathway analysis indicated that the abundant metabolites in group G were mainly involved in energy metabolism and fatty acid biosynthesis and metabolism, whereas those of group H were mainly attributed to the metabolism of unsaturated fatty acids and amino acids. Overall, the differences in lipid and amino acid metabolism in pectoral muscle appear to be responsible for the difference in meat quality between indigenous chickens and commercial laying hens. [ABSTRACT FROM AUTHOR]

A granular cell tumor (GCT) is a rare soft tissue tumor that usually arises from the striated muscle of the tongue. Few literatures have reported pectoral muscle involvement of the GCT. Herein, we report a rare case of a GCT originating from the pectoral muscle below the breast with multimodal imaging appearance.

Objectives : Breast cancer is the most commonly diagnosed type of cancer among women and a common cause of cancer-related deaths. Early diagnosis and treatment of breast cancer is critical in disease prognosis. Breast density is known to have a correlation with breast cancer. In recent years, there has been an increasing interest in the investigation of computer-aided methods for early diagnosis of breast cancer. In this study, a new fully-automated deep learning-based cascaded model was proposed for breast density assessment. In the first stage, the segmentation of adipose, fibroglandular, and pectoral muscle tissues from the digitized film mammograms of the Digital Database for Screening Mammography (DDSM) was investigated using various types of U-nets. Features extracted from the breast tissue segmentation predictions were then used to assess breast density in the second stage. Material and methods: 66 and 296 mediolateral oblique mammograms were selected from DDSM dataset for segmentation and breast density assessment systems, respectively. Different U-nets with varying number of layers and filters were implemented and the model having the highest performance was determined. U-net performance was investigated using categorical cross-entropy, Dice, Tversky, Focal Tversky, and logarithmic cosine-hyperbolic Dice loss functions. The performances of U-nets having different types of connections were investigated. The performances of U-nets having pre-trained weights from VGG16, VGG19, and ResNet50 networks in the encoding path were also investigated. Segmentation results were improved by using an image processing pipeline based on morphological operators. Segmentation performance was presented in terms of accuracy, balanced accuracy, intersection over union, and Dice's similarity coefficient (DSC) metrics. The segmentation system predictions were then used to estimate mammographic density using a machine learning pipeline by extracting features related to the fibroglandular tissue percentage. Results : Using ResNet50-U-net on the test data, average DSC scores of 82.71%, 73.39%, and 95.30% were obtained for adipose, fibroglandular, and pectoral muscle tissue segmentation, respectively. The mammogram segmentation results are 3%-12% better than the current state-of-the-art DSC in the literature when considering all of the foreground tissues concurrently. A breast density classification accuracy of 76.01% was achieved on a separate mammogram dataset, which is comparable to the recent studies in the literature. Conclusion : The proposed system can be used for automatic segmentation of mammogram into adipose, fibroglandular, and pectoral muscle tissues. The segmentation model enables the estimation of the fibroglandular-adipose tissue interface, which is recently found to be an important region for breast cancer investigations. The proposed fully-automatic breast density assessment system has a comparable performance to the ones in the literature. • A novel deep-learning based automated mammographic density assessment was proposed. • Mammogram segmentation performances of U-net variants were investigated. • The highest segmentation performance was achieved using ResNet50-U-net and Tversky loss. • High breast density assessment performance was obtained using relevant features. • Mammogram segmentation predictions enable locate critical region of interests. [ABSTRACT FROM AUTHOR]

Breast tumors are the major malignancy in females and diagnostic systems using artificial intelligence algorithms for breast imaging have shown promising results. Among many algorithms, a deep convolutional neural network (DCNN) using K-means clustering and a multiclass support vector machine model enhance the precision of categorizing breast tumors from mammogram images. Nonetheless, effective breast tumor identification is still difficult without partitioning the pectoral muscle (PM) boundary from the remaining breast area. Therefore, this article proposes an Ensemble-Net model by ensembling the transfer learning model with different pre-trained CNN structures for partitioning the PM boundary from the remaining breast area in the mammographic scans. The segmentation process has 2 phases. In the initial phase, different region-of-interests are generated that include the object according to the input images. In the secondary phase, the object class is predicted after the areas of bounding boxes are refined and a pixel-range mask is created for the entity. These 2 different phases are associated with the backbone structure which creates the pyramid hierarchy of DCNN to acquire the features from the raw images. Moreover, it employs global average pooling followed by the softmax classification to recognize the normal, benign and malignant cases. Finally, the experimental outcomes demonstrate that the Ensemble-Net achieves 96.72% accuracy than the other classical classifiers. [ABSTRACT FROM AUTHOR]

Aged chickens are often a secondary dietary choice, owing to the poor organoleptic qualities of their meat. With regard to the meat quality of chickens, the metabolic profiles of pectoral muscle in Guangyuan grey chickens (group G) and Hy-Line grey hens (group H) aged 55 weeks were compared via ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS). A total of 74 metabolites were identified with differential changes in the ion model. Lipids and lipid-like molecules comprised the largest proportion among the different metabolites. The content of myristic acid and palmitic acid were found to be higher in the pectoral muscle of group G, while group H showed significantly higher levels of glycerophospholipid molecules, such as LPC(18:2/0:0), Pi(38:5), Pc(16:0/16:0), and Pe(16:1e/14-hdohe). KEGG pathway analysis indicated that the abundant metabolites in group G were mainly involved in energy metabolism and fatty acid biosynthesis and metabolism, whereas those of group H were mainly attributed to the metabolism of unsaturated fatty acids and amino acids. Overall, the differences in lipid and amino acid metabolism in pectoral muscle appear to be responsible for the difference in meat quality between indigenous chickens and commercial laying hens.

Breast cancer is widespread worldwide and can be cured if diagnosed early. Using digital mammogram images and image processing with artificial intelligence can play an essential role in breast cancer diagnosis. As many computerized algorithms for breast cancer diagnosis have significant limitations, such as noise handling and varying or low contrast in the images, it can be difficult to segment the abnormal region. These challenges could be overcome by proposing a new pre-processing model, exploring its impact on the post-processing module, and testing it on an extensive database. In this research work, the three-step method is proposed and validated on large databases of mammography images. The first step corresponded to the database classification, followed by the second step, which removed the pectoral muscle from the mammogram image. The third stage utilized new image-enhancement techniques and a new segmentation module to detect abnormal regions in a well-enhanced image to diagnose breast cancer. The pre-and post-processing modules are based on novel image processing techniques. The proposed method was tested using data collected from different hospitals in the Qassim Health Cluster, Qassim Province, Saudi Arabia. This database contained the five categories in the Breast Imaging and Reporting and Data System and consisted of 2892 images; the proposed method is analyzed using the publicly available Mammographic Image Analysis Society database, which contained 322 images. The proposed method gives good contrast enhancement with peak-signal to noise ratio improvement of 3 dB. The proposed method provides an accuracy of approximately 92% on 2892 images of Qassim Health Cluster, Qassim Province, Saudi Arabia. The proposed method gives approximately 97% on the Mammographic Image Analysis Society database. The novelty of the proposed work is that it could work on all Breast Imaging and Reporting and Data System categories. The performance of the proposed method demonstrated its ability to improve the diagnostic performance of the computerized breast cancer detection method. [ABSTRACT FROM AUTHOR]

This study examined the quantitative relationships among ultrasonographic image attributes and chemical composition of the pectoralis major muscles in broiler chickens that received four different dietary fat supplements (Group SO: soybean oil; Group FO: flaxseed oil; Group SO+FO: soybean oil+flaxseed oil; and Group BT: beef tallow; n=10 birds/group). Ultrasonograms of birds' pectoral muscles, in a transverse (T) and longitudinal (L) plane, were obtained just before slaughter at 6 weeks of age and were subjected to digital image analyses to determine mean pixel intensity (MPI) and pixel heterogeneity values (standard deviation of numerical pixel values; MPH; a.k.a first order echotextural characteristics). Thirty-eight chemical characteristics of the muscles were determined post-mortem (crude fat, protein, and dry matter as well as fatty acid profiles) and were analyzed for correlations with the echotextural variables. A total of 12 (L-MPI: 7; L-MPH: 4; and T-MPH: 1 correlation), 5 (L-MPI: 2; L-MPH: 2; and T-MPI: 1 correlation), 15 (L-MPI: 10; T-MPI: 4; and T-MPH: 1 correlation) and 8 (L-MPI: 2; L-MPH: 1; and T-MPH: 5 correlations) significant correlations were recorded in Groups SO, FO, SO+FO and BT, respectively. When the data were pooled for all 40 birds studied, significant correlations with echotextural attributes were recorded for eighteen different chemical constituents, with the strongest overall correlation found between crude fat content and T-MPI (r=0.52, P=0.0005). In conclusion, there exists a potential application for ultrasonographic imaging in situ combined with computerized image analysis to estimate certain chemical constituents of pectoralis major muscles in broiler chickens. However, the existence and strength of correlations among ultrasonographic image attributes and muscle composition are affected by the source of dietary fat and relative abundance ("threshold concentrations") of individual chemical components. [ABSTRACT FROM AUTHOR]

Concentrations of 12 metals (As, Ba, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb and Zn) were examined in the pectoral and thigh muscle of great cormorants (Phalacrocorax carbo). The samples were collected from Central Tisza-Jászság Nature Conservation Area in Hungary. The tissue samples were analysed by inductively coupled plasma optical emission spectroscopy (ICP-OES). The aim of the study was to examine the impacts of heavy metal pollution on the water birds, determine the concentrations of the abovementioned metals in the different muscle tissues of these wetland birds, and provide the basic materials for monitoring the environmental pollution. Among the investigated elements/metals, the detected concentrations of As, Ba, Cd, Co, Cr, Mo and Ni were below the detection limit. Higher concentration of Cu, Hg, Mn and Pb was measured in the pectoral muscle compared to the thigh muscle, but only in the case of Cu and Mn were found significant differences between the tissues. In the case of the Zn concentration, the higher value was detected in the thigh muscle. There were no statistical differences between males and females in either metal concentrations. [ABSTRACT FROM AUTHOR]

Pigeon breed resources provide a genetic model for the study of phenomics. The pectoral muscles play a key role for the meat production performance of the meat pigeon and the athletic ability of the High flyers. Euro-pigeons and Silver King pigeons are commercial varieties that exhibit good meat production performance. In contrast to the domestication direction of meat pigeons, the traditional Chinese ornamental pigeon breed, High flyers, has a small and light body. Here, we investigate the molecular mechanism of the pectoral muscle development and function of pigeons using whole-genome and RNA sequencing data. The selective sweep analysis (F ST and log2 (θπ ratio)) revealed 293 and 403 positive selection genes in Euro-pigeons and Silver King, respectively, of which 65 genes were shared. With the Silver King and Euro-pigeon as the control group, the High flyers were selected for 427 and 566 genes respectively. There were 673 differentially expressed genes in the breast muscle transcriptome between the commercial meat pigeons and ornamental pigeons. Pigeon genome selection signal combined with the breast muscle transcriptome revealed that six genes (SLC16A10 , S100B , SYNE1 , HECW2 , CASQ2 and LOC110363470) from commercial varieties of pigeons and five genes (INSC , CALCB , ZBTB21 , B2M and LOC110356506) from Chinese traditional ornamental pigeons were positively selected which were involved in pathways related to muscle development and function. This study provides new insights into the selection of different directions and the genetic mechanism related to muscle development in pigeons. [ABSTRACT FROM AUTHOR]

Computer-aided diagnosis methods are being developed to assist radiologists to improve the interpretation of mammograms for the detection and diagnose of breast cancer, reduce the errors and mistakes made by human beings. In addition, it provides a more accurate and reliable classification of benign and malignant abnormalities. In the mammogram diagnosis, the pectoral muscle appears in Mediolateral oblique views (MLO) of the right and left of the breast. Considering that, the pectoral muscle has the same density as the small suspicious masses in the image and can affect/bias the results of image processing methods. This paper presents a diagnosis method to detect an abnormality in mammograms automatically. Before abnormality identification, image-processing techniques are used to correctly segment the suspicious region-of-interest (ROI). The background of the mammograms has been darkened to distinguish the breast area from any blemishes or writings that will be removed. Then the breast area has been extracted after ignoring the empty regions around the breast in mammogram images. After that, the mammogram image is inverted and the inverted image is then subtracted from the initial image. For pectoral muscle removal, a region growing method using the K-means clustering method is used. Afterward, suspicious ROI is segmented utilizing the K-means with thresholding technique. To detect abnormalities in mammograms, shape-based features, moment invariants, and also fractal dimensions are extracted from the segmented ROI. The Mini-MIAS dataset is used to evaluate the proposed method and is predominately composed of benign samples with only a tiny percent of malignant samples. To accomplish far better classifier efficiency, the SMOTE algorithm is used to present new samples from the minority classes to get a balanced dataset. Random forest classifier utilized to classify the segmented region as benign and malignant. The experimental results obtained an accuracy of 97.1%, the sensitivity of 95.1%, and the achieved specificity is 98.5%.

Pigeon breed resources provide a genetic model for the study of phenomics. The pectoral muscles play a key role for the meat production performance of the meat pigeon and the athletic ability of the High flyers. Euro-pigeons and Silver King pigeons are commercial varieties that exhibit good meat production performance. In contrast to the domestication direction of meat pigeons, the traditional Chinese ornamental pigeon breed, High flyers, has a small and light body. Here, we investigate the molecular mechanism of the pectoral muscle development and function of pigeons using whole-genome and RNA sequencing data. The selective sweep analysis (FST and log2 (θπ ratio)) revealed 293 and 403 positive selection genes in Euro-pigeons and Silver King, respectively, of which 65 genes were shared. With the Silver King and Euro-pigeon as the control group, the High flyers were selected for 427 and 566 genes respectively. There were 673 differentially expressed genes in the breast muscle transcriptome between the commercial meat pigeons and ornamental pigeons. Pigeon genome selection signal combined with the breast muscle transcriptome revealed that six genes (SLC16A10, S100B, SYNE1, HECW2, CASQ2 and LOC110363470) from commercial varieties of pigeons and five genes (INSC, CALCB, ZBTB21, B2M and LOC110356506) from Chinese traditional ornamental pigeons were positively selected which were involved in pathways related to muscle development and function. This study provides new insights into the selection of different directions and the genetic mechanism related to muscle development in pigeons.

Computer-aided diagnosis (CAD) has long become an integral part of radiological management of breast disease, facilitating a number of important clinical applications, including quantitative assessment of breast density and early detection of malignancies based on X-ray mammography. Common to such applications is the need to automatically discriminate between breast tissue and adjacent anatomy, with the latter being predominantly represented by pectoralis major (or pectoral muscle). Especially in the case of mammograms acquired in the mediolateral oblique (MLO) view, the muscle is easily confusable with some elements of breast anatomy due to their morphological and photometric similarity. As a result, the problem of automatic detection and segmentation of pectoral muscle in MLO mammograms remains a challenging task, innovative approaches to which are still required and constantly searched for. To address this problem, the present paper introduces a two-step segmentation strategy based on a combined use of data-driven prediction (deep learning) and graph-based image processing. In particular, the proposed method employs a convolutional neural network (CNN) which is designed to predict the location of breast-pectoral boundary at different levels of spatial resolution. Subsequently, the predictions are used by the second stage of the algorithm, in which the desired boundary is recovered as a solution to the shortest path problem on a specially designed graph. The proposed algorithm has been tested on three different datasets (i.e., MIAS, CBIS-DDSm, and InBreast) using a range of quantitative metrics. The results of comparative analysis show considerable improvement over state-of-the-art, while offering the possibility of model-free and fully automatic processing. The average values of dice similarity coefficient (DSC) and accuracy (ACC) on the mentioned three datasets are 97.22 ± 1.96% and 99.64±.27%, respectively.

MicroRNAs (miRNAs) are a group of crucial regulators in the process of animal growth and development. However, little is known about the expression and function of miRNAs in pigeon muscles. To identify the miRNAs participating in the rapid development of pigeon pectoral muscles and quantitate their expression levels of pectoral muscles in different age stages, we performed miRNA transcriptome analysis in pigeon pectoral muscles by sequencing small RNAs over three different age stages (1-day old, 28 days old, and 2 years old). Dual-luciferase reporter assay was applied to validate the interaction between miRNA and its target gene. We identified 304 known miRNAs, 201 conserved miRNAs, and 86 novel miRNAs in pigeon pectoral muscles. 189 differentially expressed (DE) miRNAs were screened out during pigeon development. A short time-series expression miner (STEM) analysis indicated 89 DE miRNAs were significantly clustered in a progressively decreasing expression profile, and mainly enriched in biosynthesis-related GO categories and signaling pathways for MAPK and TGF-β. Dual-luciferase reporter assay indicated that a progressively down-regulated miRNA (miR-20b-5p) could directly target Krüppel-like factor 3 (KLF3) gene. To sum-up, our data expand the repertoire of pigeon miRNAs and enhance understanding of the mechanisms underlying rapid development in squabs.

The pectoral muscle is the high-intensity region in most mediolateral oblique (MLO) views of mammograms. Since it appears at the same intensity as most abnormalities it should be removed for successful classification. Removal of pectoral muscle is often a challenging task since its position, size and shape are different for different patients and it may not occur at all. In this paper, an efficient technique for the detection and removal of pectoral muscle is proposed. The algorithm is tested and proved efficient over a wide range of pectoral muscle types and datasets based on IOU and RMSE value. [ABSTRACT FROM AUTHOR]

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Abstract Background In digital mammography, finding accurate breast profile segmentation of women’s mammogram is considered a challenging task. The existence of the pectoral muscle may mislead the diagnosis of cancer due to its high-level similarity to breast body. In addition, some other challenges due to manifestation of the breast body pectoral muscle in the mammogram data include inaccurate estimation of the density level and assessment of the cancer cell. The discrete differentiation operator has been proven to eliminate the pectoral muscle before the analysis processing. Methods We propose a novel approach to remove the pectoral muscle in terms of the mediolateral-oblique observation of a mammogram using a discrete differentiation operator. This is used to detect the edges boundaries and to approximate the gradient value of the intensity function. Further refinement is achieved using a convex hull technique. This method is implemented on dataset provided by MIAS and 20 contrast enhanced digital mammographic images. Results To assess the performance of the proposed method, visual inspections by radiologist as well as calculation based on well-known metrics are observed. For calculation of performance metrics, the given pixels in pectoral muscle region of the input scans are calculated as ground truth. Conclusions Our approach tolerates an extensive variety of the pectoral muscle geometries with minimum risk of bias in breast profile than existing techniques.

The aim of this study was to compare the effects of active and passive strategies on the recovery response after a high-volume bench press protocol. Twenty-five resistance-trained men (mean 6 SD: age = 25.8 6 3.6 years; body mass = 87.1 6 12.1 kg; and height = 177.4 6 4.9 cm) performed a high-volume bench press session (8 sets of 10 reps at 70% of 1 repetition maximum). Subsequently, they were randomly assigned to an active recovery (AR) group (n = 11) or to a passive recovery (PR) group (n = 14). Active recovery consisted of light bench press sessions performed 6 hours and 30 hours after the high-volume exercise protocol. Muscle performance (bench throw power [BTP] and isometric bench press [IBP]) and morphology (muscle thickness of pectoralis major [PECMT] and of triceps brachii [TRMT]) were measured before exercise (baseline [BL]), and at 15-minute (15P), 24-hour (24P), and 48-hour (48P) postexercise. Post-exercise recovery of both maximal strength and power was accelerated in AR compared with PR. Both BTP and IBP were significantly (p, 0.001) reduced at 15P and 24P in PR, whereas changes were significant (p, 0.001) at 15P only in AR. PECMT was still significantly (p = 0.015) altered from BL at 48P in PR, whereas changes were significant (p, 0.001) at 15P only in AR. No significant interactions (p . 0.05) between PR and AR were detected for TRMT and muscle soreness. The present results indicate that AR enhances the recovery rate after high-volume exercise sessions and may be included in resistance training programs to optimize muscle adaptations. [ABSTRACT FROM AUTHOR]

Sichuan mountainous black‐bone (SMB) chicken is a small‐sized black‐feathered chicken breed with low amount of meat, while Dahen (DH) chicken has a larger body size and a faster growth rate. MicroRNAs (miRNAs) are involved in various physiological processes, but their role in chicken muscle growth remains unclear. We aimed to investigate the miRNAs and pathways participating in the muscle growth of chicken. MiRNA profiles of four SMB chickens and four DH chickens were detected by small RNA sequencing. A total of 994 known miRNAs were identified, among which gga‐miR‐1a‐3p, gga‐miR‐148‐3p and gga‐miR‐133a‐3p exhibited the highest enrichment in both breeds of chickens. Thirty‐two miRNAs were differently expressed between SMB and DH chickens. The differently expressed miRNAs were mainly associated with fatty acid metabolism, immunity and MAPK activation‐related processes. Kyoto encyclopaedia of genes and genomes (KEGG) analysis showed that miRNAs were involved in the immunity‐related and MAPK signalling pathways. Moreover, miR‐204 was downregulated in DH chicken compared with SMB chicken, and significantly inhibited the expression of MAP3K13, which is involved in the MAPK pathway. It was confirmed through luciferase reporter assays that miR‐204 specifically inhibited the activity of MAP3K13. Our results helped demonstrate the potential molecular mechanisms of muscle growth in chickens and provide valuable information for chicken breeding. [ABSTRACT FROM AUTHOR]

Phenotypic flexibility in avian metabolic rates and body composition have been well-studied in high-latitude species, which typically increase basal metabolic rate (BMR) and summit metabolism (Msum) when acclimatized to winter conditions. Patterns of seasonal metabolic acclimatization are more variable in lower-latitude birds that experience milder winters, with fewer studies investigating adjustments in avian organ and muscle masses in the context of metabolic flexibility in these regions. We quantified seasonal variation (summer vs winter) in the masses of organs and muscles frequently associated with changes in BMR (gizzard, intestines and liver) and Msum (heart and pectoral muscles), in white-browed sparrow-weavers (Plocepasser mahali). We also measured pectoral muscle thickness using a portable ultrasound system to determine whether we could non-lethally estimate muscle size. A concurrent study measured seasonal changes in BMR and Msum in the same population of sparrow-weavers, but different individuals. There was no seasonal variation in the dry masses of the gizzard, intestines or liver of sparrow-weavers, and during the same period, BMR did not vary seasonally. We found significantly higher heart (~ 18% higher) and pectoral muscle (~ 9% higher) dry mass during winter, although ultrasound measurements did not detect seasonal changes in pectoral muscle size. Despite winter increases in pectoral muscle mass, Msum was ~ 26% lower in winter compared to summer. To the best of our knowledge, this is the first study to report an increase in avian pectoral muscle mass but a concomitant decrease in thermogenic capacity. [ABSTRACT FROM AUTHOR]

This study set out to examine associations among echotextural, physicochemical and sensory attributes of the pectoralis major muscles in 17-week-old organic turkeys (B.U.T. Big-6) varying in the amount of wheat and oat grain in daily feed rations (Group C: complete feed only; Group Exp1: 5–30% of wheat and 0–20% of oat; and Group Exp2: 5–50% of wheat and 0–50% of oat; n = 15 turkeys/group). Digital ultrasonograms of the left pectoral muscle in four different planes (longitudinal-L, transverse-T, and two oblique planes-O1 and O2) were obtained with a 5.0-MHz linear-array transducer just before slaughter. Mean numerical pixel intensity (MPI) and pixel heterogeneity (MPH) of the muscle parenchyma were computed using the ImageProPlus® analytical software. Ten significant correlations between echotextural attributes and various meat characteristics were recorded in Group C, one in Group Exp1, and eight in Group Exp2. When data were pooled for all birds studied, there were twelve significant correlations (p < 0.05); all but one correlation (between MPH and moisture) were for physical and sensory characteristics of meat samples. Computer-assisted analysis is a potential method to determine moisture as well as physical (e.g., coloration) and sensory (e.g., aroma) characteristics of pectoralis major muscles in organic turkeys.

In this study, an unusual case of a patient who was previously operated on a ruptured breast implant following silicone granuloma associated with pectoral muscle involvement is reported. A 72-year-old woman had undergone breast augmentation surgery when she was 52-year-old and silicone implant rupture 10 years later. After 10 years of ruptured silicone implant, her mammography showed diffuse, multiple high-density nodules in the left breast. The pectoral muscle was significantly hypertrophic. The magnetic resonance imaging showed that the pectoral muscle was quite hypertrophic and had heterogeneous enhancement. In clinical consideration and the presence of the suspected malignancy, a biopsy was performed. The histological analysis identified pectoral muscle and breast tissue, which had been mainly replaced by giant cells, along with an apparent foreign body response. Silicone granuloma can present itself as a soft tissue mass. Malignancy is the most important differential diagnosis. Meticulous follow-up is recommended for these patients.

Given the growing interest of consumers in naturally produced meat, we decided to examine the muscles of Ross 308 broiler chickens kept in an ecological system, with the division into two research groups depending on sex. All the analyses were carried out using the appropriate methods recommended by the AOAC and in accordance with the Polish standards (PN), which are described in detail in the relevant section of the publication. The aim of the experiment was to investigate the effect of sex on the growth performance, carcass traits and meat quality, as well as the fatty acid profile and histological parameters, of the pectoral muscles from organic broiler chickens. A total of 60 one-day-old Ross 308 chickens (half males and half females) were divided into two groups, according to the sex, and reared under organic conditions (Org.) until 82 days of age (ten birds, i.e., five males and five females in each of three pens; replications for experimental groups). Compared with the female group, the male group had a higher final BW and carcass weight (p < 0.05). The males had a better growth and slaughter performance than the females. The meat quality traits and fatty acids content were also affected by sex. The meat from females showed a significantly higher (p < 0.05) protein, dry matter, fiber diameter and shear force and a significantly lower (p < 0.05) fat level than the male group. In this research, the suitability of these fast-growing broilers for natural and organic production systems has been researched with regard to the performance, meat quality and histological characteristics of the muscles.

Objective:The aim of this study was to present our experience in embedding exposed subcutaneous venous ports in the pectoral muscle.Methods:In this study, six patients who consulted between January 2006 and November 2013 for exposed subcutaneous venous ports were retrospectively evaluated. The ports were placed in the pectoral muscle.Results:The patients were followed up for 4–9 months. Oncologic chemotherapies were initiated at the end of the third week after surgery.Conclusion:With the results of our preliminary observations, we believe that embedding exposed subcutaneous venous ports in the pectoral muscle is a practical and safe method for cachectic patients.