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Introduction: Different types of mastectomies leave different amounts of residual breast tissue. The significance of the residual breast volume (RBV) is not clear. Therefore, we developed an MRI tool that allows to easily assess the RBV. In this study we evaluated factors associated with RBV after skin or nipple sparing mastectomy (SSM/NSM) in breast cancer BRCA pathogenic variant (PV) carriers who underwent both therapeutic and risk reducing SSM/NSM and its relation to breast cancer outcomes using an innovative MRI-based tool. Methods: Data of breast cancer BRCA PV who were treated between 2006 and 2020 were retrieved from of the oncogenetics unit databases. Only patients who underwent SSM/NSM and had a postoperative breast MRI available for analysis were included. Data collected included demographics, clinicopathological features, and outcomes. The MRI tool was developed by a breast cancer imaging laboratory. A logistic regression test and 95% confidence interval (CI) were used to assess the associated risk of increased RBV. A forward stepwise linear regression was used to correlate tumour-patient specific factors and RBV, and a Kaplan–Meier curve to show the probability of locoregional relapse. Results: A total of 84 patients undergoing 89 mastectomies were included. At a median follow-up of 98 months, 5 local, 2 regional, and 4 distant recurrences were observed. RBV was not significantly related with breast cancer outcomes (p value = NS). A higher body mass index (BMI) was associated with a higher RBV (p < 0.0001). A larger number of involved axillary nodes was associated with a smaller RBV (p = 0.025). The RBV on the risk-reducing mastectomy side was significantly higher compared to the breast cancer side (p value = 0.007). Local recurrences occurred in the vicinity of the primary tumour. [ABSTRACT FROM AUTHOR]

Bone tissue and breast tissue are interrelated, as demonstrated by breast microcalcifications, breast cancer bone metastases, bone morphogenetic proteins, and Wnt signaling. In addition, osteoblasts and osteoclasts represent an important switch of tumor cell dormancy during bone metastasis. Damage to both types of tissues mentioned above is highly prevalent, especially in postmenopausal women, and manifests itself in osteoporosis and breast cancer. Sea buckthorn (Elaeagnus rhamnoides L.), a botanical drug with high antioxidant, antitumor, anti-inflammatory, immunomodulatory, and regenerative properties, has great therapeutic potential due to the unique composition of its bioactive metabolites. This review aimed to summarize the current knowledge from in vitro and in vivo studies on the effect of sea buckthorn, as well as its most widespread flavonoids isorhamnetin, quercetin, and kaempferol, on bone and breast tissue health. In vitro studies have revealed the beneficial impacts of sea buckthorn and aforementioned flavonoids on both bone health (bone remodeling, mineralization, and oxidative stress) and breast tissue health (cancer cell proliferation, apoptosis, tumor growth, and metastatic behavior). In vivo studies have documented their protective effects against disturbed bone microarchitecture and reduced bone strength in animal models of osteoporosis, as well as against tumor expansion and metastatic properties in animal xenograft models. In any case, further research and clinical trials are needed to carefully evaluate the potential therapeutic benefits of sea buckthorn and its flavonoids. Based on the available information, however, it can be concluded that these bioactive metabolites favorably affect both bone and breast tissue health. [ABSTRACT FROM AUTHOR]

Background A subset of females with breast implants have reported a myriad of nonspecific systemic symptoms collectively termed systemic symptoms associated with breast implants (SSBI). SSBI symptoms are similar to manifestations associated with autoimmune and connective tissue disorders. Breast tissue is rich in adipose cells, comprised of lipids. Insertion of an implant creates an oxidative environment leading to lipid oxidation. Oxylipins can influence immune responses and inflammatory processes. Objectives In this study we explored the abundance of a spectrum of oxylipins in the periprosthetic tissue surrounding the breast implant. Because oxylipins are immunogenic, we sought to determine if they were associated with the SSBI patients. We have also attempted to determine if the common manifestations exhibited by such patients have any association with oxylipin abundance. Methods The study included 120 patients divided into 3 cohorts. We analyzed 46 patients with breast implants exhibiting manifestations associated with SSBI; 29 patients with breast implants not exhibiting manifestations associated with SSBI (control cohort I, non-SSBI); and 45 patients without implants (control cohort II, no-implant tissue). Lipid extraction and oxylipin quantification were performed with liquid chromatography mass spectrometry (LC-MS/MS). LC-MS/MS targeted analysis of the breast adipose tissue was performed. Results Of the 15 oxylipins analyzed, 5 exhibited increased abundance in the SSBI cohort when compared to the non-SSBI and no-implant cohorts. Conclusions The study documents the association of the oxylipins with each manifestation reported by the patient. This study provides an objective assessment of the subjective questionnaire, highlighting which symptoms may be more relevant than the others. Level of Evidence: 4 [ABSTRACT FROM AUTHOR]

Hyperspectral imaging has shown great promise for diagnostic applications, particularly in cancer surgery. However, non-bulk tissue-related spectral variations complicate the data analysis. Common techniques, such as standard normal variate normalization, often lead to a loss of amplitude and scattering information. This study investigates a novel approach to address these spectral variations in hyperspectral images of optical phantoms and excised human breast tissue. Our method separates surface and volume reflectance, hypothesizing that spectral variability arises from significant variations in surface reflectance across pixels. An illumination setup was developed to measure samples with a hyperspectral camera from different axial positions but with identical zenith angles. This configuration, combined with a novel data analysis approach, allows for the estimation and separation of surface reflectance for each direction and volume reflectance across all directions. Validated with optical phantoms, our method achieved an 83% reduction in spectral variability. Its functionality was further demonstrated in excised human breast tissue. Our method effectively addresses variations caused by surface reflectance or glare while conserving surface reflectance information, which may enhance sample analysis and evaluation. It benefits samples with unknown refractive index spectra and can be easily adapted and applied across a wide range of fields where hyperspectral imaging is used. [ABSTRACT FROM AUTHOR]

Bone tissue and breast tissue are interrelated, as demonstrated by breast microcalcifications, breast cancer bone metastases, bone morphogenetic proteins, and Wnt signaling. In addition, osteoblasts and osteoclasts represent an important switch of tumor cell dormancy during bone metastasis. Damage to both types of tissues mentioned above is highly prevalent, especially in postmenopausal women, and manifests itself in osteoporosis and breast cancer. Sea buckthorn (Elaeagnus rhamnoides L.), a botanical drug with high antioxidant, antitumor, anti-inflammatory, immunomodulatory, and regenerative properties, has great therapeutic potential due to the unique composition of its bioactive metabolites. This review aimed to summarize the current knowledge from in vitro and in vivo studies on the effect of sea buckthorn, as well as its most widespread flavonoids isorhamnetin, quercetin, and kaempferol, on bone and breast tissue health. In vitro studies have revealed the beneficial impacts of sea buckthorn and aforementioned flavonoids on both bone health (bone remodeling, mineralization, and oxidative stress) and breast tissue health (cancer cell proliferation, apoptosis, tumor growth, and metastatic behavior). In vivo studies have documented their protective effects against disturbed bone microarchitecture and reduced bone strength in animal models of osteoporosis, as well as against tumor expansion and metastatic properties in animal xenograft models. In any case, further research and clinical trials are needed to carefully evaluate the potential therapeutic benefits of sea buckthorn and its flavonoids. Based on the available information, however, it can be concluded that these bioactive metabolites favorably affect both bone and breast tissue health.

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Breast cancer has become the cancer with the highest incidence rate in the world. Robot-assisted puncture is gradually being applied to breast puncture biopsy procedures. In this paper, to address the problem of lesion drift existing in the current work of breast puncture robots, a breast tissue manipulation system based on uncalibrated vision is designed to manipulate breast tissue to move the puncture target to a position that is easy to puncture, optimizing the puncture path to reduce procedure failure rates. Firstly, the Canny operator and SURF algorithm extract the target contour and features from the breast images captured by the camera. An improved algorithm combining stochastic Hough transform and direct least squares is proposed to enhance the accuracy of the target localization in the images. Secondly, uncalibrated vision is introduced into breast tissue manipulation, the image-deformation Jacobi matrix is derived from the system model. Then, the online estimation of the image-deformation Jacobi matrix is carried out using the Unscented Kalman Filter (UKF) algorithm. The robotic arm is controlled by the vision controller to accomplish the manipulation task. Simulations verify the high performance of the control scheme based on the UKF algorithm for online estimation of the image-deformation Jacobian matrix. Finally, breast tissue manipulation experimental results show that the manipulation system successfully shifts the breast tissue deformation target to the desired target position, and the manipulation accuracy meets the clinical requirements. The breast tissue morphology control is achieved, which verifies the effectiveness and feasibility of the design in this paper.

Ectopic breast tissue (EBT) is breast tissue located outside the normal anatomic boundaries of the breasts, developing due to incomplete embryological regression of the mammary ridges. EBT can develop anywhere along the milk line, with the axilla being the most common site. While generally benign, EBT can undergo malignant transformation. This case report discusses a 24-year-old female with locally advanced invasive ductal carcinoma in the axillary EBT, highlighting its clinical presentation, diagnostic process, and management in a resource-limited setting. The patient underwent wide local excision and axillary lymph node dissection followed by adjuvant chemotherapy and radiotherapy, achieving a favorable short-term outcome. This case underscores the importance of considering EBT in differential diagnosis of axillary masses and the need for tailored treatment strategies in such settings. [ABSTRACT FROM AUTHOR]

(1) Background: Hyperspectral imaging has emerged as a promising margin assessment technique for breast-conserving surgery. However, to be implicated intraoperatively, it should be both fast and capable of yielding high-quality images to provide accurate guidance and decision-making throughout the surgery. As there exists a trade-off between image quality and data acquisition time, higher resolution images come at the cost of longer acquisition times and vice versa. (2) Methods: Therefore, in this study, we introduce a deep learning spatial–spectral reconstruction framework to obtain a high-resolution hyperspectral image from a low-resolution hyperspectral image combined with a high-resolution RGB image as input. (3) Results: Using the framework, we demonstrate the ability to perform a fast data acquisition during surgery while maintaining a high image quality, even in complex scenarios where challenges arise, such as blur due to motion artifacts, dead pixels on the camera sensor, noise from the sensor's reduced sensitivity at spectral extremities, and specular reflections caused by smooth surface areas of the tissue. (4) Conclusion: This gives the opportunity to facilitate an accurate margin assessment through intraoperative hyperspectral imaging. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging has shown great promise for diagnostic applications, particularly in cancer surgery. However, non-bulk tissue-related spectral variations complicate the data analysis. Common techniques, such as standard normal variate normalization, often lead to a loss of amplitude and scattering information. This study investigates a novel approach to address these spectral variations in hyperspectral images of optical phantoms and excised human breast tissue. Our method separates surface and volume reflectance, hypothesizing that spectral variability arises from significant variations in surface reflectance across pixels. An illumination setup was developed to measure samples with a hyperspectral camera from different axial positions but with identical zenith angles. This configuration, combined with a novel data analysis approach, allows for the estimation and separation of surface reflectance for each direction and volume reflectance across all directions. Validated with optical phantoms, our method achieved an 83% reduction in spectral variability. Its functionality was further demonstrated in excised human breast tissue. Our method effectively addresses variations caused by surface reflectance or glare while conserving surface reflectance information, which may enhance sample analysis and evaluation. It benefits samples with unknown refractive index spectra and can be easily adapted and applied across a wide range of fields where hyperspectral imaging is used.

Cancer is a multi-factorial disease, and the etiology of breast cancer (BC) is due to a combination of both genetic and environmental factors. Breast tissue shows a unique microbiota, Proteobacteria and Firmicutes are the most abundant bacteria in breast tissue, and several studies have shown that the microbiota of healthy breast differs from that of BC. Breast microbiota appears to be correlated with different characteristics of the tumor, and prognostic clinicopathologic features. It also appears that there are subtle differences between the microbial profiles of the healthy control and high-risk patients. Genetic predisposition is an extremely important risk factor for BC. BRCA1/2 germline mutations and Li-Fraumeni syndrome are DNA repair deficiency syndromes inherited as autosomal dominant characters that substantially increase the risk of BC. These syndromes exhibit incomplete penetrance of BC expression in carrier subjects. The action of breast microbiota on carcinogenesis might explain why women with a mutation develop cancer and others do not. Among the potential biological pathways through which the breast microbiota may affect tumorigenesis, the most relevant appear to be DNA damage caused by colibactin and other bacterial-derived genotoxins, β-glucuronidase-mediated estrogen deconjugation and reactivation, and HPV-mediated cancer susceptibility. In conclusion, in patients with a genetic predisposition, an unfavorable breast microbiota may be co-responsible for the onset of BC. Prospectively, the ability to modulate the microbiota may have an impact on disease onset and progression in patients at high risk for BC. [ABSTRACT FROM AUTHOR]

The digital holographic technique is an interferometric method that provides comprehensive information on morphological traits such as cell layer thickness and shape as well as access to biophysical attributes of cells like refractive index, dry mass, and volume. This method helps characterize sample structures in three dimensions both statically and dynamically, even for transparent objects like living biological cells. This research work captures the digital holograms of breast tissues and analyzes the malignancy of the tissue using a deep learning technique. It enables dynamic measurement of the sample under investigation. Different transfer learning models such as Inception, DenseNet, SqueezeNet, VGG, and ResNet are incorporated in this work. The parameters accuracy, precision, sensitivity, and F1 score of different models are compared and found that the ResNet model outperforms better compared to other models. [ABSTRACT FROM AUTHOR]

Ectopic mammary gland tissue in the vulva is an exceptionally rare disease. We present a case of a 62-year-old woman with a left vulvar mass of 30 years duration that progressively increased in size. The patient reported having pressure and discomfort, especially during movement. Surgical excision was performed, and a histopathological examination revealed a well-differentiated ectopic breast. We also review other cases of vulvar ectopic breast to further comprehend the characteristics of this rare disease. Clinicians and pathologists should always consider it as a differential diagnosis when presented with a vulvar mass.

Simple Summary: To minimize the risk of cancer recurrence, it is crucial for surgeons to assess the resection margins (surface) of surgical specimens during breast-conserving surgeries to determine whether the tumor has been removed entirely. However, this is often not easy and also current techniques lack to aid the surgeons. In our study, we used a hyperspectral imaging technique to overcome this challenge. To assess the resection margins with hyperspectral imaging, a classification model should be developed, which requires a dataset with accurate ground-truth labels. Since it is difficult to establish such a dataset, we introduced a novel approach based on hyperspectral unmixing to enable an accurate correlation between hyperspectral measurements and histology ground-truth labels. Subsequently, we developed a classification model for tumor tissue detection on the lumpectomy resection surface of 189 patients. We achieved a sensitivity of 94% and a specificity of 85%, which demonstrated the potential of hyperspectral imaging for breast-conserving surgeries. (1) Background: Assessing the resection margins during breast-conserving surgery is an important clinical need to minimize the risk of recurrent breast cancer. However, currently there is no technique that can provide real-time feedback to aid surgeons in the margin assessment. Hyperspectral imaging has the potential to overcome this problem. To classify resection margins with this technique, a tissue discrimination model should be developed, which requires a dataset with accurate ground-truth labels. However, establishing such a dataset for resection specimens is difficult. (2) Methods: In this study, we therefore propose a novel approach based on hyperspectral unmixing to determine which pixels within hyperspectral images should be assigned to the ground-truth labels from histopathology. Subsequently, we use this hyperspectral-unmixing-based approach to develop a tissue discrimination model on the presence of tumor tissue within the resection margins of ex vivo breast lumpectomy specimens. (3) Results: In total, 372 measured locations were included on the lumpectomy resection surface of 189 patients. We achieved a sensitivity of 0.94, specificity of 0.85, accuracy of 0.87, Matthew's correlation coefficient of 0.71, and area under the curve of 0.92. (4) Conclusion: Using this hyperspectral-unmixing-based approach, we demonstrated that the measured locations with hyperspectral imaging on the resection surface of lumpectomy specimens could be classified with excellent performance. [ABSTRACT FROM AUTHOR]

(1) Background: Hyperspectral imaging has emerged as a promising margin assessment technique for breast-conserving surgery. However, to be implicated intraoperatively, it should be both fast and capable of yielding high-quality images to provide accurate guidance and decision-making throughout the surgery. As there exists a trade-off between image quality and data acquisition time, higher resolution images come at the cost of longer acquisition times and vice versa. (2) Methods: Therefore, in this study, we introduce a deep learning spatial–spectral reconstruction framework to obtain a high-resolution hyperspectral image from a low-resolution hyperspectral image combined with a high-resolution RGB image as input. (3) Results: Using the framework, we demonstrate the ability to perform a fast data acquisition during surgery while maintaining a high image quality, even in complex scenarios where challenges arise, such as blur due to motion artifacts, dead pixels on the camera sensor, noise from the sensor’s reduced sensitivity at spectral extremities, and specular reflections caused by smooth surface areas of the tissue. (4) Conclusion: This gives the opportunity to facilitate an accurate margin assessment through intraoperative hyperspectral imaging.

Abstract Background Formalin-fixed, paraffin-embedded (FFPE) tissues have many advantages for identification of risk biomarkers, including wide availability and potential for extended follow-up endpoints. However, RNA derived from archival FFPE samples has limited quality. Here we identified parameters that determine which FFPE samples have the potential for successful RNA extraction, library preparation, and generation of usable RNAseq data. Methods We optimized library preparation protocols designed for use with FFPE samples using seven FFPE and Fresh Frozen replicate pairs, and tested optimized protocols using a study set of 130 FFPE biopsies from women with benign breast disease. Metrics from RNA extraction and preparation procedures were collected and compared with bioinformatics sequencing summary statistics. Finally, a decision tree model was built to learn the relationship between pre-sequencing lab metrics and qc pass/fail status as determined by bioinformatics metrics. Results Samples that failed bioinformatics qc tended to have low median sample-wise correlation within the cohort (Spearman correlation 4). The median RNA concentration and pre-capture library Qubit values for qc failed samples were 18.9 ng/ul and 2.08 ng/ul respectively, which were significantly lower than those of qc pass samples (40.8 ng/ul and 5.82 ng/ul). We built a decision tree model based on input RNA concentration, input library qubit values, and achieved an F score of 0.848 in predicting QC status (pass/fail) of FFPE samples. Conclusions We provide a bioinformatics quality control recommendation for FFPE samples from breast tissue by evaluating bioinformatic and sample metrics. Our results suggest a minimum concentration of 25 ng/ul FFPE-extracted RNA for library preparation and 1.7 ng/ul pre-capture library output to achieve adequate RNA-seq data for downstream bioinformatics analysis.

Abstract Background Normal breast tissue is utilized in tissue‐based studies of breast carcinogenesis. While gene expression in breast tumor tissue is well explored, our knowledge of transcriptomic signatures in normal breast tissue is still incomplete. The aim of this study was to investigate variability of gene expression in a large sample of normal breast tissue biopsies, according to breast cancer related exposures (obesity, smoking, alcohol, hormone therapy, and parity). Methods We analyzed gene expression profiles from 311 normal breast tissue biopsies from cancer‐free, post‐menopausal women, using Illumina bead chip arrays. Principal component analysis and K‐means clustering was used for initial analysis of the dataset. The association of exposures and covariates with gene expression was determined using linear models for microarrays. Results Heterogeneity of the breast tissue and cell composition had the strongest influence on gene expression profiles. After adjusting for cell composition, obesity, smoking, and alcohol showed the highest numbers of associated genes and pathways, whereas hormone therapy and parity were associated with negligible gene expression differences. Conclusion Our results provide insight into associations between major exposures and gene expression profiles and provide an informative baseline for improved understanding of exposure‐related molecular events in normal breast tissue of cancer‐free, post‐menopausal women.

Breast cancer is a leading cause of mortality among women. The patient's survival rate is uncertain due to the limitations in the accuracy of diagnosis and effective monitoring during cancer treatment. The key to efficaciously controlling cancer on a larger scale is effective diagnosis at an early stage of cancer by distinguishing the vital signatures of the diseased from the normal breast tissue. The breast tissue is a heterogeneous turbid media that exhibits multifaceted bulk tissue properties. Various sensing modalities can yield distinct tissue behavior for cancer and adjacent normal tissues, serving as a basis for cancer diagnosis. A novel multimodal diagnostic tool that can concurrently assess the optical, electrical, and mechanical bulk tissue properties can substantially augment the clinical findings such as histopathology, potentially aiding the clinician to establish an accurate and rapid diagnosis of cancer. This review aims to discuss the clinical and engineering aspects along with the unmet challenges of these physical sensing modalities, primarily in the field of optical, electrical, and mechanical. The challenges of combining two or more of these sensing modalities that can significantly enhance the effectiveness of the clinical diagnostic tools are further investigated. [ABSTRACT FROM AUTHOR]

Background: The inclusive symptom of breast cancer is a new mass or lump. Breast tumors may be a participation of dissimilar types or combination of invasive and in situ cancer. The positron emitting radionuclides may be employed to treat the breast. Methods: The various configurations of breast may be optimized to decrease the absorbed radiation dose, since the average glandular dose is regularly utilized as a criterion in radiation-risk detection. In this study, two configurations in cubic-rectangular (CR) and cylindrical-conical (CC) sketches along with appropriate assumptions were modelled and simulated via Monte Carlo technique, and the acquired dose rate (DR) values were compared. Results: The amounts of scattering and absorption cross sections of radiations next to the DR value reduced as the height of the CR sketch decreases. The mean dose rate value in the CR sketch reduced by 96.83% compared to that in the CC sketch. Additionally, raising the distance from the nipple (or raising the depth) reduced the DR values for both configurations. While the source of positron emitter was close to the right breast, the relative difference of the DR values between two sketches had major influence, for the CR outline which was high at the lower depths, and decreased linearly by increasing the depth. The accumulated DR value reduced severely in the CR sketch rather than the CC one. Conclusion: This study may be helpful to inspect deformed breast tissue in treatment planning.

Introduction: Breast tissue in infancy is a rather undescribed phenomenon. We aimed to describe the prevalence and progression of palpable breast tissue in healthy boys and girls aged 0-1 years and to evaluate clinical markers, individual serum hormone concentrations as well as combined hormone profiles as determinants of the persistence of breast tissue. Methods: In total, 233 term infants (119 boys, 114 girls) were included and followed from birth until 1 year of age in The COPENHAGEN Minipuberty Study (ClinicalTrials.gov #NTC02784184). Infants were followed up to six times with a clinical examination and serum sampling. Principal component analyses (PCAs) produced combined hormone profiles. Results: A total of 98% of all infants aged 0-1 year exhibited breast tissue at some point. 50% still had breast tissue present at 0.5-0.6 years in girls and 0.3- 0.4 years in boys (‘persistent’). At one year, more girls than boys had breast tissue present (p=0.010). Most clinical and hormonal markers did not differ in infants with/without persistent breast tissue. However, in those with persistent breast tissue, estradiol (first visit, girls, p=0.034), androstenedione, corticosterone, cortisol (first visit, boys, all p<0.050), length (first visit, boys, p=0.030), and testicular volume (0.3-0.4 years, p=0.040) were higher, while IGF-I (0.3-0.4, boys, p=0.033) was lower. In boys, a combined, PCA-derived hormone profile (first visit) was able to predict the persistence of breast tissue (area under the curve=83%) better than any single marker. Discussion: Palpable breast tissue in infancy is common in both sexes although it persists in significantly more girls than boys at one year of age. Data supports both the early origin of breast tissue (in utero- and early postnatal) as well as a role of endogenous hormone production in later development and maintenance. [ABSTRACT FROM AUTHOR]

This study describes the first in vivo acoustic attenuation measurements of breast tissue undertaken using a novel phase-insensitive detection technique employing a differential pyroelectric sensor. The operation of the sensor is thermal in nature, with its output signal being dictated by the acoustic power integrated over its surface. The particularly novel feature of the sensor lies in its differential principle of operation, which significantly enhances its immunity to background acoustic and vibration noise. A large area variant of the sensor was used to detect ultrasonic energy generated by an array of 14 discrete 3.2-MHz plane piston transducers, transmitted through pendent breasts in water. The transduction and reception capability represent key parts of a prototype Quantitative Ultrasound Computed Tomography Test Facility developed at the National Physical Laboratory to study the efficacy of phase-insensitive ultrasound computed tomography of breast phantoms containing a range of appropriate inclusions, in particular, the measurement uncertainties associated with quantitative reconstructions of the acoustic attenuation coefficient. For this study, attenuation coefficient measurements were made using 1-D projections on 12 nominally healthy study volunteers, whose age ranged from 19 to 65 years. Averaged or bulk attenuation coefficient values were generated in the range 1.7–4.6 dBcm $^{-{1}}$ at 3.2 MHz and have been compared with existing literature, derived from in vivo and ex vivo studies. Results are encouraging and indicate that the relatively simple technique could be applied as a robust method for assessing the properties of breast tissue, particularly the balance of fatty (adipose) and fibroglandular components. [ABSTRACT FROM AUTHOR]

IntroductionBreast tissue in infancy is a rather undescribed phenomenon. We aimed to describe the prevalence and progression of palpable breast tissue in healthy boys and girls aged 0-1 years and to evaluate clinical markers, individual serum hormone concentrations as well as combined hormone profiles as determinants of the persistence of breast tissue.MethodsIn total, 233 term infants (119 boys, 114 girls) were included and followed from birth until 1 year of age in The COPENHAGEN Minipuberty Study (ClinicalTrials.gov #NTC02784184). Infants were followed up to six times with a clinical examination and serum sampling. Principal component analyses (PCAs) produced combined hormone profiles.ResultsA total of 98% of all infants aged 0-1 year exhibited breast tissue at some point. 50% still had breast tissue present at 0.5-0.6 years in girls and 0.3-0.4 years in boys (‘persistent’). At one year, more girls than boys had breast tissue present (p=0.010). Most clinical and hormonal markers did not differ in infants with/without persistent breast tissue. However, in those with persistent breast tissue, estradiol (first visit, girls, p=0.034), androstenedione, corticosterone, cortisol (first visit, boys, all p

Background: Formalin-fixed, paraffin-embedded (FFPE) tissues have many advantages for identification of risk biomarkers, including wide availability and potential for extended follow-up endpoints. However, RNA derived from archival FFPE samples has limited quality. Here we identified parameters that determine which FFPE samples have the potential for successful RNA extraction, library preparation, and generation of usable RNAseq data. Methods: We optimized library preparation protocols designed for use with FFPE samples using seven FFPE and Fresh Frozen replicate pairs, and tested optimized protocols using a study set of 130 FFPE biopsies from women with benign breast disease. Metrics from RNA extraction and preparation procedures were collected and compared with bioinformatics sequencing summary statistics. Finally, a decision tree model was built to learn the relationship between pre-sequencing lab metrics and qc pass/fail status as determined by bioinformatics metrics. Results: Samples that failed bioinformatics qc tended to have low median sample-wise correlation within the cohort (Spearman correlation < 0.75), low number of reads mapped to gene regions (< 25 million), or low number of detectable genes (11,400 # of detected genes with TPM > 4). The median RNA concentration and pre-capture library Qubit values for qc failed samples were 18.9 ng/ul and 2.08 ng/ul respectively, which were significantly lower than those of qc pass samples (40.8 ng/ul and 5.82 ng/ul). We built a decision tree model based on input RNA concentration, input library qubit values, and achieved an F score of 0.848 in predicting QC status (pass/fail) of FFPE samples. Conclusions: We provide a bioinformatics quality control recommendation for FFPE samples from breast tissue by evaluating bioinformatic and sample metrics. Our results suggest a minimum concentration of 25 ng/ul FFPE-extracted RNA for library preparation and 1.7 ng/ul pre-capture library output to achieve adequate RNA-seq data for downstream bioinformatics analysis. [ABSTRACT FROM AUTHOR]

This work presents a breast cancer detection modality using a metasurface as the imaging medium and a microwave source. The fundamental concept is to record the electromagnetic energy, which, when transmitted through the breast, scatters differently according to the variation in the electrical properties of the breast tissue. The energy captured by the metasurface forms an impression image of the breast under test. This impression is used as a detection tool to determine the presence of a tumor inside the breast. A convolution neural network (CNN) is applied to enhance detection accuracy and provide quantitative data about tumors when present. The CNN results show the possibility of finding a tumor with a 10 mm diameter and distinguish between healthy and unhealthy breasts with 93% accuracy while determining the size and the size with location with an accuracy of 91% and 74%, respectively. [ABSTRACT FROM AUTHOR]

BACKGROUND: Characterization of normal and malignant breast tissues using X-ray scattering techniques has shown promising results and applications. OBJECTIVE: To examine possibility of characterizing normal and malignant breast tissues using the scattered photon distribution of polyenergetic beams of 30 kV X-rays. METHODS: A Monte Carlo simulation is upgraded so that it is capable of simulating input mammographic X-ray spectra from different target-filter combinations, tracing photon transport, and producing the distribution of scattered photons. The target-filter combinations include Mo-Mo, Mo-Al, Mo-Rh, Rh-Rh, Rh-Al, W-Rh, and W-Al. Analysis of obtained scattered photon distribution is carried out by comparing the ratio of count under the peak in the momentum transfer region from 0 to 1.55 nm–1, to that in the region from 1.6 to 9.1 nm–1 (covering the regions of scattering from fat and soft tissue, respectively) for breast samples with different percentages of normal tissue (0–100%). RESULTS: Mo-Mo target-filter combination shows a high linear dependence of the count under peak ratio on the percentage of normal tissue in breast samples (R2 = 0.9513). Despite slightly less linear than Mo-Mo, target-filter combinations other than Rh-Rh, W-Rh, and W-Al produce high linear responses (R2 > 0.9) CONCLUSION: Mo-Mo target-filter combination would probably be the most relevant in characterizing normal and malignant breast tissues from their scattered photon distribution. [ABSTRACT FROM AUTHOR]

PCB 126 is a pervasive, dioxin-like chemical pollutant which can activate the aryl hydrocarbon receptor (AhR). Despite being banned from the market, PCB 126 can be detected in breast milk to this day. The extent to which interindividual variation impacts the adverse responses to this chemical in the breast tissue remains unclear. This study aimed to investigate the impact of 3 nM PCB 126 on gene expression in a panel of genetically diverse benign human breast epithelial cell (HBEC) cultures and patient derived breast tissues. Six patient derived HBEC cultures were treated with 3 nM PCB 126. RNAseq was used to interrogate the impact of exposure on differential gene expression. Gene expression changes from the top critical pathways were confirmed via qRT-PCR in a larger panel of benign patient derived HBEC cultures, as well as in patient-derived breast tissue explant cultures. RNAseq analysis of HBEC cultures revealed a signature of 144 genes significantly altered by 3 nM PCB 126 treatment. Confirmation of 8 targets using a panel of 12 HBEC cultures and commercially available breast cell lines demonstrated that while the induction of canonical downstream target gene, CYP1A1, was consistent across our primary HBECs, other genes including AREG, S100A8, IL1A, IL1B, MMP7, and CCL28 exhibited significant variability across individuals. The dependence on the activity of the aryl hydrocarbon receptor was confirmed using inhibitors. PCB 126 can induce significant and consistent changes in gene expression associated with xenobiotic metabolism in benign breast epithelial cells. Although the induction of most genes was reliant on the AhR, significant variability was noted between genes and individuals. These data suggest that there is a bifurcation of the pathway following AhR activation that contributes to the variation in interindividual responses.

Metoclopramide (MCP) is a drug that has been widely used in recent years due to its hyperprolactinaemia effect on mothers during breastfeeding. The aim of this study was to investigate the proliferative changes that MCP may cause in the maternal breast tissue. In this study, 18 Wistar albino young–adult breastfeeding mothers with their offspring were divided into three groups: control group, low‐dose MCP‐applied group and high‐dose MCP‐applied group. The experiment was carried out during the lactation period and at the end of 21 days. Prolactin, BrdU and Ki‐67 breast tissue distributions were evaluated by immunohistochemistry, and tissue levels were evaluated biochemically by the ELISA method. According to ELISA and immunohistochemistry results in breast tissue, there was no significant difference between Ki‐67 and BrdU results in all groups. Metoclopramide did not change the expression of proliferation molecules Ki‐67 and BrdU in breast tissue. These results suggested that while metoclopramide increases breast proliferation, it does not have the risk of transforming the tissue into a tumour. [ABSTRACT FROM AUTHOR]

In this article, a new method is proposed to find tumor's location. This process is based on the arrangement of sensors; the phase and distance to the cancer are given as well. Extraction of tumor distance and phase by Snell's law is the number of received pulses and the delay of receiving Signal. The transmitter antenna is in a fixed position, and the receiver rotates at a certain angular velocity around the tissue. Considering this information, a package solution in polar coordinates is presented. Meanwhile, angle and range information are extracted. Then the maximum probability estimate of the target location is given. This paper applies experimentally to simulated random data. [ABSTRACT FROM AUTHOR]

Electromagnetic thermal therapies for cancer treatment, such as microwave hyperthermia, aim to heat up a targeted tumour site to temperatures within 40 and 44 °C. Computational simulations used to investigate such heating systems employ the Pennes' bioheat equation to model the heat exchange within the tissue, which accounts for several tissue properties: density, specific heat capacity, thermal conductivity, metabolic heat generation rate, and blood perfusion rate. We present a review of these thermal and physiological properties relevant for hyperthermia treatments of breast including fibroglandular breast, fatty breast, and breast tumours. The data included in this review were obtained from both experimental measurement studies and estimated properties of human breast tissues. The latter were used in computational studies of breast thermal treatments. The measurement methods, where available, are discussed together with the estimations and approximations considered for values where measurements were unavailable. The review concludes that measurement data for the thermal and physiological properties of breast and tumour tissue are limited. Fibroglandular and fatty breast tissue properties are often approximated from those of generic muscle or fat tissue. Tumour tissue properties are mostly obtained from approximating equations or assumed to be the same as those of glandular tissue. We also present a set of reliable data, which can be used for more accurate modelling and simulation studies to better treat breast cancer using thermal therapies. [ABSTRACT FROM AUTHOR]

ABSTRACT Microbiota studies have reported changes in the microbial composition of the breast upon cancer development. However, results are inconsistent and limited to the later phases of cancer development (after diagnosis). We analyzed and compared the resident bacterial taxa of histologically normal breast tissue (healthy, H, n = 49) with those of tissues donated prior to (prediagnostic, PD, n = 15) and after (adjacent normal, AN, n = 49, and tumor, T, n = 46) breast cancer diagnosis (n total = 159). DNA was isolated from tissue samples and submitted for Illumina MiSeq paired-end sequencing of the V3-V4 region of the 16S gene. To infer bacterial function in breast cancer, we predicted the functional bacteriome from the 16S sequencing data using PICRUSt2. Bacterial compositional analysis revealed an intermediary taxonomic signature in the PD tissue relative to that of the H tissue, represented by shifts in Bacillaceae, Burkholderiaceae, Corynebacteriaceae, Streptococcaceae, and Staphylococcaceae. This compositional signature was enhanced in the AN and T tissues. We also identified significant metabolic reprogramming of the microbiota of the PD, AN, and T tissue compared with the H tissue. Further, preliminary correlation analysis between host transcriptome profiling and microbial taxa and genes in H and PD tissues identified altered associations between the human host and mammary microbiota in PD tissue compared with H tissue. These findings suggest that compositional shifts in bacterial abundance and metabolic reprogramming of the breast tissue microbiota are early events in breast cancer development that are potentially linked with cancer susceptibility. IMPORTANCE The goal of this study was to determine the role of resident breast tissue bacteria in breast cancer development. We analyzed breast tissue bacteria in healthy breast tissue and breast tissue donated prior to (precancerous) and after (postcancerous) breast cancer diagnosis. Compared to healthy tissue, the precancerous and postcancerous breast tissues demonstrated differences in the amounts of breast tissue bacteria. In addition, breast tissue bacteria exhibit different functions in pre-cancerous and post-cancerous breast tissues relative to healthy tissue. These differences in function are further emphasized by altered associations of the breast tissue bacteria with gene expression in the human host prior to cancer development. Collectively, these analyses identified shifts in bacterial abundance and metabolic function (dysbiosis) prior to breast tumor diagnosis. This dysbiosis may serve as a therapeutic target in breast cancer prevention.

Bovine leukemia virus (BLV), a deltaretrovirus, causes B-cell leukemia/lymphoma in cattle and is prevalent in herds globally. A previous finding of antibodies against BLV in humans led us to examine the possibility of human infection with BLV. We focused on breast tissue because, in cattle, BLV DNA and protein have been found to be more abundant in mammary epithelium than in lymphocytes. In human breast tissue specimens, we identified BLV DNA by using nested liquid-phase PCR and DNA sequencing. Variations from the bovine reference sequence were infrequent and limited to base substitutions. In situ PCR and immunohistochemical testing localized BLV to the secretory epithelium of the breast. Our finding of BLV in human tissues indicates a risk for the acquisition and proliferation of this virus in humans. Further research is needed to determine whether BLV may play a direct role in human disease.

Abstract Background Archived formalin fixed paraffin embedded (FFPE) samples are valuable clinical resources to examine clinically relevant morphology features and also to study genetic changes. However, DNA quality and quantity of FFPE samples are often sub-optimal, and resulting NGS-based genetics variant detections are prone to false positives. Evaluations of wet-lab and bioinformatics approaches are needed to optimize variant detection from FFPE samples. Results As a pilot study, we designed within-subject triplicate samples of DNA derived from paired FFPE and fresh frozen breast tissues to highlight FFPE-specific artifacts. For FFPE samples, we tested two FFPE DNA extraction methods to determine impact of wet-lab procedures on variant calling: QIAGEN QIAamp DNA Mini Kit (“QA”), and QIAGEN GeneRead DNA FFPE Kit (“QGR”). We also used negative-control (NA12891) and positive control samples (Horizon Discovery Reference Standard FFPE). All DNA sample libraries were prepared for NGS according to the QIAseq Human Breast Cancer Targeted DNA Panel protocol and sequenced on the HiSeq 4000. Variant calling and filtering were performed using QIAGEN Gene Globe Data Portal. Detailed variant concordance comparisons and mutational signature analysis were performed to investigate effects of FFPE samples compared to paired fresh frozen samples, along with different DNA extraction methods. In this study, we found that five times or more variants were called with FFPE samples, compared to their paired fresh-frozen tissue samples even after applying molecular barcoding error-correction and default bioinformatics filtering recommended by the vendor. We also found that QGR as an optimized FFPE-DNA extraction approach leads to much fewer discordant variants between paired fresh frozen and FFPE samples. Approximately 92% of the uniquely called FFPE variants were of low allelic frequency range ( T|G > A” mutational signature known to be representative of FFPE artifacts resulting from cytosine deamination. Based on control samples and FFPE-frozen replicates, we derived an effective filtering strategy with associated empirical false-discovery estimates. Conclusions Through this study, we demonstrated feasibility of calling and filtering genetic variants from FFPE tissue samples using a combined strategy with molecular barcodes, optimized DNA extraction, and bioinformatics methods incorporating genomics context such as mutational signature and variant allelic frequency.

The current breast cancer detection techniques are mostly invasive and suffer from high cost, high false rate and inefficacy in early detection. These limitations can be subdued by the development of a non-invasive microwave detection system whose performance is predominantly dependent on the antenna used in the system. The designing of a compact wideband antenna and matching its impedance with breast phantom is a challenging task. In this paper, we have designed a compact antenna matched with the breast phantom operating in wideband frequency from 1 to 6 GHz capable to detect the dielectric (or impedance) contrast of the benign and malignant tissue. The impedance of the antenna is matched to a cubically shaped breast phantom and a very small tumor (volume=1 cm3). The antenna is tuned to the possible range of electrical properties of breast phantom and tumour (permittivity ranging from 10 to 20 and conductivity from 1.5 to 2.5 S/m). The return loss (S11), E-field distribution and specific absorption rate (SAR) are simulated. The operating band of antenna placed near the phantom without the tumor was found to be (1.11-5.47)GHz and with tumor inside the phantom is (1.29-5.50)GHz. Results also show that the SAR of the antenna is within the safety limit. [ABSTRACT FROM AUTHOR]

This article presents a modeling and analysis of a tunable graphene-based sensor for breast tumor detection operating in the terahertz frequency range using the wave concept iterative process (WCIP) method. Through the novel implementation approach of the biological tissues in the WCIP method, we can integrate the normal and tumor of human breast tissues into this algorithm. At the beginning, the details of the algorithms of human breast tissue and graphene modeling in the WCIP method are presented. In order to verify the results obtained by the WCIP algorithm, we presented a comparative study with the CST simulator. Then, using the WCIP method, the numerical simulation results demonstrate the capability of the proposed sensor to detect the normal breast tissue with good sensitivity of 7.11 (THz/RIU) and breast tumor with a high sensitivity of 8.21 THz/RIU and large figure of merits of 17.51 and 20.23 RIU−1, respectively. Finally, the simplicity, efficiency and tunability are the benefits of the proposed sensor, which makes it suitable for breast tumor detection in the THz band. [ABSTRACT FROM AUTHOR]

Electromagnetic thermal therapies for cancer treatment, such as microwave hyperthermia, aim to heat up a targeted tumour site to temperatures within 40 and 44 °C. Computational simulations used to investigate such heating systems employ the Pennes’ bioheat equation to model the heat exchange within the tissue, which accounts for several tissue properties: density, specific heat capacity, thermal conductivity, metabolic heat generation rate, and blood perfusion rate. We present a review of these thermal and physiological properties relevant for hyperthermia treatments of breast including fibroglandular breast, fatty breast, and breast tumours. The data included in this review were obtained from both experimental measurement studies and estimated properties of human breast tissues. The latter were used in computational studies of breast thermal treatments. The measurement methods, where available, are discussed together with the estimations and approximations considered for values where measurements were unavailable. The review concludes that measurement data for the thermal and physiological properties of breast and tumour tissue are limited. Fibroglandular and fatty breast tissue properties are often approximated from those of generic muscle or fat tissue. Tumour tissue properties are mostly obtained from approximating equations or assumed to be the same as those of glandular tissue. We also present a set of reliable data, which can be used for more accurate modelling and simulation studies to better treat breast cancer using thermal therapies.

This paper proposes a simple and computationally efficient algorithm for the detection and localization of early tumors (<5 mm) in the breast tissues. The feasibility of the proposed technique is demonstrated with three-dimensional EM simulators and validated with experimental results on a breast phantom using wideband patch antenna operating in 2.4–4.7 GHz band. The proposed method identifies the tumor location and the depth of the tumor. This is accomplished by employing a mean value approach and an efficient algorithm. Average value of the backscattered signal received by the antenna at different location is considered in the mean value technique. The depth of tumor is identified by Ground Penetrating Radar algorithm which employs the backscattered signals from the breast tissues. These data are converted to B-scan image which in turn gives the depth of the tumor. The results obtained in the experiment are in congruence with the simulated results. In the experimental analysis, the tumor location is identified when the size of the tumor is large owing to the low sensitivity of the antenna. [ABSTRACT FROM AUTHOR]

Objective: To compare the staining quality between rapid hematoxylin and eosin (H&E) staining and routine H&E staining of frozen breast tissue sections., Methods: In this cross-sectional observational study, 120 frozen breast tissue sections were randomly assigned to rapid or routine H&E staining ( n =  60 per group). Rapid H&E staining used a 7:1 mixture of modified Gill's hematoxylin and alcohol-soluble 1% eosin Y. The staining quality of each section was evaluated and scored. A score of >7 was considered excellent, a score of 6 to 7 good, and a score of ≤5 poor., Results: The staining time for rapid staining was approximately 3 minutes, whereas that of routine staining was approximately 12 minutes. There were no significant differences in the staining quality scores or proportions of sections in each grade between the two staining methods. The proportions of sections that were classified as excellent or good were 96.7% and 98.3% for rapid and routine staining, respectively., Conclusions: In frozen breast tissue sections, rapid H&E staining may provide staining quality that is comparable to that of routine staining, while markedly reducing the staining time., Competing Interests: Declaration of conflicting interestsThe authors declare that there is no conflict of interest.

Automated segmentation of histopathological images is a challenging task to detect cancerous cells in breast tissue. Recent reviews state high accuracy to segment image, but depends on user input, say window area size, time steps, level set, magnification factor and so on. To extract the region of interest effectively, the subject expert performs post-processing operations several times on the segmentation results with different input values for different parameters say, area opening, fill holes and selects most appropriate enhanced image required for further analysis. The authors proposed an automated segmentation technique followed by self-driven post-processing operations to detect cancerous cells effectively. The post-processing method itself determines the value of different parameters for different operations based on segmented results obtained. The proposed technique has the following features: (i) technique is context sensitive; (ii) no prior setting of time step, weighted area coefficient parameters is required; (iii) magnification independent; (iv) post-processing operations are self-driven which enhance segmentation results adaptively. The experimental results are compared with four state-of-the-art techniques: fuzzy C-means, spatial fuzzy C-means, spatial neutrosophic distance regularised level set and convolutional neural network-based PangNet. Experimental results obtained on two publicly available data sets show that the proposed technique outperforms effectively.

Abstract Background Mammographic density is a known risk factor for breast cancer, but the underlying pathologic characteristics are not well understood. The current analysis investigated the expression of several markers of interest, e.g., inflammation and growth, with mammographic density (MD) in normal and malignant breast tissue specimens from 279 women of the Multiethnic Cohort (MEC). Methods Breast cancer cases, recruited from a nested case-control study within the MEC, provided mammograms for density evaluation. Protein expression (COX-2, TNF-α, TGF-β, IGF-1R, IGFBP-2, and vimentin) was assessed by immunohistochemical detection. Linear regression was applied to evaluate the relation between marker expression and percent density and to compute adjusted means with 95% confidence intervals (CI) by marker status while adjusting for confounders. Results Due to missing cores and tissue, normal tissue could only be evaluated for COX-2 and vimentin. No significant associations with mammographic density were detected for all markers analyzed. For inflammatory markers (TNF-α, COX-2, and TGF-β) in tumor tissue, MD were non-significantly higher with stronger expression but the differences were very small. For example, the mean MD values for no, weak, and strong TNF-α expression were 35% (95% CI 24–47%), 39% (95% CI 29–48%), and 38% (95% CI 27–50%). In a posthoc analysis among postmenopausal women only, the difference across categories of TNF-α expression increased to 25% (95% CI 12–39%), 35% (95% CI 23–48%), and 35% (95% CI 20–49%). Conclusions The current analysis offers little support for an involvement of immunohistochemical markers representing inflammatory and growth factor pathways as predictors of breast density.

Abstract Background Monte Carlo simulations were run to estimate the dose variations generated by thedifference arising from the chemical composition of the tissues. Methods CT datasets of five breast cancer patients were selected. Mammary gland was delineated as clinical target volume CTV, as well as CTV_lob and CTV_fat, being the lobular and fat fractions of the entire mammary gland. Patients were planned for volumetric modulated arc therapy technique, optimized in the Varian Eclipse treatment planning system. CT, structures and plans were imported in PRIMO, based on Monte Carlo code Penelope, to run three simulations: AdiMus, where the adipose and muscle tissues were automatically assigned to fat and lobular fractions of the breast; Adi and Mus, where adipose and muscle, respectively were assigned to the whole mammary gland. The specific tissue density was kept identical from the CT dataset. Differences in mean doses in the CTV_lob and CTV_fat structures were evaluated for the different tissue assignments. Differences generated by the tissue composition and estimated by Acuros dose calculations in Eclipse were also analysed. Results From Monte Carlo simulations, the dose in the lobular fraction of the breast, when adipose tissue is assigned in place of muscle, is overestimated by 1.25 ± 0.45%; the dose in the fat fraction of the breast with muscle tissue assignment is underestimated by 1.14 ± 0.51%. Acuros showed an overestimation of 0.98 ± 0.06% and an underestimation of 0.21 ± 0.14% in the lobular and fat portions, respectively. Reason of this dissimilarity resides in the fact that the two calculations, Monte Carlo and Acuros, differently manage the range of CT numbers and the material assignments, having Acuros an overlapping range, where two tissues are both present in defined proportions. Conclusion Although not clinically significant, the dose deposition difference in the lobular and connective fat fraction of the breast tissue lead to an improved knowledge of the possible dose distribution and homogeneity in the breast radiation treatment.

Automated segmentation of histopathological images is a challenging task to detect cancerous cells in breast tissue. Recent reviews state high accuracy to segment image, but depends on user input, say window area size, time steps, level set, magnification factor and so on. To extract the region of interest effectively, the subject expert performs post‐processing operations several times on the segmentation results with different input values for different parameters say, area opening, fill holes and selects most appropriate enhanced image required for further analysis. The authors proposed an automated segmentation technique followed by self‐driven post‐processing operations to detect cancerous cells effectively. The post‐processing method itself determines the value of different parameters for different operations based on segmented results obtained. The proposed technique has the following features: (i) technique is context sensitive; (ii) no prior setting of time step, weighted area coefficient parameters is required; (iii) magnification independent; (iv) post‐processing operations are self‐driven which enhance segmentation results adaptively. The experimental results are compared with four state‐of‐the‐art techniques: fuzzy C‐means, spatial fuzzy C‐means, spatial neutrosophic distance regularised level set and convolutional neural network‐based PangNet. Experimental results obtained on two publicly available data sets show that the proposed technique outperforms effectively. [ABSTRACT FROM AUTHOR]

Human milk contains a dynamic and complex site-specific microbiome, which is not assembled in an aleatory way, formed by organized microbial consortia and networks. Presence of some genera, such as Staphylococcus, Streptococcus, Corynebacterium, Cutibacterium (formerly known as Propionibacterium), Lactobacillus , Lactococcus and Bifidobacterium , has been detected by both culture-dependent and culture-independent approaches. DNA from some gut-associated strict anaerobes has also been repeatedly found and some studies have revealed the presence of cells and/or nucleic acids from viruses, archaea, fungi and protozoa in human milk. Colostrum and milk microbes are transmitted to the infant and, therefore, they are among the first colonizers of the human gut. Still, the significance of human milk microbes in infant gut colonization remains an open question. Clinical studies trying to elucidate the question are confounded by the profound impact of non-microbial human milk components to intestinal microecology. Modifications in the microbiota of human milk may have biological consequences for infant colonization, metabolism, immune and neuroendocrine development, and for mammary health. However, the factors driving differences in the composition of the human milk microbiome remain poorly known. In addition to colostrum and milk, breast tissue in lactating and non-lactating women may also contain a microbiota, with implications in the pathogenesis of breast cancer and in some of the adverse outcomes associated with breast implants. This and other open issues, such as the origin of the human milk microbiome, and the current limitations and future prospects are addressed in this review. [ABSTRACT FROM AUTHOR]