FRaeppli, a multispectral imaging toolbox for cell tracing and dense tissue analysis in zebrafish

Visualizing cell shapes, interactions and lineages of differentiating cells is instrumental for understanding organ development and repair. Across species, strategies for stochastic multicolour labelling have greatly facilitated tracking cells in in vivo and mapping neuronal connectivity. Nevertheless, integrating multi-fluorophore information into the context of developing tissues in zebrafish is challenging given their cytoplasmic localization and spectral incompatibility with commonly used fluorescent markers. Here, we developed FRaeppli (Fish-Raeppli) expressing bright membrane- or nuclear-targeted fluorescent proteins for efficient cell shape analysis and tracking. High spatiotemporal activation flexibility is provided by the Gal4/UAS system together with Cre/lox and/or PhiC31integrase. The distinct spectra of the FRaeppli fluorescent proteins allow simultaneous imaging with GFP and infrared subcellular reporters or tissue landmarks. By tailoring hyperspectral protocols for time-efficient acquisition, we demonstrate FRaeppli s suitability for live imaging of complex internal organs, like the liver. Combining FRaeppli with polarity markers revealed previously unknown canalicular topologies between differentiating hepatocytes, reminiscent of the mammalian liver, suggesting shared developmental mechanisms. The multispectral FRaeppli toolbox thus enables the comprehensive analysis of intricate cellular morphologies, topologies and tissue lineages at single-cell resolution in zebrafish.

Dense Tissue Pattern Characterization Using Deep Neural Network

Breast tumors are from the common infections among women around the world. Classifying the various types of breast tumors contribute to treating breast tumors more efficiently. However, this classification task is often hindered by dense tissue patterns captured in mammograms. The present study has been proposed a dense tissue pattern characterization framework using deep neural network. A total of 322 mammograms belonging to the mini-MIAS dataset and 4880 mammograms from DDSM dataset have been taken, and an ROI of fixed size 224 × 224 pixels from each mammogram has been extracted. In this work, tedious experimentation has been executed using different combinations of training and testing sets using different activation function with AlexNet, ResNet-18 model. Data augmentation has been used to create a similar type of virtual image for proper training of the DL model. After that, the testing set is applied on the trained model to validate the proposed model. During experiments, four different activation functions ‘sigmoid’, ‘tanh’, ‘ReLu’, and ‘leakyReLu’ are used, and the outcome for each function has been reported. It has been found that activation function ‘ReLu’ perform always outstanding with respect to others. For each experiment, classification accuracy and kappa coefficient have been computed. The obtained accuracy and kappa value for MIAS dataset using ResNet-18 model is 91.3% and 0.803, respectively. For DDSM dataset, the accuracy of 92.3% and kappa coefficient value of 0.846 are achieved. After the combination of both dataset images, the achieved accuracy is 91.9%, and kappa coefficient value is 0.839 using ResNet-18 model. Finally, it has been concluded that the ResNet-18 model and ReLu activation function yield outstanding performance for the task.

Microvalve Bioprinting of MSC-Chondrocyte Co-Cultures

Recent improvements within the fields of high-throughput screening and 3D tissue culture have provided the possibility of developing in vitro micro-tissue models that can be used to study diseases and screen potential new therapies. This paper reports a proof-of-concept study on the use of microvalve-based bioprinting to create laminar MSC-chondrocyte co-cultures to investigate whether the use of MSCs in ACI procedures would stimulate enhanced ECM production by chondrocytes. Microvalve-based bioprinting uses small-scale solenoid valves (microvalves) to deposit cells suspended in media in a consistent and repeatable manner. In this case, MSCs and chondrocytes have been sequentially printed into an insert-based transwell system in order to create a laminar co-culture, with variations in the ratios of the cell types used to investigate the potential for MSCs to stimulate ECM production. Histological and indirect immunofluorescence staining revealed the formation of dense tissue structures within the chondrocyte and MSC-chondrocyte cell co-cultures, alongside the establishment of a proliferative region at the base of the tissue. No stimulatory or inhibitory effect in terms of ECM production was observed through the introduction of MSCs, although the potential for an immunomodulatory benefit remains. This study, therefore, provides a novel method to enable the scalable production of therapeutically relevant micro-tissue models that can be used for in vitro research to optimise ACI procedures.

The Fat-glandular Interface and Breast Tumor Locations: Appearances on Ultrasound Tomography Are Supported by Quantitative Peritumoral Analyses

Objective To analyze the preferred tissue locations of common breast masses in relation to anatomic quadrants and the fat-glandular interface (FGI) using ultrasound tomography (UST). Methods Ultrasound tomography scanning was performed in 206 consecutive women with 298 mammographically and/or sonographically visible, benign and malignant breast masses following written informed consent to participate in an 8-site multicenter, Institutional Review Board-approved cohort study. Mass locations were categorized by their anatomic breast quadrant and the FGI, which was defined by UST as the high-contrast circumferential junction of fat and fibroglandular tissue on coronal sound speed imaging. Quantitative UST mass comparisons were done for each tumor and peritumoral region using mean sound speed and percentage of fibroglandular tissue. Chi-squared and analysis of variance tests were used to assess differences. Results Cancers were noted at the FGI in 95% (74/78) compared to 51% (98/194) of fibroadenomas and cysts combined (P < 0.001). No intra-quadrant differences between cancer and benign masses were noted for tumor location by anatomic quadrants (P = 0.66). Quantitative peritumoral sound speed properties showed that cancers were surrounded by lower mean sound speeds (1477 m/s) and percent fibroglandular tissue (47%), compared to fibroadenomas (1496 m/s; 65.3%) and cysts (1518 m/s; 84%) (P < 0.001; P < 0.001, respectively). Conclusion Breast cancers form adjacent to fat and UST localized the vast majority to the FGI, while cysts were most often completely surrounded by dense tissue. These observations were supported by quantitative peritumoral analyses of sound speed values for fat and fibroglandular tissue.

Mimowolna sylwa. O książce Anny Kaszuby-Dębskiej „Bruno. Epoka genialna”

The article offers a critical discussion of Anna Kaszuba-Dębska’s book Bruno. Epoka genialna [Bruno. The Age of Genius]. The reviewer argues that the main feature of Kaszuba-Dębska narrative is its heterogeneity; it manifests itself through the renouncement of the authorial voice, which it replaced by a dense tissue of quotations, as well as through the extensive discussion of cultural contexts and the lack of a consistently outlined dominant theme organizing the course of the protagonist’s life. For this reason, the reviewer regards Kaszuba-Dębska’s publication as a text reminiscent of a silva rerum (cf. commonplace book), whose heterogeneity, however, seems to stem from the author’s lack of control over her material, rather than from an attempt to cross the borders of the biographical genre.

Use of Low-Dose Tamoxifen to Increase Mammographic Screening Sensitivity in Premenopausal Women

Increased breast density decreases mammographic sensitivity due to masking of cancers by dense tissue. Tamoxifen exposure reduces mammographic density and, therefore, should improve screening sensitivity. We modelled how low-dose tamoxifen exposure could be used to increase mammographic sensitivity. Mammographic sensitivity was calculated using the KARMA prospective screening cohort. Two models were fitted to estimate screening sensitivity and detected tumor size based on baseline mammographic density. BI-RADS-dependent sensitivity was estimated. The results of the 2.5 mg tamoxifen arm of the KARISMA trial were used to define expected changes in mammographic density after six months exposure and to predict changes in mammographic screening sensitivity and detected tumor size. Rates of interval cancers and detection of invasive tumors were estimated for women with mammographic density relative decreases by 10–50%. In all, 517 cancers in premenopausal women were diagnosed in KARMA: 287 (56%) screen-detected and 230 (44%) interval cancers. Screening sensitivities prior to tamoxifen, were 76%, 69%, 53%, and 46% for BI-RADS density categories A, B, C, and D, respectively. After exposure to tamoxifen, modelled screening sensitivities were estimated to increase by 0% (p = 0.35), 2% (p < 0.01), 5% (p < 0.01), and 5% (p < 0.01), respectively. An estimated relative density decrease by ≥20% resulted in an estimated reduction of interval cancers by 24% (p < 0.01) and reduction in tumors >20 mm at detection by 4% (p < 0.01). Low-dose tamoxifen has the potential to increase mammographic screening sensitivity and thereby reduce the proportion of interval cancers and larger screen-detected cancers.

Random effects tumour growth models for identifying image markers of mammography screening sensitivity

IntroductionPercentage mammographic density has long been recognised as a marker of breast cancer risk and of mammography sensitivity. There may be other image markers of screening sensitivity and efficient statistical approaches would be helpful for establishing them from large scale epidemiological and screening data.MethodsWe compare a novel random effects continuous tumour growth model (which includes a screening sensitivity submodel) to logistic regression (with interval vs. screen-detected cancer as the dependent variable) in terms of statistical power to detect image markers of screening sensitivity. We do this by carrying out a simulation study. We also use continuous tumour growth modelling to quantify the roles of dense tissue scatter (measured as skewness of the intensity gradient) and percentage mammographic density in screening sensitivity. This is done by using mammograms and information on tumour size, mode of detection and screening history from 1,845 postmenopausal women diagnosed with invasive breast cancer, in Sweden between 1993 and 1995.ResultsThe statistical power to detect a marker of screening sensitivity was larger for our continuous tumour growth model than it was for logistic regression. For the settings considered in this paper, the percentage increase in power ranged from 34 to 56%. In our analysis of data from Swedish breast cancer patients, using our continuous growth model, when including both percentage mammographic density and dense tissue scatter in the screening sensitivity submodel, only the latter variable was significantly associated with sensitivity. When included one at a time, both markers were significantly associated (p-values of 5.7 × 10−3 and 1.0 × 10−5 for percentage mammographic density and dense tissue scatter, respectively).ConclusionsOur continuous tumour growth model is useful for finding image markers of screening sensitivity and for quantifying their role, using large scale epidemiological and screening data. Clustered dense tissue is associated with low mammography screening sensitivity.

Whole Breast Sound Speed Measurement from US Tomography Correlates Strongly with Volumetric Breast Density from Mammography

Objective To assess the feasibility of using tissue sound speed as a quantitative marker of breast density. Methods This study was carried out under an Institutional Review Board–approved protocol (written consent required). Imaging data were selected retrospectively based on the availability of US tomography (UST) exams, screening mammograms with volumetric breast density data, patient age of 18 to 80 years, and weight less than 300 lbs. Sound speed images from the UST exams were used to measure the volume of dense tissue, the volume averaged sound speed (VASS), and the percent of high sound speed tissue (PHSST). The mammographic breast density and volume of dense tissue were estimated with three-dimensional (3D) software. Differences in volumes were assessed with paired t-tests. Spearman correlation coefficients were calculated to determine the strength of the correlations between the mammographic and UST assessments of breast density. Results A total of 100 UST and 3D mammographic data sets met the selection criteria. The resulting measurements showed that UST measured a more than 2-fold larger volume of dense tissue compared to mammography. The differences were statistically significant (P &lt; 0.001). A strong correlation of rS = 0.85 (95% CI: 0.79–0.90) between 3D mammographic breast density (BD) and the VASS was noted. This correlation is significantly stronger than those reported in previous two-dimensional studies (rS = 0.85 vs rS = 0.71). A similar correlation was found for PHSST and mammographic BD with rS = 0.86 (95% CI: 0.80–0.90). Conclusion The strong correlations between UST parameters and 3D mammographic BD suggest that breast sound speed should be further studied as a potential new marker for inclusion in clinical risk models.

Association of Interactions Between Mammographic Density Phenotypes and Established Risk Factors With Breast Cancer Risk, by Tumor Subtype and Menopausal Status

Previous studies suggest that the association between mammographic density (MD) and breast cancer risk might be modified by other breast cancer risk factors. In this study, we assessed multiplicative interactions between MD measures and established risk factors on the risk of invasive breast cancer overall and according to menopausal and estrogen receptor status. We used data on 2,137 cases and 4,346 controls from a nested case-control study within the Nurses’ Health Study (1976–2004) and Nurses’ Health Study II (1989–2007), whose data on percent mammographic density (PMD) and absolute area of dense tissue and nondense tissue (NDA) were available. No interaction remained statistically significant after adjusting for number of comparisons. For breast cancer overall, we observed nominally significant interactions (P &lt; 0.05) between nulliparity and PMD/NDA, age at menarche and area of dense tissue, and body mass index and NDA. Individual nominally significant interactions across MD measures and risk factors were also observed in analyses stratified by either menopausal or estrogen receptor status. Our findings help provide further insights into potential mechanisms underlying the association between MD and breast cancer.