Recently, we discovered a cDNA in teleost ovarian follicle cells belonging to the zinc transporter ZIP9 subfamily (SLC39A9) encoding a protein with characteristics of a membrane androgen receptor ...(mAR). Here, we demonstrate that human ZIP9 expressed in MDA-MB-468 breast cancer cells and stably overexpressed in human prostate cancer PC-3 cells (PC-3-ZIP9) also displays the ligand binding and signaling characteristics of a specific, high-affinity mAR. Testosterone treatment of MDA-MB-468 and PC-3-ZIP9 cells caused activation of G proteins and second messenger pathways as well as increases in intracellular free zinc concentrations that were accompanied by induction of apoptosis. 1,2,6,7-3H-testosterone binding and these responses were abrogated in MDA-MB-468 cells after ZIP9 small interfering RNA (siRNA) treatment and absent in PC-3 cells transfected with empty vector, confirming that ZIP9 functions as an mAR. Testosterone treatment caused up-regulation of proapoptotic genes Bax (Bcl-2-associated X protein), p53 (tumor protein p53), and JNK (c-Jun N-terminal kinases) in both cell lines and increased expression of Bax, Caspase 3, and cytochrome C proteins. Treatment with a zinc chelator or a MAPK inhibitor blocked testosterone-induced increases in Bax, p53, and JNK mRNA expression. The results suggest that both androgen signaling and zinc transporter functions of ZIP9 mediate testosterone promotion of apoptosis. ZIP9 is widely expressed in human tissues and up-regulated in malignant breast and prostate tissues, suggesting that it is a potential therapeutic target for treating breast and prostate cancers. These results provide the first evidence for a mechanism mediated by a single protein through which steroid and zinc signaling pathways interact to regulate physiological functions in mammalian cells.
Circadian rhythm disturbances are observed in, e.g., aging and neurodegenerative diseases and are associated with an increased incidence of obesity and diabetes. We subjected male C57Bl/6J mice to ...constant light 12‐h light‐light (LL) cycle to examine the effects of a disturbed circadian rhythm on energy metabolism and insulin sensitivity. In vivo electrophysiological recordings in the central pacemaker of the suprachiasmatic nuclei (SCN) revealed an immediate reduction in rhythm amplitude, stabilizing at 44% of normal amplitude values after 4 d LL. Food intake was increased (+26%) and energy expenditure decreased (–13%), and we observed immediate body weight gain (d 4: +2.4%, d 14: +5.0%). Mixed model analysis revealed that weight gain developed more rapidly in response to LL as compared to high fat. After 4 wk in LL, the circadian pattern in feeding and energy expenditure was completely lost, despite continuing low‐amplitude rhythms in the SCN and in behavior, whereas weight gain had stabilized. Hyperinsulinemic‐euglycemic clamp analysis revealed complete abolishment of normal circadian variation in insulin sensitivity in LL. In conclusion, a reduction in amplitude of the SCN, to values previously observed in aged mice, is sufficient to induce a complete loss of circadian rhythms in energy metabolism and insulin sensitivity.—Coomans, C. P., van den Berg, S. A. A., Houben, T., van Klinken, J.‐B., van den Berg, R., Pronk, A. C. M., Havekes, L. M., Romijn, J. A., Willems van Dijk, K., Biermasz, N. R., Meijer, J. H. Detrimental effects of constant light exposure and high‐fat diet on circadian energy metabolism and insulin sensitivity. FASEB J. 27, 1721–1732 (2013). www.fasebj.org
The blood-brain barrier (BBB) is a unique feature of the human body, preserving brain homeostasis and preventing toxic substances to enter the brain. However, in various neurodegenerative diseases, ...the function of the BBB is disturbed. Mechanisms of the breakdown of the BBB are incompletely understood and therefore a realistic model of the BBB is essential. We present here the smallest model of the BBB yet, using a microfluidic chip, and the immortalized human brain endothelial cell line hCMEC/D3. Barrier function is modulated both mechanically, by exposure to fluid shear stress, and biochemically, by stimulation with tumor necrosis factor alpha (TNF-α), in one single device. The device has integrated electrodes to analyze barrier tightness by measuring the transendothelial electrical resistance (TEER). We demonstrate that hCMEC/D3 cells could be cultured in the microfluidic device up to 7 days, and that these cultures showed comparable TEER values with the well-established Transwell assay, with an average (± SEM) of 36.9 Ω.cm
2
(± 0.9 Ω.cm
2
) and 28.2 Ω.cm
2
(± 1.3 Ω.cm
2
) respectively. Moreover, hCMEC/D3 cells on chip expressed the tight junction protein Zonula Occludens-1 (ZO-1) at day 4. Furthermore, shear stress positively influenced barrier tightness and increased TEER values with a factor 3, up to 120 Ω.cm
2
. Subsequent addition of TNF-α decreased the TEER with a factor of 10, down to 12 Ω.cm
2
. This realistic microfluidic platform of the BBB is very well suited to study barrier function in detail and evaluate drug passage to finally gain more insight into the treatment of neurodegenerative diseases.
The reliability of Type Ia Supernovae (SNe Ia) may be limited by the imprint of their galactic origins. To investigate the connection between supernovae and their host characteristics, we developed ...an improved method to estimate the stellar population age of the host as well as the local environment around the site of the supernova. We use a Bayesian method to estimate the star formation history and mass weighted age of a supernova's environment by matching observed spectral energy distributions to a synthesized stellar population. Applying this age estimator to both the photometrically and spectroscopically classified Sloan Digital Sky Survey II supernovae (N = 103), we find a 0.114 0.039 mag "step" in the average Hubble residual at a stellar age of ∼8 Gyr; it is nearly twice the size of the currently popular mass step. We then apply a principal component analysis on the SALT2 parameters, host stellar mass, and local environment age. We find that a new parameter, PC1, consisting of a linear combination of stretch, host stellar mass, and local age, shows a very significant (4.7 ) correlation with Hubble residuals. There is a much broader range of PC1 values found in the Hubble flow sample when compared with the Cepheid calibration galaxies. These samples have mildly statistically different average PC1 values, at ∼2.5 , resulting in at most a 1.3% reduction in the evaluation of H0. Despite accounting for the highly significant trend in SN Ia Hubble residuals, there remains a 9% discrepancy between the most recent precision estimates of H0 using SN Ia and the CMB.
Purpose
To study the influence of gradient echo–based contrasts as input channels to a 3D patch‐based neural network trained for synthetic CT (sCT) generation in canine and human populations.
Methods
...Magnetic resonance images and CT scans of human and canine pelvic regions were acquired and paired using nonrigid registration. Magnitude MR images and Dixon reconstructed water, fat, in‐phase and opposed‐phase images were obtained from a single T1‐weighted multi‐echo gradient‐echo acquisition. From this set, 6 input configurations were defined, each containing 1 to 4 MR images regarded as input channels. For each configuration, a UNet‐derived deep learning model was trained for synthetic CT generation. Reconstructed Hounsfield unit maps were evaluated with peak SNR, mean absolute error, and mean error. Dice similarity coefficient and surface distance maps assessed the geometric fidelity of bones. Repeatability was estimated by replicating the training up to 10 times.
Results
Seventeen canines and 23 human subjects were included in the study. Performance and repeatability of single‐channel models were dependent on the TE‐related water–fat interference with variations of up to 17% in mean absolute error, and variations of up to 28% specifically in bones. Repeatability, Dice similarity coefficient, and mean absolute error were statistically significantly better in multichannel models with mean absolute error ranging from 33 to 40 Hounsfield units in humans and from 35 to 47 Hounsfield units in canines.
Conclusion
Significant differences in performance and robustness of deep learning models for synthetic CT generation were observed depending on the input. In‐phase images outperformed opposed‐phase images, and Dixon reconstructed multichannel inputs outperformed single‐channel inputs.
Abstract
Magnetic charge propagation in spin-ice materials has yielded a paradigm-shift in science, allowing the symmetry between electricity and magnetism to be studied. Recent work is now ...suggesting the spin-ice surface may be important in mediating the ordering and associated phase space in such materials. Here, we detail a 3D artificial spin-ice, which captures the exact geometry of bulk systems, allowing magnetic charge dynamics to be directly visualized upon the surface. Using magnetic force microscopy, we observe vastly different magnetic charge dynamics along two principal directions. For a field applied along the surface termination, local energetics force magnetic charges to nucleate over a larger characteristic distance, reducing their magnetic Coulomb interaction and producing uncorrelated monopoles. In contrast, applying a field transverse to the surface termination yields highly correlated monopole-antimonopole pairs. Detailed simulations suggest it is the difference in effective chemical potential as well as the energy landscape experienced during dynamics that yields the striking differences in monopole transport.
Purpose
Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging ...and Reporting Data System (BI‐RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI‐RADS categories, i.e., “scattered density” and “heterogeneously dense”. The aim of this work was to investigate a deep learning‐based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI‐RADS category in current clinical workflow.
Methods
In this study, we constructed a convolutional neural network (CNN)‐based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board‐certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier.
Results
The AUC was 0.9421 when the CNN‐model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples. Using the pretrained model followed by a fine‐tuning process with as few as 500 mammogram images led to an AUC of 0.9265. After removing the potentially inaccurately labeled images, AUC was increased to 0.9882 and 0.9857 for without and with the pretrained model, respectively, both significantly higher (P < 0.001) than when using the full imaging dataset.
Conclusions
Our study demonstrated high classification accuracies between two difficult to distinguish breast density categories that are routinely assessed by radiologists. We anticipate that our approach will help enhance current clinical assessment of breast density and better support consistent density notification to patients in breast cancer screening.
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