Suppression of eukaryotic initiation factor 4E prevents chemotherapy-induced alopecia

BACKGROUND: Chemotherapy-induced hair loss (alopecia) (CIA) is one of the most feared side effects of chemotherapy among cancer patients. There is currently no pharmacological approach to minimize CIA, although one strategy that has been proposed involves protecting normal cells from chemotherapy by transiently inducing cell cycle arrest. Proof-of-concept for this approach, known as cyclotherapy, has been demonstrated in cell culture settings. METHODS: The eukaryotic initiation factor (eIF) 4E is a cap binding protein that stimulates ribosome recruitment to mRNA templates during the initiation phase of translation. Suppression of eIF4E is known to induce cell cycle arrest. Using a novel inducible and reversible transgenic mouse model that enables RNAi-mediated suppression of eIF4E in vivo, we assessed the consequences of temporal eIF4E suppression on CIA. RESULTS: Our results demonstrate that transient inhibition of eIF4E protects against cyclophosphamide-induced alopecia at the organismal level. At the cellular level, this protection is associated with an accumulation of cells in G1, reduced apoptotic indices, and was phenocopied using small molecule inhibitors targeting the process of translation initiation. CONCLUSIONS: Our data provide a rationale for exploring suppression of translation initiation as an approach to prevent or minimize cyclophosphamide-induced alopecia.1U01 CA168409 - NCI NIH HHS; P01 CA 87497 - NCI NIH HHS; P30 CA008748 - NCI NIH HHS; MOP-106530 - Canadian Institutes of Health Research; P01 CA013106 - NCI NIH HH

Zero-shot Clustering of Embeddings with Self-Supervised Learnt Encoders

We explore whether self-supervised pretrained models can provide a useful representation space for datasets they were not trained on, and whether these representations can be used to group novel unlabelled data into meaningful clusters. To this end, we conduct experiments using image representation encoders pretrained on ImageNet using a variety of self-supervised training techniques. These encoders are deployed on image datasets that were not seen during training, without fine-tuning, and we investigate whether their embeddings can be clustered with conventional clustering algorithms. We find that it is possible to create well-defined clusters using self-supervised feature encoders, especially when using the Agglomerative Clustering method, and that it is possible to do so even for very fine-grained datasets such as NABirds. We also find indications that the Silhouette score is a good proxy of cluster quality when no ground-truth is available

FISSA: A neuropil decontamination toolbox for calcium imaging signals

In vivo calcium imaging has become a method of choice to image neuronal population activity throughout the nervous system. These experiments generate large sequences of images. Their analysis is computationally intensive and typically involves motion correction, image segmentation into regions of interest (ROIs), and extraction of fluorescence traces of each ROI. Out of focus fluorescence from surrounding neuropil and other cells can strongly contaminate the signal assigned to a given ROI. In this study, we introduce the FISSA toolbox (Fast Image Signal Separation Analysis) for neuropil decontamination. Given pre-defined ROIs, the FISSA toolbox automatically extracts the surrounding local neuropil and performs blind-source separation with non-negative matrix factorization. Using both simulated and in vivo data, we show that this toolbox performs similarly or better than existing published methods. FISSA requires only little RAM, allowing for fast processing of large datasets even on a standard laptop. The FISSA toolbox is available in Python, with an option for MATLAB format outputs, and can easily be integrated into existing workflows. It is available from Github and the standard Python repositories

Chelator-Free Radiolabeling of SERRS Nanoparticles for Whole-Body PET and Intraoperative Raman Imaging

A single contrast agent that offers whole-body non-invasive imaging along with the superior sensitivity and spatial resolution of surface-enhanced resonance Raman scattering (SERRS) imaging would allow both pre-operative mapping and intraoperative imaging and thus be highly desirable. We hypothesized that labeling our recently reported ultrabright SERRS nanoparticles with a suitable radiotracer would enable pre-operative identification of regions of interest with whole body imaging that can be rapidly corroborated with a Raman imaging device or handheld Raman scanner in order to provide high precision guidance during surgical procedures. Here we present a straightforward new method that produces radiolabeled SERRS nanoparticles for combined positron emission tomography (PET)-SERRS tumor imaging without requiring the attachment of molecular chelators. We demonstrate the utility of these PET-SERRS nanoparticles in several proof-of-concept studies including lymph node (LN) tracking, intraoperative guidance for LN resection, and cancer imaging after intravenous injection. We anticipate that the radiolabeling method presented herein can be applied generally to nanoparticle substrates of various materials by first coating them with a silica shell and then applying the chelator-free protocol

Tips for implementing multigrid methods on domains containing holes

As part of our development of a computer code to perform 3D `constrained evolution' of Einstein's equations in 3+1 form, we discuss issues regarding the efficient solution of elliptic equations on domains containing holes (i.e., excised regions), via the multigrid method. We consider as a test case the Poisson equation with a nonlinear term added, as a means of illustrating the principles involved, and move to a "real world" 3-dimensional problem which is the solution of the conformally flat Hamiltonian constraint with Dirichlet and Robin boundary conditions. Using our vertex-centered multigrid code, we demonstrate globally second-order-accurate solutions of elliptic equations over domains containing holes, in two and three spatial dimensions. Keys to the success of this method are the choice of the restriction operator near the holes and definition of the location of the inner boundary. In some cases (e.g. two holes in two dimensions), more and more smoothing may be required as the mesh spacing decreases to zero; however for the resolutions currently of interest to many numerical relativists, it is feasible to maintain second order convergence by concentrating smoothing (spatially) where it is needed most. This paper, and our publicly available source code, are intended to serve as semi-pedagogical guides for those who may wish to implement similar schemes.Comment: 18 pages, 11 figures, LaTeX. Added clarifications and references re. scope of paper, mathematical foundations, relevance of work. Accepted for publication in Classical & Quantum Gravit

Requirement of NF-κB activation to suppress p53-independent apoptosis induced by oncogenic ras

The ras proto-oncogene is frequently mutated in human tumors and functions to chronically stimulate signal transduction cascades resulting in the synthesis or activation of specific transcription factors, including Ets, c-Myc, c-Jun, and nuclear factor kappa B (NF-kappaB). These Ras-responsive transcription factors are required for transformation, but the mechanisms by which these proteins facilitate oncogenesis have not been fully established. Oncogenic Ras was shown to initiate a p53-independent apoptotic response that was suppressed through the activation of NF-kappaB. These results provide an explanation for the requirement of NF-kappaB for Ras-mediated oncogenesis and provide evidence that Ras-transformed cells are susceptible to apoptosis even if they do not express the p53 tumor-suppressor gene product