Successful Use of Squeezed-Fat Grafts to Correct a Breast Affected by Poland Syndrome

This study attempted to reconstruct deformities of a Poland syndrome patient using autologous fat tissues. All injected fat tissues were condensed by squeezing centrifugation. Operations were performed four times with intervals over 6 months. The total injection volume was 972 ml, and the maintained volume of 628 ml was measured by means of a magnetic resonance image (MRI). The entire follow-up period was 4.5 years. After surgery, several small cysts and minimal calcifications were present but no significant complications. The cosmetic outcomes and volume maintenance rates were excellent despite the overlapped large-volume injections. In conclusion, higher condensation of fat tissues through squeezing centrifugation would help to achieve better results in volume maintenance and reduce complications. It is necessary, however, to perform more comparative studies with many clinical cases for a more scientific analysis. The study experiments with squeezed fat simply suggest a hypothesis that squeezing centrifugation could select healthier cells through pressure disruption of relatively thinner membranes of larger, more vulnerable and more mature fat cells

Assessing The Factual Accuracy of Generated Text

We propose a model-based metric to estimate the factual accuracy of generated text that is complementary to typical scoring schemes like ROUGE (Recall-Oriented Understudy for Gisting Evaluation) and BLEU (Bilingual Evaluation Understudy). We introduce and release a new large-scale dataset based on Wikipedia and Wikidata to train relation classifiers and end-to-end fact extraction models. The end-to-end models are shown to be able to extract complete sets of facts from datasets with full pages of text. We then analyse multiple models that estimate factual accuracy on a Wikipedia text summarization task, and show their efficacy compared to ROUGE and other model-free variants by conducting a human evaluation study

Lets Talk about Race: Identity, Chatbots, and AI

Why is it so hard for chatbots to talk about race? This work explores how the biased contents of databases, the syntactic focus of natural language processing, and the opaque nature of deep learning algorithms cause chatbots difficulty in handling race-talk. In each of these areas, the tensions between race and chatbots create new opportunities for people and machines. By making the abstract and disparate qualities of this problem space tangible, we can develop chatbots that are more capable of handling race-talk in its many forms. Our goal is to provide the HCI community with ways to begin addressing the question, how can chatbots handle race-talk in new and improved ways

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery

In silico design and experimental validation of sirnas targeting conserved regions of multiple hepatitis c virus genotypes

© 2016 ElHefnawi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. RNA interference (RNAi) is a post-transcriptional gene silencing mechanism that mediates the sequence-specific degradation of targeted RNA and thus provides a tremendous opportunity for development of oligonucleotide-based drugs. Here, we report on the design and validation of small interfering RNAs (siRNAs) targeting highly conserved regions of the hepatitis C virus (HCV) genome. To aim for therapeutic applications by optimizing the RNAi efficacy and reducing potential side effects, we considered different factors such as target RNA variations, thermodynamics and accessibility of the siRNA and target RNA, and off-target effects. This aim was achieved using an in silico design and selection protocol complemented by an automated MysiRNA-Designer pipeline. The protocol included the design and filtration of siRNAs targeting highly conserved and accessible regions within the HCV internal ribosome entry site, and adjacent core sequences of the viral genome with high-ranking efficacy scores. Off-target analysis excluded siRNAs with potential binding to human mRNAs. Under this strict selection process, two siRNAs (HCV353 and HCV258) were selected based on their predicted high specificity and potency. These siRNAs were tested for antiviral efficacy in HCV genotype 1 and 2 replicon cell lines. Both in silico-designed siRNAs efficiently inhibited HCV RNA replication, even at low concentrations and for short exposure times (24h); they also exceeded the antiviral potencies of reference siRNAs targeting HCV. Furthermore, HCV353 and HCV258 siRNAs also inhibited replication of patient-derived HCV genotype 4 isolates in infected Huh-7 cells. Prolonged treatment of HCV replicon cells with HCV353 did not result in the appearance of escape mutant viruses. Taken together, these results reveal the accuracy and strength of our integrated siRNA design and selection protocols. These protocols could be used to design highly potent and specific RNAi-based therapeutic oligonucleotide interventions

Features of siRNAs validated in HCV replicon assays.

<p>siRNA sequences targeting HCV IRES subdomains III and IV.</p

Scheme of HCV IRES and binding sites of siRNAs used in this study.

<p>The HCV IRES and the adjacent core sequence with the binding sites of the selected <i>in silico</i>-designed and reference siRNAs (HCV258, HCV 321, HCV353, and HCV360) used in this study are presented here. The first nucleotide of each individual siRNA binding to the IRES is underlined. Three of the most critical IRES loops (IIId, IIIf, and IV) have been targeted by HCV353 and HCV258 siRNAs. The 5′NTR scheme was modified with permission from the Nature Publishing Group (Lukavsky <i>et al</i>. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159211#pone.0159211.ref011" target="_blank">11</a>]).</p

Curing of HCV replicon cells with IRES-specific siRNAs.

<p>(A) Subgenomic HCV JFH-1 replicon cells expressing an NS5A-GFP fusion protein were treated in the absence of G418 twice per week with 50 nM of siRNAs, either individually (scramble, HCV321 or HCV353) or in combination (50 nM HCV321 and 50 nM HCV353) for 8 weeks as depicted. HCV RNA replication was determined twice per week using GFP expression. (B) siRNA transfection was discontinued after 8 weeks, replicon cells were treated with 500 μg/mL G418 for 4 weeks, and resistant cell clones were stained using crystal violet.</p