Increased Expression of Musashi-1 Evidences Mesenchymal Repair in Maxillary Sinus Floor Elevation

This study aimed to analyze the expression of Musashi-1 (MSI1) in maxillary native bone and grafted bone after maxillary sinus floor elevation. To do so, fifty-seven bone biopsies from 45 participants were studied. Eighteen samples were collected from native bone while 39 were obtained 6 months after maxillary sinus grafting procedures. Musashi-1 was analyzed by immunohistochemistry and RT-PCR. MSI1 was detected in osteoblasts and osteocytes in 97.4% (38/39) of grafted areas. In native bone, MSI1 was detected in only 66.6% (12/18) of the biopsies, mainly in osteocytes. Detection of MSI1 was significantly higher in osteoprogenitor mesenchymal cells of grafted biopsies (p < 0.001) but minor in smooth muscle and endothelial cells; no expression was detected in adipocytes. The mesenchymal cells of the non-mineralized tissue of native bone showed very low nuclear expression of MSI1, in comparison to fusiform cells in grafted areas (0.28(0.13) vs. 2.10(0.14), respectively; p < 0.001). Additionally, the detection of MSI1 mRNA was significantly higher in biopsies from grafted areas than those from native bone (1.00(0.51) vs. 60.34(35.2), respectively; p = 0.029). Thus, our results regardig the significantly higher detection of Musashi-1 in grafted sites than in native bone reflects its importance in the remodeling/repair events that occur after maxillary sinus floor elevation in humans.This investigation was partially supported by Research Groups #CTS-138 and #CTS-1028 (Junta de Andalucía, Spain). MPM was supported by the Andalucía Talent Hub Program from the Andalusian Knowledge Agency (co-funded by the European Union’s Seventh Framework Program, Marie Skłodowska-Curie actions (COFUND – Grant Agreement n° 291780) and the Ministry of Economy, Innovation, Science and Employment of the Junta de Andalucía)

The Role of Scientific Source Credibility and Goodwill in Public Skepticism Toward GM Foods

The complex web of political-economic relations that constitutes biotechnology coupled with a contentious history of public resistance, illustrates the power of perceptions of credibility in mediating individuals’ judgements about GMOs. To more accurately measure what contributes to public skepticism of GM foods, the present research applies a multidimensional model of source credibility comprised of scientific understanding, integrity, agreement, concern, trust, and goodwill (bias). Testing the Anti-Reflexivity Thesis in a new context, we also explore the role of attitudes about science and economic innovation by analyzing associations between political ideology and beliefs about the potential impacts of GM foods. Using data from the Pew Research Center’s American Trends Panel, we find evidence of politically polarized perceptions of GM scientists’ credibility and public beliefs about the environmental risks and benefits of GM foods. Results suggest that political ideology is indirectly associated with beliefs about GM impacts on the food supply, largely through perceptions of goodwill, the so-called “lost” dimension of source credibility. Because demand for biotechnology products like gene edited foods is expected to increase, consumer beliefs about GMOs will likely have significant implications for the future of the bioeconomy

The prognostic association of SPAG5 gene expression in breast cancer patients with systematic therapy

Background: Despite much effort on the treatment of breast cancer over the decades, a great uncertainty regarding the appropriate molecular biomarkers and optimal therapeutic strategy still exists. This research was performed to analyze the association of SPAG5 gene expression with clinicopathological factors and survival outcomes. Methods: We used a breast cancer database including 5667 patients with a mean follow-up of 69 months. Kaplan-Meier survival analyses for relapse free survival (RFS), overall survival (OS), and distant metastasis-free survival (DMFS) were performed. In addition, ROC analysis was performed to validate SPAG5 as a prognostic candidate gene. Results: Mean SPAG5 expression value was significantly higher with some clinicopathological factors that resulted in tumor promotion and progression, including poor differentiated type, HER2 positive or TP53 mutated breast cancer. Based on ROC-analysis SPAG 5 is a suitable prognostic marker of poor survival. In patients who received chemotherapy alone, SPAG5 had only a moderate and not significant predictive impact on survival outcomes. However, in hormonal therapy, high SPAG5 expression could strongly predict prognosis with detrimental RFS (HR = 1.57, 95% CI 1.2-2.06, p = 0.001), OS (HR = 2, 95% CI 1.05-3.8, p = 0.03) and DMFS (HR = 2.36, 95% CI 1.57-3.54, p < 0.001), respectively. In addition, SPAG5 could only serve as a survival predictor in ER+, but not ER- breast cancer patients. Patients might also be at an increased risk of relapse despite being diagnosed with a lower grade cancer (well differentiated type). Conclusions: SPAG5 could be used as an independent prognostic and predictive biomarker that might have clinical utility, especially in ER+ breast cancer patients who received hormonal therapy. © 2019 The Author(s)

Estas son algunas de las habilidades blandas demandadas en Colombia

Este producto forma parte de una serie de infografías de divulgación científica que buscan reseñar algunas de las investigaciones más importantes en las que ha tenido participación la Universidad EAFIT, publicadas en las revistas especializadas más prestigiosas del mund

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License