Natural stimulus responsive scaffolds/cells for bone tissue engineering : influence of lysozyme upon scaffold degradation and osteogenic differentiation of cultured marrow stromal cells induced by CaP coatings

This work proposes the use of nonporous, smart, and stimulus responsive chitosan-based scaffolds for bone tissue engineering applications. The overall vision is to use biodegradable scaffolds based on chitosan and starch that present properties that will be regulated by bone regeneration, with the capability of gradual in situ pore formation. Biomimetic calcium phosphate (CaP) coatings were used as a strategy to incorporate lysozyme at the surface of chitosan-based materials with the main objective of controlling and tailoring their degradation profile as a function of immersion time. To confirm the concept, degradation tests with a lysozyme concentration similar to that incorporated into CaP chitosan-based scaffolds were used to study the degradation of the scaffolds and the formation of pores as a function of immersion time. Degradation studies with lysozyme (1.5 g=L) showed the formation of pores, indicating an increase of porosity (*5–55% up to 21 days) resulting in porous threedimensional structures with interconnected pores. Additional studies investigated the influence of a CaP biomimetic coating on osteogenic differentiation of rat marrow stromal cells (MSCs) and showed enhanced differentiation of rat MSCs seeded on the CaP-coated chitosan-based scaffolds with lysozyme incorporated. At all culture times, CaP-coated chitosan-based scaffolds with incorporated lysozyme demonstrated greater osteogenic differentiation of MSCs, bone matrix production, and mineralization as demonstrated by calcium deposition measurements, compared with controls (uncoated scaffolds). The ability of these CaP-coated chitosan-based scaffolds with incorporated lysozyme to create an interconnected pore network in situ coupled with the demonstrated positive effect of these scaffolds upon osteogenic differentiation of MSCs and mineralized matrix production illustrates the strong potential of these scaffolds for application in bone tissue engineering strategies.The authors would like to acknowledge Dr. Serena Danti. This work was supported by the European NoE EX-PERTISSUES (NMP3-CT-2004-500283), the European STREP HIPPOCRATES (NMP3-CT-2003-505758), and the Portuguese Foundation for Science and Technology (FCT) through POCTI and/or FEDER programs. This work was also supported by a grant from the National Institutes of Health (NIH; R01 DE15164) (A. G. M.) and a Bioengineering Research Partnership with the Baylor College of Medicine through the National Institute of Biomedical Imaging and Bioengineering (NIH Grant 5 R01 EB005173-02). F. K. K. is supported by a training fellowship from the Keck Center Nanobiology Training Program of the Gulf Coast Consortia (NIH Grant 5 T90 DK070121-03)

"Smart'' and stimulus responsive chitosan-based scaffolds/cells for bone tissue engineering: Influence of lysozyme upon scaffold degradation and osteogenic differentiation of cultured marrow stromal cells induced by cap coatings

[Excerpt] The present study reports the use of non-porous, ‘‘smart’’ and stimulus responsive chitosan-based scaffolds with the capability of gradual in situ pore formation for bone tissue engineering applications. Biomimetic calcium phosphate (CaP) coatings were used as a strategy to incorporate lysozyme at the surface of chitosan based materials the main objective of controlling their degradation profile as a function of immersion time. In order to confirm the concept, degradation tests with concentration similar to those incorporated into CaP chitosan-based scaffolds were used to study the degradation of the scaffolds and the formation of pores as function of immersion time. Degradation studies with lysozyme (1.5 g/L) showed the formation of pores, indicating an increase of porosity (~5% - 55% up to 21 days) resulting in porous 3-D structures with interconnected pores. […]info:eu-repo/semantics/publishedVersio

The R136 star cluster hosts several stars whose individual masses greatly exceed the accepted 150 Msun stellar mass limit

Spectroscopic analyses of H-rich WN5-6 stars within the young star clusters NGC 3603 and R136 are presented, using archival HST & VLT spectroscopy, & high spatial resolution near-IR photometry. We derive high T* for the WN stars in NGC 3603 (T*~42+/-2 kK) & R136 (T*~53+/-3 kK) plus clumping-corrected dM/dt ~ 2-5x10^-5 Msun/yr which closely agree with theoretical predictions. These stars make a disproportionate contribution to the global budget of their host clusters. R136a1 alone supplies ~7% of N(LyC) of the entire 30 Dor region. Comparisons with stellar models calculated for the main-sequence evolution of 85-500 Msun suggest ages of ~1.5 Myr & M_init in the range 105 - 170 Msun for 3 systems in NGC 3603, plus 165-320 Msun for 4 stars in R136. Our high stellar masses are supported by dynamical mass determinations for the components of NGC 3603 A1. We consider the predicted L_X of the R136 stars if they were close, colliding wind binaries. R136c is consistent with a colliding wind binary system. However, short period, colliding wind systems are excluded for R136a WN stars if mass ratios are of order unity. Widely separated systems would have been expected to harden owing to early dynamical encounters with other massive stars in such a dense environment. From simulated star clusters, whose constituents are randomly sampled from the Kroupa IMF, both clusters are consistent with a tentative upper mass limit of ~300 Msun. The Arches cluster is either too old, exhibits a deficiency of very massive stars, or more likely stellar masses have been underestimated - M_init for the most luminous stars in the Arches cluster approach 200 Msun according to contemporary stellar & photometric results. The potential for stars greatly exceeding 150 Msun within metal-poor galaxies suggests that such pair-instability SNe could occur within the local universe, as has been claimed for SN 2007bi (abridged).Comment: 20 pages, 14 figures, accepted for MNRAS. Version with higher resolution figures is available from http://pacrowther.staff.shef.ac.uk/R136.pdf See also http://www.eso.org/public/news/eso1030/ from Wed 21 from noon (CEST

A comparative analysis reveals weak relationships between ecological factors and beta diversity of stream insect metacommunities at two spatial levels.

The hypotheses that beta diversity should increase with decreasing latitude and increase with spatial extent of a region have rarely been tested based on a comparative analysis of multiple datasets, and no such study has focused on stream insects. We first assessed how well variability in beta diversity of stream insect metacommunities is predicted by insect group, latitude, spatial extent, altitudinal range, and dataset properties across multiple drainage basins throughout the world. Second, we assessed the relative roles of environmental and spatial factors in driving variation in assemblage composition within each drainage basin. Our analyses were based on a dataset of 95 stream insect metacommunities from 31 drainage basins distributed around the world. We used dissimilarity-based indices to quantify beta diversity for each metacommunity and, subsequently, regressed beta diversity on insect group, latitude, spatial extent, altitudinal range, and dataset properties (e.g., number of sites and percentage of presences). Within each metacommunity, we used a combination of spatial eigenfunction analyses and partial redundancy analysis to partition variation in assemblage structure into environmental, shared, spatial, and unexplained fractions. We found that dataset properties were more important predictors of beta diversity than ecological and geographical factors across multiple drainage basins. In the within-basin analyses, environmental and spatial variables were generally poor predictors of variation in assemblage composition. Our results revealed deviation from general biodiversity patterns because beta diversity did not show the expected decreasing trend with latitude. Our results also call for reconsideration of just how predictable stream assemblages are along ecological gradients, with implications for environmental assessment and conservation decisions. Our findings may also be applicable to other dynamic systems where predictability is low

Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidatesfor targeted treatment.

Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs\u27 recapitulation of human tumors

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14