Engineered polyester-PEG nanoparticles prepared through a “grafting through” strategy and post-functionalization via Michael type addition

Free radical polymerization (FRP) is widely used in industrial processes as an efficient and versatile method to engineer polymeric nanoparticles (PNPs) of controlled size, narrowly distributed, and of well-defined surface properties. Functional Poly(ε-caprolactone) (PCL) and poly(lactic acid) (PLA) can be utilized as macromonomers in FRP in combination with a co-polymerizable poly(ethylene glycol) (PEG), to achieve aqueous dispersions of PNPs composed of a hydrophobic polyester core and a hydrophilic PEG shell of tuneable size. For several industrial and biological applications, PNPs also need surface functionalization to provide specific physicochemical characteristics, including stimuli-responsiveness, and bioactivity. In this work, a flexible “grafting through” strategy based on Ring opening polymerization (ROP) and FRP was proposed to obtain engineered polyester-PEG nanoparticles functionalized with acrylate groups on the hydrophilic shell. The presence of acrylates allows a versatile surface functionalization through Michael-type addition with a thiolated ligand (peptide), in aqueous solution under physiological pH, with the advantage of high conversion and absence of reaction side products. A cysteine-containing cyclic RGD was used as model peptide for conjugation, due to its potential application as ligand for endothelial cells. Results indicated that active cell targeting can be achieved by using this surface functionalization approach

Netrin-1 and Its Receptor DCC Are Causally Implicated in Melanoma Progression

International audienc

Germline TIM-3 Mutations Characterize Sub-Cutaneous Panniculitis T-Cell Lymphomas with Hemophagocytic Lymphohistiocytic Syndrome

International audienc

Germline HAVCR2 mutations altering TIM-3 characterize subcutaneous panniculitis-like T cell lymphomas with hemophagocytic lymphohistiocytic syndrome

International audienc

Correction: The 5th edition of The World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms (vol 36, pg 1720, 2022)

10.1038/s41375-023-01962-5LEUKEMIA3791944-195

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset