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Background: Multiple radiomics models have been proposed for grading glioma using different algorithms, features, and sequences of magnetic resonance imaging. The research seeks to assess the present overall performance of radiomics for grading glioma., Methods: A systematic literature review of the databases Ovid MEDLINE PubMed, and Ovid EMBASE for publications published on radiomics for glioma grading between 2012 and 2023 was performed. The systematic review was carried out following the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analysis., Results: In the meta-analysis, a total of 7654 patients from 40 articles, were assessed. R-package mada was used for modeling the joint estimates of specificity (SPE) and sensitivity (SEN). Pooled event rates across studies were performed with a random-effects meta-analysis. The heterogeneity of SPE and SEN were based on the χ2 test. Overall values for SPE and SEN in the differentiation between high-grade gliomas (HGGs) and low-grade gliomas (LGGs) were 84% and 91%, respectively. With regards to the discrimination between World Health Organization (WHO) grade 4 and WHO grade 3, the overall SPE was 81% and the SEN was 89%. The modern non-linear classifiers showed a better trend, whereas textural features tend to be the best-performing (29%) and the most used., Conclusions: Our findings confirm that present radiomics' diagnostic performance for glioma grading is superior in terms of SEN and SPE for the HGGs vs. LGGs discrimination task when compared to the WHO grade 4 vs. 3 task., Competing Interests: The authors declare no conflict of interest., (© 2024 The Author(s). Published by IMR Press.)

Background and Objective: Numerous radiomics-based models have been proposed to discriminate between central nervous system (CNS) gliomas and primary central nervous system lymphomas (PCNSLs). Given the heterogeneity of the existing models, we aimed to define their overall performance and identify the most critical variables to pilot future algorithms., Methods: A systematic review of the literature and a meta-analysis were conducted, encompassing 12 studies and a total of 1779 patients, focusing on radiomics to differentiate gliomas from PCNSLs. A comprehensive literature search was performed through PubMed, Ovid MEDLINE, Ovid EMBASE, Web of Science, and Scopus databases. Overall sensitivity (SEN) and specificity (SPE) were estimated. Event rates were pooled using a random-effects meta-analysis, and the heterogeneity was assessed using the χ2 test., Results: The overall SEN and SPE for differentiation between CNS gliomas and PCNSLs were 88% (95% CI = 0.83 - 0.91) and 87% (95% CI = 0.83 - 0.91), respectively. The best-performing features were the ones extracted from the Gray Level Run Length Matrix (GLRLM; ACC 97%), followed by those obtained from the Neighboring Gray Tone Difference Matrix (NGTDM; ACC 93%), and shape-based features (ACC 91%). The 18F-FDG-PET/CT was the best-performing imaging modality (ACC 97%), followed by the MRI CE-T1W (ACC 87% - 95%). Most studies applied a cross-validation analysis (92%)., Conclusion: The current SEN and SPE of radiomics to discriminate CNS gliomas from PCNSLs are high, making radiomics a helpful method to differentiate these tumor types. The best-performing features are the GLRLM, NGTDM, and shape-based features. The 18F-FDG-PET/CT imaging modality is the best-performing, while the MRI CE-T1W is the most used., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Garaba, Aslam, Ponzio, Panciani, Brinjikji, Fontanella and De Maria.)

Purpose: To better define the overall performance of the current radiomics-based models for the discrimination of pediatric posterior fossa tumors., Methods: A comprehensive literature search of the databases PubMed, Ovid MEDLINE, Ovid EMBASE, Web of Science, and Scopus was designed and conducted by an experienced librarian. We estimated overall sensitivity (SEN) and specificity (SPE). Event rates were pooled across studies using a random-effects meta-analysis, and the χ 2 test was performed to assess the heterogeneity., Results: Overall SEN and SPE for differentiation between MB, PA, and EP were found to be promising, with SEN values of 93% (95% CI = 0.88-0.96), 83% (95% CI = 0.66-0.93), and 85% (95% CI = 0.71-0.93), and corresponding SPE values of 87% (95% CI = 0.82-0.90), 95% (95% CI = 0.90-0.98) and 90% (95% CI = 0.84-0.94), respectively. For MB, there is a better trend for LR classifiers, while textural features are the most used and the best performing (ACC 96%). As for PA and EP, a synergistic employment of LR and NN classifiers, accompanied by geometrical or morphological features, demonstrated superior performance (ACC 94% and 96%, respectively)., Conclusions: The diagnostic performance is high, making radiomics a helpful method to discriminate these tumor types. In the forthcoming years, we expect even more precise models.

The prognosis of renal cell carcinoma (RCC) malignant neoplasms deeply relies on an accurate determination of the histological subtype, which currently involves the light microscopy visual analysis of histological slides, considering notably tumor architecture and cytology. RCC subtyping is therefore a time-consuming and tedious process, sometimes requiring expert review, with great impact on diagnosis, prognosis and treatment of RCC neoplasms. In this study, we investigate the automatic RCC subtyping classification of 91 patients, diagnosed with clear cell RCC, papillary RCC, chromophobe RCC, or renal oncocytoma, through deep learning based methodologies. We show how the classification performance of several state-of-the-art Convolutional Neural Networks (CNNs) are perfectible among the different RCC subtypes. Thus, we introduce a new classification model leveraging a combination of supervised deep learning models (specifically CNNs) and pathologist's expertise, giving birth to a hybrid approach that we termed ExpertDeepTree (ExpertDT). Our findings prove ExpertDT's superior capability in the RCC subtyping task, with respect to traditional CNNs, and suggest that introducing some expert-based knowledge into deep learning models may be a valuable solution for complex classification cases., (© 2023. Springer Nature Limited.)

Motivation: Computer-aided analysis of biological images typically requires extensive training on large-scale annotated datasets, which is not viable in many situations. In this paper, we present Generative Adversarial Network Discriminator Learner (GAN-DL), a novel self-supervised learning paradigm based on the StyleGAN2 architecture, which we employ for self-supervised image representation learning in the case of fluorescent biological images., Results: We show that Wasserstein Generative Adversarial Networks enable high-throughput compound screening based on raw images. We demonstrate this by classifying active and inactive compounds tested for the inhibition of SARS-CoV-2 infection in two different cell models: the primary human renal cortical epithelial cells (HRCE) and the African green monkey kidney epithelial cells (VERO). In contrast to previous methods, our deep learning-based approach does not require any annotation, and can also be used to solve subtle tasks it was not specifically trained on, in a self-supervised manner. For example, it can effectively derive a dose-response curve for the tested treatments., Availability and Implementation: Our code and embeddings are available at https://gitlab.com/AlesioRFM/gan-dl StyleGAN2 is available at https://github.com/NVlabs/stylegan2 ., (© 2022. The Author(s).)

Metal coordination between polyphenols and metal cations like Fe 3+ allows to produce conformal homogeneous and robust coatings on a vast variety of materials. The deposition kinetics and the stability of the obtained films are however only poorly investigated. In the present article it is shown that rough, granular but pinhole free coatings up to 50nm in thickness can be obtained in a one pot manner using pyrocatechol (Pyr)/Fe 3+ mixtures at different stoichiometries (with Fe 3+ /Pyr ratios equal to 0.55 or 1.10) provided the deposition time is extended up to 24h. More importantly, we show that these films are dissolved upon oxidation of Pyr in cyclic voltammetry experiments. When the films deposited during short durations are rinsed with buffer and subsequently re-exposed to Pyr containing solution, they undergo partial dissolution most probably through a ligand exchange process. Such a dissolution process does not occur anymore in the same conditions, when the deposition time is increased above 5h. All Pyr-Fe 3+ based films can be stabilized by a post-deposition of a polyelectrolyte multilayer film based on the alternated adsorption of poly(allylamine hydrochloride) and the sodium salt of poly(styrene sulfonate). The deposition of 5 bilayers of these polyelectrolytes allows suppressing the dissolution of Pyr-Fe 3+ based films produced for short deposition times., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Polydopamine (PDA) coatings appear as a universal functionalization methodology allowing to coat the surface of almost all kinds of known materials with a conformal, stable, robust and reactive material. Relatively few investigations were dedicated to the incorporation of other molecules in PDA coatings during their deposition from dopamine solutions under oxidative conditions. Herein we rely on the assumption that the basic building blocks of PDA could be porphyrin like tetramers (as well as higher order oligomers) of 5,6-dihydroxyindole and we investigate the influence of a cationic Cu(II) phtalocyanine, namely Alcian Blue (AB), on the deposition kinetics and on the properties of PDA films. We demonstrate that AB is indeed incorporated in the PDA films to yield a composite PDA-AB coating displaying the optical features of both PDA and AB. The amount of incorporated dye depends on its concentration in solution. The obtained PDA-AB films have a smaller thickness than their related PDA counterparts, a different morphology and a higher permeability to the anionic hexacyanoferrate redox probe. In addition, the incorporation of AB in the films is not homogeneous through their thickness as inferred by means of X-ray photoelectron spectroscopy. The reason for this interesting finding is discussed on the basis of the interactions between AB and PDA as well as on the basis of the structure of PDA films., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Anionic and cationic surfactants such as sodium dodecylsulfate (SDS) and hexadecyltrimethylammonium bromide (HTAB) are able to control the size of "polydopamine" particles produced from dopamine solutions and to simultaneously strongly inhibit the deposition of "polydopamine" on surfaces. Indeed, dynamic light scattering experiments allowed to show that the hydrodynamic radius of polydopamine progressively decreases from about 1 μm to a few nanometer upon an increase in the SDS and CTAB concentration. At the highest surfactant concentration used (50 mM) the size of the aggregates is only slightly larger than the size of the surfactant micelles. On the other hand, the non-ionic Triton X-100 surfactant has no significant influence on both phenomena. It is suggested that the observed effect originates from the anionic and cationic surfactants acting as a template in which the growth of "polydopamine" is confined., (Copyright © 2014 Elsevier Inc. All rights reserved.)

The authors report the importance of the use of vascular homografts in treatment of prosthetic infections. To have good results is very important to obtain vascular segments prelevated and preserved in the best conditions. The importance of the explant is fundamental. The authors report the right sequence of organs' explant and the technique to obtain good vascular segments; for this purpose is important the coordination of the surgical equips taking part in the explant.