SynONIM: A Comprehensive Database of Synthetic Oligonucleotide Modifications and Impurities to Aid in Their Characterization by Mass Spectrometry.

Given the resurgence of oligonucleotides in the biotherapeutic space, there is a profound focus on their characterization by mass spectrometry. These therapeutic moieties commonly employ synthetic modifications to aid in increasing efficacy and stability; however, these modifications can also increase the complexity of mass spectrometry data analysis. Additionally, various stress conditions can affect both the observed level and type of impurities stemming from the variety of utilized modifications.

Lipid Based Formulations in Hard Gelatin and HPMC Capsules: a Physical Compatibility Study.

Purpose: To investigate the compatibility between hard gelatin and HPMC capsules with a range of different isotropic lipid based formulations containing multiple excipients.

Methods: The miscibility was investigated for 350 systems applying five different oils (Labrafac ™ lipophile WL1349, Maisine® CC, Captex 300 EP/NF, olive oil, and Capmul MCM EP/NF), five different surfactans (Labrasol ® ALF, Labrafil M 2125 CS, Kolliphor ® ELP, Kolliphor ® HS 15, Tween 80) and three different cosolvents (propylene glycol, polyethylene glycol 400, and Transcutol ® HP). For the isotropic systems capsule compatibility was investigated in both gelatin and HPMC capsules at 25°C at 40% and 60% relative humidity by examining physical damages to the capsules and weight changes after storage.

The successive projection algorithm as an initialization method for brain tumor segmentation using non-negative matrix factorization.

Non-negative matrix factorization (NMF) has become a widely used tool for additive parts-based analysis in a wide range of applications. As NMF is a non-convex problem, the quality of the solution will depend on the initialization of the factor matrices. In this study, the successive projection algorithm (SPA) is proposed as an initialization method for NMF.

Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization.

Background: Segmentation of gliomas in multi-parametric (MP-)MR images is challenging due to their heterogeneous nature in terms of size, appearance and location. Manual tumor segmentation is a time-consuming task and clinical practice would benefit from (semi-) automated segmentation of the different tumor compartments.

Methods: We present a semi-automated framework for brain tumor segmentation based on non-negative matrix factorization (NMF) that does not require prior training of the method.

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI.

Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies.