Mantle cell lymphoma (MCL) is an incurable B-cell malignancy characterized by a high clinical variability. Therefore, there is a critical need to define parameters that identify high-risk patients ...for aggressive disease and therapy resistance. B-cell receptor (BCR) signaling is crucial for MCL initiation and progression and is a target for therapeutic intervention. We interrogated BCR signaling proteins (SYK, LCK, BTK, PLCγ2, p38, AKT, NF-κB p65, and STAT5) in 30 primary MCL samples using phospho-specific flow cytometry. Anti-IgM modulation induced heterogeneous BCR signaling responses among samples allowing the identification of two clusters with differential responses. The cluster with higher response was associated with shorter progression free survival (PFS) and overall survival (OS). Moreover, higher constitutive AKT activity was predictive of inferior response to the Bruton's tyrosine kinase inhibitor (BTKi) ibrutinib. Time-to-event analyses showed that MCL international prognostic index (MIPI) high-risk category and higher STAT5 response were predictors of shorter PFS and OS whilst MIPI high-risk category and high SYK response predicted shorter OS. In conclusion, we identified BCR signaling properties associated with poor clinical outcome and resistance to ibrutinib, thus highlighting the prognostic and predictive significance of BCR activity and advancing our understanding of signaling heterogeneity underlying clinical behavior of MCL.
Computational approaches to detect the signals of adverse drug reactions are powerful tools to monitor the unattended effects that users experience and report, also preventing death and serious ...injury. They apply statistical indices to affirm the validity of adverse reactions reported by users. The methodologies that scan fixed duration intervals in the lifetime of drugs are among the most used. Here we present a method, called TEDAR, in which ranges of varying length are taken into account. TEDAR has the advantage to detect a greater number of true signals without significantly increasing the number of false positives, which are a major concern for this type of tools. Furthermore, early detection of signals is a key feature of methods to prevent the safety of the population. The results show that TEDAR detects adverse reactions many months earlier than methodologies based on a fixed interval length.
•Detection of signals in pharmacovigilance datasets is improved by temporal dynamics.•TEDAR detects a greater number of true signals without increasing false positives.•TEDAR detects adverse reactions months before the other methodologies.
The joint modeling of genetic data and brain imaging information allows for determining the pathophysiological pathways of neurodegenerative diseases such as Alzheimer’s disease (AD). This task has ...typically been approached using mass-univariate methods that rely on a complete set of Single Nucleotide Polymorphisms (SNPs) to assess their association with selected image-derived phenotypes (IDPs). However, such methods are prone to multiple comparisons bias and, most importantly, fail to account for potential cross-feature interactions, resulting in insufficient detection of significant associations. Ways to overcome these limitations while reducing the number of traits aim at conveying genetic information at the gene level and capturing the integrated genetic effects of a set of genetic variants, rather than looking at each SNP individually. Their associations with brain IDPs are still largely unexplored in the current literature, though they can uncover new potential genetic determinants for brain modulations in the AD continuum. In this work, we explored an explainable multivariate model to analyze the genetic basis of the grey matter modulations, relying on the AD Neuroimaging Initiative (ADNI) phase 3 dataset. Cortical thicknesses and subcortical volumes derived from T1-weighted Magnetic Resonance were considered to describe the imaging phenotypes. At the same time the genetic counterpart was represented by gene variant scores extracted by the Sequence Kernel Association Test (SKAT) filtering model. Moreover, transcriptomic analysis was carried on to assess the expression of the resulting genes in the main brain structures as a form of validation. Results highlighted meaningful genotype–phenotype interactionsas defined by three latent components showing a significant difference in the projection scores between patients and controls. Among the significant associations, the model highlighted EPHX1 and BCAS1 gene variant scores involved in neurodegenerative and myelination processes, hence relevant for AD. In particular, the first was associated with decreased subcortical volumes and the second with decreasedtemporal lobe thickness. Noteworthy, BCAS1 is particularly expressed in the dentate gyrus. Overall, the proposed approach allowed capturing genotype–phenotype interactions in a restricted study cohort that was confirmed by transcriptomic analysis, offering insights into the underlying mechanisms of neurodegeneration in AD in line with previous findings and suggesting new potential disease biomarkers.
Display omitted
•PLS reveals genotype–phenotype interactions in AD continuum.•Using SKAT filtering, gene-based variant scores were derived in a restricted cohort.•Gene variant scores are associated with brain modulations.•EPHX1 and BCAS1 are tied to neurodegeneration.•Transcriptomic validation reinforces model insights.
Many scientific applications entail solving the subgraph isomorphism problem, i.e., given an input pattern graph, find all the subgraphs of a (usually much larger) target graph that are structurally ...equivalent to that input. Because subgraph isomorphism is NP-complete, methods to solve it have to use heuristics. This work evaluates subgraph isomorphism methods to assess their computational behavior on a wide range of synthetic and real graphs. Surprisingly, our experiments show that, among the leading algorithms, certain heuristics based only on pattern graphs are the most efficient.
COVID-19 is characterized by severe inflammation during the acute phase and increased aortic stiffness in the early postacute phase. In other models, aortic stiffness is improved after the reduction ...of inflammation. We aimed to evaluate the mid- and long-term effects of COVID-19 on vascular and cardiac autonomic function. The primary outcome was aortic pulse wave velocity (aPWV).
The cross-sectional Study-1 included 90 individuals with a history of COVID-19 and 180 matched controls. The longitudinal Study-2 included 41 patients with COVID-19 randomly selected from Study-1 who were followed-up for 27 weeks.
Study-1: Compared with controls, patients with COVID-19 had higher aPWV and brachial PWV 12 to 24 (but not 25-48) weeks after COVID-19 onset, and they had higher carotid Young's elastic modulus and lower distensibility 12 to 48 weeks after COVID-19 onset. In partial least squares structural equation modeling, the higher the hs-CRP (high-sensitivity C-reactive protein) at hospitalization was, the higher the aPWV 12 to 48 weeks from COVID-19 onset (path coefficient: 0.184;
=0.04). Moreover, aPWV (path coefficient: -0.186;
=0.003) decreased with time. Study-2: mean blood pressure and carotid intima-media thickness were comparable at the end of follow-up, whereas aPWV (-9%;
=0.01), incremental Young's elastic modulus (-17%;
=0.03), baroreflex sensitivity (+28%;
=0.049), heart rate variability triangular index (+15%;
=0.01), and subendocardial viability ratio (+12%;
=0.01×10
) were significantly improved. There was a trend toward improvement in brachial PWV (-6%;
=0.14) and carotid distensibility (+18%;
=0.05). Finally, at the end of follow-up (48 weeks after the onset of COVID-19) aPWV (+6%;
=0.04) remained significantly higher in patients with COVID-19 than in control subjects.
COVID-19-related arterial stiffening involves several arterial tree portions and is partially resolved in the long-term.
