This article reports the results of quantitative intra- and intergeneric taxonomic relationships among Micrococcaceae strains and a novel endophytic bacterium (SG) isolated from a suspension culture of Arabidopsis thaliana (L.) Heynh in our laboratory. The known strain Rothia sp. ND6WE1A was used as a reference one for SG. Whole-genome sequencing and phylogenetic analysis were based on the 16S rRNA test. Quantitative analysis for the nucleotide identity (ANI) and calculation of evolutionary distances were based on the identified amino acids (AAI) test indicating the generic assignment of the reference strain within and between the identified monophyletic groups of Micrococcaceae. The amino acid data structure of Rothia sp. ND6WE1A was compared against the UniProt database (250 million records) of close lineage of Micrococcaceae, including other Rothia spp. These data presented unique and evolutionary amino acid alignments, eventually expected in the new SG isolate as well. The metagenomic entries of the respective genome and proteome, characterized at the genus and species levels, could be considered for evolutionary taxonomic reclassification of the isolated and the reference strain (SG + Rothia sp. ND6WE1A). Therefore, our results warrant further investigations on the isolated SG strain.
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http://dx.doi.org/10.1007/s00203-024-03896-7 | DOI Listing |
Purpose: In glioblastoma, the therapeutically intractable and resistant phenotypes can be derived from glioma stem cells, which often have different underlying mechanisms from non-stem glioma cells. Aberrant signaling across the EGFR-PTEN-AKT-mTOR pathways have been shown as common drivers of glioblastoma. Revealing the inter and intra-cellular heterogeneity within glioma stem cell populations in relations to signaling patterns through these pathways may be key to precision diagnostic and therapeutic targeting of these cells.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Ace Alzheimer Center Barcelona - International University of Catalunya (UIC), Barcelona, Spain.
Background: Alzheimer's disease (AD) is a complex disorder with a strong genetic component, yet many genetic risk factors remain unknown. Integrating genome-wide association studies (GWAS) and high-throughput proteomic platforms is a useful strategy to evaluate protein quantitative trait loci (pQTLs) and to detect candidate genes and pathways involved in AD. Due to the novelty of these techniques, the identification of reliable protein measures through a comprehensive quality control is mandatory.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Center for Cognitive Neurology, New York University Langone Health, New York, NY, USA.
Background: A decline in gait has been associated with an escalated risk of cognitive decline and changes in Alzheimer's disease (AD) biomarkers, thus offering prognostic insight. However, the utility of gait analysis in preclinical stages of AD is unclear, and prior studies have primarily used qualitative or gross measures of gait. Furthermore, gait analysis has predominantly been performed in cohorts of non-Hispanic Whites.
View Article and Find Full Text PDFBackground: Alzheimer's Disease (AD) is a major neurodegenerative disorder characterized by amyloid deposits in brain tissues and representing a continuously increasing global burden in need of disease-modifying therapeutic options. Amyloid beta 1-42 and 1-40 peptides and the amyloid beta 1-42/1-40 ratio are hallmarks of AD and are commonly monitored in Cerebro-Spinal Fluid (CSF) along other AD biomarkers, to support diagnosis and management of AD patients. Over the past few years, blood-based AD biomarkers have emerged as highly relevant and more practical alternatives to CSF biomarkers, and further technical performance characterization of the associated assays would be beneficial to the AD research and medical community.
View Article and Find Full Text PDFBackground: Quantitative EEG measures can be used as biosignatures of disease conditions. As such, the effect of interventions/treatments can be studied by longitudinal analysis of changes in these measures. The consistency of these measures can be assessed by test-retest reliability scores such as intra-class correlation coefficient (ICC) that depends on intra- and inter-subject variability.
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