Publications by authors named "A Maxwell"
Background: A kidney biopsy is an essential investigation for diagnosis but is invasive and associated with complications. Delaying or missing the opportunity to diagnose kidney disease could result in adverse patient outcomes. This study aimed to examine attitudes to kidney biopsy across the world.
View Article and Find Full Text PDFIn this Letter, we present a small series of novel bacterial topoisomerase inhibitors (NTBIs) that exhibit both potent inhibition of DNA gyrase and potent antimycobacterial activity. The disclosed crystal structure of DNA gyrase in complex with DNA and compound from this NBTI series reveals the binding mode of an NBTI in the GyrA binding pocket and confirms the presence and importance of halogen bonding for the excellent on-target potency. In addition, we have shown that compound is a promising DNA gyrase inhibitor, with an IC for gyrase of 0.
View Article and Find Full Text PDFBackground: DNA methylation differences are associated with kidney function and diabetic kidney disease (DKD), but prospective studies are scarce. Therefore, we aimed to study DNA methylation in a prospective setting in the Finnish Diabetic Nephropathy Study type 1 diabetes (T1D) cohort.
Methods: We analysed baseline blood sample-derived DNA methylation (Illumina's EPIC array) of 403 individuals with normal albumin excretion rate (early progression group) and 373 individuals with severe albuminuria (late progression group) and followed-up their DKD progression defined as decrease in eGFR to <60 mL/min/1.
Non-adherence to immunosuppressive medications in kidney transplant recipients- a systematic scoping review.
Transplant Rev (Orlando)
December 2024
Background: Rejection and graft failure remain common in kidney transplant recipients. Non-adherence to immunosuppressive medications is considered a major contributary factor to reduced long-term graft survival, particularly in younger people. Improvements in clinical practice based on adherence studies has been minimal.
View Article and Find Full Text PDFArticle Synopsis
- Convolutional neural networks (CNNs) are revolutionizing the analysis of geospatial and Earth observation data, particularly in pixel-level classification tasks.
- The newly introduced geodl R package enables deep learning applications in R for geospatial data without requiring Python integration, simplifying the user experience.
- geodl efficiently processes raster-based data using the terra package and includes features for creating raster masks, training models, and assessing performance with common metrics.