Publications by authors named "M P Hacker"
Background: Cardiac involvement in amyloid light chain (AL) amyloidosis significantly influences prognosis, necessitating timely diagnosis and meticulous risk stratification.
Objectives: This prospective study aimed to delineate the molecular phenotypes of AL cardiac amyloidosis (AL-CA) by characterizing fibro-amyloid deposition using F-florbetapir and gallium-68-labeled fibroblast activation protein inhibitor-04 (Ga-FAPI-04) positron emission tomography (PET)/computed tomography (CT) imaging. The authors also proposed a novel molecular stratification methodology for prognosis.
Background: Poly (ADP-ribose) polymerase (PARP) enzymes are crucial for the repair of DNA single-strand breaks and have become key therapeutic targets in homologous recombination-deficient cancers, including prostate cancer. To enable non-invasive monitoring of PARP-1 expression, several PARP-1-targeting positron emission tomography (PET) tracers have been developed. Here, we aimed to preclinically investigate [carbonyl-C]DPQ as an alternative PARP-1 PET tracer as it features a strongly distinct chemotype compared to the frontrunners [F]FluorThanatrace and [F]PARPi.
View Article and Find Full Text PDFIdentifying the determinants of pregnancy loss is a critical public health concern. However, pregnancy loss is often not noticed, and even when it is, it is inconsistently recorded. Thus, past studies have been limited to medically-identified losses or small, highly selected cohorts, which can lead to biased or non-generalizable results.
View Article and Find Full Text PDFArticle Synopsis
- Radical prostatectomy (RP) is a standard treatment for localized prostate cancer, but accurately detecting extraprostatic extension (EPE) before surgery is difficult, which can lead to suboptimal treatments.
- This study explores using explainable machine learning to improve EPE detection by analyzing data from newly diagnosed prostate cancer patients who underwent PET/MRI imaging and subsequent surgery.
- The results showed machine learning models outperformed traditional imaging methods in predicting EPE, which could lead to better clinical decision-making and improved patient outcomes.
Objectives: Anemia in pregnancy has negative impacts on maternal and neonatal morbidity and mortality and has been described as an issue of health equity. The primary aim of our study was to describe rates of anemia near delivery and assess whether this correlates with neighbourhood-level income status.
Methods: We conducted a retrospective cohort study of pregnant individuals delivering from January 2012 through December 2022 at two large academic centres.