Publications by authors named "B D O'Malley"
Background: Complexity stratification for CHD is an integral part of clinical research due to its heterogenous clinical presentation and outcomes. To support our ongoing research efforts into CHD requiring disease severity stratifications, a simplified CHD severity classification system was developed and verified, with potential utility for clinical researchers without specialist CHD knowledge or access to clinical/medical records.
Method: A two-tiered analysis approach was undertaken.
Article Synopsis
- Chemotherapy combined with immune checkpoint inhibitors (ICIs) is used to boost immunotherapy effectiveness, but certain tumors, especially triple-negative breast cancer (TNBC), often remain unresponsive.
- The study identifies IRE1α, an ER stress sensor, as a key factor that limits the immune-boosting effects of taxane chemotherapy in these tumors by silencing double-stranded RNA (dsRNA) and preventing a type of inflammatory cell death called pyroptosis.
- Inhibiting IRE1α allows taxane to produce more dsRNA, which activates immune responses, transforming PD-L1-negative TNBC tumors into ones that are sensitive to immunotherapy.
Introduction: The cause of most CHD is unknown and considered complex, implicating genetic and environmental factors in disease causation. The Kids Heart BioBank was established in 2003 to accelerate genetic investigations into CHD.
Methods: Recruitment includes patients undergoing interventions for CHD at The Children's Hospital at Westmead.
We hypothesized that slowed oxygen uptake ( ) kinetics for exercise transitions to higher power outputs (PO) within the steady state (SS) domain would increase the mean response time (MRT) with increasing exercise intensity during incremental exercise. Fourteen highly trained cyclists (mean ± standard deviation []; age (39 ± 6) years [yr]; and peak = (61 ± 9) mL/kg/min performed a maximal, ramp incremental cycling test and on separate days, four 6-min bouts of cycling at 30%, 45%, 65% & 75% of their incremental peak PO (Wpeak). SS trial data were used to calculate the MRT and verified by mono-exponential and linear curve fitting.
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- Reliable AI in medical diagnoses requires effective uncertainty quantification (UQ), but current methods can be impractical for clinical use.
- The proposed UQ approach utilizes deep neuroevolution (DNE) to efficiently create an ensemble of accurate models, particularly analyzing language lateralization maps from rs-fMRI scans.
- Results show that DNE-based UQ aligns well with expert assessments, indicating its potential reliability for identifying uncertainties in medical imaging, especially with out-of-distribution data.