Publications by authors named "D R Black"

Background: LIGHT (oLaparib In HRD-Grouped Tumor types; NCT02983799) prospectively evaluated olaparib treatment in patients with platinum-sensitive relapsed ovarian cancer (PSROC) assigned to cohorts by known BRCA mutation (BRCAm) and homologous recombination deficiency (HRD) status: germline BRCAm (gBRCAm), somatic BRCAm (sBRCAm), HRD-positive non-BRCAm, and HRD-negative. At the primary analysis, olaparib treatment demonstrated activity across all cohorts, with greatest efficacy in terms of objective response rate and progression-free survival observed in the g/sBRCAm cohorts. The authors report final overall survival (OS).

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Patients with LCHADD develop progressive chorioretinopathy with vision loss over time. To date, no data on the impact of vision loss on patient vision-specific activities of daily living or quality of life have been reported. We used validated ophthalmic patient-reported outcome measures (PROMs) to compare the impact of patient-perceived visual function to visual acuity and an ophthalmologist-graded stage of LCHADD chorioretinopathy.

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We have proposed to the Food and Drug Administration (FDA) that treatment-related increases in total hip bone mineral density (TH BMD) at two years could be a surrogate endpoint for fracture risk reduction in clinical trials. The qualification of a surrogate includes a strong association of the surrogate with the clinical outcome. We compiled a large database of individual patient data (IPD) through the FNIH-ASBMR-SABRE project, and this analysis aimed to assess the relationship between baseline BMD and fracture risk in the placebo groups.

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Recently, an African ancestry-specific Parkinson disease (PD) risk signal was identified at the gene encoding glucocerebrosidase (GBA1). This variant ( rs3115534 -G) is carried by ~50% of West African PD cases and imparts a dose-dependent increase in risk for disease. The risk variant has varied frequencies across African ancestry groups but is almost absent in European and Asian ancestry populations.

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Article Synopsis
  • * Two deep learning models were developed to better correct for these issues; one uses labeled data while the other is semi-supervised, both trained on known concentrations of protoporphyrin IX (PpIX).
  • * Evaluations showed that these models had significantly higher correlation coefficients for PpIX concentration detection compared to classical methods, with the semi-supervised model also performing better on human data, reducing false positives by 36%.
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