Publications by authors named "D M Parker"

Because of the urgent need for new antibiotics to treat drug-resistant bacterial pathogens, we employed an assay that rapidly screens large quantities of compounds for their ability to interfere with bacterial protein synthesis, in particular, the delivery of amino acids to the ribosome via tRNA and elongation factor Tu (EF-Tu). We have identified a drug lead, named MGC-10, which kills Gram-positive bacteria, including methicillin-resistant (MRSA), with a MIC of 6 µM, while being harmless to mammalian cells in that concentration range. The antibacterial activity of MGC-10 was broad against over 50 strains of antibiotic-resistant samples obtained from hospital infections, where MGC-10 inhibited all tested strains of MRSA.

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The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis. Two recently developed post hoc ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples.

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Background: Patients with body mass index (BMI) ≥50 have more obesity-associated medical problems and often require more aggressive surgical management. Few single-institution comparative studies have been published examining this specific population.

Objectives: The study aims to compare the weight loss and diabetes remission effects of sleeve gastrectomy (SG), Roux-en-Y gastric bypass (RYGB), and biliopancreatic diversion with duodenal switch (BPD/DS).

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Staphylococcus aureus has evolved mechanisms to cope with low iron (Fe) availability in host tissues. Staphylococcus aureus uses the ferric uptake transcriptional regulator (Fur) to sense titers of cytosolic Fe. Upon Fe depletion, apo-Fur relieves transcriptional repression of genes utilized for Fe uptake.

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This work investigates the use of configuration state imaging together with deep neural networks to develop quantitative MRI techniques for deployment in an interventional setting. A physics modeling technique for inhomogeneous fields and heterogeneous tissues is presented and used to evaluate the theoretical capability of neural networks to estimate parameter maps from configuration state signal data. All tested normalization strategies achieved similar performance in estimating and .

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