Publications by authors named "A Saar"

Article Synopsis
  • This study looks at how doctors can figure out who might have memory problems after spine surgery in older patients, focusing on those 60 and up.
  • They're using special tests before surgery, like brain scans and blood flow tests, to see if a certain measurement (the pulsatility index) can predict if someone will have issues like confusion after surgery.
  • The results showed that about 1 in 5 patients had post-operative delirium, and older patients were more likely to experience it, with specific test scores helping to predict these problems.
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. Chronic kidney disease (CKD) and diabetes mellitus (DM) contribute significantly to cardiovascular disease (CVD) and mortality, with prevalence increasing. The evolving demographic of myocardial infarction (MI) patients, influenced by sedentary lifestyles and advanced medical care, lacks understanding regarding the interplay of CKD, DM, age, and post-MI mortality.

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Arthritis associated with Lyme disease is frequent in regions of the United States where the illness is widespread; nonetheless, periprosthetic joint infections (PJI) caused by Lyme are exceptionally rare. As of October 2023, only five cases of Lyme PJI have been documented in the literature. Four of these cases were managed successfully with surgical intervention, while one was managed successfully with oral and IV antibiotics.

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The OPLS all-atom force field was updated and applied to modeling unsaturated hydrocarbons, alcohols, and ethers. Testing has included gas-phase conformational energetics, properties of pure liquids, and free energies of hydration. Monte Carlo statistical mechanics (MC) calculations were used to model 60 liquids.

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Geometric deep learning is one of the main workhorses for harnessing the power of big data to predict molecular properties such as aqueous solubility, which is key to the pharmacokinetic improvement of drug candidates. Two ensembles of graph neural network architectures were built, one based on spectral convolution and the other on spatial convolution. The pretrained models, denoted respectively as SolNet-GCN and SolNet-GAT, significantly outperformed the existing neural networks benchmarked on a validation set of 207 molecules.

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