Re-evaluation of Cyclic Peptide Binding to Neurotensin Receptor 1 by Shifting the Peptide Register during Synthesis.

The head-to-tail cyclic peptide [Arg-Lys-Pro-Tyr-Tle-Leu] (peptide , where Tle is l--Leu) has previously been reported to bind to neurotensin receptor 1 (NTS1) (pKi = 5.97). Upon seeking to reproduce this finding, we found that peptide did not have a measurable affinity for NTS1.

Witnessing Self-Affirming Moments in Persons with Dementia While Interacting with Therapy Dogs: A Case Report.

Individuals, families, and communities are impacted by Alzheimer's disease and other dementias worldwide. In Canada and elsewhere, family members commonly see loved ones living with dementia experience difficult moments, including anxiety, stress, and fear. Struggling health care systems strive to apply the latest evidence-based interventions.

A randomized, placebo-controlled first-in-human study of oral TQS-168 in healthy volunteers: Assessment of safety, tolerability, pharmacokinetics, pharmacodynamics, and food effect.

TQS-168, a first-in-class small-molecule inducer of peroxisome proliferator-activated receptor gamma coactivator 1-alpha gene expression, is in development for the treatment of amyotrophic lateral sclerosis. A single-ascending-dose (SAD) and multiple-ascending-dose (MAD) study of TQS-168 was carried out in healthy male subjects to investigate safety, tolerability, pharmacokinetics (PK), food effect, and preliminary pharmacodynamic effects (PD). Since solubility enhancement could be beneficial, assessment of three formulations was incorporated into the study using an integrated rapid manufacturing and clinical testing approach.

Graph Neural Network-Based Molecular Property Prediction with Patch Aggregation.

Graph neural networks (GNNs) have emerged as powerful tools for quantum chemical property prediction, leveraging the inherent graph structure of molecular systems. GNNs depend on an edge-to-node aggregation mechanism for combining edge representations into node representations. Unfortunately, existing learnable edge-to-node aggregation methods substantially increase the number of parameters and, thus, the computational cost relative to simple sum aggregation.