No abstract

Luminescent semiconductor ~9.5 nm nanoparticles (quantum dots: QDs) have intrinsic physiochemical and optical properties which enable us to begin to understand the mechanisms of nanoparticle mediated chemical/drug delivery. Here, we demonstrate the ability of CdSe/ZnS core/shell QDs surface functionalized with a zwitterionic compact ligand to deliver a cell-penetrating lipopeptide to the developing chick embryo brain without any apparent toxicity. Functionalized QDs were conjugated to the palmitoylated peptide WGDap-(Palmitoyl)VKIKKP9GGH6, previously shown to uniquely facilitate endosomal escape, and microinjected into the embryonic chick spinal cord canal at embryo day 4 (E4). We were subsequently able to follow the labeling of spinal cord extension into the ventricles, migratory neuroblasts, maturing brain cells, and complex structures such as the choroid plexus. QD intensity extended throughout the brain, and peaked between E8 and E11 when fluorescence was concentrated in the choroid plexus before declining to hatching (E21/P0). We observed no abnormalities in embryonic patterning or embryo survival, and mRNA in situ hybridization confirmed that, at key developmental stages, the expression pattern of genes associated with different brain cell types (brain lipid binding protein, Sox-2, proteolipid protein and Class III-β-Tubulin) all showed a normal labeling pattern and intensity. Our findings suggest that we can use chemically modified QDs to identify and track neural stem cells as they migrate, that the choroid plexus clears these injected QDs/nanoparticles from the brain after E15, and that they can deliver drugs and peptides to the developing brain.

Background: We evaluated the effectiveness of four upper airway ultrasonographic parameters in predicting difficult intubation (DI). The validity of models based on combined ultrasonography-based parameters was also investigated. Methods: In a prospective, observational, double-blinded cohort trial, 1043 ASA-PS I-III patients without anticipated difficult airway, undergoing tracheal intubation under general anesthesia were enrolled. Preoperatively, their tongue thickness (TT), invisibility of hyoid bone (VH), and anterior neck soft tissue thickness from skin to thyrohyoid membrane (ST) and hyoid bone (SH) respectively, were measured under sublingual and submandibular ultrasonographic scans. Based on tracheal intubation, they were categorized as easy intubation (EI) or DI. The logistic regression, youden index, and receiver operator characteristic analysis were used.

Reinforcement learning methods trained on few environments rarely learn policies that generalize to unseen environments. To improve generalization, we incorporate the inherent sequential structure in reinforcement learning into the representation learning process. This approach is orthogonal to recent approaches, which rarely exploit this structure explicitly. Specifically, we introduce a theoretically motivated policy similarity metric (PSM) for measuring behavioral similarity between states. PSM assigns high similarity to states for which the optimal policies in those states as well as in future states are similar. We also present a contrastive representation learning procedure to embed any state similarity metric, which we instantiate with PSM to obtain policy similarity embeddings (PSEs 1 ). We demonstrate that PSEs improve generalization on diverse benchmarks, including LQR with spurious correlations, a jumping task from pixels, and Distracting DM Control Suite. Source code would be made available at agarwl.github.io/pse.

No abstract