Navigating Informed Consent and Patient Safety in Surgery: Lessons for Medical Students and Junior Trainees.

AbstractIn the operating room, patient safety is of paramount importance. Medical students and junior trainees, despite their primary role as students, may play active roles in assessing patient safety and reporting suspected errors. Active consent is one layer of patient safety that is continuously assessed by several team members. This article examines an instance where patient consent may have been violated. Through the lens of trainee and senior perspectives, we discuss the ethical principles at stake and provide recommendations for medical student and junior trainee involvement in patient care when an error is suspected.

GraphVAMPnets for uncovering slow collective variables of self-assembly dynamics.

Uncovering slow collective variables (CVs) of self-assembly dynamics is important to elucidate its numerous kinetic assembly pathways and drive the design of novel structures for advanced materials through the bottom-up approach. However, identifying the CVs for self-assembly presents several challenges. First, self-assembly systems often consist of identical monomers, and the feature representations should be invariant to permutations and rotational symmetries. Physical coordinates, such as aggregate size, lack high-resolution detail, while common geometric coordinates like pairwise distances are hindered by the permutation and rotational symmetry challenges. Second, self-assembly is usually a downhill process, and the trajectories often suffer from insufficient sampling of backward transitions that correspond to the dissociation of self-assembled structures. Popular dimensionality reduction methods, such as time-structure independent component analysis, impose detailed balance constraints, potentially obscuring the true dynamics of self-assembly. In this work, we employ GraphVAMPnets, which combines graph neural networks with a variational approach for Markovian process (VAMP) theory to identify the slow CVs of the self-assembly processes. First, GraphVAMPnets bears the advantages of graph neural networks, in which the graph embeddings can represent self-assembly structures in high-resolution while being invariant to permutations and rotational symmetries. Second, it is built upon VAMP theory, which studies Markov processes without forcing detailed balance constraints, which addresses the out-of-equilibrium challenge in the self-assembly process. We demonstrate GraphVAMPnets for identifying slow CVs of self-assembly kinetics in two systems: the aggregation of two hydrophobic molecules and the self-assembly of patchy particles. We expect that our GraphVAMPnets can be widely applied to molecular self-assembly.

Quantum Mechanics/Molecular mechanics calculations predict A1, not A2, is present in melanopsin (Opn4m) of red-eared slider turtles (Trachemys scripta elegans).

Melanopsin is a photopigment that plays a role in non-visual, light-driven, cellular processes such as modulation of circadian rhythms, retinal vascular development, and the pupillary light reflex (PLR). In this study, computational methods were used to understand which chromophore is harbored by melanopsin in red-eared slider turtles (Trachemys scripta elegans). In mammals, the vitamin A derivative 11-cis-retinal (A1) is the chromophore, which provides functionality for melanopsin. However, in red-eared slider turtles, a member of the reptilian class, the identity of the chromophore remains unclear. Red-eared slider turtles, similar to other freshwater vertebrates, possess visual pigments that harbor a different vitamin A derivative, 11-cis-3,4-didehydroretinal (A2), making their pigments more sensitive to red-light than blue-light, therefore, suggesting the chromophore to be the A2 derivative instead of the A1. To help resolve the chromophore identity, in this work, computational homology models of melanopsin in red-eared slider turtles were first constructed. Next, quantum mechanics/molecular mechanics (QM/MM) calculations were carried out to compare how A1 and A2 derivatives bind to melanopsin. Time dependent density functional theory (TDDFT) calculations were then used to determine the excitation energy of the pigments. Lastly, calculated excitation energies were compared to experimental spectral sensitivity data from responses by the irises of red-eared sliders. Contrary to what was expected, our results suggest that melanopsin in red-eared slider turtles is more likely to harbor the A1 chromophore than the A2. Furthermore, a glutamine (Q622.56) and tyrosine (Y853.28) residue in the chromophore binding pocket are shown to play a role in the spectral tuning of the chromophore.

Disease mutations and phosphorylation alter the allosteric pathways involved in autoinhibition of protein phosphatase 2A.

Mutations in protein phosphatase 2A (PP2A) are connected to intellectual disability and cancer. It has been hypothesized that these mutations might disrupt the autoinhibition and phosphorylation-induced activation of PP2A. Since they are located far from both the active and substrate binding sites, it is unclear how they exert their effect. We performed allosteric pathway analysis based on molecular dynamics simulations and combined it with biochemical experiments to investigate the autoinhibition of PP2A. In the wild type (WT), the C-arm of the regulatory subunit B56δ obstructs the active and substrate binding sites exerting a dual autoinhibition effect. We find that the disease mutant, E198K, severely weakens the allosteric pathways that stabilize the C-arm in the WT. Instead, the strongest allosteric pathways in E198K take a different route that promotes exposure of the substrate binding site. To facilitate the allosteric pathway analysis, we introduce a path clustering algorithm for lumping pathways into channels. We reveal remarkable similarities between the allosteric channels of E198K and those in phosphorylation-activated WT, suggesting that the autoinhibition can be alleviated through a conserved mechanism. In contrast, we find that another disease mutant, E200K, which is in spatial proximity of E198, does not repartition the allosteric pathways leading to the substrate binding site; however, it may still induce exposure of the active site. This finding agrees with our biochemical data, allowing us to predict the activity of PP2A with the phosphorylated B56δ and provide insight into how disease mutations in spatial proximity alter the enzymatic activity in surprisingly different mechanisms.

An Efficient Path Classification Algorithm Based on Variational Autoencoder to Identify Metastable Path Channels for Complex Conformational Changes.

Conformational changes (i.e., dynamic transitions between pairs of conformational states) play important roles in many chemical and biological processes. Constructing the Markov state model (MSM) from extensive molecular dynamics (MD) simulations is an effective approach to dissect the mechanism of conformational changes. When combined with transition path theory (TPT), MSM can be applied to elucidate the ensemble of kinetic pathways connecting pairs of conformational states. However, the application of TPT to analyze complex conformational changes often results in a vast number of kinetic pathways with comparable fluxes. This obstacle is particularly pronounced in heterogeneous self-assembly and aggregation processes. The large number of kinetic pathways makes it challenging to comprehend the molecular mechanisms underlying conformational changes of interest. To address this challenge, we have developed a path classification algorithm named latent-space path clustering (LPC) that efficiently lumps parallel kinetic pathways into distinct metastable path channels, making them easier to comprehend. In our algorithm, MD conformations are first projected onto a low-dimensional space containing a small set of collective variables (CVs) by time-structure-based independent component analysis (tICA) with kinetic mapping. Then, MSM and TPT are constructed to obtain the ensemble of pathways, and a deep learning architecture named the variational autoencoder (VAE) is used to learn the spatial distributions of kinetic pathways in the continuous CV space. Based on the trained VAE model, the TPT-generated ensemble of kinetic pathways can be embedded into a latent space, where the classification becomes clear. We show that LPC can efficiently and accurately identify the metastable path channels in three systems: a 2D potential, the aggregation of two hydrophobic particles in water, and the folding of the Fip35 WW domain. Using the 2D potential, we further demonstrate that our LPC algorithm outperforms the previous path-lumping algorithms by making substantially fewer incorrect assignments of individual pathways to four path channels. We expect that LPC can be widely applied to identify the dominant kinetic pathways underlying complex conformational changes.

Catastrophic Antiphospholipid Syndrome Following Lower Extremity Arterial Bypass Surgery: Case Report and Evidence Review.

Catastrophic antiphospholipid syndrome (CAPS) is a rare life threatening presentation of antiphospholipid syndrome. Surgery has been proposed as one of the triggering factors for this life threatening entity. There are no detailed published reports in the current literature describing CAPS as a complication after surgery. We report a case of a 21 year old that developed CAPS postoperatively and discuss the multidisciplinary approach for diagnosis and management.

Use of non-partitioned ventilator tubing results in dead-space ventilation hypercarbia.

We present a case of severe postoperative hypercarbia in a patient with severe COPD. Hypercarbia and respiratory acidosis continued to increase despite maximal ventilation, bronchodilator therapy, sedation, and paralysis. Mistaken use of non-partitioned ventilator circuit was the cause of the hypercarbia. The ventilator's self-test function failed to detect the error. We changed to a partitioned-lumen circuit, with much less ventilation dead space, and the hypercarbia resolved immediately.