Using machine learning to predict five-year transplant-free survival among infants with hypoplastic left heart syndrome.

Hypoplastic left heart syndrome (HLHS) is a congenital malformation commonly treated with palliative surgery and is associated with significant morbidity and mortality. Risk stratification models have often relied upon traditional survival analyses or outcomes data failing to extend beyond infancy. Individualized prediction of transplant-free survival (TFS) employing machine learning (ML) based analyses of outcomes beyond infancy may provide further valuable insight for families and healthcare providers along the course of a staged palliation. Data from both the Pediatric Heart Network (PHN) Single Ventricle Reconstruction (SVR) trial and Extension study (SVR II), which extended cohort follow up for five years was used to develop ML-driven models predicting TFS. Models incrementally incorporated features corresponding to successive phases of care, from pre-Stage 1 palliation (S1P) through the stage 2 palliation (S2P) hospitalization. Models trained with features from Pre-S1P, S1P operation, and S1P hospitalization all demonstrated time-dependent area under the curves (td-AUC) beyond 0.70 through 5 years following S1P, with a model incorporating features through S1P hospitalization demonstrating particularly robust performance (td-AUC 0.838 (95% CI 0.836-0.840)). Machine learning may offer a clinically useful alternative means of providing individualized survival probability predictions, years following the staged surgical palliation of hypoplastic left heart syndrome.

A machine-learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset.

BACKGROUND: Pediatric anesthesia has evolved to a high level of patient safety, yet a small chance remains for serious perioperative complications, even in those traditionally considered at low risk. In practice, prediction of at-risk patients currently relies on the American Society of Anesthesiologists Physical Status (ASA-PS) score, despite reported inconsistencies with this method. AIMS: The goal of this study was to develop predictive models that can classify children as low risk for anesthesia at the time of surgical booking and after anesthetic assessment on the procedure day. METHODS: Our dataset was derived from APRICOT, a prospective observational cohort study conducted by 261 European institutions in 2014 and 2015. We included only the first procedure, ASA-PS classification I to III, and perioperative adverse events not classified as drug errors, reducing the total number of records to 30 325 with an adverse event rate of 4.43%. From this dataset, a stratified train:test split of 70:30 was used to develop predictive machine learning algorithms that could identify children in ASA-PS class I to III at low risk for severe perioperative critical events that included respiratory, cardiac, allergic, and neurological complications. RESULTS: Our selected models achieved accuracies of >0.9, areas under the receiver operating curve of 0.6-0.7, and negative predictive values >95%. Gradient boosting models were the best performing for both the booking phase and the day-of-surgery phase. CONCLUSIONS: This work demonstrates that prediction of patients at low risk of critical PAEs can be made on an individual, rather than population-based, level by using machine learning. Our approach yielded two models that accommodate wide clinical variability and, with further development, are potentially generalizable to many surgical centers.

Examining Implicit Bias Differences in Pediatric Surgical Fellowship Letters of Recommendation Using Natural Language Processing.

OBJECTIVE: We analyzed the prevalence and type of bias in letters of recommendation (LOR) for pediatric surgical fellowship applications from 2016-2021 using natural language processing (NLP) at a quaternary care academic hospital. DESIGN: Demographics were extracted from submitted applications. The Valence Aware Dictionary for sEntiment Reasoning (VADER) model was used to calculate polarity scores. The National Research Council dataset was used for emotion and intensity analysis.  The Kruskal-Wallis H-test was used to determine statistical significance.  SETTING: This study took place at a single, academic, free standing quaternary care children's hospital with an ACGME accredited pediatric surgery fellowship. PARTICIPANTS: Applicants to a single pediatric surgery fellowship were selected for this study from 2016 to 2021. A total of 182 individual applicants were included and 701 letters of recommendation were analyzed. RESULTS: Black applicants had the highest mean polarity (most positive), while Hispanic applicants had the lowest.  Overall differences between polarity distributions were not statistically significant.   The intensity of emotions showed that differences in "anger" were statistically significant (p=0.03).  Mean polarity was higher for applicants that successfully matched in pediatric surgery. DISCUSSION: This study identified differences in LORs based on racial and gender demographics submitted as part of pediatric surgical fellowship applications to a single training program. The presence of bias in letters of recommendation can lead to inequities in demographics to a given program. While difficult to detect for humans, natural language processing is able to detect bias as well as differences in polarity and emotional intensity. While the types of emotions identified in this study are highly similar among race and gender groups, the intensity of these emotions revealed differences, with "anger" being most significant. CONCLUSION: From this work, it can be concluded that bias in LORs, as reflected as differences in polarity, which is likely a result of the intensity of the emotions being used and not the types of emotions being expressed.   Natural language processing shows promise in identification of subtle areas of bias that may influence an individual's likelihood of successful matching.

Secondary structure of peptides mimicking the Gly-rich regions of major ampullate spidroin protein 1 and 2.

Spider dragline silk has highly desirable material properties, possessing high extensibility, strength, and biocompatibility. Before it is spun, the constituent proteins are stored in a concentrated dope that is void of fibrils. To investigate the structural properties of the amorphous fiber regions in the dope, computer simulations were performed on model peptides representing the N. clavipes Gly-rich regions. Analysis of the secondary structure found predominantly turns, bends and coils; a small 31-helical population decreased with increasing concentration. Interestingly, the population of 31-helices saw a large increase in octanol. These results indicate that the unusual 31-helical secondary structure of the Gly-rich region of the fiber is a consequence of the spinning process, and that the low dielectric environment of the fiber may assist in favoring this structure.

One-Bead-Two-Compound Thioether Bridged Macrocyclic γ-AApeptide Screening Library against EphA2.

Identification of molecular ligands that recognize peptides or proteins is significant but poses a fundamental challenge in chemical biology and biomedical sciences. Development of cyclic peptidomimetic library is scarce, and thus discovery of cyclic peptidomimetic ligands for protein targets is rare. Herein we report the unprecedented one-bead-two-compound (OBTC) combinatorial library based on a novel class of the macrocyclic peptidomimetics γ-AApeptides. In the library, we utilized the coding peptide tags synthesized with Dde-protected α-amino acids, which were orthogonal to solid phase synthesis of γ-AApeptides. Employing the thioether linkage, the desired macrocyclic γ-AApeptides were found to be effective for ligand identification. Screening the library against the receptor tyrosine kinase EphA2 led to the discovery of one lead compound that tightly bound to EphA2 (Kd = 81 nM) and potently antagonized EphA2-mediated signaling. This new approach of macrocyclic peptidomimetic library may lead to a novel platform for biomacromolecular surface recognition and function modulation.

Molecular dynamics simulations and molecular flooding studies of the retinoid X-receptor ligand binding domain.

Bexarotene is an FDA approved retinoid X-receptor (RXR) agonist for the treatment of cutaneous T-cell lymphoma, and its use in other cancers and Alzheimer's disease is being investigated. The drug causes serious side effects, which might be reduced by chemical modifications of the molecule. To rationalize known agonists and to help identify sites for potential substitutions we present molecular simulations in which the RXR ligand-binding domain was flooded with a large number of drug-like molecules, and molecular dynamics simulations of a series of bexarotene-like ligands bound to the RXR ligand-binding domain. Based on the flooding simulations, two regions of interest for ligand modifications were identified: a hydrophobic area near the bridgehead and another near the fused ring. In addition, positional fluctuations of the phenyl ring were generally smaller than fluctuations of the fused ring of the ligands. Together, these observations suggest that the fused ring might be a good target for the design of higher affinity bexarotene-like ligands, while the phenyl ring is already optimized. In addition, notable differences in ligand position and interactions between the RXRα and RXRß were observed, as well as differences in hydrogen bonding and solvation, which might be exploited in the development of subspecies-specific ligands.

Importance of ß2-ß3 Loop Motion for the Increased Binding and Decreased Selectivity of the ΔLL Mutant of the Human Papillomavirus Type 6 E2 Protein.

The binding affinity of the human papillomavirus type 6 E2 protein is strongly mediated by the sequence of the DNA linker region, with high affinity for the AATT linker and low affinity for the CCGG linker. When two terminal leucine residues are removed from the protein, the level of binding to both strands increases, but unequally, resulting in a significant decrease in selectivity for the AATT linker strand. To rationalize this behavior, we performed molecular dynamics simulations of the wild-type and mutant protein in the apo state and bound to DNA with high-affinity AATT and low-affinity CCGG linker strands. While no stable contacts were made between the ß2-ß3 loop and DNA in the wild type, this loop was repositioned in the mutant complexes and formed electrostatic contacts with the DNA backbone. More contacts were formed when the mutant was bound to the CCGG linker strand than to the AATT linker strand, resulting in a more favorable change in interaction energy for the CCGG strand. In addition, significant differences in correlated motions were found, which further explained the differences in binding. The simulations suggest that ß2-ß3 loop motions are responsible for the increased affinity and decreased selectivity of the mutant protein.

Modeling, synthesis, and biological evaluation of potential retinoid X receptor (RXR) selective agonists: novel analogues of 4-[1-(3,5,5,8,8-pentamethyl-5,6,7,8-tetrahydro-2-naphthyl)ethynyl]benzoic acid (bexarotene) and (E)-3-(3-(1,2,3,4-tetrahydro-1,1,4,4,6-pentamethylnaphthalen-7-yl)-4-hydroxyphenyl)acrylic acid (CD3254).

Three unreported analogues of 4-[1-(3,5,5,8,8-pentamethyl-5-6-7-8-tetrahydro-2-naphthyl)ethynyl]benzoic acid (1), otherwise known as bexarotene, as well as four novel analogues of (E)-3-(3-(1,2,3,4-tetrahydro-1,1,4,4,6-pentamethylnaphthalen-7-yl)-4-hydroxyphenyl)acrylic acid (CD3254), are described and evaluated for their retinoid X receptor (RXR) selective agonism. Compound 1 has FDA approval as a treatment for cutaneous T-cell lymphoma (CTCL), although treatment with 1 can elicit side-effects by disrupting other RXR-heterodimer receptor pathways. Of the seven modeled novel compounds, all analogues stimulate RXR-regulated transcription in mammalian 2 hybrid and RXRE-mediated assays, possess comparable or elevated biological activity based on EC50 profiles, and retain similar or improved apoptotic activity in CTCL assays compared to 1. All novel compounds demonstrate selectivity for RXR and minimal crossover onto the retinoic acid receptor (RAR) compared to all-trans-retinoic acid, with select analogues also reducing inhibition of other RXR-dependent pathways (e.g., VDR-RXR). Our results demonstrate that further improvements in biological potency and selectivity of bexarotene can be achieved through rational drug design.

Modeling, synthesis and biological evaluation of potential retinoid X receptor-selective agonists: novel halogenated analogues of 4-[1-(3,5,5,8,8-pentamethyl-5,6,7,8-tetrahydro-2-naphthyl)ethynyl]benzoic acid (bexarotene).

The synthesis of halogenated analogues of 4-[1-(3,5,5,8,8-pentamethyl-5,6,7,8-tetrahydro-2-naphthyl)ethynyl]benzoic acid (1), known commonly as bexarotene, and their evaluation for retinoid X receptor (RXR)-specific agonist performance is described. Compound 1 is FDA approved to treat cutaneous T-cell lymphoma (CTCL); however, bexarotene treatment can induce hypothyroidism and elevated triglyceride levels, presumably by disrupting RXR heterodimer pathways for other nuclear receptors. The novel halogenated analogues in this study were modeled and assessed for their ability to bind to RXR and stimulate RXR homodimerization in an RXRE-mediated transcriptional assay as well as an RXR mammalian-2-hybrid assay. In an array of eight novel compounds, four analogues were discovered to promote RXR-mediated transcription with EC(50) values similar to that of 1 and are selective RXR agonists. Our approach also uncovered a periodic trend of increased binding and homodimerization of RXR when substituting a halogen atom for a proton ortho to the carboxylic acid on 1.