Neuraxial clonidine is not associated with lower post-cesarean opioid consumption or pain scores in parturients on chronic buprenorphine therapy: a retrospective cohort study.

PURPOSE: Adequate post-cesarean delivery analgesia can be difficult to achieve for women diagnosed with opioid use disorder receiving buprenorphine. We sought to determine if neuraxial clonidine administration is associated with decreased opioid consumption and pain scores following cesarean delivery in women receiving chronic buprenorphine therapy. METHODS: This was a retrospective cohort study at a tertiary care teaching hospital of women undergoing cesarean delivery with or without neuraxial clonidine administration while receiving chronic buprenorphine. The primary outcome was opioid consumption (in morphine milligram equivalents) 0-6 h following cesarean delivery. Secondary outcomes included opioid consumption 0-24 h post-cesarean, median postoperative pain scores 0-24 h, and rates of intraoperative anesthetic supplementation. Multivariable analysis evaluating the adjusted effects of neuraxial clonidine on outcomes was conducted using linear regression, proportional odds model, and logistic regression separately. RESULTS: 196 women met inclusion criteria, of which 145 (74%) received neuraxial clonidine while 51 (26%) did not. In univariate analysis, there was no significant difference in opioid consumption 0-6 h post-cesarean delivery between the clonidine (8 [IQR 0, 15]) and control (1 [IQR 0, 8]) groups (P = 0.14). After adjusting for potential confounders, there remained no significant association with neuraxial clonidine administration 0-6 h (Difference in means 2.77, 95% CI [- 0.89 to 6.44], P = 0.14) or 0-24 h (Difference in means 8.56, 95% CI [- 16.99 to 34.11], P = 0.51). CONCLUSION: In parturients receiving chronic buprenorphine therapy at the time of cesarean delivery, neuraxial clonidine administration was not associated with decreased postoperative opioid consumption, median pain scores, or the need for intraoperative supplementation.

Alcohol Prevention in Urgent and Emergency Care (APUEC): Development and Evaluation of Workforce Digital Training on Screening, Brief Intervention, and Referral for Treatment.

Excessive alcohol consumption carries a significant health, social and economic burden. Screening, brief intervention and referral to treatment (SBIRT) is one approach to identifying patients with excessive alcohol consumption and providing interventions to help them reduce their drinking. However, healthcare workers in urgent and emergency care settings do not routinely integrate SBIRT into clinical practice and raise a lack of training as a barrier to SBIRT delivery. Therefore, "Alcohol Prevention in Urgent and Emergency Care" (APUEC) training was developed, delivered, and evaluated. APUEC is a brief, stand-alone, multimedia, interactive digital training package for healthcare workers. The aim of APUEC is to increase positive attitudes, knowledge, confidence and skills related to SBIRT through the provision of (a) education on the impact of alcohol and the role of urgent and emergency care in alcohol prevention, and (b) practical guidance on patient assessment, delivery of brief advice and making referral decisions. Development involved collaborative-participatory design approaches and a rigorous six-step ASPIRE methodology (involving n = 28 contributors). APUEC was delivered to healthcare workers who completed an online survey (n = 18) and then participated in individual qualitative interviews (n = 15). Analysis of data was aligned with Levels 1-3 of the Kirkpatrick Model of Training Evaluation. Survey data showed that all participants (100%) found the training useful and would recommend it to others. Insights from the qualitative data showed that APUEC digital training increases healthcare workers' perceived knowledge, confidence and skills related to alcohol prevention in urgent and emergency care settings. Participants viewed APUEC to be engaging and relevant to urgent and emergency care workers. This digital training was perceived to be useful for workforce skills development and supporting the implementation of SBIRT in clinical practice. While the impact of APUEC on clinician behaviour and patient outcomes is yet to be tested, APUEC digital training could easily be embedded within education and continuing professional development programmes for healthcare workers and healthcare trainees of any discipline. Ultimately, this may facilitate the integration of SBIRT into routine care and contribute to population health improvement.

Assessing the performance of approximate density functional theory on 95 experimentally characterized Fe(II) spin crossover complexes.

Spin crossover (SCO) complexes, which exhibit changes in spin state in response to external stimuli, have applications in molecular electronics and are challenging materials for computational design. We curate a dataset of 95 Fe(II) SCO complexes (SCO-95) from the Cambridge Structural Database that have available low- and high-temperature crystal structures and, in most cases, confirmed experimental spin transition temperatures (T1/2). We study these complexes using density functional theory (DFT) with 30 functionals spanning across multiple rungs of "Jacob's ladder" to understand the effect of exchange-correlation functional on electronic and Gibbs free energies associated with spin crossover. We specifically assess the effect of varying the Hartree-Fock exchange fraction (aHF) in structures and properties within the B3LYP family of functionals. We identify three best-performing functionals, a modified version of B3LYP (aHF = 0.10), M06-L, and TPSSh, that accurately predict SCO behavior for the majority of the complexes. While M06-L performs well, MN15-L, a more recently developed Minnesota functional, fails to predict SCO behavior for all complexes, which could be the result of differences in datasets used for parametrization of M06-L and MN15-L and also the increased number of parameters for MN15-L. Contrary to observations from prior studies, double-hybrids with higher aHF values are found to strongly stabilize high-spin states and therefore exhibit poor performance in predicting SCO behavior. Computationally predicted T1/2 values are consistent among the three functionals but show limited correlation to experimentally reported T1/2 values. These failures are attributed to the lack of crystal packing effects and counter-anions in the DFT calculations that would be needed to account for phenomena such as hysteresis and two-step SCO behavior. The SCO-95 set thus presents opportunities for method development, both in terms of increasing model complexity and method fidelity.

Identifying Underexplored and Untapped Regions in the Chemical Space of Transition Metal Complexes.

We survey more than 240 000 crystallized mononuclear transition metal complexes (TMCs) to identify trends in preferred geometric structure and metal coordination. While we observe that an increased level of d filling correlates with a lower coordination number preference, we note exceptions, and we observe undersampling of 4d/5d transition metals and 3p-coordinating ligands. For the one-third of mononuclear TMCs that are octahedral, analysis of the 67 symmetry classes of their ligand environments reveals that complexes often contain monodentate ligands that may be removable, forming an open site amenable to catalysis. Due to their use in catalysis, we analyze trends in coordination by tetradentate ligands in terms of the capacity to support multiple metals and the variability of coordination geometry. We identify promising tetradentate ligands that co-occur in crystallized complexes with labile monodentate ligands that would lead to reactive sites. Literature mining suggests that these ligands are untapped as catalysts, motivating proposal of a promising octa-functionalized porphyrin.

Architector for high-throughput cross-periodic table 3D complex building.

Rare-earth and actinide complexes are critical for a wealth of clean-energy applications. Three-dimensional (3D) structural generation and prediction for these organometallic systems remains a challenge, limiting opportunities for computational chemical discovery. Here, we introduce Architector, a high-throughput in-silico synthesis code for s-, p-, d-, and f-block mononuclear organometallic complexes capable of capturing nearly the full diversity of the known experimental chemical space. Beyond known chemical space, Architector performs in-silico design of new complexes including any chemically accessible metal-ligand combinations. Architector leverages metal-center symmetry, interatomic force fields, and tight binding methods to build many possible 3D conformers from minimal 2D inputs including metal oxidation and spin state. Over a set of more than 6,000 x-ray diffraction (XRD)-determined complexes spanning the periodic table, we demonstrate quantitative agreement between Architector-predicted and experimentally observed structures. Further, we demonstrate out-of-the box conformer generation and energetic rankings of non-minimum energy conformers produced from Architector, which are critical for exploring potential energy surfaces and training force fields. Overall, Architector represents a transformative step towards cross-periodic table computational design of metal complex chemistry.

Readability, Content, and Quality of Online Patient Education Materials on Anesthesia and Neurotoxicity in the Pediatric Population.

OBJECTIVE: Internet-based patient education materials (PEMs) are often above the recommended sixth grade reading level recommended by the U.S. Department of Health and Human Services. In 2016 the U.S. Food and Drug Administration (FDA) released a warning statement against use of general anesthetic drugs in children and pregnant women due to concerns about neurotoxicity. The aim of this study is to evaluate readability, content, and quality of Internet-based PEMs on anesthesia in the pediatric population and neurotoxicity. STUDY DESIGN: The websites of U.S. medical centers with pediatric anesthesiology fellowship programs were searched for PEMs pertaining to pediatric anesthesia and neurotoxicity. Readability was assessed. PEM content was evaluated using matrices specific to pediatric anesthesia and neurotoxicity. PEM quality was assessed with the Patient Education Material Assessment Tool for Print. A one-sample t-test was used to compare the readability of the PEMs to the recommended sixth grade reading level. RESULTS: We identified 27 PEMs pertaining to pediatric anesthesia and eight to neurotoxicity. Mean readability of all PEMs was greater than a sixth grade reading (p <0.001). While only 13% of PEMs on anesthesia for pediatric patient mentioned the FDA warning, 100% of the neurotoxicity materials did. PEMs had good understandability (83%) and poor actionability (60%). CONCLUSION: The readability, content, and quality of PEMs are poor and should be improved to help parents and guardians make informed decisions about their children's health care. KEY POINTS: · The FDA issued a warning statement against the use of general anesthetic drugs in children and pregnant women.. · Readability, content, and quality of Internet-based patient education materials on the topic of neurotoxicity are poor.. · Improving the readability, content, and quality of PEMs could aid parents in making important health care decisions..

Ligand additivity relationships enable efficient exploration of transition metal chemical space.

To accelerate the exploration of chemical space, it is necessary to identify the compounds that will provide the most additional information or value. A large-scale analysis of mononuclear octahedral transition metal complexes deposited in an experimental database confirms an under-representation of lower-symmetry complexes. From a set of around 1000 previously studied Fe(II) complexes, we show that the theoretical space of synthetically accessible complexes formed from the relatively small number of unique ligands is significantly (∼816k) larger. For the properties of these complexes, we validate the concept of ligand additivity by inferring heteroleptic properties from a stoichiometric combination of homoleptic complexes. An improved interpolation scheme that incorporates information about cis and trans isomer effects predicts the adiabatic spin-splitting energy to around 2 kcal/mol and the HOMO level to less than 0.2 eV. We demonstrate a multi-stage strategy to discover leads from the 816k Fe(II) complexes within a targeted property region. We carry out a coarse interpolation from homoleptic complexes that we refine over a subspace of ligands based on the likelihood of generating complexes with targeted properties. We validate our approach on nine new binary and ternary complexes predicted to be in a targeted zone of discovery, suggesting opportunities for efficient transition metal complex discovery.

Exploiting Ligand Additivity for Transferable Machine Learning of Multireference Character across Known Transition Metal Complex Ligands.

Accurate virtual high-throughput screening (VHTS) of transition metal complexes (TMCs) remains challenging due to the possibility of high multireference (MR) character that complicates property evaluation. We compute MR diagnostics for over 5,000 ligands present in previously synthesized octahedral mononuclear transition metal complexes in the Cambridge Structural Database (CSD). To accomplish this task, we introduce an iterative approach for consistent ligand charge assignment for ligands in the CSD. Across this set, we observe that the MR character correlates linearly with the inverse value of the averaged bond order over all bonds in the molecule. We then demonstrate that ligand additivity of the MR character holds in TMCs, which suggests that the TMC MR character can be inferred from the sum of the MR character of the ligands. Encouraged by this observation, we leverage ligand additivity and develop a ligand-derived machine learning representation to train neural networks to predict the MR character of TMCs from properties of the constituent ligands. This approach yields models with excellent performance and superior transferability to unseen ligand chemistry and compositions.

Molecular orbital projectors in non-empirical jmDFT recover exact conditions in transition-metal chemistry.

Low-cost, non-empirical corrections to semi-local density functional theory are essential for accurately modeling transition-metal chemistry. Here, we demonstrate the judiciously modified density functional theory (jmDFT) approach with non-empirical U and J parameters obtained directly from frontier orbital energetics on a series of transition-metal complexes. We curate a set of nine representative Ti(III) and V(IV) d1 transition-metal complexes and evaluate their flat-plane errors along the fractional spin and charge lines. We demonstrate that while jmDFT improves upon both DFT+U and semi-local DFT with the standard atomic orbital projectors (AOPs), it does so inefficiently. We rationalize these inefficiencies by quantifying hybridization in the relevant frontier orbitals. To overcome these limitations, we introduce a procedure for computing a molecular orbital projector (MOP) basis for use with jmDFT. We demonstrate this single set of d1 MOPs to be suitable for nearly eliminating all energetic delocalization and static correlation errors. In all cases, MOP jmDFT outperforms AOP jmDFT, and it eliminates most flat-plane errors at non-empirical values. Unlike DFT+U or hybrid functionals, jmDFT nearly eliminates energetic delocalization and static correlation errors within a non-empirical framework.

Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles.

Virtual high-throughput screening (VHTS) with density functional theory (DFT) and machine-learning (ML)-acceleration is essential in rapid materials discovery. By necessity, efficient DFT-based workflows are carried out with a single density functional approximation (DFA). Nevertheless, properties evaluated with different DFAs can be expected to disagree for cases with challenging electronic structure (e.g., open-shell transition-metal complexes, TMCs) for which rapid screening is most needed and accurate benchmarks are often unavailable. To quantify the effect of DFA bias, we introduce an approach to rapidly obtain property predictions from 23 representative DFAs spanning multiple families, "rungs" (e.g., semi-local to double hybrid) and basis sets on over 2000 TMCs. Although computed property values (e.g., spin state splitting and frontier orbital gap) differ by DFA, high linear correlations persist across all DFAs. We train independent ML models for each DFA and observe convergent trends in feature importance, providing DFA-invariant, universal design rules. We devise a strategy to train artificial neural network (ANN) models informed by all 23 DFAs and use them to predict properties (e.g., spin-splitting energy) of over 187k TMCs. By requiring consensus of the ANN-predicted DFA properties, we improve correspondence of computational lead compounds with literature-mined, experimental compounds over the typically employed single-DFA approach.

Deciphering Cryptic Behavior in Bimetallic Transition-Metal Complexes with Machine Learning.

We demonstrate an alternative, data-driven approach to uncovering structure-property relationships for the rational design of heterobimetallic transition-metal complexes that exhibit metal-metal bonding. We tailor graph-based representations of the metal-local environment for these complexes for use in multiple linear regression and kernel ridge regression (KRR) models. We curate a set of 28 experimentally characterized complexes to develop a multiple linear regression model for oxidation potentials. We achieve good accuracy (mean absolute error of 0.25 V) and preserve transferability to unseen experimental data with a new ligand structure. We also train a KRR model on a subset of 330 structurally characterized heterobimetallics to predict the degree of metal-metal bonding. This KRR model predicts relative metal-metal bond lengths in the test set to within 5%, and analysis of key features reveals the fundamental atomic contributions (e.g., the valence electron configuration) that most strongly influence the behavior of these complexes. Our work provides guidance for rational bimetallic design, suggesting that properties, including the formal shortness ratio, should be transferable from one period to another.

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning.

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.

Peripartum Considerations for the Transgender Parturient: A Case Report.

Despite efforts by medical and social activists, transgender parturients encounter barriers to adequate and gender-inclusive health care, resources, and support. We present a case of a 38-year-old transgender man presenting for induction of labor at term. Our case highlights the importance of multidisciplinary planning, appropriate gender-related language, and interventions that may ameliorate gender dysphoria during childbirth. Because some transgender men may desire childbirth, we recommend that health care providers become familiar with and respectful of the unique considerations for this patient population in the peripartum setting.

Solvent manipulation of the pre-reduction metal-ligand complex and particle-ligand binding for controlled synthesis of Pd nanoparticles.

Understanding how to control the nucleation and growth rates is crucial for designing nanoparticles with specific sizes and shapes. In this study, we show that the nucleation and growth rates are correlated with the thermodynamics of metal-ligand/solvent binding for the pre-reduction complex and the surface of the nanoparticle, respectively. To obtain these correlations, we measured the nucleation and growth rates by in situ small angle X-ray scattering during the synthesis of colloidal Pd nanoparticles in the presence of trioctylphosphine in solvents of varying coordinating ability. The results show that the nucleation rate decreased, while the growth rate increased in the following order, toluene, piperidine, 3,4-lutidine and pyridine, leading to a large increase in the final nanoparticle size (from 1.4 nm in toluene to 5.0 nm in pyridine). Using density functional theory (DFT), complemented by 31P nuclear magnetic resonance and X-ray absorption spectroscopy, we calculated the reduction Gibbs free energies of the solvent-dependent dominant pre-reduction complex and the solvent-nanoparticle binding energy. The results indicate that lower nucleation rates originate from solvent coordination which stabilizes the pre-reduction complex and increases its reduction free energy. At the same time, DFT calculations suggest that the solvent coordination affects the effective capping of the surface where stronger binding solvents slow the nanoparticle growth by lowering the number of active sites (not already bound by trioctylphosphine). The findings represent a promising advancement towards understanding the microscopic connection between the metal-ligand thermodynamic interactions and the kinetics of nucleation and growth to control the size of colloidal metal nanoparticles.

Heteroatom Tracing Reveals the 30-Atom Au-Ag Bimetallic Nanocluster as a Dimeric Structure.

Understanding the formation of face-centered cubic (fcc) nanostructures at the atomic level remains a major task. With atomically precise nanoclusters (NCs) as model systems, herein we devised an atom-tracing strategy by heteroatom doping into Au30(SR)18 (SR = S-tC4H9) to label the specific positions in M30(SR)18 NCs (M = Au/Ag), which clearly reveals the dimeric nature of M30. Interestingly, the specific position is also consistent with the Ag-doping site in M21(SR)15. Electronic orbital analysis shows intrinsic orbital localization at the two specific positions in M30, which are decisive to the electronic structure of M30, regardless of Au or Ag occupancy. The fcc dimeric NC, which would not be discovered without Ag tracing, provides a possible explanation for the wide accessibility of nonsuperatomic Au-SR NCs.

Heterometal-Doped M23 (M = Au/Ag/Cd) Nanoclusters with Large Dipole Moments.

Dipole moment (µ) is a critical parameter for molecules and nanomaterials as it affects many properties. In metal-thiolate (SR) nanoclusters (NCs), µ is commonly low (0-5 D) compared to quantum dots. Herein, we report a doping strategy to give giant dipoles (∼18 D) in M23 (M = Au/Ag/Cd) NCs, falling in the experimental trend for II-VI quantum dots. In M23 NCs, high µ is caused by the Cd-Br bond and the arrangement of heteroatoms along the C3 axis. Strong dipole-dipole interactions are observed in crystalline state, with energy exceeding 5 kJ/mol, directing a "head-to-tail" alignment of Au22-nAgnCd1(SR)15X (SR = adamantanethiolate) dipoles. The alignment can be controlled by µ via doping. The optical absorption peaks of M23 show solvent polarity-dependent shifts (∼25 meV) with negative solvatochromism. Detailed electronic structures of M23 are revealed by density functional theory and time-dependent DFT calculations. Overall, the doping strategy for obtaining large dipole moments demonstrates an atomic-level design of clusters with useful properties.

Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions.

Determination of ground-state spins of open-shell transition-metal complexes is critical to understanding catalytic and materials properties but also challenging with approximate electronic structure methods. As an alternative approach, we demonstrate how structure alone can be used to guide assignment of ground-state spin from experimentally determined crystal structures of transition-metal complexes. We first identify the limits of distance-based heuristics from distributions of metal-ligand bond lengths of over 2000 unique mononuclear Fe(II)/Fe(III) transition-metal complexes. To overcome these limits, we employ artificial neural networks (ANNs) to predict spin-state-dependent metal-ligand bond lengths and classify experimental ground-state spins based on agreement of experimental structures with the ANN predictions. Although the ANN is trained on hybrid density functional theory data, we exploit the method-insensitivity of geometric properties to enable assignment of ground states for the majority (ca. 80-90%) of structures. We demonstrate the utility of the ANN by data-mining the literature for spin-crossover (SCO) complexes, which have experimentally observed temperature-dependent geometric structure changes, by correctly assigning almost all (>95%) spin states in the 46 Fe(II) SCO complex set. This approach represents a promising complement to more conventional energy-based spin-state assignment from electronic structure theory at the low cost of a machine learning model.

Unfolding adsorption on metal nanoparticles: Connecting stability with catalysis.

Metal nanoparticles have received substantial attention in the past decades for their applications in numerous areas, including medicine, catalysis, energy, and the environment. Despite these applications, the fundamentals of adsorption on nanoparticle surfaces as a function of nanoparticle size, shape, metal composition, and type of adsorbate are yet to be found. Herein, we introduce the first universal adsorption model that accounts for detailed nanoparticle structural characteristics, metal composition, and different adsorbates by combining first principles calculations with machine learning. Our model fits a large number of data and accurately predicts adsorption trends on nanoparticles (both monometallic and alloy) that have not been previously seen. In addition to its application power, the model is simple and uses tabulated and rapidly calculated data for metals and adsorbates. We connect adsorption with stability behavior of nanoparticles, thus advancing the design of optimal nanoparticles for applications of interest, such as catalysis.

Rethinking Heterometal Doping in Ligand-Protected Metal Nanoclusters.

Heterometal doping is a promising avenue toward tailoring properties of ligand-protected metal nanoclusters for specific applications. Though successful doping has been demonstrated in several structures, the underlying reasons for the dopant preference on occupying specific locations on the nanocluster with different concentrations remain unclear. In this study we apply our thermodynamic stability model, originally developed for ligand-protected monometallic nanoclusters, to rationalize the synthetic accessibility, dopant location, and concentrations of various heterometals on ligand-protected Au nanoclusters. Importantly, we demonstrate that the thermodynamic stability theory is a significant step forward in accurately describing doping effects on nanoclusters using first-principles calculations. With our computational predictions being in excellent agreement with a series of experiments, we introduce the thermodynamic stability theory as a new method for bimetallic nanocluster prediction.