Hospital Testing of the Effectiveness of Co-Designed Educational Materials to Improve Patient and Visitor Knowledge and Confidence in Reporting Patient Deterioration.

BACKGROUND: Co-designed educational materials could significantly improve the likelihood of patients and visitors (consumers) escalating care through hospital systems. The objective was to investigate patients' and visitors' knowledge and confidence in recognizing and reporting patient deterioration in hospitals before and after exposure to educational materials. METHODS: A multimethod design involved a convenience sample of patients and visitors at a South Australian hospital. Knowledge and confidence of participants to report patient deterioration was assessed using a validated questionnaire. Baseline group was surveyed, and a second group was surveyed after exposure to a poster and on-hold message relating to consumer-initiated escalation-of-care. Nominal data were examined using chi-square analysis, and ordinal data using the Mann-Whitney U test. Open-ended questions were examined using thematic analysis. RESULTS: A total of 407 participants completed the study, 203 undertook the baseline survey, and 204 the postintervention survey. Respondents exposed to the educational materials reported significantly higher recognition of responsibility to report concerns about patient deterioration compared to controls (86.3% vs. 73.1%; p = 0.007). Respondents exposed to the educational materials also had better ability to identify signs that a patient was becoming sicker compared to controls (77.5% vs. 71.3%, p = 0.012). Four overarching themes emerged from the questions: patient/visitor understanding of key messages, patient/visitor recognition of deterioration, patient/visitor response to deterioration and patient/visitor recommendations. CONCLUSION: Following educational interventions, patients and visitors report improved awareness of their role in recognizing and responding to clinical deterioration. They advise additional active interventions and caution that the materials should accommodate language, cultural, and disability needs.

Impact of prostate radiotherapy on survival outcomes in clinically node-positive prostate cancer: A multicentre retrospective analysis.

PURPOSE: To evaluate clinical outcomes for cN1M0 prostate cancer treated with varied modalities. MATERIALS AND METHODS: Men with radiological stage cN1M0 prostate cancer on conventional imaging, treated from 2011-2019 with various modalities across four centres in the UK were included. Demographics, tumour grade and stage, and treatment details were collected. Biochemical and radiological progression-free survival (bPFS, rPFS) and overall survival (OS) were estimated using Kaplan Meier analyses. Potential factors impacting survival were tested with univariable log-rank test and multivariable Cox-proportional hazards model. RESULTS: Total 337 men with cN1M0 prostate cancer were included, 47% having Gleason grade group 5 disease. Treatment modalities included androgen deprivation therapy (ADT) in 98.9% men, either alone (19%) or in combinations including prostate radiotherapy (70%), pelvic nodal radiotherapy (38%), docetaxel (22%), or surgery (7%). At median follow up of 50 months, 5-year bPFS, rPFS, and OS were 62.7%, 71.0%, and 75.8% respectively. Prostate radiotherapy was associated with significantly higher bPFS (74.1% vs 34.2%), rPFS (80.7% vs 44.3%) and OS (86.7% vs 56.2%) at five years (log rank p < 0.001 each). On multivariable analysis including age, Gleason grade group, tumour stage, ADT duration, docetaxel, and nodal radiotherapy, benefit of prostate radiotherapy persisted for bPFS [HR 0.33 (95% CI 0.18-0.62)], rPFS [HR 0.25 (0.12-0.51)], and OS [HR 0.27 (0.13-0.58)] (p < 0.001 each). Impact of nodal radiotherapy or docetaxel was not established due to small subgroups. CONCLUSION: Addition of prostate radiotherapy to ADT in cN1M0 prostate cancer yielded improved disease control and overall survival independent of other tumour and treatment factors.

Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium.

OBJECTIVE: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep-learning models in glioblastoma care pathways. Additionally, to understand the most common imaging studies and image contrasts to inform the development of potentially robust deep-learning models. METHODS: MR imaging data were analysed from a random sample of five patients from the prospective cohort across five participating sites of the ZGBM consortium. Reported clinical and treatment data alongside DICOM header information were analysed to understand treatment pathway imaging schedules. RESULTS: All sites perform all structural imaging at every stage in the pathway except for the presurgical study, where in some sites only contrast-enhanced T1-weighted imaging is performed. Diffusion MRI is the most common non-structural imaging type, performed at every site. CONCLUSION: The imaging protocol and scheduling varies across the UK, making it challenging to develop machine-learning models that could perform robustly at other centres. Structural imaging is performed most consistently across all centres. ADVANCES IN KNOWLEDGE: Successful translation of deep-learning models will likely be based on structural post-treatment imaging unless there is significant effort made to standardise non-structural or peri-operative imaging protocols and schedules.

Exploring chemical space in non-targeted analysis: a proposed ChemSpace tool.

Non-targeted analysis (NTA) using high-resolution mass spectrometry allows scientists to detect and identify a broad range of compounds in diverse matrices for monitoring exposure and toxicological evaluation without a priori chemical knowledge. NTA methods present an opportunity to describe the constituents of a sample across a multidimensional swath of chemical properties, referred to as "chemical space." Understanding and communicating which region of chemical space is extractable and detectable by an NTA workflow, however, remains challenging and non-standardized. For example, many sample processing and data analysis steps influence the types of chemicals that can be detected and identified. Accordingly, it is challenging to assess whether analyte non-detection in an NTA study indicates true absence in a sample (above a detection limit) or is a false negative driven by workflow limitations. Here, we describe the need for accessible approaches that enable chemical space mapping in NTA studies, propose a tool to address this need, and highlight the different ways in which it could be implemented in NTA workflows. We identify a suite of existing predictive and analytical tools that can be used in combination to generate scores that describe the likelihood a compound will be detected and identified by a given NTA workflow based on the predicted chemical space of that workflow. Higher scores correspond to a higher likelihood of compound detection and identification in a given workflow (based on sample extraction, data acquisition, and data analysis parameters). Lower scores indicate a lower probability of detection, even if the compound is truly present in the samples of interest. Understanding the constraints of NTA workflows can be useful for stakeholders when results from NTA studies are used in real-world applications and for NTA researchers working to improve their workflow performance. The hypothetical ChemSpaceTool suggested herein could be used in both a prospective and retrospective sense. Prospectively, the tool can be used to further curate screening libraries and set identification thresholds. Retrospectively, false detections can be filtered by the plausibility of the compound identification by the selected NTA method, increasing the confidence of unknown identifications. Lastly, this work highlights the chemometric needs to make such a tool robust and usable across a wide range of NTA disciplines and invites others who are working on various models to participate in the development of the ChemSpaceTool. Ultimately, the development of a chemical space mapping tool strives to enable further standardization of NTA by improving method transparency and communication around false detection rates, thus allowing for more direct method comparisons between studies and improved reproducibility. This, in turn, is expected to promote further widespread applications of NTA beyond research-oriented settings.

Reaction dynamics of Diels-Alder reactions from machine learned potentials.

Recent advances in the development of reactive machine-learned potentials (MLPs) promise to transform reaction modelling. However, such methods have remained computationally expensive and limited to experts. Here, we employ different MLP methods (ACE, NequIP, GAP), combined with automated fitting and active learning, to study the reaction dynamics of representative Diels-Alder reactions. We demonstrate that the ACE and NequIP MLPs can consistently achieve chemical accuracy (±1 kcal mol-1) to the ground-truth surface with only a few hundred reference calculations. These strategies are shown to enable routine ab initio-quality classical and quantum dynamics, and obtain dynamical quantities such as product ratios and free energies from non-static methods. For ambimodal reactions, product distributions were found to be strongly dependent on the QM method and less so on the type of dynamics propagated.

Computational Modeling of Supramolecular Metallo-organic Cages-Challenges and Opportunities.

Self-assembled metallo-organic cages have emerged as promising biomimetic platforms that can encapsulate whole substrates akin to an enzyme active site. Extensive experimental work has enabled access to a variety of structures, with a few notable examples showing catalytic behavior. However, computational investigations of metallo-organic cages are scarce, not least due to the challenges associated with their modeling and the lack of accurate and efficient protocols to evaluate these systems. In this review, we discuss key molecular principles governing the design of functional metallo-organic cages, from the assembly of building blocks through binding and catalysis. For each of these processes, computational protocols will be reviewed, considering their inherent strengths and weaknesses. We will demonstrate that while each approach may have its own specific pitfalls, they can be a powerful tool for rationalizing experimental observables and to guide synthetic efforts. To illustrate this point, we present several examples where modeling has helped to elucidate fundamental principles behind molecular recognition and reactivity. We highlight the importance of combining computational and experimental efforts to speed up supramolecular catalyst design while reducing time and resources.

Heterometathesis of diphosphanes (R2P-PR2) with dichalcogenides (R'E-ER', E = O, S, Se, Te).

The reactions of R2P-PR2 with R'E-ER', (where E = Se, S, O, Te) to give R2P-ER' have been explored experimentally and computationally. The reaction of Ph2P-PPh2 with PhSe-SePh gives Ph2P-SePh (1) rapidly and quantitatively. The P-P/Se-Se reaction is inhibited by the addition of the radical scavenger TEMPO which is consistent with a radical mechanism for the heterometathesis reaction. Compound 1 has been fully characterised, including by X-ray crystallography. A range of other Ar2P-SeR (R = Ph, nBu or CH2CH2CO2H) have also been prepared and characterised. The reaction of 1 with [Mo(CO)4(nbd)] (nbd = norbornadiene) gives two products which, from their characteristic 31P NMR data, have been identified as cis-[Mo(CO)4(Ph2PSePh-P)2] (8) and the mixed-donor complex cis-[Mo(CO)4(Ph2P-SePh-P)(Ph2P-SePh-Se)] (9). It is deduced that the P and Se atoms in ligand 1 have comparable capacity to coordinate to Mo(0). The reaction of Ph2P-PPh2 with PhS-SPh gives Ph2P-SPh (2) quantitatively but no reaction was observed between Ph2P-PPh2 and PhTe-TePh. Heterometathesis between Ph2P-PPh2 and tBuO-OtBu does not occur thermally but has been observed under UV irradiation to give Ph2P-OtBu along with P(V) oxidation by-products. DFT calculations have been carried out to illuminate why heterometatheses with dichalcogenides R'E-ER' occur readily when E = S and Se but not when E = O and Te. The calculations show that heterometathesis is predicted to be thermodynamically favourable for E = O, S and Se and unfavourable for E = Te. The fact that a metathesis reaction between Ph2P-PPh2 with tBuO-OtBu is not observed in the absence of UV radiation, is therefore due to kinetics.

A transferable active-learning strategy for reactive molecular force fields.

Predictive molecular simulations require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct such potentials by fitting energies and forces to high-level quantum-mechanical data, but doing so typically requires considerable human intervention and data volume. Here we show that, by leveraging hierarchical and active learning, accurate Gaussian Approximation Potential (GAP) models can be developed for diverse chemical systems in an autonomous manner, requiring only hundreds to a few thousand energy and gradient evaluations on a reference potential-energy surface. The approach uses separate intra- and inter-molecular fits and employs a prospective error metric to assess the accuracy of the potentials. We demonstrate applications to a range of molecular systems with relevance to computational organic chemistry: ranging from bulk solvents, a solvated metal ion and a metallocage onwards to chemical reactivity, including a bifurcating Diels-Alder reaction in the gas phase and non-equilibrium dynamics (a model SN2 reaction) in explicit solvent. The method provides a route to routinely generating machine-learned force fields for reactive molecular systems.

You're Worried, We're Listening: Online Testing of the Effectiveness of Education Materials to Improve Consumer Knowledge and Confidence in Reporting Patient Deterioration.

OBJECTIVES: Early identification of patient deterioration in hospital is important to reduce mortality, avoidable morbidity, length of stay, and associated healthcare costs. By closely observing physical and behavioral changes, deteriorating patients are more likely to be identified. Patients and family at the bedside can play an important role in reporting deterioration if made aware of how to do so. Therefore, the objective of this study was to undertake an online evaluation of educational materials designed to improve consumers' knowledge and confidence to report patient deterioration. METHODS: A convenience sample was used to recruit community-based participants for an online survey. A self-designed validated instrument was used to undertake a preintervention and postintervention test involving 3 types of educational materials. Quantitative data were analyzed with Wilcoxon signed rank test to compare participants' knowledge and confidence before and after exposure to the intervention. Conventional content analyses examined responses on key messages and recommendations to improve the educational materials. RESULTS: A total of 84 respondents completed both prequestionnaires and postquestionnaires. After exposure to the education materials, analyses confirmed that knowledge and confidence scores were significantly higher than baseline measures. Content analyses indicated a clear understanding of the key messages presented in the materials. Four main recommendations were made regarding the education materials. CONCLUSIONS: Participants readily identified the key messages in the educational materials and demonstrated increased knowledge and confidence to report concerns about deterioration. Further research is required to determine the efficacy of the educational materials in relation to consumer behavior.

Development and Validation of an Evaluation Tool of Consumers' Knowledge and Confidence to Report Patient Deterioration in Hospitals.

OBJECTIVES: Studies have shown that many preventable hospital deaths may be reduced through early reporting of deterioration by patients and their visitors to health professionals. Engagement of patients and families for safer health care was recommended by the World Health Organization. As no validated tool was identified, the objective of the study was to develop and validate a tool to measure the impact of messages contained in self-developed educational materials on consumers' knowledge and confidence to report patient deterioration. METHODS: A tool was created using consumer-based input. A test-retest methodology was used 2 weeks apart, using 37 participants sourced from consumer groups. Fourteen 5-point Likert scale questionnaire items were categorized into knowledge and confidence components to identify the impact of the materials in both of these areas. RESULTS: Test-retest reliability confirmed 10 of the 14 proposed questionnaire items (4 knowledge items and 6 confidence) had at least moderate κ agreement ranging from 0.42 to 0.69 and an intraclass correlation coefficient of 0.66. An exploratory factor analysis of the 10 remaining items indicated a 2-factor solution had the best interpretability accounting for 96.9% of variability. The Cronbach α and factor loadings for the factors, knowledge, and confidence suggested an acceptable level of reliability and validity. CONCLUSIONS: The study confirms our tool for assessment of educational materials to be a reliable, context-specific, and validated instrument. The tool is an appropriate instrument to assess the effectiveness of educational materials in public awareness campaigns that focus on consumer reporting of patient deterioration within hospitals.

Radical-initiated P,P-metathesis reactions of diphosphanes: evidence from experimental and computational studies.

By combining the diphosphanes Ar2P-PAr2, where Ar = C6H5, 4-C6H4Me, 4-C6H4OMe, 3,5-C6H3(CF3)2, it has been shown that P,P-metathesis generally occurs rapidly under ambient conditions. DFT calculations have shown that the stability of unsymmetrical diphosphanes Z2P-PZ'2 is a function of the difference between the Z and Z' substituents in terms of size and electronegativity. Of the mechanisms that were calculated for the P,P-metathesis, the most likely was considered to be one involving Ar2P˙ radicals. The observations that photolysis increases the rate of the P,P-metatheses and TEMPO inhibits it, are consistent with a radical chain process. The P,P-metathesis reactions that involve (o-Tol)2P-P(o-Tol)2 are anomalously slow and, in the absence of photolysis, were only observed to take place in CHCl3 and CH2Cl2. The role of the chlorinated solvent is ascribed to the formation of Ar2PCl which catalyses the P,P-metathesis. The slow kinetics observed with (o-Tol)2P-P(o-Tol)2 is tentatively attributed to the o-CH3 groups quenching the (o-Tol)2P˙ radicals or inhibiting the metathesis reaction sterically.

autodE: Automated Calculation of Reaction Energy Profiles- Application to Organic and Organometallic Reactions.

Calculating reaction energy profiles to aid in mechanistic elucidation has long been the domain of the expert computational chemist. Here, we introduce autodE (https://github.com/duartegroup/autodE), an open-source Python package capable of locating transition states (TSs) and minima and delivering a full reaction energy profile from 1D or 2D chemical representations. autodE is broadly applicable to study organic and organometallic reaction classes, including addition, substitution, elimination, migratory insertion, oxidative addition, and reductive elimination; it accounts for conformational sampling of both minima and TSs and is compatible with many electronic structure packages. The general applicability of autodE is demonstrated in complex multi-step reactions, including cobalt- and rhodium-catalyzed hydroformylation and an Ireland-Claisen rearrangement.

Carbonylative C-C Bond Activation of Aminocyclopropanes Using a Temporary Directing Group Strategy.

Temporary directing groups (TDGs) underpin a range of C-C bond activation methodologies; however, the use of TDGs for the regiocontrolled activation of cyclopropane C-C bonds is underdeveloped. In this report, we show how an unusual ring contraction process can be harnessed for TDG-based carbonylative C-C bond activations of cyclopropanes. The method involves the transient installation of an isocyanate-derived TDG, rather than relying on carbonyl condensation events as used in previous TDG-enabled C-C bond activations.

Synergistic Noncovalent Catalysis Facilitates Base-Free Michael Addition.

Carbon-carbon bond-forming processes that involve the deprotonation of a weakly acidic C-H pro-nucleophile using a strong Brønsted base are central to synthetic methodology. Enzymes also catalyze C-C bond formation from weakly C-H acidic substrates; however, they accomplish this at pH 7 using only collections of noncovalent interactions. Here, we show that a simple, bioinspired synthetic cage catalyzes Michael addition reactions using only Coulombic and other weak interactions to activate various pro-nucleophiles and electrophiles. The anion-stabilizing property of the cage promotes spontaneous pro-nucleophile deprotonation, suggesting acidity enhancement equivalent to several pKa units. Using a second noncovalent reagent-commercially available 18-crown-6-facilitates catalytic base-free addition of several challenging Michael partners. The cage's microenvironment also promotes high diastereoselectivity compared to a conventional base-catalyzed reaction.

cgbind: A Python Module and Web App for Automated Metallocage Construction and Host-Guest Characterization.

Metallocages offer a diverse and underexplored region of chemical space in which to search for novel catalysts and substrate hosts. However, the ability to tailor such structures toward applications in binding and catalysis is a challenging task. Here, we present an open-source computational toolkit, cgbind, that facilitates the construction, characterization, and prediction of functional metallocages. It employs known structural scaffolds as starting points and computationally efficient approaches for property evaluation. We demonstrate the ability of cgbind to construct libraries of cages with varied topologies and linker functionalities, generate accurate geometries (RMSD < 1.5 Å to crystal structures), and predict substrate binding with accuracy on par with semiempirical QM, all in seconds. The cgbind code presented here is freely available at github.com/duartegroup/cgbind and also via a web-based graphical user interface at cgbind.chem.ox.ac.uk. The protocol described here paves the way for high-throughput virtual screening of potential supramolecular structures, accelerating the search for new hosts and catalysts.

Rhodacyclopentanones as Linchpins for the Atom Economical Assembly of Diverse Polyheterocycles.

We outline a conceptual blueprint that provides direct and atom economical access to a wide range of complex polyheterocycles. Our method capitalizes on the ambiphilic reactivity of rhodacyclopentanones that arise upon exposure of cyclopropanes to Rh(I) catalysts and CO. Using this approach, a wide array of polycyclizations are achieved, including variants that involve powerful dearomatizations and medium ring formations.

Host-Guest-Induced Electron Transfer Triggers Radical-Cation Catalysis.

Modifying the reactivity of substrates by encapsulation is a fundamental principle of capsule catalysis. Here we show an alternative strategy, wherein catalytic activation of otherwise inactive quinone "co-factors" by a simple Pd2L4 capsule promotes a range of bulk-phase, radical-cation cycloadditions. Solution electron-transfer experiments and cyclic voltammetry show that the cage anodically shifts the redox potential of the encapsulated quinone by a significant 1 V. Moreover, the capsule also protects the reduced semiquinone from protonation, thus transforming the role of quinones from stoichiometric oxidants into catalytic single-electron acceptors. We envisage that the host-guest-induced release of an "electron hole" will translate to various forms of non-encapsulated catalysis that involve other difficult-to-handle, highly reactive species.

Rationalizing the Activity of an "Artificial Diels-Alderase": Establishing Efficient and Accurate Protocols for Calculating Supramolecular Catalysis.

Self-assembled cages have emerged as novel platforms to explore bioinspired catalysis. While many different size and shape supramolecular structures are now readily accessible, only a few are known to accelerate chemical reactions under substoichiometric conditions. These limited examples point to a poor understanding of cage catalysis in general, limiting the ability to design new systems. Here we show that a simple and efficient density-functional-theory-based methodology, informed by explicitly solvated molecular dynamics and coupled cluster calculations, is sufficient to accurately reproduce experimental guest binding affinities (MAD = 1.9 kcal mol-1) and identify the catalytic Diels-Alder proficiencies (>80% accuracy) of two homologous Pd2L4 metallocages with a variety of substrates. This analysis reveals how subtle structural differences in the cage framework affect binding and catalysis. These effects manifest in a smaller distortion and more favorable interaction energy for the catalytic cage compared to the inactive structure. This study gives detailed insight that would otherwise be difficult to obtain from experiments, providing new opportunities in the design of catalytically active supramolecular cages.