Using machine learning to predict UK and Japanese secondary students' life satisfaction in PISA 2018

BACKGROUND

Life satisfaction is a key component of students' subjective well-being due to its impact on academic achievement and lifelong health. Although previous studies have investigated life satisfaction through different lenses, few of them employed machine learning (ML) approaches.

OBJECTIVE

Using ML algorithms, the current study predicts secondary students' life satisfaction from individual-level variables.

METHOD

Two supervised ML models, random forest (RF) and k-nearest neighbours (KNN), were developed based on the UK data and the Japan data in PISA 2018.

RESULTS

Findings show that (1) both models yielded better performance on the UK data than on the Japanese data; (2) the RF model outperformed the KNN model in predicting students' life satisfaction; (3) meaning in life, student competition, teacher support, exposure to bullying and ICT resources at home and at school played important roles in predicting students' life satisfaction.

CONCLUSIONS

Theoretically, this study highlights the multi-dimensional nature of life satisfaction and identifies several key predictors. Methodologically, this study is the first to use ML to explore the predictors of life satisfaction. Practically, it serves as a reference for improving secondary students' life satisfaction.

Effect of MR head coil geometry on deep-learning-based MR image reconstruction

PURPOSE

To investigate whether parallel imaging-imposed geometric coil constraints can be relaxed when using a deep learning (DL)-based image reconstruction method as opposed to a traditional non-DL method.

THEORY AND METHODS

Traditional and DL-based MR image reconstruction approaches operate in fundamentally different ways: Traditional methods solve a system of equations derived from the image data whereas DL methods use data/target pairs to learn a generalizable reconstruction model. Two sets of head coil profiles were evaluated: (1) 8-channel and (2) 32-channel geometries. A DL model was compared to conjugate gradient SENSE (CG-SENSE) and L1-wavelet compressed sensing (CS) through quantitative metrics and visual assessment as coil overlap was increased.

RESULTS

Results were generally consistent between experiments. As coil overlap increased, there was a significant (p < 0.001) decrease in performance in most cases for all methods. The decrease was most pronounced for CG-SENSE, and the DL models significantly outperformed (p < 0.001) their non-DL counterparts in all scenarios. CS showed improved robustness to coil overlap and signal-to-noise ratio (SNR) versus CG-SENSE, but had quantitatively and visually poorer reconstructions characterized by blurriness as compared to DL. DL showed virtually no change in performance across SNR and very small changes across coil overlap.

CONCLUSION

The DL image reconstruction method produced images that were robust to coil overlap and of higher quality than CG-SENSE and CS. This suggests that geometric coil design constraints can be relaxed when using DL reconstruction methods.

Chronic disease prevention and screening outcomes for patients with and without financial difficulty: a secondary analysis of the BETTER WISE cluster randomised controlled trial

OBJECTIVE

Building on Existing Tools To improvE chronic disease pRevention and screening in primary care Wellness of cancer survIvorS and patiEnts (BETTER WISE) was designed to assess the effectiveness of a cancer and chronic disease prevention and screening (CCDPS) programme. Here, we compare outcomes in participants living with and without financial difficulty.

DESIGN

Secondary analysis of a cluster-randomised controlled trial.

SETTING

Patients of 59 physicians from 13 clinics enrolled between September 2018 and August 2019.

PARTICIPANTS

596 of 1005 trial participants who responded to a financial difficulty screening question at enrolment.

INTERVENTION

1-hour CCDPS visit versus usual care.

OUTCOME MEASURES

Eligibility for a possible 24 CCDPS actions was assessed at baseline and the primary outcome was the percentage of eligible items that were completed at 12-month follow-up. We also compared the change in response to the financial difficulty screening question between baseline and follow-up.

RESULTS

55 of 265 participants (20.7%) in the control group and 69 of 331 participants (20.8%) in the intervention group reported living with financial difficulty. The primary outcome was 29% (95% CI 26% to 33%) for intervention and 23% (95% CI 21% to 26%) for control participants without financial difficulty (p=0.01). Intervention and control participants with financial difficulty scored 28% (95% CI 24% to 32%) and 32% (95% CI 27% to 38%), respectively (p=0.14). In participants who responded to the financial difficulty question at both time points (n=302), there was a net decrease in the percentage of participants who reported financial difficulty between baseline (21%) and follow-up (12%, p<0.001) which was similar in the control and intervention groups. The response rate to this question was only 51% at follow-up.

CONCLUSION

The BETTER intervention improved uptake of CCDPS manoeuvres in participants without financial difficulty, but not in those living with financial difficulty. Improving CCDPS for people living with financial difficulty may require a different clinical approach or that social determinants be addressed concurrently with clinical and lifestyle needs or both.

TRIAL REGISTRATION NUMBER

ISRCTN21333761.

Oxidation-induced ambiphilicity triggers N-N bond formation and dinitrogen release in octahedral terminal molybdenum(v) nitrido complexes

Coupling of octahedral, terminal d1 molybdenum(v) nitrido complexes supported by a dianionic pentadentate ligand via N-N bond formation to give μ-dinitrogen complexes was found to be thermodynamically feasible but faces significant kinetic barriers. However, upon oxidation, a kinetically favored nucleophilic/electrophilic N-N bond forming mechanism was enabled to give monocationic μ-dinitrogen dimers. Computational and experimental evidence for this "oxidation-induced ambiphilic nitrido coupling" mechanism is presented. The factors influencing release of dinitrogen from the resulting μ-dinitrogen dimers were also probed and it was found that further oxidation to a dicationic species is required to induce (very rapid) loss of dinitrogen. The mechanistic path discovered for N-N bond formation and dinitrogen release follows an ECECC sequence (E = "electrochemical step"; C = "chemical step"). Experimental evidence for the intermediacy of a highly electrophilic, cationic d0 molybdenum(vi) nitrido in the N-N bond forming mechanism via trapping with an isonitrile reagent is also discussed. Together these results are relevant to the development of molecular catalysts capable of mediating ammonia oxidation to dihydrogen and dinitrogen.

Behavioral and Neurophysiological Implications of Pathological Human Tau Expression in Serotonin Neurons

Alzheimer's disease (AD) is a progressive degenerative disorder that results in a severe loss of brain cells and irreversible cognitive decline. Memory problems are the most recognized symptoms of AD. However, approximately 90% of patients diagnosed with AD suffer from behavioral symptoms, including mood changes and social impairment years before cognitive dysfunction. Recent evidence indicates that the dorsal raphe nucleus (DRN) is among the initial regions that show tau pathology, which is a hallmark feature of AD. The DRN harbors serotonin (5-HT) neurons, which are critically involved in mood, social, and cognitive regulation. Serotonergic impairment early in the disease process may contribute to behavioral symptoms in AD. However, the mechanisms underlying vulnerability and contribution of the 5-HT system to AD progression remain unknown. Here, we performed behavioral and electrophysiological characterizations in mice expressing a phosphorylation-prone form of human tau (hTauP301L) in 5-HT neurons. We found that pathological tau expression in 5-HT neurons induces anxiety-like behavior and alterations in stress-coping strategies in female and male mice. Female mice also exhibited social disinhibition and mild cognitive impairment in response to 5-HT neuron-specific hTauP301L expression. Behavioral alterations were accompanied by disrupted 5-HT neuron physiology in female and male hTauP301L expressing mice with exacerbated excitability disruption in females only. These data provide mechanistic insights into the brain systems and symptoms impaired early in AD progression, which is critical for disease intervention.

Bending a Cumulene with Electrons: Stepwise Chemical Reduction and Structural Study of a Tetraaryl[4]Cumulene

Chemical reduction of a [4]cumulene with cesium metal was explored, and the structural changes stemming from electron acquisition are detailed using X-ray crystallography. It is found that the [4]cumulene undergoes dramatic geometric changes upon stepwise reduction, including bending of the cumulenic core and twisting of the endgroups from orthogonal to planar. The structural deformation is consistent with early theoretical reports that suggest that the twisting should occur upon reduction of both even and odd [n]cumulenes. The current results, on the other hand, are inconsistent with a previous experimental study of a [3]cumulene in which the predicted twisting is not observed upon reduction. DFT calculations reveal that the barrier to deformation is an order of magnitude lower in a [3]cumulene than a [4]cumulene, allowing the barrier to be overcome in the solid-state.

Analyses of plasma metabolites using a high performance four-channel CIL LC-MS method and identification of metabolites associated with enteric methane emissions in beef cattle

Reducing enteric methane (one greenhouse gas) emissions from beef cattle not only can be beneficial in reducing global warming, but also improve efficiency of nutrient utilization in the production system. However, direct measurement of enteric methane emissions on individual cattle is difficult and expensive. The objective of this study was to detect plasma metabolites that are associated with enteric methane emissions in beef cattle. Average enteric methane emissions (CH4) per day (AVG_DAILYCH4) for each individual cattle were measured using the GreenFeed emission monitoring (GEM) unit system, and beef cattle with divergent AVG_DAILYCH4 from Angus (n = 10 for the low CH4 group and 9 for the high CH4 group), Charolais (n = 10 for low and 10 for = high), and Kinsella Composite (n = 10 for low and 10 for high) populations were used for plasma metabolite quantification and metabolite-CH4 association analyses. Blood samples of these cattle were collected near the end of the GEM system tests and a high performance four-channel chemical isotope labeling (CIL) liquid chromatography (LC) mass spectrometer (MS) method was applied to identify and quantify concentrations of metabolites. The four-channel CIL LC-MS method detected 4235 metabolites, of which 1105 were found to be significantly associated with AVG_DAILYCH4 by a t-test, while 1305 were significantly associated with AVG_DAILYCH4 by a regression analysis at p<0.05. Both the results of the t-test and regression analysis revealed that metabolites that were associated with enteric methane emissions in beef cattle were largely breed-specific whereas 4.29% to 6.39% CH4 associated metabolites were common across the three breed populations and 11.07% to 19.08% were common between two breed populations. Pathway analyses of the CH4 associated metabolites identified top enriched molecular processes for each breed population, including arginine and proline metabolism, arginine biosynthesis, butanoate metabolism, and glutathione metabolism for Angus; beta-alanine metabolism, pyruvate metabolism, glycolysis / gluconeogenesis, and citrate cycle (TCA cycle) for Charolais; phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arginine biosynthesis, and arginine and proline metabolism for Kinsella Composite. The detected CH4 associated metabolites and enriched molecular processes will help understand biological mechanisms of enteric methane emissions in beef cattle. The detected CH4 associated plasma metabolites will also provide valuable resources to further characterize the metabolites and verify their utility as biomarkers for selection of cattle with reduced methane emissions.

Intended and unintended consequences of strategies to meet performance benchmarks for rheumatologist referrals in a centralized intake system

RATIONALE

Timely assessment of a chronic condition is critical to prevent long-term irreversible consequences. Patients with inflammatory arthritis (IA) symptoms require diagnosis by a rheumatologist and intervention initiation to minimize potential joint damage. With limited rheumatologist capacity, meeting urgency wait time benchmarks can be challenging. We investigate the impact of the maximum wait time guarantee (MWTG) policy and referral volume changes in a rheumatology central intake (CI) system on meeting this challenge.

METHODS

We applied a system simulation approach to model a high-volume CI rheumatology clinic. Model parameters were based on the referral and triage data from the CI and clinic appointment data. We compare the wait time performance of the current distribution policy MWTG and when referral volumes change.

RESULTS

The MWTG policy ensures 100% of new patients see a rheumatologist within their urgency wait time benchmark. However, the average wait time for new patients increased by 51% (178-269 days). A 10% decrease in referrals resulted in a 76% decrease on average wait times (178-43 days) for new patients and an increase in the number of patients seen by a rheumatologist within 1 year of the initial visit.

CONCLUSION

An MWTG policy can result in intended and unintended consequences-ensuring that all patients meet the wait time benchmarks but increasing wait times overall. Relatively small changes in referral volume significantly impact wait times. These relationships can assist clinic managers and policymakers decide on the best approach to manage referrals for better system performance.

Hippocampal ripples coincide with "up-state" and spindles in retrosplenial cortex

During NREM sleep, hippocampal sharp-wave ripple (SWR) events are thought to stabilize memory traces for long-term storage in downstream neocortical structures. Within the neocortex, a set of distributed networks organized around retrosplenial cortex (RS-network) interact preferentially with the hippocampus purportedly to consolidate those traces. Transient bouts of slow oscillations and sleep spindles in this RS-network are often observed around SWRs, suggesting that these two activities are related and that their interplay possibly contributes to memory consolidation. To investigate how SWRs interact with the RS-network and spindles, we combined cortical wide-field voltage imaging, Electrocorticography, and hippocampal LFP recordings in anesthetized and sleeping mice. Here, we show that, during SWR, "up-states" and spindles reliably co-occur in a cortical subnetwork centered around the retrosplenial cortex. Furthermore, retrosplenial transient activations and spindles predict slow gamma oscillations in CA1 during SWRs. Together, our results suggest that retrosplenial-hippocampal interaction may be a critical pathway of information exchange between the cortex and hippocampus.

MINNO: An Open Source Software for Refining Metabolic Networks and Investigating Complex Network Activity Using Empirical Metabolomics Data

Metabolomics is a powerful tool for uncovering biochemical diversity in a wide range of organisms. Metabolic network modeling is commonly used to frame metabolomics data in the context of a broader biological system. However, network modeling of poorly characterized nonmodel organisms remains challenging due to gene homology mismatches which lead to network architecture errors. To address this, we developed the Metabolic Interactive Nodular Network for Omics (MINNO), a web-based mapping tool that uses empirical metabolomics data to refine metabolic networks. MINNO allows users to create, modify, and interact with metabolic pathway visualizations for thousands of organisms, in both individual and multispecies contexts. Herein, we illustrate the use of MINNO in elucidating the metabolic networks of understudied species, such as those of the Borrelia genus, which cause Lyme and relapsing fever diseases. Using a hybrid genomics-metabolomics modeling approach, we constructed species-specific metabolic networks for threeBorrelia species. Using these empirically refined networks, we were able to metabolically differentiate these species via their nucleotide metabolism, which cannot be predicted from genomic networks. Additionally, using MINNO, we identified 18 missing reactions from the KEGG database, of which nine were supported by the primary literature. These examples illustrate the use of metabolomics for the empirical refining of genetically constructed networks and show how MINNO can be used to study nonmodel organisms.

Localized Medium Concentration Electrolyte with Fast Kinetics for Lithium Metal Batteries

Localized high-concentration electrolyte is widely acknowledged as a cutting-edge electrolyte for the lithium metal anode. However, the high fluorine content, either from high-concentration salts or from highly fluorinated diluents, results in significantly higher production costs and an increased environmental burden. Here, we have developed a novel electrolyte termed "Localized Medium-Concentration Electrolyte" (LMCE) to effectively address these issues. This LMCE is designed and produced by diluting a medium concentration (0.5 M-1.5 M) electrolyte which is incompatible with lithium metal anode before diluting. It has ultralow concentration (0.1 M) and demonstrates remarkable compatibility with lithium metal anode. Surprisingly, our LMCE, despite having an ultralow concentration (0.1 M), exhibits excellent kinetics in Li/Cu, Li/Li, LiFePO4 /Li, and NCM811/Li batteries. Additionally, LMCE effectively inhibits the corrosion of the Al current collector caused by LiTFSI salt under high voltage (>4 V) conditions. This groundbreaking LMCE design transforms the seemingly "incompatible" into the "compatible", opening up new avenues for exploring various electrolyte formulations, including all liquid electrolyte-based batteries.

Assessing morphological preservation of gastrointestinal parasites from fecal samples of wild capuchin monkeys (Cebus imitator) stored in ethanol versus formalin

The copromicroscopic identification of gastrointestinal parasites is a common, cost-effective method vital to understanding host-parasite interactions. However, its efficacy depends on effective preservation of the samples. In this study, we compare the preservation of ethanol and formalin preserved gastrointestinal parasites collected from a wild population of Costa Rican capuchin monkeys (Cebus imitator). Fecal samples were collected, halved, and stored in either 10% formalin or 96% ethanol at ambient temperature, then microscopically screened for the presence of parasites. Parasites were morphologically identified and rated based on their preservation using a newly developed rubric. We identified more parasitic morphotypes in formalin-preserved samples but found no difference in the number of parasites per fecal gram (PFG) between mediums. There was no difference in the PFG of two most prevalent parasite morphotypes, Filariopsis barretoi larvae and Strongyle-type eggs, and while Filariopsis larvae were better preserved in formalin, strongyle eggs showed no preservation difference between mediums. Our results support the suitability of both ethanol and formalin for morphological parasite identification in samples stored over 1 year, describe the morphological changes and challenges associated with parasite degradation, and highlight the potential for future studies to use both morphological and molecular methods in non-invasively collected samples.

Haplotype phasing of CYP2D6: an allelic ratio method using Agena MassARRAY data

Pharmacogenomics aims to use the genetic information of an individual to personalize drug prescribing. There is evidence that pharmacogenomic testing before prescription may prevent adverse drug reactions, increase efficacy, and reduce cost of treatment. CYP2D6 is a key pharmacogene of relevance to multiple therapeutic areas. Indeed, there are prescribing guidelines available for medications based on CYP2D6 enzyme activity as deduced from CYP2D6 genetic data. The Agena MassARRAY system is a cost-effective method of detecting genetic variation that has been clinically applied to other genes. However, its clinical application to CYP2D6 has to date been limited by weaknesses such as the inability to determine which haplotype was present in more than one copy for individuals with more than two copies of the CYP2D6 gene. We report application of a new protocol for CYP2D6 haplotype phasing of data generated from the Agena MassARRAY system. For samples with more than two copies of the CYP2D6 gene for which the prior consensus data specified which one was present in more than one copy, our protocol was able to conduct CYP2D6 haplotype phasing resulting in 100% concordance with the prior data. In addition, for three reference samples known to have more than two copies of CYP2D6 but for which the exact number of CYP2D6 genes was unknown, our protocol was able to resolve the number for two out of the three of these, and estimate the likely number for the third. Finally, we demonstrate that our method is applicable to CYP2D6 hybrid tandem configurations.

Deployment of a human-centred clinical decision support system for pulmonary embolism: evaluation of impact on quality of diagnostic decisions

Pulmonary embolism (PE) is a serious condition that presents a diagnostic challenge for which diagnostic errors often happen. The literature suggests that a gap remains between PE diagnostic guidelines and adherence in healthcare practice. While system-level decision support tools exist, the clinical impact of a human-centred design (HCD) approach of PE diagnostic tool design is unknown.

DESIGN

Before-after (with a preintervention period as non-concurrent control) design study.

SETTING

Inpatient units at two tertiary care hospitals.

PARTICIPANTS

General internal medicine physicians and their patients who underwent PE workups.

INTERVENTION

After a 6-month preintervention period, a clinical decision support system (CDSS) for diagnosis of PE was deployed and evaluated over 6 months. A CDSS technical testing phase separated the two time periods.

MEASUREMENTS

PE workups were identified in both the preintervention and CDSS intervention phases, and data were collected from medical charts. Physician reviewers assessed workup summaries (blinded to the study period) to determine adherence to evidence-based recommendations. Adherence to recommendations was quantified with a score ranging from 0 to 1.0 (the primary study outcome). Diagnostic tests ordered for PE workups were the secondary outcomes of interest.

RESULTS

Overall adherence to diagnostic pathways was 0.63 in the CDSS intervention phase versus 0.60 in the preintervention phase (p=0.18), with fewer workups in the CDSS intervention phase having very low adherence scores. Further, adherence was significantly higher when PE workups included the Wells prediction rule (median adherence score=0.76 vs 0.59, p=0.002). This difference was even more pronounced when the analysis was limited to the CDSS intervention phase only (median adherence score=0.80 when Wells was used vs 0.60 when Wells was not used, p=0.001). For secondary outcomes, using both the D-dimer blood test (42.9% vs 55.7%, p=0.014) and CT pulmonary angiogram imaging (61.9% vs 75.4%, p=0.005) was lower during the CDSS intervention phase.

CONCLUSION

A clinical decision support intervention with an HCD improves some aspects of the diagnostic decision, such as the selection of diagnostic tests and the use of the Wells probabilistic prediction rule for PE.

Adsorption Dynamics of Uremic Toxins to Novel Modified Magnetic Nanoparticles

Kidney dysfunction leads to the retention of metabolites in the blood compartment, some of which reach toxic levels. Uremic toxins are associated with the progression of kidney disease and other symptoms of kidney failure (i.e., nausea, itchiness, and hypertension). Toxin removal ameliorates symptoms and reduces further organ damage, but membrane-based methods are inadequate for this purpose. Engineered adsorbents may facilitate enhanced removal of retained toxins, especially those bound strongly by proteins. Poly 2-(methacryloyloxy)ethyl phosphorylcholine-co-β-cyclodextrin (p(MPC-co-PMβCD)) coated magnetic nanoparticles are synthesized, characterized for their physicochemical properties (Fourier-transform infrared spectroscopy (FTIR), nuclear magnetic resonance (NMR), thermogravimetric analysis(TGA), gel permeation chromatography (GPC), and transmission electron microscope (TEM), and evaluated toxin adsorption from a complex solution for the first time to quantify the effects of film chemistry and incubation time on the adsorbed toxinome (the collection of toxins). Uremic toxins are bound by even "low-fouling" polymer films themselves; providing further insight into how small molecule interactions with "low-fouling" films may affect protein-surface interactions. These results suggest a dynamic interaction between toxins and surfaces that is not driven by solution concentration alone. This knowledge will help advance the design of novel adsorbent films for clearing uremic toxins.

A Review of Cognitive Support Systems in the Operating Room

BACKGROUND

In recent years, numerous innovative yet challenging surgeries, such as minimally invasive procedures, have introduced an overwhelming amount of new technologies, increasing the cognitive load for surgeons and potentially diluting their attention. Cognitive support technologies (CSTs) have been in development to reduce surgeons' cognitive load and minimize errors. Despite its huge demands, it still lacks a systematic review.

METHODS

Literature was searched up until May 21st, 2021. Pubmed, Web of Science, and IEEExplore. Studies that aimed at reducing the cognitive load of surgeons were included. Additionally, studies that contained an experimental trial with real patients and real surgeons were prioritized, although phantom and animal studies were also included. Major outcomes that were assessed included surgical error, anatomical localization accuracy, total procedural time, and patient outcome.

RESULTS

A total of 37 studies were included. Overall, the implementation of CSTs had better surgical performance than the traditional methods. Most studies reported decreased error rate and increased efficiency. In terms of accuracy, most CSTs had over 90% accuracy in identifying anatomical markers with an error margin below 5 mm. Most studies reported a decrease in surgical time, although some were statistically insignificant.

DISCUSSION

CSTs have been shown to reduce the mental workload of surgeons. However, the limited ergonomic design of current CSTs has hindered their widespread use in the clinical setting. Overall, more clinical data on actual patients is needed to provide concrete evidence before the ubiquitous implementation of CSTs.

Controlling viscous fingering instabilities of complex fluids

Despite their aesthetic elegance, wavy or fingering patterns emerge when a fluid of low viscosity pushes another immiscible fluid of high viscosity in a porous medium, producing an incomplete sweep and hampering several crucial technologies. Some examples include chromatography, printing, coating flows, oil-well cementing, as well as large-scale technologies of groundwater and enhanced oil recovery. Controlling such fingering instabilities is notoriously challenging and unresolved for complex fluids of varying viscosity because the fluids' mobility contrast is often predetermined and yet the predominant drive in determining a stable, flat or unstable, wavy interface. Here we show, experimentally and theoretically, how to suppress or control the primary viscous fingering patterns of a common type of complex fluids (of shear-thinning with a low yield stress) using a radially tapered cell of linearly varying gap thickness, h(r). Experimentally, we displace a complex viscous (PAA) solution with gas under a constant flow rate (Q), varied between 0.02 and 2 slpm (standard liter per minute), in a radially converging cell with a constant gap-thickness gradient, [Formula: see text]. A stable, uniform interface emerges at low Q and in a steeper cell (i.e., greater [Formula: see text]) for the complex fluids, whereas unstable fingering pattern at high Q and smaller [Formula: see text]. Our theoretical predictions with a simplified linear stability analysis show an agreeable stability criterion with experimental data, quantitatively offering strategies to control complex fluid-fluid patterns and displacements in microfluidics and porous media.

An intriguing vision for transatlantic collaborative health data use and artificial intelligence development

Our traditional approach to diagnosis, prognosis, and treatment, can no longer process and transform the enormous volume of information into therapeutic success, innovative discovery, and health economic performance. Precision health, i.e., the right treatment, for the right person, at the right time in the right place, is enabled through a learning health system, in which medicine and multidisciplinary science, economic viability, diverse culture, and empowered patient's preferences are digitally integrated and conceptually aligned for continuous improvement and maintenance of health, wellbeing, and equity. Artificial intelligence (AI) has been successfully evaluated in risk stratification, accurate diagnosis, and treatment allocation, and to prevent health disparities. There is one caveat though: dependable AI models need to be trained on population-representative, large and deep data sets by multidisciplinary and multinational teams to avoid developer, statistical and social bias. Such applications and models can neither be created nor validated with data at the country, let alone institutional level and require a new dimension of collaboration, a cultural change with the establishment of trust in a precompetitive space. The Data for Health (#DFH23) conference in Berlin and the Follow-Up Workshop at Harvard University in Boston hosted a representative group of stakeholders in society, academia, industry, and government. With the momentum #DFH23 created, the European Health Data Space (EHDS) as a solid and safe foundation for consented collaborative health data use and the G7 Hiroshima AI process in place, we call on citizens and their governments to fully support digital transformation of medicine, research and innovation including AI.

Overview and Discussion of the Competition on Legal Information, Extraction/Entailment (COLIEE) 2023

We summarize the 10th Competition on Legal Information Extraction and Entailment. In this tenth edition, the competition included four tasks on case law and statute law. The case law component includes an information retrieval task (Task 1), and the confirmation of an entailment relation between an existing case and a selected unseen case (Task 2). The statute law component includes an information retrieval task (Task 3), and an entailment/question-answering task based on retrieved civil code statutes (Task 4). Participation was open to any group based on any approach. Ten different teams participated in the case law competition tasks, most of them in more than one task. We received results from 8 teams for Task 1 (22 runs) and seven teams for Task 2 (18 runs). On the statute law task, there were 9 different teams participating, most in more than one task. 6 teams submitted a total of 16 runs for Task 3, and 9 teams submitted a total of 26 runs for Task 4. We describe the variety of approaches, our official evaluation, and analysis of our data and submission results.

Legal Information Retrieval and Entailment Using Transformer-based Approaches

The challenge of information overload in the legal domain increases every day. The COLIEE competition has created four challenge tasks that are intended to encourage the development of systems and methods to alleviate some of that pressure: a case law retrieval (Task 1) and entailment (Task 2), and a statute law retrieval (Task 3) and entailment (Task 4). Here we describe our methods for Task 1 and Task 4. In Task 1, we used a sentence-transformer model to create a numeric representation for each case paragraph. We then created a histogram of the similarities between a query case and a candidate case. The histogram is used to build a binary classifier that decides whether a candidate case should be noticed or not. In Task 4, our approach relies on fine-tuning a pre-trained DeBERTa large language model (LLM) trained on SNLI and MultiNLI datasets. Our method for Task 4 was ranked third among eight participating teams in the COLIEE 2023 competition. For Task 4, We also compared the performance of the DeBERTa model with those of a knowledge distillation model and ensemble methods including Random Forest and Voting.

Simultaneous estimation of the temporal and spatial extent of animal migration using step lengths and turning angles

BACKGROUND

Animals of many different species, trophic levels, and life history strategies migrate, and the improvement of animal tracking technology allows ecologists to collect increasing amounts of detailed data on these movements. Understanding when animals migrate is important for managing their populations, but is still difficult despite modelling advancements.

METHODS

We designed a model that parametrically estimates the timing of migration from animal tracking data. Our model identifies the beginning and end of migratory movements as signaled by change-points in step length and turning angle distributions. To this end, we can also use the model to estimate how an animal's movement changes when it begins migrating. In addition to a thorough simulation analysis, we tested our model on three datasets: migratory ferruginous hawks (Buteo regalis) in the Great Plains, barren-ground caribou (Rangifer tarandus groenlandicus) in northern Canada, and non-migratory brown bears (Ursus arctos) from the Canadian Arctic.

RESULTS

Our simulation analysis suggests that our model is most useful for datasets where an increase in movement speed or directional autocorrelation is clearly detectable. We estimated the beginning and end of migration in caribou and hawks to the nearest day, while confirming a lack of migratory behaviour in the brown bears. In addition to estimating when caribou and ferruginous hawks migrated, our model also identified differences in how they migrated; ferruginous hawks achieved efficient migrations by drastically increasing their movement rates while caribou migration was achieved through significant increases in directional persistence.

CONCLUSIONS

Our approach is applicable to many animal movement studies and includes parameters that can facilitate comparison between different species or datasets. We hope that rigorous assessment of migration metrics will aid understanding of both how and why animals move.

The impact of the COVID-19 pandemic on transfers between long-term care and emergency departments across Alberta

BACKGROUND

Long-term care (LTC) was overwhelmingly impacted by COVID-19 and unnecessary transfer to emergency departments (ED) can have negative health outcomes. This study aimed to explore how the COVID-19 pandemic impacted LTC to ED transfers and hospitalizations, utilization of community paramedics and facilitated conversations between LTC and ED physicians during the first four waves of the pandemic in Alberta, Canada.

METHODS

In this retrospective population-based study, administrative databases were linked to identify episodes of care for LTC residents who resided in facilities in Alberta, Canada. This study included data from January 1, 2018 to December 31, 2021 to capture outcomes prior to the onset of the pandemic and across the first four waves. Individuals were included if they visited an emergency department, received care from a community paramedic or whose care involved a facilitated conversation between LTC and ED physicians during this time period.

RESULTS

Transfers to ED and hospitalizations from LTC have been gradually declining since 2018 with a sharp decline seen during wave 1 of the pandemic that was greatest in the lowest-priority triage classification (CTAS 5). Community paramedic visits were highest during the first two waves of the pandemic before declining in subsequent waves; facilitated calls between LTC and ED physicians increased during the waves.

CONCLUSIONS

There was a reduction in number of transfers from LTC to EDs and in hospitalizations during the first four waves of the pandemic. This was supported by increased conversations between LTC and ED physicians, but was not associated with increased community paramedic visits. Additional work is needed to explore how programs such as community paramedics and facilitated conversations between LTC and ED providers can help to reduce unnecessary transfers to hospital.

PathBank 2.0-the pathway database for model organism metabolomics

PathBank (https://pathbank.org) and its predecessor database, the Small Molecule Pathway Database (SMPDB), have been providing comprehensive metabolite pathway information for the metabolomics community since 2010. Over the past 14 years, these pathway databases have grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in computing technology. This year's update, PathBank 2.0, brings a number of important improvements and upgrades that should make the database more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of primary or canonical pathways (from 1720 to 6951); (ii) a massive increase in the total number of pathways (from 110 234 to 605 359); (iii) significant improvements to the quality of pathway diagrams and pathway descriptions; (iv) a strong emphasis on drug metabolism and drug mechanism pathways; (v) making most pathway images more slide-compatible and manuscript-compatible; (vi) adding tools to support better pathway filtering and selecting through a more complete pathway taxonomy; (vii) adding pathway analysis tools for visualizing and calculating pathway enrichment. Many other minor improvements and updates to the content, the interface and general performance of the PathBank website have also been made. Overall, we believe these upgrades and updates should greatly enhance PathBank's ease of use and its potential applications for interpreting metabolomics data.

DrugBank 6.0: the DrugBank Knowledgebase for 2024

First released in 2006, DrugBank (https://go.drugbank.com) has grown to become the 'gold standard' knowledge resource for drug, drug-target and related pharmaceutical information. DrugBank is widely used across many diverse biomedical research and clinical applications, and averages more than 30 million views/year. Since its last update in 2018, we have been actively enhancing the quantity and quality of the drug data in this knowledgebase. In this latest release (DrugBank 6.0), the number of FDA approved drugs has grown from 2646 to 4563 (a 72% increase), the number of investigational drugs has grown from 3394 to 6231 (a 38% increase), the number of drug-drug interactions increased from 365 984 to 1 413 413 (a 300% increase), and the number of drug-food interactions expanded from 1195 to 2475 (a 200% increase). In addition to this notable expansion in database size, we have added thousands of new, colorful, richly annotated pathways depicting drug mechanisms and drug metabolism. Likewise, existing datasets have been significantly improved and expanded, by adding more information on drug indications, drug-drug interactions, drug-food interactions and many other relevant data types for 11 891 drugs. We have also added experimental and predicted MS/MS spectra, 1D/2D-NMR spectra, CCS (collision cross section), RT (retention time) and RI (retention index) data for 9464 of DrugBank's 11 710 small molecule drugs. These and other improvements should make DrugBank 6.0 even more useful to a much wider research audience ranging from medicinal chemists to metabolomics specialists to pharmacologists.

Perspectives of parents partnering with physical therapists to deliver intensive rehabilitation for their young children with perinatal stroke: A qualitative study

BACKGROUND

Parental participation in their young children's rehabilitation has been promoted to increase intervention intensity, but parents' perspectives on increased involvement remain unclear. The objective of this study was to explore parents' experiences partnering with physical therapists (PTs) to administer early, intensive rehabilitation to their young children with cerebral palsy (CP).

METHODS

Twelve mothers and one father of children between 8 months and 3 years old with CP were interviewed. Semistructured interviews were conducted before and after parents partnered with a PT to deliver a 12 week activity-based intervention targeting their child's lower extremities. The intervention occurred in the child's home and in PT clinical sites. Interviews were audio recorded and transcribed verbatim. Interpretive description was used as the methodological framework.

RESULTS

The three themes were (1) focus on maximizing the child's potential, (2) participation in intensive rehabilitation is challenging, and (3) the importance of a positive experience with therapy. Families discussed a number of barriers and facilitators to participating in intensive rehabilitation.

CONCLUSIONS

These results provide considerations for clinicians and researchers to facilitate meaningful engagement of parents in their young children's rehabilitation. This is especially important with increasing evidence for early, activity-based interventions for young children with CP.

Early childhood trajectories of domain-specific developmental delay and gestational age at birth: An analysis of the All Our Families cohort

OBJECTIVE

To describe developmental domain-specific trajectories from ages 1 through 5 years and to estimate the association of trajectory group membership with gestational age for children born between ≥34 and <41 weeks gestation.

METHODS

Using data from the All Our Families cohort, trajectories of the domain-specific Ages & Stages Questionnaire scores were identified and described using group-based trajectory modeling for children born ≥34 and <41 weeks of gestation (n = 2664). The trajectory groups association with gestational age was estimated using multinomial logistic regression.

RESULTS

Across the five domains, 4-5 trajectory groups were identified, and most children experienced changing levels of risk for delay over time. Decreasing gestational age increases the Relative risk of delays in fine motor (emerging high risk: 1.46, 95% CI: 1.19-1.80; resolving moderate risk: 1.11, 95% CI: 1.03-1.21) and gross motor (resolving high risk: 1.21, 95% CI: 1.04-1.42; and consistent high risk: 1.64, 95% CI: 1.20-2.24) and problem solving (consistent high risk: 1.58 (1.09-2.28) trajectory groups compared to the consistent low risk trajectory groups.

CONCLUSION

This study highlights the importance of longitudinal analysis in understanding developmental processes; most children experienced changing levels of risk of domain-specific delay over time instead of having a consistent low risk pattern. Gestational age had differential effects on the individual developmental domains after adjustment for social, demographic and health factors, indicating a potential role of these factors on trajectory group membership.

Spatiotemporal signaling underlies progressive vascular rarefaction in myocardial infarction

Therapeutic angiogenesis represents a promising avenue to revascularize the ischemic heart. Its limited success is partly due to our poor understanding of the cardiac stroma, specifically mural cells, and their response to ischemic injury. Here, we combine single-cell and positional transcriptomics to assess the behavior of mural cells within the healing heart. In response to myocardial infarction, mural cells adopt an altered state closely associated with the infarct and retain a distinct lineage from fibroblasts. This response is concurrent with vascular rarefaction and reduced vascular coverage by mural cells. Positional transcriptomics reveals that the infarcted heart is governed by regional-dependent and temporally regulated programs. While the remote zone acts as an important source of pro-angiogenic signals, the infarct zone is accentuated by chronic activation of anti-angiogenic, pro-fibrotic, and inflammatory cues. Together, our work unveils the spatiotemporal programs underlying cardiac repair and establishes an association between vascular deterioration and mural cell dysfunction.

The Effect of Deuteration and Homologation of the Lactam Ring of Nirmatrelvir on Its Biochemical Properties and Oxidative Metabolism

This study explores the relationship between structural alterations of nirmatrelvir, such as homologation and deuteration, and metabolic stability of newly synthesized derivatives. We developed a reliable synthetic protocol toward dideutero-nirmatrelvir and its homologated analogues with high isotopic incorporation. Deuteration of the primary metabolic site of nirmatrelvir provides a 3-fold improvement of its human microsomal stability but is accompanied by an increased metabolism rate at secondary sites. Homologation of the lactam ring allows the capping group modification to decrease and delocalize the molecule's lipophilicity, reducing the metabolic rate at secondary sites. The effect of deuteration was less pronounced for the 6-membered lactam than for its 5-membered analogue in human microsomes, but the trend is reversed in the case of mouse microsomes. X-ray data revealed that the homologation of the lactam ring favors the orientation of the drug's nitrile warhead for interaction with the catalytic sulfur of the SARS-CoV-2 Mpro, improving its binding. Comparable potency against SARS-CoV-2 Mpro from several variants of concern and selectivity over human cysteine proteases cathepsin B, L, and S was observed for the novel deuterated/homologated derivative and nirmatrelvir. Synthesized compounds displayed a large interspecies variability in hamster, rat, and human hepatocyte stability assays. Overall, we aimed to apply a rational approach in changing the physicochemical properties of the drug to refine its biochemical and biological parameters.

Disease-Associated Mutations in Tau Encode for Changes in Aggregate Structure Conformation

The accumulation of tau fibrils is associated with neurodegenerative diseases, which are collectively termed tauopathies. Cryo-EM studies have shown that the packed fibril core of tau adopts distinct structures in different tauopathies, such as Alzheimer's disease, corticobasal degeneration, and progressive supranuclear palsy. A subset of tauopathies are linked to missense mutations in the tau protein, but it is not clear whether these mutations impact the structure of tau fibrils. To answer this question, we developed a high-throughput protein purification platform and purified a panel of 37 tau variants using the full-length 0N4R splice isoform. Each of these variants was used to create fibrils in vitro, and their relative structures were studied using a high-throughput protease sensitivity platform. We find that a subset of the disease-associated mutations form fibrils that resemble wild-type tau, while others are strikingly different. The impact of mutations on tau structure was not clearly associated with either the location of the mutation or the relative kinetics of fibril assembly, suggesting that tau mutations alter the packed core structures through a complex molecular mechanism. Together, these studies show that single-point mutations can impact the assembly of tau into fibrils, providing insight into its association with pathology and disease.

Predicting Impact Outcomes and Maximum Spreading of Drop Impact on Heated Nanostructures Using Machine Learning

Accurate prediction of droplet behavior upon impact on a heated nanostructured surface is vital for various industrial applications. In this study, we leverage multiple data-driven machine learning (ML) techniques to model the impact outcome and droplet spreading, employing existing experimental data. Our approach incorporates a comprehensive range of critical control parameters, such as the impact velocity (V), surface temperature (Ts), nanopillars' packing fraction (ϕ), and surface roughness (r). We obtain optimal results when utilizing the artificial neural network classification (ANNC) to construct a phase diagram that encompasses all of the experimental impact behaviors. Additionally, we utilize the support vector regression (SVR) method to model the maximum spreading factor (βmax) as a function of the Weber number (We), defined as the ratio of droplet kinetic to surface energy, and Ts for each surface combination. Consistent with previous experimental observations, our results illustrate that nanostructures not only introduce distinct impact behaviors, such as central jetting, but also influence the boundaries among the deposition, rebound, and splashing regimes within the phase diagram. An increase in ϕ at a constant r promotes deposition and spreading events, while increasing r at a constant ϕ results in enhanced heat transfer to promote the Leidenfrost effect for the rebound regime and a greater disturbance of the liquid lamella to trigger splashing. The SVR prediction reveals the existence of a We-number threshold governed by the nanostructure parameters. Beyond this threshold, the maximum spreading factor (βmax) of a spreading droplet becomes independent of the surface temperature (Ts) as We increases, suggesting that fluid properties are likely the dominating factors influencing the spreading dynamics in the extreme We range.

An Automated Centrifugal Microfluidic Platform for Efficient Multistep Blood Sample Preparation and Clean-Up towards Small Ion-Molecule Analysis

Sample preparation for mass spectroscopy typically involves several liquid and solid phase clean-ups, extractions, and other unit operations, which are labour-intensive and error-prone. We demonstrate a centrifugal microfluidic platform that automates the whole blood sample's preparation and clean-up by combining traditional liquid-phase and multiple solid-phase extractions for applications in mass spectroscopy (MS)-based small molecule detection. Liquid phase extraction was performed using methanol to precipitate proteins in plasma separated from a blood sample under centrifugal force. The preloaded solid phase composed of C18 beads then removed lipids with a combination of silica particles, which further cleaned up any remaining proteins. We further integrated the application of this sample prep disc with matrix-assisted laser desorption/ionization (MALDI) MS by using glancing angle deposition films, which further cleaned up the processed sample by segregating the electrolyte background from the sample salts. Additionally, hydrophilic interaction liquid chromatography (HILIC) MS was employed for detecting targeted free amino acids. Therefore, several representative ionic metabolites, including several amino acids and organic acids from blood samples, were analysed by both MALDI-MS and HILIC-MS to demonstrate the performance of this sample preparation disc. The fully automated blood sample preparation procedure only took 35 mins, with a throughput of three parallel units.

Data-driven analysis and prediction of stable phases for high-entropy alloy design

High-entropy alloys (HEAs) represent a promising class of materials with exceptional structural and functional properties. However, their design and optimization pose challenges due to the large composition-phase space coupled with the complex and diverse nature of the phase formation dynamics. In this study, a data-driven approach that utilizes machine learning (ML) techniques to predict HEA phases and their composition-dependent phases is proposed. By employing a comprehensive dataset comprising 5692 experimental records encompassing 50 elements and 11 phase categories, we compare the performance of various ML models. Our analysis identifies the most influential features for accurate phase prediction. Furthermore, the class imbalance is addressed by employing data augmentation methods, raising the number of records to 1500 in each category, and ensuring a balanced representation of phase categories. The results show that XGBoost and Random Forest consistently outperform the other models, achieving 86% accuracy in predicting all phases. Additionally, this work provides an extensive analysis of HEA phase formers, showing the contributions of elements and features to the presence of specific phases. We also examine the impact of including different phases on ML model accuracy and feature significance. Notably, the findings underscore the need for ML model selection based on specific applications and desired predictions, as feature importance varies across models and phases. This study significantly advances the understanding of HEA phase formation, enabling targeted alloy design and fostering progress in the field of materials science.

Repeatability of adaptation in sunflowers reveals that genomic regions harbouring inversions also drive adaptation in species lacking an inversion

Local adaptation commonly involves alleles of large effect, which experience fitness advantages when in positive linkage disequilibrium (LD). Because segregating inversions suppress recombination and facilitate the maintenance of LD between locally adapted loci, they are also commonly found to be associated with adaptive divergence. However, it is unclear what fraction of an adaptive response can be attributed to inversions and alleles of large effect, and whether the loci within an inversion could still drive adaptation in the absence of its recombination-suppressing effect. Here, we use genome-wide association studies to explore patterns of local adaptation in three species of sunflower: Helianthus annuus, Helianthus argophyllus, and Helianthus petiolaris, which each harbour a large number of species-specific inversions. We find evidence of significant genome-wide repeatability in signatures of association to phenotypes and environments, which are particularly enriched within regions of the genome harbouring an inversion in one species. This shows that while inversions may facilitate local adaptation, at least some of the loci can still harbour mutations that make substantial contributions without the benefit of recombination suppression in species lacking a segregating inversion. While a large number of genomic regions show evidence of repeated adaptation, most of the strongest signatures of association still tend to be species-specific, indicating substantial genotypic redundancy for local adaptation in these species.

Developing non-response weights to account for attrition-related bias in a longitudinal pregnancy cohort

BACKGROUND

Prospective cohorts may be vulnerable to bias due to attrition. Inverse probability weights have been proposed as a method to help mitigate this bias. The current study used the "All Our Families" longitudinal pregnancy cohort of 3351 maternal-infant pairs and aimed to develop inverse probability weights using logistic regression models to predict study continuation versus drop-out from baseline to the three-year data collection wave.

METHODS

Two methods of variable selection took place. One method was a knowledge-based a priori variable selection approach, while the second used Least Absolute Shrinkage and Selection Operator (LASSO). The ability of each model to predict continuing participation through discrimination and calibration for both approaches were evaluated by examining area under the receiver operating curve (AUROC) and calibration plots, respectively. Stabilized inverse probability weights were generated using predicted probabilities. Weight performance was assessed using standardized differences of baseline characteristics for those who continue in study and those that do not, with and without weights (unadjusted estimates).

RESULTS

The a priori and LASSO variable selection method prediction models had good and fair discrimination with AUROC of 0.69 (95% Confidence Interval [CI]: 0.67-0.71) and 0.73 (95% CI: 0.71-0.75), respectively. Calibration plots and non-significant Hosmer-Lemeshow Goodness of Fit Tests indicated that both the a priori (p = 0.329) and LASSO model (p = 0.242) were well-calibrated. Unweighted results indicated large (> 10%) standardized differences in 15 demographic variables (range: 11 - 29%), when comparing those who continued in the study with those that did not. Weights derived from the a priori and LASSO models reduced standardized differences relative to unadjusted estimates, with the largest differences of 13% and 5%, respectively. Additionally, when applying the same LASSO variable selection method to develop weights in future data collection waves, standardized differences remained below 10% for each demographic variable.

CONCLUSION

The LASSO variable selection approach produced robust weights that addressed non-response bias more than the knowledge-driven approach. These weights can be applied to analyses across multiple longitudinal waves of data collection to reduce bias.

Towards High Efficiency and Rapid Production of Room-Temperature Liquid Metal Wires Compatible with Electronic Prototyping Connectors

We present in this work new methodologies to produce, refine, and interconnect room-temperature liquid-metal-core thermoplastic elastomer wires that have extreme extendibility (>500%), low production time and cost at scale, and may be integrated into commonly used electrical prototyping connectors like a Japan Solderless Terminal (JST) or Dupont connectors. Rather than focus on the development of a specific device, the aim of this work is to demonstrate strategies and processes necessary to achieve scalable production of liquid-metal-enabled electronics and address several key challenges that have been present in liquid metal systems, including leak-free operation, minimal gallium corrosion of other electrode materials, low liquid metal consumption, and high production rates. The ultimate goal is to create liquid-metal-enabled rapid prototyping technologies, similar to what can be achieved with Arduino projects, where modification and switching of components can be performed in seconds, which enables faster iterations of designs. Our process is focused primarily on fibre-based liquid metal wires contained within thermoplastic elastomers. These fibre form factors can easily be integrated with wearable sensors and actuators as they can be sewn or woven into fabrics, or cast within soft robotic components.

Machine learning analyses identify multi-modal frailty factors that selectively discriminate four cohorts in the Alzheimer's disease spectrum: a COMPASS-ND study

BACKGROUND

Frailty indicators can operate in dynamic amalgamations of disease conditions, clinical symptoms, biomarkers, medical signals, cognitive characteristics, and even health beliefs and practices. This study is the first to evaluate which, among these multiple frailty-related indicators, are important and differential predictors of clinical cohorts that represent progression along an Alzheimer's disease (AD) spectrum. We applied machine-learning technology to such indicators in order to identify the leading predictors of three AD spectrum cohorts; viz., subjective cognitive impairment (SCI), mild cognitive impairment (MCI), and AD. The common benchmark was a cohort of cognitively unimpaired (CU) older adults.

METHODS

The four cohorts were from the cross-sectional Comprehensive Assessment of Neurodegeneration and Dementia dataset. We used random forest analysis (Python 3.7) to simultaneously test the relative importance of 83 multi-modal frailty indicators in discriminating the cohorts. We performed an explainable artificial intelligence method (Tree Shapley Additive exPlanation values) for deep interpretation of prediction effects.

RESULTS

We observed strong concurrent prediction results, with clusters varying across cohorts. The SCI model demonstrated excellent prediction accuracy (AUC = 0.89). Three leading predictors were poorer quality of life ([QoL]; memory), abnormal lymphocyte count, and abnormal neutrophil count. The MCI model demonstrated a similarly high AUC (0.88). Five leading predictors were poorer QoL (memory, leisure), male sex, abnormal lymphocyte count, and poorer self-rated eyesight. The AD model demonstrated outstanding prediction accuracy (AUC = 0.98). Ten leading predictors were poorer QoL (memory), reduced olfaction, male sex, increased dependence in activities of daily living (n = 6), and poorer visual contrast.

CONCLUSIONS

Both convergent and cohort-specific frailty factors discriminated the AD spectrum cohorts. Convergence was observed as all cohorts were marked by lower quality of life (memory), supporting recent research and clinical attention to subjective experiences of memory aging and their potentially broad ramifications. Diversity was displayed in that, of the 14 leading predictors extracted across models, 11 were selectively sensitive to one cohort. A morbidity intensity trend was indicated by an increasing number and diversity of predictors corresponding to clinical severity, especially in AD. Knowledge of differential deficit predictors across AD clinical cohorts may promote precision interventions.

A Scoping Review of Adult Inpatient Satisfaction with Mental Health Services

Patient satisfaction with hospital services has been increasingly discussed as an important indicator of healthcare quality. It has been demonstrated that improving patient satisfaction is associated with better compliance with treatment plans and a decrease in patient complaints regarding doctors' and nurses' misconduct. This scoping review's objective is to investigate the pertinent literature on the experiences and satisfaction of patients with mental disorders receiving inpatient psychiatric care. Our goals are to highlight important ideas and explore the data that might serve as a guide to enhance the standard of treatment and patient satisfaction in acute mental health environments. This study is a scoping review that was designed in adherence with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) statement. A systematic search was conducted in the following databases: PubMed, MEDLINE, PsycINFO, CINAHL, and EMBASE. A comprehensive review was completed, including articles from January 2012 to June 2022. Qualitative and quantitative studies were included in this review based on our eligibility criteria, such as patient satisfaction as a primary outcome, adult psychiatric inpatients, and non-review studies published in the English language. Studies were considered ineligible if they included nonpsychiatric patients or patients with neurocognitive disorders, review studies, or study measure outcomes other than inpatient satisfaction. For the eligible studies, data extraction was conducted, information was summarized, and the findings were reported. A total of 31 studies representing almost all the world's continents were eligible for inclusion in this scoping review. Different assessment tools and instruments were used in the included studies to measure the level of patients' satisfaction. The majority of the studies either utilized a pre-existing or newly created inpatient satisfaction questionnaire that appeared to be reliable and of acceptable quality. This review has identified a variety of possible factors that affect patients' satisfaction and can be used as a guide for service improvement. More than half of the included studies revealed that the following factors were strongly recommended to enhance inpatient satisfaction with care: a clear discharge plan, less coercive treatment during the hospital stay, more individualized, higher quality information and teaching about the mental disorder to patients by staff, better therapeutic relationships with staff, and specific treatment components that patients enjoy, such as physical exercise sessions and music therapy. Patients also value staff who spend more time with them. The scope of patient satisfaction with inpatient mental health services is a growing source of concern. Patient satisfaction is associated with better adherence to treatment regimens and fewer complaints against health care professionals. This scoping review has identified several patient satisfaction research gaps as well as important determinants of satisfaction and how to measure and utilize patient satisfaction as a guide for service quality improvement. It would be useful for future research and reviews to consider broadening their scope to include the satisfaction of psychiatric patients with innovative services, like peer support groups and other technologically based interventions like text for support. Future research also could benefit from utilizing additional technological tools, such as electronic questionnaires.

Group Versus Individual Diabetes Education for Persons With Experience of Homelessness in Canada

PURPOSE

The purpose of the study was to explore various forms of diabetes self-management education (DSME), including group and individual sessions, for persons with lived experiences of homelessness (PWLEH) in Canada.

METHODS

A qualitative descriptive study using open-ended interviews with health care and homeless sector service providers was utilized to serve those experiencing homelessness in 5 cities across Canada. NVivo qualitative data analysis software was used to facilitate thematic analysis, focusing on variations in DSME for PWLEH.

RESULTS

We conducted interviews with 96 unique health and social care providers. Four themes were identified through focused coding of interviews. First, the use of a harm reduction approach during diabetes education tailored to PWLEH considered patients' access to food, medications, and supplies and other comorbidities, including mental health and substance use disorders. The second theme related to the unsuitability of the curriculum in mainstream diabetes education in a group setting for PWLEH. Third, the role of group education in community building is to create supportive relationships among members. The final theme was the importance of trust and confidentiality in DSME, which were most easily maintained during individual education, compared to group formats.

CONCLUSIONS

Overall, PWLEH experience unique challenges in managing diabetes. DSME adapted to these individuals' unique needs may be more successful and could be delivered in both individual and group settings.

Genomics of Preaxostyla Flagellates Illuminates the Path Towards the Loss of Mitochondria

The notion that mitochondria cannot be lost was shattered with the report of an oxymonad Monocercomonoides exilis, the first eukaryote arguably without any mitochondrion. Yet, questions remain about whether this extends beyond the single species and how this transition took place. The Oxymonadida is a group of gut endobionts taxonomically housed in the Preaxostyla which also contains free-living flagellates of the genera Trimastix and Paratrimastix. The latter two taxa harbour conspicuous mitochondrion-related organelles (MROs). Here we report high-quality genome and transcriptome assemblies of two Preaxostyla representatives, the free-living Paratrimastix pyriformis and the oxymonad Blattamonas nauphoetae. We performed thorough comparisons among all available genomic and transcriptomic data of Preaxostyla to further decipher the evolutionary changes towards amitochondriality, endobiosis, and unstacked Golgi. Our results provide insights into the metabolic and endomembrane evolution, but most strikingly the data confirm the complete loss of mitochondria for all three oxymonad species investigated (M. exilis, B. nauphoetae, and Streblomastix strix), suggesting the amitochondriate status is common to a large part if not the whole group of Oxymonadida. This observation moves this unique loss to 100 MYA when oxymonad lineage diversified.

Amino Acids as Potential Precursors to Odorous Compounds in Tap Water during Spring Runoff Events

The onset of spring runoff in northern climates and tap water odor events are difficult to predict because common water quality parameters cannot fully explain the intermittent odor events that occurred over past decades. Studies have shown that small polar water-soluble compounds, such as amino acids (AAs), leach first from ice/snowmelt. AAs are known to produce odorous compounds, such as aldehydes and chloroaldimines, upon chlorination. Therefore, we proposed that AAs may serve as markers for small and soluble organics that contribute to the odor of chlorinated tap water. Here, we studied the occurrence of AAs in source water collected at two water treatment plants and the odor profiles of tap water at >300 homes during the 2021 and 2022 spring runoff events. AA concentrations were at baseline levels (<100 ng/L) during the 2021 runoff but much higher (up to 5500 ng/L) in 2022 and associated with an escalation in odor complaints. AA concentrations peaked at the onset of the 2022 spring runoff and corresponded with the strongest reported odor intensities in tap water. We obtained high resolution MS and MS/MS spectra of chloroaldimines and confirmed the formation of chloroaldimines under chlorination of the six AAs detected in source water. The results indicate that AAs signal the onset of spring runoff and represent small polar water-soluble compounds that may contribute to tap water odor problems.

Cortical reactivation of spatial and non-spatial features coordinates with hippocampus to form a memory dialogue

Episodic memories comprise diverse attributes of experience distributed across neocortical areas. The hippocampus is integral to rapidly binding these diffuse representations, as they occur, to be later reinstated. However, the nature of the information exchanged during this hippocampal-cortical dialogue remains poorly understood. A recent study has shown that the secondary motor cortex carries two types of representations: place cell-like activity, which were impaired by hippocampal lesions, and responses tied to visuo-tactile cues, which became more pronounced following hippocampal lesions. Using two-photon Ca2+ imaging to record neuronal activities in the secondary motor cortex of male Thy1-GCaMP6s mice, we assessed the cortical retrieval of spatial and non-spatial attributes from previous explorations in a virtual environment. We show that, following navigation, spontaneous resting state reactivations convey varying degrees of spatial (trajectory sequences) and non-spatial (visuo-tactile attributes) information, while reactivations of non-spatial attributes tend to precede reactivations of spatial representations surrounding hippocampal sharp-wave ripples.

Exploring the outcomes of research engagement using the observation method in an online setting

OBJECTIVE

The objective of this study was to explore the outcomes of research engagement (patient engagement, PE) in the context of qualitative research.

DESIGN

We observed engagement in two groups comprised of patients, clinicians and researchers tasked with conducting a qualitative preference exploration project in inflammatory bowel disease. One group was led by a patient research partner (PLG, partner led group) and the other by an academic researcher (RLG, researcher led group). A semistructured guide and a set of critical outcomes of research engagement were used as a framework to ground our analysis.

SETTING

The study was conducted online.

PARTICIPANTS

Patient research partners (n=5), researchers (n=5) and clinicians (n=4) participated in this study.

MAIN OUTCOME MEASURES

Transcripts of meetings, descriptive and reflective observation data of engagement during meetings and email correspondence between group members were analysed to identify the outcomes of PE.

RESULTS

Both projects were patient-centred, collaborative, meaningful, rigorous, adaptable, ethical, legitimate, understandable, feasible, timely and sustainable. Patient research partners (PRPs) in both groups wore dual hats as patients and researchers and influenced project decisions wearing both hats. They took on advisory and operational roles. Collaboration seemed easier in the PLG than in the RLG. The RLG PRPs spent more time than their counterparts in the PLG sharing their experience with biologics and helping their group identify a meaningful project question. A formal literature review informed the design, project materials and analysis in the RLG, while the formal review informed the project materials and analysis in the PLG. A PRP in the RLG and the PLG lead leveraged personal connections to facilitate recruitment. The outcomes of both projects were meaningful to all members of the groups.

CONCLUSIONS

Our findings show that engagement of PRPs in research has a positive influence on the project design and delivery in the context of qualitative research in both the patient-led and researcher-led group.

Repeated multi-domain cognitive training prevents cognitive decline, anxiety and amyloid pathology found in a mouse model of Alzheimer disease

Education, occupation, and an active lifestyle, comprising enhanced social, physical, and mental components are associated with improved cognitive functions in aged people and may delay the progression of various neurodegenerative diseases including Alzheimer's disease. To investigate this protective effect, 3-month-old APPNL-G-F/NL-G-F mice were exposed to repeated single- or multi-domain cognitive training. Cognitive training was given at the age of 3, 6, & 9 months. Single-domain cognitive training was limited to a spatial navigation task. Multi-domain cognitive training consisted of a spatial navigation task, object recognition, and fear conditioning. At the age of 12 months, behavioral tests were completed for all groups. Then, mice were sacrificed, and their brains were assessed for pathology. APPNL-G-F/NL-G-F mice given multi-domain cognitive training compared to APPNL-G-F/NL-G-F control group showed an improvement in cognitive functions, reductions in amyloid load and microgliosis, and a preservation of cholinergic function. Additionally, multi-domain cognitive training improved anxiety in APPNL-G-F/NL-G-F mice as evidenced by measuring thigmotaxis behavior in the Morris water maze. There were mild reductions in microgliosis in the brain of APPNL-G-F/NL-G-F mice with single-domain cognitive training. These findings provide causal evidence for the potential of certain forms of cognitive training to mitigate the cognitive deficits in Alzheimer disease.

Plasma essential fatty acid on hospital admission is a marker of COVID-19 disease severity

It is important for allocation of resources to predict those COVID patients at high risk of dying or organ failure. Early signals to initiate cellular events of host immunity can be derived from essential fatty acid metabolites preceding the cascade of proinflammatory signals. Much research has focused on understanding later proinflammatory responses. We assessed if remodelling of plasma phospholipid content of essential fatty acids by the COVID-19 virus provides early markers for potential death and disease severity. Here we show that, at hospital admission, COVID-19 infected subjects who survive exhibit higher proportions of C20:4n-6 in plasma phospholipids concurrent with marked proinflammatory cytokine elevation in plasma compared to healthy subjects. In contrast, more than half of subjects who die of this virus exhibit very low C18:2n-6 and C20:4n-6 content in plasma phospholipids on hospital admission compared with healthy control subjects. Moreover, in these subjects who die, the low level of primary inflammatory signals indicates limited or aberrant stimulation of host immunity. We conclude that COVID-19 infection results in early fundamental remodelling of essential fatty acid metabolism. In subjects with high mortality, it appears that plasma n-6 fatty acid content is too low to stimulate cellular events of host immunity.

Multidisciplinary Support To Access living donor Kidney Transplant (MuST AKT): A Clinical Research Protocol for a Pilot Randomized Controlled Trial to Increase Living Kidney Donation

Background

Living donor kidney transplantation (LDKT) is the optimal treatment for eligible patients with kidney failure, although it is underutilized. Contextually tailored patient- and family-centered interventions may be effective to increase LDKT.

Objective

We outline a protocol to test the feasibility of the Multidisciplinary Support To Access living donor Kidney Transplant (MuST AKT) intervention designed to increase LDKT.

Design

Non-blinded single-center pilot randomized controlled trial with a qualitative interview component.

Setting

Academic transplant referral center in Northern Alberta Region with a population of more than 2 million in its catchment area.

Patients

English-speaking patients of the age range 18 to 75 years who are referred for kidney transplantation are eligible to participate.

Measurements

Feasibility will be assessed by indicators of recruitment, retention, and completion rates, treatment fidelity, adherence to intervention, engagement in intervention, and acceptability.

Methods

Participants will be randomly assigned 1:1 to either standard care (control) or the experimental group who receive standard care plus the MuST AKT intervention, a person-centered program designed to assist and enable the kidney transplant candidate to achieve what is required to receive an LDKT. The intervention consists of an introductory session and 4 intervention sessions delivered in-person or virtually.

Limitations

Inferences cannot be drawn regarding the efficacy/effectiveness of the MuST AKT intervention. This study is non-blinded.

Conclusions

This pilot study is the first step in our broader initiative to increase LDKT in our health care jurisdiction. The results of this study will be used to inform the development of a future definitive randomized controlled trial.

Trial registration number

NCT04666545.

Understanding the Formation Mechanisms of Silicon Particles from the Thermal Disproportionation of Hydrogen Silsesquioxane

Crystalline silicon particles sustaining Mie resonances are readily obtained from the thermal processing of hydrogen silsesquioxane (HSQ). Here, the mechanisms involved in silicon particle formation and growth from HSQ are investigated through real-time in situ analysis using an environmental transmission electron microscope and X-ray diffractometer. The nucleation of Si nanodomains is observed starting around 1000 °C. For the first time, a highly mobile intermediate phase is experimentally observed, thus demonstrating a previously unknown growth mechanism. At least two growth processes occur simultaneously: the coalescence of small particles into larger particles and growth mode by particle displacement through the matrix toward the HSQ grain surface. Postsynthetic characterization by scanning electron microscopy further supports the latter growth mechanism. The gaseous environment employed during synthesis impacts particle formation and growth under both in situ and ex situ conditions, impacting the particle yield and structural homogeneity. Understanding the formation mechanisms of particles provides promising pathways for reducing the energy cost of this synthetic route.

Effects of Dietary Intake of Arsenosugars and Other Organic Arsenic Species on Studies of Arsenic Methylation Efficiency in Humans

Extensive research has used dimethylarsinic acid (DMA) in urine as a marker of arsenic methylation. The premise is that humans methylate inorganic arsenicals to monomethylarsonic acid (MMA) and DMA and excrete these arsenic species into the urine. However, DMA in urine not only comes from the methylation of inorganic arsenic but also could be a result of metabolism of other arsenic species, such as arsenosugars and arsenolipids. Most environmental health and epidemiological studies of arsenic methylation might have overlooked confounding factors that contribute to DMA in urine. Here we critically evaluate reported studies that used methylation indexes, concentration ratios of methylated arsenicals, or the percentage of DMA in urine as markers of arsenic methylation efficiency. Dietary intake of arsenosugars potentially confounds the calculation and interpretation of the arsenic methylation efficiencies. Many studies have not considered incidental dietary intake of arsenosugars, arsenolipids, and other organic arsenic species. Future studies should consider the dietary intake of diverse arsenic species and their potential effect on the urinary concentrations of DMA.

Change Management Accompanying Implementation of Decision Support for Prevention of Acute Kidney Injury in Cardiac Catheterization Units: Program Report

Purpose of program

Different models exist to guide successful implementation of electronic health tools into clinical practice. The Contrast Reducing Injury Sustained by Kidneys (Contrast RISK) initiative introduced an electronic decision support tool with physician audit and feedback into all of the cardiac catheterization facilities in Alberta, Canada, with the goal of preventing contrast-associated acute kidney injury (CA-AKI) following coronary angiography and intervention. This report describes the change management approaches used by the initiative and end-user's feedback on these processes.

Sources of information and methods

The Canada Health Infoway Change Management model was used to address 6 activities relevant to project implementation: governance and leadership, stakeholder engagement, communications, workflow analysis and integration, training and education, and monitoring and evaluation. Health care providers and invasive cardiologists from all sites completed preimplementation, usability, and postimplementation surveys to assess integration and change success.

Key findings

Prior to implementation, 67% of health providers were less than satisfied with processes to determine appropriate contrast dye volumes, 47% were less than satisfied with processes for administering adequate intravenous fluids, and 68% were less than satisfied with processes to ensure follow-up of high-risk patients. 48% of invasive cardiologists were less than satisfied with preprocedural identification of patients at risk of acute kidney injury (AKI). Following implementation, there were significant increases among health providers in the odds of satisfaction with processes for identifying those at high risk of AKI (odds ratio [OR] 3.01, 95% confidence interval [CI] 1.36-6.66, P = .007), quantifying the appropriate level of contrast dye for each patient (OR 6.98, 95% CI 3.06-15.91, P < .001), determining the optimal amount of IV fluid for each patient (OR 1.86, 95% CI 0.88-3.91, P = .102), and following up of kidney function of high risk patients (OR 5.49, 95%CI 2.45-12.30, P < .001). There were also significant increases among physicians in the odds of satisfaction with processes for identifying those at high risk of AKI (OR 19.53, 95% CI 3.21-118.76, P = .001), quantifying the appropriate level of contrast dye for each patient (OR 26.35, 95% CI 4.28-162.27, P < .001), and for following-up kidney function of high-risk patients (OR 7.72, 95% CI 1.62-36.84.30, P = .010). Eighty-nine percent of staff perceived the initiative as being successful in changing clinical practices to reduce the risk of CA-AKI. Physicians uniformly agreed that the system was well-integrated into existing workflows, while 42% of health providers also agreed.

Implications

The Canada Health Infoway Change Management model was an effective framework for guiding implementation of an electronic decision support tool and audit and feedback intervention to improve processes for AKI prevention within cardiac catheterization units.

Development of a DNAzyme Walker for the Detection of APE1 in Living Cancer Cells

DNAzyme walker technology is a compelling option for bioanalytical and drug delivery applications. While nucleic acid and protein targets have been used to activate DNAzyme walkers, investigations into enzyme-triggered DNAzyme walkers in living cells are still in their early stages. The base excision repair (BER) pathway presents an array of enzymes that are overexpressed in cancer cells. Here, we introduce a DNAzyme walker system that sensitively and specifically detects the BER enzyme apurinic/apyrimidinic endodeoxyribonuclease 1 (APE1). We constructed the DNAzyme walker on the surface of 20 nm-diameter gold nanoparticles. We achieved a detection limit of 160 fM of APE1 in a buffer and in whole cell lysate equivalent to the amount of APE1 in a single HeLa cell in a sample volume of 100 μL. Confocal imaging of the DNAzyme walking reveals a cytoplasmic distribution of APE1 in HeLa cells. Walking activity is tunable to exogenous Mn2+ concentrations and the uptake of the DNAzyme walker system does not require transfection assistance. We demonstrate the investigative potential of the DNAzyme walker for up-regulated or overactive enzyme biomarkers of the BER pathway in cancer cells.

Development of the Strengths, Skills, and Goals Matrix: a tool for facilitating strengths-based adolescent and young adult engagement in research

BACKGROUND

The involvement of adolescents and young adults (AYAs) with lived experience of health and mental health conditions as partners in research is increasing given the prominence of participatory approaches to research, including patient-oriented research (POR). Much of the relevant research is conducted by graduate students. While guiding AYA engagement frameworks and models exist, the processes of partnering with AYAs in patient-oriented graduate-level research projects have not been well established. Co-developed tools and practices are required to support strengths-based, developmentally appropriate AYA-graduate student partnerships.

OBJECTIVES

The objectives of this commentary are: (1) to share the processes of partnership between a graduate student and five Young Adult Research Partners (YARP), (2) to describe the co-design and implementation of the Strengths, Skills, and Goals Matrix (SSGM), a tool for facilitating strengths-based AYA engagement in research, and (3) to outline considerations for applying this tool across a variety of research contexts with patient partners.

MAIN BODY

Within the YARP-graduate student partnership, the SSGM offered extensive benefits, including tangible skill development, peer mentorship, and rapport building among all members. This tool offers strategies for strengths-based engagement practices which emphasize AYAs' preferences and goals throughout POR projects. Practical recommendations and considerations for applying the SSGM within graduate-level research and beyond are described, including the importance of connecting AYAs' current (and desired) skills to specific tasks within the research project and resulting outputs.

CONCLUSIONS

The SSGM has possible relevance in a variety of settings given its broadly applicable structure. Future research could explore the adaptation, application, and evaluation of the SSGM across research contexts to determine its feasibility and ease of implementation.

PATIENT OR PUBLIC CONTRIBUTION

This article was conceived of and co-authored by five young adult research partners. The YARP co-designed the SSGM presented in this article, the figures, and substantially contributed to the preparation of the article.