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Chemical mechanisms describe the atmospheric transformations of organic and inorganic species and connect air emissions to secondary species such as ozone, fine particles, and hazardous air pollutants (HAPs) like formaldehyde. Recent advances in our understanding of several chemical systems and shifts in the drivers of atmospheric chemistry warrant updates to mechanisms used in chemical transport models such as the Community Multiscale Air Quality (CMAQ) modeling system. This work builds on the Regional Atmospheric Chemistry Mechanism version 2 (RACM2) and develops the Community Regional Atmospheric Chemistry Multiphase Mechanism (CRACMM) version 1.0, which demonstrates a fully coupled representation of chemistry leading to ozone and secondary organic aerosol (SOA) with consideration of HAPs. CRACMMv1.0 includes 178 gas-phase species, 51 particulate species, and 508 reactions spanning gas-phase and heterogeneous pathways. To support estimation of health risks associated with HAPs, nine species in CRACMM cover 50 % of the total cancer and 60 % of the total non-cancer emission-weighted toxicity estimated for primary HAPs from anthropogenic and biomass burning sources in the US, with the coverage of toxicity higher (> 80 %) when secondary formaldehyde and acrolein are considered. In addition, new mechanism species were added based on the importance of their emissions for the ozone, organic aerosol, or atmospheric burden of total reactive organic carbon (ROC): sesquiterpenes, furans, propylene glycol, alkane-like low- to intermediate-volatility organic compounds (9 species), low- to intermediate-volatility oxygenated species (16 species), intermediate-volatility aromatic hydrocarbons (2 species), and slowly reacting organic carbon. Intermediate- and lower-volatility organic compounds were estimated to increase the coverage of anthropogenic and biomass burning ROC emissions by 40 % compared to current operational mechanisms. Autoxidation, a gas-phase reaction particularly effective in producing SOA, was added for C10 and larger alkanes, aromatic hydrocarbons, sesquiterpenes, and monoterpene systems including second-generation aldehydes. Integrating the radical and SOA chemistry put additional constraints on both systems and enabled the implementation of previously unconsidered SOA pathways from phenolic and furanone compounds, which were predicted to account for ∼ 30 % of total aromatic hydrocarbon SOA under typical atmospheric conditions. CRACMM organic aerosol species were found to span the atmospherically relevant range of species carbon number, number of oxygens per carbon, and oxidation state with a slight high bias in the number of hydrogens per carbon. In total, 11 new emitted species were implemented as precursors to SOA compared to current CMAQv5.3.3 representations, resulting in a bottom-up prediction of SOA, which is required for accurate source attribution and the design of control strategies. CRACMMv1.0 is available in CMAQv5.4.

The novel coronavirus disease 2019 (COVID-19) significantly disrupted therapy service delivery for children with disabilities and their families. Parents of children with disabilities have been particularly impacted as a large degree of responsibility has been placed on them to both manage and deliver therapies remotely. However, little is known regarding whether sociodemographic factors are associated with parents' perceptions of therapy service delivery during COVID-19. This study explored the relationship between sociodemographic factors and parents' satisfaction with therapies for children with disabilities during COVID-19.Two hundred seven parents of children with disabilities completed an online survey battery that included the Family-Provider Partnership Scale and sociodemographic characteristics and assessed their satisfaction with their child[ren]'s therapies during COVID-19.Access to telehealth, receipt of only school-based therapies, parent education, number of household essential workers, and total number of children were associated with satisfaction with therapy service and/or the family-provider partnership.By better understanding the association between sociodemographic factors and parent perception of therapy service delivery, providers can better support families in optimizing service delivery during the remainder of COVID-19 mitigation efforts and during future periods of service disruption. This study provides insight into the sociodemographic characteristics that are associated with lower levels of satisfaction and thus require more tailored support from providers. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Condensable particulate matter (CPM) emitted from stationary combustion and mobile sources exhibits high emissions and a large proportion of organic components. However, CPM is not generally measured when conducting emission surveys of PM in most countries, including China. Consequently, previous emission inventories have not included emission rates for CPM. Here, we construct an emission inventory of CPM in China with a focus on organic aerosols (OAs) based on collected CPM emission information. Results show that OA emissions are enhanced twofold after the inclusion of CPM in a new inventory for China for the years 2014 and 2017. Considering organic CPM emissions and model representations of secondary OA (SOA) formation from CPM, a series of sensitivity cases have been simulated here using the three-dimensional Community Multiscale Air Quality (CMAQ) model to estimate the contributions of CPM emissions to atmospheric OA and fine PM (PM2.5, particulate matter with aerodynamic diameter not exceeding 2.5 µm) concentrations in China. Compared with observations at a Beijing site during a haze episode from 14 October to 14 November 2014, estimates of the temporal average primary OA (POA) and SOA concentrations were greatly improved after including the CPM effects. These scenarios demonstrated the significant contributions of CPM emissions from stationary combustion and mobile sources to the POA (51 %–85 %​​​​​​​), SOA (42 %–58 %), and total OA concentrations (45 %–75 %). Furthermore, the contributions of CPM emissions to total OA concentrations were demonstrated over the 2 major cities and 26 other cities of the Beijing–Tianjin–Hebei region (hereafter referred to as the “BTH2 + 26 cities”) in December 2018, with average contributions of up to 49 %, 53 %, 54 %, and 50 % for Handan, Shijiazhuang, Xingtai, and Dezhou, respectively. Correspondingly, the inclusion of CPM emissions also narrowed the gap between simulated and observed PM2.5 concentrations over the BTH2 + 26 cities. These results improve the simulation performance of atmospheric OA and PM2.5 and may also provide important implications for the sources of OA.

Objective To provide high-quality data for coronavirus disease 2019 (COVID-19) research, we validated derived COVID-19 clinical indicators and 22 associated machine learning phenotypes, in the Mass General Brigham (MGB) COVID-19 Data Mart. Methods Fifteen reviewers performed a retrospective manual chart review for 150 COVID-19-positive patients in the data mart. To support rapid chart review for a wide range of target data, we offered a natural language processing (NLP)-based chart review tool, the Digital Analytic Patient Reviewer (DAPR). For this work, we designed a dedicated patient summary view and developed new 127 NLP logics to extract COVID-19 relevant medical concepts and target phenotypes. Moreover, we transformed DAPR for research purposes so that patient information is used for an approved research purpose only and enabled fast access to the integrated patient information. Lastly, we performed a survey to evaluate the validation difficulty and usefulness of the DAPR. Results The concepts for COVID-19-positive cohort, COVID-19 index date, COVID-19-related admission, and the admission date were shown to have high values in all evaluation metrics. However, three phenotypes showed notable performance degradation than the positive predictive value in the prepandemic population. Based on these results, we removed the three phenotypes from our data mart. In the survey about using the tool, participants expressed positive attitudes toward using DAPR for chart review. They assessed that the validation was easy and DAPR helped find relevant information. Some validation difficulties were also discussed. Conclusion Use of NLP technology in the chart review helped to cope with the challenges of the COVID-19 data validation task and accelerated the process. As a result, we could provide more reliable research data promptly and respond to the COVID-19 crisis. DAPR's benefit can be expanded to other domains. We plan to operationalize it for wider research groups.

Motivation The i2b2 platform is used at major academic health institutions and research consortia for querying for electronic health data. However, a major obstacle for wider utilization of the platform is the complexity of data loading that entails a steep curve of learning the platform’s complex data schemas. To address this problem, we have developed the i2b2-etl package that simplifies the data loading process, which will facilitate wider deployment and utilization of the platform. Results We have implemented i2b2-etl as a Python application that imports ontology and patient data using simplified input file schemas and provides inbuilt record number de-identification and data validation. We describe a real-world deployment of i2b2-etl for a population-management initiative at MassGeneral Brigham. Availability and implementation i2b2-etl is a free, open-source application implemented in Python available under the Mozilla 2 license. The application can be downloaded as compiled docker images. A live demo is available at https://i2b2clinical.org/demo-i2b2etl/ (username: demo, password: Etl@2021). Supplementary information Supplementary data are available at Bioinformatics online.

Mobile sources are responsible for a substantial controllable portion of the reactive organic carbon (ROC) emitted to the atmosphere, especially in urban environments of the United States (U.S.). We update existing methods for calculating mobile source organic particle and vapor emissions in the U.S. with over a decade of laboratory data that parameterize the volatility and organic aerosol (OA) potential of emissions from onroad vehicles, nonroad engines, aircraft, marine vessels, and locomotives. We find that existing emission factor information from teflon filters combined with quartz filters collapses into simple relationships and can be used to reconstruct the complete volatility distribution of ROC emissions. This new approach consists of source-specific filter artifact corrections and state-of-the-science speciation including explicit intermediate volatility organic compounds (IVOCs), yielding the first bottom-up volatility-resolved inventory of U.S. mobile source emissions. Using the Community Multiscale Air Quality model, we estimate mobile sources account for 20–25 % of the IVOC concentrations and 4.4–21.4 % of ambient OA. The updated emissions and air quality model reduce biases in predicting fine-particle organic carbon in winter, spring, and autumn throughout the U.S. (4.3–11.3 % reduction in normalized bias). We identify key uncertain parameters that align with current state-of-the-art research measurement challenges.

This study describes a modeling framework, model evaluation, and source apportionment to understand the causes of Los Angeles (LA) air pollution. A few major updates are applied to the Community Multiscale Air Quality (CMAQ) Model with high spatial resolution (1 km × 1 km). The updates include dynamic traffic emissions based on real-time on-road information and recent emission factors and secondary organic aerosol (SOA) schemes to represent volatile chemical products (VCP). Meteorology is well-predicted compared to ground-based observations, and the emission rates from multiple sources (i.e., on-road, volatile chemical product, area, point, biogenic, and sea spray) are quantified. Evaluation of the CMAQ model shows that ozone is well-predicted despite inaccuracies in nitrogen oxide (NOx) predictions. Particle matter (PM) is underpredicted compared to concurrent measurements made with an aerosol mass spectrometer (AMS) in Pasadena. Inorganic aerosol is well-predicted while SOA is underpredicted. Modeled SOA consists of mostly organic nitrates and products from oxidation of alkane-like intermediate volatility organic compounds (IVOCs) and has missing components that behave like less-oxidized oxygenated organic aerosol (LO-OOA). Source apportionment demonstrates that the urban areas of the LA Basin and vicinity are NOx-saturated (VOC-sensitive) with the largest sensitivity of O3 to changes in VOCs in the urban core. Differing oxidative capacities in different regions impact the nonlinear chemistry leading to PM and SOA formation, which is quantified in this study.

The Integrated Source Apportionment Method (ISAM) has been revised in the Community Multiscale Air Quality (CMAQ) model. This work updates ISAM to maximize its flexibility, particularly for ozone (O3) modeling, by providing multiple attribution options, including products inheriting attribution fully from nitrogen oxide reactants, fully from volatile organic compound (VOC) reactants, equally from all reactants, or dynamically from NOx or VOC reactants based on the indicator gross production ratio of hydrogen peroxide (H2O2) to nitric acid (HNO3). The updated ISAM has been incorporated into the most recent publicly accessible versions of CMAQ (v5.3.2 and beyond). This study's primary objective is to document these ISAM updates and demonstrate their impacts on source apportionment results for O3 and its precursors. Additionally, the ISAM results are compared with the Ozone Source Apportionment Technology (OSAT) in the Comprehensive Air-quality Model with Extensions (CAMx) and the brute-force method (BF). All comparisons are performed for a 4 km horizontal grid resolution application over the northeastern US for a selected 2 d summer case study (9 and 10 August 2018). General similarities among ISAM, OSAT, and BF results add credibility to the new ISAM algorithms. However, some discrepancies in magnitude or relative proportions among tracked sources illustrate the distinct features of each approach, while others may be related to differences in model formulation of chemical and physical processes. Despite these differences, OSAT and ISAM still provide useful apportionment data by identifying the geographical and temporal contributions of O3 and its precursors. Both OSAT and ISAM attribute the majority of O3 and NOx contributions to boundary, mobile, and biogenic sources, whereas the top three contributors to VOCs are found to be biogenic, boundary, and area sources.

Airborne particulate matter (PM) is a pollutant of concern not only because of its adverse effects on human health but also on visibility and the radiative budget of the atmosphere. PM can be considered as a sum of solid/liquid species covering a wide range of particle sizes with diverse chemical composition. Organic aerosols may be emitted (primary organic aerosols, POA), or formed in the atmosphere following reaction of volatile organic compounds (secondary organic aerosols, SOA), but some of these compounds may partition between the gas and aerosol phases depending upon ambient conditions. This review focuses on carbonaceous PM and gaseous precursors emitted by road traffic, including ultrafine particles (UFP) and polycyclic aromatic hydrocarbons (PAHs) that are clearly linked to the evolution and formation of carbonaceous species. Clearly, the solid fraction of PM has been reduced during the last two decades, with the implementation of after-treatment systems abating approximately 99% of primary solid particle mass concentrations. However, the role of brown carbon and its radiative effect on climate and the generation of ultrafine particles by nucleation of organic vapour during the dilution of the exhaust remain unclear phenomena and will need further investigation. The increasing role of gasoline vehicles on carbonaceous particle emissions and formation is also highlighted, particularly through the chemical and thermodynamic evolution of organic gases and their propensity to produce particles. The remaining carbon-containing particles from brakes, tyres and road wear will still be a problem even in a future of full electrification of the vehicle fleet. Some key conclusions and recommendations are also proposed to support the decision makers in view of the next regulations on vehicle emissions worldwide.

Objective The increasing translation of artificial intelligence (AI)/machine learning (ML) models into clinical practice brings an increased risk of direct harm from modeling bias; however, bias remains incompletely measured in many medical AI applications. This article aims to provide a framework for objective evaluation of medical AI from multiple aspects, focusing on binary classification models. Materials and Methods Using data from over 56 000 Mass General Brigham (MGB) patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), we evaluate unrecognized bias in 4 AI models developed during the early months of the pandemic in Boston, Massachusetts that predict risks of hospital admission, ICU admission, mechanical ventilation, and death after a SARS-CoV-2 infection purely based on their pre-infection longitudinal medical records. Models were evaluated both retrospectively and prospectively using model-level metrics of discrimination, accuracy, and reliability, and a novel individual-level metric for error. Results We found inconsistent instances of model-level bias in the prediction models. From an individual-level aspect, however, we found most all models performing with slightly higher error rates for older patients. Discussion While a model can be biased against certain protected groups (ie, perform worse) in certain tasks, it can be at the same time biased towards another protected group (ie, perform better). As such, current bias evaluation studies may lack a full depiction of the variable effects of a model on its subpopulations. Conclusion Only a holistic evaluation, a diligent search for unrecognized bias, can provide enough information for an unbiased judgment of AI bias that can invigorate follow-up investigations on identifying the underlying roots of bias and ultimately make a change.

Background The rotator cuff is a group of muscles and tendons which support the shoulder joint. Rotator cuff disease is a frequent cause of morbidity in adulthood. Aims The aims of his study are to determine the prevalence and patterns of rotator cuff derangement in symptomatic patients using MRI and to attempt to correlate identified patterns of disease with age and gender Methods Five hundred ninety-seven patients attending for MRI of the shoulder with atraumatic shoulder pain were included for study. Patients’ age and gender was recorded. Record was made of the presence or absence of rotator cuff derangement and of degenerative change in the AC and glenohumeral joints. Correlation was made between age and gender. Results There were 358 males (60%) and 239 females (40%) with a mean age of 49.4 ± 17.1 years. Subacromial bursitis was identified in 517 patients. A normal supraspinatus tendon was identified in 219 patients and supraspinatus full thickness tearing was identified in 102 patients. A normal AC joint was identified in 267 patients while degenerative AC joint changes were identified in 370 patients. A significant correlation was identified between age and rotator cuff derangement (p p Conclusion The extent of rotator cuff and AC joint derangement increases with ageing. Impingement appears to trigger a cascade of events in sequence, from isolated subacromial bursitis through to supraspinatus tendon tearing. Patterns of rotator cuff derangement are similar in men and women.

Comprehensive genome annotation is essential to understand the impact of clinically relevant variants. However, the absence of a standard for clinical reporting and browser display complicates the process of consistent interpretation and reporting. To address these challenges, Ensembl/GENCODE1and RefSeq2launched a joint initiative, the Matched Annotation from NCBI and EMBL-EBI (MANE) collaboration, to converge on human gene and transcript annotation and to jointly define a high-value set of transcripts and corresponding proteins. Here, we describe the MANE transcript sets for use as universal standards for variant reporting and browser display. The MANE Select set identifies a representative transcript for each human protein-coding gene, whereas the MANE Plus Clinical set provides additional transcripts at loci where the Select transcripts alone are not sufficient to report all currently known clinical variants. Each MANE transcript represents an exact match between the exonic sequences of an Ensembl/GENCODE transcript and its counterpart in RefSeq such that the identifiers can be used synonymously. We have now released MANE Select transcripts for 97% of human protein-coding genes, including all American College of Medical Genetics and Genomics Secondary Findings list v3.0 (ref.3) genes. MANE transcripts are accessible from major genome browsers and key resources. Widespread adoption of these transcript sets will increase the consistency of reporting, facilitate the exchange of data regardless of the annotation source and help to streamline clinical interpretation.

Exposure to ozone and fine particle (PM(2.5)) air pollution results in premature death. These pollutants are predominantly secondary in nature and can form from nitrogen oxides (NO(X)), sulfur oxides (SO(X)), and volatile organic compounds (VOCs). Predicted health benefits for emission reduction scenarios often incompletely account for VOCs as precursors as well as the secondary organic aerosol (SOA) component of PM(2.5). Here, we show that anthropogenic VOC emission reductions are more than twice as effective as equivalent fractional reductions of SO(X) or NO(X) at reducing air pollution-associated cardiorespiratory mortality in the United States. A 25% reduction in anthropogenic VOC emissions from 2016 levels is predicted to avoid 13,000 premature deaths per year, and most (85%) of the VOC-reduction benefits result from reduced SOA with the remainder from ozone. While NO(X) (−5.7 ± 0.2 % yr(−1)) and SO(X) (−12 ± 1 % yr(−1)) emissions have declined precipitously across the U.S. since 2002, anthropogenic VOC emissions (−1.8 ± 0.3 % yr(−1)) and concentrations of non-methane organic carbon (−2.4 ± 1.0 % yr(−1)) have changed less. This work indicates preferentially controlling VOCs could yield significant benefits to human health.

The inhibitory effect of metformin on mTOR signaling and cell viability in CAL27 and SCC47 cells.

supplementary methods and figure legends

Metformin may reduce the progression of head and neck squamous cell carcinoma (HNSCC); however, whether metformin acts by altering the host metabolism or targets cancer-initiating cells remains poorly understood. This gap in knowledge has prevented the stratification of patient populations who are most likely to benefit from metformin treatment. Here, we explored whether metformin acts directly on HNSCC cells to inhibit aberrant cell growth. To investigate the tumor cell autonomous effects of metformin, we engineered representative HPV− and HPV+ HNSCC cells harboring typical genetic alternations to express the yeast mitochondrial NADH dehydrogenase (NDI1) protein, which is insensitive to metformin. NDI1 expression rescued the inhibitory effects of metformin on mitochondrial complex I, abolished the ability of metformin to activate AMP-activated protein kinase, and inhibited mTOR signaling both in vitro and in vivo, and was sufficient to render metformin ineffective to prevent HNSCC tumor growth. This experimental system provided an opportunity to identify metformin-regulated transcriptional programs linked to cancer cell growth inhibition in the tumor microenvironment. Remarkably, computational analysis of the metformin-induced transcriptome revealed that metformin downregulated gene expression signatures associated with cancer stemness and epithelial–mesenchymal transition, concomitant with increased expression of squamous differentiation genes. These findings support that metformin may act directly on cancer-initiating cells to prevent their progression to HNSCC, which may inform the selection of patients at risk of developing HNSCC in future early-stage clinical trials.Significance:Metformin's ability to directly target HNSCC-initiating cells instead of exerting cancer preventive activity based solely on its systemic effects may inform the selection of patients in future precision prevention trials.

PDF file - 348KB, Figure S1: Isogenic IDH1 mutation compromises metabolic reprogramming under hypoxia. Figure S2: Simulated and measured uncorrected MIDs. Figure S3: Compromised Reductive TCA Metabolism is specific to cells with mutant IDH1. Figure S4: Cells with endogenous IDH1 and IDH2 mutations respond differently to mitochondrial stress. Figure S5: Inhibition of mutant IDH1 does not rescue reprogramming of TCA metabolism. Figure S6: Cells expressing mutant IDH1 are sensitive to pharmacological inhibition of mitochondrial oxidative metabolism.

PDF file - 93KB, Estimates Fluxes for HCT116 Parental and IDH1 R132H/+ 2H1 cells under Normoxia and Hypoxia (2 percent Oxygen).

PDF file - 136KB

Oncogenic mutations in isocitrate dehydrogenase 1 and 2 (IDH1/2) occur in several types of cancer, but the metabolic consequences of these genetic changes are not fully understood. In this study, we performed 13C metabolic flux analysis on a panel of isogenic cell lines containing heterozygous IDH1/2 mutations. We observed that under hypoxic conditions, IDH1-mutant cells exhibited increased oxidative tricarboxylic acid metabolism along with decreased reductive glutamine metabolism, but not IDH2-mutant cells. However, selective inhibition of mutant IDH1 enzyme function could not reverse the defect in reductive carboxylation activity. Furthermore, this metabolic reprogramming increased the sensitivity of IDH1-mutant cells to hypoxia or electron transport chain inhibition in vitro. Lastly, IDH1-mutant cells also grew poorly as subcutaneous xenografts within a hypoxic in vivo microenvironment. Together, our results suggest therapeutic opportunities to exploit the metabolic vulnerabilities specific to IDH1 mutation. Cancer Res; 74(12); 3317–31. ©2014 AACR.

Volatile chemical products (VCPs) are a significant source of reactive organic carbon emissions in the United States, with a substantial fraction (>20% by mass) serving as secondary organic aerosol (SOA) precursors. Here, we incorporate a new nationwide VCP inventory into the Community Multiscale Air Quality (CMAQ) model, with VCP-specific updates to better model air quality impacts. Model results indicate that VCPs most enhance anthropogenic SOA in densely populated areas, with population-weighted annual average SOA increasing 15 – 30% in Southern California and New York City due to VCP emissions (contribution of 0.2 – 0.5 μg m(−3)). Annually, VCP emissions enhance total population-weighted PM(2.5) by ~5% in California, ~3% in New York, New Jersey, and Connecticut, and 1 – 2% in most other states. While the maximum daily 8-hr ozone enhancements from VCP emissions are more modest, their influence can cause a several ppb increase on select days in major cities. Printing Inks, Cleaning Products, and Paints & Coatings product use categories contribute ~75% to the modeled VCP-derived SOA and Cleaning Products, Paints & Coatings, and Personal Care Products contribute ~81% to the modeled VCP-derived ozone. Overall, VCPs enhance multiple criteria pollutants throughout the United States, with the largest impacts in urban cores.

Background Sarcopenia is a muscle disease that is linked to the effects of ageing, chronic diseases, physical inactivity, and poor nutrition. In Ireland, there is a lack of readily available information on sarcopenia in older adults. The aim of this study was to describe the prevalence and associated factors of sarcopenia in community dwelling older adults attending a day hospital service in Ireland, using the European Working Group of Sarcopenia in Older People (EWGSOP) 2019 guidelines. Methods An observational cross-sectional study was conducted, where a consecutive series of older adults attending a day hospital service were invited to participate. The measure of primary interest was the diagnosis of sarcopenia using the EWGSOP 2019 guidelines. Muscle strength was assessed using both, handheld grip strength and the Five Times Sit to Stand Test. Muscle mass was assessed using appendicular skeletal mass adjusted for height using raw data from Bio Impedance Analysis. Physical performance was measured using the Short Physical Performance Battery and the Timed Up and Go Test. We also collected other Comprehensive Geriatric Assessment measures including cognition, nutrition, frailty and physical activity. Results A total of 134 participants took part in the study. The mean age was 81.7 (SD +/− 7.1). Sixty-one percent (N = 82) were female. The prevalence of sarcopenia ranged from 27–37% depending on the assessment tool used to assess muscle strength. Sarcopenia was associated with older age, frailty, reduced nutritional state, poor physical performance and reduced anthropometric measures, irrespective of how muscle strength was measured. Independently associated factors differed depending on muscle strength test, except for older age. Conclusion The prevalence of sarcopenia in the day hospital ranged from 27–37%. The assessment tool used to assess muscle strength influenced both the prevalence and associated factors of sarcopenia, suggesting scope for further research.

Background The ability of an older adult to walk independently outdoors in their community assists with maintaining independence, a healthy lifestyle and a good quality of life. The study qualitatively explored older persons’ perceptions and experiences of the variables associated with their ability to walk outdoors in their community, through the use of photographs and discussion. Methods This qualitative study employed Photovoice Methodology (Wang and Burris, 1997). Inclusion criteria were community dwelling older adults over 65 years old, attending a day hospital, able to ambulate at least 10 metres with/without an assistive device and no visual or fine motor limitations that would preclude using a camera. Participants attended an information session and received and practiced with a single use camera. Over one week they took photographs of perceived barriers or facilitators to community ambulation. The developed photographs formed the basis for focus group discussions. Focus groups were recorded; transcribed and thematic analysis was used to identify key themes. Results Eight participants, mean age of 78.8 years completed the Photovoice substudy. Five of the participants were female and six lived alone. One participant had experienced a fall in the past six months. Three key themes were identified; personal, environmental and strategic factors, all associated with an older adults’ ability to ambulate in the community. Each of these themes had a number of subthemes highlighting barriers and facilitators to community ambulation in this population. Conclusion This novel study design was well received. It provided real life experiences, insights and rich valuable data that may not be captured by other means. The findings demonstrate the multifactorial nature of independent community ambulation, both the personal and environmental factors associated with independent community ambulation in older adults. Physiotherapists should adopt a broader, holistic approach to the assessment and treatment of older adults, to promote the achievement of independent community ambulation.

Objective To validate a previously published machine learning model of delirium risk in hospitalized patients with coronavirus disease 2019 (COVID-19). Method Using data from six hospitals across two academic medical networks covering care occurring after initial model development, we calculated the predicted risk of delirium using a previously developed risk model applied to diagnostic, medication, laboratory, and other clinical features available in the electronic health record (EHR) at time of hospital admission. We evaluated the accuracy of these predictions against subsequent delirium diagnoses during that admission. Results Of the 5102 patients in this cohort, 716 (14%) developed delirium. The model's risk predictions produced a c-index of 0.75 (95% CI, 0.73–0.77) with 27.7% of cases occurring in the top decile of predicted risk scores. Model calibration was diminished compared to the initial COVID-19 wave. Conclusion This EHR delirium risk prediction model, developed during the initial surge of COVID-19 patients, produced consistent discrimination over subsequent larger waves; however, with changing cohort composition and delirium occurrence rates, model calibration decreased. These results underscore the importance of calibration, and the challenge of developing risk models for clinical contexts where standard of care and clinical populations may shift.