Improving Patient Outcomes by Preventing Airway Injuries Associated with Video Laryngoscopes

Video laryngoscopy poses a higher risk of airway injury than direct laryngoscopy when used for difficult intubations in the operating room. Education related to manufacturer guidelines, in addition to, routine simulation practice are vital to improving efficacy and patient safety when using video laryngoscopes. The purpose of this project was to provide education to anesthesia providers regarding the potential risk of airway injury through a presentation and mid-fidelity simulation experience. An in-person PowerPoint presentation was delivered to anesthesia providers, along with a simulation experience using an intubation trainer manikin to demonstrate the proper technique when using the GlideScope and McGRATH MAC Video Laryngoscope. A pocket reference tool was also created and distributed to all participants after the presentation. The participants completed a 10-question pre-test before the presentation and a 10-question post-test immediately following the presentation and simulation experience. The results from both tests were evaluated to determine the overall impact and effectiveness of the presentation and simulation experience. A post-implementation survey was also utilized, which consisted of an 11-question Likert Scale Survey with two open-ended questions. This was used to collect participant perceptions on how well the material was presented and the usefulness of the educational tools provided. The results of this quality improvement project implied there was an immediate impact on the participants’ level of knowledge related to the proper use of video laryngoscopes to avoid injury to the patient. The survey results suggested that the participants had an increase in confidence of video laryngoscope use when presented with a difficult intubation, along with the reinforcement of accurate use according to manufacturer guidelines. Recommendations of this project would be to continue to provide awareness to anesthesia providers of the potential airway injury risk associated with video laryngoscopy with further education and simulation experiences

Instilling Resilience in Children of Poverty

More than sixteen million children are currently living in poverty in the United States (NCCP, 2015). If these children fail to develop resilience, then they will continue to live in the cycle of generational poverty. Generational poverty is where a family continues to live in poverty from generation to generation. In order to develop resilience, strategies must be implemented within schools in order to nurture resilience in children. This research study focused on resiliency and instilling resiliency in children living in poverty. Knowledge from administrators, teachers, and parents was gathered in order to create strategies to instill resilience in children of poverty. Administrators, teachers, and parents that participated in this study were individuals from Title I schools. Title I schools are schools where at least 50% of the school is on free/reduced lunch. In order for a student to be placed on free/reduced lunch, the family must be making a low enough income and considered to be living in poverty. Literature on resilience was used in order to support the ideas of educational personnel and parents. Concepts found in both interviews and literature was combined in order to form strategies that can be implemented inside of a classroom. Four strategies were created using concepts found in the study

Higher Rates of Head Contacts, Body Checking, and Suspected Injuries in Ringette Than Female Ice Hockey:Time to Ring in Opportunities for Prevention

Objective: Ringette is a popular team ice sport in Canada, primarily played by females. Bodychecking is prohibited at all levels of play. This study used video-analysis to evaluate physical contact (PC), head contact (HC), and suspected injury and concussion incidence rates (IR) in youth ringette. Study Design: Cross-sectional. Subjects: Youth ringette players from the 2021-2022 season playing in the U16 (ages 14-15) or U19 (ages 16-18) age groups (A or AA levels). Games were filmed from regular season, provincials, and nationals (AA only). Observation Technique: Game video-recordings were analyzed using Dartfish video-analysis software. Validated criteria were used to assess trunk PC intensity (levels 1-3=lower-intensity PC, levels 4-5=higher-intensity bodychecking), HC type (HC1=direct player-to-player, HC2=indirect), suspected injury (concussion, non-concussion), and penalty enforcement. Outcome Measures: Multivariable Poisson regression analyses (adjusted for cluster by teamgame, offset by game-minutes) were used to estimate PC, HC, and suspected injury and concussion IRs. Incidence rate ratios (IRR) were used to compare IR across age groups, levels of play, and game types. Proportions of bodychecks and HC1s penalized were reported. Results: Seventy-eight team-games were included (U16 n=40, U19 n=38; A n=30, AA n=48; regular season n=30, provincials n=32, nationals n=16). The overall bodychecking IR was 17.34/100 team-minutes (95% CI:14.80-20.33), HC 19.09/100 team-minutes (95% CI:16.7421.78), suspected injury 1.53/100 team-minutes (95% CI:1.13-2.09), and suspected concussion 0.74/100 team-minutes (95% CI:0.48-1.13). Only 29% (95% CI:24.97-32.59) of bodychecks and 7% (95% CI:4.76-9.70) of HC1s were penalized. No differences were found in bodychecking, HCs, or suspected injury and concussion IRs between age groups or levels of play. Bodychecking IRs were 64% (IRR=1.64; 95% CI:1.13-2.39) higher in provincials and 24% (IRR=1.24; 95% CI:1.02-1.50) higher in nationals than regular season games. A 31% (IRR=0.69; 95% CI:0.49-0.97) lower rate of HCs was reported in national games compared to provincial games. Bodychecking was the most common mechanism for concussion (70%) and nonconcussion injuries (67%), with concussions most often associated with HC2s (62.5%). Conclusions: Bodychecking and HC1 IRs were high among youth ringette players, despite rules prohibiting them. Future research should target prevention strategies aimed to reduce HC1s and bodychecking to reduce injury and concussion IRs in youth ringette

Molecular Genetic Influences on Normative and Problematic Alcohol Use in a Population-Based Sample of College Students

Background: Genetic factors impact alcohol use behaviors and these factors may become increasingly evident during emerging adulthood. Examination of the effects of individual variants as well as aggregate genetic variation can clarify mechanisms underlying risk. Methods: We conducted genome-wide association studies (GWAS) in an ethnically diverse sample of college students for three quantitative outcomes including typical monthly alcohol consumption, alcohol problems, and maximum number of drinks in 24 h. Heritability based on common genetic variants (h2SNP) was assessed. We also evaluated whether risk variants in aggregate were associated with alcohol use outcomes in an independent sample of young adults. Results: Two genome-wide significant markers were observed: rs11201929 in GRID1 for maximum drinks in 24 h, with supportive evidence across all ancestry groups; and rs73317305 in SAMD12 (alcohol problems), tested only in the African ancestry group. The h2SNP estimate was 0.19 (SE = 0.11) for consumption, and was non-significant for other outcomes. Genome-wide polygenic scores were significantly associated with alcohol outcomes in an independent sample. Conclusions: These results robustly identify genetic risk for alcohol use outcomes at the variant level and in aggregate. We confirm prior evidence that genetic variation in GRID1impacts alcohol use, and identify novel loci of interest for multiple alcohol outcomes in emerging adults. These findings indicate that genetic variation influencing normative and problematic alcohol use is, to some extent, convergent across ancestry groups. Studying college populations represents a promising avenue by which to obtain large, diverse samples for gene identification

Digital proxemics: Designing social and collaborative interaction in virtual environments

Behaviour in virtual environments might be informed by our experiences in physical environments, but virtual environments are not constrained by the same physical, perceptual, or social cues. Instead of replicating the properties of physical spaces, one can create virtual experiences that diverge from reality by dynamically manipulating environmental, aural, and social properties. This paper explores digital proxemics, which describe how we use space in virtual environments and how the presence of others influences our behaviours, interactions, and movements. First, we frame the open challenges of digital proxemics in terms of activity, social signals, audio design, and environment. We explore a subset of these challenges through an evaluation that compares two audio designs and two displays with different social signal affordances: head-mounted display (HMD) versus desktop PC. We use quantitative methods using instrumented tracking to analyse behaviour, demonstrating how personal space, proximity, and attention compare between desktop PC and HMDs

Aportes para la enseñanza de las ciencias naturales

Se organiza en cuatro secciones. La primera presenta el enfoque de la enseñanza de las ciencias naturales en la región, a partir de la revisión del análisis curricular que sirve como marco de evaluación de las pruebas, especificando los propósitos, objetivos, características y orientación de la enseñanza de esta disciplina. La segunda hace una presentación de la prueba TERCE, detallando los aprendizajes que evalúa. La tercera sección, muestra los resultados de los estudiantes en los distintos dominios y procesos cognitivos evaluados en las pruebas de tercer y sexto grados. En la cuarta sección se entregan ejemplos de preguntas representativas de distintos niveles de logro en las pruebas y se aportan sugerencias o propuestas de prácticas pedagógicas para promover que los estudiantes alcancen los niveles más avanzados

Nilearn for new use cases: Scaling up computational and community efforts

Introduction Nilearn (https://nilearn.github.io) is a well-established Python package that provides statistical and machine learning tools for fast and easy analysis of brain images with instructive documentation and a friendly community. This focus has led to its current position as a crucial part of the neuroimaging community’s open-source software ecosystem, supporting efficient and reproducible science [1]. It has been continuously developed over the past 10 years, currently with 900 stars, 500 forks, and 176 contributors on GitHub. Nilearn leverages and builds upon other central Python machine learning packages, such as Scikit-Learn [2], that are extensively used, tested, and optimized by a large scientific and industrial community. In recent years, efforts in Nilearn have been focused on meeting evolving community needs by increasing General Linear Model (GLM) support, interfacing with initiatives like fMRIPrep and BIDS, and improving the user documentation. Here we report on progress regarding our current priorities. Methods Nilearn is developed to be accessible and easy-to-use for researchers and the open-source community. It features user-focused documentation that includes a user guide and an example gallery as well as comprehensive contribution guidelines. Nilearn is also presented in tutorials and workshops throughout the year including the Montreal Artificial Intelligence and Neuroscience (MAIN) Educational Workshop, the OHBM Brainhack event, and for the Chinese Open Science Network. The community is encouraged to ask questions, report bugs, make suggestions for improvements or new features, and make direct contributions to the source code. We use the platforms Neurostars, GitHub, and Discord to interact with contributors and users on a daily basis. Nilearn adheres to best practices in software development including using version control, unit testing, and requiring multiple reviews of contributions. We also have a continuous integration infrastructure set up to automate many aspects of our development process and make sure our code is continuously tested and up-to-date. Results Nilearn supports methods such as image manipulation and processing, decoding, functional connectivity analysis, GLM, multivariate pattern analysis, along with plotting volumetric and surface data. In the latest release, cluster-level and TFCE-based family-wise error rate (FWER) control have been added to support the mass univariate and GLM analysis modules, expanding from the already implemented voxel-level correction method (see Fig1). Optimizing Nilearn’s maskers is also underway such as the recently added classes for handling multi-subject 4D image data. These also provide the option to use parallelization to speed up computation. In addition, Nilearn has introduced a new theme to update the documentation making the website more readable and accessible (https://nilearn.github.io/). This change also sets the stage for further improvement and modernisation of several aspects of the documentation, like the user guide. Development on Nilearn’s interfaces module added a new function to write BIDS-compatible model results to disk. This and further development of the BIDS interface will facilitate interaction with other relevant community tools such as FitLins [3]. Finally, several surface plotting enhancements are in progress including improving the API for background maps (see Fig2). Conclusion Nilearn is extensively used by researchers of the neuroimaging community due to its implementations of well-founded methods and visualization tools which are often essential in brain imaging research for quality control and communicating results. Recent work has highlighted areas where more active work is needed to scale the project both technically and socially, including: working with large datasets, better supporting analyses on the cortical surface, and advancing standard practice in neuroimaging statistics through active community outreach. References [1] Poldrack, R., Gorgolewski, K., Varoquaux, G. (2019). Computational and Informatic Advances for Reproducible Data Analysis in Neuroimaging Annual Review of Biomedical Data Science 2(1), 119-138. https://dx.doi.org/10.1146/annurev-biodatasci-072018-021237 [2] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, 12, 2825-2830. [3] Markiewicz, C. J., De La Vega, A., Wagner, A., Halchenko, Y. O., Finc, K., Ciric, R., Goncalves, M., Nielson, D. M., Kent, J. D., Lee, J. A., Bansal, S., Poldrack, R. A., Gorgolewski, K. J. (2022). poldracklab/fitlins: 0.11.0 (0.11.0). Zenodo. https://doi.org/10.5281/zenodo.7217447This poster was presented at OHBM 2023

Estimation and validation of InSAR-derived surface displacements at temperate raised peatlands

Peatland surface motion derived from satellite-based Interferometry of Synthetic Aperture Radar (InSAR) is potentially a proxy for groundwater level variations and greenhouse gas emissions from peat soils. Ground validation of these motions at equivalent temporal resolution has proven problematic, either because of limitations of traditional surveying methods or because of limitations with past InSAR time-series approaches. Novel camera-based instrumentation has enabled in-situ measurement of peat surface from mid-2019 to mid-2022 at two large temperate raised bogs undergoing restoration – Cors Fochno and Cors Caron, in mid-Wales, United Kingdom. The cameras provided continuous measurements at sub-millimetre precision and sub-daily temporal resolution. From these data and Sentinel-1 acquisitions spanning mid-2015 to early-2023, we demonstrate that accurate peat surface motion can be derived by InSAR when a combination of interferometric networks with long and short temporal baselines is used. The InSAR time series data closely match the in-situ data at both bogs, and in particular recover well the annual peat surface oscillations of 10-40 mm. Pearson's values for the point-wise correlation of in-situ and InSAR displacements are 0.8–0.9, while 76% of differences are < ±5 mm and 93% are < ±10 mm. RMSE values between multi-annual in-situ and InSAR peat surface displacement rates are ~7 mm·yr−1 and decrease to ∼3.5 mm for individual peat surface motion measurements. Larger differences mainly occur during drought periods. Multi-annual displacement velocities rates based on InSAR indicate long-term subsidence at Cors Caron (maximum −7 mm·yr−1), while Cors Fochno exhibits subsidence at the centre and uplift at the margins (−9 mm·yr−1 to +5 mm·yr−1). Because of the annual peat surface oscillations, however, more robust ground validation of the long-term peat surface motion rates derived from InSAR requires longer time-series of in-situ measurements than are presently available. Nonetheless, the InSAR-derived surface motion rates correlate well spatially with both peat dome elevation and peat thickness. In addition, the annual oscillations in surface motion are synchronous with or lag slightly behind groundwater level changes. A coarse ratio of 10:1 is observed between annual changes in groundwater level and peat surface displacement. Satellite-based InSAR derived from a fusion of short- and long-term temporal baseline networks can thus enable accurate monitoring of hydrologically driven surface motions of moderately degraded to intact temperate raised peatlands

EEPD1 Rescues Stressed Replication Forks and Maintains Genome Stability by Promoting End Resection and Homologous Recombination Repair

Replication fork stalling and collapse is a major source of genome instability leading to neoplastic transformation or cell death. Such stressed replication forks can be conservatively repaired and restarted using homologous recombination (HR) or non-conservatively repaired using micro-homology mediated end joining (MMEJ). HR repair of stressed forks is initiated by 5' end resection near the fork junction, which permits 3' single strand invasion of a homologous template for fork restart. This 5' end resection also prevents classical non-homologous end-joining (cNHEJ), a competing pathway for DNA double-strand break (DSB) repair. Unopposed NHEJ can cause genome instability during replication stress by abnormally fusing free double strand ends that occur as unstable replication fork repair intermediates. We show here that the previously uncharacterized Exonuclease/Endonuclease/Phosphatase Domain-1 (EEPD1) protein is required for initiating repair and restart of stalled forks. EEPD1 is recruited to stalled forks, enhances 5' DNA end resection, and promotes restart of stalled forks. Interestingly, EEPD1 directs DSB repair away from cNHEJ, and also away from MMEJ, which requires limited end resection for initiation. EEPD1 is also required for proper ATR and CHK1 phosphorylation, and formation of gamma-H2AX, RAD51 and phospho-RPA32 foci. Consistent with a direct role in stalled replication fork cleavage, EEPD1 is a 5' overhang nuclease in an obligate complex with the end resection nuclease Exo1 and BLM. EEPD1 depletion causes nuclear and cytogenetic defects, which are made worse by replication stress. Depleting 53BP1, which slows cNHEJ, fully rescues the nuclear and cytogenetic abnormalities seen with EEPD1 depletion. These data demonstrate that genome stability during replication stress is maintained by EEPD1, which initiates HR and inhibits cNHEJ and MMEJ

Towards a rational design of solid drug nanoparticles with optimised pharmacological properties.

Solid drug nanoparticles (SDNs) are a nanotechnology with favourable characteristics to enhance drug delivery and improve the treatment of several diseases, showing benefit for improved oral bioavailability and injectable long-acting medicines. The physicochemical properties and composition of nanoformulations can influence the absorption, distribution, and elimination of nanoparticles; consequently, the development of nanoparticles for drug delivery should consider the potential role of nanoparticle characteristics in the definition of pharmacokinetics. The aim of this study was to investigate the pharmacological behaviour of efavirenz SDNs and the identification of optimal nanoparticle properties and composition. Seventy-seven efavirenz SDNs were included in the analysis. Cellular accumulation was evaluated in HepG2 (hepatic) and Caco-2 (intestinal), CEM (lymphocyte), THP1 (monocyte), and A-THP1 (macrophage) cell lines. Apparent intestinal permeability (Papp) was measured using a monolayer of Caco-2 cells. The Papp values were used to evaluate the potential benefit on pharmacokinetics using a physiologically based pharmacokinetic model. The generated SDNs had an enhanced intestinal permeability and accumulation in different cell lines compared to the traditional formulation of efavirenz. Nanoparticle size and excipient choice influenced efavirenz apparent permeability and cellular accumulation, and this appeared to be cell line dependent. These findings represent a valuable platform for the design of SDNs, giving an empirical background for the selection of optimal nanoparticle characteristics and composition. Understanding how nanoparticle components and physicochemical properties influence pharmacological patterns will enable the rational design of SDNs with desirable pharmacokinetics