A Simulated Tracheostomy Tube Change Educational Intervention to Promote Competency Among Novice Healthcare Professionals: A Repeated Measures Study.

Objective: To evaluate an educational intervention to promote confidence, knowledge, and skills in tracheostomy tube change among nursing students. Methods: The study, conducted at the at the Johns Hopkins Center for Immersive Learning and Digital Innovation, enrolled nursing students without prior experience in tracheostomy tube change. The intervention included a pre-recorded presentation, faculty demonstrations with a Tracheostomy Care Training Simulation Model, and participant practice demonstrating skills. Primary outcomes included confidence, knowledge, and competency with tracheostomy tube changes. Secondary outcomes included number of attempts required to achieve competency and time required per attempt. The study followed STROBE guidelines with repeated measure design. Results: Participants in the study (n=50) had a mean age of 30 years, were predominantly female (83%) with a bachelor's degree (76%), most often in the third semester of nursing school (45%). Participants showed a mean improvement of 3.58 points out of five (SD: 0.56, P<0.001) across 11 pre- and post-test items. Every confidence assessment improved, with the largest increase in assessing tube placement. Knowledge assessments improved across all eight test items in the first test-retest interval, showing an improvement of 1.14 points out of five (SD: 0.89, P<0.001). Competency assessment improved in the first test-retest interval of 1.01 points out of five (SD: 0.65, P<0.001). On serial assessments, time to complete tracheostomy tube change decreased from 2.39 to 0.60 minutes. Faculty deemed 95% of participants competent after only one skill testing iteration. Conclusion: An educational intervention, combining digital presentations with interactive faculty-led simulations and practical skill assessments, successfully elevated nursing students' confidence, knowledge, and competency in tracheostomy tube changes.

Elevating Tracheostomy Care Through Data-Driven Innovation: What Can Education, Evidence-Based Practice, and Quality Improvement Learn from One Another?

The past decade has witnessed unprecedented progress in tracheostomy care, through communication, dissemination, and implementation of key drivers including interprofessional education, team-based care, standardized protocols, patient and family engagement, and data-driven practice. Improved safety, efficiency, and quality of tracheostomy care reflects contributions from fields of competency-based education, evidence-based practice, and quality improvement. These elements are interconnected, reinforcing one another to enhance patient care. Competency-based interactive education emphasizes active and practical learning through simulations and case studies, which enhance the clinical skills essential for high-quality care. These educational strategies are grounded in clinical research, ensuring that care practices are continually updated and aligned with the latest evidence, thereby bridging the gap between research findings and clinical application. Quality improvement processes such as Plan-Do-Study-Act (PDSA) cycles refine care delivery in real-world settings. Implementation science promotes the uptake of evidence-based practices, ensuring that discoveries translate to improved health outcomes, quality of care, and overall system performance. In each of these domains, patient and family engagement ensures alignment with patient needs and values. The Global Tracheostomy Collaborative leverages this integrated approach through international educational symposia and webinars, comprehensive data analyses, and a learning community that promotes innovative technologies like in situ simulation and augmented and virtual reality. Together, these approaches enhance the learning and application of best practices in tracheostomy care. The continuous, dynamic interaction of education, research, and quality improvement, grounded in patient-centered care, fosters excellence and innovation in care of patients with tracheostomy.

The Role of Astrocytes in CNS Disorders: Historic and Contemporary Views.

This Special Issue of Cells presents a collection of 22 published, peer-reviewed articles on the theme of "Astrocytes in CNS Disorders," including 9 reviews of the evidence implicating astrocytes in the etiology of specific disorders, and 13 original research papers providing such evidence [...].

Neural general circulation models for weather and climate.

General circulation models (GCMs) are the foundation of weather and climate prediction1,2. GCMs are physics-based simulators that combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. Recently, machine-learning models trained on reanalysis data have achieved comparable or better skill than GCMs for deterministic weather forecasting3,4. However, these models have not demonstrated improved ensemble forecasts, or shown sufficient stability for long-term weather and climate simulations. Here we present a GCM that combines a differentiable solver for atmospheric dynamics with machine-learning components and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best machine-learning and physics-based methods. NeuralGCM is competitive with machine-learning models for one- to ten-day forecasts, and with the European Centre for Medium-Range Weather Forecasts ensemble prediction for one- to fifteen-day forecasts. With prescribed sea surface temperature, NeuralGCM can accurately track climate metrics for multiple decades, and climate forecasts with 140-kilometre resolution show emergent phenomena such as realistic frequency and trajectories of tropical cyclones. For both weather and climate, our approach offers orders of magnitude computational savings over conventional GCMs, although our model does not extrapolate to substantially different future climates. Our results show that end-to-end deep learning is compatible with tasks performed by conventional GCMs and can enhance the large-scale physical simulations that are essential for understanding and predicting the Earth system.

Interferon subverts an AHR-JUN axis to promote CXCL13+ T cells in lupus.

Systemic lupus erythematosus (SLE) is prototypical autoimmune disease driven by pathological T cell-B cell interactions1,2. Expansion of T follicular helper (TFH) and T peripheral helper (TPH) cells, two T cell populations that provide help to B cells, is a prominent feature of SLE3,4. Human TFH and TPH cells characteristically produce high levels of the B cell chemoattractant CXCL13 (refs. 5,6), yet regulation of T cell CXCL13 production and the relationship between CXCL13+ T cells and other T cell states remains unclear. Here, we identify an imbalance in CD4+ T cell phenotypes in patients with SLE, with expansion of PD-1+/ICOS+ CXCL13+ T cells and reduction of CD96hi IL-22+ T cells. Using CRISPR screens, we identify the aryl hydrocarbon receptor (AHR) as a potent negative regulator of CXCL13 production by human CD4+ T cells. Transcriptomic, epigenetic and functional studies demonstrate that AHR coordinates with AP-1 family member JUN to prevent CXCL13+ TPH/TFH cell differentiation and promote an IL-22+ phenotype. Type I interferon, a pathogenic driver of SLE7, opposes AHR and JUN to promote T cell production of CXCL13. These results place CXCL13+ TPH/TFH cells on a polarization axis opposite from T helper 22 (TH22) cells and reveal AHR, JUN and interferon as key regulators of these divergent T cell states.

Association of USMLE Step 1 Pass/Fail Reporting with Interview and Match Outcomes.

OBJECTIVE: To investigate interview and match outcomes of medical students who received pass/fail USMLE reporting vs medical students with numeric scoring during the same period. DESIGN: Retrospective analysis of a cross-sectional survey-based study. SETTING: United States 2023 residency match. PARTICIPANTS: Medical student applicants in the 2023 residency match cycle who responded to the Texas Seeking Transparency in Application to Residency (STAR) survey. RESULTS: Among 6756 applicants for the 2023 match, 496 (7.3%) took USMLE Step 1 with pass/fail reporting. Pass/fail reporting was associated with lower USMLE Step 2-CK scores (245.9 vs 250.7), fewer honored clerkships (2.4 vs 3.1), and lower Alpha Omega Alpha membership (12.5% vs 25.2%) (all p < 0.001). Applicants with numeric USMLE Step 1 scores received more interview offers after adjusting for academic performance (beta coefficient 1.04 (95% CI 0.28-1.79); p = 0.007). Numeric USMLE Step 1 scoring was associated with more interview offers in nonsurgical specialties (beta coefficient 1.64 [95% CI 0.74-2.53]; p < 0.001), but not in general surgery (beta coefficient 3.01 [95% CI -0.82 to 6.84]; p = 0.123) or surgical subspecialties (beta coefficient 1.92 [95% CI -0.78 to 4.62]; p = 0.163). Numeric USMLE Step 1 scoring was not associated with match outcome. CONCLUSIONS: Applicants with numeric USMLE Step 1 scoring had stronger academic profiles than those with pass/fail scoring; however, adjusted analyses found only weak associations with interview or match outcomes. Further research is warranted to assess longitudinal outcomes.

Endothelial cell sphingosine 1-phosphate receptor 1 restrains VE-cadherin cleavage and attenuates experimental inflammatory arthritis.

In rheumatoid arthritis, inflammatory mediators extravasate from blood into joints via gaps between endothelial cells (ECs), but the contribution of ECs is not known. Sphingosine 1-phosphate receptor 1 (S1PR1), widely expressed on ECs, maintains the vascular barrier. Here, we assessed the contribution of vascular integrity and EC S1PR1 signaling to joint damage in mice exposed to serum-induced arthritis (SIA). EC-specific deletion of S1PR1 or pharmacological blockade of S1PR1 promoted vascular leak and amplified SIA, whereas overexpression of EC S1PR1 or treatment with an S1PR1 agonist delayed SIA. Blockade of EC S1PR1 induced membrane metalloproteinase-dependent cleavage of vascular endothelial cadherin (VE-cadherin), a principal adhesion molecule that maintains EC junctional integrity. We identified a disintegrin and a metalloproteinase domain 10 (ADAM10) as the principal VE-cadherin "sheddase." Mice expressing a stabilized VE-cadherin construct had decreased extravascular VE-cadherin and vascular leakage in response to S1PR1 blockade, and they were protected from SIA. Importantly, patients with active rheumatoid arthritis had decreased circulating S1P and microvascular expression of S1PR1, suggesting a dysregulated S1P/S1PR1 axis favoring vascular permeability and vulnerability. We present a model in which EC S1PR1 signaling maintains homeostatic vascular barrier function by limiting VE-cadherin shedding mediated by ADAM10 and suggest this signaling axis as a therapeutic target in inflammatory arthritis.

Association of Medical School Gap Year Research and Degree Programs With Otolaryngology Interview and Match Outcomes.

OBJECTIVE: To investigate whether a gap year for either research or a master's degree is associated with interview offers or match outcomes among otolaryngology applicants. METHODS: Using the Texas Seeking Transparency in Application to Residency (Texas STAR) database, we conducted a cross-sectional analysis of otolaryngology applicants from 2018 to 2022. Applicants were stratified based on the presence and type of gap year during medical school. Applicant characteristics, signaling, research productivity, and application costs were analyzed, with primary outcomes including number of interview offers and match status. RESULTS: Among 564 otolaryngology applicant respondents to the Texas STAR survey, 160 (28%) reported a gap year, including 64 (40%) applicants participating in a research year, 65 (41%) completing a Master of Public Health or Science (MPH and MSc), and 31 (19%) completing a Master of Business Administration, Education, or other degree (MBA and MEd). Gap-year applicants who completed a research year or MPH/MSc degree received more interview offers (P < .01) than MBA, MEd applicants, or those without a gap year. Applicants with a research year had the most publications, oral presentations, abstracts, posters, and research experiences (all P < .01). When controlling for USMLE scores, clerkship honors, and applications submitted, applicants completing a research year or an MPH/MSc-degree received increased interview offers (P < .01). No significant differences were seen in expenditures or match rates. CONCLUSIONS: Research and MPH/MSc gap years were associated with increased residency interview offers but not increased match success. Further longitudinal studies are needed to assess how yearlong experiences affect long-term career outcomes.

Programming patchy particles for materials assembly design.

Direct design of complex functional materials would revolutionize technologies ranging from printable organs to novel clean energy devices. However, even incremental steps toward designing functional materials have proven challenging. If the material is constructed from highly complex components, the design space of materials properties rapidly becomes too computationally expensive to search. On the other hand, very simple components such as uniform spherical particles are not powerful enough to capture rich functional behavior. Here, we introduce a differentiable materials design model with components that are simple enough to design yet powerful enough to capture complex materials properties: rigid bodies composed of spherical particles with directional interactions (patchy particles). We showcase the method with self-assembly designs ranging from open lattices to self-limiting clusters, all of which are notoriously challenging design goals to achieve using purely isotropic particles. By directly optimizing over the location and interaction of the patches on patchy particles using gradient descent, we dramatically reduce the computation time for finding the optimal building blocks.

Granzyme K drives a newly-intentified pathway of complement activation.

Granzymes are a family of serine proteases mainly expressed by CD8+ T cells, natural killer cells, and innate-like lymphocytes1,2. Although their major role is thought to be the induction of cell death in virally infected and tumor cells, accumulating evidence suggests some granzymes can regulate inflammation by acting on extracellular substrates2. Recently, we found that the majority of tissue CD8+ T cells in rheumatoid arthritis (RA) synovium, inflammatory bowel disease and other inflamed organs express granzyme K (GZMK)3, a tryptase-like protease with poorly defined function. Here, we show that GZMK can activate the complement cascade by cleaving C2 and C4. The nascent C4b and C2a fragments form a C3 convertase that cleaves C3, allowing further assembly of a C5 convertase that cleaves C5. The resulting convertases trigger every major event in the complement cascade, generating the anaphylatoxins C3a and C5a, the opsonins C4b and C3b, and the membrane attack complex. In RA synovium, GZMK is enriched in areas with abundant complement activation, and fibroblasts are the major producers of complement C2, C3, and C4 that serve as targets for GZMK-mediated complement activation. Our findings describe a previously unidentified pathway of complement activation that is entirely driven by lymphocyte-derived GZMK and proceeds independently of the classical, lectin, or alternative pathways. Given the widespread abundance of GZMK-expressing T cells in tissues in chronic inflammatory diseases and infection, GZMK-mediated complement activation is likely to be an important contributor to tissue inflammation in multiple disease contexts.

Leukemia inhibitory factor (LIF) receptor amplifies pathogenic activation of fibroblasts in lung fibrosis.

Fibrosis drives end-organ damage in many diseases. However, clinical trials targeting individual upstream activators of fibroblasts, such as TGFß, have largely failed. Here, we target the leukemia inhibitory factor receptor (LIFR) as a "master amplifier" of multiple upstream activators of lung fibroblasts. In idiopathic pulmonary fibrosis (IPF), the most common fibrotic lung disease, we found that lung myofibroblasts had high LIF expression. Further, TGFß1, one of the key drivers of fibrosis, upregulated LIF expression in IPF fibroblasts. In vitro anti-LIFR antibody blocking on human IPF lung fibroblasts reduced induction of profibrotic genes downstream of TGFß1, IL-4 and IL-13. Further, siRNA silencing of LIFR in IPF precision cut lung slices reduced expression of fibrotic proteins. Together, we find that LIFR drives an autocrine positive feedback loop that amplifies and sustains pathogenic activation of IPF fibroblasts downstream of multiple external stimuli, implicating LIFR as a therapeutic target in fibrosis. Significance Statement: Fibroblasts have a central role in the pathogenesis of fibrotic diseases. However, due to in part to multiple profibrotic stimuli, targeting a single activator of fibroblasts, like TGFß, has not yielded successful clinical treatments. We hypothesized that a more effective therapeutic strategy is identifying a downstream "master amplifier" of a range of upstream profibrotic stimuli. This study identifies the leukemia inhibitory factor receptor (LIFR) on fibrotic lung fibroblasts amplifies multiple profibrotic stimuli, such as IL-13 and TGFß. Blocking LIFR reduced fibrosis in ex vivo lung tissue from patients with idiopathic pulmonary fibrosis (IPF). LIFR, acting as a master amplifier downstream of fibroblast activation, offers an alternative therapeutic strategy for fibrotic diseases.

Clonal associations between lymphocyte subsets and functional states in rheumatoid arthritis synovium.

Rheumatoid arthritis (RA) is an autoimmune disease involving antigen-specific T and B cells. Here, we perform single-cell RNA and repertoire sequencing on paired synovial tissue and blood samples from 12 seropositive RA patients. We identify clonally expanded CD4 + T cells, including CCL5+ cells and T peripheral helper (Tph) cells, which show a prominent transcriptomic signature of recent activation and effector function. CD8 + T cells show higher oligoclonality than CD4 + T cells, with the largest synovial clones enriched in GZMK+ cells. CD8 + T cells with possibly virus-reactive TCRs are distributed across transcriptomic clusters. In the B cell compartment, NR4A1+ activated B cells, and plasma cells are enriched in the synovium and demonstrate substantial clonal expansion. We identify synovial plasma cells that share BCRs with synovial ABC, memory, and activated B cells. Receptor-ligand analysis predicted IFNG and TNFRSF members as mediators of synovial Tph-B cell interactions. Together, these results reveal clonal relationships between functionally distinct lymphocyte populations that infiltrate the synovium of patients with RA.

Global predictors of tracheostomy-related pressure injury in the COVID-19 era: A study of secondary data.

OBJECTIVES: To determine the incidence and risk factors of tracheostomy-related pressure injuries (TRPI) and examine the COVID-19 pandemic's impact on TRPI incidence. DESIGN: Secondary analysis of Global Tracheostomy Collaborative database and a multi-center hospital system's electronic medical records. SETTING: 27 hospitals, primarily in the United States, United Kingdom, and Australasia. PATIENTS: 6,400 adults and 2,405 pediatric patients hospitalized with tracheostomy between 1 January 2019 and 31 December 2021. MEASUREMENT: TRPI as a binary outcome, reported as odds ratios. RESULTS: TRPI incidence was 4.69 % in adults and 5.65 % in children. For adults, associated risks were female sex (OR: 0.64), severe obesity (OR: 2.62), ICU admission (OR: 2.05), cuffed tracheostomy (OR: 1.49), fenestrated tracheostomy (OR: 15.37), percutaneous insertion (OR: 2.03) and COVID-19 infection (OR: 1.66). For children, associated risks were diabetes mellitus (OR: 4.31) and ICU admission (OR: 2.68). TRPI odds increased rapidly in the first 60 days of stay. Age was positively associated with TRPI in adults (OR: 1.014) and children (OR: 1.060). Black patients had higher TRPI incidence than white patients; no moderating effects of race were found. Hospital cluster effects (adults ICC: 0.227; children ICC: 0.138) indicated unmeasured hospital-level factors played a significant role. CONCLUSIONS: Increasing age and length of stay up to 60 days are TRPI risk factors. Other risks for adults were female sex, severe obesity, cuffed/fenestrated tracheostomy, percutaneous insertion, and COVID-19; for children, diabetes mellitus and FlexTend devices were risks. Admission during the COVID-19 pandemic had contrasting effects for adults and children. Additional research is needed on unmeasured hospital-level factors. IMPLICATIONS FOR CLINICAL PRACTICE: These findings can guide targeted interventions to reduce TRPI incidence and inform tracheostomy care during public health crises. Hospital benchmarking of tracheostomy-related pressure injuries is needed.

Recurrent flow patterns as a basis for two-dimensional turbulence: Predicting statistics from structures.

A dynamical systems approach to turbulence envisions the flow as a trajectory through a high-dimensional state space [Hopf, Commun. Appl. Maths 1, 303 (1948)]. The chaotic dynamics are shaped by the unstable simple invariant solutions populating the inertial manifold. The hope has been to turn this picture into a predictive framework where the statistics of the flow follow from a weighted sum of the statistics of each simple invariant solution. Two outstanding obstacles have prevented this goal from being achieved: 1) paucity of known solutions and 2) the lack of a rational theory for predicting the required weights. Here, we describe a method to substantially solve these problems, and thereby provide compelling evidence that the probability density functions (PDFs) of a fully developed turbulent flow can be reconstructed with a set of unstable periodic orbits. Our method for finding solutions uses automatic differentiation, with high-quality guesses constructed by minimizing a trajectory-dependent loss function. We use this approach to find hundreds of solutions in turbulent, two-dimensional Kolmogorov flow. Robust statistical predictions are then computed by learning weights after converting a turbulent trajectory into a Markov chain for which the states are individual solutions, and the nearest solution to a given snapshot is determined using a deep convolutional autoencoder. In this study, the PDFs of a spatiotemporally chaotic system have been successfully reproduced with a set of simple invariant states, and we provide a fascinating connection between self-sustaining dynamical processes and the more well-known statistical properties of turbulence.

The chromatin landscape of pathogenic transcriptional cell states in rheumatoid arthritis.

Synovial tissue inflammation is a hallmark of rheumatoid arthritis (RA). Recent work has identified prominent pathogenic cell states in inflamed RA synovial tissue, such as T peripheral helper cells; however, the epigenetic regulation of these states has yet to be defined. Here, we examine genome-wide open chromatin at single-cell resolution in 30 synovial tissue samples, including 12 samples with transcriptional data in multimodal experiments. We identify 24 chromatin classes and predict their associated transcription factors, including a CD8 + GZMK+ class associated with EOMES and a lining fibroblast class associated with AP-1. By integrating with an RA tissue transcriptional atlas, we propose that these chromatin classes represent 'superstates' corresponding to multiple transcriptional cell states. Finally, we demonstrate the utility of this RA tissue chromatin atlas through the associations between disease phenotypes and chromatin class abundance, as well as the nomination of classes mediating the effects of putatively causal RA genetic variants.