Percutaneous mechanical thrombectomy in acute pulmonary embolism: Outcomes from a safety-net hospital.

BACKGROUND: Our study aims to present clinical outcomes of mechanical thrombectomy (MT) in a safety-net hospital. METHODS: This is a retrospective study of intermediate or high-risk pulmonary embolism (PE) patients who underwent MT between October 2020 and May 2023. The primary outcome was 30-day mortality. RESULTS: Among 61 patients (mean age 57.6 years, 47% women, 57% Black) analyzed, 12 (19.7%) were classified as high-risk PE, and 49 (80.3%) were intermediate-risk PE. Of these patients, 62.3% had Medicaid or were uninsured, 50.8% lived in a high poverty zip code. The prevalence of normotensive shock in intermediate-risk PE patients was 62%. Immediate hemodynamic improvements included 7.4 mmHg mean drop in mean pulmonary artery pressure (-21.7%, p < 0.001) and 93% had normalization of their cardiac index postprocedure. Thirty-day mortality for the entire cohort was 5% (3 patients) and 0% when restricted to the intermediate-risk group. All 3 patients who died at 30 days presented with cardiac arrest. There were no differences in short-term mortality based on race, insurance type, citizenship status, or socioeconomic status. All-cause mortality at most recent follow up was 13.1% (mean follow up time of 13.4 ± 8.5 months). CONCLUSION: We extend the findings from prior studies that MT demonstrates a favorable safety profile with immediate improvement in hemodynamics and a low 30-day mortality in patients with acute PE, holding true even with relatively higher risk and more vulnerable population within a safety-net hospital.

Relationship Between the Mixed Venous-to-Arterial Carbon Dioxide Gradient and Cardiac Index in Acute Pulmonary Embolism.

BACKGROUND: Among patients with acute pulmonary embolism (PE) undergoing mechanical thrombectomy, the cardiac index (CI) is frequently reduced even among those without clinically apparent shock. The purpose of this study was to describe the mixed venous-to-arterial carbon dioxide gradient (CO2 gap), a surrogate of perfusion adequacy, among patients with acute PE undergoing mechanical thrombectomy. METHODS: This was a single-center retrospective study of consecutive patients with PE undergoing mechanical thrombectomy and simultaneous pulmonary artery catheterization over a 3-year period. RESULTS: Of 107 patients, 97 had simultaneous mixed venous and arterial blood gas measurements available. The CO2 gap was elevated (>6 mmHg) in 51% of the cohort and in 49% of patients with intermediate-risk PE. A reduced CI (≤2.2 L/min/m2) was associated with an increased odds (OR = 7.9; 95% CI 3.49-18.1, p < 0.001) for an elevated CO2 gap. There was an inverse relationship between CI and CO2 gap. For every 1 L/min/m2 decrease in the CI, the CO2 gap increased by 1.3 mmHg (p = 0.001). Among patients with an elevated baseline CO2 gap >6 mmHg, thrombectomy improved CO2 gap, CI, and mixed venous oxygen saturation. When the CO2 gap was dichotomized above and below 6, there was no difference in in-hospital mortality (9% vs. 0%; p = 0.10, HR: 1.24; 95% CI: 0.97-1.60; P = 0.085). CONCLUSIONS: Among patients with acute PE undergoing mechanical thrombectomy, the CO2 gap is abnormal in nearly 50% of patients and inversely related to CI. Further studies should examine the relationship between markers of perfusion and outcomes in this population to refine risk stratification.

Advancing medical affair capabilities and insight generation through machine learning techniques.

BACKGROUND: Pharmaceutical companies are increasingly leveraging machine learning techniques to optimize healthcare research, drug development, and medical affairs activities. AI (artificial intelligence) tools such as chatbots, virtual digital assistants, and research tools have been explored to varying degrees of maturity in industries such as consumer goods or software technology. However, there continues to be untapped opportunities within the pharmaceutical industry to employ these technologies for enhanced engagement and education with healthcare professionals (HCPs). Pharmacists, situated at the crossroads of clinical sciences and innovation, have the potential to elevate their role and significance within the pharmaceutical industry by developing and leveraging such technologies. METHODS: To address this, the python-coded tool, Medical Information (MI) Data Uses For AI Semantic Analysis (MUFASA), utilizes state-of-the-art Sentence Transformer library, clustering, and visualization techniques. MUFASA harnesses unsolicited MI data with AI technology, improving efficiency and providing actionable medical affairs intelligence for targeted content delivery to HCPs. RESULTS: MUFASA optimizes medical affairs activities through its distinctive features: semantic search, cluster analysis, and visualization. Its proficiency in understanding inquiries, as demonstrated through 3D vector mapping and clustering tests, enhances the efficiency of MI and Medical Science Liaison (MSL) case handling. It proves invaluable in training new staff, bolstering response uniformity, and mitigating compliance risks. Leveraging the HDBSCAN algorithm, MUFASA's cluster analysis uncovers deep insights and discerns actionable themes from large inquiry data sets. The visualization graphs, generated from semantic searches, support evidence-based decisions by tracking the effectiveness of initiatives and monitoring trend shifts. Collectively, MUFASA enriches strategic decision-making, cultivates actionable insights, and bolsters healthcare professional engagement. CONCLUSION: There are numerous opportunities for innovation within the intersection of healthcare and data science. Pharmaceutical manufacturers, with one of their medical affairs responsibilities being the collection of unsolicited inquiries, particularly from HCPs, stand poised to leverage machine learning capabilities to optimize its processes. The abundance of data generated by the growing effort to use it in meaningful ways presents an opportunity for pharmaceutical companies to harness machine learning techniques.

Persistence is key: unresolved immune dysfunction is lethal in both COVID-19 and non-COVID-19 sepsis.

Introduction: Severe COVID-19 and non-COVID-19 pulmonary sepsis share pathophysiological, immunological, and clinical features, suggesting that severe COVID-19 is a form of viral sepsis. Our objective was to identify shared gene expression trajectories strongly associated with eventual mortality between severe COVID-19 patients and contemporaneous non-COVID-19 sepsis patients in the intensive care unit (ICU) for potential therapeutic implications. Methods: Whole blood was drawn from 20 COVID-19 patients and 22 non-COVID-19 adult sepsis patients at two timepoints: ICU admission and approximately a week later. RNA-Seq was performed on whole blood to identify differentially expressed genes and significantly enriched pathways. Using systems biology methods, drug candidates targeting key genes in the pathophysiology of COVID-19 and sepsis were identified. Results: When compared to survivors, non-survivors (irrespective of COVID-19 status) had 3.6-fold more "persistent" genes (genes that stayed up/downregulated at both timepoints) (4,289 vs. 1,186 genes); these included persistently downregulated genes in T-cell signaling and persistently upregulated genes in select innate immune and metabolic pathways, indicating unresolved immune dysfunction in non-survivors, while resolution of these processes occurred in survivors. These findings of persistence were further confirmed using two publicly available datasets of COVID-19 and sepsis patients. Systems biology methods identified multiple immunomodulatory drug candidates that could target this persistent immune dysfunction, which could be repurposed for possible therapeutic use in both COVID-19 and sepsis. Discussion: Transcriptional evidence of persistent immune dysfunction was associated with 28-day mortality in both COVID-19 and non-COVID-19 septic patients. These findings highlight the opportunity for mitigating common mechanisms of immune dysfunction with immunomodulatory therapies for both diseases.

Post-COVID symptoms are associated with endotypes reflecting poor inflammatory and hemostatic modulation.

Introduction: Persistent symptoms after COVID-19 infection ("long COVID") negatively affects almost half of COVID-19 survivors. Despite its prevalence, its pathophysiology is poorly understood, with multiple host systems likely affected. Here, we followed patients from hospital to discharge and used a systems-biology approach to identify mechanisms of long COVID. Methods: RNA-seq was performed on whole blood collected early in hospital and 4-12 weeks after discharge from 24 adult COVID-19 patients (10 reported post-COVID symptoms after discharge). Differential gene expression analysis, pathway enrichment, and machine learning methods were used to identify underlying mechanisms for post-COVID symptom development. Results: Compared to patients with post-COVID symptoms, patients without post-COVID symptoms had larger temporal gene expression changes associated with downregulation of inflammatory and coagulation genes over time. Patients could also be separated into three patient endotypes with differing mechanistic trajectories, which was validated in another published patient cohort. The "Resolved" endotype (lowest rate of post-COVID symptoms) had robust inflammatory and hemostatic responses in hospital that resolved after discharge. Conversely, the inflammatory/hemostatic responses of "Suppressive" and "Unresolved" endotypes (higher rates of patients with post-COVID symptoms) were persistently dampened and activated, respectively. These endotypes were accurately defined by specific blood gene expression signatures (6-7 genes) for potential clinical stratification. Discussion: This study allowed analysis of long COVID whole blood transcriptomics trajectories while accounting for the issue of patient heterogeneity. Two of the three identified and externally validated endotypes ("Unresolved" and "Suppressive") were associated with higher rates of post-COVID symptoms and either persistently activated or suppressed inflammation and coagulation processes. Gene biomarkers in blood could potentially be used clinically to stratify patients into different endotypes, paving the way for personalized long COVID treatment.

Severe COVID-19 and non-COVID-19 severe sepsis converge transcriptionally after a week in the intensive care unit, indicating common disease mechanisms.

Introduction: Severe COVID-19 and non-COVID-19 pulmonary sepsis share pathophysiological, immunological, and clinical features. To what extent they share mechanistically-based gene expression trajectories throughout hospitalization was unknown. Our objective was to compare gene expression trajectories between severe COVID-19 patients and contemporaneous non-COVID-19 severe sepsis patients in the intensive care unit (ICU). Methods: In this prospective single-center observational cohort study, whole blood was drawn from 20 COVID-19 patients and 22 non-COVID-19 adult sepsis patients at two timepoints: ICU admission and approximately a week later. RNA-Seq was performed on whole blood to identify differentially expressed genes and significantly enriched pathways. Results: At ICU admission, despite COVID-19 patients being almost clinically indistinguishable from non-COVID-19 sepsis patients, COVID-19 patients had 1,215 differentially expressed genes compared to non-COVID-19 sepsis patients. After one week in the ICU, the number of differentially expressed genes dropped to just 9 genes. This drop coincided with decreased expression of antiviral genes and relatively increased expression of heme metabolism genes over time in COVID-19 patients, eventually reaching expression levels seen in non-COVID-19 sepsis patients. Both groups also had similar underlying immune dysfunction, with upregulation of immune processes such as "Interleukin-1 signaling" and "Interleukin-6/JAK/STAT3 signaling" throughout disease compared to healthy controls. Discussion: Early on, COVID-19 patients had elevated antiviral responses and suppressed heme metabolism processes compared to non-COVID-19 severe sepsis patients, although both had similar underlying immune dysfunction. However, after one week in the ICU, these diseases became indistinguishable on a gene expression level. These findings highlight the importance of early antiviral treatment for COVID-19, the potential for heme-related therapeutics, and consideration of immunomodulatory therapies for both diseases to treat shared immune dysfunction.

Predicting severity in COVID-19 disease using sepsis blood gene expression signatures.

Severely-afflicted COVID-19 patients can exhibit disease manifestations representative of sepsis, including acute respiratory distress syndrome and multiple organ failure. We hypothesized that diagnostic tools used in managing all-cause sepsis, such as clinical criteria, biomarkers, and gene expression signatures, should extend to COVID-19 patients. Here we analyzed the whole blood transcriptome of 124 early (1-5 days post-hospital admission) and late (6-20 days post-admission) sampled patients with confirmed COVID-19 infections from hospitals in Quebec, Canada. Mechanisms associated with COVID-19 severity were identified between severity groups (ranging from mild disease to the requirement for mechanical ventilation and mortality), and established sepsis signatures were assessed for dysregulation. Specifically, gene expression signatures representing pathophysiological events, namely cellular reprogramming, organ dysfunction, and mortality, were significantly enriched and predictive of severity and lethality in COVID-19 patients. Mechanistic endotypes reflective of distinct sepsis aetiologies and therapeutic opportunities were also identified in subsets of patients, enabling prediction of potentially-effective repurposed drugs. The expression of sepsis gene expression signatures in severely-afflicted COVID-19 patients indicates that these patients should be classified as having severe sepsis. Accordingly, in severe COVID-19 patients, these signatures should be strongly considered for the mechanistic characterization, diagnosis, and guidance of treatment using repurposed drugs.

TPWshiny: an interactive R/Shiny app to explore cell line transcriptional responses to anti-cancer drugs.

SUMMARY: The NCI Transcriptional Pharmacodynamics Workbench (NCI TPW) is an extensive compilation of directly measured transcriptional responses to anti-cancer agents across the well-characterized NCI-60 cancer cell lines. The NCI TPW data are publicly available through a web interface that allows limited user interaction with the data. We developed 'TPWshiny' as a standalone, easy to install, R application to facilitate more interactive data exploration.With no programming skills required, TPWshiny provides an intuitive and comprehensive graphical interface to help researchers understand the response of tumor cell lines to 15 therapeutic agents. The data are presented in interactive scatter plots, heatmaps, time series and Venn diagrams. Data can be queried by drug concentration, time point, gene and tissue type. Researchers can download the data for further analysis. AVAILABILITY AND IMPLEMENTATION: Users can download the ready-to-use, self-extracting package for Windows or macOS, and R source code from the project website (https://brb.nci.nih.gov/TPWshiny/). TPWshiny documentation and additional information can be found on the project website.

Characterization of the immune cell landscape in CRC: Clinical implications of tumour-infiltrating leukocytes in early- and late-stage CRC.

Introduction: Colorectal cancer (CRC) is the third leading cause of cancer-related deaths globally. Tumour-infiltrating leukocytes play an important role in cancers, including CRC. We therefore sought to characterize the impact of tumour-infiltrating leukocytes on CRC prognosis. Methods: To determine whether the immune cell profile within CRC tissue could influence prognosis, we employed three computational methodologies (CIBERSORT, xCell and MCPcounter) to predict abundance of immune cell types, based on gene expression. This was done using two patient cohorts, TCGA and BC Cancer Personalized OncoGenomics (POG). Results: We observed significant differences in immune cell composition between CRC and normal adjacent colon tissue, as well as differences in based on method of analysis. Evaluation of survival based on immune cell types revealed dendritic cells as a positive prognostic marker, consistently across methodologies. Mast cells were also found to be a positive prognostic marker, but in a stage-dependent manner. Unsupervised cluster analysis demonstrated that significant differences in immune cell composition has a more pronounced effect on prognosis in early-stage CRC, compared to late-stage CRC. This analysis revealed a distinct group of individuals with early-stage CRC which have an immune infiltration signature that indicates better survival probability. Conclusions: Taken together, characterization of the immune landscape in CRC has provided a powerful tool to assess prognosis. We anticipate that further characterization of the immune landscape will facilitate use of immunotherapies in CRC.

3D-Printed Personal Protective Face Shields During the COVID-19 Pandemic: A Survey of Canadian Frontline Workers.

Background During the coronavirus disease 2019 pandemic, three-dimensional (3D) printing was utilized to rapidly produce face shields for frontline workers in response to an acute shortage of personal protective equipment (PPE). In this study, we examine the perceived utility and performance of 3D-printed (3DP) face shields through a survey of frontline workers in Ontario, Canada. Methodology Frontline workers who received community-produced 3DP face shields from the Canadian initiative "3DPPE GTHA" (March-December 2020) were invited to participate in the study. The survey response rate was 54.3%. Of 63 respondents, 39 were patient-facing and 24 were community-facing frontline workers. Participants were asked to rate performance measures in 10 categories on a five-point Likert scale. Data were categorized by organization and frontline worker type, and a t-test was used to determine statistically significant differences among subgroups. Results The mean preference for 3DP face shields among respondents was 3.2 out of 5 (95% confidence interval [CI]: 2.1-4.3). Community-facing respondents reported significantly greater overall utility scores for 3DP face shields (3.58, 95% CI: 3.38-3.79) compared to respondents working in a patient-facing profession (2.95, 95% CI: 2.77-3.13; p < 0.05). However, no differences were reported in portability and compatibility with other PPE. Respondents from organizations with large service volumes reported significantly lower overall utility scores (2.67, 95% CI: 2.44-2.89) than respondents in organizations with smaller service volumes (3.45, 95% CI: 3.28-3.62; p < 0.05). Conclusions Community-facing frontline workers and those from smaller service volume organizations endorse higher utility for 3DP face shields than patient-facing frontline workers. Despite this, frontline workers generally rate 3DP face shields positively. 3DP face shields are a viable option for personal and community use and can be used to supplement supply in a community setting.

The incidence of stroke in patients with early echocardiography after acute myocardial infarction.

Left ventricular (LV) thrombus formation after ST-elevation myocardial infarction (STEMI) increases the risk of stroke. In our center, most echocardiograms are performed within 2 days post-STEMI. However, LV thrombi often become visible later. We assessed the 1-year incidence of stroke in patients without LV thrombus on echocardiography performed early (1-2 days) vs. later (day ≥ 3) post-STEMI. This retrospective observational study included 416 patients with acute STEMI. Patients with atrial fibrillation were excluded. All patients underwent echocardiography during admission. Patients with stroke within 12 months post-STEMI were identified from the hospital charts and administrative databases. Most echocardiograms (75%) were performed ≤ 2 days post-STEMI. LV thrombus was identified in 12 patients. One (8.3%) patient with LV thrombus and 10 (2.5%) patients without LV thrombus suffered stroke within 12 months post-STEMI. Most patients with stroke had apical akinesis. Most strokes occurred during the index admission or within 67 days of STEMI. There was no significant difference in the incidence of stroke between the patients with early vs. later echocardiography post-STEMI. The incidence of stroke after STEMI is low and similar between patients with echocardiography performed early vs. later post-STEMI which supports our current clinical practice. Importantly, most strokes occur in patients without LV thrombus on early echocardiography. High reported mortality rate associated with stroke following STEMI justifies the need for further validation in prospective studies to identify patients who may benefit from repeat imaging to detect later LV thrombus formation, and how this will impact patient outcomes and healthcare costs.

Overcoming vaccine deployment challenges among the hardest to reach: lessons from polio elimination in India.

After more than 30 years of efforts to eliminate polio, India was certified polio free by WHO in 2014. The final years prior to polio elimination were characterised by concentrated efforts to vaccinate hard-to-reach groups in the state of Uttar Pradesh, including migrant workers, religious minority Muslims and impoverished communities with poor pre-existing social support systems. This article aims to describe the management strategies employed by India to improve the deployment and acceptance of vaccines among hard-to-reach groups in Uttar Pradesh in the final years prior to polio elimination.Three main management principles contributed to polio elimination among the hardest to reach in Uttar Pradesh: bundling of health services, local stakeholder engagement and accountability mechanisms for public health initiatives. In an effort to market the polio campaign as an authentic health-oriented programme, vaccine acceptance was improved by packaging other basic healthcare services such as routine check-ups and essential medications. India also prioritised local stakeholder engagement by using influential community leaders to reach vaccine hesitant groups. Lastly, the accountability mechanisms developed between non-profit organisations and decision-makers in the field ensured accurate reporting and identified deficiencies in healthcare worker training. The lessons learnt from India's polio vaccination programme have important implications for the implementation of future mass vaccination initiatives, particularly when trying to reach vulnerable communities.

Inhibition of nucleophosmin 1 suppresses colorectal cancer tumor growth of patient -derived xenografts via activation of p53 and inhibition of AKT.

The nucleophosmin 1 (NPM1) protein is frequently overexpressed in various cancers compared to normal tissues and represents a potential biomarker for maliganancy. However, its role in colorectal cancer (CRC) is still not fully understood. In this report, we show that NPM1 levels in CRC correlate with prognosis and sensitivity to chemotherapy. NPM1 expression was found to be significantly increased in CRC tumors (P < .001) and was associated with poor overall 5-year survival (P < .05). For individuals with Stage IV disease, this represented a reduction in survival by 11 months (P < .01; HR = 0.38, CI [0.21, 0.69]. In vitro, we show that NPM1 gene silencing enhanced the chemosensitivity of CRC cells and that pharmacological inhibition of NPM1 by NSC348884 triggered the onset of programmed cell death. Our immunofluorescence microscopy and immunoblot analyses also revealed that blocking NPM1 function sensitized CRC cells to chemotherapy-induced apoptosis through a mechanism that involves proteins in the AKT pathway. Consistent with the in vitro data, our patient-derived CRC xenograft model showed that inhibition of NPM1 suppressed tumor growth and attenuated AKT signaling in vivo. Moreover, LY294002, an inhibitor of the PI3K/AKT pathway, restored the chemosensitivity of CRC cells expressing high levels of NPM1. The findings that NPM1's expression in CRC tissue correlates with prognosis and supports anti-apoptotic activity mediated by AKT signaling, further our understanding of the role of NPM1 in CRC.

The current perspective of the PA 1957 gas well pillar study and its implications for longwall gas well pillars.

Many states rely upon the Pennsylvania 1957 Gas Well Pillar Study to evaluate the coal barrier surrounding gas wells. The study included 77 gas well failure cases that occurred in the Pittsburgh and Freeport coal seams over a 25-year span. At the time, coal was mined using the room-and-pillar mining method with full or partial pillar recovery, and square or rectangle pillars surrounding the gas wells were left to protect the wells. The study provided guidelines for pillar sizes under different overburden depths up to 213 m (700 ft). The 1957 study has also been used to determine gas well pillar sizes in longwall mines since longwall mining began in the 1970 s. The original study was developed for room-and-pillar mining and could be applied to gas wells in longwall chain pillars under shallow cover. However, under deep cover, severe deformations in gas wells have occurred in longwall chain pillars. Presently, with a better understanding of coal pillar mechanics, new insight into subsidence movements induced by retreat mining, and advances in numerical modeling, it has become both critically important and feasible to evaluate the adequacy of the 1957 study for longwall gas well pillars. In this paper, the data from the 1957 study is analyzed from a new perspective by considering various factors, including overburden depth, failure location, failure time, pillar safety factor (SF), and floor pressure. The pillar SF and floor pressure are calculated by considering abutment pressure induced by full pillar recovery. A statistical analysis is performed to find correlations between various factors and helps identify the most significant factors for the stability of gas wells influenced by retreat mining. Through analyzing the data from the 1957 study, the guidelines for gas well pillars in the 1957 study are evaluated for their adequacy for room-and-pillar mining and their applicability to longwall mining. Numerical modeling is used to model the stability of gas wells by quantifying the mining-induced stresses in gas well casings. Results of this study indicate that the guidelines in the 1957 study may be appropriate for pillars protecting conventional gas wells in both room-and-pillar mining and longwall mining under overburden depths up to 213 m (700 ft), but may not be sufficient for protective pillars under deep cover. The current evaluation of the 1957 study provides not only insights about potential gas well failures caused by retreat mining but also implications for what critical considerations should be taken into account to protect gas wells in longwall mining.

Assessing Longwall Gateroad Ground Response and Support Alternatives.

Ground falls in longwall gateroad entries remain a concern in modern longwall operations. The gateroads are subject to changing horizontal and vertical ground stress induced by longwall extraction. These stress changes can result in failure of the strata around an entry leading to large deformations of the entry roof, floor, and ribs. The gateroad support systems are required to control the failed strata while maintaining safe access to the longwall face and unimpeded ventilation. This paper presents research that was conducted to better understand the stability issues in gateroad excavations and to develop procedures for evaluating support and layout alternatives for longwall gateroads. Using the results of a field-monitoring program and numerical model analysis of case histories, a conceptual model of gateroad support needs was developed. The conceptual model formed the basis for developing a set of equations that can be used to estimate likely roof sag and support loading for given roof geology and longwall-induced loading conditions. The developed equations were used to compare predicted gateroad stability to field study results, showing satisfactory agreement. The calculation procedures are used to demonstrate their application in assessing support alternatives at a case study mine. It is concluded that the developed analysis procedures provide realistic assessments of likely ground stability and can be used to evaluate alternative gateroad support systems at operating longwall mines.

Converting to Connect: A Rapid RE-AIM Evaluation of the Digital Conversion of a Clerkship Curriculum in the Age of COVID-19.

BACKGROUND: With the advent of the 2019 coronavirus pandemic, a decision was made to remove medical students from clinical rotations for their own safety. This forced students on a core emergency medicine (EM) rotation at McMaster University to immediately cease all in-person activities. An urgent need for a virtual curriculum emerged. METHODS: A virtual curriculum consisting of asynchronous case-based learning on Slack, ask-me-anything webinars, and online e-modules was created to fill the need. We describe a program evaluation using the RE-AIM framework and a social networking analysis of participants. RESULTS: Medical students (n = 23) and 11 facilitators (five residents, six faculty members) participated in this pilot study. Faculty members sent a mean (±SD) of 115 (±117) messages (n = 6), and mean (±SD) message counts for students and residents were 49.96 (±25; n = 23) and 39 (±38; n = 5), respectively. A total of 62,237 words were written by the participants, with a mean of 1,831 per person. Each message consisted of a mean (±SD) of 25 words (±29). Students rapidly acquitted themselves to digital technology. Using the RE-AIM framework we highlight the feasibility of a virtual curriculum, discuss demands on faculty time, and reflect on strategies to engage learners. CONCLUSIONS: The use of asynchronous digital curricula creates opportunities for faculty-resident interaction and engagement. We report the successful deployment of a viable model for undergraduate EM training for senior medical students in the COVID-19 era of physical distancing.

Optimization of community-led 3D printing for the production of protective face shields.

BACKGROUND: As the healthcare system faced an acute shortage of personal protective equipment (PPE) during the COVID-19 pandemic, the use of 3D printing technologies became an innovative method of increasing production capacity to meet this acute need. Due to the emergence of a large number of 3D printed face shield designs and community-led PPE printing initiatives, this case study examines the methods and design best optimized for community printers who may not have the resources or experience to conduct such a thorough analysis. CASE PRESENTATION: We present the optimization of the production of 3D printed face shields by community 3D printers, as part of an initiative aimed at producing PPE for healthcare workers. The face shield frames were manufactured using the 3DVerkstan design and were coupled with an acetate sheet to assemble a complete face shield. Rigorous quality assurance and decontamination protocols ensured community-printed PPE was satisfactory for healthcare use. CONCLUSION: Additive manufacturing is a promising method of producing adequate face shields for frontline health workers because of its versatility and quick up-start time. The optimization of stacking and sanitization protocols allowed 3D printing to feasibly supplement formal public health responses in the face of a global pandemic.