Barriers to integrating portable Magnetic Resonance Imaging systems in emergency medical service ambulances for stroke care.

This study examines the barriers to integrating portable Magnetic Resonance Imaging (MRI) systems into ambulance services to enable effective triaging of patients to the appropriate hospitals for timely stroke care and potentially reduce door-to-needle time for thrombolytic administration. The study employs a qualitative methodology using a digital twin of the patient handling process developed and demonstrated through semi-structured interviews with 18 participants, including 11 paramedics from an Emergency Medical Services system and seven neurologists from a tertiary stroke care centre. The interview transcripts were thematically analysed to determine the barriers based on the Systems Engineering Initiative for Patient Safety framework. Key barriers include the need for MRI operation skills, procedural complexities in patient handling, space constraints, and the need for training and policy development. Potential solutions are suggested to mitigate these barriers. The findings can facilitate implementing MRI systems in ambulances to expedite stroke treatment.

This study investigates the challenges of integrating portable MRI systems into ambulances for faster stroke care. It identifies key barriers such as operational skills, procedural complexities, space constraints, and policy development needs, and offers a few solutions to improve emergency stroke treatment.

Enantioselective Synthesis of Tetra-substituted 3-Hydroxyphthalide Esters by Isothiourea-Catalysed Acylative Dynamic Kinetic Resolution.

A general and highly enantioselective method for the preparation of tetra-substituted 3-hydroxyphthalide esters via isothiourea-catalysed acylative dynamic kinetic resolution (DKR) is reported. Using (2S,3R)-HyperBTM (5 mol%) as the catalyst, the scope and limitations of this methodology have been extensively probed, with high enantioselectivity and good to excellent yields observed (>40 examples, up to 99%, 99:1 er). Substitution of the aromatic core within the 3-hydroxyphthalide skeleton, as well as aliphatic and aromatic substitution at C(3)-, is readily tolerated. A diverse range of anhydrides, including those from bioactive and pharmaceutically relevant acids, can also be used. The high enantioselectivity observed in this DKR process has been probed computation, with a key substrate heteroatom donor O•••acyl-isothiouronium interaction identified through DFT analysis as necessary for enantiodiscrimination.

Concurrent inhibition of ALK and SRC kinases disrupts the ALK lung tumor cell proteome.

Precision oncology has revolutionized the treatment of ALK-positive lung cancer with targeted therapies. However, an unmet clinical need still to address is the treatment of refractory tumors that contain drug-induced resistant mutations in the driver oncogene or exhibit resistance through the activation of diverse mechanisms. In this study, we established mouse tumor-derived cell models representing the two most prevalent EML4-ALK variants in human lung adenocarcinomas and characterized their proteomic profiles to gain insights into the underlying resistance mechanisms. We showed that Eml4-Alk variant 3 confers a worse response to ALK inhibitors, suggesting its role in promoting resistance to targeted therapy. In addition, proteomic analysis of brigatinib-treated cells revealed the upregulation of SRC kinase, a protein frequently activated in cancer. Co-targeting of ALK and SRC showed remarkable inhibitory effects in both ALK-driven murine and ALK-patient-derived lung tumor cells. This combination induced cell death through a multifaceted mechanism characterized by profound perturbation of the (phospho)proteomic landscape and a synergistic suppressive effect on the mTOR pathway. Our study demonstrates that the simultaneous inhibition of ALK and SRC can potentially overcome resistance mechanisms and enhance clinical outcomes in ALK-positive lung cancer patients. ONE SENTENCE SUMMARY: Co-targeting ALK and SRC enhances ALK inhibitor response in lung cancer by affecting the proteomic profile, offering hope for overcoming resistance and improving clinical outcomes.

Defining the Educational Needs for a Community-Based Hematology/Oncology Career: A National Needs Assessment.

PURPOSE: Little is known about the specific needs during training for hematology/oncology providers practicing in community-based settings. We conducted a national survey of hematologists/oncologists employed in community or academic-community hybrid settings to delineate their educational needs. METHODS: An electronic questionnaire was developed and distributed nationally through professional organizations. We primarily assessed whether survey participants received any specific training during fellowship for community-based practice. Participants were also surveyed regarding training experiences that might have affected their preparation. Relative risk (RR) and 95% CI were calculated using modified Poisson regression to identify factors associated with receiving training specifically for community-based settings. RESULTS: Of 125 participants from across 25 states, 63% were male and 58% identified as White. Less than half (41.6%, binomial 95% CI, 32.8 to 50.7) received any training in a community-based setting. Participants identified rotations in community settings (47%), direct mentorship from community-based physicians (40%), and longitudinal clinic in a community setting (36%) as experiences that would have been valuable. Specific curricula of interest included medical operations and administration (63%), health policy (35%), and quality improvement (27%). Respondents in clinical practice for <10 years were more likely to have received any training specifically for a community-based career (RR, 2.13 [95% CI, 1.18 to 3.86]). CONCLUSION: Our study demonstrates substantial unmet needs as they relate to deliberately training fellows destined for community-based careers. Prospective design of clinical training and curricula emphasizing longitudinal exposures to and key aspects of health care delivery in the community setting are paramount to achieving optimal goal-concordant hematology/oncology training during fellowship.

Comparison of direct oral anticoagulants versus low-molecular-weight heparin in primary and metastatic brain cancers: a meta-analysis and systematic review.

BACKGROUND: The safety and efficacy of direct-acting oral anticoagulants (DOACs) for therapeutic anticoagulation in the setting of primary or metastatic brain cancer is not known. OBJECTIVES: To conduct a meta-analysis and systematic review of studies that compare the risk of intracranial hemorrhage (ICH) in patients with brain cancer treated with DOACs vs low-molecular-weight heparin (LMWH). METHODS: A literature search was conducted using PubMed, EMBASE, and Cochrane databases. Summary statistics were obtained by calculating the risk ratio (RR), and heterogeneity across studies was estimated using the I2 statistic. A total of 10 retrospective studies (n = 1638) met criteria for inclusion. The primary endpoint was the pooled RR for ICH in patients with brain tumors receiving anticoagulation with DOACs compared with those receiving LMWH. Secondary analyses included the risk of fatal ICH in each subgroup. RESULTS: The pooled RR for ICH in patients receiving DOACs vs those receiving LMWH was 0.65 (95% CI, 0.36-1.17; P = .15; I2 = 50%). In studies evaluating primary brain cancer, there was a reduction in risk of ICH with DOACs (RR, 0.35; 95% CI, 0.18-0.69; P = .003; I2 = 0%). In patients with metastatic brain cancer, there was no difference in the risk of ICH with the type of anticoagulation (RR, 1.05; 95% CI, 0.71-1.56; P = .80; I2 = 0%). The overall risk of fatal ICH was not different between anticoagulants. CONCLUSION: The risk of ICH in patients with brain cancer receiving therapeutic anticoagulation varies by anticoagulation agent and diagnosis of primary or metastatic disease.

Fentanyl-Induced Serotonin Syndrome 5 Days After Cessation of Serotonergic Agents: A Case Report.

A 21-year-old patient with intellectual disability was admitted for gastroenteritis due to serotonergic medication overdose, and subsequently developed serotonin syndrome. Her symptoms initially improved after the cessation of serotonergic medications, but worsened 5 days later after fentanyl administration during general anesthesia. On emergence, she had convulsions and was nonresponsive. Subsequent imaging and electroencephalography did not demonstrate intracranial pathology or seizure activity. We suspect she had an exacerbation of her serotonin syndrome. She recovered successfully after supportive care. This case demonstrates that common medications used during anesthesia such as fentanyl can provoke serotonin syndrome, even several days after serotonergic drug discontinuation.

Jet or wet? Droplet post-impact regimes on concave contours.

Droplet collision and subsequent spreading or wetting interactions with the solid substrate exhibit rich and interesting physics and are also important for various utilities. The fluid dynamics becomes more interesting and insightful when the wettability and geometry of the surface are tuned and altered. This study investigates the post-impact regimes of droplet impact on hydrophilic and superhydrophobic concave profile grooves (having dimensions comparable to that of the droplet). The post-collision hydrodynamics for such substrate-droplet system is three-dimensional, as in addition to droplet dynamics in the azimuthal direction, liquid jets may also be generated in the axial direction of the groove. Thereby the system may either lead to wetting or jetting, depending on the impact conditions. The effect of the impact Weber number (We) on the jet velocity, non-dimensional spreading width (γ) and non-dimensional south-pole film thickness (h*) has been probed and quantified. The observations reveal that the role of the wettability of the substrate is more profound in the recoiling stage than in the spreading stage, because inertial forces dominate in the latter. It is also noted that the spreading width increases and south-pole height decreases with increasing the impact Weber number. The opposite trend is noted upon increasing the groove concavity by altering just one dimension of the groove. The jet velocity is found to be the highest immediately after the impact and eventually decreases in a nonlinear fashion. Further, it has been found that the jet velocity increases with increasing the impact Weber number and that this effect is more prominent for superhydrophobic surfaces. A semi-analytical framework has been proposed to predict the jet velocity evolution in terms of governing Weber (We) and capillary (Ca) numbers. The predictions of the proposed model are in good agreement with the experimental observations.

Marangoni Flows in a Bilayer Liquid Microfilm Interface on Wave-Contoured Hot Substrates.

This study explores the thermal Marangoni hydrodynamics in an immiscible, binary-liquid thin-film system, which is open to the gas phase at the top and rests on a heated substrate with wavy topology. The sinusoidal contour of the heated (constant-temperature) substrate results in temperature gradients along the liquid-liquid and liquid-gas interfaces, causing fluctuations in the interfacial tension, ultimately leading to Marangoni hydrodynamics in the liquid-liquid films. This type of flow is notable in liquid film coatings on patterned surfaces, which are widely used in MEMS/NEMS applications (Weinstein, S. J.; Palmer, H. J. Liquid Film Coating: Scientific Principles and Their Technological Implications; 1997, pp 19-62; Palacio, M.; Bhushan, B. Adv. Mater. 2008, 20, 1194-1198) and biological cell sorting operations (Witek, M. A.; Freed, I. M.; Soper, S. A. Anal. Chem. 2019, 92, 105-131). We solve the coupled Navier-Stokes and energy equations by the perturbation technique to obtain approximate analytical solutions and an understanding of the thermal and hydrodynamic transport in the system domain. Our study explores the parametric influence of the relative thermal conductivity of the liquid layers (k), film thickness ratio (r), and the system's Biot number (Bi) on these transport phenomena. While the strength of the thermal Marangoni effect that is generated reduces with an increase in the relative thermal conductivity (k), the impact of r depends on the k value. We observe that for k > 1 the intensity of Marangoni flow increases with r; however, the opposite holds for k < 1. Furthermore, larger values of Bi induce higher resistance to the vertical conduction from the wavy substrate compared to the convection resistance offered at the top surface, destructively interfering with the ability of the patterned substrate to generate interfacial temperature fluctuations and hence weakening the Marangoni flow.

Nephrotic syndrome following COVID-19 vaccination: a systematic review.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 infection has caused significant morbidity and mortality. Vaccines produced against this virus have proven highly effective. However, adverse events following vaccination have also been reported. One of them is nephrotic syndrome, that can be associated with different pathologic pictures. This review aims to provide a wider understanding of incidence, etiopathogenesis, and management of nephrotic syndrome following vaccination against SARS-CoV-2. METHODS AND RESULTS: A literature search was undertaken using appropriate keywords in various databases like PubMed, Google Scholar, Europe PMC, and Science Direct. Twenty-one articles were included following qualitative assessment. Data of 74 patients from these articles were included. DISCUSSION: The pathogenesis of nephrotic syndrome following COVID vaccination has been widely attributed to the activation of angiotensin-converting enzyme-2 receptors, leading to podocyte effacement. Relapses have also been reported in patients with prior history of nephrotic syndrome following COVID-19 vaccination. A renal biopsy is necessary to identify the histopathological picture. Management of COVID-19 vaccine-induced nephrotic syndrome was mainly reported as successfully attainable with corticosteroids and supportive management. CONCLUSION: Further investigations will help in establishing an early diagnosis and salvaging kidney function.

Joint liver and hepatic lesion segmentation in MRI using a hybrid CNN with transformer layers.

Backgound and Objective: Deep learning-based segmentation of the liver and hepatic lesions therein steadily gains relevance in clinical practice due to the increasing incidence of liver cancer each year. Whereas various network variants with overall promising results in the field of medical image segmentation have been successfully developed over the last years, almost all of them struggle with the challenge of accurately segmenting hepatic lesions in magnetic resonance imaging (MRI). This led to the idea of combining elements of convolutional and transformer-based architectures to overcome the existing limitations. METHODS: This work presents a hybrid network called SWTR-Unet, consisting of a pretrained ResNet, transformer blocks as well as a common Unet-style decoder path. This network was primarily applied to single-modality non-contrast-enhanced liver MRI and additionally to the publicly available computed tomography (CT) data of the liver tumor segmentation (LiTS) challenge to verify the applicability on other modalities. For a broader evaluation, multiple state-of-the-art networks were implemented and applied, ensuring direct comparability. Furthermore, correlation analysis and an ablation study were carried out, to investigate various influencing factors on the segmentation accuracy of the presented method. RESULTS: With Dice similarity scores of averaged 98±2% for liver and 81±28% lesion segmentation on the MRI dataset and 97±2% and 79±25%, respectively on the CT dataset, the proposed SWTR-Unet proved to be a precise approach for liver and hepatic lesion segmentation with state-of-the-art results for MRI and competing accuracy in CT imaging. CONCLUSION: The achieved segmentation accuracy was found to be on par with manually performed expert segmentations as indicated by inter-observer variabilities for liver lesion segmentation. In conclusion, the presented method could save valuable time and resources in clinical practice.

The SARS-CoV-2 UTR's Intrudes Host RBP's and Modulates Cellular Splicing.

SARS-CoV-2 is a novel coronavirus that causes a potentially fatal respiratory disease known as coronavirus disease (COVID-19) and is responsible for the ongoing pandemic with increasing mortality. Understanding the host-virus interaction involved in SARS-CoV-2 pathophysiology will enhance our understanding of the mechanistic basis of COVID-19 infection. The characterization of post-transcriptional gene regulatory networks, particularly pre-mRNA splicing, and the identification and characterization of host proteins interacting with the 5' and 3'UTRs of SARS-CoV-2 will improve our understanding of post-transcriptional gene regulation during SARS-CoV-2 pathogenesis. Here, we demonstrate that either SARS-CoV-2 infection or exogenous overexpression of the 5' and 3'UTRs of the viral genomic RNAs, results in reduced mRNA levels possibly due to modulation of host cell pre-mRNA splicing. Further, we have investigated the potential RNA-binding proteins interacting with the 5' and 3'UTRs, using in-silico approaches. Our results suggest that 5' and 3'UTRs indeed interact with many RNA-binding proteins. Our results provide a primer for further investigations into the UTR-mediated regulation of splicing and related molecular mechanisms in host cells.

Utilizing Deep Learning Models and Transfer Learning for COVID-19 Detection from X-Ray Images.

COVID-19 has been a global pandemic. Flattening the curve requires intensive testing, and the world has been facing a shortage of testing equipment and medical personnel with expertise. There is a need to automate and aid the detection process. Several diagnostic tools are currently being used for COVID-19, including X-Rays and CT-scans. This study focuses on detecting COVID-19 from X-Rays. We pursue two types of problems: binary classification (COVID-19 and No COVID-19) and multi-class classification (COVID-19, No COVID-19 and Pneumonia). We examine and evaluate several classic models, namely VGG19, ResNet50, MobileNetV2, InceptionV3, Xception, DenseNet121, and specialized models such as DarkCOVIDNet and COVID-Net and prove that ResNet50 models perform best. We also propose a simple modification to the ResNet50 model, which gives a binary classification accuracy of 99.20% and a multi-class classification accuracy of 86.13%, hence cementing the ResNet50's abilities for COVID-19 detection and ability to differentiate pneumonia and COVID-19. The proposed model's explanations were interpreted via LIME which provides contours, and Grad-CAM, which provides heat-maps over the area(s) of interest of the classifier, i.e., COVID-19 concentrated regions in the lungs, and realize that LIME explains the results better. These explanations support our model's ability to generalize. The proposed model is intended to be deployed for free use.

Incidental diagnosis of a rare pancreatic plasmacytoma with plasmablastic features.

Multiple myeloma is a plasma cell neoplasm that accounts for 10% of all hematologic malignancies, characterized by malignant proliferation of monoclonal plasma cells in the bone marrow. It predominantly affects men 60 to 70 years of age. Plasmacytoma is a discrete mass of neoplastic monoclonal plasma cells that may be osseous or extramedullary. Though extramedullary plasmacytomas are uncommon, they can involve any tissue or organ. Only a few cases of pancreatic involvement have been reported. We report a case of a 78-year-old woman with a long-standing history of multiple myeloma noted to have pancreatic tail involvement with plasmacytoma with plasmablastic features. Multiple myeloma with plasmablastic transformation has a poor prognosis; hence, a multidisciplinary team approach is crucial to identify and initiate appropriate management in these cases.

Operando Microscopy Diagnosis of the Onset of Lithium Plating in Transparent Lithium-Ion Full Cells.

The fast-charging capability is critical for the wide adoption of electric vehicles (EVs), which, however, can result in lithium (Li) plating on the graphite anode and thus aggravate cell degradation and increase the safety risk. Li plating is also prone to occur during charging at low temperatures. In this work, we fabricate Li-ion full cells in transparent glass capillaries to probe the real-time dynamic evolution of the lithiated phases throughout the graphite anode toward the onset of lithium plating during fast charging and under low temperatures. We observed that Li plating can occur well before 70% state of charge (SOC), even at a low C-rate and at room temperature. Our operando experiments provide the direct proof that subtle features in the electrochemical responses are caused by the Li plating, which can be utilized to improve battery management strategy. Mathematical simulations confirm that the local overpotential due to the strong concentration polarization is the root cause of the axial reaction heterogeneity in the graphite anode and the Li plating on the fully lithiated particles.

Sublabial Approach Versus Modified Denker's Procedure: A Comparative Study of Pre-maxillary Tissue Clearance in Covid-Associated Mucormycosis.

To compare the efficacy of the sublabial and modified Denker's procedure in clearance of fungal disease from the anterior wall of maxilla and the pre-maxillary area i.e. the difficult areas of maxillary sinus. A prospective observational study was conducted over a period of 2 months (April 21-June 21) in the ENT department of Sawai Man Singh hospital, Jaipur. All the patients with clinical involvement of the premaxilla or the cheek abutting the anterior wall of maxilla were included in the study population. Cases matched in both groups were subjected to debridement either by the sublabial or the modified denker's approach. Outcomes were measured by assessing the daily CRP values, post-operative DNE every 3 days after pack removal, and imaging at the end of 1 month. Repeat biopsies were performed in patients that still had persistent symptoms after getting operated on. 16 of the 60 patients (26.6%) operated on by the endoscopic approach showed evidence of residual disease on follow-up whereas only 5 patients (9.6%) in the other category had a similar outcome. Most of the recidivism was seen in the anterior maxilla. Lower rates of complications were found following debridement by the sublabial approach. Since the sublabial approach is the most direct approach for the key areas of fungal involvement of maxillary sinus, it is recommended over the modified Denker's procedure for disease clearance from pre maxilla and the anterior wall of maxillary sinus. This view is also supported by the lower rates of complications encountered following the former.

StRegA: Unsupervised anomaly detection in brain MRIs using a compact context-encoding variational autoencoder.

Expert interpretation of anatomical images of the human brain is the central part of neuroradiology. Several machine learning-based techniques have been proposed to assist in the analysis process. However, the ML models typically need to be trained to perform a specific task, e.g., brain tumour segmentation or classification. Not only do the corresponding training data require laborious manual annotations, but a wide variety of abnormalities can be present in a human brain MRI - even more than one simultaneously, which renders a representation of all possible anomalies very challenging. Hence, a possible solution is an unsupervised anomaly detection (UAD) system that can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out-of-distribution samples. Such a technique can then be used to detect anomalies - lesions or abnormalities, for example, brain tumours, without explicitly training the model for that specific pathology. Several Variational Autoencoder (VAE) based techniques have been proposed in the past for this task. Even though they perform very well on controlled artificially simulated anomalies, many of them perform poorly while detecting anomalies in clinical data. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA), which is more robust on clinical data and shows its applicability in detecting anomalies such as tumours in brain MRIs. The proposed pipeline achieved a Dice score of 0.642 ± 0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859 ± 0.112 while detecting artificially induced anomalies, while the best performing baseline achieved 0.522 ± 0.135 and 0.783 ± 0.111, respectively.

Content-based medical image retrieval system for lung diseases using deep CNNs.

Content-based image retrieval (CBIR) systems are designed to retrieve images that are relevant, based on detailed analysis of latent image characteristics, thus eliminating the dependency of natural language tags, text descriptions, or keywords associated with the images. A CBIR system maintains high-level image visuals in the form of feature vectors, which the retrieval engine leverages for similarity-based matching and ranking for a given query image. In this paper, a CBIR system is proposed for the retrieval of medical images (CBMIR) for enabling the early detection and classification of lung diseases based on lung X-ray images. The proposed CBMIR system is built on the predictive power of deep neural models for the identification and classification of disease-specific features using transfer learning based models trained on standard COVID-19 Chest X-ray image datasets. Experimental evaluation on the standard dataset revealed that the proposed approach achieved an improvement of 49.71% in terms of precision, averaging across various distance metrics. Also, an improvement of 26.55% was observed in the area under precision-recall curve (AUPRC) values across all subclasses.

Intussusception Caused by Peutz-Jeghers Syndrome.

A 20-year-old female patient with a family history significant for Peutz-Jeghers syndrome presented to the hospital multiple times with complaints of abdominal pain. On the initial visit to the hospital, the patient underwent small bowel resection for small bowel obstruction secondary to intussusception, following which she visited the hospital again one year later for similar complaints and underwent reduction of multiple points of intussusception of the small bowel without any resection of the same. Eventually, the patient underwent resection of the small bowel for the second time, along with tumor resections. The importance of follow-up in patients with Peutz-Jeghers is particularly essential, in part, because it is vital to monitor the tumors, their size, and number to prevent surgical intestinal complications, anemia, and also to eventually monitor for carcinomatous changes.

Impacts of Pokémon GO on route and mode choice decisions: exploring the potential for integrating augmented reality, gamification, and social components in mobile apps to influence travel decisions.

This study aims to understand the impacts of Pokémon GO, a popular location-based augmented reality (AR) mobile gaming app, on route and mode choices. Pokémon GO leverages AR to introduce virtual objects at fixed and dynamic locations that translate through the app interface to incentives in the real world that potentially influence users' route and mode choices. Its gaming nature and social components can possibly enhance long-term user engagement through applying the characteristics of game elements and providing opportunities for competition, collaboration, companionship, and social reinforcement. An online survey is conducted to collect the self-reported behavior of a group of Pokémon GO users to explore its impacts on the following aspects of travel behavior: (1) the frequency of changing the route to interact with virtual objects; (2) the likelihood of carpooling more instead of driving alone for more in-app collaboration; and (3) the likelihood of shifting mode from drive alone to public transit, walking, and cycling if provided with additional incentives. The ordered survey responses including frequency and likelihood are analyzed using random parameters ordered probit models to account for the unobserved heterogeneity across users and identify subpopulations of travelers who are more susceptible to the influence of Pokémon GO. The modeling results identify four types of variables (attitude and perceptions related to Pokémon GO, app engagement, play style, and sociodemographic characteristics) that affect users' travel behavior. The results illustrate that such apps with integrated AR, gamification, and social components can be used by policymakers to influence various aspects of travel behavior. The study findings and insights can provide valuable feedback to system operators for designing such apps to dynamically manage traffic in real-time and promote long-term sustainable mode shifts.

The impacts of the COVID-19 pandemic on transportation employment: A comparative analysis.

The COVID-19 pandemic caused a variety of social, economic, and environmental changes. This paper examines the employment-related impacts of the pandemic on workers in the transportation industry compared to other industries, and within different transportation sectors. We estimated random effects logistic regression models to test the following three hypotheses using the monthly Current Population Survey micro-data. One, the transportation industry experienced a greater incidence of unemployment than other industries. Two, there is heterogeneity in employment impacts within the transportation sector. Three, specific sectors within the transportation industry experienced more employment impacts than other essential industries, as designated by the Centers for Disease Control and Prevention (CDC) Phase 1a vaccination guidelines. Model results highlight that workers in the transportation sector were 20.6% more likely to be unemployed because of the pandemic than workers in non-transportation industries. Model results also indicate large intra-sector heterogeneities in employment impacts within the transportation sector. Taxi and limousine drivers were 28 times more likely to be unemployed compared to essential workers. Scenic and sightseeing transportation workers were 23.8 times more likely to be unemployed compared to essential workers. On the other end of the spectrum, however, postal workers and pipeline workers were 84% and 67% less likely to be unemployed compared to essential workers, respectively. From a policy perspective, these results suggest that attention to several aspects of transportation work is needed in the coming years to prepare for future interruptions to the transportation industry.