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Pancreatic duct dilation can be considered an early sign of pancreatic ductal adenocarcinoma (PDAC). However, there is little existing research focused on dilated pancreatic duct segmentation as a potential screening tool for people without PDAC. Dilated pancreatic duct segmentation is difficult due to the lack of readily available labeled data and strong voxel imbalance between the pancreatic duct region and other regions. To overcome these challenges, we propose a two-step approach for dilated pancreatic duct segmentation from abdominal computed tomography (CT) volumes using fully convolutional networks (FCNs).Our framework segments the pancreatic duct in a cascaded manner. The pancreatic duct occupies a tiny portion of abdominal CT volumes. Therefore, to concentrate on the pancreas regions, we use a public pancreas dataset to train an FCN to generate an ROI covering the pancreas and use a 3D U-Net-like FCN for coarse pancreas segmentation. To further improve the dilated pancreatic duct segmentation, we deploy a skip connection on each corresponding resolution level and an attention mechanism in the bottleneck layer. Moreover, we introduce a combined loss function based on Dice loss and Focal loss. Random data augmentation is adopted throughout the experiments to improve the generalizability of the model.We manually created a dilated pancreatic duct dataset with semi-automated annotation tools. Experimental results showed that our proposed framework is practical for dilated pancreatic duct segmentation. The average Dice score and sensitivity were 49.9% and 51.9%, respectively. These results show the potential of our approach as a clinical screening tool.We investigate an automated framework for dilated pancreatic duct segmentation. The cascade strategy effectively improved the segmentation performance of the pancreatic duct. Our modifications to the FCNs together with random data augmentation and the proposed combined loss function facilitate automated segmentation.

Researchers have theorized that exposure to racial discrimination may impair executive functioning. The limited existing data broadly support this notion and suggest that discrimination may exert acute and persistent effects on executive functioning, potentially because of the cognitive demands associated with responding to discrimination. However, it is unclear if discrimination is differentially associated with different core executive functions. Further, the effects may vary depending on the timing of exposure, as recent or acute exposure to discrimination may operate on executive functioning through different mechanisms than exposure across the lifetime. The current study evaluates the relations of both recent and lifetime exposure to racial discrimination to three core executive functions (i.e., cognitive flexibility, inhibitory control and working memory) using a racially and ethnically diverse sample (n = 319). In fully adjusted models, recent discrimination was negatively associated with cognitive flexibility and working memory but not with inhibitory control. These data are consistent with the broader literature on acute stress effects on core executive functions and may have implications for understanding the effects of discrimination on health. Further research is warranted to understand the course and mechanisms of effects of lifetime and recent discrimination on core executive functions.

BackgroundDarier, Hailey-Hailey, and Grover’s diseases are rare non-autoimmune acantholytic skin diseases. While these diseases have different underlying causes, they share defects in cell-cell adhesion in the epidermis and desmosome organization.ObjectiveTo better understand the underlying mechanisms leading to disease in these conditions we performed RNA-seq on lesional skin samples from Darier, Hailey-Hailey, and Grover’s disease patients.MethodsRNA-seq and bioinformatics analyses were performed on banked paraffin embedded diagnostic samples from each disease. For detailed Methods, please see the Methods section in this article’s Online Repository atwww.jacionline.org.ResultsThe transcriptomic profiles of Darier, Hailey-Hailey, and Grover’s disease were found to share a remarkable overlap, which did not extend to other common inflammatory skin diseases, psoriasis and atopic dermatitis. Analysis of enriched pathways showed a shared upregulation in keratinocyte differentiation and Th17 inflammatory pathways, and a decrease in cell adhesion and actin organization pathways in Darier, Hailey-Hailey, and Grover’s disease. Direct comparison to atopic dermatitis and psoriasis showed that the downregulation in actin organization pathways was a unique feature in Darier, Hailey-Hailey, and Grover’s disease.Further, upstream regulator analysis suggests that a decrease in SRF/MRTF activity may be responsible for the downregulation of actin organization pathways. Staining for MRTFA in lesional skin samples showed a decrease in nuclear MRTFA in patient skin compared to normal skin.ConclusionThese findings highlight the significant level of similarity in the transcriptome of Darier, Hailey-Hailey, and Grover’s disease, and identify decreases in actin organization pathways as a unique signature present in these conditions.Key MessagesDarier Disease, Hailey-Hailey Disease, and Grover’s Disease share similar transcriptional profiles suggesting common mechanisms of pathogenesis.SRF/MRTFA activity is reduced in Darier Disease, Hailey-Hailey Disease and Grover’s disease, implicating actin organization in acantholysis.

Background When a new guideline is published there is a need to understand how its recommendations can best be implemented in real-world practice. Yet, guidelines are often published with little to no roadmap for organizations to follow to promote adherence to their recommendations. The purpose of this study was to evaluate the impact of using a common process model to implement a single clinical practice guideline across multiple physical therapy clinical settings. Methods Five organizationally distinct sites with physical therapy services for patients with peripheral vestibular hypofunction participated. The Knowledge to Action model served as the foundation for implementation of a newly published guideline. Site leaders conducted preliminary gap surveys and face-to-face meetings to guide physical therapist stakeholders’ identification of target-behaviors for improved guideline adherence. A 6-month multimodal implementation intervention included local opinion leaders, audit and feedback, fatigue-resistant reminders, and communities of practice. Therapist adherence to target-behaviors for the 6 months before and after the intervention was the primary outcome for behavior change. Results Therapist participants at all sites indicated readiness for change and commitment to the project. Four sites with more experienced therapists selected similar target behaviors while the fifth, with more inexperienced therapists, identified different goals. Adherence to target behaviors was mixed. Among four sites with similar target behaviors, three had multiple areas of statistically significantly improved adherence and one site had limited improvement. Success was most common with behaviors related to documentation and offering patients low technology resources to support home exercise. A fifth site showed a trend toward improved therapist self-efficacy and therapist behavior change in one provider location. Conclusions The Knowledge to Action model provided a common process model for sites with diverse structures and needs to implement a guideline in practice. Multimodal, active interventions, with a focus on auditing adherence to therapist-selected target behaviors, feedback in collaborative monthly meetings, fatigue-resistant reminders, and developing communities of practice was associated with long-term improvement in adherence. Local rather than external opinion leaders, therapist availability for community building meetings, and rate of provider turnover likely impacted success in this model. Trial registration This study does not report the results of a health care intervention on human participants.

Melanoma is an aggressive cancer typically arising from transformation of melanocytes residing in the basal layer of the epidermis, where they are in direct contact with surrounding keratinocytes. The role of keratinocytes in shaping the melanoma tumor microenvironment remains understudied. We previously showed that temporary loss of the keratinocyte-specific cadherin, Desmoglein 1 (Dsg1) controls paracrine signaling between normal melanocytes and keratinocytes to stimulate the protective tanning response. Here, we provide evidence that melanoma cells hijack this intercellular communication by secreting factors that keep Dsg1 expression low in surrounding keratinocytes, which in turn generate their own paracrine signals that enhance melanoma spread through CXCL1/CXCR2 signaling. Evidence suggests a model whereby paracrine signaling from melanoma cells increases levels of the transcriptional repressorSnai2(Slug), and consequently decreases the Dsg1 transcriptional activatorGrhl1(Grainyhead like 1), a known promoter of epidermal differentiation and barrier function. Together, these data support the idea that paracrine crosstalk between melanoma cells and keratinocytes resulting in chronic keratinocyte Dsg1 reduction contributes to melanoma cell movement associated with tumor progression.

Deep learning models are currently the cornerstone of artificial intelligence in medical imaging. While progress is still being made, the generic technological core of convolutional neural networks (CNNs) has had only modest innovations over the last several years, if at all. There is thus a need for improvement. More recently, transformer networks have emerged that replace convolutions with a complex attention mechanism, and they have already matched or exceeded the performance of CNNs in many tasks. Transformers need very large amounts of training data, even more than CNNs, but obtaining well-curated labeled data is expensive and difficult. A possible solution to this issue would be transfer learning with pretraining on a self-supervised task using very large amounts of unlabeled medical data. This pretrained network could then be fine-tuned on specific medical imaging tasks with relatively modest data requirements. The authors believe that the availability of a large-scale, three-dimension–capable, and extensively pretrained transformer model would be highly beneficial to the medical imaging and research community. In this article, authors discuss the challenges and obstacles of training a very large medical imaging transformer, including data needs, biases, training tasks, network architecture, privacy concerns, and computational requirements. The obstacles are substantial but not insurmountable for resourceful collaborative teams that may include academia and information technology industry partners. © RSNA, 2022 Keywords: Computer-aided Diagnosis (CAD), Informatics, Transfer Learning, Convolutional Neural Network (CNN)

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site’s data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare. Federated learning, a method for training artificial intelligence algorithms that protects data privacy, was used to predict future oxygen requirements of symptomatic patients with COVID-19 using data from 20 different institutes across the globe.

Background Knowledge on the impact of stoma formation surgery on course of disease, psychological well-being, quality of life (QoL) and working capacity in patients with IBD is scarce. Methods To investigate this, we analysed prospectively followed-up patients from the Swiss inflammatory bowel disease cohort study (SIBDCS), comparing ulcerative colitis (UC) and Crohn’s disease (CD) with vs without stoma surgery, for both temporary and permanent stoma formation. Results From a total of, 3825 SIBDCS patients, 352 patients were included in our analysis -, 176 with stoma surgery (52.8% and, 47.2% with CD and UC, respectively) - amongst these, 111 with permanent and, 65 with temporary stoma - matched with, 176 patients without stoma. Mean and maximal disease activity was lower in patients with vs. without stoma overall in CD but not in UC (Fig., 1, A/B). However, in UC, disease activity was lower in permanent vs. temporary stoma patients (Fig., 1, C/D). Figure, 1 A-D: Average disease activity with mean Crohn’s disease activity index (CDAI; A) in CD and Modified Truelove & Witts activity index (MTWAI; B) and UC for patients with vs. without stoma as well as (within the stoma group) permanents vs. closed ostomy (C,D). Furthermore, active EIM as well as need for steroids, immuno-modulators and biologics were lower in stoma vs. control patients. Amongst stoma patients, quality of life and psychological wellbeing was similar in permanent vs. closed stoma (Inflammatory Bowel Disease Quality of Life questionnaire Total score (IBDQ); Hospital Anxiety and Depression Scale Total Score (HADS); Posttraumatic Diagnostic Scale Total Score (PDS); and effort-reward ratio – all mean values obtained during prospective SIBDCS follow-up). However, we found a significantly lower (i.e. more favourable) PDS avoidance score in permanent vs. closed ostomy (Fig., 2A). Working capacity was lower in permanent vs. closed stoma patients (Fig. 2B). Figure, 2 A,B: IBDQ, HADS total, PDS total and Effort-Reward ratio (A), Working status, absentism from work and need for disability insurance (B) in permanent vs. closed IBD patients with stoma. Conclusion In CD patients with stoma surgery disease activity was significantly reduced after ostomy. Moreover, in UC we observed a benefit of patients with permanent compared to their counter-parts with temporary stoma to control disease activity. We found no differences in psychological functioning and QoL between temporary and closed stoma, with the exception of higher avoidance indicative of posttraumatic stress in the latter. In contrast, working capacity revealed to be higher closed vs. permanent ostomy.

Background Approximately 40% of pancreatic tumors smaller than 2 cm are missed at abdominal CT. Purpose To develop and to validate a deep learning (DL)-based tool able to detect pancreatic cancer at CT. Materials and Methods Retrospectively collected contrast-enhanced CT studies in patients diagnosed with pancreatic cancer between January 2006 and July 2018 were compared with CT studies of individuals with a normal pancreas (control group) obtained between January 2004 and December 2019. An end-to-end tool comprising a segmentation convolutional neural network (CNN) and a classifier ensembling five CNNs was developed and validated in the internal test set and a nationwide real-world validation set. The sensitivities of the computer-aided detection (CAD) tool and radiologist interpretation were compared using the McNemar test. Results A total of 546 patients with pancreatic cancer (mean age, 65 years ± 12 [SD], 297 men) and 733 control subjects were randomly divided into training, validation, and test sets. In the internal test set, the DL tool achieved 89.9% (98 of 109; 95% CI: 82.7, 94.9) sensitivity and 95.9% (141 of 147; 95% CI: 91.3, 98.5) specificity (area under the receiver operating characteristic curve [AUC], 0.96; 95% CI: 0.94, 0.99), without a significant difference (

Three-dimensional (3D) human organotypic skin cultures provide a physiologically relevant model that recapitulates in vivo skin features. Most commonly, organotypic skin cultures are created by seeding isolated epidermal keratinocytes onto a collagen/fibroblast plug and lifting to an air liquid interface. These conditions are sufficient to drive stratification and differentiation of the keratinocytes to form an epidermal-like sheet with remarkable similarities to human epidermis. Coupled with genetic or pharmacological treatments, these cultures provide a powerful tool for elucidating keratinocyte biology. Recent focus has been placed on increasing the utility of organotypic skin cultures by incorporating other cell types that are present in the skin, such as melanocytes, immune cells, and other cells. Here we describe a step-by-step protocol for the isolation of neonatal human epidermal keratinocytes and melanocytes from foreskins, and the creation of organotypic skin cultures that include both cell types. We also describe methods that can be used to assess melanocyte behavior in these organotypic cultures, including methods for whole mount staining, measurement of melanocyte dendricity, staining for pigment, and 5-bromo-2'-deoxyuridine (BrdU) labeling for identification of proliferating cells. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Isolation of primary cells Alternate Protocol: Isolation of primary cells using differential trypsinization Basic Protocol 2: Organotypic culture protocol Support Protocol 1: Culture and maintenance of NHEKs and melanocytes Support Protocol 2: Lentiviral transduction of melanocytes Support Protocol 3: Retroviral transduction of NHEKs Support Protocol 4: Whole mount immunostaining protocol Support Protocol 5: Measuring melanocyte dendricity Support Protocol 6: Fontana-Masson staining protocol Support Protocol 7: BrdU labeling and staining.

Medical image annotation is a major hurdle for developing precise and robust machine learning models. Annotation is expensive, time-consuming, and often requires expert knowledge, particularly in the medical field. Here, we suggest using minimal user interaction in the form of extreme point clicks to train a segmentation model which, in effect, can be used to speed up medical image annotation. An initial segmentation is generated based on the extreme points utilizing the random walker algorithm. This initial segmentation is then used as a noisy supervision signal to train a fully convolutional network that can segment the organ of interest, based on the provided user clicks. Through experimentation on several medical imaging datasets, we show that the predictions of the network can be refined using several rounds of training with the prediction from the same weakly annotated data. Further improvements are shown utilizing the clicked points within a custom-designed loss and attention mechanism. Our approach has the potential to speed up the process of generating new training datasets for the development of new machine learning and deep learning-based models for, but not exclusively, medical image analysis., Comment: 13 pages, 6 figures, 1 table

Syncope is a common emergency department (ED) chief complaint. Rarely, syncope can be the result of right ventricular outflow obstruction from an intracardiac tumor, such as an intracardiac extension of intravenous leiomyomatosis (IVL). Typically, this type of tumor is confined to the pelvic veins, but in very rare cases, it can extend through the inferior vena cava into the right atrium. Point-of-care ultrasound (POCUS) can be a crucial tool in the ED for identifying intracardiac tumors presenting as syncope and expediting clinical management. We present the case of a 39-year-old female with no prior medical history that presented to the ED having experienced dyspnea on exertion and two syncopal episodes prior to ED admission. POCUS use in the ED elucidated the presence of a right atrial mass and further imaging showed a mass on the patient's uterus. After surgical removal of a portion of the atrial mass, a subsequent biopsy revealed it had leiomyoma-like features; as such, the patient was diagnosed with IVL. This case illustrates the importance of using POCUS in the ED to help determine the etiology of syncope. Although intracardiac extensions of IVL are rare, it is important for emergency physicians to keep this diagnosis in the differential in patients with symptoms or risk factors suggestive of IVL with intracardiac extension.

Objectives The potential impact of social distancing policies during the coronavirus disease 2019 (COVID-19) pandemic on social isolation and loneliness is of increasing global concern. Although many studies focus primarily on loneliness, patterns of social isolation—particularly physical and digital isolation—are understudied. We examined changes in social isolation, physical isolation, digital isolation, and loneliness in U.S. adults older than 50 before and during the lockdown. Methods Two waves of the Health and Retirement Study, a national panel sample of U.S. adults older than 50 years, were used. Fixed-effects regression models were fitted to identify within-person change from 2016 to 2020 to examine the impact of social distancing policies during the pandemic. Results There was an increase in physical isolation and social isolation among respondents during the COVID-19 social distancing policies. However, respondents experienced no change in digital isolation or loneliness. The increase in physical isolation was only present for people with high COVID-19 concern, whereas people with low concern experienced no change in physical isolation. Discussion Despite an increase in physical isolation due to the social distancing policies, U.S. adults aged older than 50 stayed connected through digital contact and were resilient in protecting themselves from loneliness.

It is generally recognized that lives are saved by administering high-quality cardiopulmonary resuscitation (CPR) to patients in cardiac arrest. A focused assessment with sonography for trauma (FAST) examination is an effective and non–invasive method for detecting rare complications of CPR, such as hemorrhage from abdominal visceral injury. We report the case of a 56-year-old female suffering from intra-abdominal hemorrhage caused by a liver laceration following CPR. The hemoperitoneum was diagnosed by a FAST examination. Although severe complications of CPR are rare, they can be easily detected with the use of a FAST examination. A FAST examination should be considered as a post-resuscitation approach to assess for life-threatening complications in all patients following cardiopulmonary resuscitation.

Intrauterine devices (IUDs) are used worldwide. The 2 types that are used are the levonorgestrel IUD and a copper containing IUD. This is a case study of a 30-year-old female with a levonorgestrel IUD who was diagnosed with a ruptured ectopic pregnancy in the emergency department (ED). Point-of-care urine pregnancy test and point-of-care ultrasound (POCUS) were vital in making this diagnosis and should be utilized in patients assigned female at birth who present with abdominal pain.

A better understanding of temporal relationships between chest CT and labs may provide a reference for disease severity over the disease course. Generalized curves of lung opacity volume and density over time can be used as standardized references from well before symptoms develop to over a month after recovery, when residual lung opacities remain. 739 patients with COVID-19 underwent CT and RT-PCR in an outbreak setting between January 21st and April 12th, 2020. 29 of 739 patients had serial exams (121 CTs and 279 laboratory measurements) over 50 ± 16 days, with an average of 4.2 sequential CTs each. Sequential volumes of total lung, overall opacity and opacity subtypes (ground glass opacity [GGO] and consolidation) were extracted using deep learning and manual segmentation. Generalized temporal curves of CT and laboratory measurements were correlated. Lung opacities appeared 3.4 ± 2.2 days prior to symptom onset. Opacity peaked 1 day after symptom onset. GGO onset was earlier and resolved later than consolidation. Lactate dehydrogenase, and C-reactive protein peaked earlier than procalcitonin and leukopenia. The temporal relationships of quantitative CT features and clinical labs have distinctive patterns and peaks in relation to symptom onset, which may inform early clinical course in patients with mild COVID-19 pneumonia, or may shed light upon chronic lung effects or mechanisms of medical countermeasures in clinical trials.

Objective To demonstrate enabling multi-institutional training without centralizing or sharing the underlying physical data via federated learning (FL). Materials and Methods Deep learning models were trained at each participating institution using local clinical data, and an additional model was trained using FL across all of the institutions. Results We found that the FL model exhibited superior performance and generalizability to the models trained at single institutions, with an overall performance level that was significantly better than that of any of the institutional models alone when evaluated on held-out test sets from each institution and an outside challenge dataset. Discussion The power of FL was successfully demonstrated across 3 academic institutions while avoiding the privacy risk associated with the transfer and pooling of patient data. Conclusion Federated learning is an effective methodology that merits further study to enable accelerated development of models across institutions, enabling greater generalizability in clinical use.

Akbar Rasulov’s provocative discussion of the ‘The Curious Case of the International Law of Democracy and the Politics of International Legal Scholarship’ makes two remarkable assertions: i) that the critics of the democratic entitlement thesis won a decisive victory in the contest to influence ‘the conventional wisdom’ within international legal scholarship; and ii) that the critiques objectively served ‘a fundamentally reactionary political agenda’. Beyond overstating both the critiques’ harshness and their impact, Rasulov is too quick to associate their methodological orthodoxy with ‘right-wing’ outcomes, neglecting to appreciate that their authors’ primary objective was to resist neo-colonialist tendencies. Whereas departures from standard source doctrines may in an earlier era have been directed towards redress of power imbalances inherited from colonialism, the ‘pro-democratic’ departures of the post-Cold War era tended to license impositions on the self-government of the poorer and weaker states. The democratic entitlement’s critics sought precisely to conserve gains that had earlier been won by sectors of the international community resistant to neo-colonialism.

In previous research, xylem sap of angiosperms has been found to include low concentrations of nanoparticles and polar lipids. A major goal of this study was to test predictions arising from the hypothesis that the nanoparticles consist largely of polar lipids from the original cell content of vessel elements. These predictions included that polar lipid and nanoparticle concentrations would be correlated, that they both do not pass through pit membranes and that they do not vary seasonally because they originate from living vessel element cells. We collected xylem sap of six temperate angiosperm species over the whole year to consider seasonal variation. Concentrations of nanoparticles and lipids in xylem sap and contamination control samples were measured with a NanoSight device and mass spectrometry. We found that the concentration of nanoparticles and polar lipids was (i) diluted when an increasing amount of sap was extracted, (ii) significantly correlated to each other for three species, (iii) affected by vessel anatomy, (iv) very low and largely different in chemical composition from contamination controls and (v) hardly variable among seasons. Moreover, there was a minor freezing–thawing effect with respect to nanoparticle amount and size. Xylem sap lipids included polar galactolipids and phospholipids in all species and neutral triacylglycerols in two species. These findings support the predictions and, by implication, the underlying hypothesis that nanoparticles in xylem sap consist of polar lipids from the original cell content of living vessel element cells. Further research is needed to examine the formation and stability of nanoparticles concerning lipid composition and multiphase interactions among gas, liquid and solid phases in xylem conduits of living plants.

ContextEnd-of-life care (EOLC) communication is beneficial but underutilized, particularly in conditions with a variable course such as chronic obstructive pulmonary disease (COPD) and congestive heart failure (CHF). Physicians’ emotional distress intolerance has been identified as a barrier to EOLC communication. However, studies of emotional distress intolerance in EOLC have largely relied on anecdotal reports, qualitative data, or observational studies of physician–patient communication. A free-standing measure of multiple dimensions of distress tolerance is warranted to enable the identification of individuals experiencing distress intolerance and to facilitate the effective targeting of interventions to improve distress tolerance.ObjectivesThis study provides preliminary data on the reliability and validity of the Physician Distress Intolerance (PDI) scale. We examine potential subdimensions of emotional distress intolerance.MethodFamily medicine and internal medicine physicians completed the PDI, read vignettes describing patients with COPD or CHF, and indicated whether they initiated or delayed EOLC communication with their patients with similar conditions.ResultsExploratory and confirmatory factor analyses were performed on separate samples. Confirmatory factor analysis confirmed that a three-factor solution was superior to a two- or one-factor solution. Three subscales were created: Anticipating Negative Emotions, Intolerance of Uncertainty, and Iatrogenic Harm. The full scale and subscales had adequate internal consistency and demonstrated evidence of validity. Higher scores on the PDI, indicating greater distress intolerance, were negatively associated with initiation and positively associated with delay of EOLC communication. Subscales provided unique information.Significance of resultsThe PDI can contribute to research investigating and addressing emotional barriers to EOLC communication.

International audience; The neon isotopic chain displays a rich phenomenology, ranging from clustering in the ground-state of the self-conjugate doubly open-shell stable $^{20}$Ne isotope to the physics of the island of inversion around the neutron-rich $^{30}$Ne isotope. This second (i.e. Paper II) of the present series proposes an extensive ab initio study of neon isotopes based on two complementary many-body methods, i.e. the quasi-exact in-medium no-core shell model (IM-NCSM) and the projected generator coordinate method (PGCM) that is ideally suited to capturing strong static correlations associated with shape deformation and fluctuations. Calculations employ a state-of-the-art generation of chiral effective field theory Hamiltonians and evaluate the associated systematic uncertainties. In spite of missing so-called dynamical correlations, which can be added via the multi-reference perturbation theory proposed in the first paper (i.e. Paper I) of the present series [1], the PGCM is shown to be a suitable method to tackle the low-lying spectroscopy of complex nuclei. Still, describing the physics of the island of inversion constitutes a challenge that seems to require the inclusion of dynamical correlations. This is addressed in the third paper (i.e. Paper III) of the present series [2].

With advancements in whole slide imaging technology and improved understanding of the features of pathologist workstations required for digital slide evaluation, many institutions are investigating broad digital pathology adoption. The benefits of digital pathology evaluation include remote access to study or diagnostic case materials and integration of analysis and reporting tools. Diagnosis based on whole slide images is established in human medical pathology, and the use of digital pathology in toxicologic pathology is increasing. However, there has not been broad adoption in toxicologic pathology, particularly in the context of regulatory studies, due to lack of precedence. To address this topic, as well as practical aspects, the European Society of Toxicologic Pathology coordinated an expert international workshop to assess current applications and challenges and outline a set of minimal requirements needed to gain future regulatory acceptance for the use of digital toxicologic pathology workflows in research and development, so that toxicologic pathologists can benefit from digital slide technology.

Emergency physicians often rely on heuristics to facilitate clinical decisions due to the large volume of patients they see daily. Consequently, they are vulnerable to error and bias. We report the case of a 69-year-old male that presented to the emergency department (ED) with shortness of breath, productive cough, and dyspnea on exertion. One day prior to ED admission, he was diagnosed with bronchitis; however, point-of-care ultrasound (POCUS) in the ED identified acute pulmonary embolism. This case illustrates the potential dangers of anchoring bias and shows the benefits of using point-of-care ultrasound of the lungs and heart to assist in the diagnosis of acute pulmonary embolism.

The World Health Organization (WHO) categorized coronavirus disease 2019 (COVID-19) as a pandemic due to its high contagion rate and widespread infectivity in February 2020. In the United States (US), one of the public health concerns is the adequacy of resources to treat infected cases. We describe a case of a previously well, 9-year-old obese boy who presented to the emergency department with shortness of breath, fever, abdominal pain and cough with chest pain. He was diagnosed with COVID-19 through significant family contact, confirmed by polymerase chain reaction and found to be at high risk of venous thromboembolism due to abnormal d-dimer. Lung point-of-care ultrasound (POCUS) in the emergency department observed significant lung pathology, including pleural thickening, consolidation, and B lines. A chest X-ray (CXR) found bilateral ground glass opacities and interstitial prominences consistent with viral pneumonia. Our case suggests that lung POCUS can provide adequate and rapid imaging to assess lung pathology of COVID-19 in a pediatric patient. As there is limited literature on use of lung POCUS in pediatric patients infected with SARS-CoV-2, our case emphasizes its function as a potentially efficient modality in bedside assessment.