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Rebecca L Ball,1 Palak Bajaj,1,2 Kathryn A Whitehead1,2 1Department of Chemical Engineering, 2Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA Abstract: The broadest clinical application of siRNA therapeutics will be facilitated by drug-loaded delivery systems that maintain stability and potency for long times under ambient conditions. In the present study, we seek to better understand the stability and effect of storage conditions on lipidoid nanoparticles (LNPs), which have been previously shown by our group and others to potently deliver RNA to various cell and organ targets both in vitro and in vivo. Specifically, this study evaluates the influence of pH, temperature, and lyophilization on LNP efficacy in HeLa cells. When stored under aqueous conditions, we found that refrigeration (2°C) kept LNPs the most stable over 150 days compared to storage in the -20°C freezer or at room temperature. Because the pH of the storage buffer was not found to influence stability, it is suggested that the LNPs be stored under physiologically appropriate conditions (pH 7) for ease of use. Although aggregation and loss of efficacy were observed when LNPs were subjected to freeze–thaw cycles, their stability was retained with the use of the cryoprotectants, trehalose, and sucrose. Initially, lyophilization of the LNPs followed by reconstitution in aqueous buffer also led to reductions in efficacy, most likely due to aggregation upon reconstitution. Although the addition of ethanol to the reconstitution buffer restored efficacy, this approach is not ideal, as LNP solutions would require dialysis prior to use. Fortunately, we found that the addition of trehalose or sucrose to LNP solutions prior to lyophilization facilitated room temperature storage and reconstitution in aqueous buffer without diminishing delivery potency. Keywords: lipid nanoparticles, nanoparticle stability, lyophilization, lyoprotectants, nanoparticle storage, siRNA delivery

Background: Available data on radiologists' missed cervical spine fractures are based primarily on studies using human reviewers to identify errors on re-evaluation; such studies do not capture the full extent of missed fractures. Objective: To use machine-learning (ML) models to identify cervical spine fractures on CT missed by interpreting radiologists, characterize the nature of these fractures, and assess their clinical significance. Methods: This retrospective study included all cervical spine CT examinations performed in adult patients in the emergency department between January 1, 2018 and December 31, 2022. Examinations reported as negative for cervical spine fracture were processed by seven award-winning ML models from the 2022 RSNA Cervical Spine Fracture AI Challenge; examinations classified as positive by at least four of seven models were considered to have ML-detected fractures. Two neuroradiologists independently reviewed examinations with ML-detected fractures, using ML-derived heat maps, to identify those representing true missed fractures. The neuroradiologists further assessed fractures' extent. Two spine surgeons independently assessed whether missed fractures were clinically significant (i.e., warranting at least one of surgical consultation, MRI, CTA, or collar immobilization). Results: The study included 6671 patients (2414 female, 4257 male; mean age, 54.6±22.1 years) who underwent a total of 6979 cervical spine CT examinations. Interpreting radiologists reported 6378 examinations as negative for fracture. Of these, 356 had ML-detected fractures (i.e., positive by ≥4 of 7 models). The neuroradiologists classified 40 of these examinations, in 39 unique patients, as having true fractures. ML-detected missed true fractures involved 51 unique sites, most commonly the C7 transverse process (n=12), C5 spinous process (n=12), and C6 spinous process (n=8). The surgeons considered missed fractures clinically significant in 15/40 examinations [MRI and collar immobilization (n=7), MRI and surgical evaluation (n=1), CTA (n=9)]. Interobserver agreement, expressed as kappa, was 0.88 between neuroradiologists for true fracture classification and 0.94 between surgeons for clinical significance classification. Conclusion: ML models identified cervical spine fractures missed by radiologists. These fractures were further characterized to systematically highlight radiologists' common misses. Clinical Impact: This ML-based framework can be applied in quality improvement efforts, to help refine radiologists' search patterns based on prone-to-miss findings.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence . This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To evaluate the performance of the winning machine learning (ML) models from the 2023 RSNA Abdominal Trauma Detection Artificial Intelligence Challenge. Materials and Methods The competition was hosted on Kaggle and took place between July 26, 2023, to October 15, 2023. The multicenter competition dataset consisted of 4,274 abdominal trauma CT scans in which solid organs (liver, spleen and kidneys) were annotated as healthy, low-grade or high-grade injury. Studies were labeled as positive or negative for the presence of bowel/mesenteric injury and active extravasation. In this study, performances of the 8 award-winning models were retrospectively assessed and compared using various metrics, including the area under the receiver operating characteristic curve (AUC), for each injury category. The reported mean values of these metrics were calculated by averaging the performance across all models for each specified injury type. Results The models exhibited strong performance in detecting solid organ injuries, particularly high-grade injuries. For binary detection of injuries, the models demonstrated mean AUC values of 0.92 (range:0.91-0.94) for liver, 0.91 (range:0.87-0.93) for splenic, and 0.94 (range:0.93-0.95) for kidney injuries. The models achieved mean AUC values of 0.98 (range:0.96-0.98) for high-grade liver, 0.98 (range:0.97-0.99) for high-grade splenic, and 0.98 (range:0.97-0.98) for high-grade kidney injuries. For the detection of bowel/mesenteric injuries and active extravasation, the models demonstrated mean AUC values of 0.85 (range:0.74-0.73) and 0.85 (range:0.79-0.89) respectively. Conclusion The award-winning models from the AI challenge demonstrated strong performance in the detection of traumatic abdominal injuries on CT scans, particularly high-grade injuries. These models may serve as a performance baseline for future investigations and algorithms. ©RSNA, 2024.

Purpose To evaluate the performance of the top models from the RSNA 2022 Cervical Spine Fracture Detection challenge on a clinical test dataset of both noncontrast and contrast-enhanced CT scans acquired at a level I trauma center. Materials and Methods Seven top-performing models in the RSNA 2022 Cervical Spine Fracture Detection challenge were retrospectively evaluated on a clinical test set of 1828 CT scans (from 1829 series: 130 positive for fracture, 1699 negative for fracture; 1308 noncontrast, 521 contrast enhanced) from 1779 patients (mean age, 55.8 years ± 22.1 [SD]; 1154 [64.9%] male patients). Scans were acquired without exclusion criteria over 1 year (January-December 2022) from the emergency department of a neurosurgical and level I trauma center. Model performance was assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. False-positive and false-negative cases were further analyzed by a neuroradiologist. Results Although all seven models showed decreased performance on the clinical test set compared with the challenge dataset, the models maintained high performances. On noncontrast CT scans, the models achieved a mean AUC of 0.89 (range: 0.79-0.92), sensitivity of 67.0% (range: 30.9%-80.0%), and specificity of 92.9% (range: 82.1%-99.0%). On contrast-enhanced CT scans, the models had a mean AUC of 0.88 (range: 0.76-0.94), sensitivity of 81.9% (range: 42.7%-100.0%), and specificity of 72.1% (range: 16.4%-92.8%). The models identified 10 fractures missed by radiologists. False-positive cases were more common in contrast-enhanced scans and observed in patients with degenerative changes on noncontrast scans, while false-negative cases were often associated with degenerative changes and osteopenia. Conclusion The winning models from the 2022 RSNA AI Challenge demonstrated a high performance for cervical spine fracture detection on a clinical test dataset, warranting further evaluation for their use as clinical support tools. Keywords: Feature Detection, Supervised Learning, Convolutional Neural Network (CNN), Genetic Algorithms, CT, Spine, Technology Assessment, Head/Neck Supplemental material is available for this article. © RSNA, 2024 See also commentary by Levi and Politi in this issue.

Supplemental material is available for this article.

Unlike wild giraffe that primarily consume low starch browse, the preference of zoo-housed giraffe for consuming supplemental feeds over forage could increase the risk of digestive disorders such as ruminal acidosis. Our objective was to evaluate the effects of modifying a supplemental feed's non-fibre carbohydrate profile and physical form on nutritional, behavioural, and blood measures of giraffe in a zoological setting. Six non-lactating, adult, female reticulated giraffes were used in a two-pen modified reversal study using two dietary treatments in seven 21-day periods with data collected on days 15-21. Dietary treatments were a control feed comprised of commercially available products used at the time as the giraffe feed (GF) and an unpelleted experimental feed (EF). On a dry matter basis, GF and EF, respectively, contained 17.0% and 17.4% crude protein, 14.2% and 1.5% starch, 14.9% and 21.3% ethanol-soluble carbohydrates, 22.9% and 26.0% acid detergent fibre (ADF) and 9.50% and 14.9% ND-soluble fibre (NDSF), with modulus of fineness values of 3.62 and 4.82. Supplemental feeds, alfalfa hay, salt, and water were available for ad libitum consumption. Significance was declared at p ≤ 0.05. Intakes of hay, supplemental feeds, and total feed did not differ by diet (p > 0.28), though intakes of starch (0.93 and 0.12 kg; p = 0.05) and ADF (1.83 and 2.23 kg; p = 0.04) differed between GF and EF respectively. Giraffe behaviour values (min/48 h) were greater with EF for total eating (p = 0.04); diets were not detected as different for engagement in oral stereotypes (GF = 433, EF = 318 min/48 h; p = 0.22). Blood glucose was higher on GF than EF (99.0 and 82.3 mg/dL; p = 0.03). The lower EF blood glucose value is more similar to ranges reported for domesticated ruminants. No differences were detected for changes in body weight or body condition score in the 21-day periods (p > 0.32). Modification of supplemental feed carbohydrate profile and physical form can influence behaviour and blood glucose values of zoo-housed giraffe., (© 2024 Wiley‐VCH GmbH. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

The Radiological Society of North America (RSNA) has held artificial intelligence competitions to tackle real-world medical imaging problems at least annually since 2017. This article examines the challenges and processes involved in organizing these competitions, with a specific emphasis on the creation and curation of high-quality datasets. The collection of diverse and representative medical imaging data involves dealing with issues of patient privacy and data security. Furthermore, ensuring quality and consistency in data, which includes expert labeling and accounting for various patient and imaging characteristics, necessitates substantial planning and resources. Overcoming these obstacles requires meticulous project management and adherence to strict timelines. The article also highlights the potential of crowdsourced annotation to progress medical imaging research. Through the RSNA competitions, an effective global engagement has been realized, resulting in innovative solutions to complex medical imaging problems, thus potentially transforming health care by enhancing diagnostic accuracy and patient outcomes. Keywords: Use of AI in Education, Artificial Intelligence © RSNA, 2024.

Hundreds of inbred mouse strains and intercross populations have been used to characterize the function of genetic variants that contribute to disease. Thousands of disease-relevant traits have been characterized in mice and made publicly available. New strains and populations including consomics, the collaborative cross, expanded BXD, and inbred wild-derived strains add to existing complex disease mouse models, mapping populations, and sensitized backgrounds for engineered mutations. The genome sequences of inbred strains, along with dense genotypes from others, enable integrated analysis of trait-variant associations across populations, but these analyses are hampered by the sparsity of genotypes available. Moreover, the data are not readily interoperable with other resources. To address these limitations, we created a uniformly dense variant resource by harmonizing multiple data sets. Missing genotypes were imputed using the Viterbi algorithm with a data-driven technique that incorporates local phylogenetic information, an approach that is extendable to other model organisms. The result is a web- and programmatically accessible data service called GenomeMUSter, comprising single-nucleotide variants covering 657 strains at 106.8 million segregating sites. Interoperation with phenotype databases, analytic tools, and other resources enable a wealth of applications, including multitrait, multipopulation meta-analysis. We show this in cross-species comparisons of type 2 diabetes and substance use disorder meta-analyses, leveraging mouse data to characterize the likely role of human variant effects in disease. Other applications include refinement of mapped loci and prioritization of strain backgrounds for disease modeling to further unlock extant mouse diversity for genetic and genomic studies in health and disease., (© 2024 Ball et al.; Published by Cold Spring Harbor Laboratory Press.)

Purpose To evaluate and report the performance of the winning algorithms of the Radiological Society of North America Cervical Spine Fracture AI Challenge. Materials and Methods The competition was open to the public on Kaggle from July 28 to October 27, 2022. A sample of 3112 CT scans with and without cervical spine fractures (CSFx) were assembled from multiple sites (12 institutions across six continents) and prepared for the competition. The test set had 1093 scans (private test set: n = 789; mean age, 53.40 years ± 22.86 [SD]; 509 males; public test set: n = 304; mean age, 52.51 years ± 20.73; 189 males) and 847 fractures. The eight top-performing artificial intelligence (AI) algorithms were retrospectively evaluated, and the area under the receiver operating characteristic curve (AUC) value, F1 score, sensitivity, and specificity were calculated. Results A total of 1108 contestants composing 883 teams worldwide participated in the competition. The top eight AI models showed high performance, with a mean AUC value of 0.96 (95% CI: 0.95, 0.96), mean F1 score of 90% (95% CI: 90%, 91%), mean sensitivity of 88% (95% Cl: 86%, 90%), and mean specificity of 94% (95% CI: 93%, 96%). The highest values reported for previous models were an AUC of 0.85, F1 score of 81%, sensitivity of 76%, and specificity of 97%. Conclusion The competition successfully facilitated the development of AI models that could detect and localize CSFx on CT scans with high performance outcomes, which appear to exceed known values of previously reported models. Further study is needed to evaluate the generalizability of these models in a clinical environment. Keywords: Cervical Spine, Fracture Detection, Machine Learning, Artificial Intelligence Algorithms, CT, Head/Neck Supplemental material is available for this article. © RSNA, 2024.

The Mouse Phenome Database continues to serve as a curated repository and analysis suite for measured attributes of members of diverse mouse populations. The repository includes annotation to community standard ontologies and guidelines, a database of allelic states for 657 mouse strains, a collection of protocols, and analysis tools for flexible, interactive, user directed analyses that increasingly integrates data across traits and populations. The database has grown from its initial focus on a standard set of inbred strains to include heterogeneous mouse populations such as the Diversity Outbred and mapping crosses and well as Collaborative Cross, Hybrid Mouse Diversity Panel, and recombinant inbred strains. Most recently the system has expanded to include data from the International Mouse Phenotyping Consortium. Collectively these data are accessible by API and provided with an interactive tool suite that enables users' persistent selection, storage, and operation on collections of measures. The tool suite allows basic analyses, advanced functions with dynamic visualization including multi-population meta-analysis, multivariate outlier detection, trait pattern matching, correlation analyses and other functions. The data resources and analysis suite provide users a flexible environment in which to explore the basis of phenotypic variation in health and disease across the lifespan., (© 2023. The Author(s).)

The Many Hosts of Mycobacteria (MHM) meeting series brings together basic scientists, clinicians and veterinarians to promote robust discussion and dissemination of recent advances in our knowledge of numerous mycobacterial diseases, including human and bovine tuberculosis (TB), nontuberculous mycobacteria (NTM) infection, Hansen's disease (leprosy), Buruli ulcer and Johne's disease. The 9th MHM conference (MHM9) was held in July 2022 at The Ohio State University (OSU) and centered around the theme of "Confounders of Mycobacterial Disease." Confounders can and often do drive the transmission of mycobacterial diseases, as well as impact surveillance and treatment outcomes. Various confounders were presented and discussed at MHM9 including those that originate from the host (comorbidities and coinfections) as well as those arising from the environment (e.g., zoonotic exposures), economic inequality (e.g. healthcare disparities), stigma (a confounder of leprosy and TB for millennia), and historical neglect (a confounder in Native American Nations). This conference report summarizes select talks given at MHM9 highlighting recent research advances, as well as talks regarding the historic and ongoing impact of TB and other infectious diseases on Native American Nations, including those in Southwestern Alaska where the regional TB incidence rate is among the highest in the Western hemisphere., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos., (© 2023. The Author(s).)

This dataset is composed of cervical spine CT images with annotations related to fractures; it is available at https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection/., Competing Interests: Disclosures of conflicts of interest: H.M.L. No relevant relationships. E.C. No relevant relationships. T.R. No relevant relationships. F.C.K. Consultant for MD.ai and GE HealthCare; member of the Radiological Society of North America (RSNA) Machine Learning Steering Committee member and the Society for Imaging Informatics in Medicine Machine Learning Education Subcommittee (both unpaid). L.M.P. Associate editor for Radiology: Artificial Intelligence; patents planned, issued, or pending: US-20220051060-A1, “Methods for creating privacy-protecting synthetic data leveraging a constrained generative ensemble model,” and US-20220051402-A1, “Systems for automated lesion detection and related methods.” J.T. Provided expert witness deposition for Phillips, Spallas, & Angstadt in October 2022, unrelated to this article. R.L.B. Support from RSNA to author. E.G. No relevant relationships. K.W.Y. No relevant relationships. M.H. No relevant relationships. A.L.S. Author’s lab receives funding from the National Institutes of Health (NIH); member of the advisory board for the National Cancer Institute Imaging Data Commons. J.S. No relevant relationships. D.B. No relevant relationships. S.A. No relevant relationships. A.P.L. No relevant relationships. M.I.G.A. No relevant relationships. J.O.J. No relevant relationships. J.J.P. Author’s lab receives funding from the NIH, which pays this author’s salary. M.L. No relevant relationships. H.D. No relevant relationships. E.A. No relevant relationships. A.Y. No relevant relationships. Y.M. No relevant relationships. J.K.C. Grants or contracts from GE HealthCare and Genentech; technology licensed to Boston AI; consulting fees from Siloam Vision; deputy editor of Radiology: Artificial Intelligence. A.E.F. Standing director, liaison for information technology, of RSNA board of directors; member of RSNA News editorial board., (© 2023 by the Radiological Society of North America, Inc.)

Hundreds of inbred laboratory mouse strains and intercross populations have been used to functionalize genetic variants that contribute to disease. Thousands of disease relevant traits have been characterized in mice and made publicly available. New strains and populations including the Collaborative Cross, expanded BXD and inbred wild-derived strains add to set of complex disease mouse models, genetic mapping resources and sensitized backgrounds against which to evaluate engineered mutations. The genome sequences of many inbred strains, along with dense genotypes from others could allow integrated analysis of trait - variant associations across populations, but these analyses are not feasible due to the sparsity of genotypes available. Moreover, the data are not readily interoperable with other resources. To address these limitations, we created a uniformly dense data resource by harmonizing multiple variant datasets. Missing genotypes were imputed using the Viterbi algorithm with a data-driven technique that incorporates local phylogenetic information, an approach that is extensible to other model organism species. The result is a web- and programmatically-accessible data service called GenomeMUSter ( https://muster.jax.org ), comprising allelic data covering 657 strains at 106.8M segregating sites. Interoperation with phenotype databases, analytic tools and other resources enable a wealth of applications including multi-trait, multi-population meta-analysis. We demonstrate this in a cross-species comparison of the meta-analysis of Type 2 Diabetes and of substance use disorders, resulting in the more specific characterization of the role of human variant effects in light of mouse phenotype data. Other applications include refinement of mapped loci and prioritization of strain backgrounds for disease modeling to further unlock extant mouse diversity for genetic and genomic studies in health and disease.

Currently, there are limited antemortem tests available to evaluate renal function in Florida manatees ( Trichechus manatus latirostris ). Although few reports of renal pathology in manatees exist in the veterinary literature, debilitated animals that present to rehabilitation centers are frequently dehydrated, may have sustained renal trauma secondary to watercraft injury, and may experience ischemic events related to coagulation disorders leading to renal compromise. Clinicians remain limited to the analysis of blood urea nitrogen, creatinine levels, and urinalysis (if urine is obtained) to determine the extent of renal insufficiency, which may not accurately reflect renal function. This poses a diagnostic challenge for clinicians in discerning how critical renal compromise is to the animal's overall health and prognosis. For the first phase of this study, retrospective symmetric dimethylarginine (SDMA) values were determined from banked serum or plasma samples from 14 wild Florida manatees that had been collected while the animals were under rehabilitation in zoological institutions prior to their deaths. Values of SDMA for nine samples from eight manatees with known renal disease, as determined by histopathology, were compared to SDMA values for seven samples from six manatees with no reported renal lesions on histopathology. Values of SDMA from wild Florida manatees with known renal disease (mean 33.56 µg/dl ± 13.15, P = 0.017) were statistically significantly elevated when compared to SDMA values from manatees with no reported renal lesions (mean = 18.71 µg/dl ± 6.9) on histopathology. For the second phase of the study, serum or plasma samples were obtained from wild manatee populations from two geographically separate presumed-healthy wild manatee populations ( n = 57). Although the upper limit was higher, serum SDMA concentrations from presumed-healthy wild manatees were comparable to those reported in small animal and equine medicine at 5.88-16.97 µg/dl.

The Diversity Outbred (DO) mice and their inbred founders are widely used models of human disease. However, although the genetic diversity of these mice has been well documented, their epigenetic diversity has not. Epigenetic modifications, such as histone modifications and DNA methylation, are important regulators of gene expression and, as such, are a critical mechanistic link between genotype and phenotype. Therefore, creating a map of epigenetic modifications in the DO mice and their founders is an important step toward understanding mechanisms of gene regulation and the link to disease in this widely used resource. To this end, we performed a strain survey of epigenetic modifications in hepatocytes of the DO founders. We surveyed four histone modifications (H3K4me1, H3K4me3, H3K27me3, and H3K27ac), as well as DNA methylation. We used ChromHMM to identify 14 chromatin states, each of which represents a distinct combination of the four histone modifications. We found that the epigenetic landscape is highly variable across the DO founders and is associated with variation in gene expression across strains. We found that epigenetic state imputed into a population of DO mice recapitulated the association with gene expression seen in the founders, suggesting that both histone modifications and DNA methylation are highly heritable mechanisms of gene expression regulation. We illustrate how DO gene expression can be aligned with inbred epigenetic states to identify putative cis -regulatory regions. Finally, we provide a data resource that documents strain-specific variation in the chromatin state and DNA methylation in hepatocytes across nine widely used strains of laboratory mice., (© 2023 Tyler et al.; Published by Cold Spring Harbor Laboratory Press.)

Histamine plays pivotal role in normal physiology and dysregulated production of histamine or signaling through histamine receptors (HRH) can promote pathology. Previously, we showed that Bordetella pertussis or pertussis toxin can induce histamine sensitization in laboratory inbred mice and is genetically controlled by Hrh1/HRH1. HRH1 allotypes differ at three amino acid residues with P 263 -V 313 -L 331 and L 263 -M 313 -S 331 , imparting sensitization and resistance respectively. Unexpectedly, we found several wild-derived inbred strains that carry the resistant HRH1 allotype (L 263 -M 313 -S 331 ) but exhibit histamine sensitization. This suggests the existence of a locus modifying pertussis-dependent histamine sensitization. Congenic mapping identified the location of this modifier locus on mouse chromosome 6 within a functional linkage disequilibrium domain encoding multiple loci controlling sensitization to histamine. We utilized interval-specific single-nucleotide polymorphism (SNP) based association testing across laboratory and wild-derived inbred mouse strains and functional prioritization analyses to identify candidate genes for this modifier locus. Atg7, Plxnd1, Tmcc1, Mkrn2, Il17re, Pparg, Lhfpl4, Vgll4, Rho and Syn2 are candidate genes within this modifier locus, which we named Bphse, enhancer of Bordetella pertussis induced histamine sensitization. Taken together, these results identify, using the evolutionarily significant diversity of wild-derived inbred mice, additional genetic mechanisms controlling histamine sensitization., (© 2023. The Author(s).)

The Mouse Phenome Database (MPD; https://phenome.jax.org; RRID:SCR_003212), supported by the US National Institutes of Health, is a Biomedical Data Repository listed in the Trans-NIH Biomedical Informatics Coordinating Committee registry. As an increasingly FAIR-compliant and TRUST-worthy data repository, MPD accepts phenotype and genotype data from mouse experiments and curates, organizes, integrates, archives, and distributes those data using community standards. Data are accompanied by rich metadata, including widely used ontologies and detailed protocols. Data are from all over the world and represent genetic, behavioral, morphological, and physiological disease-related characteristics in mice at baseline or those exposed to drugs or other treatments. MPD houses data from over 6000 strains and populations, representing many reproducible strain types and heterogenous populations such as the Diversity Outbred where each mouse is unique but can be genotyped throughout the genome. A suite of analysis tools is available to aggregate, visualize, and analyze these data within and across studies and populations in an increasingly traceable and reproducible manner. We have refined existing resources and developed new tools to continue to provide users with access to consistent, high-quality data that has translational relevance in a modernized infrastructure that enables interaction with a suite of bioinformatics analytic and data services., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Although patients generally prefer oral drug delivery to injections, low permeability of the gastrointestinal tract makes this method impossible for most biomacromolecules. One potential solution is codelivery of macromolecules, including therapeutic proteins or nucleic acids, with intestinal permeation enhancers; however, enhancer use has been limited clinically by modest efficacy and toxicity concerns surrounding long-term administration. Here, we hypothesized that plant-based foods, which are well tolerated by the gastrointestinal tract, may contain compounds that enable oral macromolecular absorption without causing adverse effects. Upon testing more than 100 fruits, vegetables, and herbs, we identified strawberry and its red pigment, pelargonidin, as potent, well-tolerated enhancers of intestinal permeability. In mice, an oral capsule formulation comprising pelargonidin and a 1 U/kg dose of insulin reduced blood glucose levels for over 4 h, with bioactivity exceeding 100% relative to subcutaneous injection. Effects were reversible within 2 h and associated with actin and tight junction rearrangement. Furthermore, daily dosing of mice with pelargonidin for 1 mo resulted in no detectable side effects, including weight loss, tissue damage, or inflammatory responses. These data suggest that pelargonidin is an exceptionally effective enhancer of oral protein uptake that may be safe for routine pharmaceutical use.

The emergence of the COVID-19 pandemic over a relatively brief interval illustrates the need for rapid data-driven approaches to facilitate clinical decision making. We examined a machine learning process to predict inpatient mortality among COVID-19 patients using clinical and chest radiographic data. Modeling was performed with a de-identified dataset of encounters prior to widespread vaccine availability. Non-imaging predictors included demographics, pre-admission clinical history, and past medical history variables. Imaging features were extracted from chest radiographs by applying a deep convolutional neural network with transfer learning. A multi-layer perceptron combining 64 deep learning features from chest radiographs with 98 patient clinical features was trained to predict mortality. The Local Interpretable Model-Agnostic Explanations (LIME) method was used to explain model predictions. Non-imaging data alone predicted mortality with an ROC-AUC of 0.87 ± 0.03 (mean ± SD), while the addition of imaging data improved prediction slightly (ROC-AUC: 0.91 ± 0.02). The application of LIME to the combined imaging and clinical model found HbA1c values to contribute the most to model prediction (17.1 ± 1.7%), while imaging contributed 8.8 ± 2.8%. Age, gender, and BMI contributed 8.7%, 8.2%, and 7.1%, respectively. Our findings demonstrate a viable explainable AI approach to quantify the contributions of imaging and clinical data to COVID mortality predictions.

Serum levels of alpha-tocopherol (vitamin E) and 25-hydroxyvitamin D (vitamin D) were measured in 25 wild Florida manatees (Trichechus manatus latirostris) from Crystal River National Wildlife Refuge, Citrus County, Florida, US. The samples were collected during the 2017 and 2018 winter health assessments. Reference intervals for alpha-tocopherol were 0.15-3.2 µg/L with mean value of 0.76 µg/mL and for 25-hydroxyvitamin D were 4.3-23 nmol/L with a mean of 13.88 nmol/L. Four, long-term captive manatees and nine manatees in rehabilitation were available for comparisons. Alpha-tocopherol levels were within the established range for free-ranging manatees. Mean 25-hydroxyvitamin D levels in long-term captive and manatees in rehabilitation fell well outside the reference interval for free-ranging manatees, being six- to sevenfold higher. The absence of significant differences in alpha-tocopherol between wild and captive manatees may not have any significant direct clinical implications but may warrant an investigation into other antioxidant mechanisms in manatees. The elevated 25-hydroxyvitamin D levels seen in captivity were unexpected and, in a few individuals, may pose a health challenge. Calcium is known to be elevated in captive manatees as well; a close examination of a potential hypervitaminosis D and hypercalcemia may be warranted., (© Wildlife Disease Association 2021.)

Florida manatees ( Trichechus manatus latirostris ) frequently present to rehabilitation care facilities for various conditions, including boat strike trauma, cold stress syndrome, and brevetoxicosis. Throughout the course of treatment, antimicrobial use to treat respiratory disease is frequently warranted. To date, clinicians have extrapolated dosages based on established information available in bovine and equine medicine. The routes of administration, efficacy, and treatment intervals are considerations in dealing with critical wild animals. The use of tulathromycin, a triamilide antibiotic, has been studied in multiple domestic species of economic importance, including cattle, small ruminants, and swine, and has revealed efficacy against respiratory diseases. Given this information, this antibiotic has also been used in manatees with positive clinical outcomes. This study employed sparse sampling and evaluated banked plasma samples at various time intervals post-tulathromycin administration obtained during the clinical treatment course of nine animals during their rehabilitation. Preliminary pharmacokinetic analysis following administration of a single dose estimated a half-life of 33.75 h and volume of distribution per fraction absorbed (Vz/F = 4.29 L/kg). The pharmacokinetic behavior of tulathromycin in Florida manatees can be used to optimize dosage regimens in this species.

As the field of metabolomics develops further, investigations of how the metabolome is affected following thermal injury may be helpful to inform diagnostics and guide treatments. In this study, changes to the metabolome were tested and validated in a murine burn injury model. After a 30% total body surface scald injury or sham procedure sera and skin biopsies were collected at 1, 2, 6, or 24 hr. Burn-specific changes in the metabolome were detected compared to sham animals. The sera metabolome exhibited a more rapid response to burn injury than that of the skin and it peaked more proximal to injury (6 vs 24 hr). Progression of metabolic response in the skin was less synchronous and showed a higher overlap of the significantly modified metabolites (SMMs) among tested time-points. Top affected pathways identified by SMMs of skin included inositol phosphate metabolism, ascorbate and alderate metabolism, caffeine metabolism, and the pentose phosphate pathway. Future research is warranted in human and larger animal models to further elucidate the role of metabolomic perturbations and the pathophysiology following burn injury., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Burn Association.)

Objective: The aim of this study is to evaluate the association between burn injury and admission plasma levels of Syndecan-1 (SDC-1) and Tissue Factor Pathway Inhibitor (TFPI), and their ability to predict 30-day mortality., Background: SDC-1 and TFPI are expressed by vascular endothelium and shed into the plasma as biomarkers of endothelial damage. Admission plasma biomarker levels have been associated with morbidity and mortality in trauma patients, but this has not been well characterized in burn patients.Methods: This cohort study enrolled burn patients admitted to a regional burn center between 2013 and 2017. Blood samples were collected within 4 h of admission and plasma SDC-1 and TFPI were quantified by ELISA. Demographics and injury characteristics were collected prospectively. The primary outcome was 30-day in-hospital mortality., Results: Of 158 patients, 74 met inclusion criteria. Most patients were male with median age of 41.5 years and burn TBSA of 20.5%. The overall mortality rate was 20.3%. Admission SDC-1 and TFPI were significantly higher among deceased patients. Plasma SDC-1 >34 ng/mL was associated with a 32-times higher likelihood of mortality [OR: 32.65 (95% CI, 2.67-399.78); P = 0.006] and a strong predictor of mortality (area under the ROC [AUROC] 0.92). TFPI was associated with a nine-times higher likelihood of mortality [OR: 9.59 (95% CI, 1.02-89.75); P = 0.002] and a fair predictor of mortality (AUROC 0.68)., Conclusions: SDC-1 and TFPI are associated with a higher risk of 30-day mortality. We propose the measurement of SDC-1 on admission to identify burn patients at high risk of mortality. However, further investigation with a larger sample size is warranted., Competing Interests: The authors report no conflicts of interest., (Copyright © 2020 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Shock Society.)

Supplemental material is available for this article., Competing Interests: Disclosures of Conflicts of Interest: E.C. disclosed no relevant relationships. F.C.K. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: consultant for MD.ai; employed by DASA as Head of AI. Other relationships: disclosed no relevant relationships. S.B.H. disclosed no relevant relationships. C.C.W. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: institution received grant from NIBIB and has MIDRC grant pending; author receives royalties from Elsevier. Other relationships: disclosed no relevant relationships. M.P.L. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: board member of Nines Radiology, SegMed, and BunkerHill; author has stock/stock options in Nines Radiology, SegMed, and BunkerHill. Other relationships: disclosed no relevant relationships. L.M.P. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: editorial board member of Radiology: Artificial Intelligence. Other relationships: disclosed no relevant relationships J.K.C. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: institution has grants pending with GE and Genentech; author receives support for travel/accommodation/meeting expenses from IBM; deputy editor of Radiology: Artificial Intelligence. Other relationships: disclosed no relevant relationships. R.L.B. Activities related to the present article: author paid consulting fee or honorarium from RSNA. Activities not related to the present article: employed by Stanford University. Other relationships: disclosed no relevant relationships. G.S. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: board member of MD.ai (no compensation); consultant for MD.ai (no compensation); stock/stock options in MD.ai; editorial board member of Radiology: Artificial Intelligence. Other relationships: disclosed no relevant relationships. A.S. Activities related to the present article: employee of MD.ai. Activities not related to the present article: employed by MD.ai. Other relationships: disclosed no relevant relationships. S.S.H. disclosed no relevant relationships. E.A. disclosed no relevant relationships. M.L. disclosed no relevant relationships. P.K. disclosed no relevant relationships. K.A.M. disclosed no relevant relationships. D.C.N.R. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: speaker for Bayer de Mexico (author gave one lecture for the International Day of Radiology and received speaker training from Bayer). Other relationships: disclosed no relevant relationships. J.W.S. disclosed no relevant relationships. P. Germaine disclosed no relevant relationships. E.C.L. disclosed no relevant relationships. T.A. disclosed no relevant relationships. P. Gupta disclosed no relevant relationships. M.J. disclosed no relevant relationships. F.U.K. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: editorial board member of Radiology: Cardiothoracic Imaging; employed at UT Southwestern Medical Center. Other relationships: disclosed no relevant relationships. C.T.L. disclosed no relevant relationships. S.S. disclosed no relevant relationships. J.W.R. disclosed no relevant relationships. C.C.B. disclosed no relevant relationships. J.M. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: institution has grant from GE Healthcare; editorial board member of Radiology: Artificial Intelligence. Other relationships: disclosed no relevant relationships., (2021 by the Radiological Society of North America, Inc.)

Background: Intracavitary irrigation is a routine component of many surgical procedures, especially in those involving a contaminated field. Normal saline remains the irrigant of choice for most surgeons. Hypochlorous acid is a weak acid that produces hypochlorite ions with antimicrobial properties. Reducing microbial concentration during intracavitary irrigation is a potential benefit of using hypochlorous acid solution over normal saline. In this study, the safety of hypochlorous acid solution for intracavitary lavage was compared with normal saline in a rat model of 3 surgical procedures-laminectomy, thoracotomy, and laparotomy., Methods: The intracavitary space was lavaged with either normal saline or hypochlorous acid. The procedures were also completed using Dakin's solution (sodium hypochlorite) as a comparator, given its known cytotoxicity. On postoperative day 5, necropsies of all animals were performed and relevant organs and blood samples obtained. Histology (hematoxylin and eosin staining) was used to examine biopsies of the collected organs for signs of inflammation, blood vessel integrity, and necrosis. Immunohistochemistry staining for caspase-3 was used to identify apoptotic cells., Results: There were no differences in outcomes (survival, pain, and time to recovery) or histology between animals lavaged with hypochlorous acid and normal saline. Intact organ-specific architecture was observed in both groups. In comparison, rats treated with Dakin's solution demonstrated significant capsular fibrosis and hemorrhage. Furthermore, significant apoptosis was noted within the bowel mesentery of the group treated with Dakin's solution when stained for caspase-3., Conclusion: Hypochlorous acid is safe for lavage of intraperitoneal, intrathecal, and intrathoracic cavities. Further studies should be conducted to demonstrate efficacy of hypochlorous acid in an infected field., (Copyright © 2021 The Author(s).)

Tuberculosis (TB) is the leading cause of preventable death in HIV-positive patients, and yet often remains undiagnosed and untreated. Chest x-ray is often used to assist in diagnosis, yet this presents additional challenges due to atypical radiographic presentation and radiologist shortages in regions where co-infection is most common. We developed a deep learning algorithm to diagnose TB using clinical information and chest x-ray images from 677 HIV-positive patients with suspected TB from two hospitals in South Africa. We then sought to determine whether the algorithm could assist clinicians in the diagnosis of TB in HIV-positive patients as a web-based diagnostic assistant. Use of the algorithm resulted in a modest but statistically significant improvement in clinician accuracy ( p  = 0.002), increasing the mean clinician accuracy from 0.60 (95% CI 0.57, 0.63) without assistance to 0.65 (95% CI 0.60, 0.70) with assistance. However, the accuracy of assisted clinicians was significantly lower ( p  < 0.001) than that of the stand-alone algorithm, which had an accuracy of 0.79 (95% CI 0.77, 0.82) on the same unseen test cases. These results suggest that deep learning assistance may improve clinician accuracy in TB diagnosis using chest x-rays, which would be valuable in settings with a high burden of HIV/TB co-infection. Moreover, the high accuracy of the stand-alone algorithm suggests a potential value particularly in settings with a scarcity of radiological expertise., Competing Interests: Competing InterestsThe authors declare no competing interests., (© The Author(s) 2020.)

Burn-induced coagulopathy is not well understood, and consensus on diagnosis, prevention, and treatments are lacking. In this review, literature on burn-induced (and associated) coagulopathy is presented along with the current understanding of the effects of burn injury on the interactions among coagulation, fibrinolysis, and inflammation in the acute resuscitative phase and reconstructive phase of care. The role of conventional tests of coagulopathy and functional assays like thromboelastography or thromboelastometry will also be discussed. Finally, reported methods for the prevention and treatment of complications related to burn-induced coagulopathy will be reviewed.

A retrospective study of admission data of 401 West Indian manatees (Trichechus manatus latirostris) presented to the David A. Straz Jr. Manatee Critical Care Center at ZooTampa at Lowry Park (ZooTampa) for rehabilitation from August 1991 through October 2017. Causes of admittance, location of rescue, gender, and age class were all recorded for each manatee admitted. Admittance categories as defined by the Florida Fish and Wildlife Conservation Commission (FWC) included watercraft collisions, natural causes, entanglement, entrapment, orphaned calves, captive born, mothers of rescued calves, calves of rescued mothers, human, and other. The admitted population was primarily from the southwest and northwest coasts and related waterways of Florida. The gender difference was relatively equivocal (54% female) while the adults comprised 79% of the admissions. The overall total admissions increased steadily over the study period as did the admissions for each individual categories of admission. Watercraft collisions and natural causes combined were 71% of all admissions for the entire study period and are the dominant causes of admission. Watercraft collisions are more likely to occur during May through October, whereas natural causes of admittance are more likely to occur between December and March. Rehabilitated manatees may reduce overall manatee mortality and can provide insight into population-based health concerns if evaluated appropriately. Future efforts can incorporate physical examination findings, hematology, biochemistry profiles, and ancillary diagnostic testing to continue to improve the individual welfare of this marine mammal in its natural range. Admissions data could also potentially serve the wider conservation and recovery efforts if it is proven that the data obtained is at least as informative as that obtained by the carcass salvage program. Limited conservation resources could then be re-directed as new challenges arise with the expanding population and potentially expanding range of this species., Competing Interests: The authors have declared that no competing interests exist.

The clinical translation of messengerRNA (mRNA) drugs has been slowed by a shortage of delivery vehicles that potently and safely shuttle mRNA into target cells. Here, we describe the properties of a particularly potent branched-tail lipid nanoparticle that delivers mRNA to >80% of three major liver cell types. We characterize mRNA delivery spatially, temporally, and as a function of injection type. Following intravenous delivery, our lipid nanoparticle induced greater protein expression than two benchmark lipids, C12-200 and DLin-MC3-DMA, at an mRNA dose of 0.5 mg/kg. Lipid nanoparticles were sufficiently potent to codeliver three distinct mRNAs (firefly luciferase, mCherry, and erythropoietin) and, separately, Cas9 mRNA and single guide RNA (sgRNA) for proof-of-concept nonviral gene editing in mice. Furthermore, our branched-tail lipid nanoparticle was neither immunogenic nor toxic to the liver. Together, these results demonstrate the unique potential of this lipid material to improve the management of diseases rooted in liver dysfunction.

Disparities in organ acceptance practices exacerbate donor heart nonuse and lead to increased waiting times and mortality for heart transplant candidates. We studied disparities in donor heart acceptance among US transplant centers and their relations to posttransplant outcomes. Candidate, potential transplant recipient match run, and deceased donor data were obtained from the United Network for Organ Sharing. We analyzed donor, candidate, and transplant center characteristics with respect to organ acceptance, offer acceptance, number of offers before acceptance (organ sequence number), and association with posttransplant mortality. A total of 693 420 donor heart offers made between April 2007 and December 2015 were included. We identified great variability in donor heart acceptance practices among US heart transplant centers. We identified donor and recipient characteristics that were strongly associated with heart organ and offer acceptance, and organ sequence number, and identified inconsistencies among centers with respect to how these characteristics influenced acceptance decisions. Finally, we identified characteristics that were highly predictive of donor heart nonuse and were not associated with increased recipient mortality, which may guide future efforts aimed at increasing use of available hearts for transplantation., (© 2019 The American Society of Transplantation and the American Society of Transplant Surgeons.)

To achieve organization and function, engineered tissues require a scaffold that supports cell adhesion, alignment, growth, and differentiation. For skeletal muscle tissue engineering, decellularization has been an approach for fabricating 3D scaffolds that retain biological architecture. While many decellularization approaches are focused on utilizing animal muscle as the starting material, decellularized plants are a potential source of highly structured cellulose-rich scaffolds. Here, we assessed the potential for a variety of decellularized plant scaffolds to promote mouse and human muscle cell alignment and differentiation. After decellularizing a range of fruits and vegetables, we identified the green-onion scaffold to have appropriate surface topography for generating highly confluent and aligned C2C12 and human skeletal muscle cells (HSMCs). The topography of the green-onion cellulose scaffold contained a repeating pattern of grooves that are approximately 20 μm wide by 10 μm deep. The outer white section of the green onion had a microstructure that guided C2C12 cell differentiation into aligned myotubes. Quantitative analysis of C2C12 and HSMC alignment revealed an almost complete anisotropic organization compared to 2D isotropic controls. Our results demonstrate that the decellularized green onion cellulose scaffolds, particularly from the outer white bulb segment, provide a simple and low-cost substrate to engineer aligned human skeletal muscle.

Background: Acute diarrhoeal disease management often requires rehydration alone without antibiotics. However, non-indicated antibiotics are frequently ordered and this is an important driver of antimicrobial resistance. The mHealth Diarrhoea Management (mHDM) trial aimed to establish whether electronic decision support improves rehydration and antibiotic guideline adherence in resource-limited settings., Methods: A cluster randomised controlled trial was done at ten district hospitals in Bangladesh. Inclusion criteria were patients aged 2 months or older with uncomplicated acute diarrhoea. Admission orders were observed without intervention in the pre-intervention period, followed by randomisation to electronic (rehydration calculator) or paper formatted WHO guidelines for the intervention period. The primary outcome was rate of intravenous fluid ordered as a binary variable. Generalised linear mixed-effect models, accounting for hospital clustering, served as the analytical framework; the analysis was intention to treat. The trial is registered with ClinicalTrials.gov (NCT03154229) and is completed., Findings: From March 11 to Sept 10, 2018, 4975 patients (75·6%) of 6577 screened patients were enrolled. The intervention effect for the primary outcome showed no significant differences in rates of intravenous fluids ordered as a function of decision-support type. Intravenous fluid orders decreased by 0·9 percentage points for paper electronic decision support and 4·2 percentage points for electronic decision support, with a 4·2-point difference between decision-support types in the intervention period (paper 98·7% [95% CI 91·8-99·8] vs electronic 94·5% [72·2-99·1]; p interaction =0·31). Adverse events such as complications and mortality events were uncommon and could not be statistically estimated., Interpretation: Although intravenous fluid orders did not change, electronic decision support was associated with increases in the volume of intravenous fluid ordered and decreases in antibiotics ordered, which are consistent with WHO guidelines., Funding: US National Institutes of Health., (Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0. license. Published by Elsevier Ltd.. All rights reserved.)

Pulmonary embolism (PE) is a life-threatening clinical problem and computed tomography pulmonary angiography (CTPA) is the gold standard for diagnosis. Prompt diagnosis and immediate treatment are critical to avoid high morbidity and mortality rates, yet PE remains among the diagnoses most frequently missed or delayed. In this study, we developed a deep learning model-PENet, to automatically detect PE on volumetric CTPA scans as an end-to-end solution for this purpose. The PENet is a 77-layer 3D convolutional neural network (CNN) pretrained on the Kinetics-600 dataset and fine-tuned on a retrospective CTPA dataset collected from a single academic institution. The PENet model performance was evaluated in detecting PE on data from two different institutions: one as a hold-out dataset from the same institution as the training data and a second collected from an external institution to evaluate model generalizability to an unrelated population dataset. PENet achieved an AUROC of 0.84 [0.82-0.87] on detecting PE on the hold out internal test set and 0.85 [0.81-0.88] on external dataset. PENet also outperformed current state-of-the-art 3D CNN models. The results represent successful application of an end-to-end 3D CNN model for the complex task of PE diagnosis without requiring computationally intensive and time consuming preprocessing and demonstrates sustained performance on data from an external institution. Our model could be applied as a triage tool to automatically identify clinically important PEs allowing for prioritization for diagnostic radiology interpretation and improved care pathways via more efficient diagnosis., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2020.)

Background: Intracoronary acetylcholine (Ach) provocation testing is the gold standard for assessing coronary endothelial function. However, dosing regimens of Ach are quite varied in the literature, and there are limited data evaluating the optimal dose. We evaluated the dose-response relationship between Ach and minimal lumen diameter (MLD) by sex and studied whether incremental intracoronary Ach doses given during endothelial function testing improve its diagnostic utility., Methods: We evaluated 65 men and 212 women with angina and no obstructive coronary artery disease who underwent endothelial function testing using the highest tolerable dose of intracoronary Ach, up to 200 μg. Epicardial endothelial dysfunction was defined as a decrease in MLD >20% after intracoronary Ach by quantitative coronary angiography. We used a linear mixed effects model to evaluate the dose-response relationship. Deming regression analysis was done to compare the %MLD constriction after incremental doses of intracoronary Ach., Results: The mean age was 53.5 years. Endothelial dysfunction was present in 186 (68.1%). Among men with endothelial dysfunction, there was a significant decrease in MLD/10 µg of Ach at doses above 50 μg and 100 µg, while this decrease in MLD was not observed in women ( P <0.001). The %MLD constriction at 20 μg versus 50 μg and 50 μg versus 100 μg were not equivalent while the %MLD constriction at 100 μg versus 200 μg were equivalent., Conclusions: Women and men appear to have different responses to Ach during endothelial function testing. In addition to having a greater response to intracoronary Ach at all doses, men also demonstrate an Ach-MLD dose-response relationship with doses up to 200 μg, while women have minimal change in MLD with doses above 50 µg. An incremental dosing regimen during endothelial function testing appears to improve the diagnostic utility of the test and should be adjusted based on the sex of the patient.

Artificial intelligence (AI) algorithms continue to rival human performance on a variety of clinical tasks, while their actual impact on human diagnosticians, when incorporated into clinical workflows, remains relatively unexplored. In this study, we developed a deep learning-based assistant to help pathologists differentiate between two subtypes of primary liver cancer, hepatocellular carcinoma and cholangiocarcinoma, on hematoxylin and eosin-stained whole-slide images (WSI), and evaluated its effect on the diagnostic performance of 11 pathologists with varying levels of expertise. Our model achieved accuracies of 0.885 on a validation set of 26 WSI, and 0.842 on an independent test set of 80 WSI. Although use of the assistant did not change the mean accuracy of the 11 pathologists ( p  = 0.184, OR = 1.281), it significantly improved the accuracy ( p  = 0.045, OR = 1.499) of a subset of nine pathologists who fell within well-defined experience levels (GI subspecialists, non-GI subspecialists, and trainees). In the assisted state, model accuracy significantly impacted the diagnostic decisions of all 11 pathologists. As expected, when the model's prediction was correct, assistance significantly improved accuracy ( p  = 0.000, OR = 4.289), whereas when the model's prediction was incorrect, assistance significantly decreased accuracy ( p  = 0.000, OR = 0.253), with both effects holding across all pathologist experience levels and case difficulty levels. Our results highlight the challenges of translating AI models into the clinical setting, and emphasize the importance of taking into account potential unintended negative consequences of model assistance when designing and testing medical AI-assistance tools., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2020.)

Background: Impaired epicardial coronary vasomotion is a potential mechanism of angina and a predictor of adverse cardiovascular outcomes in patients without angiographic evidence of obstructive coronary artery disease (CAD). We sought to evaluate the association of asymmetric dimethylarginine (ADMA)-a marker of nitric oxide-mediated vascular dysfunction-with epicardial coronary vasomotor dysfunction in this select population., Methods: Invasive testing for epicardial vasomotor dysfunction was performed using intracoronary acetylcholine in the left anterior descending coronary artery. Impaired vasomotor response was defined as a luminal constriction of >20% on quantitative coronary angiography. Plasma ADMA levels were measured using high performance liquid chromatography. A robust multivariate linear mixed-effect model approach and Akaike information criterion were used to determine predictors of vasomotor dysfunction., Results: In 191 patients with angina in the absence of obstructive CAD, abnormal epicardial vasomotion was observed in 137 (71.7%) patients. Median ADMA rose as the extent of impairment progressed: none (0.48 [0.44-0.59] μM), any (0.51 [0.46-0.60] μM, p = 0.12), focal (0.54 [0.49,0.61] μM, p = 0.17), and diffuse (0.55 [0.49,0.63] μM, p = 0.02). In unadjusted analysis, ADMA was highly predictive of vasomotor dysfunction (χ 2 =15.1, p = 0.002). Notably, ADMA remained a significant predictor even after adjusting for other factors in the best fit model (χ 2 =10.0, p = 0.02)., Conclusions: ADMA is an independent predictor of epicardial coronary vasomotor dysfunction in patients with angina in the absence of obstructive CAD. These data support a very early mechanistic role of ADMA in the continuum of atherosclerotic heart disease., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Purpose: Caco-2 monolayers are the most common model of the intestinal epithelium and are critical to the development of oral drug delivery strategies and gastrointestinal disease treatments. However, current monolayer systems are cost- and/or time-intensive, hampering progress. This study evaluates two separate methods to reduce resource input: FB Essence as a fetal bovine serum (FBS) alternative and a new, 3-day Caco-2 system deemed "thrifty, rapid intestinal monolayers" (TRIM)., Methods: Caco-2 cells were cultured with FB Essence and compared to cells in 10% FBS for proliferation and monolayer formation. TRIM were compared to commonly-used 21-day and Corning® HTS monolayer systems, as well as mouse intestines, for permeability behavior, epithelial gene expression, and tight junction arrangement., Results: No amount of FB Essence maintained Caco-2 cells beyond 10 passages. In contrast, TRIM compared favorably in permeability and gene expression to intestinal tissues. Furthermore, TRIM cost $109 and required 1.3 h of time per 24-well plate, compared to $164 and 3.7 h for 21-day monolayers, and $340 plus 1.0 h for the HTS system., Conclusions: TRIM offer a new approach to generating Caco-2 monolayers that resemble the intestinal epithelium. They are anticipated to accelerate the pace of in vitro intestinal experiments while easing financial burden.

Purpose: The aim of the study was to refine and validate the NeuroImaging Radiological Interpretation System (NIRIS), which was developed to predict management and clinical outcome based on noncontrast head computerized tomography findings in patients suspected of acute traumatic brain injury (TBI)., Methods: We assessed the performance of the NIRIS score in a prospective, single-center cohort of patients suspected of TBI (n = 648) and compared the performance of NIRIS with that of the Marshall and Rotterdam scoring systems. We also revised components of the NIRIS scoring system using decision tree methodologies implemented on pooled data from the retrospective and prospective studies (N = 1190)., Results: The NIRIS performed similarly to the Marshall and Rotterdam scoring systems in predicting mortality and markedly better in terms of predicting more granular elements of disposition and management of TBI patients, such as admission, follow-up imaging, intensive care unit stay, and neurosurgical procedures. The revised NIRIS classification correctly predicted disposition and outcome in 91.2% (331/363) after excluding patients with other major extracranial traumatic injuries or intracranial nontraumatic injuries., Conclusions: The present study further demonstrates the predictive value of NIRIS in guiding standardized clinical management and decision-making regarding treatment options for TBI patients.

In major ischemic stroke caused by a large artery occlusion, neuronal loss varies considerably across individuals without revascularization. This study aims to identify which patient characteristics are most highly associated with this variability. Demographic and clinical information were retrospectively collected on a registry of 878 patients. Imaging biomarkers including Alberta Stroke Program Early CT score, noncontrast head computed tomography infarct volume, perfusion computed tomography infarct core and penumbra, occlusion site, collateral score, and recanalization status were evaluated on the baseline and early follow-up computed tomography images. Infarct growth rates were calculated by dividing infarct volumes by the time elapsed between the computed tomography scan and the symptom onset. Collateral score was graded into four levels (0, 1, 2, and 3) in comparison with the normal side. Correlation of perfusion computed tomography and noncontrast head computed tomography infarct volumes and infarct growth rates were estimated with the nonparametric Spearman's rank correlation. Conditional inference trees were used to identify the clinical and imaging biomarkers that were most highly associated with the infarct growth rate and modified Rankin Scale at 90 days. Two hundred and thirty-two patients met the inclusion criteria for this study. The median infarct growth rates for perfusion computed tomography and noncontrast head computed tomography were 11.2 and 6.2 ml/log(min) in logarithmic model, and 18.9 and 10.4 ml/h in linear model, respectively. Noncontrast head computed tomography and perfusion computed tomography infarct volumes and infarct growth rates were significantly correlated (rho=0.53; P < 0.001). Collateral status was the strongest predictor for infarct growth rates. For collateral=0, the perfusion computed tomography and noncontrast head computed tomography infarct growth rate were 31.56 and 16.86 ml/log(min), respectively. Patients who had collateral >0 and penumbra volumes>92 ml had the lowest predicted perfusion computed tomography infarct growth rates (6.61 ml/log(min)). Collateral status was closely related to the diversity of infarct growth rates, poor collaterals were associated with a faster infarct growth rates and vice versa.

Importance: Deep learning has the potential to augment clinician performance in medical imaging interpretation and reduce time to diagnosis through automated segmentation. Few studies to date have explored this topic., Objective: To develop and apply a neural network segmentation model (the HeadXNet model) capable of generating precise voxel-by-voxel predictions of intracranial aneurysms on head computed tomographic angiography (CTA) imaging to augment clinicians' intracranial aneurysm diagnostic performance., Design, Setting, and Participants: In this diagnostic study, a 3-dimensional convolutional neural network architecture was developed using a training set of 611 head CTA examinations to generate aneurysm segmentations. Segmentation outputs from this support model on a test set of 115 examinations were provided to clinicians. Between August 13, 2018, and October 4, 2018, 8 clinicians diagnosed the presence of aneurysm on the test set, both with and without model augmentation, in a crossover design using randomized order and a 14-day washout period. Head and neck examinations performed between January 3, 2003, and May 31, 2017, at a single academic medical center were used to train, validate, and test the model. Examinations positive for aneurysm had at least 1 clinically significant, nonruptured intracranial aneurysm. Examinations with hemorrhage, ruptured aneurysm, posttraumatic or infectious pseudoaneurysm, arteriovenous malformation, surgical clips, coils, catheters, or other surgical hardware were excluded. All other CTA examinations were considered controls., Main Outcomes and Measures: Sensitivity, specificity, accuracy, time, and interrater agreement were measured. Metrics for clinician performance with and without model augmentation were compared., Results: The data set contained 818 examinations from 662 unique patients with 328 CTA examinations (40.1%) containing at least 1 intracranial aneurysm and 490 examinations (59.9%) without intracranial aneurysms. The 8 clinicians reading the test set ranged in experience from 2 to 12 years. Augmenting clinicians with artificial intelligence-produced segmentation predictions resulted in clinicians achieving statistically significant improvements in sensitivity, accuracy, and interrater agreement when compared with no augmentation. The clinicians' mean sensitivity increased by 0.059 (95% CI, 0.028-0.091; adjusted P = .01), mean accuracy increased by 0.038 (95% CI, 0.014-0.062; adjusted P = .02), and mean interrater agreement (Fleiss κ) increased by 0.060, from 0.799 to 0.859 (adjusted P = .05). There was no statistically significant change in mean specificity (0.016; 95% CI, -0.010 to 0.041; adjusted P = .16) and time to diagnosis (5.71 seconds; 95% CI, 7.22-18.63 seconds; adjusted P = .19)., Conclusions and Relevance: The deep learning model developed successfully detected clinically significant intracranial aneurysms on CTA. This suggests that integration of an artificial intelligence-assisted diagnostic model may augment clinician performance with dependable and accurate predictions and thereby optimize patient care.

Purpose: To investigate whether selected carotid computed tomography angiography (CTA) quantitative features can predict 10-year atherosclerotic cardiovascular disease (ASCVD) risk scores., Methods: One hundred seventeen patients with calculated ASCVD risk scores were considered. A semiautomated imaging analysis software was used to segment and quantify plaque features. Eighty patients were randomly selected to build models using 14 imaging variables and the calculated ASCVD risk score as the end point (continuous and binarized). The remaining 37 patients were used as the test set to generate predicted ASCVD scores. The predicted and observed ASCVD risk scores were compared to assess properties of the predictive model., Results: Nine of 14 CTA imaging variables were included in a model that considered the plaque features in a continuous fashion (model 1) and 6 in a model that considered the plaque features dichotomized (model 2). The predicted ASCVD risk scores were 18.87% ± 13.26% and 18.39% ± 11.6%, respectively. There were strong correlations between the observed ASCVD and the predicted ASCVDs, with r = 0.736 for model 1 and r = 0.657 for model 2. The mean biases between observed ASCVD and predicted ASCVDs were -1.954% ± 10.88% and -1.466% ± 12.04%, respectively., Conclusions: Selected quantitative imaging carotid features extracted from the semiautomated carotid artery analysis can predict the ASCVD risk scores.

Purpose: To develop a statistical approach that compares patient selection strategies across clinical trials and apply this approach to acute ischemic stroke clinical trials to identify the optimal inclusion criteria., Methods: We developed a statistical approach that compares the number needed to treat to achieve one success (NNT) along with the number needed to screen to achieve one success (NNS) and assesses if there are significant differences in inclusion criteria, treatment course, and clinical outcome among patients that may have been included/excluded in the trials. We applied this approach to the study population from four recent positive acute stroke clinical trials: MR CLEAN, EXTEND-IA, ESCAPE, and SWIFT PRIME, applying published trial criteria to an independent registry of 612 acute stroke patients, since we did not have access to the complete trial data., Results: Although reported NNT were similar for EXTEND-IA, SWIFT PRIME and ESCAPE, and somewhat higher for MR CLEAN, NNS varied across the trials from 21 for EXTEND-IA, 27 for MR CLEAN, to 46 for ESCAPE and 64 for SWIFT PRIME, reflecting less and more stringent inclusion criteria, respectively. Although there were significant differences in imaging biomarkers and other clinical characteristics among patients that may have been included/excluded in the trials, these differences did not translate to significant differences in treatment course or clinical outcomes., Conclusions: Our study proposes a robust statistical approach that can be applied to a larger pooled trial dataset, if made available, to objectively compare across clinical trials and inform inclusion criteria of future trials. Pooled analysis of the acute stroke trial data is needed to determine which imaging biomarker inclusion criteria are critical and which may be relaxed. If this procedure were applied across the pooled trial data, it could decrease costs and refine the design of future trials to be the most efficacious for the greatest number of patients.

Cell differentiation is driven by changes in gene expression that manifest as changes in cellular phenotype or function. Altered cellular phenotypes, stemming from genetic mutations or other perturbations, are widely assumed to directly correspond to changes in the transcriptome and vice versa. Here, we exploited the cytologically well-defined Prdm9 mutant mouse as a model of developmental arrest to test whether parallel programs of cellular differentiation and gene expression are tightly coordinated, or can be disassociated. By comparing cytological phenotype markers and transcriptomes in wild-type and mutant spermatocytes, we identified multiple instances of cellular and molecular uncoupling in Prdm9 -/- mutants. Most notably, although Prdm9 -/- germ cells undergo cytological arrest in a late-leptotene/zygotene stage, they nevertheless develop gene expression signatures characteristic of later developmental substages. These findings suggest that transcriptomic changes may not reliably map to cellular phenotypes in developmentally perturbed systems.

The potential of mRNA therapeutics will be realized only once safe and effective delivery systems are established. Unfortunately, delivery vehicle development is stymied by an inadequate understanding of how the molecular properties of a vehicle confer efficacy. Here, a small library of lipidoid materials is used to elucidate structure-function relationships and identify a previously unappreciated parameter-lipid nanoparticle surface ionization-that correlates with mRNA delivery efficacy. The two most potent materials of the library, 306O 10 and 306O i10 , induce substantial luciferase expression in mice following a single 0.75 mg kg -1 mRNA dose. These lipidoids, which have ten-carbon tails and identical molecular weights, vary only in that the 306O 10 tail is straight and the 306O i10 tail has a one-carbon branch. Remarkably, this small difference in structure conferred a tenfold improvement in 306O i10 efficacy. The enhanced potency of this branched-tail lipidoid is attributed to its strong surface ionization at the late endosomal pH of 5.0. A secondary lipidoid library confirms that O i10 materials ionize more strongly and deliver mRNA more potently than lipidoids containing linear tails. Together, these data highlight the exquisite control that lipid chemistry exerts on the mRNA delivery process and show that branched-tail lipids facilitate protein expression in animals., (© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)