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Background and aims: Reliable lead screening methods are necessary to support early identification of lead exposure in children. Sample collection using dried blood spots (DBS) offers advantages compared to traditional venipuncture and capillary collection. Here, we describe and compare three lead DBS inductively coupled plasma-mass spectrometry (ICP-MS) methods for lead screening. Materials and methods: Lead was extracted from Whatman 903 protein saver cards punches and analyzed by ICP-MS across three independent clinical laboratories. Each laboratory evaluated the performance of aqueous and matrix-matched DBS calibrators using external quality control samples (WI State of Laboratory of Hygiene Program). Leftover patient samples (n = 39) were used for an interlaboratory comparison of lead DBS. Lead DBS results were compared to whole blood methods. Results: The DBS ICP-MS methods using matrix-matched DBS calibrators had superior performance to the aqueous calibrations. There was a strong correlation between lead measured in DBS (matrix-matched) and whole blood for the three methods evaluated. Conclusion: Lead can be measured accurately by ICP-MS in DBS samples when matrix-matched calibrators are used. External quality control programs are valuable to assess the performance of DBS methods. DBS lead ICP-MS methods are a robust analytical option for lead screening even though the limitations of DBS are well recognized.

Abstract In malignant primary brain tumors, cancer cells infiltrate into the peritumoral brain structures which results in inevitable recurrence. Quantitative assessment of infiltrative heterogeneity in the peritumoral region, the area where biopsy or resection can be hazardous, is important for clinical decision making. Here, we derive a novel set of Artificial intelligence (AI)-based markers capturing the heterogeneity of tumor infiltration, by characterizing free water movement restriction in the peritumoral region using Diffusion Tensor Imaging (DTI)-based free water volume fraction maps. We leverage the differences in the peritumoral region of metastasis and glioblastomas, the former consisting of vasogenic versus the latter containing infiltrative edema, to extract a voxel-wise deep learning-based peritumoral microenvironment index (PMI). Descriptive characteristics of locoregional hubs of uniformly high PMI values are then extracted as AI-based markers to capture distinct aspects of infiltrative heterogeneity. The proposed markers are utilized to stratify patients’ survival and IDH1 mutation status on a population of 275 adult-type diffuse gliomas (CNS WHO grade 4). Our results show significant differences in the proposed markers between patients with different overall survival and IDH1 mutation status (t test, Wilcoxon rank sum test, linear regression; p

Background: As of July 2022, there have been more than 91.3 million cases of COVID-19 and nearly 1.03 million deaths in the United States alone. In addition, many people who survived COVID-19 had long-term symptoms, such as fatigue, dyspnea, loss of smell and taste, depression, and anxiety. Objectives: The purpose of our study is to evaluate the status of COVID-19 patients who were previously hospitalized. Methods: We conducted a single-center retrospective cohort study at Texas Tech University Health Sciences Center and its affiliated University Medical Center under IRB of L21-144. We included all patients hospitalized for COVID-19 and followed up in our Internal Medicine Clinic at any time between April 1, 2020, and April 1, 2021, and reviewed follow-up data for these patients after discharge. Results: A total of 128 patients were included; 59 (46%) were men, and 69 (54%) were women with an average age of 59.7 ± 14.8 years. Most of the patients (n = 78, 60.9%) identified their race as Hispanic or Latino origin; the next largest group was Caucasian (n = 29, 22.65%). The average number of days until post-hospitalization follow-up was 36 ± 38 days. The 50% of the patients (n = 64) used telemedicine for follow-up visits. Important comorbidities in these patients included diabetes (n = 84, 65.6%) and hypertension (n = 94, 73.4%). Thirty-four patients (26.6%) reported respiratory symptoms at their follow-up appointments, 24 patients (18.8%) reported constitutional symptoms, 12 patients (9.4%) reported GI symptoms, and 25 patients (19.5%) reported other symptoms, such as paresthesia, lower extremity edema, or psychological symptoms. After hospital discharge, 54 patients had follow-up chest x-rays, and 41 (75.9%) still had abnormal findings consistent with COVID-19 imaging characteristics. Follow-up laboratory tests identified 44 patients (77.2%, 57 tested) with elevated D-dimer levels, 44 patients (78.6%, 56 tested) with high ferritin levels, and 21patients (35.6%, 59 tested) with elevated troponin T HS levels. Conclusion: Long-lasting COVID-19 symptoms in these patients included respiratory symptoms (26.6%), constitutional symptoms (18.8%), GI symptoms (9.4%), and other symptoms, such as paresthesia, lower extremity edema, or psychological symptoms (19.5%). The rate of telehealth follow-up was 50%. Many patients had elevated inflammatory markers that will need follow up to determine the clinical implications.

Androstenedione (ASD) is a biomarker used in the diagnostic workup of hyperandrogenism, congenital adrenal hyperplasia, premature adrenarche, and polycystic ovary syndrome (PCOS). The Elecsys ASD competitive electrochemiluminescence immunoassay (Roche Diagnostics, Indianapolis, IN) is a new assay recently available in the US. Objective: This study evaluated the analytical and clinical performance of the Elecsys ASD assay. Design & Methods: We evaluated the linearity/analytical measuring range (AMR), precision, and accuracy of the Elecsys ASD assay on the cobas e601 analyzer. ASD was measured in serum/plasma in the Elecsys ASD, Immulite (Siemens Medical Solutions USA, Inc. Malvern, PA), and LC-MS/MS assays. Reference intervals (RI) were evaluated across genders, menopausal status, and in children. Statistical analysis was performed using EP evaluator and R program. Results: The Elecsys ASD assay had a linear response across the AMR. The intra- and inter-assay coefficients of variation at various concentrations were ≤4.5%. The Elecsys ASD assay had a mean difference of −0.04 ng/mL (−1.7%) with the LC-MS/MS assay, whereas the Immulite assay had a mean difference of 1.17 ng/mL (66%) and −1.22 ng/mL (−38%) compared to the LC-MS/MS and Elecsys ASD assays, respectively. The Roche recommended RIs for healthy men (0.280–1.52 ng/mL) and postmenopausal women (0.187–1.07 ng/mL) were successfully verified. The RIs for children were adopted from published data. For pre-menopausal women, a RI of

Abstract Tumor types are classically distinguished based on biopsies of the tumor itself, as well as a radiological interpretation using diverse MRI modalities. In the current study, the overarching goal is to demonstrate that primary (glioblastomas) and secondary (brain metastases) malignancies can be differentiated based on the microstructure of the peritumoral region. This is achieved by exploiting the extracellular water differences between vasogenic edema and infiltrative tissue and training a convolutional neural network (CNN) on the Diffusion Tensor Imaging (DTI)-derived free water volume fraction. We obtained 85% accuracy in discriminating extracellular water differences between local patches in the peritumoral area of 66 glioblastomas and 40 metastatic patients in a cross-validation setting. On an independent test cohort consisting of 20 glioblastomas and 10 metastases, we got 93% accuracy in discriminating metastases from glioblastomas using majority voting on patches. This level of accuracy surpasses CNNs trained on other conventional DTI-based measures such as fractional anisotropy (FA) and mean diffusivity (MD), that have been used in other studies. Additionally, the CNN captures the peritumoral heterogeneity better than conventional texture features, including Gabor and radiomic features. Our results demonstrate that the extracellular water content of the peritumoral tissue, as captured by the free water volume fraction, is best able to characterize the differences between infiltrative and vasogenic peritumoral regions, paving the way for its use in classifying and benchmarking peritumoral tissue with varying degrees of infiltration.

IntroductionThe lateral habenula (LHb) is an epithalamic nucleus associated with negative valence and affective disorders. It receives input via the stria medullaris (SM) and sends output via the fasciculus retroflexus (FR). Here, we use tractography to reconstruct and characterize this pathway.MethodsMulti-shell human diffusion magnetic resonance imaging (dMRI) data was obtained from the human connectome project (HCP) (n = 20, 10 males) and from healthy controls (n = 10, 6 males) scanned at our institution. We generated LHb afferents and efferents using probabilistic tractography by selecting the pallidum as the seed region and the ventral tegmental area as the output target.ResultsWe were able to reconstruct the intended streamlines in all individuals from the HCP dataset and our dataset. Our technique also aided in identification of the LHb. In right-handed individuals, the streamlines were significantly more numerous in the left hemisphere (mean ratio 1.59 ± 0.09, p = 0.04). In left-handed individuals, there was no hemispheric asymmetry on average (mean ratio 1.00 ± 0.09, p = 1.0). Additionally, these streamlines were significantly more numerous in females than in males (619.9 ± 159.7 vs. 225.9 ± 66.03, p = 0.04).ConclusionWe developed a method to reconstruct the SM and FR without manual identification of the LHb. This technique enables targeting of these fiber tracts as well as the LHb. Furthermore, we have demonstrated that there are sex and hemispheric differences in streamline number. These findings may have therapeutic implications and warrant further investigation.

OBJECTIVES/GOALS: The effect of immunosuppressive metabolites on anti-tumor immunity in human papillomavirus (HPV)-associated vs carcinogen-driven head and neck cancer is unknown. The objective of this study is to define the extent to which metabolites impair this response and identify novel metabolic targets for enhancing anti-tumor immunity. METHODS/STUDY POPULATION: HPV-associated and carcinogen-driven head and neck squamous cell carcinoma specimens were frozen following surgical excision, and tumor sections were cut onto glass slides. Slides were coated in alpha-cyano-4-hydroxy-cinnamic acid (CHCA) matrix and subjected to mass spectrometry imaging using matrix-assisted laser desorption ionization (MALDI) on a Bruker SolariX XR 12T Hybrid QqFT-ICR mass spectrometer run in positive mode. Slides were then stained for immunohistochemistry (IHC) using markers of CD8 T cells, macrophages (CD163), B cells (CD20), and tumor cells (panCK). Mass spectrometry imaging and IHC spatially resolved data will be co-registered and metabolite intensity in regions of interest (cell types) quantified. RESULTS/ANTICIPATED RESULTS: A total of seven HPV-associated (three metastatic lymph nodes and four primary tumors) and six carcinogen-driven (primary tumors) HNSC specimens were subjected to MALDI and IHC. Metabolites significantly enriched in HPV-associated HNSC relative to carcinogen-driven HNSC include 2,3-diphosphoglyceric acid, xanthine, 2,3,5-Trichloromaleylacetate, and indole-3-carboxyaldehyde. Metabolites significantly enriched in carcinogen-driven HNSC relative to HPV-associated HNSC include hesperetin 3'-O-sulfate, hypoxanthine, phosphorylcholine, and L-homocysteine sulfonic acid. In ongoing analyses, we anticipate identifying a relationship between CD8+ T cell enriched vs depleted regions and immunosuppressive metabolites (e.g., kynurenine, adenosine monophosphate). DISCUSSION/SIGNIFICANCE: Defining the extent to which CD8+ T cells interact with the metabolic milieu of the microenvironment will provide a foundation for metabolic Precision Medicine. Strategically targeting metabolic pathways to enhance the anti-tumor immune response will be leveraged for the design and implementation of immune modulatory metabolic therapy.

Vaccinations against COVID-19 infection have become a contentious issue in the United States. Multiple segments of society, including healthcare workers, have expressed concerns regarding the need for vaccination and the safety of current vaccines. Many hospital-based nurses have helped care for patients with severe COVID-19 infections. An anonymous online survey was sent to the nursing staff at University Medical Center in Lubbock, TX, USA, through a hospital-based email system to determine vaccination status and attitudes towards the COVID-19 vaccine and other routine vaccines. Multivariable regression analysis was used to determine factors associated with vaccination. A total of 251 nurses responded to this survey; 211 nurses (83.7%) had received the vaccine. Almost all nurses (242, 96%) had received all childhood vaccinations, and 231 (91.7%) had received an influenza vaccination in the prior year. A minority of nurses (75, 29.8%) supported mandatory vaccination for healthcare workers. The reasons for declining vaccination included the possibility that diet and alternative medications provided better protection against COVID-19. This survey demonstrates that over 80% of nurses working in a hospital managing very sick patients with COVID-19 infection had been vaccinated. However, nurses who did not take the annual influenza vaccine and did not consider other protective measures useful (such as mask-wearing) were significantly less likely to vaccinate. Nurses can provide an important resource for conversations with the public and patients about vaccine initiatives.

Abstract Background Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition. The degree to which the brain development in ASD deviates from typical brain development, and how this deviation relates to observed behavioral outcomes at the individual level are not well-studied. We hypothesize that the degree of deviation from typical brain development of an individual with ASD would relate to observed symptom severity. Methods The developmental changes in anatomical (cortical thickness, surface area, and volume) and diffusion metrics (fractional anisotropy and apparent diffusion coefficient) were compared between a sample of ASD (n = 247) and typically developing children (TDC) (n = 220) aged 6–25. Machine learning was used to predict age (brain age) from these metrics in the TDC sample, to define a normative model of brain development. This model was then used to compute brain age in the ASD sample. The difference between chronological age and brain age was considered a developmental deviation index (DDI), which was then correlated with ASD symptom severity. Results Machine learning model trained on all five metrics accurately predicted age in the TDC (r = 0.88) and the ASD (r = 0.85) samples, with dominant contributions to the model from the diffusion metrics. Within the ASD group, the DDI derived from fractional anisotropy was correlated with ASD symptom severity (r = − 0.2), such that individuals with the most advanced brain age showing the lowest severity, and individuals with the most delayed brain age showing the highest severity. Limitations This work investigated only linear relationships between five specific brain metrics and only one measure of ASD symptom severity in a limited age range. Reported effect sizes are moderate. Further work is needed to investigate developmental differences in other age ranges, other aspects of behavior, other neurobiological measures, and in an independent sample before results can be clinically applicable. Conclusions Findings demonstrate that the degree of deviation from typical brain development relates to ASD symptom severity, partially accounting for the observed heterogeneity in ASD. Our approach enables characterization of each individual with reference to normative brain development and identification of distinct developmental subtypes, facilitating a better understanding of developmental heterogeneity in ASD.

COIVD-19 vaccination of medical students is essential since these students will have contact with patients and will become future healthcare leaders. Hence, we surveyed medical students at Texas Tech University Health Science Center in Lubbock, TX, and received 234 responses. The majority of students were vaccinated against COVID-19 (215/234; 91.8%) and reported pro-vaccine attitudes, such as support for a COVID-19 booster shot (191/234; 81.6%) and an annual COVID-19 vaccine (186/234; 79.5%). Among those who did not receive the COVID-19 vaccine, the most frequent reasons included waiting for more evidence (16/19; 84.2%) and concern about the side effects (15/19; 79.0%). These results indicate that medical students consider vaccination important and suggest that students can provide an important resource for patients and public education.

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

Recent research suggests that in vitro neural networks created from dissociated neurons may be used for computing and performing machine learning tasks. To develop a better artificial intelligent system, a hybrid bio-silicon computer is worth exploring, but its performance is still inferior to that of a silicon-based computer. One reason may be that a living neural network has many intrinsic properties, such as random network connectivity, high network sparsity, and large neural and synaptic variability. These properties may lead to new design considerations, and existing algorithms need to be adjusted for living neural network implementation. This work investigates the impact of neural variations and random connections on inference with learning algorithms. A two-layer hybrid bio-silicon platform is constructed and a five-step design method is proposed for the fast development of living neural network algorithms. Neural variations and dynamics are verified by fitting model parameters with biological experimental results. Random connections are generated under different connection probabilities to vary network sparsity. A multi-layer perceptron algorithm is tested with biological constraints on the MNIST dataset. The results show that a reasonable inference accuracy can be achieved despite the presence of neural variations and random network connections. A new adaptive pre-processing technique is proposed to ensure good learning accuracy with different living neural network sparsity.

Background Coronavirus-2 (COVID-19) has caused a worldwide pandemic since December 2019. Since then, clinical trials with vaccines have been started and completed, and at present, 3 COVID-19 vaccines have been approved for use in the United States. Healthcare providers were among the first to get vaccinated, but the precise attitudes of healthcare workers toward vaccination are uncertain. Objective To understand residents and fellows’ attitudes toward vaccination and record any side effects after vaccination. Methods We conducted an anonymous survey that was open from 3-1-2021 to 3-12-2021 using distribution lists from the Graduate Medical Education office on the Lubbock campus of the Texas Tech University Health Sciences Center after getting approval from the Institutional Review Board (L21-088). Results Eighty-one residents and fellows (26.6% out of 304) responded to our survey. Among those who responded, 63 (77.8 %) were between 25 and 35 years old, and 41 (50.6%) were males. Seventy-seven (95.1%) received the vaccine (Pfizer-BioNTech), 78 (96.3%) reported that they supported vaccination, and 3 (3.7%) reported that they did not want vaccination. Eight members (9.8%) had tested positive for COVID-19 infection before vaccination, but only 1 (1.23%) had tested positive for COVID-19 antibodies. All residents and fellows reported side effects after the vaccination, including pain at the injection site (77; 100%), local redness (9; 11.6%), local swelling (13; 16.8%), fever (25; 32.5%), fatigue (25; 32.5%), chills (34; 44.1 %), headache (38; 49.4%). Conclusions Most medical trainees have a high interest in COVID-19 vaccination; however, a few reported that they did not want vaccination.

After publication of this supplement [1, 2], it was brought to our attention that due to an error authors were missing in the following abstracts. This has now been included in this correction.

Scientific software development processes are understood to be distinct from commercial software development practices due to uncertain and evolving states of scientific knowledge. Sustaining these software products is a recognized challenge, but under-examined is the usability and usefulness of such tools to their scientific end users. User research is a well-established set of techniques (e.g., interviews, mockups, usability tests) applied in commercial software projects to develop foundational, generative, and evaluative insights about products and the people who use them. Currently these approaches are not commonly applied and discussed in scientific software development work. The use of user research techniques in scientific environments can be challenging due to the nascent, fluid problem spaces of scientific work, varying scope of projects and their user communities, and funding/economic constraints on projects. In this paper, we reflect on our experiences undertaking a multi-method user research process in the Deduce project. The Deduce project is investigating data change to develop metrics, methods, and tools that will help scientists make decisions around data change. There is a lack of common terminology since the concept of systematically measuring and managing data change is under explored in scientific environments. To bridge this gap we conducted user research that focuses on user practices, needs, and motivations to help us design and develop metrics and tools for data change. This paper contributes reflections and the lessons we have learned from our experiences. We offer key takeaways for scientific software project teams to effectively and flexibly incorporate similar processes into their projects.

Characterization of healthy versus pathological tissue in the peritumoral area is confounded by the presence of edema, making free water estimation the key concern in modeling tissue microstructure. Most methods that model tissue microstructure are either based on advanced acquisition schemes not readily available in the clinic or are not designed to address the challenge of edema. This underscores the need for a robust free water elimination (FWE) method that estimates free water in pathological tissue but can be used with clinically prevalent single-shell diffusion tensor imaging data. FWE in single-shell data requires the fitting of a bi-compartment model, which is an ill-posed problem. Its solution requires optimization, which relies on an initialization step. We propose a novel initialization approach for FWE, FERNET, which improves the estimation of free water in edematous and infiltrated peritumoral regions, using single-shell diffusion MRI data. The method has been extensively investigated on simulated data and healthy dataset. Additionally, it has been applied to clinically acquired data from brain tumor patients to characterize the peritumoral region and improve tractography in it.

Quality assessment of diffusion MRI (dMRI) data is essential prior to any analysis, so that appropriate pre-processing can be used to improve data quality and ensure that the presence of MRI artifacts do not affect the results of subsequent image analysis. Manual quality assessment of the data is subjective, possibly error-prone, and infeasible, especially considering the growing number of consortium-like studies, underlining the need for automation of the process. In this paper, we have developed a deep-learning-based automated quality control (QC) tool, QC-Automator, for dMRI data, that can handle a variety of artifacts such as motion, multiband interleaving, ghosting, susceptibility, herringbone, and chemical shifts. QC-Automator uses convolutional neural networks along with transfer learning to train the automated artifact detection on a labeled dataset of ∼332,000 slices of dMRI data, from 155 unique subjects and 5 scanners with different dMRI acquisitions, achieving a 98% accuracy in detecting artifacts. The method is fast and paves the way for efficient and effective artifact detection in large datasets. It is also demonstrated to be replicable on other datasets with different acquisition parameters.

I explore some of the interconnections between inferences that participants make about one another’s (verbal) conduct, the implications they attribute to prior turns at talk, and the indirectness with which recipients may respond to enquiries - in short, the interconnections between inference, implication and indirectness. These are explored in the context of naturally occurring conversations (UK and US), from the methodological perspective of Conversation Analysis. Because inference has come to be associated closely with Grice’s concept of implicature, I begin by setting out my reasons for not following Grice’s path, preferring instead to revert to ‘implication’, namely the implication that a recipient finds in and attributes to a prior turn. My purpose here is to avoid the cognitive conceptualisation of speakers’ intentions that Grice supposed are associated with implicature. I argue that inference features in the understanding of and response to all turns at talk; it is not restricted to some special kind of utterance, as seems to be conveyed in Grice’s conceptualisation. The inferences that recipients make are evident in cases in which they respond not to what the prior speaker asked ‘literally,’ but to the inferred agenda of an enquiry. Moreover, recipients may respond indirectly to prior enquiries, thereby ‘side-stepping’ implications they attribute to those enquiries. In various ways, inference and indirection may on occasions be associated with practices for avoidance in conversation.

Objective: Does papillary squamotransitional cell carcinoma (PSTCC) behave differently from conventional squamous cell carcinoma of the cervix and does PSTCC have true transitional cell differentiation?Materials and Methods: Twenty cases of PSTCC were identified from archival files. Clinical data were compiled. Immunoperoxidase stains for uroplakin III, p63 and p16 were performed on available tissue blocks.Results: Patients ranged in age from 27 to 85 years. Twelve patients were FIGO Stage I, 4 were Stage II, and 2 were Stage III. Thirteen patients had clinical follow-up ranging from 5 to 132 months. Three patients subsequently had more extensive disease than initial clinical staging indicated. Nine patients had no tumor progression, three had local recurrence and one had metastatic disease. Eight cases were strongly immunoreactive for p63 and p16 and 14 were negative for uroplakin III.Conclusions: PSTCC lacks true transitional cell differentiation and probably shares similar clinicopathologic features with conventional cervical squamous cell carcinoma. [ABSTRACT FROM AUTHOR]

Online learning is coming of age. ‘Traditional’ universities are embracing online components to courses, online courses, and even complete online programs. With the advantage of distance and time insensitivity for the learning process, there appears to be a growing sense that this form of teaching and learning has strong pedagogical merit. Research has shown that students do comparatively well in this new format. There is, however, a lack of evidence illustrating particular strengths and weaknesses of online teaching and learning. This paper discusses experiences with a single course taught using two forms: (1) traditional place-based, and (2) a form of asynchronous learning network (ALN) defined as interactive virtual seminars. Differences in learning performance are tested using longitudinal observations. In a course comprised of both conceptual material and the application of techniques, the students performed overall equally well in either place-based or virtual format. Their degree of learning, however, differed significantly between conceptual and technique-based material. Implications are promising, showing that there are relative strengths to be exploited in both place-based and virtual formats.

Digital and intelligent buildings are critical to realizing efficient building energy operations and a smart grid. With the increasing digitalization of processes throughout the life cycle of buildings, data exchanged between stakeholders and between building systems have grown significantly. However, a lack of semantic interoperability between data in different systems is still prevalent and hinders the development of energy-oriented applications that can be reused across buildings, limiting the scalability of innovative solutions. Addressing this challenge, our review paper systematically reviews metadata schemas and ontologies that are at the foundation of semantic interoperability necessary to move toward improved building energy operations. The review finds 40 schemas that span different phases of the building life cycle, most of which cover commercial building operations and, in particular, control and monitoring systems. The paper’s deeper review and analysis of five popular schemas identify several gaps in their ability to fully facilitate the work of a building modeler attempting to support three use cases: energy audits, automated fault detection and diagnosis, and optimal control. Our findings demonstrate that building modelers focused on energy use cases will find it difficult, labor intensive, and costly to create, sustain, and use semantic models with existing ontologies. This underscores the significant work still to be done to enable interoperable, usable, and maintainable building models. We make three recommendations for future work by the building modeling and energy communities: a centralized repository with a search engine for relevant schemas, the development of more use cases, and better harmonization and standardization of schemas in collaboration with industry to facilitate their adoption by stakeholders addressing varied energy-focused use cases.

Myoclonus is a movement disorder characterized by involuntary, sudden, brief muscle jerks caused by muscular contraction (positive myoclonus) or inhibition (negative myoclonus).1,2 Myoclonus is generally a medical sign and not a diagnosis. It can occur in multiple disorders. A short differential diagnosis list includes anoxic brain injury, multiple sclerosis, Parkinson's disease, subacute sclerosing panencephalitis, and Creutzfeldt-Jakob disease. One way to classify the etiologies is through review of the clinical presentation and comorbid conditions. This article presents a review of anoxic brain injury related myoclonus. Post-anoxic myoclonus (PAM) can develop in either acute or chronic phase. Acute PAM occurs within hours after hypoxic event; chronic PAM (Lance-Adams syndrome) develops in survivors several days to weeks after the episodes of brain hypoxia.

Innate immune sensing of viral infection results in type I interferon (IFN) production and inflammasome activation. Type I IFNs, primarily IFN-α and IFN-β, are produced by all cell types upon virus infection and promote an antiviral state in surrounding cells by inducing the expression of IFN-stimulated genes. Type I IFN production is mediated by Toll-like receptor (TLR) 3 in HCV infected hepatocytes. Type I IFNs are also produced by plasmacytoid dendritic cells (pDC) after sensing of HIV and HCV through TLR7 in the absence of productive pDC infection. Inflammasomes are multi-protein cytosolic complexes that integrate several pathogen-triggered signaling cascades ultimately leading to caspase-1 activation and generation pro-inflammatory cytokines including interleukin (IL)-18 and IL-1β. Here, we demonstrate that HIV and HCV activate the inflammasome, but not Type I IFN production, in monocytes and macrophages in an infection-independent process that requires clathrin-mediated endocytosis and recognition of the virus by distinct endosomal TLRs. Knockdown of each endosomal TLR in primary monocytes by RNA interference reveals that inflammasome activation in these cells results from HIV sensing by TLR8 and HCV recognition by TLR7. Despite its critical role in type I IFN production by pDCs stimulated with HIV, TLR7 is not required for inflammasome activation by HIV. Similarly, HCV activation of the inflammasome in monocytes does not require TLR3 or its downstream signaling adaptor TICAM-1, while this pathway leads to type I IFN in infected hepatocytes. Monocytes and macrophages do not produce type I IFN upon TLR8 or TLR7 sensing of HIV or HCV, respectively. These findings reveal a novel infection-independent mechanism for chronic viral induction of key anti-viral programs and demonstrate distinct TLR utilization by different cell types for activation of the type I IFN vs. inflammasome pathways of inflammation.

We are reporting a case of familial thoracic aortic aneurysm and dissection in a 26-year-old man with no significant past medical history and a family history of dissecting aortic aneurysm in his mother at the age of 40. The patient presented with cough, shortness of breath, and chest pain. Chest X-ray showed bilateral pulmonary infiltrates. CT scan of the chest showed a dissection of the ascending aorta. The patient underwent aortic dissection repair and three months later he returned to our hospital with new complaints of back pain. CT angiography showed a new aortic dissection extending from the left carotid artery through the bifurcation and into the iliac arteries. The patient underwent replacement of the aortic root, ascending aorta, total aortic arch, and aortic valve. The patient recovered well postoperatively. Genetic studies of the patient and his children revealed no mutations in ACTA2, TGFBR1, TGFBR2, TGFB2, MYH11, MYLK, SMAD3, or FBN1. This case report focuses on a patient with familial TAAD and discusses the associated genetic loci and available screening methods. It is important to recognize potential cases of familial TAAD and understand the available screening methods since early diagnosis allows appropriate management of risk factors and treatment when necessary.

Abstract Background Methylation of DNA is a common mechanism for silencing genes, and aberrant methylation is increasingly being implicated in many diseases such as cancer. There is a need for robust, inexpensive methods to quantitate methylation across a region containing a number of CpGs. We describe and validate a rapid, in-tube method to quantitate DNA methylation using the melt data obtained following amplification of bisulfite modified DNA in a real-time thermocycler. Methods We first describe a mathematical method to normalise the raw fluorescence data generated by heating the amplified bisulfite modified DNA. From this normalised data the temperatures at which melting begins and finishes can be calculated, which reflect the less and more methylated template molecules present respectively. Also the T50, the temperature at which half the amplicons are melted, which represents the summative methylation of all the CpGs in the template mixture, can be calculated. These parameters describe the methylation characteristics of the region amplified in the original sample. Results For validation we used synthesized oligonucleotides and DNA from fresh cells and formalin fixed paraffin embedded tissue, each with known methylation. Using our quantitation we could distinguish between unmethylated, partially methylated and fully methylated oligonucleotides mixed in varying ratios. There was a linear relationship between T50 and the dilution of methylated into unmethylated DNA. We could quantitate the change in methylation over time in cell lines treated with the demethylating drug 5-aza-2'-deoxycytidine, and the differences in methylation associated with complete, clonal or no loss of MGMT expression in formalin fixed paraffin embedded tissues. Conclusion We have validated a rapid, simple in-tube method to quantify methylation which is robust and reproducible, utilizes easily designed primers and does not need proprietary algorithms or software. The technique does not depend on any operator manipulation or interpretation of the melt curves, and is suitable for use in any laboratory with a real-time thermocycler. The parameters derived provide an objective description and quantitation of the methylation in a specimen, and can be used to for statistical comparisons of methylation between specimens.

Abstract Background Barrett's esophagus (BE) is the metaplastic replacement of squamous with columnar epithelium in the esophagus, as a result of reflux. It is the major risk factor for the development of esophageal adenocarcinoma (EAC). Methylation of CpG dinucleotides of normally unmethylated genes is associated with silencing of their expression, and is common in EAC. This study was designed to determine at what stage, in the progression from BE to EAC, methylation of key genes occurs. Results We examined nine genes (APC, CDKN2A, ID4, MGMT, RBP1, RUNX3, SFRP1, TIMP3, and TMEFF2), frequently methylated in multiple cancer types, in a panel of squamous (19 biopsies from patients without BE or EAC, 16 from patients with BE, 21 from patients with EAC), BE (40 metaplastic, seven high grade dysplastic) and 37 EAC tissues. The methylation frequency, the percentage of samples that had any extent of methylation, for each of the nine genes in the EAC (95%, 59%, 76%, 57%, 70%, 73%, 95%, 74% and 83% respectively) was significantly higher than in any of the squamous groups. The methylation frequency for each of the nine genes in the metaplastic BE (95%, 28%, 78%, 48%, 58%, 48%, 93%, 88% and 75% respectively) was significantly higher than in the squamous samples except for CDKN2A and RBP1. The methylation frequency did not differ between BE and EAC samples, except for CDKN2A and RUNX3 which were significantly higher in EAC. The methylation extent was an estimate of both the number of methylated alleles and the density of methylation on these alleles. This was significantly greater in EAC than in metaplastic BE for all genes except APC, MGMT and TIMP3. There was no significant difference in methylation extent for any gene between high grade dysplastic BE and EAC. Conclusion We found significant methylation in metaplastic BE, which for seven of the nine genes studied did not differ in frequency from that found in EAC. This is also the first report of gene silencing by methylation of ID4 in BE or EAC. This study suggests that metaplastic BE is a highly abnormal tissue, more similar to cancer tissue than to normal epithelium.

Abstract Background Metallothionein 3 (MT3) inhibits growth in a variety of cell types. We measured MT3 gene expression by RT-PCR, and DNA methylation in the MT3 promoter by combined bisulphite restriction analysis, in four oesophageal cancer cell lines and the resected oesophagus from 64 patients with oesophageal squamous cell carcinoma (SCC). Results MT3 expression was not detected in one of the four oesophageal cell lines. The MT3 promoter was methylated in all of the oesophageal cell lines, but the degree of methylation was greater in the non-expressing cell line. After treatment with 5-aza-2'-deoxycytidine there was a reduction in the degree of methylation, and an increase in MT3 expression, in each of the cell lines (p < 0.01). Methylation was detected in 52% (33 of 64) of primary SCC and 3% (2 of 62) of histologically normal resection margins. MT3 expression was measured in 29 tumours, 17 of which had methylation of MT3. The expression of MT3 was significantly less in the methylated tumours compared to either the unmethylated tumours (p = 0.03), or the matched margin (p = 0.0005). There was not a significant difference in MT3 expression between the tumour and the margin from patients with unmethylated tumour. No correlations were observed between methylation of MT3 and survival time, patient age, gender, smoking or drinking history, tumour stage, volume, or lymph node involvement. Conclusion We conclude that MT3 expression is frequently down-regulated in oesophageal SCC, by DNA methylation, but that this is not a prognostic indicator.