Development of a respiratory virus panel test for detection of twenty human respiratory viruses by use of multiplex PCR and a fluid microbead-based assay

Virology laboratories historically have used direct fluorescent-antibody assay (DFA) and culture to detect six or seven respiratory viruses. Following the discovery of five new human respiratory viruses since 2000, there is an increasing need for diagnostic tests to detect these emerging viruses. We have developed a new test that can detect 20 different respiratory virus types/subtypes in a single 5-h test. The assay employs multiplex PCR using 14 virus-specific primer pairs, followed by a multiplexed target-specific primer extension (TSPE) reaction using 21 primers for specific respiratory virus types and subtypes. TSPE products were sorted and identified by using a fluid microsphere-based array (Universal Array; TmBioscience Corporation, Toronto, Canada) and the Luminex x-MAP system. The assay detected influenza A and B viruses; influenza A virus subtypes H1, H3, and H5 (including subtype H5N1 of the Asian lineage); parainfluenza virus types 1, 2, 3, and 4; respiratory syncytial virus types A and B; adenovirus; metapneumovirus; rhinovirus; enterovirus; and coronaviruses OC43, 229E, severe acute respiratory syndrome coronavirus, NL63, and HKU1. In a prospective evaluation using 294 nasopharyngeal swab specimens, DFA/culture detected 119 positives and the respiratory virus panel (RVP) test detected 112 positives, for a sensitivity of 97%. The RVP test detected an additional 61 positive specimens that either were not detected by DFA/culture or were positive for viruses not tested for by DFA/culture. After resolution of discordant results by using a second unique PCR assay and by using a combined reference standard of positivity, the RVP test detected 180 of 183 true positives, for a sensitivity of 98.5%, whereas DFA and culture detected only 126 of 183 true positives, for a sensitivity of 68.8%. The RVP test should improve the capabilities of hospital and public health laboratories for diagnosing viral respiratory tract infections and should assist public health agencies in identifying etiologic agents in respiratory tract infection outbreaks.

IA-FEMesh: an open-source, interactive, multiblock approach to anatomic finite element model development

Finite element (FE) analysis is a valuable tool in musculoskeletal research. The demands associated with mesh development, however, often prove daunting. In an effort to facilitate anatomic FE model development we have developed an open-source software toolkit (IA-FEMesh). IA-FEMesh employs a multiblock meshing scheme aimed at hexahedral mesh generation. An emphasis has been placed on making the tools interactive, in an effort to create a user friendly environment. The goal is to provide an efficient and reliable method for model development, visualization, and mesh quality evaluation. While these tools have been developed, initially, in the context of skeletal structures they can be applied to countless applications.

Pneumatic retinopexy. A long term follow-up study

PURPOSE

We have analysed the results of 48 consecutive cases of rhegmatogenous retinal detachment treated with pneumatic retinopexy in a long term follow-up study.

RESULTS

Thirty six of the original 48 patients were available to follow-up. We had an average follow-up period of 8.1 years and a 75% 7 year follow-up. Single operation success was achieved in 83% of patients. Redetachment occurred in 3 patients at 11, 30 and 33 months. Complications found included new breaks in 14%,, cystoid macular oedema in 8%,, and proliferative vitreoretinopathy in 3% of patients. No previously unreported complications were found in spite of the long follow-up time. Final anatomic success (after 1 or more operations) at 6 months post operatively was found in 97% of patients. At follow-up we found final anatomic success in all patients.

CONCLUSION

The use of pneumatic retinopexy instead of scleral buckling is discussed. We recommend the consideration of pneumatic retinopexy in uncomplicated selected cases of rhegmatogenous retinal detachment.

Best practices for data sharing in phylogenetic research

As phylogenetic data becomes increasingly available, along with associated data on species' genomes, traits, and geographic distributions, the need to ensure data availability and reuse become more and more acute. In this paper, we provide ten "simple rules" that we view as best practices for data sharing in phylogenetic research. These rules will help lead towards a future phylogenetics where data can easily be archived, shared, reused, and repurposed across a wide variety of projects.

A clinical comparison of the Xpert non-contact tonometer with the Goldmann applanation tonometer after penetrating keratoplasty

PURPOSE

To assess the agreement between the Xpert non-contact tonometer (NCT) and the Goldmann applanation tonometer in patients who have undergone penetrating keratoplasty.

METHODS

The study material consisted of 42 consecutive patients (43 eyes) who had undergone penetrating keratoplasty within the previous 13 months.

RESULTS

The slope of the linear relationship between the two measurement methods did not differ significantly from 1.0. The mean difference between the methods of 0.96 mmHg was not statistically significant. The range of intraindividual differences between the methods was from -9.8 to 22.8 mmHg. The standard deviation of differences was 6.62 mmHg. The 95% limits of agreement were -12.00 to 13.94 mmHg. There was no significant correlation between the central corneal thickness, astigmatism or transplant size and the difference between the methods

CONCLUSION

The Xpert NCT shows considerable variation from the Goldmann values The degree of agreement with the true IOP value remains to be shown.

Artificial Intelligence-based Tumor Segmentation in Mouse Models of Lung Adenocarcinoma

BACKGROUND

Mouse models are highly effective for studying the pathophysiology of lung adenocarcinoma and evaluating new treatment strategies. Treatment efficacy is primarily determined by the total tumor burden measured on excised tumor specimens. The measurement process is time-consuming and prone to human errors. To address this issue, we developed a novel deep learning model to segment lung tumor foci on digitally scanned hematoxylin and eosin (H&E) histology slides.

METHODS

Digital slides of 239 mice from 9 experimental cohorts were split into training (n=137), validation (n=37), and testing cohorts (n=65). Image patches of 500×500 pixels were extracted from 5× and 10× magnifications, along with binary masks of expert annotations representing ground-truth tumor regions. Deep learning models utilizing DeepLabV3+ and UNet architectures were trained for binary segmentation of tumor foci under varying stain normalization conditions. The performance of algorithm segmentation was assessed by Dice Coefficient, and detection was evaluated by sensitivity and positive-predictive value (PPV).

RESULTS

The best model on patch-based validation was DeepLabV3+ using a Resnet-50 backbone, which achieved Dice 0.890 and 0.873 on validation and testing cohort, respectively. This result corresponded to 91.3 Sensitivity and 51.0 PPV in the validation cohort and 93.7 Sensitivity and 51.4 PPV in the testing cohort. False positives could be reduced 10-fold with thresholding artificial intelligence (AI) predicted output by area, without negative impact on Dice Coefficient. Evaluation at various stain normalization strategies did not demonstrate improvement from the baseline model.

CONCLUSIONS

A robust AI-based algorithm for detecting and segmenting lung tumor foci in the pre-clinical mouse models was developed. The output of this algorithm is compatible with open-source software that researchers commonly use.

The design and applications of a recursive molecular modeling framework

In this paper, we present a recursive molecular model suitable for multiresolution analysis in a variety of application areas. Our approach allows ease of use by computer scientists and biologists by proposing a software interface for molecular analysis that hides programming details. We demonstrate that our design is flexible enough for use in desktop analytical applications, in large-scale parallel simulations, and as a server for remote molecular analysis across wide-area networks.

PhyloPen: Phylogenetic Tree Browsing Using a Pen and Touch Interface

Phylogenetic trees are used by researchers across multiple fields of study to display historical relationships between organisms or genes. Trees are used to examine the speciation process in evolutionary biology, to classify families of viruses in epidemiology, to demonstrate co-speciation in host and pathogen studies, and to explore genetic changes occurring during the disease process in cancer, among other applications. Due to their complexity and the amount of data they present in visual form, phylogenetic trees have generally been difficult to render for publication and challenging to directly interact with in digital form. To address these limitations, we developed PhyloPen, an experimental novel multi-touch and pen application that renders a phylogenetic tree and allows users to interactively navigate within the tree, examining nodes, branches, and auxiliary information, and annotate the tree for note-taking and collaboration. We present a discussion of the interactions implemented in PhyloPen and the results of a formative study that examines how the application was received after use by practicing biologists -- faculty members and graduate students in the discipline. These results are to be later used for a fully supported implementation of the software where the community will be welcomed to participate in its development.

Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group

PURPOSE

Rhabdomyosarcoma (RMS) is an aggressive soft-tissue sarcoma, which primarily occurs in children and young adults. We previously reported specific genomic alterations in RMS, which strongly correlated with survival; however, predicting these mutations or high-risk disease at diagnosis remains a significant challenge. In this study, we utilized convolutional neural networks (CNN) to learn histologic features associated with driver mutations and outcome using hematoxylin and eosin (H&E) images of RMS.

EXPERIMENTAL DESIGN

Digital whole slide H&E images were collected from clinically annotated diagnostic tumor samples from 321 patients with RMS enrolled in Children's Oncology Group (COG) trials (1998-2017). Patches were extracted and fed into deep learning CNNs to learn features associated with mutations and relative event-free survival risk. The performance of the trained models was evaluated against independent test sample data (n = 136) or holdout test data.

RESULTS

The trained CNN could accurately classify alveolar RMS, a high-risk subtype associated with PAX3/7-FOXO1 fusion genes, with an ROC of 0.85 on an independent test dataset. CNN models trained on mutationally-annotated samples identified tumors with RAS pathway with a ROC of 0.67, and high-risk mutations in MYOD1 or TP53 with a ROC of 0.97 and 0.63, respectively. Remarkably, CNN models were superior in predicting event-free and overall survival compared with current molecular-clinical risk stratification.

CONCLUSIONS

This study demonstrates that high-risk features, including those associated with certain mutations, can be readily identified at diagnosis using deep learning. CNNs are a powerful tool for diagnostic and prognostic prediction of rhabdomyosarcoma, which will be tested in prospective COG clinical trials.