Determination of suitable housekeeping genes for normalisation of quantitative real time PCR analysis of cells infected with human immunodeficiency virus and herpes viruses

The choice of an appropriate housekeeping gene for normalisation purposes has now become an essential requirement when designing QPCR experiments. This is of particular importance when using QPCR to measure viral and cellular gene transcription levels in the context of viral infections as viruses can significantly interfere with host cell pathways, the components of which traditional housekeeping genes often encode. In this study we have determined the reliability of 10 housekeeping genes in context of four heavily studied viral infections; human immunodeficiency virus type 1, herpes simplex virus type 1, cytomegalovirus and varicella zoster virus infections using a variety of cell types and virus strains. This provides researchers of these viruses with a shortlist of potential housekeeping genes to use as normalisers for QPCR experiments

Identification of genes encoding antimicrobial proteins in Langerhans cells

Langerhans cells (LCs) reside in the epidermis where they are poised to mount an antimicrobial response against microbial pathogens invading from the outside environment. To elucidate potential pathways by which LCs contribute to host defense, we mined published LC transcriptomes deposited in GEO and the scientific literature for genes that participate in antimicrobial responses. Overall, we identified 31 genes in LCs that encode proteins that contribute to antimicrobial activity, ten of which were cross-validated in at least two separate experiments. Seven of these ten antimicrobial genes encode chemokines

A One Health overview, facilitating advances in comparative medicine and translational research.

Table of contentsA1 One health advances and successes in comparative medicine and translational researchCheryl StroudA2 Dendritic cell-targeted gorilla adenoviral vector for cancer vaccination for canine melanomaIgor Dmitriev, Elena Kashentseva, Jeffrey N. Bryan, David T. CurielA3 Viroimmunotherapy for malignant melanoma in the companion dog modelJeffrey N. Bryan, David Curiel, Igor Dmitriev, Elena Kashentseva, Hans Rindt, Carol Reinero, Carolyn J. HenryA4 Of mice and men (and dogs!): development of a commercially licensed xenogeneic DNA vaccine for companion animals with malignant melanomaPhilip J. BergmanA5 Successful immunotherapy with a recombinant HER2-expressing Listeria monocytogenes in dogs with spontaneous osteosarcoma paves the way for advances in pediatric osteosarcomaNicola J. Mason, Josephine S. Gnanandarajah, Julie B. Engiles, Falon Gray, Danielle Laughlin, Anita Gaurnier-Hausser, Anu Wallecha, Margie Huebner, Yvonne PatersonA6 Human clinical development of ADXS-HER2Daniel O'ConnorA7 Leveraging use of data for both human and veterinary benefitLaura S. TremlA8 Biologic replacement of the knee: innovations and early clinical resultsJames P. StannardA9 Mizzou BioJoint Center: a translational success storyJames L. CookA10 University and industry translational partnership: from the lab to commercializationMarc JacobsA11 Beyond docking: an evolutionarily guided OneHealth approach to drug discoveryGerald J. Wyckoff, Lee Likins, Ubadah Sabbagh, Andrew SkaffA12 Challenges and opportunities for data applications in animal health: from precision medicine to precision husbandryAmado S. GuloyA13 A cloud-based programmable platform for healthHarlen D. HaysA14 Comparative oncology: One Health in actionAmy K. LeBlancA15 Companion animal diseases bridge the translational gap for human neurodegenerative diseaseJoan R. Coates, Martin L. Katz, Leslie A. Lyons, Gayle C. Johnson, Gary S. Johnson, Dennis P. O'BrienA16 Duchenne muscular dystrophy gene therapyDongsheng DuanA17 Polycystic kidney disease: cellular mechanisms to emerging therapiesJames P. CalvetA18 The domestic cat as a large animal model for polycystic kidney diseaseLeslie A. Lyons, Barbara GandolfiA19 The support of basic and clinical research by the Polycystic Kidney Disease FoundationDavid A. BaronA20 Using naturally occurring large animal models of human disease to enable clinical translation: treatment of arthritis using autologous stromal vascular fraction in dogsMark L. WeissA21 Regulatory requirements regarding clinical use of human cells, tissues, and tissue-based productsDebra A. WebsterA22 Regenerative medicine approaches to Type 1 diabetes treatmentFrancis N. KaranuA23 The zoobiquity of canine diabetes mellitus, man's best friend is a friend indeed-islet transplantationEdward J. RobbA24 One Medicine: a development model for cellular therapy of diabetesRobert J. Harman

A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes

Background: Most cardiovascular (CV)/stroke risk calculators using the integration of carotid ultrasound image-based phenotypes (CUSIP) with conventional risk factors (CRF) have shown improved risk stratification compared with either method. However such approaches have not yet leveraged the potential of machine learning (ML). Most intelligent ML strategies use follow-ups for the endpoints but are costly and time-intensive. We introduce an integrated ML system using stenosis as an endpoint for training and determine whether such a system can lead to superior performance compared with the conventional ML system.Methods: The ML-based algorithm consists of an offline and online system. The offline system extracts 47 features which comprised of 13 CRF and 34 CUSIP. Principal component analysis (PCA) was used to select the most significant features. These offline features were then trained using the event-equivalent gold standard (consisting of percentage stenosis) using a random forest (RF) classifier framework to generate training coefficients. The online system then transforms the PCA-based test features using offline trained coefficients to predict the risk labels on test subjects. The above ML system determines the area under the curve (AUC) using a 10-fold cross-validation paradigm. The above system so-called "AtheroRisk-Integrated" was compared against "AtheroRisk-Conventional", where only 13 CRF were considered in a feature set.Results: Left and right common carotid arteries of 202 Japanese patients (Toho University, Japan) were retrospectively examined to obtain 395 ultrasound scans. AtheroRisk-Integrated system [AUC=0.80, P<0.0001, 95% confidence interval (CI): 0.77 to 0.84] showed an improvement of similar to 18% against AtheroRisk-Conventional ML (AUC=0.68, P<0.0001, 95% CI: 0.64 to 0.72).Conclusions: ML-based integrated model with the event-equivalent gold standard as percentage stenosis is powerful and offers low cost and high performance CV/stroke risk assessment

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Doxycycline versus prednisolone as an initial treatment strategy for bullous pemphigoid: a pragmatic non-inferiority randomised controlled trial

Background: Bullous pemphigoid (BP) is a blistering skin disorder with increased mortality. We tested whether a strategy of starting treatment with doxycycline conveys acceptable short-term blister control whilst conferring long-term safety advantages over starting treatment with oral corticosteroids. Methods: Pragmatic multi-centre parallel-group randomised controlled trial of adults with BP (≥3 blisters ≥2 sites and linear basement membrane IgG/C3) plus economic evaluation. Participants were randomised to doxycycline (200 mg/day) or prednisolone (0·5 mg/kg/day). Localised adjuvant potent topical corticosteroids (<30 g/week) was permitted weeks 1-3. The non-inferiority primary effectiveness outcome was the proportion of participants with ≤3 blisters at 6 weeks. We assumed that doxycycline would be 25% less effective than corticosteroids with a 37% acceptable margin of noninferiority. The primary safety outcome was the proportion with severe, life-threatening or fatal treatment-related adverse events by 52 weeks. Analysis used a regression model adjusting for baseline disease severity, age and Karnofsky score, with missing data imputed. Results: 132 patients were randomised to doxycycline and 121 to prednisolone from 54 UK and 7 German dermatology centres. Mean age was 77·7 years and 68.4% had moderate to severe baseline disease. For those starting doxycycline, 83/112 (74·1%) had ≤3 blisters at 6 weeks compared with 92/101 (91·1%) for prednisolone, a difference of 18·6% favouring prednisolone (upper limit of 90% CI, 26·1%, within the predefined 37% margin). Related severe, life-threatening and fatal events at 52 weeks were 18·5% for those starting doxycycline and 36·6% for prednisolone (mITT analysis), an adjusted difference of 19·0% (95% CI, 7·9%, 30·1%, p=0·001). Conclusions: A strategy of starting BP patients on doxycycline is non-inferior to standard treatment with oral prednisolone for short-term blister control and significantly safer long-term

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors