Generic Anti-PEG Antibody Assay on ProterixBio’s (Formerly BioScale) ViBE Platform Shows Poor Reproducibility

PEGylation is a modification commonly used to increase the half-life of therapeutic proteins. The strategy for immunogenicity testing of these compounds should include methods to detect both anti-protein and anti-PEG antibodies. We previously reported a method for the detection of anti-PEG antibodies using ProterixBio’s (formerly BioScale) acoustic membrane microparticle (AMMP) technology. Our initial method development work showed the assay was capable of detecting antibodies in human serum with a sensitivity of 1 μg/mL with good reproducibility (CV?<?7%). Since the publication of this initial paper, additional experimentation was performed in an effort to validate the assay for support of clinical sample analysis. This additional data indicate that the method has high variability (CV%?>?20) and is unsuitable to support clinical sample analysis.     

Posterior cortical atrophy: a single case cognitive and radiological follow-up

Posterior cortical atrophy (PCA) is a rare neurodegenerative syndrome characterized by initial predominant visuoperceptual deficits followed by a progressive decline in other cognitive functions. This syndrome has not been as thoroughly described as other dementias, particularly from a neuropsychological evolution perspective with only a few studies describing the evolution of its cognitive progression. In this investigation we review the literature on this rare condition and we perform a 7-year neuropsychological and neuroradiological follow-up of a 64-year-old man with PCA. The subject’s deficits initially appeared in his visuoperceptual skills with later affectation appearing in language and other cognitive functions, this being coherent with the patient’s parieto-temporal atrophy evolution.     

Risks,benefits and the role of stakeholders in pathogen reduction technology

While Canada’s blood supply is currently very safe, risks remain, prompting blood suppliers to develop a more effective strategy to minimise the risk of transmitting infectious agents through blood transfusion. Pathogen reduction technology provides an additional way to protect the blood supply from new threats. However, the uptake of this new technology has been slow, reflecting the safety of the current system, the success of surveillance and screening methods, the lack of knowledge regarding pathogen reduction technology and the impact of pathogen reduction on blood quality and recipient safety. In the absence of public debate, the legacy of previous adverse events and the challenges of negotiating perceptions of risk, our objective in this article is to explore stakeholder perceptions of the challenges of introducing pathogen reduction technology in Canada. In this article, we provide a debate about risk communication and assessment drawing on data from a study of 2010 that used focus groups and interviews with key stakeholders, including media, blood suppliers, blood or blood product recipients and implementers of pathogen reduction technologies to examine stakeholders’ perceptions of risk assessment and communication. We found that there was a broad understanding among stakeholders of the need to effectively communicate the risks and benefits of pathogen reduction technology and to provide accurate information. Consequently, we predict that public acceptance of the new technology will be largely based on its perception of the risk of pathogen reduction technology.     

Factor analysis for survival time prediction with informative censoring and diverse covariates

Fulfilling the promise of precision medicine requires accurately and precisely classifying disease states. For cancer, this includes prediction of survival time from a surfeit of covariates. Such data presents an opportunity for improved prediction, but also a challenge due to high dimensionality. Furthermore, disease populations can be heterogeneous. Integrative modeling is sensible, as the underlying hypothesis is that joint analysis of multiple covariates provides greater explanatory power than separate analyses. We propose an integrative latent variable model that combines factor analysis for various data types and an exponential proportional hazards (EPH) model for continuous survival time with informative censoring. The factor and EPH models are connected through low-dimensional latent variables that can be interpreted and visualized to identify subpopulations. We use this model to predict survival time. We demonstrate this model's utility in simulation and on four Cancer Genome Atlas datasets: diffuse lower-grade glioma, glioblastoma multiforme, lung adenocarcinoma, and lung squamous cell carcinoma. These datasets have small sample sizes, high-dimensional diverse covariates, and high censorship rates. We compare the predictions from our model to three alternative models. Our model outperforms in simulation and is competitive on real datasets. Furthermore, the low-dimensional visualization for diffuse lower-grade glioma displays known subpopulations.