In vitro determination of hemoglobin A1c for diabetes diagnosis and management: technology update

It is fascinating to consider the analytical improvements that have occurred since glycated hemoglobin was first used in routine clinical laboratories for diabetes monitoring around 1977; at that time methods displayed poor precision, there were no calibrators or material with assayed values for quality control purposes. This review outlines the major improvements in hemoglobin A1c (HbA1c) measurement that have occurred since its introduction, and reflects on the increased importance of this hemoglobin fraction in the monitoring of glycemic control. The use of HbA1c as a diagnostic tool is discussed in addition to its use in monitoring the patient with diabetes; the biochemistry of HbA1c formation is described, and how these changes to the hemoglobin molecule have been used to develop methods to measure this fraction. Standardization of HbA1c is described in detail; the development of the IFCC Reference Measurement Procedure for HbA1c has enabled global standardization to be achieved which has allowed global targets to be set for glycemic control and diagnosis. The importance of factors that may interfere in the measurement of HbA1c are highlighted

Time for change: a new training programme for morpho-molecular pathologists?

The evolution of cellular pathology as a specialty has always been driven by technological developments and the clinical relevance of incorporating novel investigations into diagnostic practice. In recent years, the molecular characterisation of cancer has become of crucial relevance in patient treatment both for predictive testing and subclassification of certain tumours. Much of this has become possible due to the availability of next-generation sequencing technologies and the whole-genome sequencing of tumours is now being rolled out into clinical practice in England via the 100 000 Genome Project. The effective integration of cellular pathology reporting and genomic characterisation is crucial to ensure the morphological and genomic data are interpreted in the relevant context, though despite this, in many UK centres molecular testing is entirely detached from cellular pathology departments. The CM-Path initiative recognises there is a genomics knowledge and skills gap within cellular pathology that needs to be bridged through an upskilling of the current workforce and a redesign of pathology training. Bridging this gap will allow the development of an integrated 'morphomolecular pathology' specialty, which can maintain the relevance of cellular pathology at the centre of cancer patient management and allow the pathology community to continue to be a major influence in cancer discovery as well as playing a driving role in the delivery of precision medicine approaches. Here, several alternative models of pathology training, designed to address this challenge, are presented and appraised

The future of laboratory medicine - A 2014 perspective.

Predicting the future is a difficult task. Not surprisingly, there are many examples and assumptions that have proved to be wrong. This review surveys the many predictions, beginning in 1887, about the future of laboratory medicine and its sub-specialties such as clinical chemistry and molecular pathology. It provides a commentary on the accuracy of the predictions and offers opinions on emerging technologies, economic factors and social developments that may play a role in shaping the future of laboratory medicine

Guidelines for health surveillance in the NASA (National Aeronautics and Space Administration) workplace

The adequacy of biomedical data sheets used by the NASA medical staff for NASA employees and contractors was assessed. Procedures for developing medical histories, conducting medical examinations, and collecting toxicity data were reviewed. Recommendations for employee health maintenance and early detection of work-related abnormalities are given

Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.

Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies-amyloid plaques and cerebral amyloid angiopathy-in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate > 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist's ability suggests a route to neuropathologic deep phenotyping