Background The current paradigm of Sinoatrial Node (SAN) impulse generation: (i) is that full-scale action potentials (APs) of a common frequency are initiated at one site and are conducted within the SAN along smooth isochrones; and (ii) does not feature fine details of Ca 2+ signalling present in isolated SAN cells, in which small subcellular, subthreshold local Ca 2+ releases (LCRs) self-organize to generate cell-wide APs.Objectives To study subcellular Ca 2+ signals within and among cells comprising the SAN tissue. MethodsWe combined immunolabeling with a novel technique to detect the occurrence of LCRs and AP-induced Ca 2+ transients (APCTs) in individual pixels (chonopix) across the entire mouse SAN images. ResultsAt high magnification, Ca 2+ signals appeared markedly heterogeneous in space, amplitude, frequency, and phase among cells comprising an HCN4 + /CX43cell meshwork.The signalling exhibited several distinguishable patterns of LCR/APCT interactions within and among cells. Apparently conducting rhythmic APCTs of the meshwork were transferred to a truly conducting HCN4 -/CX43 + network of straited cells via narrow functional interfaces where different cell types intertwine, i.e. the SAN anatomical/functional unit. At low magnification, the earliest APCT of each cycle occurred within a small area of the HCN4 meshwork and subsequent APCT appearance throughout SAN pixels was discontinuous. ConclusionsWe have discovered a novel, microscopic Ca 2+ signalling paradigm of SAN operation that has escaped detection using low-resolution, macroscopic tissue isochrones employed in prior studies: APs emerge from heterogeneous subcellular subthreshold Ca 2+ 105 and is also made available for use under a CC0 license.

Age-associated changes in gene expression in skeletal muscle of healthy individuals reflect accumulation of damage and compensatory adaptations to preserve tissue integrity. To characterize these changes, RNA was extracted and sequenced from muscle biopsies collected from 53 healthy individuals (22–83 years old) of the GESTALT study of the National Institute on Aging–NIH. Expression levels of 57,205 protein-coding and non-coding RNAs were studied as a function of aging by linear and negative binomial regression models. From both models, 1134 RNAs changed significantly with age. The most differentially abundant mRNAs encoded proteins implicated in several age-related processes, including cellular senescence, insulin signaling, and myogenesis. Specific mRNA isoforms that changed significantly with age in skeletal muscle were enriched for proteins involved in oxidative phosphorylation and adipogenesis. Our study establishes a detailed framework of the global transcriptome and mRNA isoforms that govern muscle damage and homeostasis with age.

The purpose of this study is to evaluate the ability of a machine learning algorithm to classify in vivo magnetic resonance images (MRI) of human articular cartilage for development of osteoarthritis (OA). Sixty-eight subjects were selected from the Osteoarthritis Initiative (OAI) control and incidence cohorts. Progression to clinical OA was defined by the development of symptoms as quantified by the Western Ontario and McMaster Universities Arthritis (WOMAC) questionnaire three years after baseline evaluation. Multi-slice T2–weighted knee images, obtained through the OAI, of these subjects were registered using a nonlinear image registration algorithm. T2 maps of cartilage from the central weight bearing slices of the medial femoral condyle were derived from the registered images using the multiple available echo times and were classified for “progression to symptomatic OA” using the machine learning tool, weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHRM). WND-CHRM classified the isolated T2 maps for the progression to symptomatic OA with 75% accuracy. Statement of clinical significance Machine learning algorithms applied to T2 maps have the potential to provide important prognostic information for the development of OA.

No abstract

Objective The purpose of this study is to evaluate the ability of machine learning to discriminate between magnetic resonance images (MRI) of normal and pathological human articular cartilage obtained under standard clinical conditions. Method An approach to MRI classification of cartilage degradation is proposed using pattern recognition and multivariable regression in which image features from MRIs of histologically scored human articular cartilage plugs were computed using weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHRM). The WND-CHRM method was first applied to several clinically available MRI scan types to perform binary classification of normal and osteoarthritic osteochondral plugs based on the OARSI histological system. In addition, the image features computed from WND-CHRM were used to develop a multiple linear least-squares regression model for classification and prediction of an OARSI score for each cartilage plug. Results The binary classification of normal and osteoarthritic plugs yielded results of limited quality with accuracies between 36% and 70%. However, multiple linear least-squares regression successfully predicted OARSI scores and classified plugs with accuracies as high as 86%. The present results improve upon the previously-reported accuracy of classification using average MRI signal intensities and parameter values. Conclusion MRI features detected by WND-CHRM reflect cartilage degradation status as assessed by OARSI histologic grading. WND-CHRM is therefore of potential use in the clinical detection and grading of osteoarthritis.

No abstract

We present an initial molecular characterization of a morphological transition between two early aging states. In previous work, an age score reflecting physiological age was developed using a machine classifier trained on images of worm populations at fixed chronological ages throughout their lifespan. The distribution of age scores identified three stable post-developmental states and transitions. The first transition occurs at day 5 post-hatching, where a significant percentage of the population exists in both state I and state II. The temperature dependence of the timing of this transition (Q 10~1 .17) is too low to be explained by a stepwise process with an enzymatic or chemical rate-limiting step, potentially implicating a more complex mechanism. Individual animals at day 5 were sorted into state I and state II groups using the machine classifier and analyzed by microarray expression profiling. Despite being isogenic, grown for the same amount of time, and indistinguishable by eye, these two morphological states were confirmed to be molecularly distinct by hierarchical clustering and principal component analysis of the microarray results. These molecular differences suggest that pharynx morphology reflects the aging state of the whole organism. Our expression profiling yielded a gene set that showed significant overlap with those from three previous age-related studies and identified several genes not previously implicated in aging. A highly represented group of genes unique to this study is involved in targeted ubiquitin-mediated proteolysis, including Skp1-related (SKR), F-box-containing, and BTB motif adaptors.

Normal cognitive aging is associated with deficits in memory processes dependent on the hippocampus, along with large-scale changes in the hippocampal expression of many genes. Histone acetylation can broadly influence gene expression and has been recently linked to learning and memory. We hypothesized that cAMP response element binding (CREB)-binding protein (CBP), a key histone acetyltransferase, may contribute to memory decline in normal aging. Here, we quantified CBP protein levels in the hippocampus of young, aged unimpaired and aged impaired rats, classified on the basis of spatial memory capacity documented in the Morris water maze. First, CBP-immunofluorescence was quantified across the principal cell layers of the hippocampus using both low and high resolution laser scanning imaging approaches. Second, digital images of CBP immunostaining were analyzed by a multi-purpose classifier algorithm (WND-CHARM) with validated sensitivity across many types of input materials. Finally, CBP protein levels in the principal subfields of the hippocampus were quantified by quantitative western blotting. CBP levels were equivalent as a function of age and cognitive status in all analyses. The sensitivity of the techniques used was substantial, sufficient to reveal differences across the principal cell fields of the hippocampus, and to correctly classify images from young and aged animals independent of CBP-immunoreactivity. The results are discussed in the context of recent evidence suggesting that CBP decreases may be most relevant in conditions of aging that, unlike normal cognitive aging, involve significant neuron loss.