Epilepsy is a neurological disorder that affects approximately fifty million people according to the World Health Organization. While electroencephalography (EEG) plays important roles in monitoring the brain activity of patients with epilepsy and diagnosing epilepsy, an expert is needed to analyze all EEG recordings to detect epileptic activity. This method is obviously time-consuming and tedious, and a timely and accurate diagnosis of epilepsy is essential to initiate antiepileptic drug therapy and subsequently reduce the risk of future seizures and seizure-related complications. In this study, a convolutional neural network (CNN) based on raw EEG signals instead of manual feature extraction was used to distinguish ictal, preictal, and interictal segments for epileptic seizure detection. We compared the performances of time and frequency domain signals in the detection of epileptic signals based on the intracranial Freiburg and scalp CHB-MIT databases to explore the potential of these parameters. Three types of experiments involving two binary classification problems (interictal vs. preictal and interictal vs. ictal) and one three-class problem (interictal vs. preictal vs. ictal) were conducted to explore the feasibility of this method. Using frequency domain signals in the Freiburg database, average accuracies of 96.7, 95.4, and 92.3% were obtained for the three experiments, while the average accuracies for detection in the CHB-MIT database were 95.6, 97.5, and 93% in the three experiments. Using time domain signals in the Freiburg database, the average accuracies were 91.1, 83.8, and 85.1% in the three experiments, while the signal detection accuracies in the CHB-MIT database were only 59.5, 62.3, and 47.9% in the three experiments. Based on these results, the three cases are effectively detected using frequency domain signals. However, the effective identification of the three cases using time domain signals as input samples is achieved for only some patients. Overall, the classification accuracies of frequency domain signals are significantly increased compared to time domain signals. In addition, frequency domain signals have greater potential than time domain signals for CNN applications.

Osteoarthritis (OA) is a degenerative joint disease and one of the leading causes of disability in the United States and all over the world. As a disease of the whole joint, OA exhibits a complicated etiology with risk factors including, but not limited to, ageing, altered joint loading, and injury. Subchondral bone is hypothesized to be involved in OA development. However, direct evidence supporting this is lacking. We previously detected measurable transport of solute across the mineralized calcified cartilage in normal joints, suggesting a potential cross-talk between subchondral bone and cartilage. Whether this cross-talk exists in OA has not been established yet. Using two models that induced OA by either ageing or surgery (destabilization of medial meniscus, DMM), we tested the hypothesis that increased cross-talk occurs in OA. We quantified the diffusivity of sodium fluorescein (376Da), a marker of small-sized signaling molecules, within calcified joint matrix using our newly developed fluorescence loss induced by photobleaching (FLIP) method. Tracer diffusivity was found to be 0.30±0.17 and 0.33±0.20 μm2/s within the calcified cartilage and 0.12±0.04 and 0.07±0.03 μm2/s across the osteochondral interface in the aged (20–24-month-old, n=4) and DMM OA joints (5-month-old, n=5), respectively, comparable to the values for the contralateral non-operated joints in the DMM mice (0.48±0.13 and 0.12±0.06 μm2/s). Although we did not detect significant changes in tissue matrix permeability in OA joints, we found i) an increased number of vessels invading the calcified cartilage (and sometimes approaching the tidemark) in the aged (+100%) and DMM (+50%) joints relative to the normal age controls; and ii) a 60% thinning of the subchondral bone and calcified cartilage layers in the aged joints (with no significant changes detected in the DMM joints). These results suggested that the capacity for cross-talk between subchondral bone and articular cartilage could be elevated in OA. Further studies are needed to identify the direction of the cross-talk, the signaling molecules involved, and to test whether subchondral bone changes initiate OA development and could serve as a pharmaceutical target for OA treatment.

The pericellular matrix (PCM), a thin “coating” surrounding nearly all mammalian cells, plays a critical role in many cell-surface phenomena. In osteocytes, the PCM is believed to control both “outside-in” (mechanosensing) and “inside-out” (signaling molecule transport) processes. However, the osteocytic PCM is challenging to study in situ because it is thin (~100nm) and enclosed in mineralized matrix. To this end, we recently developed a novel tracer velocimetry approach that combined fluorescence recovery after photobleaching (FRAP) imaging with hydrodynamic modeling to quantify the osteocytic PCM in young murine bone (Wang et al., J Bone Miner Res. 2013; 28:1075–86). In this study, we applied the technique to older mice expressing or deficient for perlecan/HSPG2, a large heparan-sulfate proteoglycan normally secreted in osteocytic PCM. The objectives were to i) characterize transport within an altered PCM; ii) to test the sensitivity of our approach in detecting the PCM alterations; and iii) to dissect the roles of the PCM in osteocyte mechanosensing. We found that i) solute transport increases in the perlecan-deficient (hypomorphic: Hypo) mice compared with control mice; ii) PCM fiber density decreases with aging and perlecan deficiency; iii) the osteocytes in the Hypo bones are predicted to experience higher shear stress (+34%), but decreased fluid drag force (−35%) under 3N peak tibial loading, and iv) when subjected to tibial loading in a preliminary in vivo experiment, the Hypo mice did not respond to the anabolic stimuli as CTL mice. These findings support the hypothesis that the PCM fibers act as osteocyte’s sensing antennae, regulating load-induced cellular stimulations and thus bone’s sensitivity and in vivo bone adaptation. If this hypothesis is further confirmed, osteocytic PCM could be new targets to develop osteoporosis treatments by modulating bone’s intrinsic sensitivity to mechanical loading and be used to design patient-specific exercise regimens to promote bone formation.