Infrapatellar fat pad-derived mesenchymal stromal cell product for treatment of knee osteoarthritis: a first-in-human study with evaluation of the potency marker.

BACKGROUND AIMS: Infrapatellar fat pad-derived mesenchymal stromal cells (IFP-MSCs) have not yet been used in a human clinical trial. In this open-label phase 1 study, patients with knee osteoarthritis (OA) received a single intra-articular injection of autologous IFP-MSCs. Safety was assessed through physical examination of the knee joint, vital signs, laboratory tests and adverse events. Efficacy was evaluated with regard to pain and function using questionnaires, x-ray and magnetic resonance imaging (MRI). Indoleamine-2,3-dioxygenase (IDO) expression in IFP-MSCs primed with interferon gamma was used as an in vitro potency measurement in investigating the correlations of clinical outcomes. METHODS: Twelve patients with symptomatic knee OA were recruited. IFP adipose tissue was harvested from each patient's knee through surgical excision for IFP-MSC manufacturing. Cryopreserved IFP-MSCs (5 × 107 cells) were injected into the knee joint immediately after thawing. RESULTS: No significant adverse events were observed. Patients who received IFP-MSCs exhibited clinically significant pain and functional improvement at 48-week follow-up. The MRI Osteoarthritis Knee Score average was also significantly reduced from 100.2 before injection to 85.0 at 48 weeks after injection. The IDO expression of the primed IFP-MSCs of the 12 patients was correlated with clinical outcomes after injection. CONCLUSIONS: A single intra-articular injection of IFP-MSCs appears to be a safe therapy for treating knee OA and may improve disease symptoms. IDO measurement of primed IFP-MSCs has potential as a potency marker of MSC products for immunomodulatory therapy.

Associated Learning: Decomposing End-to-End Backpropagation Based on Autoencoders and Target Propagation.

Backpropagation (BP) is the cornerstone of today's deep learning algorithms, but it is inefficient partially because of backward locking, which means updating the weights of one layer locks the weight updates in the other layers. Consequently, it is challenging to apply parallel computing or a pipeline structure to update the weights in different layers simultaneously. In this letter, we introduce a novel learning structure, associated learning (AL), that modularizes the network into smaller components, each of which has a local objective. Because the objectives are mutually independent, AL can learn the parameters in different layers independently and simultaneously, so it is feasible to apply a pipeline structure to improve the training throughput. Specifically, this pipeline structure improves the complexity of the training time from O(nℓ), which is the time complexity when using BP and stochastic gradient descent (SGD) for training, to O(n+ℓ), where n is the number of training instances and ℓ is the number of hidden layers. Surprisingly, even though most of the parameters in AL do not directly interact with the target variable, training deep models by this method yields accuracies comparable to those from models trained using typical BP methods, in which all parameters are used to predict the target variable. Consequently, because of the scalability and the predictive power demonstrated in the experiments, AL deserves further study to determine the better hyperparameter settings, such as activation function selection, learning rate scheduling, and weight initialization, to accumulate experience, as we have done over the years with the typical BP method. In addition, perhaps our design can also inspire new network designs for deep learning. Our implementation is available at https://github.com/SamYWK/Associated_Learning.

Wireless Stimulus-on-Device Design for Novel P300 Hybrid Brain-Computer Interface Applications.

Improving the independent living ability of people who have suffered spinal cord injuries (SCIs) is essential for their quality of life. Brain-computer interfaces (BCIs) provide promising solutions for people with high-level SCIs. This paper proposes a novel and practical P300-based hybrid stimulus-on-device (SoD) BCI architecture for wireless networking applications. Instead of a stimulus-on-panel architecture (SoP), the proposed SoD architecture provides an intuitive control scheme. However, because P300 recognitions rely on the synchronization between stimuli and response potentials, the variation of latency between target stimuli and elicited P300 is a concern when applying a P300-based BCI to wireless applications. In addition, the subject-dependent variation of elicited P300 affects the performance of the BCI. Thus, an adaptive model that determines an appropriate interval for P300 feature extraction was proposed in this paper. Hence, this paper employed the artificial bee colony- (ABC-) based interval type-2 fuzzy logic system (IT2FLS) to deal with the variation of latency between target stimuli and elicited P300 so that the proposed P300-based SoD approach would be feasible. Furthermore, the target and nontarget stimuli were identified in terms of a support vector machine (SVM) classifier. Experimental results showed that, from five subjects, the performance of classification and information transfer rate were improved after calibrations (86.00% and 24.2 bits/ min before calibrations; 90.25% and 27.9 bits/ min after calibrations).

N-acetyl cysteine mitigates curcumin-mediated telomerase inhibition through rescuing of Sp1 reduction in A549 cells.

Curcumin is a natural compound that has been extensively observed due to its potential as an anticancer drug. Curcumin restrains cancer cell progression via telomerase activity suppression. However, the exact mechanism is still unknown. In this study, we demonstrate that the effects of curcumin on cell viability and telomerase activity can be blunted by reactive oxygen species (ROS) inhibitor N-acetyl cysteine (NAC). The ROS induced by curcumin in A549 cells was detected by flow cytometry. Using Western blot and RT-PCR, human telomerase reverse transcriptase (hTERT) decreased in the presence of curcumin. Sp1 is one of the important transcription factors in hTERT expression. Our data showed that curcumin decreases the expression of Sp1 through proteasome pathway. In addition, NAC blunted the Sp1 reduction and hTERT downregulation by curcumin. Further, reporter assay and DNA affinity precipitation assay confirmed the influence of curcumin on Sp1 in hTERT regulation. This is the first study to demonstrate that curcumin induces ROS production resulting in Sp1 binding activity inhibition and hTERT downregulation.