Rheumatoid arthritis (RA) is an autoimmune disease characterized by synovial inflammation and articular damage. Proinflammatory cytokines, antibodies, and matrix-degrading enzymes orchestrate the pathogenic events in autoimmune arthritis. Accordingly, these mediators of inflammation are the targets of several anti-arthritic drugs. However, the prolonged use of such drugs is associated with severe adverse reactions. This limitation has necessitated the search for less toxic natural plant products that possess anti-arthritic activity. Furthermore, it is imperative that the mechanism of action of such products be explored before they can be recommended for further preclinical testing. Using the rat adjuvant-induced arthritis model of human RA, we demonstrate that celastrol derived from Celastrus has potent anti-arthritic activity. This suppression of arthritis is mediated via modulation of the key proinflammatory cytokines (IL-17, IL-6, and IFN-␥) in response to the diseaserelated antigens, of the IL-6/IL-17-related transcription factor STAT3, of antibodies directed against cyclic citrullinated peptides and Bhsp65, and of the activity of matrix metalloproteinase-9 and phospho-ERK. Most of the clinical and mechanistic attributes of celastrol are similar to those of Celastrus extract. Several studies have addressed the antitumor activity of celastrol. Our study highlights the anti-arthritic activity of Celastrusderived celastrol and the underlying mechanisms. These results provide a strong rationale for further testing and validation of the use of celastrol and the natural plant extract from Celastrus as an adjunct (with conventional drugs) or alternative modality for the treatment of RA. Rheumatoid arthritis (RA)2 is a chronic debilitating autoimmune disease affecting millions of people all over the world (1-3). Both cell-mediated and humoral immune reactions participate in the pathogenesis of RA (1, 4). Several proinflammatory cytokines secreted by immune cells, including TNF-␣, IL-1␤, IL-15, IL-18, and IFN-␥, have been shown to play a role in the initiation and progression of RA (4, 5). Over the past decade, a new subset of T helper cells producing IL-17 (Th17) has become the focus of RA pathogenesis (6, 7). Induction of a Th17 response involves IL-6 and TGF-␤, as well as the transcription factors STAT3 and retinoid related orphan receptor-␥t (ROR-␥t) (8, 9). In regard to the humoral response, serum levels of anti-cyclic citrullinated protein/peptide (aCCP) antibodies correlate well with the disease severity in RA patients and serve as the specific autoantibody marker for the diagnosis of RA (10, 11). In view of the above, modulation of the proinflammatory cytokines and aCCP antibody levels is a desired goal for the management of RA.A variety of drugs are available for the treatment of RA. These include corticosteroids, nonsteroidal anti-inflammatory drugs, disease-modifying antirheumatic drugs, and biologics (1, 12). However, besides their high cost, the use of these drugs is associated with severe adverse reacti...

Background: Arthritis is characterized by bone and cartilage destruction. Many conventional drugs suppress inflammation but not bone damage. Hence, new therapeutic agents are sought. Results: Celastrus and its bioactive component celastrol inhibit osteoclastogenesis by controlling its mediators and their inducers/effectors. Conclusion: Celastrus/celastrol controls inflammation-driven bone resorption by regulating the osteoimmune cross-talk. Significance: Celastrus and celastrol are promising adjuncts to conventional drugs for arthritis treatment.

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Generally, dimensionality reduction methods, such as Principle Component Analysis (PCA) and Negative Matrix Factorization (NMF), are always applied as the preprocessing part in hyperspectral image classification so as to classify the constituent elements of every pixel in the scene efficiently. The results, however, would suffer the loss of detailed information inevitably. In this paper, deep learning frameworks, restricted Boltzmann machine (RBM) model and its deep structure deep belief networks (DBN), are introduced in hyperspectral image processing as the feature extraction and classification approach. The experiments are conducted on an airborne hyperspectral image. Further in the experiments, spatial-spectral classification is also practiced. Meanwhile, SVM with and without some classical feature extraction methods adopting before classification are employed as comparison. The results show the superior performance of the proposed approach.

This paper analyzes first-price sealed-bid auctions of standing timber organized by the French forest service, Office National des For�ts (ONF). A feature of these auctions is that they are held with random reserve prices. We consider an auction model with a random reserve price within the independent-private-value paradigm. After establishing the identification of the model, we estimate the underlying bidders' private-value distribution by using a simple two-step nonparametric procedure. This procedure allows the computation of the winners' informational rents as well as the optimal reserve price. We then simulate a first-price sealed-bid auction with the optimal announced reserve price. Empirical results show that the optimal reserve price allows the ONF to extract more of bidders' willingnesses to pay. Moreover, our results show that, though sales do not vary much, profits for the ONF would significantly increase and less timber would be sold. © 2003 President and Fellows of Harvard College and the Massachusetts Institute of Technology.