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- 저자 : ( Vaidehi. V ) (검색결과 2 건)
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A novel excisional wound pain model for evaluation of analgesics in rats
( Sergio Parra ),( Vaidehi J. Thanawala ),( Ajay Rege ),( Heather Giles ) 대한통증학회 2021 The Korean Journal of Pain Vol.34 No.2
Background: Management of pain from open wounds is a growing unmet healthcare need. However, the models available to study pain from wounds or to develop analgesics for the patients suffering from them have primarily relied on incisional models. Here, we present the first characterized and validated model of open wound pain. Methods: Unilateral full-skin excisional punch biopsy wounds on rat hind paws were evaluated for evoked pain using withdrawal responses to mechanical and thermal stimulation, and spontaneous pain was measured using hind paw weight distribution and guarding behavior. Evaluations were done before wounding (baseline) and 2-96 hours post-wounding. The model was validated by testing the effects of buprenorphine and carprofen. Results: Pain responses to all tests increased within 2 hours post-wounding and were sustained for at least 4 days. Buprenorphine caused a reversal of all four pain responses at 1 and 4 hours post-treatment compared to 0.9% saline (P < 0.001). Carprofen decreased the pain response to thermal stimulation at 1 (P ≤ 0.049) and 4 hours (P < 0.011) post-treatment compared to 0.9% saline, but not to mechanical stimulation. Conclusions: This is the first well-characterized and validated model of pain from open wounds and will allow study of the pathophysiology of pain in open wounds and the development of wound-specific analgesics.
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( Ashok Kumar P. M ),( Vaidehi. V ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.1
Detection of anomalous events from video streams is a challenging problem in many video surveillance applications. One such application that has received significant attention from the computer vision community is traffic video surveillance. In this paper, a Lossy Count based Sequential Temporal Pattern mining approach (LC-STP) is proposed for detecting spatio-temporal abnormal events (such as a traffic violation at junction) from sequences of video streams. The proposed approach relies mainly on spatial abstractions of each object, mining frequent temporal patterns in a sequence of video frames to form a regular temporal pattern. In order to detect each object in every frame, the input video is first pre-processed by applying Gaussian Mixture Models. After the detection of foreground objects, the tracking is carried out using block motion estimation by the three-step search method. The primitive events of the object are represented by assigning spatial and temporal symbols corresponding to their location and time information. These primitive events are analyzed to form a temporal pattern in a sequence of video frames, representing temporal relation between various object`s primitive events. This is repeated for each window of sequences, and the support for temporal sequence is obtained based on LC-STP to discover regular patterns of normal events. Events deviating from these patterns are identified as anomalies. Unlike the traditional frequent item set mining methods, the proposed method generates maximal frequent patterns without candidate generation. Furthermore, experimental results show that the proposed method performs well and can detect video anomalies in real traffic video data.
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