Introduction:Temperomandibular joint (TMJ) is subjected to many disorders commonly called Temperomandibular disorders (TMDs); such as TMJ hypermobility, ankylosis, internal derangement, degenerative joint disease. Internal derangement is characterized by abnormal relationship of articular disc to the condyle and disc to fossa. In past many non-invasive conservative treatment modalities were tried out for its treatment which are joint unloading, use of anti-inflammatory agents, physiotherapy etc. Now a days corticosteroids and platelet rich plasma (PRP) has been proposed as an alternative therapeutic agent. We aimed to assess whether intra articular injection of PRP in TMJ minimises the symptoms of internal derangements as compared to injection of hydrocortisone with local anaesthetic.Materials and Methods:Twenty patients for a total of 32 joints with reducible anterior disc location were divided in two groups. One group received PRP injection and the other received hydrocortisone with local anaesthetic for arthroscopy in their affected joints. Both patients and operator were blinded to the contents of injections. The patients were assessed for pain, maximum inter-incisal mouth opening and TMJ sound.Results:In the group of PRP injection, pain was markedly reduced than the group of hydrocortisone with local anesthetic; mouth opening was increased similarly in both groups and TMJ sound was experienced lesser in patients who received PRP.Conclusion:Injections of PRP were more effective in reducing the symptoms, as compared to hydrocortisone with local anaesthetics.
It has been observed in this study that the action of sodium bicarbonate in local anesthetics increases the pH levels of these solutions, thus possibly making them more effective in an acidic environment.
Image caption generation is a stimulating multimodal task. Substantial advancements have been made in thefield of deep learning notably in computer vision and natural language processing. Yet, human-generated captions are still considered better, which makes it a challenging application for interactive machine learning. In this paper, we aim to compare different transfer learning techniques and develop a novel architecture to improve image captioning accuracy. We compute image feature vectors using different state-of-the-art transferlearning models which are fed into an Encoder-Decoder network based on Stacked LSTMs with soft attention,along with embedded text to generate high accuracy captions. We have compared these models on severalbenchmark datasets based on different evaluation metrics like BLEU and METEOR.
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