This research studies the effect of CBD on the excitability of mice nociceptive trigeminal ganglion neurons. The electrophysiological parameters of the mouse trigeminal ganglion cells are measured using a Whole Cell Current Clamp Model on a primary cell culture treated with 0.5 uM CBD for 24 hours compared to a control group. After obtaining the results statistical analysis is done using a Mann Whitney-U test.
ยาที่ใช้ในกาารักษาอาการปวดในปัจจุบัน เช่น NSAIDs และยาโอปิออยด์อาจก่อให้เกิดผลข้างเคียงที่ไม่พึงประสงค์ ทำให้ไม่เหมาะสมสำหรับประชากรบางกลุ่มโดยเฉพาะผู้สูงอายุ ดังนั้นการศึกษานี้จึงทําเพื่อประเมินฤทธิของ CBD ต่อการลดความไวในการรับรู้ความเจ็บปวดเพื่อดูเป็นยาทางเลือกในการรักษาอาการปวดในอนาคต
วิทยาลัยเทคโนโลยีและนวัตกรรมวัสดุ
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คณะวิศวกรรมศาสตร์
One of the most important aspects of responding to a medical case is the response time. In general, most fatalities are due to the patient not being able to reach the hands of the doctor in time. This also includes the arrival of medical equipment to the scene. The human brain will start to degrade in function after 3 minutes of oxygen starvation which conventional road transportation method first responders presently use is usually unable to reach the site in this golden 3 minutes, resulting in fatalities during transport or before the arrival of first responders at the scene. Therefore, medical equipment transport by fully autonomous aircraft is explored. This is done through drone deliveries which is much quicker than road methods as the equipment could be flown straight to the site as it is not affected by traffic, road conditions, and navigation. In this project, we will explore an aerial delivery system for medical equipment such as Automatic External Defibrillators (AEDs), First aid equipment, and other small requested medical devices. This will be done through a DJI drone platform and their SDK application. The main goal for this project is to decrease the response time by using an autonomous aerial drone to deliver medical equipment.
คณะวิศวกรรมศาสตร์
The Thai Sign Language Generation System aims to create a comprehensive 3D modeling and animation platform that translates Thai sentences into dynamic and accurate representations of Thai Sign Language (TSL) gestures. This project enhances communication for the Thai deaf community by leveraging a landmark-based approach using a Vector Quantized Variational Autoencoder (VQVAE) and a Large Language Model (LLM) for sign language generation. The system first trains a VQVAE encoder using landmark data extracted from sign videos, allowing it to learn compact latent representations of TSL gestures. These encoded representations are then used to generate additional landmark-based sign sequences, effectively expanding the training dataset using the BigSign ThaiPBS dataset. Once the dataset is augmented, an LLM is trained to output accurate landmark sequences from Thai text inputs, which are then used to animate a 3D model in Blender, ensuring fluid and natural TSL gestures. The project is implemented using Python, incorporating MediaPipe for landmark extraction, OpenCV for real-time image processing, and Blender’s Python API for 3D animation. By integrating AI, VQVAE-based encoding, and LLM-driven landmark generation, this system aspires to bridge the communication gap between written Thai text and expressive TSL gestures, providing the Thai deaf community with an interactive, real-time sign language animation platform.