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6. Liver Histopathology in Pediatric Patients with Choledochal Cyst

Author

Kumar Mayank, Subhasis Roy Choudhury, Smita Singh, and Yogesh Kumar Sarin

Subjects
age, choledochal cyst, grading, liver histology, Pediatrics, RJ1-570, Surgery, RD1-811
Abstract

Aim: The aim is to study the various histopathological changes in the liver in pediatric patients with choledochal cyst (CC) and correlate with the presentation and type of cyst. Methods: In a prospective observational study including all pediatric patients who underwent CC excision, histopathological changes of the liver in the form of cholestasis (CHS), portal inflammation (PI), bile duct proliferation (BDP), and fibrosis were studied and graded using a scoring system. They were analyzed in relation to age, sex, symptoms, and type of the cyst. Results: All 30 patients of CC showed various degrees of histopathological changes in the liver in the form of CHS, PI, BDP, and liver fibrosis. Patients 1 years had 17/17 (100%) fusiform variety of CC (P < 0.001). Patients 1 years presented with pain abdomen (P < 0.002). Higher grades of liver fibrosis and BDP were seen in the cystic variety compared to the fusiform variety (P < 0.001). However, no significant association was found with CHS and PI (P > 1.23). Conclusions: Histopathological changes in the liver of varying grades are seen in all patients of CC. Patients of CC

Published
2024
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27. Spatio-temporal variation of C-PM2.5 (composition based PM2.5) sources using PMF*PMF (double-PMF) and single-combined PMF technique on real-time non-refractory, BC and elemental measurements during post-monsoon and winter at two sites in Delhi, India

Author

Shukla, Ashutosh Kumar, Tripathi, Sachchida Nand, Canonaco, Francesco, Lalchandani, Vipul, Sahu, Ravi, Srivastava, Deepchandra, Dave, Jay, Thamban, Navaneeth Meena, Gaddamidi, Sreenivas, Sahu, Lokesh, Kumar, Mayank, Singh, Vikram, and Rastogi, Neeraj

Published
2023
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33. COVID-19 infection, and reinfection, and vaccine effectiveness against symptomatic infection among health care workers in the setting of omicron variant transmission in New Delhi, India

Author

Malhotra, Sumit, Mani, Kalaivani, Lodha, Rakesh, Bakhshi, Sameer, Mathur, Vijay Prakash, Gupta, Pooja, Kedia, Saurabh, Sankar, Mari Jeeva, Kumar, Parmeshwar, Kumar, Arvind, H, Vikas, Ahuja, Vineet, Sinha, Subrata, Guleria, Randeep, Dua, Aman, Ahmad, Shafi, Upadhyay, Ashish Datt, Sati, Hem Chandra, Mani, Kiruba, Lokade, Amol Kumar, Devi, K. Pavithra, Johnson, Riya Marie, Gowthaman, Keerthana, Kumari, Mamta, Singh, Ritika, Kalra, Devanshi, Swetambri, Vasudha, Sharma, Shubhangi, Singh, Amit, Sharma, Vikas, Kanswal, Sunita, Sharma, Rahul, Giri, Tanika, Rajput, Simple, Mehra, Geeta, Sharma, Amit, Madan, Divya, Singh, Mukesh, Gupta, Anvita, Sharma, Shilpi, Sachdeva, Surbhi, Kumar, Mayank, Sachin, Singh, Ajay Kumar, Gohar, Naveen, Kumar, Ramu, Kanojia, Nitin, Singhania, Jyoti, Dubey, Ritu, Shukla, Sakshi, G, Abishek, Sarkar, Swarnabha, Gupta, Ishan, Rai, Sabin, Tummala, Snikitha, Reddy, Thrisha, Vadodaria, Vishwa, Sharma, Ajay, Gupta, Amit, Vats, Manish, Deori, Trideep Jyoti, Jaiswal, Abhishek, and Pandit, Sabitri

Published
2022
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48. Facial emotion recognition music player: Enhancing music experience through computer vision and machine learning.

Author

Garg, Raghav, Lathwal, Nitay, Kumar, Mayank, Chugh, Gunjan, and Lamba, Tripti

Subjects
EMOTION recognition, MUSIC & emotions, MACHINE learning, FACIAL expression & emotions (Psychology), EMOTIONAL state, COMPUTER vision
Abstract

Face Emotion Detection Music Player is an innovative idea that combines computer vision and machine learning techniques to create a unique and interactive music player. This Research Paper studies the uses of Convolutional Neural Networks (CNN) to detect and analyze facial emotions in real-time and dynamically create playlists based on detected emotions. The music player is designed to record facial expressions using a webcam or other suitable camera. To reliably identify facial expressions including happiness, sorrow, rage, surprise, and more, a CNN model is trained on a sizable collection of face images. The trained model demonstrated an astounding 74.46% accuracy in identifying emotions when combined with music player software running on a PC or other suitable device. The CNN model analyses the photos to determine the user's emotional state as they interact with the music player in real-time, as captured by the camera. Based on the detected emotions, the player automatically selects and plays songs from a predefined music collection that matches the user's emotional state. For example, if the user looks happy, the player can play happy and energetic songs, while if the user looks angry, it can play more calming and soothing music. Face Detection Music Player offers a personalized and dynamic music listening experience as the music selection is constantly updated according to the user's emotions. This research paper illustrates how computer vision and machine learning techniques may be used to produce interactive programs that can instantly adjust to the moods and preferences of their users. It has uses in user experience research, entertainment, and mental health, among others. [ABSTRACT FROM AUTHOR]

Published
2024
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