Sertoli-Leydig cell tumor (arrhenoblastoma) in adolescent age group

Arrhenoblastoma, also known as Sertoli-Leydig cell tumors or androblastomas, are very rare neoplasm of the ovaries, resulting in the overproduction of the male hormone testosterone. This is a rare tumour which accounts for less than 0.5% of all ovarian tumours. These tumours are found in women of all age groups, but are most common in young women. Presence of an ovarian tumour plus hormonal disturbances suggests a Sertoli-Leydig cell tumour. Patients present with a recent history of progressive masculinisation. Masculinisation is preceded by anovulation, oligomenorrhoea, amenorrhoea and defeminisation. Arrhenoblastomas are generally unilateral benign tumour; do not normally spread beyond the ovary, occurring in reproductive age. This work summarizes the morphological and immunohistochemical characteristics of this tumour in a 15-year old girl with clinical signs of virilisation. A 14 year old female admitted with abdominal distension, change in voice, male pattern balding and clitoromegaly in the dept. of Ob/Gy A.V.B.R.H. (Acharya Vinoba Bhave Rural Hospital) Sawangi, Wardha. Investigations included Sonography C.T scan, ascetic tap, Serum testosterone was done. She was managed by exploratory Laparotomy and follow up was advised. On follow up her serum testosterone levels and sonography was done. Here we are representing the case

Context-NER : Contextual Phrase Generation at Scale

NLP research has been focused on NER extraction and how to efficiently extract them from a sentence. However, generating relevant context of entities from a sentence has remained under-explored. In this work we introduce the task Context-NER in which relevant context of an entity has to be generated. The extracted context may not be found exactly as a substring in the sentence. We also introduce the EDGAR10-Q dataset for the same, which is a corpus of 1,500 publicly traded companies. It is a manually created complex corpus and one of the largest in terms of number of sentences and entities (1 M and 2.8 M). We introduce a baseline approach that leverages phrase generation algorithms and uses the pre-trained BERT model to get 33% ROUGE-L score. We also do a one shot evaluation with GPT-3 and get 39% score, signifying the hardness and future scope of this task. We hope that addition of this dataset and our study will pave the way for further research in this domain.Comment: 12 pages, 2 Figures, 1 Algorithm, 8 Tables. Accepted in NeurIPS 2022 - Efficient Natural Language and Speech Processing (ENLSP) Worksho