Cell biology, clinicopathological profile, and classification of gastro-enteropancreatic endocrine tumors

1998 ◽  
Vol 76 (6) ◽  
pp. 413-420 ◽  
Author(s):  
Guido Rindi ◽  
Carlo Capella ◽  
E. Solcia
Keyword(s):  
Cell Biology ◽  
Endocrine Tumors ◽  
Clinicopathological Profile

scholarly journals Morphological and Ultrastructural Characterization of Hemocytes in an Insect Model, the Hematophagous Dipetalogaster maxima (Hemiptera: Reduviidae)

Insects ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 640
Author(s):  
Natalia R. Moyetta ◽  
Fabián O. Ramos ◽  
Jimena Leyria ◽  
Lilián E. Canavoso ◽  
Leonardo L. Fruttero
Keyword(s):  
Cell Biology ◽  
Cell Types ◽  
Transmission Electron ◽  
Ultrastructural Characterization ◽  
Current Classification ◽  
Contrast Microscopy ◽  
First Time ◽  
Controversial Aspect

Hemocytes, the cells present in the hemolymph of insects and other invertebrates, perform several physiological functions, including innate immunity. The current classification of hemocyte types is based mostly on morphological features; however, divergences have emerged among specialists in triatomines, the insect vectors of Chagas’ disease (Hemiptera: Reduviidae). Here, we have combined technical approaches in order to characterize the hemocytes from fifth instar nymphs of the triatomine Dipetalogaster maxima. Moreover, in this work we describe, for the first time, the ultrastructural features of D. maxima hemocytes. Using phase contrast microscopy of fresh preparations, five hemocyte populations were identified and further characterized by immunofluorescence, flow cytometry and transmission electron microscopy. The plasmatocytes and the granulocytes were the most abundant cell types, although prohemocytes, adipohemocytes and oenocytes were also found. This work sheds light on a controversial aspect of triatomine cell biology and physiology setting the basis for future in-depth studies directed to address hemocyte classification using non-microscopy-based markers.

A Prognostic Clinicopathologic Classification of Pituitary Endocrine Tumors

2015 ◽  
Vol 44 (1) ◽  
pp. 11-18 ◽  
Author(s):  
Gérald Raverot ◽  
Alexandre Vasiljevic ◽  
Emmanuel Jouanneau ◽  
Jacqueline Trouillas
Keyword(s):  
Endocrine Tumors

Histopathology, hormone products, and clinicopathological profile of endocrine tumors of the upper small intestine: A study of 44 cases

1991 ◽  
Vol 2 (2) ◽  
pp. 92-110 ◽  
Author(s):  
Carlo Capella ◽  
Cristina Riva ◽  
Guido Rindi ◽  
Fausto Sessa ◽  
Luciana Usellini ◽  
...  
Keyword(s):  
Small Intestine ◽  
Endocrine Tumors ◽  
Clinicopathological Profile

scholarly journals Molecular classification of spontaneous endometrial adenocarcinomas in BDII rats

2009 ◽  
Vol 16 (1) ◽  
pp. 99-111 ◽  
Author(s):  
Emma Samuelson ◽  
Carola Hedberg ◽  
Staffan Nilsson ◽  
Afrouz Behboudi
Keyword(s):  
Cell Biology ◽  
Tumor Model ◽  
Molecular Classification ◽  
Female Rats ◽  
Type I ◽  
Over Expression ◽  
Molecular Alterations ◽  
Molecular Features ◽  
Endometrial Carcinomas

Female rats of the BDII/Han inbred strain are prone to spontaneously develop endometrial carcinomas (EC) that in cell biology and pathogenesis are very similar to those of human. Human EC are classified into two major groups: Type I displays endometroid histology, is hormone-dependent, and characterized by frequent microsatellite instability and PTEN, K-RAS, and CTNNB1 (β-Catenin) mutations; Type II shows non-endometrioid histology, is hormone-unrelated, displays recurrent TP53 mutation, CDKN2A (P16) inactivation, over-expression of ERBB2 (Her2/neu), and reduced CDH1 (Cadherin 1 or E-Cadherin) expression. However, many human EC have overlapping clinical, morphologic, immunohistochemical, and molecular features of types I and II. The EC developed in BDII rats can be related to type I tumors, since they are hormone-related and histologically from endometrioid type. Here, we combined gene sequencing (Pten, Ifr1, and Ctnnb1) and real-time gene expression analysis (Pten, Cdh1, P16, Erbb2, Ctnnb1, Tp53, and Irf1) to further characterize molecular alterations in this tumor model with respect to different subtypes of EC in humans. No mutation in Pten and Ctnnb1 was detected, whereas three tumors displayed sequence aberrations of the Irf1 gene. Significant down regulation of Pten, Cdh1, p16, Erbb2, and Ctnnb1 gene products was found in the tumors. In conclusion, our data suggest that molecular features of spontaneous EC in BDII rats can be related to higher-grade human type I tumors and thus, this model represents an excellent experimental tool for research on this malignancy in human.

scholarly journals BABEL enables cross-modality translation between multi-omic profiles at single-cell resolution

2020 ◽  
Author(s):  
Kevin E. Wu ◽  
Kathryn E. Yost ◽  
Howard Y. Chang ◽  
James Zou
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Data Exploration ◽  
Grand Challenge ◽  
Fine Grained ◽  
Powerful Approach ◽  
Technical Innovations ◽  
Cell Data ◽  
Human And Mouse

AbstractSimultaneous profiling of multi-omic modalities within a single cell is a grand challenge for single-cell biology. While there have been impressive technical innovations demonstrating feasibility – for example generating paired measurements of scRNA-seq and scATAC-seq – wide-spread application of joint profiling is challenging due to the experimental complexity, noise, and cost. Here we introduce BABEL, a deep learning method that translates between the transcriptome and chromatin profiles of a single cell. Leveraging a novel interoperable neural network model, BABEL can generate scRNA-seq directly from a cell’s scATAC-seq, and vice versa. This makes it possible to computationally synthesize paired multi-omic measurements when only one modality is experimentally available. Across several paired scRNA-seq and scATAC-seq datasets in human and mouse, we validate that BABEL accurately translates between these modalities for individual cells. BABEL also generalizes well to new biological contexts not seen during training. For example, starting from scATAC-seq of patient derived basal cell carcinoma (BCC), BABEL generated scRNA-seq that enabled fine-grained classification of complex cell states, despite having never seen BCC data. These predictions are comparable to analyses of the experimental BCC scRNA-seq data. We further show that BABEL can incorporate additional single-cell data modalities, such as CITE-seq, thus enabling translation across chromatin, RNA, and protein. BABEL offers a powerful approach for data exploration and hypothesis generation.

scholarly journals A Rare Case of Mixed Ductal Neuroendocrine Tumor of the Pancreas

2021 ◽  
Vol 8 (2) ◽  
pp. C20-24
Author(s):  
Vidya Viswanathan ◽  
Harsh Kumar ◽  
Charusheela Gore ◽  
Shrikant Kurhade ◽  
Rumaanah Khan
Keyword(s):  
Neuroendocrine Tumour ◽  
Rare Condition ◽  
Collision Tumor ◽  
Definitive Diagnosis ◽  
Anatomical Site ◽  
Endocrine Tumors ◽  
Microscopic Features ◽  
To Come ◽  
Adenocarcinoma Component

Collision tumors are tumors that have at least two types of tumors in the same anatomical site with no area of mixing within the transition zone. In 2010 WHO classification of neuroendocrine tumors consists of an adenocarcinoma component and a neuroendocrine carcinoma component in which each of the components accounts for 30% of the tumor. Such tumors are defined as mixed adenoneuroendocrine carcinomas. Occurrence of exocrine and endocrine tumors of the pancreas is extremely rare. The aim of our study was to describe a case in a 60 years old male who was diagnosed with this rare tumor. Gross, microscopic features and immunohistochemistry were used to diagnose this rare condition. Immunohistochemistry markers such as synaptophysin, chromogranin, EMA and Pan CK were used to come to a definitive diagnosis. Synaptophysin and chromogranin were found to be positive in the neuroendocrine component. EMA and Pan CK were found to be positive in the ductal component. Hence a diagnosis of mixed ductal neuroendocrine tumour (collision tumor) was made.

scholarly journals Deep Learning-Based In Vitro Detection Method for Cellular Impurities in Human Cell-Processed Therapeutic Products

2021 ◽  
Vol 11 (20) ◽  
pp. 9755
Author(s):  
Yasunari Matsuzaka ◽  
Shinji Kusakawa ◽  
Yoshihiro Uesawa ◽  
Yoji Sato ◽  
Mitsutoshi Satoh
Keyword(s):  
Deep Learning ◽  
Human Cell ◽  
Late Stage ◽  
Cell Biology ◽  
Prediction Models ◽  
Batch Size ◽  
Life Threatening ◽  
Induced Pluripotent

Automated detection of impurities is in demand for evaluating the quality and safety of human cell-processed therapeutic products in regenerative medicine. Deep learning (DL) is a powerful method for classifying and recognizing images in cell biology, diagnostic medicine, and other fields because it automatically extracts the features from complex cell morphologies. In the present study, we construct prediction models that recognize cancer-cell contamination in continuous long-term (four-day) cell cultures. After dividing the whole dataset into Early- and Late-stage cell images, we found that Late-stage images improved the DL performance. The performance was further improved by optimizing the DL hyperparameters (batch size and learning rate). These findings are first report for the implement of DL-based systems in disease cell-type classification of human cell-processed therapeutic products (hCTPs), that are expected to enable the rapid, automatic classification of induced pluripotent stem cells and other cell treatments for life-threatening or chronic diseases.

Sign in / Sign up

Export Citation Format

Share Document