Colonic adenomatous polyp with florid presence of monoclonal lambda Russell bodies: Case report and etiopathogenic hypothesis / Pólipo adenomatoso colónico con presencia florida de cuerpos de Russell monoclonales lambda: comunicación de un caso e hipótesis etiopatogénica

Russell bodies (RBs) are round eosinophilic intracytoplasmic inclusions formed by condensed immunoglobulins in mature plasma cells, which are called Mott cells. These cells are rarely found in the gastric tract, with even less cases reported in the colorectal region. There are still many questions about this event, as it is still unknown the relationship between the agents reported of increasing the probability of appearance of these cells and the generation of RBs. In this case report we describe the fifth patient presenting an infiltration of Mott cells in a colorectal polyp, being the second case with a monoclonal origin without a neoplastic cause, and the first one monoclonal for lambda. A comparison with previously similar reported cases is also done, and a possible etiopathogenic hypothesis proposed. (AU)

Los cuerpos de Russell (RB) son inclusiones intracitoplasmáticas eosinofílicas redondas formadas por inmunoglobulinas condensadas en las células plasmáticas maduras, que se denominan células de Mott. Estas células rara vez se encuentran en el tracto gástrico, y son aún más infrecuentes en la región colorrectal. Actualmente hay muchas dudas sobre este evento, ya que se desconoce la relación entre los agentes causantes de aumentar la probabilidad de aparición tanto de estas células como de la de RB. En este caso describimos al quinto paciente con un pólipo colorrectal, localizado en el tracto colorrectal e infiltrado por células de Mott, siendo el segundo caso de origen monoclonal sin causa neoplásica y el primero monoclonal para lambda. También se hace una comparación con casos similares previamente reportados y se propone una hipótesis etiopatogénica. (AU)

Colonic adenomatous polyp with florid presence of monoclonal lambda Russell bodies: Case report and etiopathogenic hypothesis.

Russell bodies (RBs) are round eosinophilic intracytoplasmic inclusions formed by condensed immunoglobulins in mature plasma cells, which are called Mott cells. These cells are rarely found in the gastric tract, with even less cases reported in the colorectal region. There are still many questions about this event, as it is still unknown the relationship between the agents reported of increasing the probability of appearance of these cells and the generation of RBs. In this case report we describe the fifth patient presenting an infiltration of Mott cells in a colorectal polyp, being the second case with a monoclonal origin without a neoplastic cause, and the first one monoclonal for lambda. A comparison with previously similar reported cases is also done, and a possible etiopathogenic hypothesis proposed.

Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media.

Pathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of pathologists on social media for complementary opinions. Such access to pathologists worldwide has the capacity to improve diagnostic accuracy and generate broader consensus on next steps in patient care. From Twitter we curate 13,626 images from 6,351 tweets from 25 pathologists from 13 countries. We supplement the Twitter data with 113,161 images from 1,074,484 PubMed articles. We develop machine learning and deep learning models to (i) accurately identify histopathology stains, (ii) discriminate between tissues, and (iii) differentiate disease states. Area Under Receiver Operating Characteristic (AUROC) is 0.805-0.996 for these tasks. We repurpose the disease classifier to search for similar disease states given an image and clinical covariates. We report precision@k = 1 = 0.7618 ± 0.0018 (chance 0.397 ± 0.004, mean ±stdev ). The classifiers find that texture and tissue are important clinico-visual features of disease. Deep features trained only on natural images (e.g., cats and dogs) substantially improved search performance, while pathology-specific deep features and cell nuclei features further improved search to a lesser extent. We implement a social media bot (@pathobot on Twitter) to use the trained classifiers to aid pathologists in obtaining real-time feedback on challenging cases. If a social media post containing pathology text and images mentions the bot, the bot generates quantitative predictions of disease state (normal/artifact/infection/injury/nontumor, preneoplastic/benign/low-grade-malignant-potential, or malignant) and lists similar cases across social media and PubMed. Our project has become a globally distributed expert system that facilitates pathological diagnosis and brings expertise to underserved regions or hospitals with less expertise in a particular disease. This is the first pan-tissue pan-disease (i.e., from infection to malignancy) method for prediction and search on social media, and the first pathology study prospectively tested in public on social media. We will share data through http://pathobotology.org . We expect our project to cultivate a more connected world of physicians and improve patient care worldwide.