Scientific research is mostly published in English, regardless of the researcher's nationality. However, this growing practice impairs or hinders the comprehension of professionals who depend on the results of these studies to provide adequate care for their patients. We suggest that machine translation (MT) can be used as a way of providing useful translation for biomedical articles, even though the translation itself may not be fluent. To tackle possible mistranslation that can harm a patient, we resort to crowd-sourced validation of translations. We developed a prototype of MT validation and edition, where users can vote for that translation as valid, or suggest modifications (i.e., post-editing the MT). A glossary match system is also included, aiming at terminology consistency.

Scientific research is mostly published in English, regardless of the researcher's nationality. However, this growing practice impairs or hinders the comprehension of professionals who depend on the results of these studies to provide adequate care for their patients. We suggest that machine translation (MT) can be used as a way of providing useful translation for biomedical articles, even though the translation itself may not be fluent. To tackle possible mistranslation that can harm a patient, we resort to crowd-sourced validation of translations. We developed a prototype of MT validation and edition, where users can vote for that translation as valid, or suggest modifications (i.e., post-editing the MT). A glossary match system is also included, aiming at terminology consistency.

Previous approaches to create a controlled vocabulary for Japanese have resorted to existing bilingual dictionary and transformation rules to allow such mappings. However, given the possible new terms introduced due to coronavirus disease 2019 (COVID-19) and the emphasis on respiratory and infection-related terms, coverage might not be guaranteed. We propose creating a Japanese bilingual controlled vocabulary based on MeSH terms assigned to COVID-19 related publications in this work. For such, we resorted to manual curation of several bilingual dictionaries and a computational approach based on machine translation of sentences containing such terms and the ranking of possible translations for the individual terms by mutual information. Our results show that we achieved nearly 99% occurrence coverage in LitCovid, while our computational approach presented average accuracy of 63.33% for all terms, and 84.51% for drugs and chemicals.

The coronavirus disease 2019 (COVID-19) pandemic has led to a flood of research papers and the information has been updated with considerable frequency. For society to derive benefits from this research, it is necessary to promote sharing up-to-date knowledge from these papers. However, because most research papers are written in English, it is difficult for people who are not familiar with English medical terms to obtain knowledge from them. To facilitate sharing knowledge from COVID-19 papers written in English for Japanese speakers, we tried to construct a dictionary with an open license by assigning Japanese terms to MeSH unique identifiers (UIDs) annotated to words in the texts of COVID-19 papers. Using this dictionary, 98.99% of all occurrences of MeSH terms in COVID-19 papers were covered. We also created a curated version of the dictionary and uploaded it to PubDictionary for wider use in the PubAnnotation system.

Automatically detecting mentions of pharmaceutical drugs and chemical substances is key for the subsequent extraction of relations of chemicals with other biomedical entities such as genes, proteins, diseases, adverse reactions or symptoms. The identification of drug mentions is also a prior step for complex event types such as drug dosage recognition, duration of medical treatments or drug repurposing. Formally, this task is known as named entity recognition (NER), meaning automatically identifying mentions of predefined entities of interest in running text. In the domain of medical texts, for chemical entity recognition (CER), techniques based on hand-crafted rules and graph-based models can provide adequate performance. In the recent years, the field of natural language processing has mainly pivoted to deep learning and state-of-the-art results for most tasks involving natural language are usually obtained with artificial neural networks. Competitive resources for drug name recognition in English medical texts are already available and heavily used, while for other languages such as Spanish these tools, although clearly needed were missing. In this work, we adapt an existing neural NER system, NeuroNER, to the particular domain of Spanish clinical case texts, and extend the neural network to be able to take into account additional features apart from the plain text. NeuroNER can be considered a competitive baseline system for Spanish drug and CER promoted by the Spanish national plan for the advancement of language technologies (Plan TL).
Drug Repositioning ; Learning ; Machine Learning ; Natural Language Processing ; Neural Networks (Computer) ; Neurons ; Running

OBJECTIVE: To examine the patterns of estrogen receptor (ER) and progesterone receptor (PR) expression in borderline ovarian tumors (BOTs) and ovarian carcinomas. We also assessed the disease-free survival (DFS) and overall survival (OS) in women with ovarian carcinoma, in relation to ER and/or PR expression. METHODS: We examined ER/PR expression in 38 BOTs and 172 ovarian carcinomas removed from patients treated at the State University of Campinas-UNICAMP (Brazil), from 1993 to 2008 and followed for up to 60 months using tissue microarray-based immunohistochemistry. RESULTS: Twenty-eight (73.7%) mucinous and 10 (26.3%) serous BOTs were included. Ovarian carcinomas consisted mainly of 79 (46.0%) serous, 44 (25.5%) mucinous, 17 (9.8%) endometrioid, 10 (5.8%) clear-cell types. There was no significant difference of the ER/PR expression between BOT and ovarian carcinoma (p=0.55 for ER alone, 0.90 for PR alone, and 0.12 for combined expression). The level of ER/PR expression in BOTs was significantly higher in serous than in mucinous tumors (p<0.01). In carcinomas, ER/PR was higher in serous tumors than in mucinous (p<0.01) and clear cell tumors (p=0.02), and higher in endometrioid tumors than in mucinous tumors (p<0.01). DFS was affected neither by the clinical characteristics nor by combined steroid receptor status. OS was found to be significantly worse (p<0.01) only in women with stages II-IV tumors and those with residual disease after surgery (p<0.01). CONCLUSION: Overall, serous and endometrioid tumors were predominantly ER/PR positive, whereas mucinous and clear-cell tumors were preponderantly ER/PR negative. DFS and OS were not affected by ER/PR expression.
Disease-Free Survival ; Estrogens ; Female ; Humans ; Immunohistochemistry ; Mucins ; Progesterone ; Receptors, Progesterone ; Receptors, Steroid

PURPOSE: Early diagnosis of gastric cancer is still the exception in Western countries. In the East, as in Japan and Korea, this disease is an endemic disorder. More conservative surgical procedures are frequently performed in early gastric cancer cases in these countries where sentinel lymph node navigation surgery is becoming a safe option for some patients. This study aims to evaluate preliminary outcomes of patients with early gastric cancer who underwent sentinel node navigation surgeries in Brazil, a country with non-endemic gastric cancer levels. MATERIALS AND METHODS: From September 2008 to March 2014, 14 out of 205 gastric cancer patients underwent sentinel lymph node navigation surgeries, which were performed using intraoperative, endoscopic, and peritumoral injection of patent blue dye. RESULTS: Antrectomies with Billroth I gastroduodenostomies were performed in seven patients with distal tumors. The other seven patients underwent wedge resections. Sentinel basin resections were performed in four patients, and lymphadenectomies were extended to stations 7, 8, and 9 in the other 10. Two patients received false-negative results from sentinel node biopsies, and one of those patients had micrometastasis. There was one postoperative death from liver failure in a cirrhotic patient. Another cirrhotic patient died after two years without recurrence of gastric cancer, also from liver failure. All other patients were followed-up for 13 to 79 months with no evidence of recurrence. CONCLUSIONS: Sentinel lymph node navigation surgery appears to be a safe procedure in a country with non-endemic levels of gastric cancer.
Biopsy ; Brazil ; Early Diagnosis ; Gastrectomy ; Gastroenterostomy ; Humans ; Japan ; Korea ; Liver Failure ; Lymph Node Excision ; Lymph Nodes* ; Neoplasm Micrometastasis ; Recurrence ; Sentinel Lymph Node Biopsy ; Stomach Neoplasms*

Chronic respiratory diseases (CRD) are a major public health problem worldwide. In the current epidemiological context, CRD have received much interest when considering their correlation with greater susceptibility to SARS-Cov-2 and severe disease (COVID-19). Increasingly more studies have investigated pathophysiological interactions between CRD and COVID-19.Area covered:Animal experimentation has decisively contributed to advancing our knowledge of CRD. Considering the increase in ethical restrictions in animal experimentation, researchers must focus on new experimental alternatives. Two-dimensional (2D) cell cultures have complemented animal models and significantly contributed to advancing research in the life sciences. However, 2D cell cultures have several limitations in studies of cellular interactions. Three-dimensional (3D) cell cultures represent a new and robust platform for studying complex biological processes and are a promising alternative in regenerative and translational medicine.Expert opinion:Three-dimensional cell cultures are obtained by combining several types of cells in integrated and self-organized systems in a 3D structure. These 3D cell culture systems represent an efficient methodological approach in studies of pathophysiology and lung therapy. More recently, complex 3D culture systems, such as lung-on-a-chip, seek to mimic the physiology of a lung in vivo through a microsystem that simulates alveolar-capillary interactions and exposure to air. The present review introduces and discusses 3D lung cultures as robust platforms for studies of the pathophysiology of CRD and COVID-19 and the mechanisms that underlie interactions between CRD and COVID-19.

Chronic respiratory diseases (CRD) are a major public health problem worldwide. In the current epidemiological context, CRD have received much interest when considering their correlation with greater susceptibility to SARS-Cov-2 and severe disease (COVID-19). Increasingly more studies have investigated pathophysiological interactions between CRD and COVID-19.Area covered:Animal experimentation has decisively contributed to advancing our knowledge of CRD. Considering the increase in ethical restrictions in animal experimentation, researchers must focus on new experimental alternatives. Two-dimensional (2D) cell cultures have complemented animal models and significantly contributed to advancing research in the life sciences. However, 2D cell cultures have several limitations in studies of cellular interactions. Three-dimensional (3D) cell cultures represent a new and robust platform for studying complex biological processes and are a promising alternative in regenerative and translational medicine.Expert opinion:Three-dimensional cell cultures are obtained by combining several types of cells in integrated and self-organized systems in a 3D structure. These 3D cell culture systems represent an efficient methodological approach in studies of pathophysiology and lung therapy. More recently, complex 3D culture systems, such as lung-on-a-chip, seek to mimic the physiology of a lung in vivo through a microsystem that simulates alveolar-capillary interactions and exposure to air. The present review introduces and discusses 3D lung cultures as robust platforms for studies of the pathophysiology of CRD and COVID-19 and the mechanisms that underlie interactions between CRD and COVID-19.