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Introduction: Congenital glaucoma is a disease that involves increased intraocular pressure and can result in irreversible visual deterioration. The study of epidemiology allows the delineation of the characteristics associated with patients and specific risk factors., Objective: The objective of this study was to examine epidemiological trends, place of residence, duration of gestation, sex, and race of the newborn diagnosed with congenital glaucoma in Brazil., Methods: Data from SINASC (National Live Birth System) were used to analyze the period from 2017 to 2021 in Brazil. Linear regression and analysis of variance tests were employed to assess significance. The statistical significance was determined by p<0.05., Results: A total of 47 cases of congenital glaucoma were identified in Brazil during the study period, with the highest incidence between the years of 2018 and 2021. The analysis of the distribution indicated that the states with the highest incidence were São Paulo, followed by Rio Grande do Sul and Pernambuco. Approximately 60% of cases occurred in male individuals, compared with 19 female cases. The ethnic analysis showed the highest incidence among whites and mixed. Regarding the length of pregnancy, statistical differences were observed between newborns of different periods of gestation. Infants born from pregnancies lasting between 28 and 31 weeks and 32 and 36 weeks were significant when analyzed with the group between 37 and 41 weeks., Conclusion: Studies on the mechanisms of congenital glaucoma seek to improve knowledge about the disease. Epidemiological evaluation is essential for identifying demographic and clinical patterns of the disease.

Introduction: Congenital syphilis is a complex public health issue caused by the transmission of Treponema pallidum. Brazil has high incidence rates, with a distinct transmission pattern surpassing other notifiable diseases., Objective: The objective of this study was to examine epidemiological trends, incidence rate, mortality, geographical distribution, prenatal care, and diagnostic determination timing of congenital syphilis in Paraná State., Methods: Data from Department of Informatics of the Single Health System were used to analyze the period from 2015 to 2021 in Paraná. Linear regression and t-tests were employed to assess significance. Statistical significance was determined by p<0.05., Results: A total of 5,096 notifications of congenital syphilis were recorded in Paraná over the examined period. The metropolitan region is a notable clustering of cases, following Londrina, Maringá, and Foz do Iguaçu. The age group with the highest cases is found between 20 and 24 years (34.93%). Regarding maternal education, a higher occurrence was noticed in incomplete lower secondary education mothers (22.12%). Regarding ethnic background, 3,792 women were identified as white, which was the majority of this analysis (74.41%). Diagnosed maternal syphilis throughout the prenatal phase during 2015-2018 exhibited a noteworthy increase (p<0.05). Most women received prenatal care (p<0.05), even though a significant number received the diagnosis at the delivery or after it. The average infant mortality rate associated with congenital syphilis in Paraná was 0.03., Conclusion: Paraná State serves as a representative sample of this epidemiological situation, providing significant insights into the intricacies of congenital syphilis incidence. Further comparative investigations including diverse regions within Brazil are necessary.

Summary: Relation extraction (RE) from large text collections is an important tool for database curation, pathway reconstruction, or functional omics data analysis. In practice, RE often is part of a complex data analysis pipeline requiring specific adaptations like restricting the types of relations or the set of proteins to be considered. However, current systems are either non-programmable web sites or research code with fixed functionality. We present PEDL+, a user-friendly tool for extracting protein-protein and protein-chemical associations from PubMed articles. PEDL+ combines state-of-the-art NLP technology with adaptable ranking and filtering options and can easily be integrated into analysis pipelines. We evaluated PEDL+ in two pathway curation projects and found that 59% to 80% of its extractions were helpful., Availability and Implementation: PEDL+ is freely available at https://github.com/leonweber/pedl., (© The Author(s) 2023. Published by Oxford University Press.)

The identification of chemical-protein interactions described in the literature is an important task with applications in drug design, precision medicine and biotechnology. Manual extraction of such relationships from the biomedical literature is costly and often prohibitively time-consuming. The BioCreative VII DrugProt shared task provides a benchmark for methods for the automated extraction of chemical-protein relations from scientific text. Here we describe our contribution to the shared task and report on the achieved results. We define the task as a relation classification problem, which we approach with pretrained transformer language models. Upon this basic architecture, we experiment with utilizing textual and embedded side information from knowledge bases as well as additional training data to improve extraction performance. We perform a comprehensive evaluation of the proposed model and the individual extensions including an extensive hyperparameter search leading to 2647 different runs. We find that ensembling and choosing the right pretrained language model are crucial for optimal performance, whereas adding additional data and embedded side information did not improve results. Our best model is based on an ensemble of 10 pretrained transformers and additional textual descriptions of chemicals taken from the Comparative Toxicogenomics Database. The model reaches an F1 score of 79.73% on the hidden DrugProt test set and achieves the first rank out of 107 submitted runs in the official evaluation. Database URL: https://github.com/leonweber/drugprot., (© The Author(s) 2022. Published by Oxford University Press.)