Granulomatous Mediastinitis: A Rare Cause of Pulmonary Hypertension.

A rare case of a patient with chronic obstructive pulmonary disease who developed secondary anthracofibrosis to biomass exposure, fibrosing mediastinitis due to anthracotic enlarged lymph nodes in the mediastinum, and pulmonary hypertension because of compres- sion of the lymph nodes on the pulmonary arteries is presented. This is a case report of a 71-year-old female patient who has been followed up with chronic obstructive pulmonary disease for 10 years, has no history of smoking, and has been exposed to biomass for many years. The patient, who had been hospitalized in various centers for the last 3 years due to progressive shortness of breath and dry cough, applied to us with dry cough and dyspnea complaints. On echocardiography, systolic pulmonary arterial pressure was found to be 59 mmHg. For the etiology of pulmonary hypertension, dual-energy thoracic computed tomography was performed with the suspicion of chronic thromboembolic pulmonary hypertension. No filling defect compatible with thromboembolism was detected. In right heart catheterization, mean pulmonary artery pressure was 27 mmHg, pulmonary capillary tip pressure was 7 mmHg, and pulmonary vascular resistance was 3.71 woods units. Endobronchial ultrasound was applied to the patient with the preliminary diagnoses of lymphoma, anthracosis, fibrosing mediastinitis, and infection. Widespread anthracosis was observed in all lobes and segments macroscopically. The lymph node in the subcarinal area was interpreted as anthracotic lymph node. Anthracosis is defined as black pigmentation involving the mucosal, and submucosal layers of the tracheobronchial tree and the lung parenchyma. If anthracosis is associated with luminal obliteration and/or mucosal proliferation causing obstruction, it is considered anthracofibrosis. In this case, we saw that secondary anthracofibrosis, fibrosing mediastinitis due to anthracotic enlarged lymph nodes in the mediastinum, and pulmonary hypertension may develop because of compression of the lymph nodes on the pulmonary arteries, and we wanted to draw attention to it was a rare case.

NeRNA: A negative data generation framework for machine learning applications of noncoding RNAs.

Many supervised machine learning based noncoding RNA (ncRNA) analysis methods have been developed to classify and identify novel sequences. During such analysis, the positive learning datasets usually consist of known examples of ncRNAs and some of them might even have weak or strong experimental validation. On the contrary, there are neither databases listing the confirmed negative sequences for a specific ncRNA class nor standardized methodologies developed to generate high quality negative examples. To overcome this challenge, a novel negative data generation method, NeRNA (negative RNA), is developed in this work. NeRNA uses known examples of given ncRNA sequences and their calculated structures for octal representation to create negative sequences in a manner similar to frameshift mutations but without deletion or insertion. NeRNA is tested individually with four different ncRNA datasets including microRNA (miRNA), transfer RNA (tRNA), long noncoding RNA (lncRNA), and circular RNA (circRNA). Furthermore, a species-specific case analysis is performed to demonstrate and compare the performance of NeRNA for miRNA prediction. The results of 1000 fold cross-validation on Decision Tree, Naïve Bayes and Random Forest classifiers, and deep learning algorithms such as Multilayer Perceptron, Convolutional Neural Network, and Simple feedforward Neural Networks indicate that models obtained by using NeRNA generated datasets, achieves substantially high prediction performance. NeRNA is released as an easy-to-use, updatable and modifiable KNIME workflow that can be downloaded with example datasets and required extensions. In particular, NeRNA is designed to be a powerful tool for RNA sequence data analysis.

Circular RNA-MicroRNA-MRNA interaction predictions in SARS-CoV-2 infection.

Different types of noncoding RNAs like microRNAs (miRNAs) and circular RNAs (circRNAs) have been shown to take part in various cellular processes including post-transcriptional gene regulation during infection. MiRNAs are expressed by more than 200 organisms ranging from viruses to higher eukaryotes. Since miRNAs seem to be involved in host-pathogen interactions, many studies attempted to identify whether human miRNAs could target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNAs as an antiviral defence mechanism. In this work, a machine learning based miRNA analysis workflow was developed to predict differential expression patterns of human miRNAs during SARS-CoV-2 infection. In order to obtain the graphical representation of miRNA hairpins, 36 features were defined based on the secondary structures. Moreover, potential targeting interactions between human circRNAs and miRNAs as well as human miRNAs and viral mRNAs were investigated.