Open-access database of literature derived drug-related Torsade de Pointes cases

Since an introduction of an ICH guidance in 2005, no new drugs were withdrawn from the market because of the causation of Torsade de Pointes (TdP). However, the risk of TdP is still a concern for marketed drugs. TdP is a type of polymorphic ventricular tachycardia which may lead to sudden cardiac death. QT/QTc interval prolongation is considered a sensitive, but not specific biomarker. To improve the effectiveness of studies’ workflow related to TdP risk prediction we created an extensive, structured, open-access database of drug-related TdP cases. PubMed, Google Scholar bibliographic databases, and the Internet, via the Google search engine, were searched to identify eligible reports. A total of 424 papers with a description of 634 case reports and observational studies were included. Each paper was manually examined and listed with up to 53 variables related to patient/population characteristics, general health parameters, used drugs, laboratory measurements, ECG results, clinical management, and its outcomes, as well as suspected drug’s properties and its FDA adverse reaction reports. The presented database may be considered as an extension of the recently developed and published database of drug cardiac safety-related information, part of the tox-portal project providing resources for cardiac toxicity assessment.

TOWARDS A SUSTAINABLE APPROACH OF CULTURAL TOURIST RESOURCES IN THE ORADEA METROPOLITAN AREA (OMA), ROMANIA

Metropolitan regions/areas are NUTS 3 regions or a combination of NUTS 3 regions which represent all agglomerations of at least 250,000 inhabitants (Eurostat, 2021). Oradea Metropolitan Area (NUTS3ID RO111) was established in 9th of May 2005 and has a total number of 251.570 inhabitants. The purpose of the association is to stimulate and support the growth and prosperity of the area, focused on the continuous increase of the quality of life. Besides the economic boost, OMA emerged from the need for space and leisure expressed by the core urbanites and although the periurban area is teeming with valuable cultural resources, the latter being the focus of the current study, they are little known by many of the Oradea residents. In this purpose the cultural attractions of the rural OMA were inventoried, analysed and stored in an online open-access database so that tourists can enjoy them and entrepreneurs in tourism can use them as focal points for further tourist infrastructure development. The analysis highlighted the OMA periurban area with the highest cultural attractions’ potential based on the National Methodology regarding the evaluation of the tourist potential in the basic administrative-territorial units.

A quantization assisted U-Net study with ICA and deep features fusion for breast cancer identification using ultrasonic data

Breast cancer is one of the leading causes of death in women worldwide—the rapid increase in breast cancer has brought about more accessible diagnosis resources. The ultrasonic breast cancer modality for diagnosis is relatively cost-effective and valuable. Lesion isolation in ultrasonic images is a challenging task due to its robustness and intensity similarity. Accurate detection of breast lesions using ultrasonic breast cancer images can reduce death rates. In this research, a quantization-assisted U-Net approach for segmentation of breast lesions is proposed. It contains two step for segmentation: (1) U-Net and (2) quantization. The quantization assists to U-Net-based segmentation in order to isolate exact lesion areas from sonography images. The Independent Component Analysis (ICA) method then uses the isolated lesions to extract features and are then fused with deep automatic features. Public ultrasonic-modality-based datasets such as the Breast Ultrasound Images Dataset (BUSI) and the Open Access Database of Raw Ultrasonic Signals (OASBUD) are used for evaluation comparison. The OASBUD data extracted the same features. However, classification was done after feature regularization using the lasso method. The obtained results allow us to propose a computer-aided design (CAD) system for breast cancer identification using ultrasonic modalities.

Investigation on the Prediction of Cardiovascular Events Based on Multi-Scale Time Irreversibility Analysis

Investigation of the risk factors associated with cardiovascular disease (CVD) plays an important part in the prevention and treatment of CVD. This study investigated whether alteration in the multi-scale time irreversibility of sleeping heart rate variability (HRV) was a risk factor for cardiovascular events. The D-value, based on analysis of multi-scale increments in HRV series, was used as the measurement of time irreversibility. Eighty-four subjects from an open-access database (i.e., the Sleep Heart Health Study) were included in this study. None of them had any CVD history at baseline; 42 subjects had cardiovascular events within 1 year after baseline polysomnography and were classed as the CVD group, and the other 42 subjects in the non-CVD group were age matched with those in the CVD group and had no cardiovascular events during the 15-year follow-up period. We compared D-values of sleeping HRV between the CVD and non-CVD groups and found that the D-values of the CVD group were significantly lower than those of the non-CVD group on all 10 scales, even after adjusting for gender and body mass index. Moreover, we investigated the performance of a machine learning model to classify CVD and non-CVD subjects. The model, which was fed with a feature space based on the D-values on 10 scales and trained by a random forest algorithm, achieved an accuracy of 80.8% and a positive prediction rate of 86.7%. These results suggest that the decreased time irreversibility of sleeping HRV is an independent predictor of cardiovascular events that could be used to assist the intelligent prediction of cardiovascular events.

An open-access database and analysis tool for perovskite solar cells based on the FAIR data principles

Large datasets are now ubiquitous as technology enables higher-throughput experiments, but rarely can a research field truly benefit from the research data generated due to inconsistent formatting, undocumented storage or improper dissemination. Here we extract all the meaningful device data from peer-reviewed papers on metal-halide perovskite solar cells published so far and make them available in a database. We collect data from over 42,400 photovoltaic devices with up to 100 parameters per device. We then develop open-source and accessible procedures to analyse the data, providing examples of insights that can be gleaned from the analysis of a large dataset. The database, graphics and analysis tools are made available to the community and will continue to evolve as an open-source initiative. This approach of extensively capturing the progress of an entire field, including sorting, interactive exploration and graphical representation of the data, will be applicable to many fields in materials science, engineering and biosciences.

Plastics degradation by hydrolytic enzymes: the Plastics-Active Enzymes Database - PAZy

Petroleum based plastics are durable and accumulate in all ecological niches. Knowledge on enzymatic degradation is sparse. Today, less than 50 verified plastics-active enzymes are known. First examples of enzymes acting on the polymers polyethylene terephthalate (PET) and polyurethane (PUR) have been reported together with a detailed biochemical and structural description. Further, very few polyamide (PA) oligomer active enzymes are known. In this paper, the current known enzymes acting on the synthetic polymers PET and PUR are briefly summarized, their published activity data were collected and integrated into a comprehensive open access database. The Plastics-Active Enzymes Database (PAZy) represents an inventory of known and experimentally verified plastics-active enzymes. Almost 3000 homologues of PET-active enzymes were identified by profile hidden Markov models. Over 2000 homologues of PUR-active enzymes were identified by BLAST. Based on multiple sequence alignments, conservation analysis identified the most conserved amino acids, and sequence motifs for PET- and PUR-active enzymes were derived.

Gcorn fungi: A Web Tool for Detecting Biases between Gene Evolution and Speciation in Fungi

(1) Background: Fungi contain several millions of species, and the diversification of fungal genes has been achieved by speciation, gene duplication, and horizontal gene transfer. Although several databases provide information on orthologous and paralogous events, these databases show no information on biases between gene mutation and speciation. Here, we designed the Gcorn fungi database to better understand such biases. (2) Methods: Amino acid sequences of fungal genes in 249 species, which contain 2,345,743 sequences, were used for this database. Homologous genes were grouped at various thresholds of the homology index, which was based on the percentages of gene mutations. By grouping genes that showed highly similar homology indices to each other, we showed functional and evolutionary traits in the phylogenetic tree depicted for the gene of interest. (3) Results: Gcorn fungi provides well-summarized information on the evolution of a gene lineage and on the biases between gene evolution and speciation, which are quantitatively identified by the Robinson–Foulds metric. The database helps users visualize these traits using various depictions. (4) Conclusions: Gcorn fungi is an open access database that provides a variety of information with which to understand gene function and evolution.

A database on differentially expressed microRNAs during rodent bladder healing

Urinary bladder wound healing relies on multiple biological events that are finely tuned in a spatial–temporal manner. MicroRNAs are small non-coding RNA molecules with regulatory functions. We hypothesized that microRNAs are important molecules in the coordination of normal urinary bladder wound healing. We aimed at identifying microRNAs expressed during bladder wound healing using Affymetrix global array for microRNA profiling of the rodent urinary bladder during healing of a surgically created wound. Results were validated in the rat bladders by real-time PCR (RT-PCR) using three of the differentially expressed (DE) microRNAs. The model was thereafter validated in human cells, by measuring the expression of eight of the DE microRNAs upon in vitro wound-healing assays in primary urothelial cells. Our results indicated that 508 (40%) of all rodent microRNAs were expressed in the urinary bladder during wound healing. Thirteen of these microRNAs (1%) were DE (false discovery rate (FDR) < 0.05, P < 0.05, |logfold|> 0.25) in wounded compared to non-wounded bladders. Bioinformatic analyses helped us to identify target molecules for the DE microRNAs, and biological pathways involved in tissue repair. All data are made available in an open-access database for other researchers to explore.

Software review: The JATSdecoder package—extract metadata, abstract and sectioned text from NISO-JATS coded XML documents; Insights to PubMed central’s open access database

JATSdecoder is a general toolbox which facilitates text extraction and analytical tasks on NISO-JATS coded XML documents. Its function JATSdecoder() outputs metadata, the abstract, the sectioned text and reference list as easy selectable elements. One of the biggest repositories for open access full texts covering biology and the medical and health sciences is PubMed Central (PMC), with more than 3.2 million files. This report provides an overview of the PMC document collection processed with JATSdecoder(). The development of extracted tags is displayed for the full corpus over time and in greater detail for some meta tags. Possibilities and limitations for text miners working with scientific literature are outlined. The NISO-JATS-tags are used quite consistently nowadays and allow a reliable extraction of metadata and text elements. International collaborations are more present than ever. There are obvious errors in the date stamps of some documents. Only about half of all articles from 2020 contain at least one author listed with an author identification code. Since many authors share the same name, the identification of person-related content is problematic, especially for authors with Asian names. JATSdecoder() reliably extracts key metadata and text elements from NISO-JATS coded XML files. When combined with the rich, publicly available content within PMCs database, new monitoring and text mining approaches can be carried out easily. Any selection of article subsets should be carefully performed with in- and exclusion criteria on several NISO-JATS tags, as both the subject and keyword tags are used quite inconsistently.

An Open-Access Database for the Evaluation of Cardio-Mechanical Signals From Patients With Valvular Heart Diseases

This paper describes an open-access database for seismo-cardiogram (SCG) and gyro-cardiogram (GCG) signals. The archive comprises SCG and GCG recordings sourced from and processed at multiple sites worldwide, including Columbia University Medical Center and Stevens Institute of Technology in the United States, as well as Southeast University, Nanjing Medical University, and the first affiliated hospital of Nanjing Medical University in China. It includes electrocardiogram (ECG), SCG, and GCG recordings collected from 100 patients with various conditions of valvular heart diseases such as aortic and mitral stenosis. The recordings were collected from clinical environments with the same types of wearable sensor patch. Besides the raw recordings of ECG, SCG, and GCG signals, a set of hand-corrected fiducial point annotations is provided by manually checking the results of the annotated algorithm. The database also includes relevant echocardiogram parameters associated with each subject such as ejection fraction, valve area, and mean gradient pressure.