ginmappeR: an unified approach for integrating gene and protein identifiers across biological sequence databases.

Summary: The proliferation of biological sequence data, due to developments in molecular biology techniques, has led to the creation of numerous open access databases on gene and protein sequencing. However, the lack of direct equivalence between identifiers across these databases difficults data integration. To address this challenge, we introduce ginmappeR, an integrated R package facilitating the translation of gene and protein identifiers between databases. By providing a unified interface, ginmappeR streamlines the integration of diverse data sources into biological workflows, so it enhances efficiency and user experience. Availability and implementation: from Bioconductor: https://bioconductor.org/packages/ginmappeR.

Effects of the July 2018 worldwide valsartan recall and shortage on global trends in antihypertensive medication use: a time-series analysis in 83 countries.

OBJECTIVES: This study aims to examine the effects of the July 2018 worldwide valsartan recall and shortage on global trends of antihypertensive medication use in 83 countries. METHODS: A time-series analysis of monthly purchases of valsartan, other angiotensin II receptor blockers (ARBs) and angiotensin-converting enzyme inhibitors (ACEIs) across 83 countries from January 2017 to July 2020 was conducted using the IQVIA MIDAS database. Trends in outcomes were investigated globally and by economic level (developed vs developing economies). The valsartan recall's impact on antihypertensive use was assessed with interventional autoregressive integrated moving average modelling. RESULTS: Global valsartan utilisation trends decreased significantly by 15.7% (-61 166 515 SU; p<0.0001), while global purchases of other ARBs increased by 44.8% (+958 069 420 SU; p=0.8523) and ACEIs increased by 1.6% (+44 106 747 SU; p=0.1102). Of the 32 developed countries, 20 (62.5%) showed a decline in 1-month percentage change in valsartan purchases, whereas only 10 out of 33 developing countries (30.3%) experienced a decrease in valsartan purchases. Mean 1-month, 3-month and 6-month percentage changes for developed countries were -1.2%, -9.3% and -12.2%, respectively, while the changes for developing countries were 25.0%, 7.3% and -1.2%. CONCLUSIONS: Global valsartan purchases substantially decreased post-recall, highlighting the far-reaching impacts of drug shortages. Opposing utilisation trends by economic level raise concerns of potential distribution of contaminated medications from developed countries to developing countries. Concerted actions for equitable global access to quality medications and mitigation of drug shortages are needed.

Corrigendum to Multi-source dataset of e-commerce products with attributes for property matching [Data in Brief 41 (2022) 107884].

[This corrects the article DOI: 10.1016/j.dib.2022.107884.].

Multi-source dataset of e-commerce products with attributes for property matching.

Schema/ontology matching consists in finding matches between types, properties and entities in heterogeneous sources of data in order to integrate them, which has become increasingly relevant with the development of web technologies and open data initiatives. One of the involved tasks is the matching of data properties, which attempts to try to find correspondences between the attributes of the entities. This is challenging due to the at times different names of equivalent properties. Furthermore, some properties may not be equivalent, but still match in 1..n relationships. These difficulties create the need for varied evaluation datasets for two reasons. First, they are needed to evaluate existing techniques in a variety of scenarios. Second, they enable the training of supervised techniques that may even become context-independent if trained with data from diverse enough contexts. To support the evaluation and training of data property matching techniques, we present a collection dataset consisting of product records from four different contexts. These datasets are the result of transforming two different existing datasets. In one of the datasets, some properties were filtered for being too noisy. The resulting processed dataset consists of json files with a listing of the product records and their properties, and a separate grouping of the properties that determines which ones match. It contains information about 2860 entities, with 4386 properties and 13350 pairwise matches.