A Data-Driven Computational Framework for Assessing the Risk of Placental Exposure to Environmental Chemicals.

A computational framework based on placental gene networks was proposed in this work to improve the accuracy of the placental exposure risk assessment of environmental compounds. The framework quantitatively characterizes the ability of compounds to cross the placental barrier by systematically considering the interaction and pathway-level information on multiple placental transporters. As a result, probability scores were generated for 307 compounds crossing the placental barrier based on this framework. These scores were then used to categorize the compounds into different levels of transplacental transport range, creating a gradient partition. These probability scores not only facilitated a more intuitive understanding of a compound's ability to cross the placental barrier but also provided valuable information for predicting potential placental disruptors. Compounds with probability scores greater than 90% were considered to have significant transplacental transport potential, whereas those with probability scores less than 80% were classified as unlikely to cross the placental barrier. Furthermore, external validation set results showed that the probability score could accurately predict the compounds known to cross the placental barrier. In conclusion, the computational framework proposed in this study enhances the intuitive understanding of the ability of compounds to cross the placental barrier and opens up new avenues for assessing the placental exposure risk of compounds.

Integrating biological knowledge for mechanistic inference in the host-associated microbiome.

Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.

Landscape analysis of available European data sources amenable for machine learning and recommendations on usability for rare diseases screening.

BACKGROUND: Patient registries and databases are essential tools for advancing clinical research in the area of rare diseases, as well as for enhancing patient care and healthcare planning. The primary aim of this study is a landscape analysis of available European data sources amenable to machine learning (ML) and their usability for Rare Diseases screening, in terms of findable, accessible, interoperable, reusable(FAIR), legal, and business considerations. Second, recommendations will be proposed to provide a better understanding of the health data ecosystem. METHODS: In the period of March 2022 to December 2022, a cross-sectional study using a semi-structured questionnaire was conducted among potential respondents, identified as main contact person of a health-related databases. The design of the self-completed questionnaire survey instrument was based on information drawn from relevant scientific publications, quantitative and qualitative research, and scoping review on challenges in mapping European rare disease (RD) databases. To determine database characteristics associated with the adherence to the FAIR principles, legal and business aspects of database management Bayesian models were fitted. RESULTS: In total, 330 unique replies were processed and analyzed, reflecting the same number of distinct databases (no duplicates included). In terms of geographical scope, we observed 24.2% (n = 80) national, 10.0% (n = 33) regional, 8.8% (n = 29) European, and 5.5% (n = 18) international registries coordinated in Europe. Over 80.0% (n = 269) of the databases were still active, with approximately 60.0% (n = 191) established after the year 2000 and 71.0% last collected new data in 2022. Regarding their geographical scope, European registries were associated with the highest overall FAIR adherence, while registries with regional and "other" geographical scope were ranked at the bottom of the list with the lowest proportion. Responders' willingness to share data as a contribution to the goals of the Screen4Care project was evaluated at the end of the survey. This question was completed by 108 respondents; however, only 18 of them (16.7%) expressed a direct willingness to contribute to the project by sharing their databases. Among them, an equal split between pro-bono and paid services was observed. CONCLUSIONS: The most important results of our study demonstrate not enough sufficient FAIR principles adherence and low willingness of the EU health databases to share patient information, combined with some legislation incapacities, resulting in barriers to the secondary use of data.

Advancing the allergenicity assessment of new proteins using a text mining resource.

With a society increasingly demanding alternative protein food sources, new strategies for evaluating protein safety issues, such as allergenic potential, are needed. Large-scale and systemic studies on allergenic proteins are hindered by the limited and non-harmonized clinical information available for these substances in dedicated databases. A missing key information is that representing the symptomatology of the allergens, especially given in terms of standard vocabularies, that would allow connecting with other biomedical resources to carry out different studies related to human health. In this work, we have generated the first resource with a comprehensive annotation of allergens' symptomatology, using a text-mining approach that extracts significant co-mentions between these entities from the scientific literature (PubMed, ∼36 million abstracts). The method identifies statistically significant co-mentions between the textual descriptions of the two types of entities in the literature as indication of relationship. 1,180 clinical signs extracted from the Human Phenotype Ontology, the Medical Subject Heading terms of PubMed together with other allergen-specific symptoms, were linked to 1,036 unique allergens annotated in two main allergen-related public databases via 14,009 relationships. This novel resource, publicly available through an interactive web interface, could serve as a starting point for future manually curated compilation of allergen symptomatology.

Incidence and Risk Factors Epilepsy in Patients with Dementia: A Population-Based Study Using Regional Healthcare Databases in Umbria.

Background: Dementia is prevalent among the elderly, also representing a risk for seizures/epilepsy. Estimations of epilepsy risk in dementia patients are not widely available. Objective: Our research aims to ascertain the incidence of epilepsy and its associated risk factors in subjects with dementia in the Umbria region, based on data from healthcare databases. Methods: In this retrospective study based on the healthcare administrative database of Umbria, we identified all patients diagnosed with dementia from 2013 to 2017, based on ICD-9-CM codes. For epilepsy ascertainment, we used a validated algorithm that required an EEG and the prescription of one or more anti-seizure medications post-dementia diagnosis. A case-control analysis was conducted, matching five non-dementia subjects by gender and age to each dementia patient. Cox proportional hazards models were then utilized in the analysis. Results: We identified 7,314 dementia cases, also including 35,280 age- and sex-matched control subjects. Out of patients with dementia, 148 individuals (2.02%) were diagnosed with epilepsy. We observed a progressive increase in the cumulative incidence of seizures over time, registering 1.45% in the first year following the diagnosis, and rising to 1.96% after three years. Analysis using Cox regression revealed a significant association between the development of epilepsy and dementia (HR = 4.58, 95% CI = 3.67-5.72). Additional risk factors were male gender (HR = 1.35, 95% CI = 1.07-1.69) and a younger age at dementia onset (HR = 1.03, 95% CI=1.02-1.04). Conclusions: Dementia increases epilepsy risk, especially with early onset and male gender. Clinicians should have a low threshold to suspect seizures in dementia cases.

Direct Health Care Costs Associated With a Multicomponent COPD Exacerbation Intervention.

BACKGROUND: Health care costs attributed to COPD have been estimated at $4.7 trillion globally in the next 30 years. With the global burden of COPD rising, identification of interventions that might lead to health care cost savings is an imperative. Although many studies report the effect of COPD self-management interventions on subject outcomes and health care utilization, few data describe their effect on health care costs. METHODS: Using data linkage and established case-costing methods with provincial Canadian health databases, we established public health care costs (acute and community) for 12 months following randomization for the 462 participants enrolled in our randomized controlled trial of the Program of Integrated Care for Patients with COPD and Multiple Comorbidities. RESULTS: Total median (interquartile range) in-hospital costs in the 12 months follow-up for all (intervention and control) 462 trial participants were CAD $4,769 ($417-16,834) (equivalent to US $3,566 [$312-12,588]). Total costs incurred in the community were higher at CAD $8,011 ($4,749-13,831) (equivalent to US $5,990 [$3,551-10,342]). Controlling for sex, income quintile, Johns Hopkins Aggregated Diagnosis Groups score, and living in an urban locality, we found lower community health care costs but no differences in acute care costs for participants receiving our multicomponent COPD exacerbation prevention management intervention compared to usual care. CONCLUSIONS: Controlling for important confounders, we found lower public community health care costs but no difference in acute health care costs with our multicomponent COPD exacerbation prevention management intervention compared to usual care. Community health care costs were almost double those incurred compared to acute health care costs. Given this finding, although most COPD exacerbation management interventions generally focus on reducing the use of acute care, interventions that enable health care cost savings in the community require further exploration.

FoodEx2 Harmonization of the Food Consumption Database from the Italian IV SCAI Children's Survey.

Estimating the habitual food and nutrient intakes of a population is based on dietary assessment methods that collect detailed information on food consumption. Establishing the list of foods to be used for collecting data in dietary surveys is central to standardizing data collection. Comparing foods across different data sources is always challenging. Nomenclatures, detail, and classification into broad food groups and sub-groups can vary considerably. The use of a common system for classifying and describing foods is an important prerequisite for analyzing data from different sources. At the European level, EFSA has addressed this need through the development and maintenance of the FoodEx2 classification system. The aim of this work is to present the FoodEx2 harmonization of foods, beverages, and food supplements consumed in the IV SCAI children's survey carried out in Italy. Classifying foods into representative food categories predefined at European level for intake and exposure assessment may lead to a loss of information. On the other hand, a major advantage is the comparability of data from different national databases. The FoodEx2 classification of the national food consumption database represented a step forward in the standardization of the data collection and registration. The large use of FoodEx2 categories at a high level of detail (core and extended terms) combined with the use of descriptors (facets) has minimized information loss and made the reference food categories at country level comparable with different food databases at national and international level.

Drug-drug interactions with oral anticoagulants: information consistency assessment of three commonly used online drug interactions databases in Switzerland.

Background: Toxicity or treatment failure related to drug-drug interactions (DDIs) are known to significantly affect morbidity and hospitalization rates. Despite the availability of numerous databases for DDIs identification and management, their information often differs. Oral anticoagulants are deemed at risk of DDIs and a leading cause of adverse drug events, most of which being preventable. Although many databases include DDIs involving anticoagulants, none are specialized in them. Aim and method: This study aims to compare the DDIs information content of four direct oral anticoagulants and two vitamin K antagonists in three major DDI databases used in Switzerland: Lexi-Interact, Pharmavista, and MediQ. It evaluates the consistency of DDIs information in terms of differences in severity rating systems, mechanism of interaction, extraction and documentation processes and transparency. Results: This study revealed 2'496 DDIs for the six anticoagulants, with discrepant risk classifications. Only 13.2% of DDIs were common to all three databases. Overall concordance in risk classification (high, moderate, and low risk) was slight (Fleiss' kappa = 0.131), while high-risk DDIs demonstrated a fair agreement (Fleiss' kappa = 0.398). The nature and the mechanism of the DDIs were more consistent across databases. Qualitative assessments highlighted differences in the documentation process and transparency, and similarities for availability of risk classification and references. Discussion: This study highlights the discrepancies between three commonly used DDI databases and the inconsistency in how terminology is standardised and incorporated when classifying these DDIs. It also highlights the need for the creation of specialised tools for anticoagulant-related interactions.

Incidence, risk factors, and outcomes of chylothorax after cardiac procedure in the United States.

Background: To examine the epidemiology and risk factors of chylothorax after cardiac procedure in the United States using a contemporary nationally representative database. Methods: We identified postoperative chylothorax events through National Inpatient Sample database (2016-2019) and compared baseline demographics, comorbidities, and in-hospital outcomes between hospitalizations with and without postoperative chylothorax. The Cochrane-Armitage test was used to analyze trends in incidence rates. Multivariable Poisson regression models were used to identify potential risk factors for postoperative chylothorax after cardiac procedure. Results: A total of 819 (0.24%) admissions were associated with postoperative chylothorax. The crude and standardized incidence rates of chylothorax were 23.7 (95%CI, 22.1-25.4) and 61.5 per 10,000 cardiac procedure-related admissions, respectively, with no significant temporal change in incidence rate over the study period (Ptrend = 0.5249). Infants [adjusted rate ratio (aRR), 117.3, 95% confidence interval (CI), 94.5-145.5] and children (aRR, 60.2, 95%CI, 48.0-75.5) were more likely to develop chylothorax compared to adults. Heart and great vessel procedures (aRR, 4.36, 95%CI, 3.61-5.26), septal repair (aRR, 1.91, 95%CI, 1.58-2.29), heart transplant (aRR, 5.68, 95%CI, 4.55-7.10) and pericardial procedures (aRR, 4.04, 95%CI, 3.32-4.91) were associated with elevated risk for chylothorax. Admissions with chylothorax were associated with higher inpatient mortality (4.9% vs. 3.0%, p<0.0001), longer inpatient stay, higher costs and greater perioperative complication burden. Conclusions: Following cardiac procedures, chylothorax is an uncommon but serious complication that affects the prognosis. The analysis reveals varying incidence rates across age groups and specific surgical procedures, with infants at elevated risk.

Databases of ligand-binding pockets and protein-ligand interactions.

Many research groups and institutions have created a variety of databases curating experimental and predicted data related to protein-ligand binding. The landscape of available databases is dynamic, with new databases emerging and established databases becoming defunct. Here, we review the current state of databases that contain binding pockets and protein-ligand binding interactions. We have compiled a list of such databases, fifty-three of which are currently available for use. We discuss variation in how binding pockets are defined and summarize pocket-finding methods. We organize the fifty-three databases into subgroups based on goals and contents, and describe standard use cases. We also illustrate that pockets within the same protein are characterized differently across different databases. Finally, we assess critical issues of sustainability, accessibility and redundancy.

Testes size seen through the glass of amphibian care.

Despite the great diversity of parental care types found in amphibians, studies linking them to post-copulatory sexually selected traits are scarce, presumably due to a lack of data. Valencia-Aguilar et al. used fieldwork and museum collections to show that paternal care appears to trade-off with testes size in glass frogs.

Internal validation of gestational age estimation algorithms in healthcare databases using pregnancies conceived with fertility procedures.

Fertility procedures recorded in healthcare databases can be used to estimate the start of pregnancy, which can serve as a reference standard to validate gestational age estimates based on International Classification of Diseases (ICD) codes. In a cohort of 17,398 pregnancies conceived by fertility procedures in MarketScan (2011-2020), we estimated gestational age at the end of pregnancy using algorithms based on (1) days since fertility procedure (the reference); (2) ICD-9/ICD-10 (before/after October 2015) codes indicating gestational length recorded at the end of pregnancy (method A); and (3) ICD-10 enhanced with Z3A codes denoting specific gestation weeks recorded at prenatal visits (method B). We calculated the proportion of pregnancies with an estimated gestational age within 14 days of the reference. Method A accuracy was similar for ICD-9 and ICD-10 codes. After 2015, method B was more accurate than method A: For term births, within-14-day-agreements were 90.8% for method A and 98.7% for method B. Corresponding estimates were 70.1% and 95.6% for preterm births; 35.3% and 92.6% for stillbirths; 54.3% and 64.2% for spontaneous abortions; and 16.7% and 84.6% for elective terminations. ICD-10-based algorithms that incorporate Z3A codes improve the accuracy of gestational age estimation in healthcare databases, especially for preterm and non-live births.

The MongoDB injection dataset: A comprehensive collection of MongoDB - NoSQL injection attempts and vulnerabilities.

We present the 'NoSQL Injection Dataset for MongoDB, a comprehensive collection of data obtained from diverse projects focusing on NoSQL attacks on MongoDB databases. In the present era, we can classify databases into three main types: structured, semi-structured, and unstructured. While structured databases have played a prominent role in the past, unstructured databases like MongoDB are currently experiencing remarkable growth. Consequently, the vulnerabilities associated with these databases are also increasing. Hence, we have gathered a comprehensive dataset comprising 400 NoSQL injection commands. These commands are segregated into two categories: 221 malicious commands and 179 benign commands. The dataset was meticulously curated by combining both manually authored commands and those acquired through web scraping from reputable sources. The collected dataset serves as a valuable resource for studying and analysing NoSQL injection vulnerabilities, offering insights into potential security threats and aiding in the development of robust protection mechanisms against such attacks. The dataset includes a blend of complex and simple commands that have been enhanced. The dataset is well-suited for machine learning and data analysis, especially for security enthusiasts. The security professionals can use this dataset to train or fine tune the AI-models or LLMs in order to achieve higher attack detection accuracy. The security enthusiasts can also augment this dataset to generate more NoSQL commands and create robust security tools.

Towards uniform case-identification criteria in observational studies on peripheral arterial disease: A scoping review.

OBJECTIVE: The diagnosis peripheral arterial disease (PAD) is commonly applied for symptoms related to atherosclerotic obstructions in the lower extremity, though its clinical manifestations range from an abnormal Ankle Brachial Index to critical limb ischemia. Subsequently, management and prognosis of PAD vary widely with the disease stage. A critical aspect is how this variation is addressed in administrative databases-based studies that rely on diagnosis codes for case identification. The objective of this scoping review is to inventory the identification strategies used in studies on PAD that rely on administrative databases, to map the pros and cons of the ICD codes applied, and propose a first outline for a consensus framework for case identification in administrative databases. METHODS: Registry-based reports published between 2010 to 2021 were identified through a systematic PubMed search. Studies were sub-categorized on the basis of the expressed study focus: claudication, critical limb ischemia, or general peripheral arterial disease and the ICD code(s) applied for case identification mapped. RESULTS: Ninety studies were identified, of which thirty-six (40%) did not specify the grade of PAD studied. Forty-nine (54%) articles specified PAD grade studied. Five (6%) articles specified different PAD subgroups in methods and baseline demographics, but not in further analyses. Mapping of the ICD codes applied for case identification for studies that specified the PAD grade studied indicated a remarkable heterogeneity, overlap, and inconsistency. CONCLUSION: A large proportion of registry-based studies on PAD fails to define the study focus. In addition, inconsistent strategies are used for PAD case-identification in studies that report a focus. These findings challenge study validity, and interfere with inter-study comparison. This scoping review provides a first initiative for a consensus framework for standardized case selection in administrative studies on PAD. It is anticipated that more uniform coding will improve study validity, and facilitate inter-study comparisons.

Review of predicting protein stability changes upon variations.

Forecasting alterations in protein stability caused by variations holds immense importance. Improving the thermal stability of proteins is important for biomedical and industrial applications. This review discusses the latest methods for predicting the effects of mutations on protein stability, databases containing protein mutations and thermodynamic parameters, and experimental techniques for efficiently assessing protein stability in high-throughput settings. Various publicly available databases for protein stability prediction are introduced. Furthermore, state-of-the-art computational approaches for anticipating protein stability changes due to variants are reviewed. Each method's types of features, base algorithm, and prediction results are also detailed. Additionally, some experimental approaches for verifying the prediction results of computational methods are introduced. Finally, the review summarizes the progress and challenges of protein stability prediction and discusses potential models for future research directions.

Exploring the impact of co-exposure timing on drug-drug interactions in signal detection through spontaneous reporting system databases: a scoping review.

INTRODUCTION: Drug-drug interactions (DDIs) are defined as the pharmacological effects produced by the concomitant administration of two or more drugs. To minimize false positive signals and ensure their validity when analyzing Spontaneous Reporting System (SRS) databases, it has been suggested to incorporate key pharmacological principles, such as temporal plausibility. AREAS COVERED: The scoping review of the literature was completed using MEDLINE from inception to March 2023. Included studies had to provide detailed methods for identifying DDIs in SRS databases. Any methodological approach and adverse event were accepted. Descriptive analyzes were excluded as we focused on automatic signal detection methods. The result is an overview of all the available methods for DDI signal detection in SRS databases, with a specific focus on the evaluation of the co-exposure time of the interacting drugs. It is worth noting that only a limited number of studies (n = 3) have attempted to address the issue of overlapping drug administration times. EXPERT OPINION: Current guidelines for signal validation focus on factors like the number of reports and temporal association, but they lack guidance on addressing overlapping drug administration times, highlighting a need for further research and method development.

Treemendous: an R package for integrating taxonomic information across backbones.

Standardizing and translating species names from different databases is key to the successful integration of data sources in biodiversity research. There are numerous taxonomic name-resolution applications that implement increasingly powerful name-cleaning and matching approaches, allowing the user to resolve species relative to multiple backbones simultaneously. Yet there remains no principled approach for combining information across these underlying taxonomic backbones, complicating efforts to combine and merge species lists with inconsistent and conflicting taxonomic information. Here, we present Treemendous, an open-source software package for the R programming environment that integrates taxonomic relationships across four publicly available backbones to improve the name resolution of tree species. By mapping relationships across the backbones, this package can be used to resolve datasets with conflicting and inconsistent taxonomic origins, while ensuring the resulting species are accepted and consistent with a single reference backbone. The user can chain together different functionalities ranging from simple matching to a single backbone, to graph-based iterative matching using synonym-accepted relations across all backbones in the database. In addition, the package allows users to 'translate' one tree species list into another, streamlining the assimilation of new data into preexisting datasets or models. The package provides a flexible workflow depending on the use case, and can either be used as a stand-alone name-resolution package or in conjunction with existing packages as a final step in the name-resolution pipeline. The Treemendous package is fast and easy to use, allowing users to quickly merge different data sources by standardizing their species names according to the regularly updated database. By combining taxonomic information across multiple backbones, the package increases matching rates and minimizes data loss, allowing for more efficient translation of tree species datasets to aid research into forest biodiversity and tree ecology.

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

Fossil Collection at the Zoology Museum of the University of Concepción: enhancing understanding of Chile's past biodiversity.

Background: The digital inventory of paleontological material stored in Chilean museums is highly relevant as it increases accessibility to information, both locally and over long distances, while reducing wear and tear on specimens caused by physical manipulation. The Fossil Collection database of the Museum of Zoology of the University of Concepción (UCC_MZUC_FOS) includes 144 records, with the main representatives being marine invertebrates of the Bivalvia, Echinoidea and Gastropoda classes. Notable species include Encopecalderensis, Hemiasterwayensis, Zygochlamyspatagonica and Retrotapesexalbidus, most of which come from important Chilean fossil sites. Material was collected between 1970 and 2017, with a large portion of it being donated and identified by Professor Emeritus Hugo I. Moyano and Dr. Alberto Larraín. Although the specimens contained in the resource offer basic collecting information, they substantially contribute to sharing knowledge on the fossils kept in the museums throughout the country, while providing data on their distribution. New information: This resource corresponds to the first publication of data on faunal fossils from a museum collection in Chile on the Global Biodiversity Information Facility (GBIF) platform, thereby enhancing the understanding and documentation of Chile's paleontological heritage and its national biodiversity.