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.

Augusta: From RNA-Seq to gene regulatory networks and Boolean models

Computational models of gene regulations help to understand regulatory mechanisms and are extensively used in a wide range of areas, e.g., biotechnology or medicine, with significant benefits. Unfortunately, there are only a few computational gene regulatory models of whole genomes allowing static and dynamic analysis due to the lack of sophisticated tools for their reconstruction. Here, we describe Augusta, an open-source Python package for Gene Regulatory Network (GRN) and Boolean Network (BN) inference from the high-throughput gene expression data. Augusta can reconstruct genome-wide models suitable for static and dynamic analyses. Augusta uses a unique approach where the first estimation of a GRN inferred from expression data is further refined by predicting transcription factor binding motifs in promoters of regulated genes and by incorporating verified interactions obtained from databases. Moreover, a refined GRN is transformed into a draft BN by searching in the curated model database and setting logical rules to incoming edges of target genes, which can be further manually edited as the model is provided in the SBML file format. The approach is applicable even if information about the organism under study is not available in the databases, which is typically the case for non-model organisms including most microbes. Augusta can be operated from the command line and, thus, is easy to use for automated prediction of models for various genomes. The Augusta package is freely available at github.com/JanaMus/Augusta. Documentation and tutorials are available at augusta.readthedocs.io.

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.

Evidence for the druggability of aldosterone targets in heart failure: A bioinformatics and data science-driven decision-making approach

BACKGROUND

Aldosterone plays a key role in the neurohormonal drive of heart failure. Systematic prioritization of drug targets using bioinformatics and database-driven decision-making can provide a competitive advantage in therapeutic R&D. This study investigated the evidence on the druggability of these aldosterone targets in heart failure.

METHODS

The target disease predictability of mineralocorticoid receptors (MR) and aldosterone synthase (AS) in cardiac failure was evaluated using Open Targets target-disease association scores. The Open Targets database collections were downloaded to MongoDB and queried according to the desired aggregation level, and the results were retrieved from the Europe PMC (data type: text mining), ChEMBL (data type: drugs), Open Targets Genetics Portal (data type: genetic associations), and IMPC (data type: genetic associations) databases. The target tractability of MR and AS in the cardiovascular system was investigated by computing activity scores in a curated ChEMBL database using supervised machine learning.

RESULTS

The medians of the association scores of the MR and AS groups were similar, indicating a comparable predictability of the target disease. The median of the MR activity scores group was significantly lower than that of AS, indicating that AS has higher target tractability than MR [Hodges-Lehmann difference 0.62 (95%CI 0.53-0.70, p < 0.0001]. The cumulative distributions of the overall multiplatform association scores of cardiac diseases with MR were considerably higher than with AS, indicating more advanced investigations on a wider range of disorders evaluated for MR (Kolmogorov-Smirnov D = 0.36, p = 0.0009). In curated ChEMBL, MR had a higher cumulative distribution of activity scores in experimental cardiovascular assays than AS (Kolmogorov-Smirnov D = 0.23, p < 0.0001). Documented clinical trials for MR in heart failures surfaced in database searches, none for AS.

CONCLUSIONS

Although its clinical development has lagged behind that of MR, our findings indicate that AS is a promising therapeutic target for the treatment of cardiac failure. The multiplatform-integrated identification used in this study allowed us to comprehensively explore the available scientific evidence on MR and AS for heart failure therapy.

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.

Computational resources and chemoinformatics for translational health research

The integration of computational resources and chemoinformatics has revolutionized translational health research. It has offered a powerful set of tools for accelerating drug discovery. This chapter overviews the computational resources and chemoinformatics methods used in translational health research. The resources and methods can be used to analyze large datasets, identify potential drug candidates, predict drug-target interactions, and optimize treatment regimens. These resources have the potential to transform the drug discovery process and foster personalized medicine research. We discuss insights into their various applications in translational health and emphasize the need for addressing challenges, promoting collaboration, and advancing the field to fully realize the potential of these tools in transforming healthcare.

Antiviral Drug Target Identification and Ligand Discovery

This chapter intends to provide a general overview of web-based resources available for antiviral drug discovery studies. First, we explain how the structure for a potential viral protein target can be obtained and then highlight some of the main considerations in preparing for the application of receptor-based molecular docking techniques. Thereafter, we discuss the resources to search for potential drug candidates (ligands) against this target protein receptor, how to screen them, and preparing their analogue library. We make specific reference to free, online, open-source tools and resources which can be applied for antiviral drug discovery studies.

Exploring the Role of Chemoinformatics in Accelerating Drug Discovery: A Computational Approach

Cheminformatics and its role in drug discovery is expected to be the privileged approach in handling large number of chemical datasets. This approach contributes toward the pharmaceutical development and assessment of chemical compounds at a faster rate efficiently. Additionally, as technological advancement impacts research, cheminformatics is being used more and more in the field of health science. This chapter describes the concepts of cheminformatics along with its involvement in drug discovery with a case study.

Unleashing the global potential of public health: A framework for future pandemic response

Public health emergencies, especially pandemics, need to be managed globally, and on several levels, emphasizing the importance of leadership, communication, and synchronization of measures, data, and management plans in contrast to the management of the Coronavirus-19 pandemic, which illustrated diverse strategies employed by various nations. This paper aims to review and discuss whether globalized diseases in a globalized world should be managed by globalized public health. Using a systematic literature search, followed by a non-systematic literature review, selected studies were grouped into topics, and analyzed, using content analysis to enhance the conclusive results. The results present a roadmap towards a re-envisioned framework highlighting key areas of focus: data-driven decision-making, robust technology infrastructure, global cooperation, and ongoing public health education, as part of a coordinated global response. This article reveals the weaknesses of current pandemic management systems and recommends new steps to further strengthen the management of future pandemics.

Evidence-based literature review: De-duplication a cornerstone for quality

Evidence-based literature reviews play a vital role in contemporary research, facilitating the synthesis of knowledge from multiple sources to inform decision-making and scientific advancements. Within this framework, de-duplication emerges as a part of the process for ensuring the integrity and reliability of evidence extraction. This opinion review delves into the evolution of de-duplication, highlights its importance in evidence synthesis, explores various de-duplication methods, discusses evolving technologies, and proposes best practices. By addressing ethical considerations this paper emphasizes the significance of de-duplication as a cornerstone for quality in evidence-based literature reviews.

Computational approaches for identifying disease-causing mutations in proteins

Advancements in genome sequencing have expanded the scope of investigating mutations in proteins across different diseases. Amino acid mutations in a protein alter its structure, stability and function and some of them lead to diseases. Identification of disease-causing mutations is a challenging task and it will be helpful for designing therapeutic strategies. Hence, mutation data available in the literature have been curated and stored in several databases, which have been effectively utilized for developing computational methods to identify deleterious mutations (drivers), using sequence and structure-based properties of proteins. In this chapter, we describe the contents of specific databases that have information on disease-causing and neutral mutations followed by sequence and structure-based properties. Further, characteristic features of disease-causing mutations will be discussed along with computational methods for identifying cancer hotspot residues and disease-causing mutations in proteins.

Exploring DrugCentral: from molecular structures to clinical effects

DrugCentral, accessible at https://drugcentral.org , is an open-access online drug information repository. It covers over 4950 drugs, incorporating structural, physicochemical, and pharmacological details to support drug discovery, development, and repositioning. With around 20,000 bioactivity data points, manual curation enhances information from several major digital sources. Approximately 724 mechanism-of-action (MoA) targets offer updated drug target insights. The platform captures clinical data: over 14,300 on- and off-label uses, 27,000 contraindications, and around 340,000 adverse drug events from pharmacovigilance reports. DrugCentral encompasses information from molecular structures to marketed formulations, providing a comprehensive pharmaceutical reference. Users can easily navigate basic drug information and key features, making DrugCentral a versatile, unique resource. Furthermore, we present a use-case example where we utilize experimentally determined data from DrugCentral to support drug repurposing. A minimum activity threshold t should be considered against novel targets to repurpose a drug. Analyzing 1156 bioactivities for human MoA targets suggests a general threshold of 1 µM: t = 6 when expressed as - log[Activity(M)]). This applies to 87% of the drugs. Moreover, t can be refined empirically based on water solubility (S): t = 3 - logS, for logS < - 3. Alongside the drug repurposing classification scheme, which considers intellectual property rights, market exclusivity protections, and market accessibility, DrugCentral provides valuable data to prioritize candidates for drug repurposing programs efficiently.

Exploration of biomarkers for systemic lupus erythematosus by machine-learning analysis

BACKGROUND

In recent years, research on the pathogenesis of systemic lupus erythematosus (SLE) has made great progress. However, the prognosis of the disease remains poor, and high sensitivity and accurate biomarkers are particularly important for the early diagnosis of SLE.

METHODS

SLE patient information was acquired from three Gene Expression Omnibus (GEO) databases and used for differential gene expression analysis, such as weighted gene coexpression network (WGCNA) and functional enrichment analysis. Subsequently, three algorithms, random forest (RF), support vector machine-recursive feature elimination (SVM-REF) and least absolute shrinkage and selection operation (LASSO), were used to analyze the above key genes. Furthermore, the expression levels of the final core genes in peripheral blood from SLE patients were confirmed by real-time quantitative polymerase chain reaction (RT-qPCR) assay.

RESULTS

Five key genes (ABCB1, CD247, DSC1, KIR2DL3 and MX2) were found in this study. Moreover, these key genes had good reliability and validity, which were further confirmed by clinical samples from SLE patients. The receiver operating characteristic curves (ROC) of the five genes also revealed that they had critical roles in the pathogenesis of SLE.

CONCLUSION

In summary, five key genes were obtained and validated through machine-learning analysis, offering a new perspective for the molecular mechanism and potential therapeutic targets for SLE.

Comparative Analysis of Published Database Predicting MicroRNA Binding in 3'UTR of mRNA in Diverse Species

BACKGROUND

Micro-RNAs are endogenous non-coding RNA moieties of 22-27 nucleotides that play a crucial role in the regulation of various biological processes and make them useful prognostic and diagnostic biomarkers. Discovery and experimental validation of miRNA is a laborious and time-consuming process. For early prediction, multiple bioinformatics databases are available for miRNA target prediction; however, their utility can confuse amateur researchers in selecting the most appropriate tools for their study.

OBJECTIVE

This descriptive review aimed to analyse the usability of the existing database based on the following criteria: accessibility, efficiency, interpretability, updatability, and flexibility for miRNA target prediction of 3'UTR of mRNA in diverse species so that the researchers can utilize the database most appropriate to their research.

METHODS

A systematic literature search was performed in PubMed, Google Scholar and Scopus databases up to November 2022. ≥10,000 articles found online, including ⁓130 miRNA tools, which contain various information on miRNA. Out of them, 31 databases that provide information on validated 3'UTR miRNAs target databases were included and analysed in this review.

RESULTS

These miRNA database tools are being used in varied areas of biological research to select the most suitable miRNA for their experimental validation. These databases, updated until the year 2021, consist of miRNA-related data from humans, animals, mice, plants, viruses etc. They contain 525-29806351 data entries, and information from most databases is freely available on the online platform.

CONCLUSION

Reviewed databases provide significant information, but not all information is accurate or up-to-date. Therefore, Diana-TarBase and miRWalk are the most comprehensive and up-to-date databases.

Database Creator for Mass Analysis of Peptides and Proteins, DC-MAPP: A Standalone Tool for Simplifying Manual Analysis of Mass Spectral Data to Identify Peptide/Protein Sequences

Proteomic studies typically involve the use of different types of software for annotating experimental tandem mass spectrometric data (MS/MS) and thereby simplifying the process of peptide and protein identification. For such annotations, these softwares calculate the m/z values of the peptide/protein precursor and fragment ions, for which a database of protein sequences must be provided as an input file. The calculated m/z values are stored as another database, which the user usually cannot view. Database Creator for Mass Analysis of Peptides and Proteins (DC-MAPP) is a novel standalone software that can create custom databases for "viewing" the calculated m/z values of precursor and fragment ions, prior to the database search. It contains three modules. Peptide/Protein sequences as per user's choice can be entered as input to the first module for creating a custom database. In the second module, m/z values must be queried-in, which are searched within the custom database to identify protein/peptide sequences. The third module is suited for peptide mass fingerprinting, which can be used to analyze both ESI and MALDI mass spectral data. The feature of "viewing" the custom database can be helpful not only for better understanding the search engine processes, but also for designing multiple reaction monitoring (MRM) methods. Post-translational modifications and protein isoforms can also be analyzed. Since, DC-MAPP relies on the protein/peptide "sequences" for creating custom databases, it may not be applicable for the searches involving spectral libraries. Python language was used for implementation, and the graphical user interface was built with Page/Tcl, making this tool more user-friendly. It is freely available at https://vit.ac.in/DC-MAPP/.

A comprehensive review on genomic resources in medicinally and industrially important major spices for future breeding programs: Status, utility and challenges

In the global market, spices possess a high-value but low-volume commodities of commerce. The food industry depends largely on spices for taste, flavor, and therapeutic properties in replacement of cheap synthetic ones. The estimated growth rate for spices demand in the world is ∼3.19%. Since spices grow in limited geographical regions, India is one of the leading producer of spices, contributing 25-30 percent of total world trade. Hitherto, there has been no comprehensive review of the genomic resources of industrially important major medicinal spices to overcome major impediments in varietal improvement and management. This review focuses on currently available genomic resources of 24 commercially significant spices, namely, Ajwain, Allspice, Asafoetida, Black pepper, Cardamom large, Cardamom small, Celery, Chillies, Cinnamon, Clove, Coriander, Cumin, Curry leaf, Dill seed, Fennel, Fenugreek, Garlic, Ginger, Mint, Nutmeg, Saffron, Tamarind, Turmeric and Vanilla. The advent of low-cost sequencing machines has contributed immensely to the voluminous data generation of these spices, cracking the complex genomic architecture, marker discovery, and understanding comparative and functional genomics. This review of spice genomics resources concludes the perspective and way forward to provide footprints by uncovering genome assemblies, sequencing and re-sequencing projects, transcriptome-based studies, non-coding RNA-mediated regulation, organelles-based resources, developed molecular markers, web resources, databases and AI-directed resources in candidate spices for enhanced breeding potential in them. Further, their integration with molecular breeding could be of immense use in formulating a strategy to protect and expand the production of the spices due to increased global demand.

Comprehensive survey of target prediction web servers for Traditional Chinese Medicine

Traditional Chinese medicine (TCM) is characterized by multi-components, multiple targets, and complex mechanisms of action and therefore has significant advantages in treating diseases. However, the clinical application of TCM prescriptions is limited due to the difficulty in elucidating the effective substances and the lack of current scientific evidence on the mechanisms of action. In recent years, the development of network pharmacology based on drug systems research has provided a new approach for understanding the complex systems represented by TCM. The determination of drug targets is the core of TCM network pharmacology research. Over the past years, many web tools for drug targets with various features have been developed to facilitate target prediction, significantly promoting drug discovery. Therefore, this review introduces the widely used web tools for compound-target interaction prediction databases and web resources in TCM pharmacology research, and it compares and analyzes each web tool based on their basic properties, including the underlying theory, algorithms, datasets, and search results. Finally, we present the remaining challenges for the promising future of compound-target interaction prediction in TCM pharmacology research. This work may guide researchers in choosing web tools for target prediction and may also help develop more TCM tools based on these existing resources.

Meta-research on pragmatism of randomized trials: rationale and design of the PragMeta database

BACKGROUND

Pragmatic trials provide decision-oriented, real-world evidence that is highly applicable and generalizable. The interest in real-world evidence is fueled by the assumption that effects in the "real-world" are different to effects obtained under artificial, controlled, research conditions as often used for traditional explanatory trials. However, it is unknown which features of pragmatism, generalizability, and applicability would be responsible for such differences. There is a need to provide empirical evidence and promote meta-research to answer these fundamental questions on the pragmatism of randomized trials and real-world evidence. Here, we describe the rationale and design of the PragMeta database which pursues this goal ( www.PragMeta.org ).

METHODS

PragMeta is a non-commercial, open data platform and infrastructure to facilitate research on pragmatic trials. It collects and shares data from published randomized trials that either have a specific design feature or other characteristic related to pragmatism or they form clusters of trials addressing the same research question but having different aspects of pragmatism. This lays the foundation to determine the relationship of various features of pragmatism, generalizability, and applicability with intervention effects or other trial characteristics. The database contains trial data actively collected for PragMeta but also allows to import and link existing datasets of trials collected for other purposes, forming a large-scale meta-database. PragMeta captures data on (1) trial and design characteristics (e.g., sample size, population, intervention/comparison, outcome, longitudinal structure, blinding), (2) effects estimates, and (3) various determinants of pragmatism (e.g., the use of routinely collected data) and ratings from established tools used to determine pragmatism (e.g., the PRagmatic-Explanatory Continuum Indicator Summary 2; PRECIS-2). PragMeta is continuously provided online, inviting the meta-research community to collaborate, contribute, and/or use the database. As of April 2023, PragMeta contains data from > 700 trials, mostly with assessments on pragmatism.

CONCLUSIONS

PragMeta will inform a better understanding of pragmatism and the generation and interpretation of real-world evidence.

Pharmacovigilance of herbal medicines: Concerns and future prospects

ETHNOPHARMACOLOGICAL RELEVANCE

The use of herbal medicines for prophylaxis, prevention, and treatment of various ailments is rising throughout the world because they are thought to be safer than allopathic treatments, which they are. However, several investigations have documented the toxicity and adverse drug reactions (ADR) of certain formulations and botanicals if not consumed wisely.

AIM OF THE STUDY

The goal of the current study is to address herbal medication pharmacovigilance (PV) modeling and related considerations for improved patient safety. Also, focus is laid on the comprehensive and critical analysis of the current state of PV for herbal medications at the national and international levels.

MATERIALS AND METHODS

Targeted review also known as focused literature review methodology was utilized for exploring the data from various scientific platforms such as Science Direct, Wiley Online Library, Springer, PubMed, Google Scholar using "pharmacovigilance, herbal medicine, traditional medicine, ADR, under reporting, herb toxicity, herb interactions" as keywords along with standard literature pertaining to herbal medicines that is published by the WHO and other international and national organizations etc. The botanical names mentioned in the present article were authenticated using World Flora Online database.

RESULTS

The historical developments paving the way for PV in regulatory setup were also discussed, along with various criteria's for monitoring herbal medicine, ADR of herbs, phytoconstituents, and traditional medicines, herb-drug interactions, modes of reporting ADR, databases for reporting ADR's, provisions of PV in regulatory framework of different nations, challenges and way forward in PV are discussed in detail advocating a robust drug safety ecosystem for herbal medicines.

CONCLUSION

Despite recent efforts to encourage the reporting of suspected ADRs linked to herbal medicines, such as expanding the programme and adding community pharmacists and other healthcare professionals as recognized reporters, the number of herbal ADR reports received by the regulatory bodies remains comparatively low. Since users often do not seek professional advice or report if they have side effects, under-reporting, is anticipated to be significant for herbal medications. There are inadequate quality control methods, poor regulatory oversight considering herbs used in food and botanicals, and unregulated distribution channels. In addition, botanical identity, traceability of herbs, ecological concerns, over-the-counter (OTC) herbal medicines, patient-physicians barriers requires special focus by the regulatory bodies for improved global safety of herbal medicines.

Taxonomy and Biological Control: New Challenges in an Old Relationship

Biological control and taxonomy are continuously developing fields with remarkable impacts on society. At least 80 years of literature have documented this relationship, which remains essentially the same in its mutualistic nature, as well as in its major challenges. From the perspective of Brazilian taxonomists, we discuss the impacts of important scientific and social developments that directly affect research in these areas, posing new challenges for this lasting relationship. The increasing restrictions and concerns regarding the international transit of organisms require improvements in research related to risk assessment for exotic biological control agents and also stimulate prospecting within the native biota. In our view, this is a positive situation that can foster a closer relationship between taxonomists and applied entomologists, as well as local surveys and taxonomic studies that are necessary before new programs and agents can be implemented. We discuss the essential role of molecular biology in this context, as an iconic example of the synergy between applied sciences and natural history. As our society comes to need safer and more sustainable solutions for food security and the biodiversity crisis, scientific progress will build upon this integration, where biological control and taxonomy play an essential role.

Risk-adjusted benchmarking of long-term overall survival in patients with HER2-positive early-stage Breast cancer: A Swedish retrospective cohort study

AIM

The main objective of the current study was to explore the value of risk-adjustment when comparing (i.e. benchmarking) long-term overall survival (OS) in breast cancer (BC) between Swedish regions. We performed risk-adjusted benchmarking of 5- and 10-year OS after HER2-positive early BC diagnosis between Sweden's two largest healthcare regions, constituting approximately a third of the total population in Sweden.

METHODS

All patients diagnosed with HER2-positive early-stage BC between 01-01-2009 and 31-12-2016 in healthcare regions Stockholm-Gotland and Skane were included in the study. Cox proportional hazards model was used for risk-adjustment. Unadjusted (i.e. crude) and adjusted 5- and 10-year OS was benchmarked between the two regions.

RESULTS

The crude 5-year OS was 90.3% in the Stockholm-Gotland region and 87.8% in the Skane region. The crude 10-year OS was 81.7% in the Stockholm-Gotland region and 77.3% in the Skane region. However, when adjusted for age, menopausal status and tumour biology, there was no significant OS disparity between the regions, neither at the 5-year nor 10-year follow-up.

CONCLUSION

This study showed that risk-adjustment is relevant when benchmarking OS in BC, even when comparing regions from the same country that share the same national treatment guidelines. This is, to our knowledge, the first published risk-adjusted benchmarking of OS in HER2-positive BC.

The effectiveness of digital storytelling in teaching medical information searching

BACKGROUND

Novel technologies development has created a new path for education. Digital storytelling (DST) is one of the educational approaches used in universities and scientific centers. We aimed to investigate the effect of DST on Scientific Information Search (SIS) and Information Seeking Anxiety (ISA) in students.

MATERIALS AND METHODS

This mixed-method study utilized the pre-test-post-test method containing test and control groups. We used the simple random sampling method (available) and used the formula to determine the sample size. Forty-two people participated in the study. A researcher-made questionnaire was used to collect SIS data and standard questionnaire for ISA data. The teaching approaches were accomplished using DST and the conventional methods in test and control groups, respectively. Using SPSS v. 22, we did paired-sample T-test and independent sample T-test to compare the mean score in before and after intervention in each group. Also Analysis of Covariancetest was used for considering post-test result as dependent variable, groups as independent variables and pre-test score as covariate.

RESULTS

The results showed significant changes in mean score between the post-test and pre-test of both questionnaire in both groups. Also, in the post-test, compared to the control group, the experimental group obtained higher scores for SIS, which was statistically significant, and obtained lower scores for ISA, but it was not statistically significant.

CONCLUSIONS

It can be concluded that the DST method has a positive impact on learning and reducing ISA compared to the conventional ones, and students' interest and participation in learning have increased using DST method.

Advances in Computational and Bioinformatics Tools and Databases for Designing and Developing a Multi-Epitope-Based Peptide Vaccine

A vaccine is defined as a biologic preparation that trains the immune system, boosts immunity, and protects against a deadly microbial infection. They have been used for centuries to combat a variety of contagious illnesses by means of subsiding the disease burden as well as eradicating the disease. Since infectious disease pandemics are a recurring global threat, vaccination has emerged as one of the most promising tools to save millions of lives and reduce infection rates. The World Health Organization reports that immunization protects three million individuals annually. Currently, multi-epitope-based peptide vaccines are a unique concept in vaccine formulation. Epitope-based peptide vaccines utilize small fragments of proteins or peptides (parts of the pathogen), called epitopes, that trigger an adequate immune response against a particular pathogen. However, conventional vaccine designing and development techniques are too cumbersome, expensive, and time-consuming. With the recent advancement in bioinformatics, immunoinformatics, and vaccinomics discipline, vaccine science has entered a new era accompanying a modern, impressive, and more realistic paradigm in designing and developing next-generation strong immunogens. In silico designing and developing a safe and novel vaccine construct involves knowledge of reverse vaccinology, various vaccine databases, and high throughput techniques. The computational tools and techniques directly associated with vaccine research are extremely effective, economical, precise, robust, and safe for human use. Many vaccine candidates have entered clinical trials instantly and are available prior to schedule. In light of this, the present article provides researchers with up-to-date information on various approaches, protocols, and databases regarding the computational designing and development of potent multi-epitope-based peptide vaccines that can assist researchers in tailoring vaccines more rapidly and cost-effectively.

Inclusion of database outgroups reduces false positives in fungal metabarcoding taxonomic assignments

Metabarcoding studies of fungal communities rely on curated databases for assigning taxonomy. Any host or other nonfungal environmental sequences that are amplified during polymerase chain reaction (PCR) are inherently assigned taxonomy by these same databases, possibly leading to ambiguous nonfungal amplicons being assigned to fungal taxa. Here, we investigated the effects of including nonfungal outgroups in a fungal taxonomic database to aid in detecting and removing these nontarget amplicons. We processed 15 publicly available fungal metabarcode data sets and discovered that roughly 40% of the reads from these studies were not fungal, although they were assigned as Fungus sp. when using a database without nonfungal outgroups. We discuss implications for metabarcoding studies and recommend assigning taxonomy using a database with outgroups to better detect these nonfungal amplicons.

Bridging Glycoinformatics and cheminformatics: integration efforts between GlyCosmos and PubChem

The GlyCosmos Glycoscience Portal (https://glycosmos.org) and PubChem (https://pubchem.ncbi.nlm.nih.gov/) are major portals for glycoscience and chemistry, respectively. GlyCosmos is a portal for glycan-related repositories, including GlyTouCan, GlycoPOST, and UniCarb-DR, as well as for glycan-related data resources that have been integrated from a variety of 'omics databases. Glycogenes, glycoproteins, lectins, pathways, and disease information related to glycans are accessible from GlyCosmos. PubChem, on the other hand, is a chemistry-based portal at the National Center for Biotechnology Information (NCBI). PubChem provides information not only on chemicals, but also genes, proteins, pathways, as well as patents, bioassays, and more, from hundreds of data resources from around the world. In this work, these two portals have made substantial efforts to integrate their complementary data to allow users to cross between these two domains. In addition to glycan structures, key information, such as glycan-related genes, relevant diseases, glycoproteins, and pathways were integrated and cross-linked with one another. The interfaces were designed to enable users to easily find, access, download, and reuse data of interest across these resources. Use cases are described illustrating and highlighting the type of content that can be investigated. In total, these integrations provide life science researchers improved awareness and enhanced access to glycan-related information.

A Survey on the Integration of Blockchains and Databases

The success of blockchain technology in cryptocurrencies reveals its potential in the data management field. Recently, there is a trend in the database community to integrate blockchains and traditional databases to obtain security, efficiency, and privacy from the two distinctive but related systems. In this survey, we discuss the use of blockchain technology in the data management field and focus on the fusion system of blockchains and databases. We first classify existing blockchain-related data management technologies by their locations on the blockchain-database spectrum. Based on the taxonomy, we discuss three types of fusion systems and analyze their design spaces and trade-offs. Then, by further investigating the typical systems and techniques of each type of fusion system and comparing the solutions, we provide insights of each fusion model. Finally, we outline the unsolved challenges and promising directions in this field and believe that fusion systems will take a more important role in data management tasks. We hope this survey can help both academia and industry to better understand the advantages and limitations of blockchain-related data management systems and develop fusion systems that meet various requirements in practice.