Fantastic databases and where to find them: Web applications for researchers in a rush.
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ABSTRACT:
SUBMITTER: Villalba GC
PROVIDER: S-EPMC8022358 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:[FeFe]-hydrogenases are complex metalloenzymes, key to microbial energy metabolism in numerous organisms. During anaerobic metabolism, they dissipate excess reducing equivalents by using protons from water as terminal electron acceptors, leading to hydrogen production. This reaction is coupled to reoxidation of specific redox partners [ferredoxins, NAD(P)H or cytochrome c3], that can be used either individually or simultaneously (via flavin-based electron bifurcation). [FeFe]-hydrogenases also serve additional physiological functions such as H2 uptake (oxidation), H2 sensing, and CO2 fixation. This broad functional spectrum is enabled by a modular architecture and vast genetic diversity, which is not fully explored and understood. This Mini Review summarises recent advancements in identifying and characterising novel [FeFe]-hydrogenases, which has led to expanding our understanding of their multiple roles in metabolism and functional mechanisms. For example, while numerous well-known [FeFe]-hydrogenases are irreversibly damaged by oxygen, some newly discovered enzymes display intrinsic tolerance. These findings demonstrate that oxygen sensitivity varies between different [FeFe]-hydrogenases: in some cases, protection requires the presence of exogenous compounds such as carbon monoxide or sulphide, while in other cases it is a spontaneous built-in mechanism that relies on a reversible conformational change. Overall, it emerges that additional research is needed to characterise new [FeFe]-hydrogenases as this will reveal further details on the physiology and mechanisms of these enzymes that will enable potential impactful applications.
| S-EPMC8924675 | biostudies-literature
Project description:Exogenous glucocorticoids are widely used in the clinic for the treatment of inflammatory disorders and hematological cancers. Unfortunately, their use is associated with debilitating side effects, including hyperglycemia, osteoporosis, mood swings, and weight gain. Despite the continued efforts of pharma as well as academia, the search for so-called selective glucocorticoid receptor modulators (SEGRMs), compounds with strong anti-inflammatory or anti-cancer properties but a reduced number or level of side effects, has had limited success so far. Although monoclonal antibody therapies have been successfully introduced for the treatment of certain disorders (such as anti-TNF for rheumatoid arthritis), glucocorticoids remain the first-in-line option for many other chronic diseases including asthma, multiple sclerosis, and multiple myeloma. This perspective offers our opinion on why a continued search for SEGRMs remains highly relevant in an era where small molecules are sometimes unrightfully considered old-fashioned. Besides a discussion on which bottlenecks and pitfalls might have been overlooked in the past, we elaborate on potential solutions and recent developments that may push future research in the right direction.
| S-EPMC7541956 | biostudies-literature
Project description:Colorectal cancer (CRC) is the third most common cancer worldwide. There is a need for the early diagnosis of CRC for a better prognostic outcome. It is, therefore, crucial to understand the CRC pathogenesis in all its aspects. In many cases, one of the main causes of cancer-related deaths is the presence of metastases. In this context, an often overlooked aspect is the metastatic tropism, since CRC, like other cancers, is more prone to metastasize some organs rather than others. Beyond the liver and lung, and differently from other types of cancers, a not usual site of CRC metastases is the bone. However, it may assume a crucial role in the development and the outcome of the disease. Therefore, this review aims to discuss the complex relations between bone markers and CRC pathogenesis, suggesting the use of these molecules as potential targets for therapeutic purposes. Different osteogenic molecules, some of whom are growth factors and are implicated in the different osteogenic pathways, have been proved to also be involved in CRC progression. Some of them are oncogenes, while others oncosuppressors, and in a future perspective, some of them may represent new potential CRC biomarkers.
| S-EPMC7464482 | biostudies-literature
Project description:BackgroundDigital contact tracing and exposure notification apps have quickly emerged as a potential solution to achieve timely and effective contact tracing for the SARS-CoV-2 virus. Nonetheless, their actual uptake remains limited. Citizens, including patients, are rarely consulted and included in the design and implementation process. Their contribution supports the acceptability of such apps, by providing upstream evidence on incentives and potential barriers that are most relevant to users. The DIGICIT (DIGITal CITizenship) project relied on patient and citizen partnership in research to better integrate public perspectives on these apps. In this paper, we present the co-construction process that led to the survey instrument used in the DIGICIT project and the interpretation of its results. This approach promotes public participation in research on contact tracing and exposure notification apps, as well as related digital health applications.ObjectivesThis article has three objectives: (1) describe the methodological process to co-construct a questionnaire and interpret the survey results with patients and citizens, (2) assess their experiences regarding this methodology, and (3) propose best practices for their involvement in digital health research.MethodsThe DIGICIT project was developed in four steps: (1) creation of the advisory committee composed of patients and citizens, (2) co-construction of a questionnaire, (3) interpretation of survey results, and (4) assessment of the experience of committee participants.ResultsOf the 25 applications received for participation in the advisory committee, we selected 12 people based on pre-established diversity criteria. Participants initially generated 84 survey questions in the first co-construction meeting, and eventually selected 36 in the final version. Participants made more than 20 recommendations when interpreting survey results and suggested carrying out focus groups with marginalized populations to increase representativity. They appreciated their inclusion early in the research process, being listened to and respected, the collective intelligence, and the method used for integrating their suggestions. They suggested that the study objectives and roles be better defined, that more time in the brainstorming sessions be allowed, and that discussion outside of meetings be encouraged.ConclusionHaving patients and citizens actively participating in this research constitutes the main methodological strength. They enriched the study from start to finish, and recommended the addition of focus groups to seek the perspective of marginalized groups that are typically under-represented from digital health research. Clear communication of the project objectives, good organization in meetings, and continuous evaluation from participants allow best practices to be achieved for patients' and citizens' involvement in digital health research. Co-construction in research generates critical study design ideas through collective intelligence. This methodology can be used in various clinical contexts and different healthcare settings.
| S-EPMC9343826 | biostudies-literature
Project description:Regression modeling is a workhorse of statistical ecology that allows to find relationships between a response variable and a set of explanatory variables. Despite being one of the fundamental statistical ideas in ecological curricula, regression modeling can be complex and subtle. This paper is intended as an applied protocol to help students understand the data, select the most appropriate models, verify assumptions, and interpret the output. Basic ecological questions are tackled using data from a fictional series, "Fantastic beasts and where to find them," with the aim to show how statistical thinking can foster curiosity, creativity and imagination in ecology, from the formulation of hypotheses to the interpretation of results.
| S-EPMC8544769 | biostudies-literature
Project description:Parvoviruses are a diverse family of small, non-enveloped DNA viruses that infect a wide variety of species, tissues and cell types. For over half a century, their intriguing biology and pathophysiology has fueled intensive research aimed at dissecting the underlying viral and cellular mechanisms. Concurrently, their broad host specificity (tropism) has motivated efforts to develop parvoviruses as gene delivery vectors for human cancer or gene therapy applications. While the sum of preclinical and clinical data consistently demonstrates the great potential of these vectors, these findings also illustrate the importance of enhancing and restricting in vivo transgene expression in desired cell types. To this end, major progress has been made especially with vectors based on Adeno-associated virus (AAV), whose capsid is highly amenable to bioengineering, repurposing and expansion of its natural tropism. Here, we provide an overview of the state-of-the-art approaches to create new AAV variants with higher specificity and efficiency of gene transfer in on-target cells. We first review traditional and novel directed evolution approaches, including high-throughput screening of AAV capsid libraries. Next, we discuss programmable receptor-mediated targeting with a focus on two recent technologies that utilize high-affinity binders. Finally, we highlight one of the latest stratagems for rational AAV vector characterization and optimization, namely, machine learning, which promises to facilitate and accelerate the identification of next-generation, safe and precise gene delivery vehicles.
| S-EPMC9318892 | biostudies-literature
Project description:Natural products (NPs) have been the centre of attention of the scientific community in the last decencies and the interest around them continues to grow incessantly. As a consequence, in the last 20 years, there was a rapid multiplication of various databases and collections as generalistic or thematic resources for NP information. In this review, we establish a complete overview of these resources, and the numbers are overwhelming: over 120 different NP databases and collections were published and re-used since 2000. 98 of them are still somehow accessible and only 50 are open access. The latter include not only databases but also big collections of NPs published as supplementary material in scientific publications and collections that were backed up in the ZINC database for commercially-available compounds. Some databases, even published relatively recently are already not accessible anymore, which leads to a dramatic loss of data on NPs. The data sources are presented in this manuscript, together with the comparison of the content of open ones. With this review, we also compiled the open-access natural compounds in one single dataset a COlleCtion of Open NatUral producTs (COCONUT), which is available on Zenodo and contains structures and sparse annotations for over 400,000 non-redundant NPs, which makes it the biggest open collection of NPs available to this date.
| S-EPMC7118820 | biostudies-literature
Project description:BackgroundTh EQUATOR Network improves the quality and transparency in health research, primarily by promoting awareness and use of reporting guidelines. In 2018, the UK EQUATOR Centre launched GoodReports.org , a website that helps authors find and use reporting guidelines. This paper describes the tool's development so far. We describe user experience and behaviour of using GoodReports.org both inside and outside a journal manuscript submission process. We intend to use our findings to inform future development and testing of the tool.MethodsWe conducted a survey to collect data on user experience of the GoodReports website. We cross-checked a random sample of 100 manuscripts submitted to a partner journal to describe the level of agreement between the tool's checklist recommendation and what we would have recommended. We compared the proportion of authors submitting a completed reporting checklist alongside their manuscripts between groups exposed or not exposed to the GoodReports tool. We also conducted a study comparing completeness of reporting of manuscript text before an author received a reporting guideline recommendation from GoodReports.org with the completeness of the text subsequently submitted to a partner journal.ResultsSeventy percent (423/599) of survey respondents rated GoodReports 8 or more out of 10 for usefulness, and 74% (198/267) said they had made changes to their manuscript after using the website. We agreed with the GoodReports reporting guideline recommendation in 84% (72/86) of cases. Of authors who completed the guideline finder questionnaire, 14% (10/69) failed to submit a completed checklist compared to 30% (41/136) who did not use the tool. Of the 69 authors who received a GoodReports reporting guideline recommendation, 20 manuscript pairs could be reviewed before and after use of GoodReports. Five included more information in their methods section after exposure to GoodReports. On average, authors reported 57% of necessary reporting items before completing a checklist on GoodReports.org and 60% after.ConclusionThe data suggest that reporting guidance is needed early in the writing process, not at submission stage. We are developing GoodReports by adding more reporting guidelines and by creating editable article templates. We will test whether GoodReports users write more complete study reports in a randomised trial targeting researchers starting to write health research articles.
| S-EPMC8520646 | biostudies-literature
Project description:BackgroundFinding duplicates is an important phase of systematic review. However, no consensus regarding the methods to find duplicates has been provided. This study aims to describe a pragmatic strategy of combining auto- and hand-searching duplicates in systematic review and to evaluate the prevalence and characteristics of duplicates.Methods and findingsLiteratures regarding portal vein thrombosis (PVT) and Budd-Chiari syndrome (BCS) were searched by the PubMed, EMBASE, and Cochrane library databases. Duplicates included one index paper and one or more redundant papers. They were divided into type-I (duplicates among different databases) and type-II (duplicate publications in different journals/issues) duplicates. For type-I duplicates, reference items were further compared between index and redundant papers. Of 10936 papers regarding PVT, 2399 and 1307 were identified as auto- and hand-searched duplicates, respectively. The prevalence of auto- and hand-searched redundant papers was 11.0% (1201/10936) and 6.1% (665/10936), respectively. They included 3431 type-I and 275 type-II duplicates. Of 11403 papers regarding BCS, 3275 and 2064 were identified as auto- and hand-searched duplicates, respectively. The prevalence of auto- and hand-searched redundant papers was 14.4% (1640/11403) and 9.1% (1039/11403), respectively. They included 5053 type-I and 286 type-II duplicates. Most of type-I duplicates were identified by auto-searching method (69.5%, 2385/3431 in PVT literatures; 64.6%, 3263/5053 in BCS literatures). Nearly all type-II duplicates were identified by hand-searching method (94.9%, 261/275 in PVT literatures; 95.8%, 274/286 in BCS literatures). Compared with those identified by auto-searching method, type-I duplicates identified by hand-searching method had a significantly higher prevalence of wrong items (47/2385 versus 498/1046, p<0.0001 in PVT literatures; 30/3263 versus 778/1790, p<0.0001 in BCS literatures). Most of wrong items originated from EMBASE database.ConclusionGiven the inadequacy of a single strategy of auto-searching method, a combined strategy of auto- and hand-searching methods should be employed to find duplicates in systematic review.
| S-EPMC3748039 | biostudies-literature
Project description:BackgroundUnderstanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks.ResultsWe selected M=10 out of N=53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015.ConclusionsOnly some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing.
| S-EPMC4466863 | biostudies-literature