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The translation of animal-based biomedical research into clinical research is often inadequate. Maximizing translation should be central to animal research on human diseases, guiding researchers in study design and animal model selection. However, practical considerations often drive the choice of animal model, which may not always reflect key patient characteristics, such as sex and age, impacting the disease's course. Despite diseases affecting both sexes, researchers frequently use male mice. To address this imbalance, journals and funding agencies have begun questioning the sex of animals used in studies and issued new guidelines. Conversely, the age of rodents is rarely discussed, even though many diseases primarily affect older patients. Young mice are commonly used, even in studies of diseases affecting older adults. Systematic comparisons between the age of rodents used and the age of patients in clinical trials are lacking. In this review, we systematically analyze the age and sex of mice used to model the five leading causes of global disability-adjusted life-years over the age of 75. We compare the results with the age and sex of patients in clinical trials focusing on Alzheimer's disease, stroke, type 2 diabetes mellitus, ischemic heart disease, and chronic obstructive pulmonary disease. We also analyze whether the age of the mice used has changed over the past decade. By systematically assessing the age and sex of the mice, we aim to initiate a discussion on the appropriate choice of animal model to improve the translatability of research results.

Background Muscarinic receptor agonism and positive allosteric modulation is a promising mechanism of action for treating psychosis, not present in most D2R-blocking antipsychotics. Xanomeline, an M1/M4-preferring agonist, has shown efficacy in late-stage clinical trials, with more compounds being investigated. Therefore, we aim to synthesize evidence on the preclinical efficacy of muscarinic receptor agonists and positive allosteric modulators in animal models of psychosis to provide unique insights and evidence-based information to guide drug development. Methods We plan a systematic review and meta-analysis of in vivo animal studies comparing muscarinic receptor agonists or positive allosteric modulators with control conditions and existing D2R-blocking antipsychotics in animals subjected to any method that induces behavioural changes of relevance for psychosis. We will identify eligible studies by searching multiple electronic databases. At least two independent reviewers will conduct the study selection and data extraction using prespecified forms and assess the risk of bias with the SYRCLE’s tool. Our primary outcomes include locomotor activity and prepulse inhibition measured with standardized mean differences. We will examine other behavioural readouts of relevance for psychosis as secondary outcomes, such as social interaction and cognitive function. We will synthesize the data using multi-level meta-analysis with a predefined random-effects structure, considering the non-independence of the data. In meta-regressions we will explore potential sources of heterogeneity from a predefined list of characteristics of the animal population, model, and intervention. We will assess the confidence in the evidence considering a self-developed instrument thatconsiders the internal and external validity of the evidence. Protocol registration PROSPERO-ID: CRD42024520914

The translation of animal-based biomedical research into clinical research is often inadequate. Maximizing translation should be central to animal research on human diseases, guiding researchers in study design and animal model selection. However, practical considerations often drive the choice of animal model, which may not always reflect key patient characteristics, such as sex and age, impacting the disease's course. Despite diseases affecting both sexes, researchers frequently use male mice. To address this imbalance, journals and funding agencies have begun questioning the sex of animals used in studies and issued new guidelines. Conversely, the age of rodents is rarely discussed, even though many diseases primarily affect older patients. Young mice are commonly used, even in studies of diseases affecting older adults. Systematic comparisons between the age of rodents used and the age of patients in clinical trials are lacking. In this review, we systematically analyze the age and sex of mice used to model the five leading causes of global disability-adjusted life-years over the age of 75. We compare the results with the age and sex of patients in clinical trials focusing on Alzheimer's disease, stroke, type 2 diabetes mellitus, ischemic heart disease, and chronic obstructive pulmonary disease. We also analyze whether the age of the mice used has changed over the past decade. By systematically assessing the age and sex of the mice, we aim to initiate a discussion on the appropriate choice of animal model to improve the translatability of research results.

Purpose: The aim of this study was to investigate the efficacy of calcitonin (CT) in animal models of experimental osteoarthritis (OA) and rheumatoid arthritis (RA), as new stabilized CT formulations are currently being introduced. Methods: A comprehensive and systemic literature search was conducted in PubMed/MEDLINE and Embase databases to identify articles with original data on CT treatment of preclinical OA and RA. Methodological quality was assessed using the Systematic Review Centre for Laboratory Animal Experimentation’s risk of bias tool for animal intervention studies. To provide summary estimates of efficacy, a meta-analysis was conducted for outcomes reported in four or more studies, using a random-effects model. Subgroup analyses were employed to correct for study specifics. Results: Twenty-six studies were ultimately evaluated and data from 16 studies could be analyzed in the meta-analysis, which included the following outcomes: bone mineral density, bone volume, levels of cross-linked C-telopeptide of type I collagen, histopathological arthritis score, and mechanical allodynia. For all considered outcome parameters, CT-treated groups were significantly superior to control groups (P = 0.002; P = 0.01; P < 0.00001; P < 0.00001; P = 0.04). For most outcomes, effect sizes were significantly greater in OA than in RA (P ≤ 0.025). High in-between study heterogeneity was detected. Conclusion: There is preclinical evidence for an antioxidant, anti-inflammatory, antinociceptive, cartilage-and bone-protective effect of CT in RA and OA. Given these effects, CT presents a promising agent for the treatment of both diseases, although the potential seems to be greater in OA.

Background Muscarinic receptor agonism is a promising mechanism of action for treating psychosis, not present in most D 2R-blocking antipsychotics. Xanomeline, an M1/M4-preferring agonist, has shown efficacy in late-stage clinical trials, with more compounds being investigated. Therefore, we aim to synthesize evidence on the preclinical efficacy of muscarinic receptor agonists in animal models of psychosis to provide unique insights and evidence-based information to guide drug development. Methods We plan a systematic review and meta-analysis of in vivo animal studies comparing muscarinic receptor agonists or positive allosteric modulators with control conditions and existing D2R-blocking antipsychotics in animals subjected to any method that induces behavioural changes of relevance for psychosis. We will identify eligible studies by searching multiple electronic databases. At least two independent reviewers will conduct the study selection and data extraction using prespecified forms and assess the risk of bias with the SYRCLE’s tool. Our primary outcomes include locomotor activity and prepulse inhibition measured with standardized mean differences. We will examine other behavioural readouts of relevance for psychosis as secondary outcomes, such as social interaction and cognitive function. We will synthesize the data using multi-level meta-analysis with a predefined random-effects structure, considering the non-independence of the data. In meta-regressions we will explore potential sources of heterogeneity from a predefined list of characteristics of the animal population, model, and intervention. We will assess the confidence in the evidence considering a self-developed instrument thatconsiders the internal and external validity of the evidence. Protocol registration PROSPERO-ID: CRD42024520914

The translation of animal-based biomedical research into clinical research is often inadequate. Maximizing translation should be central to animal research on human diseases, guiding researchers in study design and animal model selection. However, practical considerations often drive the choice of animal model, which may not always reflect key patient characteristics, such as sex and age, impacting the disease's course. Despite diseases affecting both sexes, researchers frequently use male mice. To address this imbalance, journals and funding agencies have begun questioning the sex of animals used in studies and issued new guidelines. Conversely, the age of rodents is rarely discussed, even though many diseases primarily affect older patients. Young mice are commonly used, even in studies of diseases affecting older adults. Systematic comparisons between the age of rodents used and the age of patients in clinical trials are lacking. In this review, we systematically analyze the age and sex of mice used to model the five leading causes of global disability-adjusted life-years over the age of 75. We compare the results with the age and sex of patients in clinical trials focusing on Alzheimer's disease, stroke, type 2 diabetes mellitus, ischemic heart disease, and chronic obstructive pulmonary disease. We also analyze whether the age of the mice used has changed over the past decade. By systematically assessing the age and sex of the mice, we aim to initiate a discussion on the appropriate choice of animal model to improve the translatability of research results.

Background: Effective communication is crucial for broad acceptance and applicability of alternative methods in 3R biomedical research and preclinical testing. 3D bioprinting is used to construct intricate biological structures towards functional liver models, specifically engineered for deployment as alternative models in drug screening, toxicological investigations, and tissue engineering. Despite a growing number of reviews in this emerging field, a comprehensive study, systematically assessing practices and reporting quality for bioprinted liver models is missing. Methods: In this systematic scoping review we systematically searched MEDLINE (Ovid), EMBASE (Ovid) and BioRxiv for studies published prior to June 2nd, 2022. We extracted data on methodological conduct, applied bioinks, the composition of the printed model, performed experiments and model applications. Records were screened for eligibility and data were extracted from included articles by two independent reviewers from a panel of seven domain experts specializing in bioprinting and liver biology. We used RAYYAN for the screening process and SyRF for data extraction. We used R for data analysis, and R and Graphpad PRISM for visualization. Results: Through our systematic database search we identified 1042 records, from which 63 met the eligibility criteria for inclusion in this systematic scoping review. Our findings revealed that extrusion-based printing, in conjunction with bioinks composed of natural components, emerged as the predominant printing technique in the bioprinting of liver models. Notably, the HepG2 hepatoma cell line was the most frequently employed liver cell type, despite acknowledged limitations. Furthermore, 51% of the printed models featured co-cultures with non-parenchymal cells to enhance their complexity. The included studies offered a variety of techniques for characterizing these liver models, with their primary application predominantly focused on toxicity testing. Among the frequently analyzed liver markers, albumin and urea stood out. Additionally, Cytochrome P450 (CYP) isoforms, primarily CYP3A and CYP1A, were assessed, and select studies employed nuclear receptor agonists to induce CYP activity. Conclusion: Our systematic scoping review offers an evidence-based overview and evaluation of the current state of research on bioprinted liver models, representing a promising and innovative technology for creating alternative organ models. We conducted a thorough examination of both the methodological and technical facets of model development and scrutinized the reporting quality within the realm of bioprinted liver models. This systematic scoping review can serve as a valuable template for systematically evaluating the progress of organ model development in various other domains. The transparently derived evidence presented here can provide essential support to the research community, facilitating the adaptation of technological advancements, the establishment of standards, and the enhancement of model robustness. This is particularly crucial as we work toward the long-term objective of establishing new approach methods as reliable alternatives to animal testing, with extensive and versatile applications.

Reproducible research and open science practices have the potential to accelerate scientific progress by allowing others to reuse research outputs, and by promoting rigorous research that is more likely to yield trustworthy results. However, these practices are uncommon in many fields, so there is a clear need for training that helps and encourages researchers to integrate reproducible research and open science practices into their daily work. Here, we outline eleven strategies for making training in these practices the norm at research institutions. The strategies, which emerged from a virtual brainstorming event organized in collaboration with the German Reproducibility Network, are concentrated in three areas: (i) adapting research assessment criteria and program requirements; (ii) training; (iii) building communities. We provide a brief overview of each strategy, offer tips for implementation, and provide links to resources. We also highlight the importance of allocating resources and monitoring impact. Our goal is to encourage researchers – in their roles as scientists, supervisors, mentors, instructors, and members of curriculum, hiring or evaluation committees – to think creatively about the many ways they can promote reproducible research and open science practices in their institutions.

Lack of reproducibility of research results has become a major theme in recent years. As we emerge from the COVID-19 pandemic, economic pressures and exposed consequences of lack of societal trust in science make addressing reproducibility of urgent importance. TIER2 is a new international project funded by the European Commission under their Horizon Europe programme. Covering three broad research areas (social, life and computer sciences) and two cross-disciplinary stakeholder groups (research publishers and funders) to systematically investigate reproducibility across contexts, TIER2 will significantly boost knowledge on reproducibility, create tools, engage communities, implement interventions and policy across different contexts to increase re-use and overall quality of research results in the European Research Area and global R&I, and consequently increase trust, integrity and efficiency in research.

Abstract Rigorous evidence is vital in all disciplines to ensure efficient, appropriate, and fit-for-purpose decision-making with minimised risk of unintended harm. To date, however, disciplines have been slow to share evidence synthesis frameworks, best practices, and tools amongst one another. Recent progress in collaborative digital and programmatic frameworks, such as the free and Open Source software R, have significantly expanded the opportunities for development of free-to-use, incrementally improvable, community driven tools to support evidence synthesis (e.g. EviAtlas, robvis, PRISMA2020 flow diagrams and metadat). Despite this, evidence synthesis (and meta-analysis) practitioners and methodologists who make use of R remain relatively disconnected from one another. Here, we report on a new virtual conference for evidence synthesis and meta-analysis in the R programming environment (ESMARConf) that aims to connect these communities. By designing an entirely free and online conference from scratch, we have been able to focus efforts on maximising accessibility and equity—making these core missions for our new community of practice. As a community of practice, ESMARConf builds on the success and groundwork of the broader R community and systematic review coordinating bodies (e.g. Cochrane), but fills an important niche. ESMARConf aims to maximise accessibility and equity of participants across regions, contexts, and social backgrounds, forging a level playing field in a digital, connected, and online future of evidence synthesis. We believe that everyone should have the same access to participation and involvement, and we believe ESMARConf provides a vital opportunity to push for equitability across disciplines, regions, and personal situations.

Abstract A spectre is haunting biomedical research: It appears that a substantial fraction of published research results cannot be reproduced, while spectacularly successful novel treatments developed in experimental models of disease too often fail in clinical trials. A reproducibility crisis has been proclaimed, and bench‐to‐bedside translation appears to be lost in a “valley of death”. Both predicaments, non‐reproducibility and translational roadblocks, are connected: Why should we expect to successfully “trans‐late” results to humans, if already “cis‐lation”—that is, the generalization from one experimental setting to an identical or fairly similar one—often fails?

The current report is the result of the recent EFSA‐EBTC initiative to advance the use of automation tools in systematic reviews. The working group members identified the need for data exchange standards to facilitate transferring data between SR automation tools without loss or changes in data as a first priority. Closely related objectives identified were the concurrent development of data guidance and data content standards, and a strategy for governance of the resulting data standards, including their maintenance and regular updates. These conversations led up to the organization of workshops to initiate the development of data standards, as described in the current report. The EFSA‐EBTC collaboration organized a four half‐day workshop for an extensive group of stakeholders actively involved in the development or use of SR automation tools. The main objectives were 1) to generate a common understanding of the needs and hurdles for data standards in SR automation, 2) to collate viewpoints from all relevant stakeholders on how such standards may be developed, and 3) to kick‐off the development process. Three preparatory workshops preceding the final workshop were organized to clarify the scope and objectives of the main workshop. This external scientific report contains the summary of the preparatory and main workshops, as well as the proposed strategy for the process of how these data standards could be developed. The recommended next steps for development focus on further refining the aims, analysis of the landscape, interoperability with existing standards, setting up the infrastructure (e.g., establishing a governance structure and working groups containing all essential stakeholders), and the process of actual data standard development, launching and monitoring adoption of the standards. [ABSTRACT FROM AUTHOR]

Abstract Background Here, we outline a method of applying existing machine learning (ML) approaches to aid citation screening in an on-going broad and shallow systematic review of preclinical animal studies. The aim is to achieve a high-performing algorithm comparable to human screening that can reduce human resources required for carrying out this step of a systematic review. Methods We applied ML approaches to a broad systematic review of animal models of depression at the citation screening stage. We tested two independently developed ML approaches which used different classification models and feature sets. We recorded the performance of the ML approaches on an unseen validation set of papers using sensitivity, specificity and accuracy. We aimed to achieve 95% sensitivity and to maximise specificity. The classification model providing the most accurate predictions was applied to the remaining unseen records in the dataset and will be used in the next stage of the preclinical biomedical sciences systematic review. We used a cross-validation technique to assign ML inclusion likelihood scores to the human screened records, to identify potential errors made during the human screening process (error analysis). Results ML approaches reached 98.7% sensitivity based on learning from a training set of 5749 records, with an inclusion prevalence of 13.2%. The highest level of specificity reached was 86%. Performance was assessed on an independent validation dataset. Human errors in the training and validation sets were successfully identified using the assigned inclusion likelihood from the ML model to highlight discrepancies. Training the ML algorithm on the corrected dataset improved the specificity of the algorithm without compromising sensitivity. Error analysis correction leads to a 3% improvement in sensitivity and specificity, which increases precision and accuracy of the ML algorithm. Conclusions This work has confirmed the performance and application of ML algorithms for screening in systematic reviews of preclinical animal studies. It has highlighted the novel use of ML algorithms to identify human error. This needs to be confirmed in other reviews with different inclusion prevalence levels, but represents a promising approach to integrating human decisions and automation in systematic review methodology.