Drug development: Raise standards for preclinical cancer research

A registered report survey of open research practices in psychology departments in the UK and Ireland

Open research practices seek to enhance the transparency and reproducibility of research. While there is evidence of increased uptake in these practices, such as study preregistration and open data, facilitated by new infrastructure and policies, little research has assessed general uptake of such practices across psychology university researchers. The current study estimates psychologists' level of engagement in open research practices across universities in the United Kingdom and Ireland, while also assessing possible explanatory factors that may impact their engagement. Data were collected from 602 psychology researchers in the United Kingdom and Ireland on the extent to which they have implemented various practices (e.g., use of preprints, preregistration, open data, open materials). Here we present the summarized descriptive results, as well as considering differences between various categories of researcher (e.g., career stage, subdiscipline, methodology), and examining the relationship between researcher's practices and their self-reported capability, opportunity, and motivation (COM-B) to engage in open research practices. Results show that while there is considerable variability in engagement of open research practices, differences across career stage and subdiscipline of psychology are small by comparison. We observed consistent differences according to respondent's research methodology and based on the presence of institutional support for open research. COM-B dimensions were collectively significant predictors of engagement in open research, with automatic motivation emerging as a consistently strong predictor. We discuss these findings, outline some of the challenges experienced in this study, and offer suggestions and recommendations for future research. Estimating the prevalence of responsible research practices is important to assess sustained behaviour change in research reform, tailor educational training initiatives, and to understand potential factors that might impact engagement.

The Untapped Potential of Comparative Biology in Aging Research: Insights From the Extraordinary-Long-Lived Naked Mole-Rat

The search for solutions to the vagaries of aging has, historically, been akin to searching at night in the bright light under street lamps by utilizing the few preexisting and well-established animal model systems. Throughout my career as a comparative biologist, I have ventured into the darkness across 4 continents and studied over 150 different animal species, many of which have evolved remarkable adaptations to survive on the harsh and rugged fitness landscape that exists outside of the laboratory setting. In this Fellows Forum, I will discuss the main focus of my research for the last 25 years and dig deeply into the biology of the preternaturally long-lived naked mole-rat that makes it an ideal model system for the characterization of successful strategies to combat aging.

Adherence to the Guideline for Reporting Evidence-based practice Educational interventions and Teaching (GREET) of studies on evidence-based healthcare e-learning: a cross-sectional study

OBJECTIVES

The objectives of this study are to assess reporting of evidence-based healthcare (EBHC) e-learning interventions using the Guideline for Reporting Evidence-based practice Educational interventions and Teaching (GREET) checklist and explore factors associated with compliant reporting.

DESIGN

Methodological cross-sectional study.

METHODS

Based on the criteria used in an earlier systematic review, we included studies comparing EBHC e-learning and any other form of EBHC training or no EBHC training. We searched Medline, Embase, ERIC, CINAHL, CENTRAL, SCOPUS, Web of Knowledge, PsycInfo, ProQuest and Best Evidence Medical Education up to 4 January 2023. Screening of titles, abstracts, full-text articles and data extraction was done independently by two authors. For each study, we assessed adherence to each of the 17 GREET items and extracted information on possible predictors. Adequacy of reporting for each item of the GREET checklist was judged with yes (provided complete information), no (provided no information), unclear (when insufficient information was provided), or not applicable, when the item was clearly of no relevance to the intervention described (such as for item 8-details about the instructors-in the studies which used electronic, self-paced intervention, without any tutoring). Studies' adherence to the GREET checklist was presented as percentages and absolute numbers. We performed univariate analysis to assess the association of potential adherence predictors with the GREET checklist. We summarised results descriptively.

RESULTS

We included 40 studies, the majority of which assessed e-learning or blended learning and mostly involved medical and other healthcare students. None of the studies fully reported all the GREET items. Overall, the median number of GREET items met (received yes) per study was 8 and third quartile (Q3) of GREET items met per study was 9 (min. 4 max. 14). When we used Q3 of the number of items met as cut-off point, adherence to the GREET reporting checklist was poor with 7 out of 40 studies (17.5%) reporting items of the checklist on acceptable level (adhered to at least 10 items out of 17). None of the studies reported on all 17 GREET items. For 3 items, 80% of included studies well reported information (received yes for these items): item 1 (brief description of intervention), item 4 (evidence-based practice content) and item 6 (educational strategies). Items for which 50% of included studies reported complete information (received yes for these items) included: item 9 (modes of delivery), item 11 (schedule) and 12 (time spent on learning). The items for which 70% or more of included studies did not provide information (received no for these items) included: item 7 (incentives) and item 13 (adaptations; for both items 70% of studies received no for them), item 14 (modifications of educational interventions-95% of studies received no for this item), item 16 (any processes to determine whether the materials and the educational strategies used in the educational intervention were delivered as originally planned-93% of studies received no for this item) and 17 (intervention delivery according to schedule-100% of studies received no for this item). Studies published after September 2016 showed slight improvements in nine reporting items. In the logistic regression models, using the cut-off point of Q3 (10 points or above) the odds of acceptable adherence to GREET guidelines were 7.5 times higher if adherence to other guideline (Consolidated Standards of Reporting Trials, Strengthening the Reporting of Observational Studies in Epidemiology, etc) was reported for a given study type (p=0.039), also higher number of study authors increased the odds of adherence to GREET guidance by 18% (p=0.037).

CONCLUSIONS

Studies assessing educational interventions on EBHC e-learning still poorly adhere to the GREET checklist. Using other reporting guidelines increased the odds of better GREET reporting. Journals should call for the use of appropriate use of reporting guidelines of future studies on teaching EBHC to increase transparency of reporting, decrease unnecessary research duplication and facilitate uptake of research evidence or result.

STUDY REGISTRATION NUMBER

The Open Science Framework (https://doi.org/10.17605/OSF.IO/V86FR).

The 30th Annual Prostate Cancer Foundation Scientific Retreat Report

BACKGROUND

The 30th Annual Prostate Cancer Foundation (PCF) Scientific Retreat was held at the Omni La Costa Resort in Carlsbad, CA, from October 26 to 28, 2023. A hybrid component was included for virtual attendees.

METHODS

The Annual PCF Scientific Retreat is a leading international scientific conference focused on pioneering, unpublished, and impactful studies across the spectrum of basic through clinical prostate cancer research, as well as research from related fields with significant potential for improving prostate cancer research and patient outcomes.

RESULTS

The 2023 PCF Retreat concentrated on key areas of research, including: (i) the biology of cancer stem cells and prostate cancer lineage plasticity; (ii) mechanisms of treatment resistance; (iii) emerging AI applications in diagnostic medicine; (iv) analytical and computational biology approaches in cancer research; (v) the role of nerves in prostate cancer; (vi) the biology of prostate cancer bone metastases; (vii) the contribution of ancestry and genomics to prostate cancer disparities; (viii) prostate cancer 3D genomics; (ix) progress in new targets and treatments for prostate cancer; (x) the biology and translational applications of tumor extracellular vesicles; (xi) updates from PCF TACTICAL Award teams; (xii) novel platforms for small molecule molecular glues and binding inhibitors; and (xiii) diversity, equity and inclusion strategies for advancing cancer care equity.

CONCLUSIONS

This meeting report summarizes the presentations and discussions from the 2023 PCF Scientific Retreat. We hope that sharing this information will deepen our understanding of current and emerging research and drive future advancements in prostate cancer patient care.

Effect-Size Discrepancies in Literature Versus Raw Datasets from Experimental Spinal Cord Injury Studies: A CLIMBER Meta-Analysis

Translation of spinal cord injury (SCI) therapeutics from pre-clinical animal studies into human studies is challenged by effect size variability, irreproducibility, and misalignment of evidence used by pre-clinical versus clinical literature. Clinical literature values reproducibility, with the highest grade evidence (class 1) consisting of meta-analysis demonstrating large therapeutic efficacy replicating across multiple studies. Conversely, pre-clinical literature values novelty over replication and lacks rigorous meta-analyses to assess reproducibility of effect sizes across multiple articles. Here, we applied modified clinical meta-analysis methods to pre-clinical studies, comparing effect sizes extracted from published literature to raw data on individual animals from these same studies. Literature-extracted data (LED) from numerical and graphical outcomes reported in publications were compared with individual animal data (IAD) deposited in a federally supported repository of SCI data. The animal groups from the IAD were matched with the same cohorts in the LED for a direct comparison. We applied random-effects meta-analysis to evaluate predictors of neuroconversion in LED versus IAD. We included publications with common injury models (contusive injuries) and standardized end-points (open field assessments). The extraction of data from 25 published articles yielded n = 1841 subjects, whereas IAD from these same articles included n = 2441 subjects. We observed differences in the number of experimental groups and animals per group, insufficient reporting of dropout animals, and missing information on experimental details. Meta-analysis revealed differences in effect sizes across LED versus IAD stratifications, for instance, severe injuries had the largest effect size in LED (standardized mean difference [SMD = 4.92]), but mild injuries had the largest effect size in IAD (SMD = 6.06). Publications with smaller sample sizes yielded larger effect sizes, while studies with larger sample sizes had smaller effects. The results demonstrate the feasibility of combining IAD analysis with traditional LED meta-analysis to assess effect size reproducibility in SCI.

How to fight fake papers: a review on important information sources and steps towards solution of the problem

Scientific fake papers, containing manipulated or completely fabricated data, are a problem that has reached dramatic dimensions. Companies known as paper mills (or more bluntly as "criminal science publishing gangs") produce and sell such fake papers on a large scale. The main drivers of the fake paper flood are the pressure in academic systems and (monetary) incentives to publish in respected scientific journals and sometimes the personal desire for increased "prestige." Published fake papers cause substantial scientific, economic, and social damage. There are numerous information sources that deal with this topic from different points of view. This review aims to provide an overview of these information sources until June 2024. Much more original research with larger datasets is needed, for example on the extent and impact of the fake paper problem and especially on how to detect them, as many findings are based more on small datasets, anecdotal evidence, and assumptions. A long-term solution would be to overcome the mantra of publication metrics for evaluating scientists in academia.

Targeted immunotherapy and nanomedicine for rhabdomyosarcoma: The way of the future

Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma of childhood. Histology separates two main subtypes: embryonal RMS (eRMS; 60%-70%) and alveolar RMS (aRMS; 20%-30%). The aggressive aRMS carry one of two characteristic chromosomal translocations that result in the expression of a PAX3::FOXO1 or PAX7::FOXO1 fusion transcription factor; therefore, aRMS are now classified as fusion-positive (FP) RMS. Embryonal RMS have a better prognosis and are clinically indistinguishable from fusion-negative (FN) RMS. Next to histology and molecular characteristics, RMS risk groupings are now available defining low risk tumors with excellent outcomes and advanced stage disease with poor prognosis, with an overall survival of about only 20% despite intensified multimodal treatment. Therefore, development of novel effective targeted strategies to increase survival and to decrease long-term side effects is urgently needed. Recently, immunotherapies and nanomedicine have been emerging for potent and effective tumor treatments with minimal side effects, raising hopes for effective and safe cures for RMS patients. This review aims to describe the most relevant preclinical and clinical studies in immunotherapy and targeted nanomedicine performed so far in RMS and to provide an insight in future developments.

Time-dependent proteomics and drug response in expanding cancer cells

Cancer cell line is commonly used for discovery and development of anti-cancer drugs. It is generally considered that drug response remains constant for a certain cell line due to the identity of genetics thus protein patterns. Here, we demonstrated that cancer cells continued dividing even after reaching confluence, in that the proteomics was changed continuously and dramatically with strong relevance to cell division, cell adhesion and cell metabolism, indicating time-dependent intrinsically reprogramming of cells during expansion. Of note, the inhibition effect of most anti-cancer drugs was strikingly attenuated in culture cells along with cell expansion, with the strongest change at the third day when cells were still expanding. Profiling of an FDA-approved drug library revealed that attenuation of response with cell expansion is common for most drugs, an exception was TAK165 that was a selective inhibitor of mitochondrial respiratory chain complex I. Finally, we screened a panel of natural products and identified four pentacyclic triterpenes as selective inhibitors of cancer cells under prolonged growth. Taken together, our findings underscore that caution should be taken in evaluation of anti-cancer drugs using culture cells, and provide agents selectively targeting overgrowth cancer cells.

Improving light microscopy training routines with evidence-based education

The low reproducibility of scientific data published in articles has recently become a cause of concern in many scientific fields. Data involving light microscopy is no exception. The low awareness of researchers of the technologies they use in their research has been identified as one of the main causes of the problem. Potential solutions have hinted at the need to improve technological and methodological education within research. Despite the pivotal role of microscopy core facilities in the education of researchers being well documented, facility staff (FS) often learn their trade on the job, without receiving themselves any structured education about the technology they teach others to use. Additionally, despite endorsing an important role at the highest level of education, most FS never receive any training in pedagogy, the field of research on teaching and learning methods. In this article, we argue that the low level of awareness that researchers have of microscopy stems from a knowledge gap formed between them and microscopy FS during training routines. On the one hand, FS consider that their teaching task is to explain what is needed to produce reliable data. On the other, despite understanding what is being taught, researchers fail to learn the most challenging aspects of microscopy, those involving their judgement and reasoning. We suggest that the misunderstanding between FS and researchers is due to FS not being educated in pedagogy and thus often confusing understanding and learning. To bridge this knowledge gap and improve the quality of the microscopy education available to researchers, we propose a paradigm shift where training staff at technological core facilities be acknowledged as full-fledged teachers and offered structured education not only in the technology they teach but also in pedagogy. We then suggest that training routines at facilities be upgraded to follow the principles of the Constructive Alignment pedagogical method. We give an example of how this can be applied to existing microscopy training routines. We also describe a model to define where the responsibility of FS in training researchers begins and ends. This involves a major structural change where university staff involved in teaching research technologies themselves receive appropriate education. For this to be achieved, we advocate that funding agencies, universities, microscopy and core facility organisations mobilise resources of time and funding. Such changes may involve funding the creation and development of 'Train-the-trainer' type of courses and giving incentives for FS to upgrade their technological and pedagogical knowledge, for example by including them in career paths. We believe that this paradigm shift is necessary to improve the level of microscopy education and ultimately the reproducibility of published data.

Grid/lattice therapy: consideration of small field dosimetry

Small-field dosimetry used in special procedures such as gamma knife, Cyberknife, Tomotherapy, IMRT, and VMAT has been in evolution after several radiation incidences with very significant (70%) errors due to poor understanding of the dosimetry. IAEA-TRS-483 and AAPM-TG-155 have provided comprehensive information on small-fields dosimetry in terms of code of practice and relative dosimetry. Data for various detectors and conditions have been elaborated. It turns out that with a suitable detectors dose measurement accuracy can be reasonably (±3%) achieved for 6 MV beams for fields >1×1 cm2. For grid therapy, even though the treatment is performed with small fields created by either customized blocks, multileaf collimator (MLC), or specialized devices, it is multiple small fields that creates combined treatment. Hence understanding the dosimetry in collection of holes of small field is a separate challenge that needs to be addressed. It is more critical to understand the scattering conditions from multiple holes that form the treatment grid fields. Scattering changes the beam energy (softer) and hence dosimetry protocol needs to be properly examined for having suitable dosimetric parameters. In lieu of beam parameter unavailability in physical grid devices, MLC-based forward and inverse planning is an alternative path for bulky tumours. Selection of detectors in small field measurement is critical and it is more critical in mixed beams created by scattering condition. Ramification of small field concept used in grid therapy along with major consideration of scattering condition is explored. Even though this review article is focussed mainly for dosimetry for low-energy megavoltage photon beam (6 MV) but similar procedures could be adopted for high energy beams. To eliminate small field issues, lattice therapy with the help of MLC is a preferrable choice.

NLRP3 inflammasome in sepsis: don't overlook the small steps-they can make a big difference!

"In sepsis, persistent activation of NLRP3 is associated with expansion of both monocytic and granulocytic MDSCs, along with high plasma concentration of IL-10."

Lineage-specific proteome remodeling of diverse lung cancer cells by targeted epigenetic inhibitors

Epigenetic inhibitors exhibit powerful antiproliferative and anticancer activities. However, cellular responses to small-molecule epigenetic inhibition are heterogenous and dependent on factors such as the genetic background, metabolic state, and on-/off-target engagement of individual small-molecule drugs. To determine the mechanisms that drive these heterogeneous cellular responses, we quantified chromatin, proteome, and transcriptome remodeling due to histone deacetylase inhibitor (HDACi) -treated cells derived from diverse genetic backgrounds. We utilized high-throughput sample multiplexed proteomics and integrated intelligent data acquisition methods to map proteomes of cancer cell lines in response to HDACi. We determined cell type-specific and ubiquitous cellular responses based on the quantification of 10,621 total proteins. We then established how coordinated remodeling of the proteome, transcriptome and chromatin state of HDACi treated cancer cells revealed convergent (JUN, MAP2K3, CDKN1A) and divergent (CCND3, ASF1B, BRD7) molecular phenotypes. HDACi-regulated proteins differ greatly across cell lines owing to heterogeneous molecular states of these cell lines. Finally, we demonstrated that HDACi treatment drove a highly cell-type specific response that may in part be explained by cell line-specific off-target drug engagement.

Impact of redefining statistical significance on P-hacking and false positive rates: An agent-based model

In recent years, concern has grown about the inappropriate application and interpretation of P values, especially the use of P<0.05 to denote "statistical significance" and the practice of P-hacking to produce results below this threshold and selectively reporting these in publications. Such behavior is said to be a major contributor to the large number of false and non-reproducible discoveries found in academic journals. In response, it has been proposed that the threshold for statistical significance be changed from 0.05 to 0.005. The aim of the current study was to use an evolutionary agent-based model comprised of researchers who test hypotheses and strive to increase their publication rates in order to explore the impact of a 0.005 P value threshold on P-hacking and published false positive rates. Three scenarios were examined, one in which researchers tested a single hypothesis, one in which they tested multiple hypotheses using a P<0.05 threshold, and one in which they tested multiple hypotheses using a P<0.005 threshold. Effects sizes were varied across models and output assessed in terms of researcher effort, number of hypotheses tested and number of publications, and the published false positive rate. The results supported the view that a more stringent P value threshold can serve to reduce the rate of published false positive results. Researchers still engaged in P-hacking with the new threshold, but the effort they expended increased substantially and their overall productivity was reduced, resulting in a decline in the published false positive rate. Compared to other proposed interventions to improve the academic publishing system, changing the P value threshold has the advantage of being relatively easy to implement and could be monitored and enforced with minimal effort by journal editors and peer reviewers.

Replication of null results: Absence of evidence or evidence of absence?

In several large-scale replication projects, statistically non-significant results in both the original and the replication study have been interpreted as a 'replication success.' Here, we discuss the logical problems with this approach: Non-significance in both studies does not ensure that the studies provide evidence for the absence of an effect and 'replication success' can virtually always be achieved if the sample sizes are small enough. In addition, the relevant error rates are not controlled. We show how methods, such as equivalence testing and Bayes factors, can be used to adequately quantify the evidence for the absence of an effect and how they can be applied in the replication setting. Using data from the Reproducibility Project: Cancer Biology, the Experimental Philosophy Replicability Project, and the Reproducibility Project: Psychology we illustrate that many original and replication studies with 'null results' are in fact inconclusive. We conclude that it is important to also replicate studies with statistically non-significant results, but that they should be designed, analyzed, and interpreted appropriately.

Exploring the understanding of reproducibility among stakeholders within academia and their expectations for a web-based education tool: A qualitative study

Although reproducibility is central to the scientific method, its understanding within the research community remains insufficient. We aimed to explore the perceptions of research reproducibility among stakeholders within academia, learn about possible barriers and facilitators to reproducibility-related practices, and gather their suggestions for the Croatian Reproducibility Network website. We conducted four focus groups with researchers, teachers, editors, research managers, and policymakers from Croatia (n = 23). The participants observed a lack of consensus on the core definitions of reproducibility, both generally and between disciplines. They noted that incentivization and recognition of reproducibility-related practices from publishers and institutions, alongside comprehensive education adapted to the researchers' career stage, could help with implementing reproducibility. Education was considered essential to these efforts, as it could help create a research culture based on good reproducibility-related practices and behavior rather than one driven by mandates or career advancement. This was particularly found to be relevant for growing reproducibility efforts globally. Regarding the Croatian Reproducibility Network website, the participants suggested we adapt the content to users from different disciplines or career stages and offer guidance and tools for reproducibility through which we should present core reproducibility concepts. Our findings could inform other initiatives focused on improving research reproducibility.

A powerful approach to identify replicable variants in genome-wide association studies

Replicability is the cornerstone of modern scientific research. Reliable identifications of genotype-phenotype associations that are significant in multiple genome-wide association studies (GWASs) provide stronger evidence for the findings. Current replicability analysis relies on the independence assumption among single-nucleotide polymorphisms (SNPs) and ignores the linkage disequilibrium (LD) structure. We show that such a strategy may produce either overly liberal or overly conservative results in practice. We develop an efficient method, ReAD, to detect replicable SNPs associated with the phenotype from two GWASs accounting for the LD structure. The local dependence structure of SNPs across two heterogeneous studies is captured by a four-state hidden Markov model (HMM) built on two sequences of p values. By incorporating information from adjacent locations via the HMM, our approach provides more accurate SNP significance rankings. ReAD is scalable, platform independent, and more powerful than existing replicability analysis methods with effective false discovery rate control. Through analysis of datasets from two asthma GWASs and two ulcerative colitis GWASs, we show that ReAD can identify replicable genetic loci that existing methods might otherwise miss.

Effect and Potential Mechanism of Immunotherapy on Cognitive Deficits in Animal Models of Alzheimer's Disease: A Systematic Review and Meta-Analysis

OBJECTIVE

Immunotherapy has been reported to ameliorate Alzheimer's disease (AD) in the animal model; however, the immunologic approaches and mechanisms have not been specifically described. Thus, the systematic review and meta-analysis were conducted to explore the effect and potential mechanism of immunotherapy on AD animal experiments based on behavioral indicators.

METHODS

According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the Cochrane Collaboration guidelines and the inclusion/exclusion criteria of immunotherapy in animal studies, 15 studies were systematically reviewed after extraction from a collected database of 3,742 publications. Finally, the effect and mechanism of immunotherapy on AD models were described by performing multiple subgroup analyses.

RESULTS

After immunotherapy, the escape latency was reduced by 18.15 seconds and the number of crossings over the platform location was increased by 1.60 times in the Morris Water Maze. Furthermore, compared to the control group, active and passive immunization could markedly ameliorate learning and memory impairment in 3 × Tg AD animal models, and active immunization could ameliorate the learning and memory ability of the APPswe/PS1ΔE9 AD animal model. Meanwhile, it could be speculated that cognitive dysfunction was improved by immunotherapy, perhaps mainly via reducing Aβ40, Aβ42, and Tau levels, as well as increasing IL-4 levels.

CONCLUSION

Immunotherapy significantly ameliorated the cognitive dysfunction of AD animal models by assessing behavioral indicators.

From significance testing to estimation and Open Science: How esci can help

We argue that researchers should test less, estimate more, and adopt Open Science practices. We outline some of the flaws of null hypothesis significance testing and take three approaches to demonstrating the unreliability of the p value. We explain some advantages of estimation and meta-analysis ("the new statistics"), especially as contributions to Open Science practices, which aim to increase the openness, integrity, and replicability of research. We then describe esci (estimation statistics with confidence intervals): a set of online simulations and an R package for estimation that integrates into jamovi and JASP. This software provides (a) online activities to sharpen understanding of statistical concepts (e.g., "The Dance of the Means"); (b) effects sizes and confidence intervals for a range of study designs, largely by using techniques recently developed by Bonett; (c) publication-ready visualisations that make uncertainty salient; and (d) the option to conduct strong, fair hypothesis evaluation through specification of an interval null. Although developed specifically to support undergraduate learning through the 2nd edition of our textbook, esci should prove a valuable tool for graduate students and researchers interested in adopting the estimation approach. Further information is at ( https://thenewstatistics.com).

Cross-Reactivity of N6AMT1 Antibodies with Aurora Kinase A: An Example of Antibody-Specific Non-Specificity

Primary antibodies are one of the main tools used in molecular biology research. However, the often-occurring cross-reactivity of primary antibodies complicates accurate data analysis. Our results show that three commercial polyclonal antibodies raised against N-6 adenine-specific DNA methyltransferase 1 (N6AMT1) strongly cross-react with endogenous and recombinant mitosis-associated protein Aurora kinase A (AURKA). The cross-reactivity was verified through immunofluorescence, immunoblot, and immunoprecipitation assays combined with mass spectrometry. N6AMT1 and AURKA are evolutionarily conserved proteins that are vital for cellular processes. Both proteins share the motif ENNPEE, which is unique to only these two proteins. We suggest that N6AMT1 antibodies recognise this motif in N6AMT1 and AURKA proteins and exhibit an example of "specific" non-specificity. This serves as an example of the importance of controls and critical data interpretation in molecular biology research.

Assessment of transparency indicators in space medicine

Space medicine is a vital discipline with often time-intensive and costly projects and constrained opportunities for studying various elements such as space missions, astronauts, and simulated environments. Moreover, private interests gain increasing influence in this discipline. In scientific disciplines with these features, transparent and rigorous methods are essential. Here, we undertook an evaluation of transparency indicators in publications within the field of space medicine. A meta-epidemiological assessment of PubMed Central Open Access (PMC OA) eligible articles within the field of space medicine was performed for prevalence of code sharing, data sharing, pre-registration, conflicts of interest, and funding. Text mining was performed with the rtransparent text mining algorithms with manual validation of 200 random articles to obtain corrected estimates. Across 1215 included articles, 39 (3%) shared code, 258 (21%) shared data, 10 (1%) were registered, 110 (90%) contained a conflict-of-interest statement, and 1141 (93%) included a funding statement. After manual validation, the corrected estimates for code sharing, data sharing, and registration were 5%, 27%, and 1%, respectively. Data sharing was 32% when limited to original articles and highest in space/parabolic flights (46%). Overall, across space medicine we observed modest rates of data sharing, rare sharing of code and almost non-existent protocol registration. Enhancing transparency in space medicine research is imperative for safeguarding its scientific rigor and reproducibility.

Methodological rigour in preclinical urology: a systematic review reporting research quality over a 14-year period

OBJECTIVE

To investigate the prevalence and trends of essential study design elements in preclinical urological studies, as well as key factors that may improve methodological rigour, as the demand for methodological rigour in preclinical studies is increasing since research reproducibility and transparency in the medico-scientific field are being questioned.

METHODS AND RESULTS

PubMed was searched to include preclinical urological studies published between July 2007 to June 2021. A total of 3768 articles met the inclusion criteria. Data on study design elements and animal models used were collected. Citation density was also examined as a surrogate marker of study influence. We performed an analysis of the prevalence of seven critical study design elements and temporal patterns over 14 years. Randomisation was reported in 50.0%, blinding in 15.0%, sample size estimation in 1.0%, inclusion of both sexes in 6.3%, statistical analysis in 97.1%, housing and husbandry in 47.7%, and inclusion/exclusion criteria in 5.0%. Temporal analysis showed that the implementation of these study design elements has increased, except for inclusion of both sexes and inclusion/exclusion criteria. Reporting study design elements were associated with increased citation density in randomisation and statistical analysis.

CONCLUSIONS

The risk of bias is prevalent in 14-year publications describing preclinical urological research, and the quality of methodological rigour is barely related to the citation density of the article. Yet five study design elements (randomisation, blinding, sample size estimation, statistical analysis, and housing and husbandry) proposed by both the National Institutes of Health and Animal Research: Reporting of In Vivo Experiments guidelines have been either well reported or are being well reported over time.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42022233125.

Self-reporting with checklists in artificial intelligence research on medical imaging: a systematic review based on citations of CLAIM

OBJECTIVE

To evaluate the usage of a well-known and widely adopted checklist, Checklist for Artificial Intelligence in Medical imaging (CLAIM), for self-reporting through a systematic analysis of its citations.

METHODS

Google Scholar, Web of Science, and Scopus were used to search for citations (date, 29 April 2023). CLAIM's use for self-reporting with proof (i.e., filled-out checklist) and other potential use cases were systematically assessed in research papers. Eligible papers were evaluated independently by two readers, with the help of automatic annotation. Item-by-item confirmation analysis on papers with checklist proof was subsequently performed.

RESULTS

A total of 391 unique citations were identified from three databases. Of the 118 papers included in this study, 12 (10%) provided a proof of self-reported CLAIM checklist. More than half (70; 59%) only mentioned some sort of adherence to CLAIM without providing any proof in the form of a checklist. Approximately one-third (36; 31%) cited the CLAIM for reasons unrelated to their reporting or methodological adherence. Overall, the claims on 57 to 93% of the items per publication were confirmed in the item-by-item analysis, with a mean and standard deviation of 81% and 10%, respectively.

CONCLUSION

Only a small proportion of the publications used CLAIM as checklist and supplied filled-out documentation; however, the self-reported checklists may contain errors and should be approached cautiously. We hope that this systematic citation analysis would motivate artificial intelligence community about the importance of proper self-reporting, and encourage researchers, journals, editors, and reviewers to take action to ensure the proper usage of checklists.

CLINICAL RELEVANCE STATEMENT

Only a small percentage of the publications used CLAIM for self-reporting with proof (i.e., filled-out checklist). However, the filled-out checklist proofs may contain errors, e.g., false claims of adherence, and should be approached cautiously. These may indicate inappropriate usage of checklists and necessitate further action by authorities.

KEY POINTS

• Of 118 eligible papers, only 12 (10%) followed the CLAIM checklist for self-reporting with proof (i.e., filled-out checklist). More than half (70; 59%) only mentioned some kind of adherence without providing any proof. • Overall, claims on 57 to 93% of the items were valid in item-by-item confirmation analysis, with a mean and standard deviation of 81% and 10%, respectively. • Even with the checklist proof, the items declared may contain errors and should be approached cautiously.

Dynamic, IPSC-derived hepatic tissue tri-culture system for the evaluation of liver physiologyin vitro

Availability of hepatic tissue for the investigation of metabolic processes is severely limited. While primary hepatocytes or animal models are widely used in pharmacological applications, a change in methodology towards more sustainable and ethical assays is highly desirable. Stem cell derived hepatic cells are generally regarded as a viable alternative for the above model systems, if current limitations in functionality and maturation can be overcome. By combining microfluidic organ-on-a-chip technology with individually differentiated, multicellular hepatic tissue fractions, we aim to improve overall functionality of hepatocyte-like cells, as well as evaluate cellular composition and interactions with non-parenchymal cell populations towards the formation of mature liver tissue. Utilizing a multi-omic approach, we show the improved maturation profiles of hepatocyte-like cells maintained in a dynamic microenvironment compared to standard tissue culture setups without continuous perfusion. In order to evaluate the resulting tissue, we employ single cell sequencing to distinguish formed subpopulations and spatial localization. While cellular input was strictly defined based on established differentiation protocols of parenchyma, endothelial and stellate cell fractions, resulting hepatic tissue was shown to comprise a complex mixture of epithelial and non-parenchymal fractions with specific local enrichment of phenotypes along the microchannel. Following this approach, we show the importance of passive, paracrine developmental processes in tissue formation. Using such complex tissue models is a crucial first step to develop stem cell-derivedin vitrosystems that can compare functionally with currently used pharmacological and toxicological applications.

The breathtaking world of human respiratory in vitro models: Investigating lung diseases and infections in 3D models, organoids, and lung-on-chip

The COVID-19 pandemic illustrated an urgent need for sophisticated, human tissue models to rapidly test and develop effective treatment options against this newly emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Thus, in particular, the last 3 years faced an extensive boost in respiratory and pulmonary model development. Nowadays, 3D models, organoids and lung-on-chip, respiratory models in perfusion, or precision-cut lung slices are used to study complex research questions in human primary cells. These models provide physiologically relevant systems for studying SARS-CoV-2 and, of course, other respiratory pathogens, but they are, too, suited for studying lung pathologies, such as CF, chronic obstructive pulmonary disease, or asthma, in more detail in terms of viral infection. With these models, the cornerstone has been laid for further advancing the organs by, for example, inclusion of several immune cell types or humoral immune components, combination with other organs in microfluidic organ-on-chip devices, standardization and harmonization of the devices for reliable and reproducible drug and vaccine testing in high throughput.

A pathway to strengthening open science: comments on the draft South African Ethics in Health Research Guidelines

The recently released draft South African Ethics in Health Research Guidelines: Principles, Processes and Structures (Draft Guidelines) by the National Health Research Ethics Council recognize open data and provide guiding principles for this in the context of health research in South Africa. While its inclusion is a positive development, there is room for improvement. Although the Draft Guidelines leverage the Draft National Policy on Data and Cloud, it lacks incorporation of other relevant government policies, notably the Draft National Open Science Policy, and fails to sufficiently detail the principles of open science and open access. This limited scope and lack of comprehensive definition and detailed guidance present challenges for researchers in conducting ethical and responsible health research in South Africa. It constrains the Draft Guidelines from fully aligning with national imperatives and from fostering African-centric approaches. To address these issues, it is recommended that the Draft Guidelines integrate broader policies and principles, enhance clarity through comprehensive definitions, provide detailed guidance on open access, and promote African-centric approaches. Implementing these solutions will strengthen the Draft Guidelines, aligning them with national visions of open science, and thereby harnessing the full potential of South Africa's diverse scientific community in advancing health research.

Decatastrophizing research irreproducibility

The reported inability to replicate research findings from the published literature precipitated extensive efforts to identify and correct perceived deficiencies in the execution and reporting of biomedical research. Despite these efforts, quantification of the magnitude of irreproducible research or the effectiveness of associated remediation initiatives, across diverse biomedical disciplines, has made little progress over the last decade. The idea that science is self-correcting has been further challenged in recent years by the proliferation of unverified or fraudulent scientific content generated by predatory journals, paper mills, pre-print server postings, and the inappropriate use of artificial intelligence technologies. The degree to which the field of pharmacology has been negatively impacted by these evolving pressures is unknown. Regardless of these ambiguities, pharmacology societies and their associated journals have championed best practices to enhance the experimental rigor and reporting of pharmacological research. The value of transparent and independent validation of raw data generation and its analysis in basic and clinical research is exemplified by the discovery, development, and approval of Highly Effective Modulator Therapy (HEMT) for Cystic Fibrosis (CF) patients. This provides a didactic counterpoint to concerns regarding the current state of biomedical research. Key features of this important therapeutic advance include objective construction of basic and translational research hypotheses, associated experimental designs, and validation of experimental effect sizes with quantitative alignment to meaningful clinical endpoints with input from the FDA, which enhanced scientific rigor and transparency with real world deliverables for patients in need.

FAIR reporting of clinical trials for public health practice

The number of clinical trials is rapidly growing, and automation of literature processing is becoming desirable but unresolved. Our purpose was to assess and increase the readiness of clinical trial reports for supporting automated retrieval and implementation in public health practice. We searched the Medline database for a random sample of clinical trials of HIV/AIDS management with likely relevance to public health in Africa. Five authors assessed trial reports for inclusion, extracted data, and assessed quality based on the FAIR principles of scientific data management (findable, accessible, interoperable, and reusable). Subsequently, we categorized reported results in terms of outcomes and essentials of implementation. A sample of 96 trial reports was selected. Information about the tested intervention that is essential for practical implementation was largely missing, including personnel resources needed 32·3% (.95 CI: 22·9-41·6); material/supplies needed 33·3% (.95 CI: 23·9-42·8); major equipment/building investment 42·8% (CI: 33·8-53·7); methods of educating providers 53·1% (CI: 43·1-63·4); and methods of educating the community 27·1% (CI: 18·2-36·0). Overall, 65% of studies measured health/biologic outcomes, among them, only a fraction showed any positive effects. Several specific design elements were identified that frequently make clinical trials unreal and their results unusable. To sort and interpret clinical trial results easier and faster, a new reporting structure, a practice- and retrieval-oriented trial outline with numeric outcomes (PROTON) table was developed and illustrated. Many clinical trials are either inconsequential by design or report incomprehensible results. According to the latest expectations of FAIR scientific data management, all clinical trial reports should include a consistent and practical impact-oriented table of clinical trial results.

The progressive trend of modeling and drug screening systems of breast cancer bone metastasis

Bone metastasis is considered as a considerable challenge for breast cancer patients. Various in vitro and in vivo models have been developed to examine this occurrence. In vitro models are employed to simulate the intricate tumor microenvironment, investigate the interplay between cells and their adjacent microenvironment, and evaluate the effectiveness of therapeutic interventions for tumors. The endeavor to replicate the latency period of bone metastasis in animal models has presented a challenge, primarily due to the necessity of primary tumor removal and the presence of multiple potential metastatic sites.The utilization of novel bone metastasis models, including three-dimensional (3D) models, has been proposed as a promising approach to overcome the constraints associated with conventional 2D and animal models. However, existing 3D models are limited by various factors, such as irregular cellular proliferation, autofluorescence, and changes in genetic and epigenetic expression. The imperative for the advancement of future applications of 3D models lies in their standardization and automation. The utilization of artificial intelligence exhibits the capability to predict cellular behavior through the examination of substrate materials' chemical composition, geometry, and mechanical performance. The implementation of these algorithms possesses the capability to predict the progression and proliferation of cancer. This paper reviewed the mechanisms of bone metastasis following primary breast cancer. Current models of breast cancer bone metastasis, along with their challenges, as well as the future perspectives of using these models for translational drug development, were discussed.

Statistical and Scientific Considerations Concerning the Interpretation, Replicability, and Transportability of Research Findings

To advance scientific understanding of disease processes and related intervention effects, study results should be free from bias and replicable. More broadly, investigators seek results that are transportable, that is, applicable to a perceived study population as well as in other environments and populations. We review fundamental statistical issues that arise in the analysis of observational data from disease cohorts and other sources and discuss how these issues affect the transportability and replicability of research results. Much of the literature focuses on estimating average exposure or intervention effects at the population level, but we argue for more nuanced analyses of conditional effects that reflect the complexity of disease processes.

Data standards in drug discovery: A long way to go

Each year, millions to trillions of data points are generated to evaluate the response of chemicals and biologicals to human cells in vitro and in vivo using various technologies and endpoints. Despite the vast amount of data available, the development process has not become significantly more efficient in recent years. Given the increasing use of more complex physiological models, which are time-consuming and significantly more expensive, it is crucial to maximize the value of these valuable data through improved standardization.

High replicability of newly discovered social-behavioural findings is achievable

Failures to replicate evidence of new discoveries have forced scientists to ask whether this unreliability is due to suboptimal implementation of methods or whether presumptively optimal methods are not, in fact, optimal. This paper reports an investigation by four coordinated laboratories of the prospective replicability of 16 novel experimental findings using rigour-enhancing practices: confirmatory tests, large sample sizes, preregistration and methodological transparency. In contrast to past systematic replication efforts that reported replication rates averaging 50%, replication attempts here produced the expected effects with significance testing (P < 0.05) in 86% of attempts, slightly exceeding the maximum expected replicability based on observed effect sizes and sample sizes. When one lab attempted to replicate an effect discovered by another lab, the effect size in the replications was 97% that in the original study. This high replication rate justifies confidence in rigour-enhancing methods to increase the replicability of new discoveries.

Ethical Principles in Plastic Surgery Research

Research is an integral part of medical progress that leads to better understanding of disease processes and the development of therapies to improve patient care. The medical community has an obligation and societal responsibility to review its practices and advance its knowledge to optimize care for those who entrust it with their health and well-being. While ultimately intended to benefit patients specifically and society as a whole, every laboratory and clinical investigation inherently carries an element of uncertainty and has attendant risks. These can have unintended and, at times, harmful consequences that cannot justify the knowledge gained. In order to mitigate these risks and protect human subjects involved in clinical research studies, a basic framework of ethical principles has been developed to guide responsible experimental design, execution, and data dissemination. This article provides a review of these principles and the historical context from which they were derived and explores the persistent challenges and cognitive biases that can increase susceptibility to unethical research practices.

Research rigor and reproducibility in research education: A CTSA institutional survey

We assessed the rigor and reproducibility (R&R) activities of institutions funded by the National Center for Advancing Translational Sciences (NCTSA) through a survey and website search (N = 61). Of 50 institutional responses, 84% reported incorporating some form of R&R training, 68% reported devoted R&R training, 30% monitored R&R practices, and 10% incentivized them. Website searches revealed 9 (15%) freely available training curricula, and 7 (11%) institutional programs specifically created to enhance R&R. NCATS should formally integrate R&R principles into its translational science models and institutional requirements.

Cancer researchers' experiences with and perceptions of research data sharing: Results of a cross-sectional survey

BACKGROUND

Despite wide recognition of the benefits of sharing research data, public availability rates have not increased substantially in oncology or medicine more broadly over the last decade.

METHODS

We surveyed 285 cancer researchers to determine their prior experience with sharing data and views on known drivers and inhibitors.

RESULTS

We found that 45% of respondents had shared some data from their most recent empirical publication, with respondents who typically studied non-human research participants, or routinely worked with human genomic data, more likely to share than those who did not. A third of respondents added that they had previously shared data privately, with 74% indicating that doing so had also led to authorship opportunities or future collaborations for them. Journal and funder policies were reported to be the biggest general drivers toward sharing, whereas commercial interests, agreements with industrial sponsors and institutional policies were the biggest prohibitors. We show that researchers' decisions about whether to share data are also likely to be influenced by participants' desires.

CONCLUSIONS

Our survey suggests that increased promotion and support by research institutions, alongside greater championing of data sharing by journals and funders, may motivate more researchers in oncology to share their data.

Targeted Phosphoproteomics of Human Saliva Extracellular Vesicles via Multiple Reaction Monitoring Cubed (MRM3)

Oral squamous cell carcinoma (OSCC) has become a global health problem due to its increasing incidence and high mortality rate. Early intervention through monitoring of the diagnostic biomarker levels during OSCC treatment is critical. Extracellular vesicles (EVs) are emerging surrogates in intercellular communication through transporting biomolecule cargo and have recently been identified as a potential source of biomarkers such as phosphoproteins for many diseases. Here, we developed a multiple reaction monitoring cubed (MRM3) method coupled with a novel sample preparation strategy, extracellular vesicles to phosphoproteins (EVTOP), to quantify phosphoproteins using a minimal amount of saliva (50 μL) samples from OSCC patients with high specificity and sensitivity. Our results established differential patterns in the phosphopeptide content of healthy, presurgery, and postsurgery OSCC patient groups. Notably, we discovered significantly increased salivary phosphorylated alpha-amylase (AMY) in the postsurgery group compared to the presurgery group. We hereby present the first targeted MS method with extremely high sensitivity for measuring endogenous phosphoproteins in human saliva EVs.

Artificial Intelligence for Drug Discovery: Are We There Yet?

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

Robustness and reproducibility for AI learning in biomedical sciences: RENOIR

Artificial intelligence (AI) techniques are increasingly applied across various domains, favoured by the growing acquisition and public availability of large, complex datasets. Despite this trend, AI publications often suffer from lack of reproducibility and poor generalisation of findings, undermining scientific value and contributing to global research waste. To address these issues and focusing on the learning aspect of the AI field, we present RENOIR (REpeated random sampliNg fOr machIne leaRning), a modular open-source platform for robust and reproducible machine learning (ML) analysis. RENOIR adopts standardised pipelines for model training and testing, introducing elements of novelty, such as the dependence of the performance of the algorithm on the sample size. Additionally, RENOIR offers automated generation of transparent and usable reports, aiming to enhance the quality and reproducibility of AI studies. To demonstrate the versatility of our tool, we applied it to benchmark datasets from health, computer science, and STEM (Science, Technology, Engineering, and Mathematics) domains. Furthermore, we showcase RENOIR's successful application in recently published studies, where it identified classifiers for SET2D and TP53 mutation status in cancer. Finally, we present a use case where RENOIR was employed to address a significant pharmacological challenge-predicting drug efficacy. RENOIR is freely available at https://github.com/alebarberis/renoir .

FDA-approved heterocyclic molecules for cancer treatment: Synthesis, dosage, mechanism of action and their adverse effect

As the incorporation of heterocycles increases the physical characteristics and biological activity of pharmacological molecules, heterocyclic scaffolds are commonly discovered as common cores in a wide spectrum of biologically active drugs. In the contemporary context, many heterocycles have arisen, playing vital roles in diverse pharmaceutical compounds that benefit humanity. Over 85 % of FDA-approved medication molecules contain heterocycles, and most importantly, numerous heterocyclic medicinal molecules indicate potential benefits against a range: of malignancies. The unique flexibility and dynamic core scaffold of these compounds have aided anticancer research. These medications are used to treat cancer patients by targeting particular genes, enzymes, and receptors. Aside from the drugs that are now on the market, numerous forms are being researched for their potential anti-cancer activity. Here in this review, we classified some molecules and biologically active heterocycles containing anticancer medicinal moieties approved by the FDA between 2019 and 2021 based on their use in various forms of cancer. We will focus on those that are suitable for cancer treatment, as well as the essential biochemical mechanisms of action, biological targets, synthetic methods, and inherent limiting considerations in their use.

Systematic heterogenization revisited: Increasing variation in animal experiments to improve reproducibility?

Life sciences are currently facing a reproducibility crisis. Originally, the crisis was born out of single alarming failures to reproduce findings at different times and locations. Nowadays, systematic studies indicate that the prevalence of irreproducible research does in fact exceed 50%. Viewed from a rather cynical perspective, Fett's law of the lab "Never replicate a successful experiment" has thus taken on a completely new meaning. In this respect, animal research has come under particular scrutiny, as the stakes are high in terms of both research ethics and societal impact. To counteract this, it is essential to identify sources of poor reproducibility as well as to iron out these failures. We here review the current debate, briefly discuss potential reasons, and summarize steps that have already been undertaken to improve reproducibility in animal research. By the example of classical behavioural phenotyping studies, we particularly highlight the role strict standardization plays in exacerbating the crisis, and review the concept of systematic heterogenization as an alternative strategy to deal with variation in animal studies. Briefly, we argue that systematic variation rather than strict homogenization of experimental conditions benefits the robustness of research findings, and hence their reproducibility. To this end, we will present concrete examples for systematically heterogenized experiments and provide a practical guide on how to apply systematic heterogenization in experimental practice.

Can antibodies be "vegan"? A guide through the maze of today's antibody generation methods

There is no doubt that today's life sciences would look very different without the availability of millions of research antibody products. Nevertheless, the use of antibody reagents that are poorly characterized has led to the publication of false or misleading results. The use of laboratory animals to produce research antibodies has also been criticized. Surprisingly, both problems can be addressed with the same technology. This review charts today's maze of different antibody formats and the various methods for antibody production and their interconnections, ultimately concluding that sequence-defined recombinant antibodies offer a clear path to both improved quality of experimental data and reduced use of animals.

Choice of fusion proteins, expression host, and analytics solves difficult-to-produce protein challenges in discovery research

High quality biological reagents are a prerequisite for pharmacological research. Herein a protein production screening approach, including quality assessment methods, for protein-based discovery research is presented. Trends from 2895 expression constructs representing 253 proteins screened in mammalian and bacterial hosts-91% of which are successfully expressed and purified-are discussed. Mammalian expression combined with the use of solubility-promoting fusion proteins is deemed suitable for most targets. Furthermore, cases utilizing stable cell line generation and choice of fusion protein for higher yield and quality of difficult-to-produce proteins (Leucine-rich repeat-containing G-protein coupled receptor 4 (LGR4) and Neurturin) are presented and discussed. In the case of Neurturin, choice of fusion protein impacted the target binding 80-fold. These results highlight the need for exploration of construct designs and careful Quality Control (QC) of difficult-to-produce protein reagents.

Toward a common standard for data and specimen provenance in life sciences

Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre-analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross-organizational documentation, traceability, and non-repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine-actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.

Advances in iPSC Technology in Neural Disease Modeling, Drug Screening, and Therapy

Neurodegenerative disorders (NDs) including Alzheimer's Disease, Parkinson's Disease, Amyotrophic Lateral Sclerosis (ALS), and Huntington's disease are all incurable and can only be managed with drugs for the associated symptoms. Animal models of human illnesses help to advance our understanding of the pathogenic processes of diseases. Understanding the pathogenesis as well as drug screening using appropriate disease models of neurodegenerative diseases (NDs) are vital for identifying novel therapies. Human-derived induced pluripotent stem cell (iPSC) models can be an efficient model to create disease in a dish and thereby can proceed with drug screening and identifying appropriate drugs. This technology has many benefits, including efficient reprogramming and regeneration potential, multidirectional differentiation, and the lack of ethical concerns, which open up new avenues for studying neurological illnesses in greater depth. The review mainly focuses on the use of iPSC technology in neuronal disease modeling, drug screening, and cell therapy.

Effects of home-cage elevation on behavioral tests in mice

BACKGROUND

Research reproducibility is a common problem in preclinical behavioral science. Mice are an important animal model for studying human behavioral disorders. Experimenters, processing methods, and rearing environments are the main causes of data variability in behavioral neuroscience. It is likely that mice adapt their behavior according to the environment outside the breeding cage. We speculated that mice housed on elevated shelves and mice housed on low shelves might have differently altered anxiety-like behavior toward heights.

PURPOSE

The purpose of this study was to investigate potential behavioral changes in mice raised at different heights for 3 weeks. Changes in behavior were examined using various experimental tests.

RESULTS

Mice housed on elevated shelves showed reduced anxiety-like behavior in a light/dark traffic test compared with mice housed on low shelves. There were no significant differences between the two groups in terms of activity, exploratory behavior, muscle strength, or depression-like behavior.

CONCLUSIONS

Our results indicate that different cage heights and corresponding light exposure may alter the anxiety-like behavior of mice in response to brightness. Researchers need to carefully control the cage height and light intensity experienced by the mice to produce reproducible test results.