Effective cancer therapy involves initiation of a tumor-specific immune response. Consequently, the interest in oncolytic viruses (OV) capable of triggering immunogenic cell death has sparked in recent years. However, the common use of pre-clinical models that fail to mirror patient tumor ecosystems (TES) hinders clinical translation. Here, we provide a condensed view on the intricate interplay between several aspects of TES and OV action and discuss these considerations in the view of recently developed pre-clinical human model systems. Given the urgent demand for innovative cancer treatments, the purpose of this review is to highlight the so-far overlooked complex impact of the tumor microenvironment (TME) on OV permissivity, with the intent to provide a foundation for future, more effective pre-clinical studies.

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.

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

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.

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.

Multi-cell-type, 3D microphysiological systems (MPS) that recapitulate normal organ/organ system functions and the progression of diseases are being applied in drug discovery and development programs to enable precision medicine. A critical step for this application is to demonstrate the reproducibility of the MPS and its ability to identify biologic/clinical heterogeneity from experimental variability, which requires capturing detailed metadata associated with MPS studies as well as a strong analytical approach for assessing reproducibility. Detailed metadata ensure that identical study parameters are being compared when evaluating reproducibility. We have developed the Pittsburgh reproducibility protocol (PReP), which uses a set of common statistical metrics, the coefficient of variation (CV), ANOVA, and intraclass correlation coefficient (ICC), in a pipeline as a standard approach to evaluate the intra- and interstudy reproducibility of MPS performance. The PReP can be employed to identify biological/clinical heterogeneity relevant to precision medicine.

Phase 2 trials are instrumental for designing definitive efficacy trials or attaining accelerated approval. However, high attrition of drug candidates in phase 2 trials raises questions about their supporting evidence.

We developed a typology of supporting evidence for phase 2 cancer trials. We also devised a scheme for capturing elements that enable an assessment of the strength of such evidence. Using this framework, we content analyzed supporting evidence provided in protocols of 50 randomly sampled phase 2 cancer monotherapy trials between January 2014 and January 2019, available on ClinicalTrials.gov.

Of the 50 protocols in our sample, 52% were industry funded. Most invoked supporting evidence deriving from trials against different cancers (n = 28, 56%) or preclinical studies (n = 48, 96%) but not from clinical studies involving the target drug-indication pairing (n = 23, 46%). When presenting evidence from models, only 1 (2%) protocol explained its translational relevance. Instead, protocols implied translatability by describing molecular (86%) and pathophysiological (84%) processes shared by model and target systems. Protocols often provided information for assessing the magnitude, precision, and risk of bias for supporting trials (n = 43; 93%, 91%, 47%, respectively). However, such information was often unavailable for preclinical studies (n = 49; 53%, 22%, 59%, respectively).

Supporting evidence is key to justifying the commitment of scientific resources and patients to a clinical hypothesis. Protocols often omit elements that would enable critical assessment of supporting evidence for phase 2 monotherapy cancer trials. These gaps suggest the promise of more structured approaches for presenting supporting evidence.

Research integrity is fundamental to responsible research practice. Despite attention, the intellectual structure and evolution of this field remains underexplored. This study maps the knowledge landscape of research integrity, identifying key themes, contributions, and trends.

A scientometric analysis was conducted on 6,895 records from Web of Science and Scopus (1935-2024). CiteSpace facilitated network analysis, including co-authorship, keyword co-occurrence, and co-citation patterns, while burst detection identified topics.

Research integrity studies have grown significantly since the 1980s, with interdisciplinary collaboration. Keyword and co-citation analyses reveal a shift from early discussions on scientific misconduct to concerns such as open science, AI ethics, and research governance. A collaboration network has emerged, with leading contributions from North America, Europe, and Asia.

Research integrity has matured into an interdisciplinary field, reaching academic consensus with growing integration of policies, regulations, and technology. Future research is expected to focus on AI's role in research integrity. Key areas of concern include algorithmic bias, automation ethics, and implications for scholarly publishing. Open science and transparency will remain central, particularly in addressing data fabrication, paper mills, and predatory publishing. Institutional policies will continue evolving, embedding integrity principles into governance and public engagement initiatives.

Objective. Sharing computational models offers many benefits, including increased scientific rigor during project execution, readership of the associated paper, resource usage efficiency, replicability, and reusability. In recognition of the growing practice and requirement of sharing models, code, and data, herein, we provide guidance to facilitate sharing of computational models by providing an accessible resource for regular reference throughout a project's stages.Approach. We synthesized literature on good practices in scientific computing and on code and data sharing with our experience in developing, sharing, and using models of neural stimulation, although the guidance will also apply well to most other types of computational models.Main results. We first describe the '6 R' characteristics of shared models, leaning on prior scientific computing literature, which enforce accountability and enable advancement: re-runnability, repeatability, replicability, reproducibility, reusability, and readability. We then summarize action items associated with good practices in scientific computing, including selection of computational tools during project planning, code and documentation design during development, and user instructions for deployment. We provide a detailed checklist of the contents of shared models and associated materials, including the model itself, code for reproducing published figures, documentation, and supporting datasets. We describe code, model, and data repositories, including a list of characteristics to consider when selecting a platform for sharing. We describe intellectual property (IP) considerations to balance permissive, open-source licenses versus software patents and bespoke licenses that govern and incentivize commercialization. Finally, we exemplify these practices with our ASCENT pipeline for modeling peripheral nerve stimulation.Significance. We hope that this paper will serve as an important and actionable reference for scientists who develop models-from project planning through publication-as well as for model users, institutions, IP experts, journals, funding sources, and repository platform developers.

Concerns about the replicability, reproducibility and transparency of research have ushered in a set of practices and behaviours under the umbrella of 'open research'. To this end, many new initiatives have been developed that represent procedural (i.e. behaviours and sets of commonly used practices in the research process), structural (new norms, rules, infrastructure and incentives), and community-based change (working groups, networks). The objectives of this research were to identify and outline international initiatives that enhance awareness and uptake of open research practices in the discipline of psychology. A systematic mapping review was conducted in three stages: (i) a Web search to identify open research initiatives in psychology; (ii) a literature search to identify related articles; and (iii) a hand search of grey literature. Eligible initiatives were then coded into an overarching theme of procedural, structural or community-based change. A total of 187 initiatives were identified; 30 were procedural (e.g. toolkits, resources, software), 70 structural (e.g. policies, strategies, frameworks) and 87 community-based (e.g. working groups, networks). This review highlights that open research is progressing at pace through various initiatives that share a common goal to reform research culture. We hope that this review promotes their further adoption and facilitates coordinated efforts between individuals, organizations, institutions, publishers and funders.

Doxorubicin is a crucial chemotherapy used in the treatment of triple-negative breast cancer (TNBC) patients, but elevated doxorubicin doses may induce therapeutic resistance. To overcome this limitation, we have previously established a photodynamic therapeutic (PDT)-like strategy that irradiates doxorubicin-treated cells with a low-power nonionizing blue LED device. This combined treatment increases the production of reactive oxygen species to promote cell death, consequently enabling reduced doxorubicin dosages. Yet, precisely determining the molecular mechanisms that drive this outcome is still required for advancing such PDT-like approach. Here, we aimed to correlate the expression of the inflammatory markers NF-κB, IL-8, IL-6, and IL-1β with the survival of TNBC cells submitted to our PDT-like protocol. Our results show that NF-κB/p65 nuclear levels were enhanced in MDA-MB-231 cells treated with doxorubicin and blue LED. Moreover, this PDT-like strategy increased IL-6 mRNA levels in MDA-MB-231 cells. IL-1β and IL-8 mRNA were upregulated in samples incubated with doxorubicin regardless of concomitant irradiation with blue LED. These results show that our PDT-like protocol is effective in elevating inflammatory signals, shedding light on the molecular mechanisms that underlie the efficacy of this innovative anticancer therapeutic approach.

Interpreting cancer clinical trial results often depends on addressing issues of multiplicity. When testing multiple hypotheses, unreliable findings can occur by chance due to the inflation of the type I error rate, the probability of mistakenly rejecting the null hypothesis when the null hypothesis is true. In this setting, researchers may often set the type I error rate (or the alpha level) low to limit false positive findings and the interpretation of a causal relationship where none exists. Conversely, overly conservative type I error control may result in declaring findings, that do not meet multiplicity-adjusted alpha levels, as false when they are actually true, reducing opportunities for new discovery. This presentation focuses on multiplicity adjustment in the context of clinical trials conducted within the NCI's Community Oncology Research Program (NCORP). Because federally sponsored trials often require long-term participation from patients and represent a substantial investment by taxpayers, striking the right balance between optimizing what is learned from these trials, while avoiding false positive results, should be a priority.

Research transparency is crucial for ensuring the relevance, integrity, and reliability of scientific findings. However, previous work indicates room for improvement across transparency practices. The primary objective of this study was to develop an extensible tool to provide individualized feedback and guidance for improved transparency across phases of a study. Our secondary objective was to assess the feasibility of implementing this tool to improve transparency in clinical trials.

We developed study-level "report cards" that combine tailored feedback and guidance to investigators across several transparency practices, including prospective registration, availability of summary results, and open access publication. The report cards were generated through an automated pipeline for scalability. We also developed an infosheet to summarize relevant laws, guidelines, and resources relating to transparency. To assess the feasibility of using these tools to improve transparency, we conducted a single-arm intervention study at Berlin's university medical center, the Charité - Universitätsmedizin Berlin. Investigators (n = 92) of 155 clinical trials were sent individualized report cards and the infosheet, and surveyed to assess their perceived usefulness. We also evaluated included trials for improvements in transparency following the intervention.

Survey responses indicated general appreciation for the report cards and infosheet, with a majority of participants finding them helpful to build awareness of the transparency of their trial and transparency requirements. However, improvement on transparency practices was minimal and largely limited to linking publications in registries. Investigators also commented on various challenges associated with implementing transparency, including a lack of clarity around best practices and institutional hurdles.

This study demonstrates the potential of developing and using tools, such as report cards, to provide individualized feedback at scale to investigators on the transparency of their study. While these tools were positively received by investigators, the limited improvement in transparency practices suggests that awareness alone is likely not sufficient to drive improvement. Future research and implementation efforts may adapt the tools to further practices or research areas, and explore integrated approaches that combine the report cards with incentives and institutional support to effectively strengthen transparency in research.

Endpoint Adjudication Committees (EACs) benefit the quality of randomized control trials (RCTs) where outcomes depend on subjective interpretations. However, assembling a committee to adjudicate large datasets is cumbersome. In a recent RCT, the primary outcome was time to union following operative fixation of scaphoid non-union, with real or placebo adjunctive ultrasound treatment. Union status was determined with computed tomography (CT) scans interpreted by treating surgeons and radiologists. An EAC was established to deliberate discrepancies between radiologists' and surgeons' interpretations of union status.

Three hundred sixty-four CT scans from 142 participants were collected in the RCT. The treating surgeon and an MSK radiologist categorized images by percent-union (0 %, 1-24 %, 25-49 %, 50-74 %, 75-99 %, 100 %). Union was defined as at least 50 % trabecular bridging. The EAC adjudicated those images that were deemed major discrepancies. The committee was composed of three members assembled by the committee chair, an MSK radiologist. A charter was established to guide the adjudication process. Ten minutes were allotted to each scan, including 2-3 min of an independent adjudicator's review, followed by 5-7 min of committee discussion to reach a diagnosis.

Adjudicators spent an average of seven minutes on each scan. The EAC assessed 101 CT scans from 69 patients collected across five study sites: four scans from the agreed upon group as practice interpretations, 75 major discrepancies, and 22 missing interpretations from either the initial MSK radiologist, the treating orthopaedic surgeon, or both. These were adjudicated for final union status. Twenty-eight of the images with major discrepancies were adjudicated to union, and 47 to non-union. Adjudication changed the primary outcome of time to union in 40/142 (28 %) of study participants.

This adjudication process provides a valuable research tool for reference by other clinical investigators whose RCTs' outcomes are dependent on interpretation of radiographic images.

Making progress in neuroscience research involves learning from existing data. In this perspective piece, we explore the potential of a data-driven evidence ecosystem to connect all primary data streams, and synthesis efforts to inform evidence-based research and translational success from bench to bedside. To enable this transformation, we set out how we can produce evidence designed with evidence curation in mind. All data should be findable, understandable, and easily synthesisable, using a combination of human and machine effort. This will require shifts in research culture and tailored infrastructure to support rapid dissemination, data sharing, and transparency. We also discuss improvements in the way we can synthesise evidence to better inform primary research, including the potential of emerging technologies, big-data approaches, and breaking down research silos. Through a case study in stroke research, one of the most well-established areas for synthesis efforts, we demonstrate the progress in implementing elements of this ecosystem, with an emphasis on the need for coordinated efforts between laboratory researchers and synthesists.

Replications are important for assessing the reliability of published findings. However, they are costly, and it is infeasible to replicate everything. Accurate, fast, lower-cost alternatives such as eliciting predictions could accelerate assessment for rapid policy implementation in a crisis and help guide a more efficient allocation of scarce replication resources. We elicited judgements from participants on 100 claims from preprints about an emerging area of research (COVID-19 pandemic) using an interactive structured elicitation protocol, and we conducted 29 new high-powered replications. After interacting with their peers, participant groups with lower task expertise ('beginners') updated their estimates and confidence in their judgements significantly more than groups with greater task expertise ('experienced'). For experienced individuals, the average accuracy was 0.57 (95% CI: [0.53, 0.61]) after interaction, and they correctly classified 61% of claims; beginners' average accuracy was 0.58 (95% CI: [0.54, 0.62]), correctly classifying 69% of claims. The difference in accuracy between groups was not statistically significant and their judgements on the full set of claims were correlated (r(98) = 0.48, P < 0.001). These results suggest that both beginners and more-experienced participants using a structured process have some ability to make better-than-chance predictions about the reliability of 'fast science' under conditions of high uncertainty. However, given the importance of such assessments for making evidence-based critical decisions in a crisis, more research is required to understand who the right experts in forecasting replicability are and how their judgements ought to be elicited.

Here we test the feasibility of using decision markets to select studies for replication and provide evidence about the replicability of online experiments. Social scientists (n = 162) traded on the outcome of close replications of 41 systematically selected MTurk social science experiments published in PNAS 2015-2018, knowing that the 12 studies with the lowest and the 12 with the highest final market prices would be selected for replication, along with 2 randomly selected studies. The replication rate, based on the statistical significance indicator, was 83% for the top-12 and 33% for the bottom-12 group. Overall, 54% of the studies were successfully replicated, with replication effect size estimates averaging 45% of the original effect size estimates. The replication rate varied between 54% and 62% for alternative replication indicators. The observed replicability of MTurk experiments is comparable to that of previous systematic replication projects involving laboratory experiments.

Pharmaceutical medicine professionals have to face many ethical problems during the entire life span of new medicines extending from animal studies to broad clinical practice. The primary aim of the general ethical principles governing research conducted in humans is to diminish the physical and psychological burdens of the participants in human drug studies but overlooks many additional social and ethical problems faced by medicine developers. These arise mainly at the interface connecting the profit-oriented pharmaceutical industry and the healthcare-centered medical profession cooperating in medicines development. In 2002, the International Federation of Associations of Pharmaceutical Physicians and Pharmaceutical Medicine developed the International Code of Ethical Conduct for Pharmaceutical Physicians for providing ethical advice for their members to manage the frequently competitive goals characteristic for their specialty. The ethical framework compiled by the International Federation of Associations of Pharmaceutical Physicians and Pharmaceutical Medicine serves its members by presenting morally acceptable or inacceptable behaviors in frequently encountered controversies arising from competing industrial and healthcare interests in medicines development. The authors selected this format to encourage reflection and debate for finding optimal moral conclusions in specific issues. Many recent examples of serious scientific-ethical misconduct, such as the oxycodone tragedy, the recommendations of unproven useless occasionally dangerous therapies during the coronavirus disease 2019 pandemic, and the withdrawal of many papers containing non-reproducible results, contributed to the increasing loss of trust by the public in science including pharmaceutical medicine. We are convinced that the ethical guidance developed by the International Federation of Associations of Pharmaceutical Physicians and Pharmaceutical Medicine will encourage its members to reflect intensively on optimal ethical behavior in drug development for strengthening the trust of society in innovative new medicines. Finally, considering the increasingly active participation of non-medically trained scientists in producing and applying complex biological medicines, distant monitoring methods coupled together with artificial intelligence technology in innovative clinical trials, the Ethics Working Group recommended already in 2017 measures to optimize their smooth cooperation and underlined their joint ethical responsibilities in guarding the safety and human dignity of trial participants.

Despite the wealth of single-cell multi-omics data, it remains challenging to predict the consequences of novel genetic and chemical perturbations in the human body. It requires knowledge of molecular interactions at all biological levels, encompassing disease models and humans. Current machine learning methods primarily establish statistical correlations between genotypes and phenotypes but struggle to identify physiologically significant causal factors, limiting their predictive power. Key challenges in predictive modeling include scarcity of labeled data, generalization across different domains, and disentangling causation from correlation. In light of recent advances in multi-omics data integration, we propose a new artificial intelligence (AI)-powered biology-inspired multi-scale modeling framework to tackle these issues. This framework will integrate multi-omics data across biological levels, organism hierarchies, and species to predict genotype-environment-phenotype relationships under various conditions. AI models inspired by biology may identify novel molecular targets, biomarkers, pharmaceutical agents, and personalized medicines for presently unmet medical needs.

Antibodies are extensively used in biomedical research, clinical fields, and disease treatment. However, to enhance the reproducibility and reliability of antibody-based experiments, it is crucial to have a detailed understanding of the antibody's target specificity and epitope. In this study, we developed a high-throughput and precise epitope analysis method, DECODE (Decoding Epitope Composition by Optimized-mRNA-display, Data analysis, and Expression sequencing). This method allowed identifying patterns of epitopes recognized by monoclonal or polyclonal antibodies at single amino acid resolution and predicted cross-reactivity against the entire protein database. By applying the obtained epitope information, it has become possible to develop a new 3D immunostaining method that increases the penetration of antibodies deep into tissues. Furthermore, to demonstrate the applicability of DECODE to more complex blood antibodies, we performed epitope analysis using serum antibodies from mice with experimental autoimmune encephalomyelitis (EAE). As a result, we were able to successfully identify an epitope that matched the sequence of the peptide inducing the disease model without relying on existing antigen information. These results demonstrate that DECODE can provide high-quality epitope information, improve the reproducibility of antibody-dependent experiments, diagnostics and therapeutics, and contribute to discover pathogenic epitopes from antibodies in the blood.

When researchers write down their plans for a study ahead of time and make this public, this is called pre-registration. Pre-registration allows others to see if the researchers stuck to their original plan or changed as they went along. Pre-registration is growing in popularity but we do not know how widely it is used in autism research. In this study, we looked at papers published in six major autism journals between 2011 and 2022. We found that only 2.23% of papers published in autism journals had been pre-registered. We also took a close look at a selection of the pre-registrations to check how good they were and if researchers stuck to their plans. We found that the pre-registrations generally lacked specifics, particularly about how the study was designed and the data would be analysed. We also found that only 28% of the papers closely followed the pre-registered plans or reported the changes.Based on these findings, we recommend that autism researchers consider pre-registering their work and transparently report any changes from their original plans. We have provided some recommendations for researchers and journals on how pre-registration could be better used in autism research.

By rearranging hydrogen bond donor and acceptor groups within a standard Watson-Crick geometry, DNA can add eight independently replicable nucleotides forming four additional not found in standard Terran DNA. For many applications, the orthogonal pairing of standard and nonstandard pairs offers a key advantage. However, other applications require standard and nonstandard nucleotides to communicate with each other. This is especially true when seeking to recruit high-throughput instruments (e.g., Illumina), designed to sequence standard 4-nucleotide DNA, to sequence DNA that includes added nucleotides. For this purpose, PCR workflows are needed to replace nonstandard nucleotides in (for example) a 6-letter DNA sequence by defined mixtures of standard nucleotides built from 4 nucleotides. High-throughput sequencing can then report the sequences of those mixtures to bioinformatic alignment tools, which infer the original 6-nucleotide sequence by analysis of the mixtures. Unfortunately, the intrinsic orthogonality of standard and nonstandard nucleotides often demand polymerases that violate pairing biophysics to do this replacement, leading to inefficiencies in this "transliteration" process. Thus, laboratory in vitro evolution (LIVE) using "anthropogenic evolvable genetic information systems" (AEGIS), an important "consumer" of new sequencing tools, has been slow to be democratized; robust sequencing is needed to identify the AegisBodies and AegisZymes that AEGIS-LIVE delivers. This work introduces a new way to connect synthetic and standard molecular biology: biversal nucleotides. In an example presented here, a pyrimidine analogue (pyridine-2-one, y) pairs with Watson-Crick geometry to both a nonstandard base (2-amino-8-imidazo-[1,2a]-1,3,5-triazin-[8H]-4-one, P, the Watson-Crick partner of 6-amino-5-nitro-[1H]-pyridin-2-one, Z) and a base that completes the Watson-Crick hydrogen bond pattern (2-amino-2'-deoxyadenosine, amA). PCR amplification of GACTZP DNA with dyTP delivers products where Z:P pairs are cleanly transliterated to A:T pairs. In parallel, PCR of the same GACTZP sample at higher pH delivers products where Z:P pairs are cleanly transliterated to C:G pairs. By allowing robust sequencing of 6-letter GACTZP DNA, this workflow will help democratize AEGIS-LIVE. Further, other implementations of the biversal concept can enable communication across and between standard DNA and synthetic DNA more generally.

Biomedicine today is experiencing a shift towards decentralized data collection, which promises enhanced reproducibility and collaboration across diverse laboratory environments. This inter-laboratory study evaluates the performance of biocytometry, a method utilizing engineered bioparticles for enumerating cells based on their surface antigen patterns. In centralized and aggregated inter-lab studies, biocytometry demonstrated significant statistical power in discriminating numbers of target cells at varying concentrations as low as 1 cell per 100,000 background cells. User skill levels varied from expert to beginner capturing a range of proficiencies. Measurement was performed in a decentralized environment without any instrument cross-calibration or advanced user training outside of a basic instruction manual. The results affirm biocytometry to be a viable solution for immunophenotyping applications demanding sensitivity as well as scalability and reproducibility and paves the way for decentralized analysis of rare cells in heterogeneous samples.

Recent advances in multivariate pattern recognition have fostered the search for reliable neuroimaging-based biomarkers in psychiatric conditions, including schizophrenia. These approaches consider the complex pattern of alterations in brain function and structure, overcoming the limitations of traditional univariate methods. To assess the reliability of neuroimaging-based biomarkers and the contribution of study characteristics in distinguishing individuals with schizophrenia spectrum disorder (SSD) from healthy controls (HCs), we conducted a systematic review of the studies that used multivariate pattern recognition for this objective.

We systematically searched PubMed, Scopus, and Web of Science for studies on SSD classification using multivariate pattern analysis on magnetic resonance imaging data. We employed a bivariate random-effects meta-analytic model to explore the classification of sensitivity (SE) and specificity (SP) across studies while also evaluating the moderator effects of clinical and non-clinical variables.

A total of 119 studies (with 12,723 patients with SSD and 13,196 HCs) were identified. The meta-analysis estimated a SE of 79.1% (95% confidence interval [CI], 77.1%-81.0%) and a SP of 80.0% (95% CI, 77.8%-82.0%). In particular, the Positive and Negative Syndrome Scale and the Global Assessment of Functioning scores, age, age of onset, duration of untreated psychosis, deep learning, algorithm type, features selection, and validation methods had significant effects on classification performance.

Multivariate pattern analysis reliably identifies neuroimaging-based biomarkers of SSD, achieving ∼80% SE and SP. Despite clinical heterogeneity, discernible brain modifications effectively differentiate SSD from HCs. Classification performance depends on patient-related and methodological factors crucial for the development, validation, and application of prospective models in clinical settings.

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.

AI-based methods to generate images have seen unprecedented advances in recent years challenging both image forensic and human perceptual capabilities. Accordingly, these methods are expected to play an increasingly important role in the fraudulent fabrication of data. This includes images with complicated intrinsic structures such as histological tissue samples, which are harder to forge manually. Here, we use stable diffusion, one of the most recent generative algorithms, to create such a set of artificial histological samples. In a large study with over 800 participants, we study the ability of human subjects to discriminate between these artificial and genuine histological images. Although they perform better than naive participants, we find that even experts fail to reliably identify fabricated data. While participant performance depends on the amount of training data used, even low quantities are sufficient to create convincing images, necessitating methods and policies to detect fabricated data in scientific publications.

N/A.

Emerging societal expectations from biomedical research and intensifying international scientific competition are becoming existential matters. Based on a review of pertinent evidence, this article analyzes challenges and formulates public policy recommendations for improving productivity and impact of life sciences. Critical risks include widespread quality defects of research, particularly non-reproducible results, and narrow access to scientifically sound information giving advantage to health misinformation. In funding life sciences, the simultaneous shift to nondemocratic societies is an added challenge. Simply spending more on research will not be enough in the global competition. Considering the pacesetter role of the federal government, five national policy recommendations are put forward: (i) funding projects with comprehensive expectations of reproducibility; (ii) public-private partnerships for contemporaneous quality support in laboratories; (iii) making research institutions accountable for quality control; (iv) supporting new quality filtering standards for scientific journals and repositories, and (v) establishing a new network of centers for scientific health communications.

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.

Artificial intelligence (AI) is exhibiting tremendous potential to reduce the massive costs and long timescales of drug discovery. There are however important challenges currently limiting the impact and scope of AI models.

In this perspective, the authors discuss a range of data issues (bias, inconsistency, skewness, irrelevance, small size, high dimensionality), how they challenge AI models, and which issue-specific mitigations have been effective. Next, they point out the challenges faced by uncertainty quantification techniques aimed at enhancing and trusting the predictions from these AI models. They also discuss how conceptual errors, unrealistic benchmarks and performance misestimation can confound the evaluation of models and thus their development. Lastly, the authors explain how human bias, whether from AI experts or drug discovery experts, constitutes another challenge that can be alleviated by gaining more prospective experience.

AI models are often developed to excel on retrospective benchmarks unlikely to anticipate their prospective performance. As a result, only a few of these models are ever reported to have prospective value (e.g. by discovering potent and innovative drug leads for a therapeutic target). The authors have discussed what can go wrong in practice with AI for drug discovery. The authors hope that this will help inform the decisions of editors, funders investors, and researchers working in this area.

The European Quality In Preclinical Data (EQIPD) consortium was born from the fact that publications report challenges with the robustness, rigor, and/or validity of research data, which may impact decisions about whether to proceed with further preclinical testing or to advance to clinical testing, as well as draw conclusions on the predictability of preclinical models. To address this, a consortium including multiple research laboratories from academia and industry participated in a series of electroencephalography (EEG) experiments in mice aimed to detect sources of variance and to gauge how protocol harmonisation and data analytics impact such variance. Ultimately, the goal of this first ever between-laboratory comparison of EEG recordings and analyses was to validate the principles that supposedly increase data quality, robustness, and comparability. Experiments consisted of a Localisation phase, which aimed to identify the factors that influence between-laboratory variability, a Harmonisation phase to evaluate whether harmonisation of standardized protocols and centralised processing and data analysis reduced variance, and a Ring-Testing phase to verify the ability of the harmonised protocol to generate consistent findings. Indeed, between-laboratory variability reduced from Localisation to Harmonisation and this reduction remained during the Ring-Testing phase. Results obtained in this multicentre preclinical qEEG study also confirmed the complex nature of EEG experiments starting from the surgery and data collection through data pre-processing to data analysis that ultimately influenced the results and contributed to variance in findings across laboratories. Overall, harmonisation of protocols and centralized data analysis were crucial in reducing laboratory-to-laboratory variability. To this end, it is recommended that standardized guidelines be updated and followed for collection and analysis of preclinical EEG data.

Cancer drug development remains a critical but challenging process that affects millions of patients and their families. Using biomedical informatics and artificial intelligence (AI) approaches, we assessed the regulatory and translational research landscape defining successful first-in-class drugs for patients with cancer.

This is a retrospective observational study of all novel first-in-class drugs approved by the US Food and Drug Administration (FDA) from 2018 to 2022, stratified by cancer versus noncancer drugs. A biomedical informatics pipeline leveraging interoperability standards and ChatGPT performed integration and analysis of public databases provided by the FDA, National Institutes of Health, and WHO.

Between 2018 and 2022, the FDA approved a total of 247 novel drugs, of which 107 (43.3%) were first-in-class drugs involving a new biologic target. Of these first-in-class drugs, 30 (28%) treatments were indicated for patients with cancer, including 19 (63.3%) for solid tumors and the remaining 11 (36.7%) for hematologic cancers. A median of 68 publications of basic, clinical, and other relevant translational science preceded successful FDA approval of first-in-class cancer drugs, with oncology-related treatments involving fewer median years of target-based research than therapies not related to cancer (33 v 43 years; P < .05). Overall, 94.4% of first-in-class drugs had at least 25 years of target-related research papers, while 85.5% of first-in-class drugs had at least 10 years of translational research publications.

Novel first-in-class cancer treatments are defined by diverse clinical indications, personalized molecular targets, dependence on expedited regulatory pathways, and translational research metrics reflecting this complex landscape. Biomedical informatics and AI provide scalable, data-driven ways to assess and even address important challenges in the drug development pipeline.

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.

Replicable signals from different yet conceptually related studies provide stronger scientific evidence and more powerful inference. We introduce STAREG, a statistical method for replicability analysis of high throughput experiments, and apply it to analyze spatial transcriptomic studies. STAREG uses summary statistics from multiple studies of high throughput experiments and models the the joint distribution of p-values accounting for the heterogeneity of different studies. It effectively controls the false discovery rate (FDR) and has higher power by information borrowing. Moreover, it provides different rankings of important genes. With the EM algorithm in combination with pool-adjacent-violator-algorithm (PAVA), STAREG is scalable to datasets with millions of genes without any tuning parameters. Analyzing two pairs of spatially resolved transcriptomic datasets, we are able to make biological discoveries that otherwise cannot be obtained by using existing methods.

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.

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.

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.

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.

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).

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 compounds. The molecular study of the extent of this heterogeneity often measures changes in a single cell line or using a small number of compounds. To more comprehensively profile the effects of small-molecule perturbations and their influence on these heterogeneous cellular responses, we present a molecular resource based on the quantification of chromatin, proteome, and transcriptome remodeling due to histone deacetylase inhibitors (HDACi) in non-isogenic cell lines. Through quantitative molecular profiling of 10,621 proteins, these data reveal coordinated molecular remodeling of HDACi treated cancer cells. HDACi-regulated proteins differ greatly across cell lines with consistent (JUN, MAP2K3, CDKN1A) and divergent (CCND3, ASF1B, BRD7) cell-state effectors. Together these data provide valuable insight into cell-type driven and heterogeneous responses that must be taken into consideration when monitoring molecular perturbations in culture models.

Chronic inflammation is the gate of many human illnesses and happens when the immune system is unable to suppress external attacks in the correct form. Nonetheless, the gut microbiome plays a pivotal role in keeping homeostasis in the human body and preventing inflammation. Imbalanced microbiota and many diseases can result in inflammation, which when not taken seriously, can be turned into chronic ones and ultimately lead to serious diseases such as cancer. One approach to maintaining hemostasis in the human body is consumption of probiotics as a supplement. Probiotics impact the immune functions of dendritic cells (DCs), T cells, and B cells in the gut-associated lymphoid tissue by inducing the secretion of an array of cytokines. They activate the innate immune response through their microbial-associated molecular pattern, and this activation is followed by multiple cytokine secretion and adaptive elicitation that mitigates pro-inflammatory expression levels and tumor incidence. Thus, according to several studies showing the benefit of probiotics application, alone or in combination with other agents, to induce potent immune responses in individuals against some inflammatory disorders and distinct types of cancers, this review is devoted to surveying the role of probiotics and the modulation of inflammation in some cancer models.

The rapidly advancing field of artificial intelligence (AI) has garnered substantial attention for its potential application in drug discovery and development. This opinion review critically examined the feasibility and prospects of integrating AI as a transformative tool in the pharmaceutical industry. AI, encompassing machine learning algorithms, deep learning, and data analytics, offers unprecedented opportunities to streamline and enhance various stages of drug development. This opinion review delved into the current landscape of AI-driven approaches, discussing their utilization in target identification, lead optimization, and predictive modeling of pharmacokinetics and toxicity. We aimed to scrutinize the integration of large-scale omics data, electronic health records, and chemical informatics, highlighting the power of AI in uncovering novel therapeutic targets and accelerating drug repurposing strategies. Despite the considerable potential of AI, the review also addressed inherent challenges, including data privacy concerns, interpretability of AI models, and the need for robust validation in real-world clinical settings. Additionally, we explored ethical considerations surrounding AI-driven decision-making in drug development. This opinion review provided a nuanced perspective on the transformative role of AI in drug discovery by discussing the existing literature and emerging trends, presenting critical insights and addressing potential hurdles. In conclusion, this study aimed to stimulate discourse within the scientific community and guide future endeavors to harness the full potential of AI in drug development.