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Scientists are increasingly expected to not only produce objective science but also to use their science and expertise to support the public good. Prior studies indicate that scientists see both responsibilities as important; however, it is not clear how scientists manage these responsibilities when they are perceived to be in tension. In this study, scientists at land-grant institutions (n = 87) were presented with binary choices to identify where tensions may exist and how scientists make decisions between competing responsibilities. We examined nine categories of potential tensions: direct conflicts between objectivity and public impact, engagement in public outreach, communication priorities and approach, political engagement and perceived bias, the media, funding and disclosure, supporting/protecting specific causes, diversity, and ethical decision making. Results indicate that scientists belong to three cultures of ethical thought: arbiters, advocates, and brokers. These cultures roughly align with the scientist roles described by Pielke and suggest different prioritizations of commitment to public service relative to commitment to objective science, though both commitments were valued. There was variation across cultures of thought on whether scientists should openly support policies based on their research. Broad areas of agreement across cultures of thought were also identified. Scientists of all cultures of thought indicated a willingness to see themselves in a public role, communicating openly with non-experts on issues where scientific research intersects with the public good. There was also an expressed need for scientists to receive better training and tools to manage conflicting responsibilities.

Multidrug-resistant Enterobacterales are an increasing public health concern, yet household environmental sources remain poorly defined. This study evaluated whether citizen scientists can effectively collect household surface samples using sponge sticks to detect multidrug-resistant Enterobacterales and the effectiveness of methods of bacterial recovery from household surface types. We conducted controlled laboratory experiments using extended-spectrum beta- lactamases-producing Enterobacterales and extended-spectrum cephalosporin-resistant Acinetobacter baumannii on cloth, metal, and plastic surfaces to benchmark recovery efficiency. Recovery varied by organism, with highest rates for Klebsiella pneumoniae and A. baumannii, with minimal differences across surface types. Thirteen citizen-scientists sampled their household surfaces following standardized instructions. Bacteria were recovered from most households, including 16 Enterobacterales isolates resistant to third-generation cephalosporins. Recovered species included Escherichia coli, Pantoea agglomerans, and Enterobacter cloacae complex. Antibiotic susceptibility testing and PCR revealed diverse resistance patterns, including a multidrug-resistant E. coli carrying a blaCMY AmpC cephalosporinase gene. These findings demonstrate that citizen science-based sponge stick sampling is feasible and effective for detecting multidrug-resistant Enterobacterales in household environments. This approach provides a practical method for community-based antimicrobial resistance surveillance of gram-negative bacteria, enabling identification of household environmental reservoirs of multidrug-resistant Enterobacterales that, when paired with risk factor data, can inform targeted public health interventions and future epidemiological studies. Findings can be returned to participants through accessible summaries to promote community awareness and engagement in antimicrobial resistance prevention.

For the treatment of many forms of cancer, cell- and gene-based therapies are showing promise in both pre-clinical data and clinical trials. In particular, CAR T cell therapies, of which there are now 7 FDA-approved products, have shown ground-breaking results in haematological cancers such as multiple myeloma and B cell malignancies. Recent research is also attempting to develop effective CAR T cell therapies for solid tumours, with varying success. One of the key challenges faced by CAR T cell therapy is balancing strong cytotoxic activity for an effective treatment with preventing severe and potentially lethal toxicities, such as Cytokine Release Syndrome and Immune Effector Cell-Associated Neurotoxicity Syndrome. This mini review discusses some of the potential solutions that scientists have devised to overcome toxicities and improve existing CAR T cell therapies.

Despite extensive efforts by scientists, academic institutions, and pharmaceutical companies, a safe and effective HIV/AIDS vaccine remains elusive. Most HIV-1 envelope peptide vaccine strategies have concentrated on Gp120, gp140, or gp160. HIV-1 Env trimer binding to the CD4-receptor initiates structural changes promoting the envelope's transition from a closed to an open state via an intermediate step. Broadly neutralizing antibodies target the state-1 Env conformation, while less effective antibodies typically recognize open states. However, due to virus variability, an optimal vaccine has not yet been successfully developed. In this study, focusing on the pivotal role of Gp41 in various vaccine strategies, a very large sequence dataset was utilized. These sequences were obtained from drug-naïve individuals or those undergoing antiretroviral therapy (ART). Gp41 amino acid variability was characterized genetically using a starting pool dataset of 24,505 full-length Env sequences from HIV-1 Subtype-B infected individuals. The dataset underwent hydropathy analysis, genetic distance evaluation, non-synonymous/synonymous substitution rate estimation, Shannon-Entropy calculation, and N-linked glycosylation (NLG) analysis. Similar variability between viral sequences retrieved from drug-naïve and antiretroviral-treated individuals was observed. In our dataset, ART selection pressures observed at gp41 level are minimal: 7 positions with dN/dS > 1, significant increases in entropy values, and a comparable value of glycosylation sites were highlighted. This study reinforces the importance of identifying specific single sensitizing mutations in HIV control. Gp41 remains an important vaccine target for understanding virus-host immunological interactions. Further analyses may reveal specific mechanisms related to host antiviral responses and viral regions with strong masking activity.

The Upper Guinean forests of West Africa are a major global hotspot for primate diversity, yet many areas remain understudied and insufficiently protected. Southeastern Côte d'Ivoire illustrates this conservation gap. Once home to several of West Africa's most threatened primate species, the region has experienced extensive deforestation, habitat fragmentation, and hunting pressure over recent decades. Consequently, many primate populations have declined or disappeared from large parts of their former range. The Tanoé-Ehy Forest, a swamp forest ecosystem located along the Côte d'Ivoire-Ghana border, is now one of the last refuges for several endangered primates in the region. Since 2006, a long-term conservation initiative led by Ivorian scientists, in partnership with local communities, has aimed to protect this forest and its biodiversity through a community-based conservation approach. This article presents the Tanoé-Ehy conservation initiative as a case study demonstrating how locally led conservation efforts can support both biodiversity protection and community empowerment while contributing to changes in conservation practice in African primatology. Over nearly two decades, the project has combined ecological research, participatory governance, and socio-economic initiatives. Community members actively participate in wildlife monitoring, forest surveillance, environmental education, and livelihood diversification programs designed to reduce pressure on forest resources. Beyond biodiversity protection, the initiative highlights the importance of strengthening national scientific leadership and recognizing local communities as central actors in conservation governance. The Tanoé-Ehy experience shows that effective and sustainable conservation in biodiversity-rich regions depends on integrating local knowledge, equitable partnerships, and long-term community engagement.

Trust in medical scientists shapes public engagement in health and biomedical research, yet its influence on cancer research participation is not well understood. This study assessed trust in researchers and examined its relationship with willingness to engage in diverse cancer research activities. Cross-sectional analyses of a 2023 statewide survey of US adults evaluated Trust in Medical Researcher Scale (TMRS) scores (range 0-48) and willingness to participate in cancer research activities (research studies, biobanking, and data-sharing). Associations between trust and participation were examined using descriptive statistics and adjusted logistic regression models. Among 1,780 respondents, the mean TMRS score was 27.3 ± 9.3, with most reporting moderate trust. Willingness to participate in cancer research varied across activity types. Low trust was consistently associated with reduced willingness across all activity types. Limited trust in researchers represents a significant barrier to participation in cancer research.

Snakebite envenoming is a major neglected tropical disease, in which snake venom metalloproteinases (SVMPs) are key drivers of local tissue damage, microvascular disruption, and hemostatic imbalance. Closely related SVMPs can nevertheless range from highly hemorrhagic to essentially non-hemorrhagic while sharing a conserved Zn(II) catalytic site, and the structural causes of this divergence (particularly the role of the conserved methionine-containing turn (MET-turn) beneath the metal center) remain unclear. Here, we have combined a Protein Data Bank (PDB) inspection with theoretical calculations at the PBE0-D3/def2-TZVP level to investigate the noncovalent interactions stabilizing the MET residue over the Zn(II) catalytic site in both hemorrhagic and non-hemorrhagic P-I and P-III SVMPs. Additionally, the results were analyzed using several computational tools, such as the Quantum Theory of Atoms in Molecules (QTAIM) and the Non Covalent Interaction plot (NCIplot) methodologies, revealing and quantifying S lone pair-π, CH-π and Zn-associated σ-hole (Spodium bond-like) interactions. We believe the results presented herein will be useful to those scientists devoted to bioinorganic chemistry and rational drug design as well as to expand the current knowledge of SpBs among the chemical biology community.

Polymer-based long-acting injectables (LAIs) have transformed the treatment of chronic diseases by enabling controlled drug delivery, thus reducing dosing frequency and extending therapeutic duration. Achieving controlled drug release from LAIs requires extensive optimization of the complex underlying physicochemical properties. Machine learning (ML) can accelerate LAI development by modeling the complex relationships between LAI properties and drug release. However, recent ML studies have provided limited information on key properties that modulate drug release, as existing approaches rely on time as a primary input feature, obscuring the independent contributions of material characteristics to release dynamics This paper presents a novel data transformation and explainable ML approach to synthesize actionable information from 321 LAI formulations by predicting early drug release at 24, 48, and 72 h, classifying release profile types, and predicting complete release profiles. These three experiments investigate the influence of LAI material characteristics in early and complete drug release profiles. A moderate correlation (0.37) is observed between the true and predicted drug release at 72 h, while an F1-score of 0.72 is obtained in classifying the types of drug release profiles. For the first time, we demonstrate that time-independent ML frameworks achieve equivalent performance to time-dependent approaches in predicting complete drug release profiles, including complex delayed biphasic and triphasic curves. Shapley additive explanations reveal the relative influence of material characteristics during early time points, between drug release profile classes and for complete release, which fill several gaps in previous in-vitro and ML-based studies. The novel approach and findings can provide a quantitative strategy and recommendations for scientists to optimize the drug-release dynamics of LAI. The source code for the model implementation is publicly available in1.

The application of machine learning in clinical medicine requires systematic evaluation across diverse modeling paradigms. We benchmarked 10 models, including classic machine learning, tabular deep learning, and automated machine learning (AutoML), across eight real-world clinical risk prediction datasets. Using a 10-time repeated 5-fold cross-validation protocol, we assessed discrimination, calibration, and clinical utility. Gradient boosting decision trees, particularly CatBoost, and the tabular foundation model TabPFN consistently demonstrated superior robustness, forming the top tier for performance. AutoGluon also exhibited strong competitiveness. In contrast, most other tabular deep learning models displayed significant instability. These findings indicate that advanced gradient boosting models and TabPFN represent premier strategies for building high-performance clinical risk prediction models, while AutoML offers a reliable alternative. This study provides crucial empirical guidance for clinicians and data scientists in selecting appropriate modeling strategies.

Recent advances in electrode technology - including the development of Neuropixels and SiNAPS probes - have made it possible to routinely capture spike trains from thousands of neurons distributed across the brain. Widespread dissemination of these tools has not only yielded new discoveries but also changed the way in which neuroscientific questions are asked and answered. In this article, we describe the motivations for collecting electrophysiological recordings on this scale, review the basic physical principles underlying these measurements and discuss key considerations for generating optimally useful datasets. We compare the latest devices for large-scale recordings and address challenges and opportunities in data analysis, rigour, reproducibility and data sharing. Finally, we provide a roadmap for future advances in this space. We argue that widely available hardware, software and protocols are now empowering scientists to perform experiments matched to the scale and complexity of the neural circuits that underlie complex mammalian behaviours.

Land plants underpin civilization and planetary health, yet their genomic diversity remains largely uncharted. Current resources are unstandardized and scarce, lacking reference genomes for 95% of genera, 70% of families, and 51% of orders, impeding evolutionary and functional insight. We thus propose the PLANeT initiative, an international effort to generate high-quality, standardized genomes across the plant tree of life. Integrating artificial intelligence (AI) with genomics, we will decode conserved principles to advance fundamental plant biology, biodiversity conservation, crop improvement, and natural product discovery. Engaging around 100 labs to train 1,000 scientists, we will tackle pivotal questions for a sustainable future.

Adopting a safety-centric approach, this article explores how generative artificial intelligence (AI), and more specifically, foundation models for biological sequences, can exacerbate data quality issues, technical biases, and dual-use potential, particularly in critical applications such as clinical genetics, precision medicine, and pathogen engineering. This work centres on how misuse risks emerge throughout the innovation pipeline and how these intersect with the growing accessibility of generative genomic models. Particular attention is given to dual-use governance and infrastructure hardening in sequence analysis workflows. The work aims to provide scientists, regulators, and policymakers with a toolkit to discuss beneficial innovation in genomic AI while maintaining robust safeguards against harm and misuse.

Integrating societal considerations into public health research, particularly during crises, can foster public trust, support inclusive policy-making, and enhance technology development. Interdisciplinary collaboration may enhance researchers' capacities to reflect on the societal impacts of scientific advancements as they occur. This qualitative Socio-Technical Integration Research (STIR) study investigated these claims among four early-career researchers in virology, physics, and engineering, working in a large-scale COVID-19 research project across Germany. The collaboration involved 12 weeks of protocol-based dialogue exercises, pre- and post-study interviews, and participant observation, and was analyzed using the "Midstream modulation" framework. We found that the exercises documented, and in some cases stimulated, changes in participants' awareness, attitudes, and behaviours regarding their research's broader social context. One participant grew more aware of their work's social impact over time, recognizing stakeholders beyond the laboratory. Another shifted attitudes toward science communication, while a third demonstrated greater empathy for public reactions to scientific advice. These enhanced capacities for reflexivity suggests potential of STIR for improved communication among scientists, the public, and policymakers, strengthening the science-society interface in COVID-19 and broader health research. Such collaborations can build public trust, inform interventions, and improve the translation of basic research into effective health policies.

Integration of Artificial Intelligence (AI), particularly deep learning, into medical imaging represents a profound shift in diagnostic medicine, moving from purely descriptive analysis to advanced predictive and prescriptive analytics. This Collection explores the rapid advancement of AI-driven tools in their specific fields such as oncology, cardiology, ophthalmology and so on, highlighting their potential to improve diagnostic accuracy, workflow efficiency, and personalized treatment planning. However, significant challenges remain, including the heterogeneity of medical image data, the "black box" nature of some intelligent models, and the critical hurdles of clinical integration and validation. The research presented here addresses these frontiers, showcasing innovations in algorithm development, explainable AI, and translational application. This Editorial synthesizes the contributions and outlines the essential collaborative pathway-uniting computer scientists, clinicians, and regulatory bodies-required to translate algorithmic promise into robust, trustworthy, and equitable clinical tools that genuinely improve patient care.