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Adhesion forces between macrophages and cancer cells promote early tumor development

Biochemical mechanisms of macrophage-driven tumor promotion are well documented, but the contribution of physical forces to early tumor development remains poorly understood. Here, we combine experimental analyses with physical modeling to investigate these forces in Kras G12D p53 −/− (KP) lung tumor spheroids grown in 3D. Real-time microscopy showed that tissue-resident macrophages, but not monocytes, promote early tumor growth. Using quantitative measurements, we built a physical model that recapitulates cancer cell proliferation dynamics and macrophage–tumor interactions. KP tumor cells grown alone formed a single aggregate that contracted over time due to nutrient limitation, whereas macrophages induced the formation of multiple aggregates that grew, fused, and expanded nutrient access, thereby increasing proliferation. Similar macrophage-driven growth was observed when alveolar or bone-marrow–derived macrophages were cocultured with KP or pancreatic carcinoma cells. The model predicted a redistribution of macrophages toward the periphery of aggregates, a pattern confirmed in vitro and previously observed in vivo. It also identified adhesion forces between tumor cells and macrophages as a key driver of spheroid nucleation and growth. Among candidate integrins, CD11c was highly expressed by alveolar macrophages; CD11c blockade reduced adhesion forces, prevented macrophage-driven spheroid nucleation, and impaired tumor growth. Bone-marrow-derived macrophages required simultaneous CD11b and CD11c blockade for similar effects. Finally, CD11c inhibition in RAG-Knock Out (KO) mice reduced tumor survival probability and slowed the growth of ear-implanted tumors, indicating that CD11c-dependent interactions support tumor establishment beyond the lung. Together, these findings uncover a critical physical mechanism through which macrophages promote early tumor progression.

Identity interweaving, act boundaries, illusion and reality interweaving: A study of visual narratives of scientists and citizen scientists through AI

The rise of generative artificial intelligence (AI) is transforming human-computer interaction, reshaping communication methods and altering public perceptions of science. This shift challenges traditional scientific authority, especially as citizen science gains prominence. While research on scientific rhetoric has focused on qualitative analyses in media, little attention has been given to how AI influences visual rhetorical narratives in science. This study employs computer visual analysis and quantitative rhetorical difference analysis to explore the intersection of AI and science through Visual Narrative Theory.It investigates the rhetorical differences between scientists and citizen scientists across three dimensions: narrate, act, and resonate. Findings reveal that both groups embody a mixed rhetoric of authority and proximity in the narrative dimension. In the act dimension, AI depicts scientists in professional roles while showing citizen scientists in practical roles, portraying scientists as “flowers in the greenhouse”. In the resonate dimension, scientists’ narratives often feature surreal elements, while citizen scientists present more everyday narratives.This analysis, utilizing computer vision and quantitative methods, offers a fresh perspective on the image of science in the AI era and suggests strategies for enhancing science communication and building trust in science using generative AI.

Developing statistical models as an early warning system to predict Salmonella outbreaks in wild birds

“I felt like I was providing half a service”: Challenges, solutions, and action items for paramedicine when encountering patients experiencing intimate partner violence

Introduction Intimate partner violence (IPV) is a pervasive and damaging global crisis. In response to the harmful health consequences, survivors often attempt to access the healthcare system. Paramedics are often the first point of contact with the healthcare system. Objective: Examine how the perspectives and experiences of paramedics may inform our understanding of current clinical practice and guide potential improvements for paramedicine. Methods An interpretive description qualitative approach was used to design and conduct this research. Paramedics participated in focus groups discussing the intersection of paramedicine and IPV from the practitioner perspective. De-identified focus group transcripts underwent inductive pattern recognition. From the patterns, common challenges were identified. Corresponding solutions and action items were identified. Results N = 17 paramedics (Women n = 7 (41%), Men n = 10 (59%); Mean Age 34 ± 10 years) participated in four focus groups. Even without clinical practice guidance for IPV, participants shared the service they were providing did not meet the needs of survivors. Common challenges at the intersection of paramedicine and IPV were: 1) patient barriers for help seeking, 2) individual paramedic disposition, 3) individual paramedic confidence, 4) paramedic service education, training, and readiness, 5) paramedic service guidance, 6) paramedic service configuration, and 7) interagency networks. Solutions and action items to address each challenge included updating functional education, training, infrastructure, and policy. Conclusion Participants indicated that substantial challenges exist from the paramedic perspective at the intersection of paramedicine and IPV. Solutions and action items to bolster the education, training, infrastructure, policy, and positioning of paramedics were identified, providing positive direction. Meaningful, evidence-based implementation of these results should be pursued to advance the profession. Paramedics can be positioned as expert resources for survivors of IPV, linking through to vital supports that promote positive outcomes.

Substance P in the lateral hypothalamic area regulates binge‐like eating behaviors in mice

Binge eating disorder (BED) is the most common type of eating disorder; however, the neural circuit mechanisms underlying BED remain elusive. Here, we report that tachykinin-expressing neurons in the lateral hypothalamic area (LH Tac1 neurons) are inhibited during binge-like eating behaviors in mice. We identified LH Tac1 neurons as key mediators of binge-like eating behaviors and reported that the LH Tac1 → lateral periaqueductal gray (LPAG) circuit is critical to the regulation of binge-like eating behaviors. Moreover, Substance P (SP) released by LH Tac1 neurons modulates binge-like eating behaviors by influencing the input of glutamate to LPAG cells, which receive projections from LH Tac1 neurons. In summary, these findings point to the SP as a key node in BED circuits.

Understanding discrepancies in perceived importance of patient safety measures between patients and healthcare professionals in perioperative care: An exploratory study

Background Patient safety is a critical concern in perioperative care. This study explores the discrepancies in how patients and healthcare professionals perceive the importance of perioperative patient safety outcome measures, aiming to improve the development of future Core Outcome Sets (COS). Methods Qualitative exploratory study using focus groups with healthcare professionals and patients involved in the Core Outcome Set for Patient Safety in Perioperative Care. Data were collected through online mini-focus groups and analysed using thematic qualitative text analysis. Results Communication failure emerged as the predominant cross-cutting issue across discussions, particularly in relation to discrepancies in expectations, information exchange, and understanding between healthcare professionals and patients. Three primary reasons for discrepancies in attributed importance of indicators were identified: different targets/focus; knowledge gaps; and varying importance placed on the sense of safety. Patients often emphasized subjective experiences, fears, and emotional impacts, leading them to prioritize quality of life indicators and long-term effects. In contrast, healthcare professionals focused on system-level factors and resource limitations, giving greater weight to technical and physiological outcomes. Discussion/conclusion The study findings underscore the need for a more holistic approach in developing COS, balancing technical medical outcomes with patient-centered quality of life measures.

The population structure in the Baltic herring reflects natural selection and local adaptation

How species time reproduction and adapt to environmental conditions are key topics in ecology and evolutionary biology. Here, we conducted a high-resolution population genetic analysis of Baltic herring, a subspecies of Atlantic herring ( Clupea harengus ). Genotypes at >4,500 SNPs were generated from >4,500 spawning individuals, sampled from 150 locations spanning Swedish’s eastern coast. Abiotic factors—week of spawning, latitude, temperature, salinity—were used to assess how genetic variation is shaped by temporal, spatial, and environmental gradients. Our results reaffirm strong genetic differentiation between spring- and autumn-spawning ecotypes, despite hybridization suggesting ongoing gene flow between the two ecotypes. We document significant substructuring within the spring-spawning ecotype, delineating three main, previously unidentified, genetic clusters underpinned by adaptative genetic variation associated with latitude, salinity, temperature, and spawning time. Complementary linkage disequilibrium (LD) partitioning showed that adaptive loci—especially those in inversion regions—exhibit strong elevated among-population LD, consistent with divergence maintained by local selection despite ongoing gene flow. Clinal variation in allele frequencies indicated regionally distinct selection pressures, including shifts in allele frequencies at two major supergenes (inversions) and at a suite of genes correlated with abiotic factors. Importantly, rare genetic outlier populations are identified within each geographic region which further illustrates the unexpected fine-grained population structure of Baltic herring and implies a strong homing behavior in this abundant marine fish. Overall, this study demonstrates the capacity for targeted population genetic studies to detect adaptive variation in natural populations, the outcomes of which have direct implications for sustainable fisheries and biodiversity management.

A lightweight and robust method for electrocardiogram anomaly detection and localization using multi-scale masked autoencoder

Electrocardiogram (ECG) analysis is crucial for diagnosing cardiovascular conditions. While traditional classification models require large volumes of labeled data across multiple disease categories, anomaly detection offers a flexible alternative by identifying deviations from normal patterns—an approach particularly valuable given the rarity and diversity of cardiac conditions. However, existing anomaly detection methods often rely on R-peak detection or heartbeat segmentation, which increases preprocessing complexity and reduces robustness to signal variability. To address these limitations, we propose MMAE-ECG, a multi-scale masked autoencoder designed to capture both global and local dependencies without such preprocessing steps. MMAE-ECG integrates a multi-scale masking strategy and a multi-scale attention mechanism with distinct positional embeddings, enabling a lightweight Transformer encoder to efficiently model ECG signals. Additionally, an aggregation strategy is introduced to improve anomaly score estimation. Experiments demonstrate that MMAE-ECG achieves state-of-the-art performance in both anomaly detection and localization while significantly reducing computational costs. Specifically, it requires only approximately 1/78 of the inference FLOPs and 1/18 of the trainable parameters compared to the previous leading method. Ablation studies further validate the contributions of each component, demonstrating the potential of multi-scale masked autoencoders as an effective and efficient approach for ECG anomaly detection.

Pareto optimality reveals an atlas of cellular archetypes

We sought to identify universal organizing principles behind phenotypic variation within cell types. Pareto optimality describes how trade-offs between optimal solutions account for variation, predicting that the boundary points of a data distribution reflect specialized functions. We hypothesized that transcriptomic variation was explained by Pareto optimality across all cell types. We then used the Tabula Sapiens Atlas of single-cell RNA sequencing across cell types and tissues in the human body to test this hypothesis and found that most cell types adhere to this theory. This enabled us to use this principled method to characterize the functions performed by each cell type. These phenotypes are derived from an unbiased approach and do not incorporate ideas from existing biological models or theories, and yet in many cases they recapitulate our understanding of the functions of major cell types. Ultimately, we conclude that multiobjective optimization broadly shapes the observed phenotypic variation within cell types. This finding enables us to write explicit representations of the low-dimensional manifolds on which transcriptomes of single cells reside. This can inform the design of the next generation of virtual cell language models, which aim to statistically learn low-dimensional transcriptomic manifolds.

Is check-up on demand non-inferior to routine follow-up at one year after total hip or knee arthroplasty in terms of clinical outcomes and cost-effectiveness? Protocol for a randomized stepped-wedge hybrid effectiveness de-implementation trial

Background Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are highly effective surgical procedures for patients with end-stage osteoarthritis. Due to population ageing and the rising prevalence of osteoarthritis, the demand for these procedures continues to increase, placing pressure on healthcare systems. Postoperative follow-up care contributes to this burden, yet internationally its timing and frequency after THA and TKA differ substantially. Dutch guidelines recommend routine follow-up (RFU) at 6–12 weeks and 1 year postoperatively. However, most complications are identified based on symptoms, often during unplanned visits. Consequently, the added value of a 1-year routine follow-up visit remains unclear, suggesting that alternative follow-up strategies, such as check-up on demand (COD) might reduce unnecessary visits. Materials and methods This multicenter hybrid type II effectiveness de-implementation trial uses a stepped-wedge cluster randomized design across 10 Dutch hospitals. All hospitals will sequentially transition from RFU with scheduled follow-up visits at 6–12 weeks and 1 year postoperatively to Check-Up on Demand (COD), in which patients have a scheduled visit at 6–12 weeks and receive a leaflet with instructions on when and how to contact the hospital, without a scheduled 1-year visit. A total of 1,000 patients aged ≥50 years undergoing primary THA or TKA for osteoarthritis will be included. Each participating hospital will recruit 100 patients (50 THA and 50 TKA). The primary clinical outcome is PROMIS physical functioning at 2 years (i.e., 1 year after the 1-year follow-up moment). The primary process outcome is the number of patients who have a clinical visit or X-ray related to surgery at 1 year postoperatively. Secondary outcomes include complications, surgical interventions, additional healthcare consumption, quality of life, pain, satisfaction, and costs. An economic evaluation and budget impact analysis will be conducted from healthcare and societal perspectives. The trial is registered at ClinicalTrials.gov (NCT06971757).

Elucidating the design principles for engineering plant organ size

Enhancements to crop morphology, such as the semidwarfing that helped drive the green revolution, are often driven by changes in gene expression. These are challenging to translate across species, which slows the rate of crop improvement. Synthetic transcription factors (SynTFs) offer a rapid alternative to generate targeted alterations to gene expression. However, the complexity of developmental pathways makes it unclear how to best apply them to predictably engineer morphology. In this work, we explore whether mathematical modeling can guide SynTF-based gene expression modulation to help elucidate the design principles of engineering organ size. We targeted genes in the phytohormone, gibberellin (GA), signaling pathway, which is a central regulator of cell expansion. We demonstrate that modulation of GA signaling gene expression can generate consistent dwarfing across tissues and environments in Arabidopsis thaliana , and that the degree of dwarfing is dependent on the strength of regulation, as predicted by modeling. We further validate the model’s predictive power by demonstrating its capacity to predict the qualitative impacts of different regulatory architectures for engineering organ size. Additionally, we develop expression parameterized models to quantitatively predict organ size and elucidate how temperature will affect growth. Finally, we show that these insights can be generalized for engineering organ size in tomato ( Solanum lycopersicum ). This work creates a framework for predictable engineering of an agriculturally important trait across tissues and plant species. It also serves as a proof-of-concept for how mathematical models can guide SynTF-based alterations in gene expression to enable bottom–up design of plant phenotypes.

Correction: Targeted drug screening for autism based on Cav1.2 calcium ion channel

Microbially enhanced dissolution of calcite in sinking marine particles

Evidence for the shallow cycling of calcium carbonate in the global ocean is mounting, but the mechanisms driving the dissolution of thermodynamically stable polymorphs, like aragonite and calcite, in the surface ocean remain unconstrained. Here, we quantify how microbial metabolism creates acidic microenvironments in marine particles that enhance the local dissolution of calcite despite supersaturated conditions in bulk waters. A temporal decoupling of particle deoxygenation and acidification suggests that respiration-derived carbon dioxide is not the sole driver of the observed undersaturation. Rapid dissolution occurs in particles exhibiting bacterial growth, with rates exceeding abiotic dissolution at the same bulk saturation by more than an order of magnitude. We observe the highest particle-associated dissolution rates at intermediate settling velocities, indicating that a trade-off between elevated mass transfer due to settling and bacterial respiration governs the ensuing dissolution rates. Translation of our experiments to the water column suggests that microbially driven undersaturation in marine particles may dissolve sufficient calcite in the mesopelagic ocean to extend particle transit times by eliminating this vital ballast mineral, reducing the efficiency of organic carbon sequestration.

Computational modeling-directed combination treatment with etanercept and mifepristone mitigates neuroinflammation in a mouse model of Gulf War Illness

Gulf War Illness is a chronic multi-symptom disorder experienced by over 30% of veterans from the 1990–1991 Gulf War and is increasingly recognized to be driven by underlying persistent neuroinflammation resulting from chemical and physiological exposures experienced during deployment. Despite significant advances in identifying Gulf War-relevant exposures and underlying pathobiology, effective treatment strategies for Gulf War Illness are still largely lacking. Many studies that have evaluated potential therapies for Gulf War Illness have primarily focused on a single treatment. However, through a mechanistically informed computational evaluation of blood biomarkers and gene expression in veterans with Gulf War Illness, we identified that a combination of anti-inflammatory and anti-glucocorticoid treatment may prove effective in treating Gulf War Illness. Here, we have evaluated combined treatment with the anti-TNFα drug, etanercept, and anti-glucocorticoid, mifepristone, in an established long-term mouse model of Gulf War Illness of combined physiological stress and nerve agent exposure. Supporting results from the computational modeling of this treatment, we found that this drug combination significantly alleviates the underlying neuroinflammation associated with Gulf War Illness. The fusion of computational and in vivo preclinical treatment evaluation may provide a highly useful and translationally relevant means by which to identify successful treatment paradigms for Gulf War Illness.

Leveraging antibiotic hormesis for cryptic natural product discovery

Microbial natural products are a foundational source of therapeutic agents, yet a vast majority remain inaccessible due to the limited expression of their biosynthetic gene clusters under standard laboratory conditions. To address this challenge, we developed a simple and broadly applicable screening approach based on the idea of antibiotic hormesis wherein high-dose growth-inhibitory antibiotics serve as low-dose elicitors of secondary metabolites. We generated an in-house library of all available clinical antibiotics and exposed nine phylogenetically diverse bacteria to high-dose growth inhibition and low-dose metabolite stimulation assays. The approach revealed the induction of cryptic metabolites with every strain tested. Four of these were selected for scale-up fermentation and comprehensive metabolomic analysis, leading to the identification of eight known but strain-novel cryptic metabolites and nine structurally unique, previously undescribed natural products. These findings underscore the utility of the antibiotic hormesis approach as a rapid and scalable platform for the discovery of natural products and therapeutic leads. Moreover, the consistent elicitation of cryptic metabolites highlights the generalizability of low-dose antibiotics as key signaling molecules in microbial metabolic regulation. Beyond expanding the repertoire of accessible natural products, this work lays the foundation for systematic studies into the molecular and ecological mechanisms that govern cryptic metabolite biosynthesis.

Correction: A study of the changing characteristics and influencing factors of holiday visitor vitality in Urban parks: The case of Fuzhou, China

High-latitude teleconnections drive subtropical marine bioproductivity at the dawn of the Antarctic ice sheet

The inception of the Antarctic ice sheet (AIS) marked a major global climatic reorganization of the Cenozoic, but the response of the subtropical marine biosphere remains poorly constrained. A new sediment archive from the subtropical South Atlantic (IODP Exp. 390 and 393) reveals a sevenfold increase in surface ocean bioproductivity proxy accumulation (biogenic barium) commensurate with the initial expansion of the AIS 34 Mya, and the emergence of an amplified astronomical forcing of subtropical bioproductivity that mirrors the subsequent evolution of the AIS in the early Oligocene. We find that a strong 40-kyr obliquity response characterizes subtropical bioproductivity following the initial establishment of an expansive marine-based AIS. Portions of the AIS in contact with the marine environment are sensitive to meridional heat delivery controlled by obliquity-forced interactions between the atmosphere and ocean, which can propagate to the lower latitudes via Southern Ocean overturning circulation. The surprising emergence of obliquity forcing of low-latitude bioproductivity enhances our understanding of global teleconnections and feedbacks that regulate global climate, and points to mechanisms driving global marine bioproductivity on astronomical timescales—and their intricate connections to the evolution of the cryosphere.

A lightweight deep learning framework for reliable microscopy-based diagnosis of cutaneous leishmaniasis

Cutaneous leishmaniasis (CL) is a neglected tropical and zoonotic disease affecting both human and animal health, for which microscopic examination of Giemsa-stained slides remains the diagnostic reference standard despite being time-consuming and operator-dependent. In this study, we developed a lightweight, calibration-aware deep learning framework for automated amastigote detection and slide-level diagnostic probability estimation from microscopy images. A U-Net architecture with a MobileNetV2 encoder was employed for pixel-level parasite segmentation using a weakly supervised pseudo-labeling strategy on a single-center dataset comprising 292 field-of-view images. Slide-level diagnostic probabilities were derived via probability pooling and subsequently refined using post-hoc calibration techniques, including isotonic regression, Platt scaling, and temperature scaling. Model performance was evaluated on an independent test set using segmentation metrics (Dice coefficient and Intersection-over-Union) and diagnostic reliability metrics (AUROC, Brier score, and Expected Calibration Error). The proposed framework achieved a Dice coefficient of 0.901 and an IoU of 0.820 for segmentation against pseudo-label references, with strong discriminative performance at the slide level (AUROC = 0.978). Isotonic regression markedly improved probability reliability, reducing the Brier score from 0.089 to 0.030 and the Expected Calibration Error from 0.120 to 0.023 without significantly affecting discrimination. Statistical analyses confirmed the robustness of the calibration improvements. Overall, the results demonstrate that isotonic calibration enhances the interpretability and reliability of deep-learning-based CL diagnostics. The proposed lightweight framework provides a robust foundation for microscopy-based screening and supports future validation across broader datasets and One Health–oriented diagnostic applications.

Identification and characterization of a wet adhesive protein extracted from <i>Dreissena bugensis</i> , the freshwater quagga mussel

Mechanisms of wet adhesion have evolved in several aquatic organisms over millions of years. Yet, the repertoire of synthetic biocompatible wet adhesive materials is still limited. The byssus is a well-studied proteinaceous bioadhesive structure utilized by several bivalves to support sessile lifestyles in turbulent conditions. The quagga mussel ( Dreissena bugensis ) is a freshwater byssate and a notorious invasive species in the Great Lakes region. To identify adhesive proteins in the quagga mussel byssus, we utilized quantitative proteomics and found several proteins enriched at the byssus–substrate interface. Among the identified proteins was the Dbfp7 protein family. Dbfp7 is a small, polymorphic, and mostly disordered protein that lacks significant amounts of 3,4-dihydroxyphenylalanine (DOPA), a modified amino acid found in several marine mussel byssal proteins. Atomic force microscopy nanomechanical mapping of Dbfp7 films demonstrates that this protein exhibits adhesive ability in aqueous conditions. While DOPA is critical for marine mussel adhesion, interfacial electrochemistry of freshwater adhesive plaques suggests that freshwater byssates circumvent catechol-based adhesion. The functional characterization of Dbfp7 as a freshwater mussel adhesive protein advances the understanding of fundamental requirements for biocompatible wet adhesion, a crucial step for the development of bioinspired wet adhesive materials, such as improved medical adhesives.

Prevalence and influencing factors of dry eye syndrome among pilots: A survey study

Background Dry eye syndrome (DES) is a prevalent ocular condition that significantly impacts affected individuals’ quality of life and occupational performance. This study investigates the prevalence and contributing factors of DES among pilots, which is a group particularly susceptible to environmental and occupational stressors. Methods A descriptive, observational study was conducted, which involved 794 pilots. Based on the severity of DES, these pilots were assigned into mild, moderate and severe groups. Data was collected through surveys, and analyzed using multiple linear regression, in order to determine the relationship between the DES scores and potential influencing factors. Results The study revealed that all pilots included in the present study were affected by DES, in which 88.40% of pilots experienced moderate DES and 11.60% of pilots reported severe DES. After adjusting for other covariates in the model, the multivariate analysis revealed that eyelid diseases, ocular surface disease, poor sleep quality, and fatigue were statistically significant and positively correlated to higher DES scores ( p  &lt; 0.05), while residing in the southern region and engaging in physical activities were statistically significant and negatively correlated to the DES scores ( p  &lt; 0.05). Conclusion The high prevalence of DES in pilots highlights the urgent need for tailored occupational health interventions. Strategies to mitigate DES risk should include promoting regular physical exercise, improving sleep quality, and addressing fatigue. Future research should prioritize longitudinal studies to establish causal relationships and develop targeted management approaches for this high-risk occupational group.