Spatial transcriptomics has revolutionized our understanding of tissue organization by simultaneously capturing gene expression and spatial localization within intact tissues. However, analyzing these increasingly complex datasets requires specialized expertise across computational biology, statistics, and biological context. To address this challenge, we introduce the Spatial Transcriptomics AI Agent (STAgent), an autonomous multimodal agentic AI that integrates multimodal large language models (LLMs) with specialized computational tools to transform weeks-long analysis tasks into minutes of automated processing. Unlike conventional machine learning approaches that are limited to narrow, predefined tasks, STAgent leverages the emergent capabilities of multimodal LLMs – such as flexible reasoning, contextual understanding, and cross-modal integration – which allow it to adapt to novel data, execute multi-step analyses, and generate biologically meaningful insights with minimal human input. STAgent enables autonomous deep research through integrated capabilities, including dynamic code generation for complex analytical workflows, visual reasoning for interpreting spatial patterns, real-time retrieval of relevant peer-reviewd scientific literature, and synthesis of comprehensive, actionable reports. We applied STAgent to investigate the in vivo maturation of human stem cell-derived pancreatic cells (SC-pancreas) transplanted into immunodeficient mice. We generated single-cell spatial transcriptomics data spanning multiple developmental timepoints. STAgent autonomously (1) identified the maturation of initially scattered endocrine cells into well-defined islet-like structures, with predominantly peripheral α-cells surrounding β-cell cores supported by an expanding mesenchymal network; (2) revealed strengthening endocrine-endocrine cell interactions over time and, through context-aware gene set analysis, uncovered spatially resolved biological processes driving maturation; (3) unlike traditional analytical approaches, STAgent offers mechanistic explanations of spatial patterns, contextualizing findings with relevant literatures and developing cohesive insights into human pancreatic development. This agentic approach establishes a new paradigm in spatial transcriptomics analysis by substantially lowering the expertise barrier and reducing analysis time, accelerating biological and biomedical discovery.

The accumulation of electrochemically produced bubbles is inevitable in gas-evolving reactions and can induce potential losses by theoretically increasing activation, concentration, and ohmic overpotentials. These effects are often either overstated or completely neglected in the literature, which complicates the accurate analysis of experimental results for gas evolution reactions. This study systematically identifies and quantifies the overpotential losses induced by bubbles by combining experimental results for hydrogen (HER) and oxygen evolution reactions (OER), obtained using the rotating disk electrode (RDE) technique, with simulations based on a two-dimensional transmission line model. Our results show that ohmic overpotential is the primary cause of apparent activity loss due to bubbles in RDE. This effect leads to catalyst activity misestimates exceeding 2 orders of magnitude, and Tafel slope errors of 100% at higher currents if left uncorrected. By identifying these effects, this work provides a robust framework for mitigating inaccuracies and improving the characterization of electrocatalysts for gas evolution reactions.

This summary of the second Terrestrial Very-Long-Baseline Atom Interferometry (TVLBAI) Workshop provides a comprehensive overview of our meeting held in London in April 2024 (Second Terrestrial Very-Long-Baseline Atom Interferometry Workshop, Imperial College, April 2024), building on the initial discussions during the inaugural workshop held at CERN in March 2023 (First Terrestrial Very-Long-Baseline Atom Interferometry Workshop, CERN, March 2023). Like the summary of the first workshop (Abend et al. in AVS Quantum Sci. 6:024701, 2024), this document records a critical milestone for the international atom interferometry community. It documents our concerted efforts to evaluate progress, address emerging challenges, and refine strategic directions for future large-scale atom interferometry projects. Our commitment to collaboration is manifested by the integration of diverse expertise and the coordination of international resources, all aimed at advancing the frontiers of atom interferometry physics and technology, as set out in a Memorandum of Understanding signed by over 50 institutions (Memorandum of Understanding for the Terrestrial Very Long Baseline Atom Interferometer Study).

This article is concerned with qualitative and quantitative refinements of the concepts of the log-convexity and log-concavity of positive sequences. A new class of tempered sequences is introduced, its basic properties are established and several interesting examples are provided. The new class extends the class of log-balanced sequences by including the sequences of similar growth rates, but of the opposite log-behavior. Special attention is paid to the sequences defined by two- and three-term linear recurrences with constant coefficients. For the special cases of generalized Fibonacci and Lucas sequences, we graphically illustrate the domains of their log-convexity and log-concavity. For an application, we establish the concyclicity of the points a2na2n+1,1a2n+1 for some classes of Horadam sequences (an) with positive terms.

The inverse design of metamaterial architectures presents a significant challenge, particularly for nonlinear mechanical properties involving large deformations, buckling, contact, and plasticity. Traditional methods, such as gradient-based optimization, and recent generative deep-learning approaches often rely on binary pixel-based representations, which introduce jagged edges that hinder finite element (FE) simulations and 3D printing. To overcome these challenges, we propose an inverse design framework that utilizes a signed distance function (SDF) representation combined with a conditional diffusion model. The SDF provides a smooth boundary representation, eliminating the need for post-processing and ensuring compatibility with FE simulations and manufacturing methods. A classifier-free guided diffusion model is trained to generate SDFs conditioned on target macroscopic stress-strain curves, enabling efficient one-shot design synthesis. To assess the mechanical response of the generated designs, we introduce a forward prediction model based on Neural Operator Transformers (NOT), which accurately predicts homogenized stress-strain curves and local solution fields for arbitrary geometries with irregular query meshes. This approach enables a closed-loop process for general metamaterial design, offering a pathway for the development of advanced functional materials.

Background: The accidental puncture of the supra-aortal arteries during central venous catheterization is a rare but potentially life-threatening complication. Traditional management often requires open surgical repair, which is associated with significant morbidity. This study evaluates an endovascular approach for managing such cases using an Angio-Seal™ vascular closure device (Terumo Medical Corporation, Somerset, NJ, USA). Methods: Between January 2010 and December 2024, 47 patients with misplaced catheters in supra-aortal arteries were treated at our institution. Of these, 37 cases involving subclavian artery catheter misplacements were managed using a standardized algorithm and form the focus of this study. Additional interventions, such as stent graft placement or balloon inflation, were performed as needed. Results: Primary technical success was achieved in 86.5% of cases. Four patients required stentgrafts and one balloon inflation for persistent extravasations. One patient developed a small subclavian pseudoaneurysm, which resolved spontaneously. Primary assisted technical success and clinical success rates were both 100%. Conclusions: This study demonstrates the efficacy and safety of our minimally invasive endovascular approach for managing subclavian artery catheter misplacements. With a high success rate, low complication rate, and the avoidance of open surgery, this algorithm offers a promising alternative for treating this rare but serious complication of central venous catheterization.

As editors and scholars, we have concerns with investigations that emphasize the contribution of one major factor to the development of a complex entity such as, for example, language or literacy. This phenomenon is known as the single-factor fallacy. Basically, this is asserting that there is one all-encompassing factor that causes or influences academic development even though there are certainly other factors that are critical contributors. Endorsing one factor, whether explicitly or implicitly, leads to oversimplification and overgeneralization as well as to other problems such as misleading conclusions and confirmation and citation biases. The single-factor approach results in the promotion of inappropriate educational decisions or implications regarding d/Deaf and hard of hearing (d/Dhh) children and adolescents. We discuss ways to minimize or avoid the single-factor fallacy.

Introduction Chatbots like ChatGPT have attracted a lot of interest lately due to their ability to generate human-like responses. Their reliability and accuracy are still questionable, and they are the topic of many studies in different fields. Therefore, the aim of this study was to examine the knowledge of two versions of chatbots regarding laboratory enzymology and to compare it with the average knowledge of students for the purpose of considering the use of ChatGPT in providing answers in this field. Material and methods An exam with 110 questions covering four topics was answered by students and ChatGPT-3.5 and ChatGPT-4.0. The accuracy of the answers of 52 students and ChatGPT was evaluated. The accuracy of answers between students and artificial intelligence was compared, and the percentage of passing the exam was 60%. All responses were reviewed by two authors with full interrater agreement. Results Total scores for students, ChatGPT-3.5, and ChatGPT-4.0 were 85.46%, 52.73%, and 74.55% (p < 0.05), whereby ChatGPT-4.0 achieved better results compared to the other chatbot. ChatGPT-3.5 and ChatGPT-4.0 achieved the best results on questions about enzymes in metabolism. The lowest scores for both chatbots were observed in the laboratory analysis of enzymes. Conclusion ChatGPT showed average results in the Laboratory Enzymology exam and scored lower than students. This proved that chatbots could be a potential tool for learning and eventual implementation in higher and/or medical education with extensive optimization but still cannot replace a human.

Background Adjuvant chemotherapy decisions in early-stage, hormone receptor-positive, HER2-negative breast cancer traditionally rely on clinicopathological features such as tumor size, grade, and lymph node status. However, multigene expression assays like MammaPrint offer additional prognostic information that may alter treatment recommendations. This study aimed to assess the level of agreement between MammaPrint-based genomic risk classification and chemotherapy recommendations derived from National Comprehensive Cancer Network (NCCN)-based clinical criteria in a cohort of Bosnia and Herzegovina breast cancer patients. Methods We retrospectively analyzed 66 patients with HR+/HER2-, node-negative early breast cancer treated between 2023 and 2024. All patients underwent MammaPrint testing, which classified tumors as either low risk or high risk for distant recurrence. Clinical risk was assessed using a simplified NCCN-guided algorithm, in which chemotherapy was recommended for tumors >2 cm and/or grade three histology. The primary outcome was the rate of concordance between genomic and clinical risk classifications. Statistical analysis included descriptive summaries, cross-tabulation, and Cohen’s kappa to evaluate agreement. Results Of the 66 patients analyzed, MammaPrint classified 27 (40.9%) as high risk and 39 (59.1%) as low risk. Based on NCCN criteria, 36 patients (54.5%) were considered clinically high-risk and recommended for chemotherapy, while 30 (45.5%) were not. Concordance between genomic and clinical classifications was observed in 37 patients (56.1%), while 29 patients (43.9%) showed discordant results. The most common discordance pattern was a clinically high-risk but genomically low-risk classification, observed in 19 cases (65.5% of discordant pairs). Cohen’s kappa for agreement between methods was 0.136, indicating slight agreement beyond chance. McNemar’s test yielded a χ² value of 10.0 (p = 0.036), suggesting significant asymmetry in discordance patterns. Conclusion This study highlights a substantial rate of discordance between MammaPrint genomic risk and NCCN-based clinical risk assessment. In our cohort, reliance on clinicopathological features alone would have led to different chemotherapy recommendations in over half of the cases. These findings support the clinical utility of multigene assays in refining adjuvant treatment decisions and reducing potential overtreatment in early breast cancer.