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LipoAgent: Coordinating Fine-Tuned LLM Agents for Safer Lipid Design

arXiv:2605.25250v1 Announce Type: new Abstract: Lipid nanoparticles (LNPs) are among the most clinically mature platforms for nucleic acid delivery, yet designing lipids that are both effective and biologically safe remains a major bottleneck. In practical screening, toxicity is a decision-level constraint: if a lipid is toxic, its efficiency prediction is clinically irrelevant. We propose LipoAgent, a safety-aware multi-agent LLM framework for lipid discovery. LipoAgent combines domain-specific finetuning with a conditional prediction objective that enforces toxicity as a prerequisite for efficiency prediction, and further improves reliability via multi-agent verification with lightweight human oversight when disagreement persists. Across multiple foundation models, LipoAgent achieves an average 32% relative improvement in mRNA transfection efficiency prediction compared with other reported models for lipid design. Wet-lab validation confirms that virtual screening rankings reliably translate to biological transfection outcomes. The code is publicly available at https://github.com/SAI-Lab-NYU/LipoAgent.git.

Towards Evaluation Engineering: An Empirical Study of ML Evaluation Harnesses in the Wild

arXiv:2605.24213v1 Announce Type: cross Abstract: Evaluation harnesses are software systems that orchestrate model evaluation by managing model invocation, data loading, metric computation, and result reporting. Despite their critical role in machine learning infrastructure, their operational challenges and engineering concerns have received limited attention so far. We present an empirical study of 57 evaluation harnesses, deriving a five-stage harness model and classifying 16,560 issues by workflow stage and root cause. Most harness operational challenges concentrate in the Specification stage (41.4% of issues), where harnesses integrate external models, datasets, and scoring judges. The three most frequent root causes of operational challenges are unimplemented features (24.3%), documentation gaps (20.3%), and missing input validation (17.2%), which together account for 61.7% of classified issues, spanning both defects in existing functionality and capability gaps that block intended workflows. Root causes also vary by workflow stage: environment incompatibility and external dependency breakage account for 36.2% of provisioning issues, whereas algorithmic error (25.9%) and validation gap (22.5%) dominate assessment issues. Together, these contributions establish an empirical foundation for treating evaluation engineering as a distinct software engineering concern.

Bibliometric analysis of lung cancer organoid research: trends and emerging areas of study

J Thorac Dis. 2026 Apr 30;18(4):406. doi: 10.21037/jtd-2026-0547. Epub 2026 Apr 27.

ABSTRACT

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide, posing a substantial global health burden. Despite advances in early detection, molecular profiling, and targeted therapies, patient outcomes remain unsatisfactory due to tumor heterogeneity, therapeutic resistance, and the lack of reliable preclinical models. In recent years, lung cancer organoids (LCOs), patient-derived three-dimensional (3D) culture systems, have demonstrated the ability to preserve the histological architecture and genomic features of primary tumors more faithfully than conventional models, making them a promising platform for translational research and precision medicine. This study aims to quantitatively evaluate the global research output, identify major contributors and collaboration patterns, and systematically uncover research hotspots and emerging trends in the field of LCOs through bibliometric analysis.

METHODS: A systematic bibliometric analysis was conducted using publications on LCOs retrieved from the Web of Science Core Collection (WoSCC). Articles published between 2015 and 2024 were included. A total of 356 publications were analyzed. Publication outputs, country and institutional contributions, collaboration networks, and keyword co-occurrence were evaluated using Bibliometrix (R package), VOSviewer, and CiteSpace.

RESULTS: The number of publications on LCOs has increased steadily over the past decade, reflecting growing research interest and technological advancement. China and the United States were identified as the leading contributors, accounting for the majority of publications, while Germany, South Korea, and Japan also demonstrated strong research capacity and active collaboration. Keyword and thematic analyses revealed several major research hotspots, including personalized medicine, drug response and resistance mechanisms, tumor microenvironment modeling, and immune-related interactions. Burst keyword analysis further identified emerging trends, such as co-culture systems, immunotherapy evaluation, and the integration of LCOs with high-throughput screening and multi-omics approaches.

CONCLUSIONS: LCOs have evolved into a versatile platform bridging basic research and clinical applications in lung cancer. This study provides a comprehensive overview of the current research landscape and highlights emerging directions in the field. Future research should focus on methodological standardization, optimization of organoid construction and evaluation, integration with multi-omics and immune models, and strengthened international collaboration to facilitate clinical translation and improve patient outcomes.

PMID:42182656 | PMC:PMC13190222 | DOI:10.21037/jtd-2026-0547

Bibliometric analysis of lung cancer organoid research: trends and emerging areas of study

25 May 2026 at 18:00

J Thorac Dis. 2026 Apr 30;18(4):406. doi: 10.21037/jtd-2026-0547. Epub 2026 Apr 27.

ABSTRACT

BACKGROUND: Lung cancer remains the leading cause of cancer-related mortality worldwide, posing a substantial global health burden. Despite advances in early detection, molecular profiling, and targeted therapies, patient outcomes remain unsatisfactory due to tumor heterogeneity, therapeutic resistance, and the lack of reliable preclinical models. In recent years, lung cancer organoids (LCOs), patient-derived three-dimensional (3D) culture systems, have demonstrated the ability to preserve the histological architecture and genomic features of primary tumors more faithfully than conventional models, making them a promising platform for translational research and precision medicine. This study aims to quantitatively evaluate the global research output, identify major contributors and collaboration patterns, and systematically uncover research hotspots and emerging trends in the field of LCOs through bibliometric analysis.

METHODS: A systematic bibliometric analysis was conducted using publications on LCOs retrieved from the Web of Science Core Collection (WoSCC). Articles published between 2015 and 2024 were included. A total of 356 publications were analyzed. Publication outputs, country and institutional contributions, collaboration networks, and keyword co-occurrence were evaluated using Bibliometrix (R package), VOSviewer, and CiteSpace.

RESULTS: The number of publications on LCOs has increased steadily over the past decade, reflecting growing research interest and technological advancement. China and the United States were identified as the leading contributors, accounting for the majority of publications, while Germany, South Korea, and Japan also demonstrated strong research capacity and active collaboration. Keyword and thematic analyses revealed several major research hotspots, including personalized medicine, drug response and resistance mechanisms, tumor microenvironment modeling, and immune-related interactions. Burst keyword analysis further identified emerging trends, such as co-culture systems, immunotherapy evaluation, and the integration of LCOs with high-throughput screening and multi-omics approaches.

CONCLUSIONS: LCOs have evolved into a versatile platform bridging basic research and clinical applications in lung cancer. This study provides a comprehensive overview of the current research landscape and highlights emerging directions in the field. Future research should focus on methodological standardization, optimization of organoid construction and evaluation, integration with multi-omics and immune models, and strengthened international collaboration to facilitate clinical translation and improve patient outcomes.

PMID:42182656 | PMC:PMC13190222 | DOI:10.21037/jtd-2026-0547

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