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Received — 27 May 2026 ⏭

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

✇cs.AI, q-bio.NC updates on arXiv.org
By: Leshu Li · An Lu · Haiyu Wang · Zhibin Feng · Conghui Duan · Qing Bao · Zongmin Zhao · Sai Qian Zhang
26 May 2026 at 12:00
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

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhimin Zhao · Zehao Wang · Abdul Ali Bangash · Bram Adams · Ahmed E. Hassan
26 May 2026 at 12:00
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

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Zhe Wu · Sheng Chen · Yifan Man · Min Zhao · Rongjian Xu
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

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

✇Omics In Lung
By: Zhe Wu · Sheng Chen · Yifan Man · Min Zhao · Rongjian Xu
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

Received — 8 April 2026 ⏭
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • Gradual Cognitive Externalization: From Modeling Cognition to Constituting It Zhimin Zhao
    arXiv:2604.04387v2 Announce Type: new Abstract: Developers are publishing AI agent skills that replicate a colleague's communication style, encode a supervisor's mentoring heuristics, or preserve a person's behavioral repertoire beyond biological death. To explain why, we propose Gradual Cognitive Externalization (GCE), a framework arguing that ambient AI systems, through sustained causal coupling with users, transition from modeling cognitive functions to constituting part of users' cognitive
     
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Gradual Cognitive Externalization: From Modeling Cognition to Constituting It

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhimin Zhao
7 April 2026 at 12:00
arXiv:2604.04387v2 Announce Type: new Abstract: Developers are publishing AI agent skills that replicate a colleague's communication style, encode a supervisor's mentoring heuristics, or preserve a person's behavioral repertoire beyond biological death. To explain why, we propose Gradual Cognitive Externalization (GCE), a framework arguing that ambient AI systems, through sustained causal coupling with users, transition from modeling cognitive functions to constituting part of users' cognitive architectures. GCE adopts an explicit functionalist commitment: cognitive functions are individuated by their causal-functional roles, not by substrate. The framework rests on the behavioral manifold hypothesis and a central falsifiable assumption, the no behaviorally invisible residual (NBIR) hypothesis: for any cognitive function whose behavioral output lies on a learnable manifold, no behaviorally invisible component is necessary for that function's operation. We document evidence from deployed AI systems showing that externalization preconditions are already observable, formalize three criteria separating cognitive integration from tool use (bidirectional adaptation, functional equivalence, causal coupling), and derive five testable predictions with theory-constrained thresholds.
Received — 11 March 2026 ⏭

Investigating the Effect of Hospital Infection Control Informatization on Optimizing Microbiological Specimen Submission Before Antibiotic Therapy: Failure Mode and Effects Analysis

✇Journal of Medical Internet Research
By: Jianxiong Wu · Weijiang Zhan · Jun Hua · Qinling Ge · Yuexian Zhu · Huixian Yu · Min Zhao · Xiaoyan Zhan · Bingwei Zhu · Tongqi Xiang · Longxi Lu · Tieying Dai
11 March 2026 at 04:15
Background: Antimicrobial resistance (AMR) poses a critical global health threat, with inappropriate antibiotic use being a major driver. Timely microbiological specimen submission before initiating antibiotic therapy is a cornerstone of antimicrobial stewardship (AMS), enabling pathogen-directed therapy and reducing unnecessary broad-spectrum exposure. However, suboptimal compliance remains common due to workflow interruptions, technological barriers, and behavioral factors. Failure Mode and Effects Analysis (FMEA), a proactive risk-assessment method widely used in health care quality improvement, provides a systematic framework to identify process vulnerabilities and prioritize corrective actions. Despite its increasing application, few studies have integrated FMEA with hospital informatization to optimize microbiological specimen submission workflows in routine AMS practice. Objective: This study aimed to systematically identify workflow risks affecting preantibiotic microbiological specimen submission and to design, implement, and evaluate informatization-enabled interventions using an FMEA-based framework. Methods: FMEA was conducted at a tertiary hospital in China. A multidisciplinary team identified potential failure modes across 4 domains: health information systems, personnel, administration, and external support. Risk Priority Numbers (RPNs) and Action Priority (AP) indices were calculated for each failure mode. Targeted interventions were implemented, including dual-verification barcode scanning, artificial intelligence-driven clinical decision support alerts, EHR-integrated training modules, and automated compliance dashboards. Pre- and postintervention specimen submission rates (January 2024-December 2024) were analyzed using the Mann-Kendall trend test. Results: The top 5 failure modes included PDA barcode scanning failures (RPN=175), inadequate clinical decision support (RPN=140), insufficient clinician awareness (RPN=56), suboptimal oversight mechanisms, and patient-related barriers. Postintervention, significant upward trends were observed in overall specimen submission rates (
Received — 17 February 2026 ⏭

FGBench: A Dataset and Benchmark for Molecular Property Reasoning at Functional Group-Level in Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xuan Liu · Siru Ouyang · Xianrui Zhong · Jiawei Han · Huimin Zhao
17 February 2026 at 13:00
arXiv:2508.01055v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have gained significant attention in chemistry. However, most existing datasets center on molecular-level property prediction and overlook the role of fine-grained functional group (FG) information. Incorporating FG-level data can provide valuable prior knowledge that links molecular structures with textual descriptions, which can be used to build more interpretable, structure-aware LLMs for reasoning on molecule-related tasks. Moreover, LLMs can learn from such fine-grained information to uncover hidden relationships between specific functional groups and molecular properties, thereby advancing molecular design and drug discovery. Here, we introduce FGBench, a dataset comprising 625K molecular property reasoning problems with functional group information. Functional groups are precisely annotated and localized within the molecule, which ensures the dataset's interoperability thereby facilitating further multimodal applications. FGBench includes both regression and classification tasks on 245 different functional groups across three categories for molecular property reasoning: (1) single functional group impacts, (2) multiple functional group interactions, and (3) direct molecular comparisons. In the benchmark of state-of-the-art LLMs on 7K curated data, the results indicate that current LLMs struggle with FG-level property reasoning, highlighting the need to enhance reasoning capabilities in LLMs for chemistry tasks. We anticipate that the methodology employed in FGBench to construct datasets with functional group-level information will serve as a foundational framework for generating new question-answer pairs, enabling LLMs to better understand fine-grained molecular structure-property relationships. The dataset and evaluation code are available at https://github.com/xuanliugit/FGBench.

CellINR: Implicitly Overcoming Photo-induced Artifacts in 4D Live Fluorescence Microscopy

✇cs.AI, q-bio.NC updates on arXiv.org
By: Cunmin Zhao · Ziyuan Luo · Guoye Guan · Zelin Li · Yiming Ma · Zhongying Zhao · Renjie Wan
17 February 2026 at 13:00
arXiv:2508.19300v2 Announce Type: replace-cross Abstract: 4D live fluorescence microscopy is often compromised by prolonged high intensity illumination which induces photobleaching and phototoxic effects that generate photo-induced artifacts and severely impair image continuity and detail recovery. To address this challenge, we propose the CellINR framework, a case-specific optimization approach based on implicit neural representation. The method employs blind convolution and structure amplification strategies to map 3D spatial coordinates into the high frequency domain, enabling precise modeling and high-accuracy reconstruction of cellular structures while effectively distinguishing true signals from artifacts. Experimental results demonstrate that CellINR significantly outperforms existing techniques in artifact removal and restoration of structural continuity, and for the first time, a paired 4D live cell imaging dataset is provided for evaluating reconstruction performance, thereby offering a solid foundation for subsequent quantitative analyses and biological research. The code and dataset will be public.
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