❌

Normal view

Received — 27 May 2026 ⏭

A visual analysis of the research dynamics of biomarkers for lung cancer screening

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Huange Zhu · Burong Li · Ranran Kong · Jun Yang · Zeqi Guo · Jiafeng Yin
26 May 2026 at 18:00

Clin Epigenetics. 2026 May 26;18(1):90. doi: 10.1186/s13148-026-02084-2.

ABSTRACT

BACKGROUND: Non-invasive biomarkers offer potential to improve risk stratification and early diagnosis of lung cancer, complementing low-dose computed tomography (LDCT) screening. This study employed bibliometric analysis to identify global research trends, collaborative networks, and future directions in lung cancer biomarker research. Publications on lung cancer biomarkers for screening were retrieved from the Web of Science Core Collection (WoSCC). Data processing and visualisation were performed using Citespace, VOSviewer, KH Coder, Latent Dirichlet Allocation (LDA) topic modelling, and the online bibliometric analysis platform. Burst detection analysis was performed to predict emerging research trends.

RESULTS: Analysis of 3636 publications revealed exponential growth in research output since 2014. International collaboration demonstrated a dual-core structure centred on China and the United States, with Chinese institutions showing high publication volumes and American institutions demonstrating greater citation influence. Journal citation mapping revealed three evolutionary phases: basic mechanisms-clinical translation-intelligent integration. LDA topic modelling identified 22 topics grouped into five core research directions: imaging and pathological diagnostic techniques; molecular and omics marker research; liquid biopsy and new detection technologies; clinical and translational medicine research; and tumour biology and treatment mechanisms. Burst detection analysis predicted future four priority areas: epigenetic studies centred on DNA methylation for risk prediction; treatment resistance and invasion mechanisms; liquid biopsy technology development; and targeted therapy clinical trials.

CONCLUSIONS: Lung cancer biomarker research has evolved towards multimodal, intelligent screening approaches. Future research priorities include DNA methylation-based markers, circulating microRNA signatures, and artificial intelligence-assisted diagnostic platforms to improve early detection accuracy and complement LDCT screening.

PMID:42185923 | DOI:10.1186/s13148-026-02084-2

EchoPilot: Training-Free Ultrasound Video Segmentation via Scale-Space Semantic Prompting and Reliability-Gated Memory

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ruiqiang Xiao · Zhaohu Xing · Yijun Yang · Zhenyan Han · Weiming Wang · Kaishun Wu · Lei Zhu
26 May 2026 at 12:00
arXiv:2605.25944v1 Announce Type: cross Abstract: Ultrasound video segmentation is clinically valuable yet difficult due to speckle noise, weak boundaries, and rapid anatomical deformation. Recent promptable foundation models enable point-guided segmentation, but their direct deployment in ultrasound remains unreliable: a single point provides insufficient spatial context to resolve scale ambiguity, and greedy memory updates amplify early errors into severe temporal drift. We present EchoPilot, a training-free framework for ultrasound video segmentation under sparse first-frame interaction, requiring only a single point click and an anatomical category name. EchoPilot orchestrates a frozen medical vision-language model (VLM) for semantic localization, a vision foundation model (VFM) for dense geometric feature extraction, and a promptable video segmentor for mask prediction and propagation. To resolve initialization ambiguity, we propose Scale-Space Semantic Prompting, which first selects an optimal contextual view via a parameter-free S.E.E.D. (Semantic Energy-Entropy Density) criterion, and then synthesizes geometrically precise auxiliary point prompts from dense foundation features without additional user interaction. To reduce propagation drift, a Reliability-Gated Memory update is further introduced to selectively freeze the segmentor's memory bank under uncertain predictions, preventing error accumulation. We also contribute the first dynamic fetal placenta ultrasound video segmentation dataset with 671 annotated frames. Across three ultrasound video datasets, EchoPilot achieves state-of-the-art performance under the sparse-interactive setting, consistently outperforming training-free baselines and finetuned specialists.

A review of organoid-immune co-culture platforms to model the immune microenvironment of hepatocellular carcinoma and guide immunotherapy

✇Omics in Hepatocellular
By: Wen-Wen Liu · Xi-Shuai Xie · Xiao-Jun Yang
21 May 2026 at 18:00

J Transl Med. 2026 May 20. doi: 10.1186/s12967-026-08278-9. Online ahead of print.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) is characterized by a highly immunosuppressive and heterogeneous tumor microenvironment that limits the effectiveness of current immunotherapies. Conventional two-dimensional cultures and animal models fail to fully capture patient-specific tumor-immune interactions, creating an urgent need for more physiologically relevant platforms.

MAIN BODY: This review summarizes recent advances in co-culture systems integrating patient-derived HCC organoids with defined immune cell populations to reconstruct essential features of the tumor microenvironment. We describe strategies for organoid establishment and validation, outline immune cell integration approaches, and compare static three-dimensional cultures, microfluidic organ-on-chip systems, and bioengineered multicellular platforms. We further highlight key tumor-immune interaction mechanisms that have been functionally interrogated in these systems, including immune checkpoint-mediated T-cell dysfunction, adenosine-driven metabolic suppression, and chemokine-regulated immune recruitment. Importantly, we critically evaluate current limitations, including immune cell exhaustion artifacts, lack of stromal and vascular complexity, and variability across protocols, which may affect the reproducibility and translational interpretation of these models. While emerging studies suggest potential for predicting immunotherapy responses, robust clinical validation in HCC remains limited.

CONCLUSIONS: Organoid-immune co-culture platforms represent an emerging translational framework that bridges mechanistic tumor immunology with functional precision oncology. With improved standardization and integration of multicellular bioengineering and multi-omics technologies, these systems have strong potential to guide personalized immunotherapy strategies, although further clinical validation is required.

PMID:42163357 | DOI:10.1186/s12967-026-08278-9

There are no more articles
❌