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Received today — 7 October 2026 ⏭

Postoperative circulating tumor DNA in stage II colon cancer: biological rationale, clinical evidence, and unresolved challenges

✇MRD
By: Erhua Yang · Xuefeng Wang · Jiajing Zhang · Dejiang Zhao · Zhimeng Yang · Xue Li · Guirong He · Tianhua Guo · Ruotian Wang · Baohong Yuan
26 September 2026 at 18:00

Front Oncol. 2026 Sep 11;16:1908802. doi: 10.3389/fonc.2026.1908802. eCollection 2026.

ABSTRACT

BACKGROUND: Stage II colon cancer is clinically heterogeneous. Current clinicopathologic risk factors cannot accurately identify patients with residual disease after curative resection. Circulating tumor DNA (ctDNA) has emerged as a promising biomarker for the detection of minimal residual disease (MRD), defined as microscopic residual tumor burden that remains after curative-intent treatment and is not detectable by conventional imaging, and for postoperative risk stratification.

METHODS: This narrative review summarizes the biological basis, analytical approaches, and clinical evidence regarding ctDNA in stage II colon cancer. We particularly emphasize prospective studies and randomized controlled trials.

RESULTS: Postoperative ctDNA positivity is strongly associated with increased recurrence risk and provides superior prognostic stratification compared with conventional clinicopathologic factors. Prospective studies have demonstrated that ctDNA-positive patients experience substantially higher recurrence rates, with the GALAXY study reporting a hazard ratio of 11.99 (95% CI: 8.83-16.27) for disease-free survival among patients with postoperative molecular residual disease. The DYNAMIC trial demonstrated that ctDNA-guided management reduces adjuvant chemotherapy use without compromising recurrence-free survival. However, current evidence suggests an important asymmetry in clinical utility. ctDNA negativity may support treatment de-escalation. In contrast, ctDNA positivity has not yet reliably identified patients who benefit from treatment escalation.

CONCLUSIONS: ctDNA constitutes a robust prognostic biomarker in stage II colon cancer and supports risk-adapted postoperative management. However, its predictive value for guiding treatment escalation remains unproven. Integration with clinicopathologic and molecular features is essential before routine clinical implementation.

PMID:42798930 | PMC:PMC13613144 | DOI:10.3389/fonc.2026.1908802

Received — 15 September 2026 ⏭

Tumor-derived CTHRC1 mediates ITGB3-dependent osteoclast differentiation to promote prostate cancer bone metastasis

✇Oncogene - Issue - nature.com science feeds
By: Tao Xie · Tianming Peng · Qu Li · Mingzhao Li · Maoping Cai · Qianyi Li · Yu-Zhong Yu · Jiehui Zhong · Chong Wang · Ming-Kun Chen · Zhe-Sheng Chen · Xian-Lu Song · Di Gu · Shan-Chao Zhao
11 September 2026 at 08:00

Oncogene, Published online: 11 September 2026; doi:10.1038/s41388-026-03976-6

Tumor-derived CTHRC1 mediates ITGB3-dependent osteoclast differentiation to promote prostate cancer bone metastasis
Received — 10 September 2026 ⏭

Modality-Decoupled Federated Learning for Privacy-Preserving Embodied Intelligence in 6G

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhuodong Liu · Xiangyu Li · Chunhong Yuan · Hongyang Du · Bodong Shang · Qingqing Wu · Tony Q. S. Quek · Mohsen Guizani
10 September 2026 at 12:00
arXiv:2609.09591v1 Announce Type: cross Abstract: Sixth-generation (6G) wireless networks are expected to provide a key infrastructure for large-scale embodied intelligence, where heterogeneous robots collaborate through low-latency connectivity, edge intelligence, and distributed sensing. Vision-language-action (VLA) models offer a foundation by integrating visual perception, language understanding, and action generation into a unified closed-loop policy. However, training and adapting VLA models to distributed robotic agents introduce challenges in privacy protection, communication efficiency, and model heterogeneity. Existing federated learning (FL) methods overlook the intrinsic differences among vision, language, and action pathways in parameter scale, privacy exposure, update dynamics, and tolerance to compression or perturbation. To address this issue, this article proposes FedMVLA, a modality-decoupled FL framework for privacy-preserving embodied intelligence in 6G networks. FedMVLA incorporates three mechanisms: modality-aware federated aggregation (MAFA), modality-aware privacy allocation (MAPA), and modality-aware communication compression (MACO), together with a modality-sliced transport design that routes the precision-critical action stream through a protected ultra-reliable low-latency slice. A case study on federated robotic manipulation over the Third Generation Partnership Project (3GPP)-based wireless substrate, covering fading, co-channel interference, and malicious jamming, shows that FedMVLA achieves an 84.8% task success rate, exceeds FedAvg by 22.2 percentage points, sustains a widening margin when scaling to 128 clients across eight cells, and reduces the schedule-averaged per-client uplink model-update payload by 95.6% (approximately 96%), while keeping the 95th percentile (p95) of the round-critical uplink completion time near 1.5s.
Received — 27 May 2026 ⏭

SPACE: Unifying Symmetric and Asymmetric Routing Problems for Generalist Neural Solver

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rongsheng Chen · Changliang Zhou · Canhong Yu · Yuanyao Chen · Yu Zhou · Zhuo Chen · Zhenkun Wang
26 May 2026 at 12:00
arXiv:2605.24484v1 Announce Type: new Abstract: Generalist neural routing solvers have shown great potential in solving diverse vehicle routing problems (VRPs) with a unified model. However, existing solvers are typically limited to symmetric settings or degrade in performance when switching to asymmetric settings due to input inconsistencies or inherent structural differences, substantially limiting their practicality in real-world scenarios that encompass both scenarios. To address this limitation, we define the spatial position of each node based on the relative distances to a specific set of pivots and further propose a Spatial Pivot-Aligned Coordinate-free Embedding (SPACE) framework that unifies node representation and solution generation across symmetric and asymmetric VRPs. Specifically, we construct a bidirectional Frechet representation using a novel furthest pivot sampling strategy to enable invariant node representations across distinct problem settings. Furthermore, we introduce a weight-decomposed adaptive decoding mechanism that decouples geometric perception from problem representations, mitigating the overfitting of constraint decisions to a specific geometry setting. Extensive experiments on 110 VRP variants, comprising 55 symmetric problems and their asymmetric counterparts, demonstrate that SPACE achieves promising zero-shot generalization in both symmetric and asymmetric VRPs.

SEP-Attack: A Simple and Effective Paradigm for Transfer-Based Textual Adversarial Attack

✇cs.AI, q-bio.NC updates on arXiv.org
By: Han Liu · Zhi Xu · Xiaotong Zhang · Feng Zhang · Xiaoming Xu · Wei Wang · Fenglong Ma · Hong Yu
26 May 2026 at 12:00
arXiv:2605.24958v1 Announce Type: cross Abstract: Despite the strong performance of deep neural networks in modern Web and language applications, they remain vulnerable to adversarial attacks, especially transferable attacks that generate adversarial examples using surrogate models without accessing the victim model. Transferable attacks in the text domain are still under-explored, with only a few studies addressing this challenging issue, often with suboptimal results due to equal treatment of submodels or inaccurate estimation of importance scores. To address these challenges, we propose a simple yet effective paradigm for transfer-based textual adversarial attack, named SEP-Attack. Specifically, we employ the Determinantal Point Process (DPP) to generate diverse surrogate ensemble weights, representing the transferability of submodels. Using these weights, we introduce a new metric to evaluate prediction confidence scores, which in turn are used to calculate word importance scores and generate adversarial candidates. Finally, we quantify the transferability score for each candidate and select the top ones as the final transferable adversarial examples. Experiments conducted on four datasets and two real-world APIs validate the efficacy of SEP-Attack, significantly outperforming state-of-the-art baselines.
Received — 5 March 2026 ⏭

PlaneCycle: Training-Free 2D-to-3D Lifting of Foundation Models Without Adapters

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yinghong Yu · Guangyuan Li · Jiancheng Yang
5 March 2026 at 13:00
arXiv:2603.04165v1 Announce Type: cross Abstract: Large-scale 2D foundation models exhibit strong transferable representations, yet extending them to 3D volumetric data typically requires retraining, adapters, or architectural redesign. We introduce PlaneCycle, a training-free, adapter-free operator for architecture-agnostic 2D-to-3D lifting of foundation models. PlaneCycle reuses the original pretrained 2D backbone by cyclically distributing spatial aggregation across orthogonal HW, DW, and DH planes throughout network depth, enabling progressive 3D fusion while preserving pretrained inductive biases. The method introduces no additional parameters and is applicable to arbitrary 2D networks. Using pretrained DINOv3 models, we evaluate PlaneCycle on six 3D classification and three 3D segmentation benchmarks. Without any training, the lifted models exhibit intrinsic 3D fusion capability and, under linear probing, outperform slice-wise 2D baselines and strong 3D counterparts, approaching the performance of fully trained models. With full fine-tuning, PlaneCycle matches standard 3D architectures, highlighting its potential as a seamless and practical 2D-to-3D lifting operator. These results demonstrate that 3D capability can be unlocked from pretrained 2D foundation models without structural modification or retraining. Code is available at https://github.com/HINTLab/PlaneCycle.
Received — 25 February 2026 ⏭

FinSight-Net:A Physics-Aware Decoupled Network with Frequency-Domain Compensation for Underwater Fish Detection in Smart Aquaculture

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jinsong Yang · Zeyuan Hu · Yichen Li · Hong Yu
24 February 2026 at 13:00
arXiv:2602.19437v1 Announce Type: cross Abstract: Underwater fish detection (UFD) is a core capability for smart aquaculture and marine ecological monitoring. While recent detectors improve accuracy by stacking feature extractors or introducing heavy attention modules, they often incur substantial computational overhead and, more importantly, neglect the physics that fundamentally limits UFD: wavelength-dependent absorption and turbidity-induced scattering significantly degrade contrast, blur fine structures, and introduce backscattering noise, leading to unreliable localization and recognition. To address these challenges, we propose FinSight-Net, an efficient and physics-aware detection framework tailored for complex aquaculture environments. FinSight-Net introduces a Multi-Scale Decoupled Dual-Stream Processing (MS-DDSP) bottleneck that explicitly targets frequency-specific information loss via heterogeneous convolutional branches, suppressing backscattering artifacts while compensating distorted biological cues through scale-aware and channel-weighted pathways. We further design an Efficient Path Aggregation FPN (EPA-FPN) as a detail-filling mechanism: it restores high-frequency spatial information typically attenuated in deep layers by establishing long-range skip connections and pruning redundant fusion routes, enabling robust detection of non-rigid fish targets under severe blur and turbidity. Extensive experiments on DeepFish, AquaFishSet, and our challenging UW-BlurredFish benchmark demonstrate that FinSight-Net achieves state-of-the-art performance. In particular, on UW-BlurredFish, FinSight-Net reaches 92.8% mAP, outperforming YOLOv11s by 4.8% while reducing parameters by 29.0%, providing a strong and lightweight solution for real-time automated monitoring in smart aquaculture.
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