❌

Normal view

Circulating Tumor Cells as the Liquid Biopsy Foray into Noninvasive Colorectal Cancer Screening

Cancer Epidemiol Biomarkers Prev. 2026 Apr 1;35(4):491-493. doi: 10.1158/1055-9965.EPI-25-1971.

ABSTRACT

Recently, stool- and blood-based cancer screening kits have been approved in clinical practice as convenient and noninvasive methods for colon cancer screening. One such test in long-standing practice has included the fecal immunochemical test (FIT), wherein home-based testing has rendered it a convenient initial assay to complement screening colonoscopy, despite limitations in diagnostic performance. In a recent original study published in the journal by Nguyen and colleagues, the feasibility and performance of combining FIT with circulating tumor cell (CTC) enumeration for predicting colorectal neoplasia and the risk of developing colorectal cancer were described. In this commentary, we highlight the potential of this combination as a novel colorectal cancer screening technique. The introduction of CTC as a potential colorectal cancer screening assay is timely, given the emergence of liquid biopsies that hold promise in their ability to detect a multitude of cancer-specific signals, from the detection of minimal residual disease to the detection of molecular alterations for precision therapies in oncology. We place the importance of their results in the context of the evolving landscape of stool- and blood-based colorectal cancer screening tests involving multitarget fecal DNA and cell-free DNA assays. See related article by Nguyen et al., Cancer Epidemiol Biomarkers Prev 2026;35:79-87.

PMID:41918361 | DOI:10.1158/1055-9965.EPI-25-1971

Uncertainty Gating for Cost-Aware Explainable Artificial Intelligence

arXiv:2603.29915v1 Announce Type: new Abstract: Post-hoc explanation methods are widely used to interpret black-box predictions, but their generation is often computationally expensive and their reliability is not guaranteed. We propose epistemic uncertainty as a low-cost proxy for explanation reliability: high epistemic uncertainty identifies regions where the decision boundary is poorly defined and where explanations become unstable and unfaithful. This insight enables two complementary use cases: `improving worst-case explanations' (routing samples to cheap or expensive XAI methods based on expected explanation reliability), and `recalling high-quality explanations' (deferring explanation generation for uncertain samples under constrained budget). Across four tabular datasets, five diverse architectures, and four XAI methods, we observe a strong negative correlation between epistemic uncertainty and explanation stability. Further analysis shows that epistemic uncertainty distinguishes not only stable from unstable explanations, but also faithful from unfaithful ones. Experiments on image classification confirm that our findings generalize beyond tabular data.

Impact of enriched meaning representations for language generation in dialogue tasks: A comprehensive exploration of the relevance of tasks, corpora and metrics

arXiv:2603.29518v1 Announce Type: cross Abstract: Conversational systems should generate diverse language forms to interact fluently and accurately with users. In this context, Natural Language Generation (NLG) engines convert Meaning Representations (MRs) into sentences, directly influencing user perception. These MRs usually encode the communicative function (e.g., inform, request, confirm) via DAs and enumerate the semantic content with slot-value pairs. In this work, our objective is to analyse whether providing a task demonstrator to the generator enhances the generations of a fine-tuned model. This demonstrator is an MR-sentence pair extracted from the original dataset that enriches the input at training and inference time. The analysis involves five metrics that focus on different linguistic aspects, and four datasets that differ in multiple features, such as domain, size, lexicon, MR variability, and acquisition process. To the best of our knowledge, this is the first study on dialogue NLG implementing a comparative analysis of the impact of MRs on generation quality across domains, corpus characteristics, and the metrics used to evaluate these generations. Our key insight is that the proposed enriched inputs are effective for complex tasks and small datasets with high variability in MRs and sentences. They are also beneficial in zero-shot settings for any domain. Moreover, the analysis of the metrics shows that semantic metrics capture generation quality more accurately than lexical metrics. In addition, among these semantic metrics, those trained with human ratings can detect omissions and other subtle semantic issues that embedding-based metrics often miss. Finally, the evolution of the metric scores and the excellent results for Slot Accuracy and Dialogue Act Accuracy demonstrate that the generative models present fast adaptability to different tasks and robustness at semantic and communicative intention levels.

BotVerse: Real-Time Event-Driven Simulation of Social Agents

arXiv:2603.29741v1 Announce Type: cross Abstract: BotVerse is a scalable, event-driven framework for high-fidelity social simulation using LLM-based agents. It addresses the ethical risks of studying autonomous agents on live networks by isolating interactions within a controlled environment while grounding them in real-time content streams from the Bluesky ecosystem. The system features an asynchronous orchestration API and a simulation engine that emulates human-like temporal patterns and cognitive memory. Through the Synthetic Social Observatory, researchers can deploy customizable personas and observe multimodal interactions at scale. We demonstrate BotVersevia a coordinated disinformation scenario, providing a safe, experimental framework for red-teaming and computational social scientists. A video demonstration of the framework is available at https://youtu.be/eZSzO5Jarqk.

Perfecting Human-AI Interaction at Clinical Scale. Turning Production Signals into Safer, More Human Conversations

arXiv:2603.29893v1 Announce Type: cross Abstract: Healthcare conversational AI agents shouldn't be optimized only for clean benchmark accuracy in production-first regime; they must be optimized for the lived reality of patient conversations, where audio is imperfect, intent is indirect, language shifts mid-call, and compliance hinges on how guidance is delivered. We present a production-validated framework grounded in real-time signals from 115M+ live patient-AI interactions and clinician-led testing (7K+ licensed clinicians; 500K+ test calls). These in-the-wild cues -- paralinguistics, turn-taking dynamics, clarification triggers, escalation markers, multilingual continuity, and workflow confirmations -- reveal failure modes that curated data misses and provide actionable training and evaluation signals for safety and reliability. We further show why healthcare-grade safety cannot rely on a single LLM: long-horizon dialogue and limited attention demand redundancy via governed orchestration, independent checks, and verification. Many apparent "reasoning" errors originate upstream, motivating vertical integration across contextual ASR, clarification/repair, ambient speech handling, and latency-aware model/hardware choices. Treating interaction intelligence (tone, pacing, empathy, clarification, turn-taking) as first-class safety variables, we drive measurable gains in safety, documentation, task completion, and equity in building the safest generative AI solution for autonomous patient-facing care. Deployed across more than 10 million real patient calls, Polaris attains a clinical safety score of 99.9%, while significantly improving patient experience with average patient rating of 8.95 and reducing ASR errors by 50% over enterprise ASR. These results establish real-world interaction intelligence as a critical -- and previously underexplored -- determinant of safety and reliability in patient-facing clinical AI systems.

Four Generations of Quantum Biomedical Sensors

arXiv:2603.29944v1 Announce Type: cross Abstract: Quantum sensing technologies offer transformative potential for ultra-sensitive biomedical sensing, yet their clinical translation remains constrained by classical noise limits and a reliance on macroscopic ensembles. We propose a unifying generational framework to organize the evolving landscape of quantum biosensors based on their utilization of quantum resources. First-generation devices utilize discrete energy levels for signal transduction but follow classical scaling laws. Second-generation sensors exploit quantum coherence to reach the standard quantum limit, while third-generation architectures leverage entanglement and spin squeezing to approach Heisenberg-limited precision. We further define an emerging fourth generation characterized by the end-to-end integration of quantum sensing with quantum learning and variational circuits, enabling adaptive inference directly within the quantum domain. By analyzing critical parameters such as bandwidth matching and sensor-tissue proximity, we identify key technological bottlenecks and propose a roadmap for transitioning from measuring physical observables to extracting structured biological information with quantum-enhanced intelligence.

AgentDrift: Unsafe Recommendation Drift Under Tool Corruption Hidden by Ranking Metrics in LLM Agents

arXiv:2603.12564v5 Announce Type: replace-cross Abstract: Tool-augmented LLM agents increasingly operate as multi-turn advisors in high-stakes domains, yet their evaluation relies on ranking metrics that measure what is recommended but not whether it is safe for the user. We present a paired-trajectory protocol that replays real financial dialogues under clean and contaminated tool-output conditions across eight LLMs (7B to frontier), decomposing divergence into information-channel and memory-channel mechanisms. We observe evaluation blindness: recommendation quality is preserved under contamination (UPR~1.0) while risk-inappropriate products appear in 65-93% of turns, invisible to standard NDCG. Violations are information-channel-driven, emerge at turn 1, and persist without self-correction over 23-step trajectories. Even non-extreme perturbations (within-band corruption, narrative-only attacks) evade threshold monitors while producing significant drift. Susceptibility scales with instruction-following fidelity across all eight models. Sparse autoencoder probing reveals models internally distinguish adversarial perturbations but fail to propagate this signal to output; causal interventions (activation patching, feature clamping, direct steering) confirm this representation-to-action gap is structural and resists linear repair. A safety-penalized NDCG variant (sNDCG) reduces preservation ratios to 0.51-0.74. These results motivate trajectory-level safety monitoring for deployed multi-turn agents.

Evidence of the pair-instability gap from black-hole masses

Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10359-0

LIGO–Virgo–KAGRA’s fourth Gravitational-Wave Transient Catalog shows evidence of a clear pair-instability gap in the distribution of binary black-hole secondary masses but is absent in the larger primary masses.

Reproducibility and robustness of economics and political science research

Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10251-x

Robustness checks and reproduction of analyses with existing and updated data based on 110 articles in economics and political science journals with data and code-sharing requirements found high levels of robustness and reproducibility and determined that robustness was not dependent on author characteristics or data availability.

Benralizumab versus placebo for hypereosinophilic syndrome: a randomized, placebo-controlled phase 3 trial

Nature Medicine, Published online: 31 March 2026; doi:10.1038/s41591-026-04315-8

Benralizumab (an anti-IL-5 receptor α antibody), compared to placebo, significantly reduced the risk of first flare in patients with hypereosinophilic syndrome.

Minimal Residual Disease Assessment Through ctDNA Facilitates Tailored Immunotherapy in MSI-High, NTRK1-Fusion Pancreatic Adenocarcinoma

Oncologist. 2026 Mar 30:oyag108. doi: 10.1093/oncolo/oyag108. Online ahead of print.

ABSTRACT

Pancreatic cancer remains one of the most lethal malignancies, with limited integration of precision oncology into routine clinical care. We present a unique case of a RAS wild-type, MSI-H, TMB-H pancreatic ductal adenocarcinoma harboring a TPM3-NTRK1 fusion, monitored through 13 serial liquid biopsies over 3 years. Dynamic changes in NTRK1-fusion allele frequency, tumor mutational burden, and the emergence of an NTRK1 resistance mutation guided finely tuned, situation-adapted therapeutic adjustments: rapid disease control with targeted NTRK inhibition followed by durable remission under immune checkpoint blockade. This case highlights the power of comprehensive molecular profiling and high-frequency ctDNA monitoring to capture tumor evolution and minimal residual disease. Importantly, it further demonstrates how MRD-guided surveillance enables a precise balance between fast-acting targeted therapy and the sustained effects of immunotherapy, providing a blueprint for individualized, context- driven treatment strategies in rare molecular subtypes of pancreatic cancer.

PMID:41913057 | DOI:10.1093/oncolo/oyag108

Utility of Circulating Tumor DNA-Based Liquid Biopsies in Patients with Cancer Receiving Immunotherapy

Surg Oncol Clin N Am. 2026 Apr;35(2):399-414. doi: 10.1016/j.soc.2025.12.010. Epub 2026 Feb 6.

ABSTRACT

Liquid biopsies offer a promising, noninvasive approach for monitoring and predicting responses to immunotherapy across multiple solid tumors. For the most part these are circulating tumor DNA (ctDNA) based assays. Here, we discuss the biological basis, clinical evidence, and potential applications of different types of ctDNA assays in tracking tumor dynamics, distinguishing pseudoprogression, and assessing minimal residual disease. We explore the current limitations, assay variability, and future directions, including integration with other biomarkers and real-world clinical trials aimed at validating ctDNA as a routine tool in precision immuno-oncology.

PMID:41903996 | DOI:10.1016/j.soc.2025.12.010

Prognostic value of circulating tumor DNA for minimal residual disease detection in ovarian cancer: A systematic review and meta-analysis

Crit Rev Oncol Hematol. 2026 Mar 25;222:105300. doi: 10.1016/j.critrevonc.2026.105300. Online ahead of print.

ABSTRACT

INTRODUCTION: Epithelial Ovarian Cancer (EOC) is the most lethal gynecological malignancy, with a high rate of recurrence due to minimal residual disease (MRD). Traditional surveillance methods have limited sensitivity for detecting MRD. ctDNA has emerged as a promising biomarker for real-time tumor monitoring and early detection of MRD.

METHODS: We performed a systematic search of Medline, Embase, and CENTRAL through July 2025. Eligible studies included cohort studies involving adults with EOC that reported ctDNA data, collected post-surgery or after adjuvant chemotherapy. Survival outcomes, including progression-free survival (PFS) and overall survival (OS), were extracted and stratified by ctDNA status (detectable vs. undetectable). All statistical analyses were performed at Review Manager version 5.4. This study is prospectively registered in PROSPERO (CRD420251124631).

RESULTS: A total of 1291 records were identified, of which 11 studies met eligibility criteria, encompassing 627 patients with EOC. The pooled analysis showed that ctDNA positivity after surgery was significantly associated with worse PFS (HR 3.83; 95% CI 2.55-5.77; I2= 5% p < 0.01) and OS (HR 2.84; 95% CI 1.22-6.57; I2=0; p < 0.01) compared with ctDNA-negative patients. Similarly, post-adjuvant chemotherapy detection of ctDNA yields worse PFS (HR 4.95) and OS (HR 5.95).

CONCLUSION: Our findings suggest that ctDNA is a novel instrument for MRD detection, and its presence serves as a potent prognostic indicator for recurrence and mortality in ovarian cancer. These results support integrating ctDNA into clinical trial designs and highlight its potential for risk-adapted surveillance and treatment strategies.

PMID:41895358 | DOI:10.1016/j.critrevonc.2026.105300

❌