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STAT+: Nature Medicine to investigate study that found cancer treatment is better in morning

✇STAT
By: Angus Chen

The notion that oncologists could boost immunotherapy responses simply by giving infusions in the morning, rather than late afternoon, is an attractive one. So when a clinical trial published in Nature Medicine this month showed that lung cancer patients treated in the morning had a massive reduction in the risk of progression compared to those treated in the afternoon, many scientists were intrigued, if skeptical.

Now that study is coming under fire, as multiple scientists and sleuths raise serious concerns about the data and point out inconsistencies in the trial.

These have called the study’s conclusions even further into question, which experts told STAT already lacked strong biological plausibility, and Nature Medicine appended a note on the study on Thursday that it is starting an investigation into the concerns.

Continue to STAT+ to read the full story…

© Jenny Kane/AP

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Gastric cancer occurrence and heterogeneity: integration of clinical data, multi-omics and tumor microenvironment

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Ping Li · Yafang Huang · Wanting Jiang · Haocheng Zheng · Runhua Chen · Xia Ding

Future Oncol. 2026 Feb 20:1-14. doi: 10.1080/14796694.2026.2631302. Online ahead of print.

ABSTRACT

Gastric cancer (GC) is an epithelial malignant tumor with high morbidity and mortality. In recent years, more and more studies have strengthened our understanding of how GC develops, including the origin of GC cells, precancerous lesions, gene mutations, transcriptional changes, protein translation and the tumor microenvironment. With the concept of accurate tumor therapy gradually applied to clinical practice, these data provide more reference and basis for early prevention, early screening, early detection and accurate treatment of GC.

PMID:41717787 | DOI:10.1080/14796694.2026.2631302

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MedClarify: An information-seeking AI agent for medical diagnosis with case-specific follow-up questions

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hui Min Wong · Philip Heesen · Pascal Janetzky · Martin Bendszus · Stefan Feuerriegel
arXiv:2602.17308v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for diagnostic tasks in medicine. In clinical practice, the correct diagnosis can rarely be immediately inferred from the initial patient presentation alone. Rather, reaching a diagnosis often involves systematic history taking, during which clinicians reason over multiple potential conditions through iterative questioning to resolve uncertainty. This process requires considering differential diagnoses and actively excluding emergencies that demand immediate intervention. Yet, the ability of medical LLMs to generate informative follow-up questions and thus reason over differential diagnoses remains underexplored. Here, we introduce MedClarify, an AI agent for information-seeking that can generate follow-up questions for iterative reasoning to support diagnostic decision-making. Specifically, MedClarify computes a list of candidate diagnoses analogous to a differential diagnosis, and then proactively generates follow-up questions aimed at reducing diagnostic uncertainty. By selecting the question with the highest expected information gain, MedClarify enables targeted, uncertainty-aware reasoning to improve diagnostic performance. In our experiments, we first demonstrate the limitations of current LLMs in medical reasoning, which often yield multiple, similarly likely diagnoses, especially when patient cases are incomplete or relevant information for diagnosis is missing. We then show that our information-theoretic reasoning approach can generate effective follow-up questioning and thereby reduces diagnostic errors by ~27 percentage points (p.p.) compared to a standard single-shot LLM baseline. Altogether, MedClarify offers a path to improve medical LLMs through agentic information-seeking and to thus promote effective dialogues with medical LLMs that reflect the iterative and uncertain nature of real-world clinical reasoning.
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Intent Laundering: AI Safety Datasets Are Not What They Seem

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shahriar Golchin · Marc Wetter
arXiv:2602.16729v1 Announce Type: cross Abstract: We systematically evaluate the quality of widely used AI safety datasets from two perspectives: in isolation and in practice. In isolation, we examine how well these datasets reflect real-world attacks based on three key properties: driven by ulterior intent, well-crafted, and out-of-distribution. We find that these datasets overrely on "triggering cues": words or phrases with overt negative/sensitive connotations that are intended to trigger safety mechanisms explicitly, which is unrealistic compared to real-world attacks. In practice, we evaluate whether these datasets genuinely measure safety risks or merely provoke refusals through triggering cues. To explore this, we introduce "intent laundering": a procedure that abstracts away triggering cues from attacks (data points) while strictly preserving their malicious intent and all relevant details. Our results indicate that current AI safety datasets fail to faithfully represent real-world attacks due to their overreliance on triggering cues. In fact, once these cues are removed, all previously evaluated "reasonably safe" models become unsafe, including Gemini 3 Pro and Claude Sonnet 3.7. Moreover, when intent laundering is adapted as a jailbreaking technique, it consistently achieves high attack success rates, ranging from 90% to over 98%, under fully black-box access. Overall, our findings expose a significant disconnect between how model safety is evaluated and how real-world adversaries behave.
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Be Wary of Your Time Series Preprocessing

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sofiane Ennadir · Tianze Wang · Oleg Smirnov · Sahar Asadi · Lele Cao
arXiv:2602.17568v1 Announce Type: cross Abstract: Normalization and scaling are fundamental preprocessing steps in time series modeling, yet their role in Transformer-based models remains underexplored from a theoretical perspective. In this work, we present the first formal analysis of how different normalization strategies, specifically instance-based and global scaling, impact the expressivity of Transformer-based architectures for time series representation learning. We propose a novel expressivity framework tailored to time series, which quantifies a model's ability to distinguish between similar and dissimilar inputs in the representation space. Using this framework, we derive theoretical bounds for two widely used normalization methods: Standard and Min-Max scaling. Our analysis reveals that the choice of normalization strategy can significantly influence the model's representational capacity, depending on the task and data characteristics. We complement our theory with empirical validation on classification and forecasting benchmarks using multiple Transformer-based models. Our results show that no single normalization method consistently outperforms others, and in some cases, omitting normalization entirely leads to superior performance. These findings highlight the critical role of preprocessing in time series learning and motivate the need for more principled normalization strategies tailored to specific tasks and datasets.
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STAT+: Key study of Grail’s cancer detection test fails in setback for company

✇STAT
By: Matthew Herper and Angus Chen

A blood test for detecting cancer early being developed by the diagnostics firm Grail failed to meet its main goal in a giant study being conducted with England’s National Health Service, the company said Thursday.

Grail’s test has been the standard bearer for new technologies that promise a blood test can be used to detect many different types of cancer early and eventually even to indicate to scientists where in the body to look for tumors. The company already sells its test, called Galleri, for a list price of $1,000, although it is not yet approved by the Food and Drug Administration. Grail said Thursday it sold 185,000 tests in 2025, generating $136.8 million. 

The company’s shares were down 47% in after-hours trading.

Continue to STAT+ to read the full story…

© Adobe

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STAT+: AI-guided cancer treatments, telehealth usage, and other health tech news

✇STAT
By: Mario Aguilar

You’re reading the web edition of STAT’s Health Tech newsletter, our guide to how technology is transforming the life sciences. Sign up to get it delivered in your inbox every Tuesday and Thursday.

Good morning health tech readers!

Please join me in congratulating my colleagues as STAT, who have been honored with a fourth Polk Award for our coverage of the Trump administration’s impacts on the federal health department and American science. The award recognizes the whole newsroom and in particular the work of Lizzy Lawrence covering a dramatic year of changes at the Food and Drug Administration. 

Continue to STAT+ to read the full story…

© Adobe

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Live biotherapeutics in cancer therapy

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Rasmita Mishra · Sreelakshmi S Kumar · T Sayamsmruti Panda · Soumendu Mahapatra · Punit Prasad

Prog Mol Biol Transl Sci. 2026;220:361-403. doi: 10.1016/bs.pmbts.2026.01.002. Epub 2026 Jan 23.

ABSTRACT

Cancer poses a global challenge in diagnostics and therapeutics. Treatments like chemotherapy, radiotherapy, surgery, and immunotherapy have significantly decreased the fatality rate, but drug resistance, therapy side effects, and relapse remain as major concerns. Live biotherapeutics are microorganisms that can be developed as therapeutic agents to modulate cancer pathophysiology and aid in disease management. Live biotherapeutic products (LBPs) have the potential to suppress tumour growth, enhance the effectiveness of conventional therapies, and reduce treatment-related side effects. Dysbiosis in the gut and cancer-specific tissues is linked to cancers of the colon, stomach, pancreas, and liver. Live biotherapeutics aim either to re-establish microbial balance or to employ microbes directly as anticancer tools. Both native and engineered LBPs (bacteria and viruses) represent promising interventions that may form part of next-generation cancer treatment strategies. Their clinical application draws on the integration of microbiology, immunology, synthetic biology, and oncology. LBPs can be used to target cancer cells by delivering antitumour payloads such as immune modulators, toxins, exposing cancer antigens, and molecules for targeted killing. LBPs offer advantages such as reduced systemic toxicity, overcoming drug resistance, and synergy with chemo-, radio-, and immunotherapies. Despite challenges in safety, manufacturing, regulation, and personalization, advances in synthetic biology and omics are enabling precision approaches. Future innovations such as bacteriobots, biocontainment systems, and patient-specific microbiome integration highlight their potential as next-generation cancer therapeutics.

PMID:41714084 | DOI:10.1016/bs.pmbts.2026.01.002

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Breakthrough CRISPR system could reverse antibiotic resistance crisis

✇Latest Science News -- ScienceDaily
Antibiotic resistance is racing toward a global crisis, with “superbugs” projected to cause over 10 million deaths annually by 2050. Now, scientists at UC San Diego have unveiled a powerful new CRISPR-based tool that doesn’t just fight resistant bacteria—it can actively strip away their drug resistance. Inspired by gene drives used in insects, the technology spreads a genetic “fix” through bacterial populations, even inside stubborn biofilms that shield microbes from antibiotics.
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One stem cell generates 14 million tumor-killing NK cells in major cancer breakthrough

✇Latest Science News -- ScienceDaily
Scientists in China have unveiled a breakthrough way to mass-produce powerful cancer-fighting immune cells in the lab. By engineering early-stage stem cells from cord blood—rather than trying to modify mature natural killer (NK) cells—they created a streamlined process that generates enormous numbers of highly potent NK cells, including CAR-equipped versions designed to hunt specific cancers.
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Personalized mRNA vaccine induces strong, durable immunity in hard-to-treat breast cancer

✇Nature - Issue - nature.com science feeds

Nature, Published online: 18 February 2026; doi:10.1038/d41586-026-00477-0

In an experimental trial, a personalized mRNA vaccine was tested in 14 individuals with an aggressive form of breast cancer. It trained immune cells to target cancer-specific mutant proteins, inducing strong, lasting immunity. Ten participants remained disease-free. Analysis of relapses revealed resistance mechanisms, offering essential insights for future therapies.
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Scientists discover brain switches that clear Alzheimer’s plaques

✇Latest Science News -- ScienceDaily
Researchers have identified two brain receptors that help the brain clear away amyloid beta, a hallmark of Alzheimer’s disease. By stimulating these receptors in mice, scientists increased levels of a natural amyloid-breaking enzyme, reduced buildup in the brain, and improved memory-related behavior. Because these receptors are common drug targets, the findings could open the door to affordable pill-based treatments with fewer side effects.
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Hunt Globally: Deep Research AI Agents for Drug Asset Scouting in Investing, Business Development, and Search & Evaluation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Alisa Vinogradova · Vlad Vinogradov · Luba Greenwood · Ilya Yasny · Dmitry Kobyzev · Shoman Kasbekar · Kong Nguyen · Dmitrii Radkevich · Roman Doronin · Andrey Doronichev
arXiv:2602.15019v1 Announce Type: new Abstract: Bio-pharmaceutical innovation has shifted: many new drug assets now originate outside the United States and are disclosed primarily via regional, non-English channels. Recent data suggests >85% of patent filings originate outside the U.S., with China accounting for nearly half of the global total; a growing share of scholarly output is also non-U.S. Industry estimates put China at ~30% of global drug development, spanning 1,200+ novel candidates. In this high-stakes environment, failing to surface "under-the-radar" assets creates multi-billion-dollar risk for investors and business development teams, making asset scouting a coverage-critical competition where speed and completeness drive value. Yet today's Deep Research AI agents still lag human experts in achieving high-recall discovery across heterogeneous, multilingual sources without hallucinations. We propose a benchmarking methodology for drug asset scouting and a tuned, tree-based self-learning Bioptic Agent aimed at complete, non-hallucinated scouting. We construct a challenging completeness benchmark using a multilingual multi-agent pipeline: complex user queries paired with ground-truth assets that are largely outside U.S.-centric radar. To reflect real deal complexity, we collected screening queries from expert investors, BD, and VC professionals and used them as priors to conditionally generate benchmark queries. For grading, we use LLM-as-judge evaluation calibrated to expert opinions. We compare Bioptic Agent against Claude Opus 4.6, OpenAI GPT-5.2 Pro, Perplexity Deep Research, Gemini 3 Pro + Deep Research, and Exa Websets. Bioptic Agent achieves 79.7% F1 versus 56.2% (Claude Opus 4.6), 50.6% (Gemini 3 Pro + Deep Research), 46.6% (GPT-5.2 Pro), 44.2% (Perplexity Deep Research), and 26.9% (Exa Websets). Performance improves steeply with additional compute, supporting the view that more compute yields better results.
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