Nature, Published online: 03 September 2025; doi:10.1038/d41586-025-02835-w
Iberian harvester ants can produce males that are genetically different species — plus, new insights into preventing deadly bridge collapses.
Nature, Published online: 03 September 2025; doi:10.1038/s41586-025-09478-x
DNA-methylation and gene-expression profiling of tissue sections at near single-cell resolution can be used to create detailed spatial maps showing how methylation and transcription interact to shape cell identity and tissue development.
World J Methodol. 2025 Dec 20;15(4):105516. doi: 10.5662/wjm.v15.i4.105516. eCollection 2025 Dec 20.
ABSTRACT
BACKGROUND: Artificial intelligence (AI) is transforming healthcare by improving diagnostic accuracy and predictive analytics. Periodontal diseases are recognized as risk factors for systemic conditions, including type 2 diabetes mellitus, cardiovascular disease, Alzheimer's disease, polycystic ovary syndrome, thyroid dysfunction, and post-coronavirus disease 2019 complications. These conditions exhibit complex bidirectional interactions, underscoring the importance of early detection and risk stratification. Current diagnostic tools often fail to capture these interactions at an early stage, limiting timely intervention. This study hypothesizes that AI-driven approaches can significantly improve early diagnosis and risk prediction of periodontal-systemic interactions, enhancing clinical outcomes.
AIM: To evaluate AI's role in diagnosing and predicting periodontal-systemic interactions in studies from 2010 to 2024.
METHODS: This systematic review followed PRISMA guidelines (2009) and included peer-reviewed articles from PubMed, Scopus, and Embase. Studies with large sample sizes (≥ 500 participants) were selected, focusing on AI models integrating multi-omics data and advanced imaging techniques such as cone beam computed tomography and magnetic resonance imaging. Machine learning models processed structured clinical data, deep learning models combined imaging and clinical data, and natural language processing models extracted insights from clinical notes.
RESULTS: AI applications significantly enhanced diagnostic and predictive accuracy, reducing diagnostic time by 40% and improving predictive accuracy by 25% in periodontal patients with type 2 diabetes mellitus. Studies with sample sizes of 1000-1500 participants reported diagnostic accuracy improvements up to 92%, with specificity and sensitivity rates of 94% and 90%, respectively. Increasing sample sizes over the years reflected advancements in AI, data collection, and model training, reinforcing model reliability.
CONCLUSION: AI's integration of multi-omics and imaging data has transformed early diagnosis and risk prediction in periodontal-systemic interactions, improving clinical outcomes and decision-making.
PMID:40900876 | PMC:PMC12400324 | DOI:10.5662/wjm.v15.i4.105516
Nature, Published online: 03 September 2025; doi:10.1038/d41586-025-02524-8
The discovery of an unusual reproductive system for one ant species solves a long-standing puzzle about a missing population of another ant species.
Nature, Published online: 03 September 2025; doi:10.1038/d41586-025-02751-z
Foreseeing clashes of cultures is the key to making industry-academic collaborations mutually beneficial.
Nature, Published online: 03 September 2025; doi:10.1038/d41586-025-02801-6
Some glioblastomas steal serine from their environment — a weakness that opens the door to treatment.
Meta is revising how its AI chatbots interact with users after a series of reports exposed troubling behaviour, including interactions with minors. The company told TechCrunch it is now training its bots not to engage with teenagers on topics like self-harm, suicide, or eating disorders, and to avoid romantic banter. These are temporary steps while it develops longer-term rules.
The changes follow a Reuters investigation that found Meta’s systems could generate sexualised content, including shirtless images of underage celebrities, and engage children in conversations that were romantic or suggestive. One case reported by the news agency described a man dying after rushing to an address provided by a chatbot in New York.
Meta spokesperson Stephanie Otway admitted the company had made mistakes. She said Meta is “training our AIs not to engage with teens on these topics, but to guide them to expert resources,” and confirmed that certain AI characters, like highly sexualised ones like “Russian Girl,” will be restricted.
Child safety advocates argue the company should have acted earlier. Andy Burrows of the Molly Rose Foundation called it “astounding” that bots were allowed to operate in ways that put young people at risk. He added: “While further safety measures are welcome, robust safety testing should take place before products are put on the market – not retrospectively when harm has taken place.”
The scrutiny of Meta’s AI chatbots comes amid broader worries about how AI chatbots may affect vulnerable users. A California couple recently filed a lawsuit against OpenAI, claiming ChatGPT encouraged their teenage son to take his own life. OpenAI has since said it is working on tools to promote healthier use of its technology, noting in a blog post that “AI can feel more responsive and personal than prior technologies, especially for vulnerable individuals experiencing mental or emotional distress.”
The incidents highlight a growing debate about whether AI firms are releasing products too quickly without proper safeguards. Lawmakers in several countries have already warned that chatbots, while useful, may amplify harmful content or give misleading advice to people who are not equipped to question it.
Meanwhile, Reuters reported that Meta’s AI Studio had been used to create flirtatious “parody” chatbots of celebrities like Taylor Swift and Scarlett Johansson. Testers found the bots often claimed to be the real people, engaged in sexual advances, and in some cases generated inappropriate images, including of minors. Although Meta removed several of the bots after being contacted by reporters, many were left active.
Some of the AI chatbots were created by outside users, but others came from inside Meta. One chatbot made by a product lead in its generative AI division impersonated Taylor Swift and invited a Reuters reporter to meet for a “romantic fling” on her tour bus. This was despite Meta’s policies explicitly banning sexually suggestive imagery and the direct impersonation of public figures.
The issue of AI chatbot impersonation is particularly sensitive. Celebrities face reputational risks when their likeness is misused, but experts point out that ordinary users can also be deceived. A chatbot pretending to be a friend, mentor, or romantic partner may encourage someone to share private information or even meet in unsafe situations.
The problems are not confined to entertainment. AI chatbots posing as real people have offered fake addresses and invitations, raising questions about how Meta’s AI tools are being monitored. One example involved a 76-year-old man in New Jersey who died after falling while rushing to meet a chatbot that claimed to have feelings for him.
Cases like this illustrate why regulators are watching AI closely. The Senate and 44 state attorneys general have already begun probing Meta’s practices, adding political pressure to the company’s internal reforms. Their concern is not only about minors, but also about how AI could manipulate older or vulnerable users.
Meta says it is still working on improvements. Its platforms place users aged 13 to 18 into “teen accounts” with stricter content and privacy settings, but the company has not yet explained how it plans to address the full list of problems raised by Reuters. That includes bots offering false medical advice and generating racist content.
For years, Meta has faced criticism over the safety of its social media platforms, particularly regarding children and teenagers. Now Meta’s AI chatbot experiments are drawing similar scrutiny. While the company is taking steps to restrict harmful chatbot behaviour, the gap between its stated policies and the way its tools have been used raises ongoing questions about whether it can enforce those rules.
Until stronger safeguards are in place, regulators, researchers, and parents will likely continue to press Meta on whether its AI is ready for public use.
(Photo by Maxim Tolchinskiy)
See also: Agentic AI: Promise, scepticism, and its meaning for Southeast Asia
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Diagn Cytopathol. 2025 Nov;53(11):580-591. doi: 10.1002/dc.70009. Epub 2025 Sep 4.
ABSTRACT
Colorectal cancer (CRC) is a major global health burden, ranking among the leading causes of cancer-related deaths. Despite improvements in screening and treatment, challenges such as late-stage diagnosis, high recurrence rates, and therapy resistance continue to impede optimal outcomes. Liquid biopsy, a minimally invasive technique that analyzes tumor-derived components in bodily fluids-including circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs)-is emerging as a powerful tool to transform CRC management across the disease continuum. This review provides a comprehensive overview of liquid biopsy's current and emerging applications in CRC. We examine its role in early detection, where sensitive ctDNA-based assays and epigenetic biomarkers have demonstrated the ability to identify CRC at asymptomatic or early stages, potentially improving screening uptake and compliance. Furthermore, we explore how liquid biopsy enables dynamic monitoring of treatment response and clonal evolution, facilitating the timely identification of resistance mutations and supporting personalized therapy adjustments. Innovations in multi-omics integration, artificial intelligence, and ultra-sensitive sequencing technologies are also discussed as pivotal advancements that enhance the clinical utility of liquid biopsy. Despite significant progress, the widespread adoption of liquid biopsy faces several hurdles, including assay standardization, sensitivity for low-shedding tumors, regulatory approval, and cost-effectiveness. Continued research, validation in large prospective trials, and harmonization of testing protocols are essential to overcome these challenges. Ultimately, liquid biopsy holds the potential to become a cornerstone of precision oncology in CRC, enabling earlier intervention, more tailored treatment strategies, and improved patient outcomes.
PMID:40905096 | DOI:10.1002/dc.70009
Curr Opin Immunol. 2025 Sep 1;97:102652. doi: 10.1016/j.coi.2025.102652. Online ahead of print.
ABSTRACT
PURPOSE OF REVIEW: Diagnosing sarcoidosis remains challenging. Histology findings and a variable clinical presentation can mimic other infectious, malignant, and autoimmune diseases. This review synthesizes current evidence on histopathology, sampling techniques, imaging modalities, and biomarkers and explores how emerging 'omics' and artificial intelligence tools may sharpen diagnostic accuracy.
RECENT FINDINGS: Within the typical granulomatous lesions, limited or 'burned-out' necrosis is an ancillary finding, which can be present in up to one-third of sarcoid biopsies, and demands a careful differential diagnostic work-up. Endobronchial ultrasound-guided transbronchial needle aspiration of lymph nodes has replaced mediastinoscopy as first-line sampling tool, while cryobiopsy is still under validation. Volumetric PET metrics such as total lung glycolysis and somatostatin-receptor tracers refine activity assessment; combined FDG PET/MRI improves detection of occult cardiac disease. Advanced bronchoalveolar lavage (BAL) immunophenotyping via flow cytometry and serum, BAL, and genetic biomarkers show to correlate with inflammatory burden but have low diagnostic value. Multi-omics signatures and Positron Emission Tomography with Computer Tomography radiomics, supported by deep-learning algorithms, show promising results for noninvasive diagnostic confirmation, phenotyping, and disease monitoring.
SUMMARY: No single test is conclusive for diagnosing sarcoidosis. An integrated, multidisciplinary strategy is needed. Large, multicenter, and multiethnic studies are essential to translate and validate data from emerging AI tools and -omics research into clinical routine.
PMID:40902264 | DOI:10.1016/j.coi.2025.102652
Nature Medicine, Published online: 04 September 2025; doi:10.1038/s41591-025-03921-2
Luis Diaz describes how a molecular blood assay revealed the tiniest traces of DNA from a tumor, changing how we monitor cancer.
Physiol Genomics. 2025 Sep 2. doi: 10.1152/physiolgenomics.00091.2025. Online ahead of print.
ABSTRACT
The human microbiome is emerging as a key regulator of cancer biology, modulating tumor development, immune dynamics, and therapeutic responses across diverse malignancies. In this review, recent insights are synthesized regarding how microbial communities (bacterial, fungal, and viral) shape oncogenic signaling, immune checkpoint blockade (ICB) efficacy, and metabolic reprogramming in lung, pancreatic, colorectal, breast, cervical, melanoma, and gastric cancers. Mechanistic links between microbial metabolites, intratumoral colonization, and host immune phenotypes are highlighted proposing that the microbiome constitutes a programmable axis within the tumor immune-metabolic ecosystem. Drawing on multi-omics integration and translational studies, a shift from associative profiling toward causal, spatially resolved, and intervention-ready frameworks is proposed. This perspective positions the microbiome not as a passive bystander, but as a co-evolving participant in tumor progression and treatment response, with the potential to reshape diagnostics, prognostics, and therapeutic strategies in precision oncology.
PMID:40897518 | DOI:10.1152/physiolgenomics.00091.2025
npj Digital Medicine, Published online: 01 September 2025; doi:10.1038/s41746-025-01950-2
Evolutionary game analysis of healthcare data governance in China’s experimental data trading pilots
npj Digital Medicine, Published online: 01 September 2025; doi:10.1038/s41746-025-01962-y
Deep hierarchical subtyping of multi-organ systemic sclerosis trajectories - a EUSTAR study
Nature, Published online: 02 September 2025; doi:10.1038/d41586-025-02796-0
AI-agent ethics should consider sentient non-human animals
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