Nature, Published online: 03 September 2025; doi:10.1038/d41586-025-02834-x
Two brother ants defy species borders, by sharing the same mother despite being genetically different species.
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.
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.
Artificial intelligence is fundamentally reshaping how the world operates. With its potential to automate repetitive tasks, analyze vast datasets, and augment human capabilities, the use of AI technologies is already driving changes across industries.
In health care and pharmaceuticals, machine learning and AI-powered tools are advancing disease diagnosis, reducing drug discovery timelines by as much as 50%, and heralding a new era of personalized medicine. In supply chain and logistics, AI models can help prevent or mitigate disruptions, allowing businesses to make informed decisions and enhance resilience amid geopolitical uncertainty. Across sectors, AI in research and development cycles may reduce time-to-market by 50% and lower costs in industries like automotive and aerospace by as much as 30%.
“This is one of those inflection points where I don’t think anybody really has a full view of the significance of the change this is going to have on not just companies but society as a whole,” says Patrick Milligan, chief information security officer at Ford, which is making AI an important part of its transformation efforts and expanding its use across company operations.
Given its game-changing potential—and the breakneck speed with which it is evolving—it is perhaps not surprising that companies are feeling the pressure to deploy AI as soon as possible: 98% say they feel an increased sense of urgency in the last year. And 85% believe they have less than 18 months to deploy an AI strategy or they will see negative business effects.
Companies that take a “wait and see” approach will fall behind, says Jeetu Patel, president and chief product officer at Cisco. “If you wait for too long, you risk becoming irrelevant,” he says. “I don’t worry about AI taking my job, but I definitely worry about another person that uses AI better than me or another company that uses AI better taking my job or making my company irrelevant.”
But despite the urgency, just 13% of companies globally say they are ready to leverage AI to its full potential. IT infrastructure is an increasing challenge as workloads grow ever larger. Two-thirds (68%) of organizations say their infrastructure is moderately ready at best to adopt and scale AI technologies.
Essential capabilities include adequate compute power to process complex AI models, optimized network performance across the organization and in data centers, and enhanced cybersecurity capabilities to detect and prevent sophisticated attacks. This must be combined with observability, which ensures the reliable and optimized performance of infrastructure, models, and the overall AI system by providing continuous monitoring and analysis of their behavior. Good quality, well-managed enterprise-wide data is also essential—after all, AI is only as good as the data it draws on. All of this must be supported by AI-focused company culture and talent development.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
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
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
Cell Death Discovery, Published online: 02 September 2025; doi:10.1038/s41420-025-02718-z
Protein lipoylation in cancer: metabolic reprogramming and therapeutic potential
Nat Commun. 2025 Sep 1;16(1):8161. doi: 10.1038/s41467-025-63538-4.
ABSTRACT
Immune checkpoint blockade (ICB) has improved outcomes for patients with head and neck squamous cell carcinoma (HNSCC), but predictive biomarkers remain limited. Here, we use a time-resolved, multi-omic approach in a murine HNSCC model to characterize peripheral immune responses to ICB. Single-cell transcriptomics and T/B cell receptor analyses reveal early on-treatment expansion of effector memory T and B cell repertoires in responders, preceding tumor regression. These dynamic immune features inform a composite transcriptional signature that accurately predicts ICB response in independent human HNSCC cohorts. LiBIO outperforms existing biomarkers and generalizes to melanoma, non-small cell lung cancer, and breast cancer without retraining. These findings suggest that early treatment-induced changes in circulating immune repertoires reflect the host's capacity to mount an effective antitumor response. This work provides a framework for leveraging transient peripheral immune dynamics to develop non-invasive, high-fidelity biomarkers for response to immunotherapy across cancer types.
PMID:40890155 | PMC:PMC12402333 | DOI:10.1038/s41467-025-63538-4
npj Digital Medicine, Published online: 30 August 2025; doi:10.1038/s41746-025-01966-8
Adherence to digital pregnancy care – lessons learned from the SMART start feasibility study
npj Digital Medicine, Published online: 30 August 2025; doi:10.1038/s41746-025-01945-z
Enabling secure and self determined health data sharing and consent management
Nature Medicine, Published online: 28 August 2025; doi:10.1038/s41591-025-03900-7
Trained and validated on multimodal data from 14.5 million images from multicountry datasets, a foundation model is shown to increase diagnostic and referral accuracy of clinicians when used as an assistant in a trial involving 16 ophthalmologists and 668 patients.
J Proteome Res. 2025 Sep 1. doi: 10.1021/acs.jproteome.5c00424. Online ahead of print.
ABSTRACT
Glioma is an aggressive brain tumor that requires challenging treatments. Tumor Treating Fields (TTFields), an FDA-approved therapy for glioblastoma (GBM), pleural mesothelioma, and platinum-refractory metastatic nonsmall cell lung cancer (in combination with PD-1/PD-L1 inhibitors or docetaxel), employs specific frequency electric fields to disrupt cell division and enhance treatment efficacy. However, their molecular mechanisms remain unclear. This study aimed to elucidate these mechanisms and optimize the therapeutic potential of TTFields through quantitative proteomics, phosphoproteomics, and glycoproteomics. Pathway analysis of the proteomics revealed that TTFields impact the cell cycle, DNA repair, autophagy, and DNA replication. Phosphoproteomic studies further demonstrated a marked decline in the activity of key kinases ABL1 and PDK1, while glycoproteomics highlighted disruptions in cell adhesion and ECM-receptor interactions. Notably, proteomic analysis identified an upregulation of PARP1 and BRD4 protein levels, suggesting a previously unrecognized resistance mechanism. Consistently, combining TTFields with inhibitors targeting these proteins significantly enhanced the treatment efficacy in U87 cells. Thus, this study uncovers comprehensive molecular mechanisms underlying TTFields' effects on GBM cells and supports the development of concomitant therapies to enhance treatment efficacy.
PMID:40889189 | DOI:10.1021/acs.jproteome.5c00424
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