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Plasmonic metamaterial time crystal

To achieve the ultra-fast changes needed, they hit the metamaterial with powerful laser pulses. This violently accelerated the material’s free-floating electrons, dynamically altering their energy and making their “effective mass” shift by up to 80% of their rest mass in fractions of a trillionth of a second. This massive, rapid shift forced the material into a time-crystal state.

A plasmonic metamaterial driven at terahertz frequencies achieves strong, ultrafast temporal modulation and shows a transition to the photonic time crystal regime with reduced plasmonic losses.

How molecular tethers and asynchronous replication drive parasite proliferation

Malaria parasites proliferate in an unusual way. Rather than dividing into two daughter cells like human cells, they first amplify their genetic material tenfold, hundredfold or even thousandfold before simultaneously producing a corresponding number of daughter parasites. Until now, the mechanisms controlling these processes were only partly understood.

Two recently published studies by researchers from Heidelberg University’s Faculty of Medicine, Harvard Medical School and the German Cancer Research Center (DKFZ) provide important insights into the molecular basis of this proliferation strategy and reveal how the parasite makes particularly efficient use of limited resources within infected blood cells. The findings open new perspectives for the development of future antimalarial drugs.

How Generative AI Should Transform Clinical Decision Support

Consider a primary care physician seeing a patient aged 58 years for routine follow-up. The electronic health record (EHR) alerts that the patient is eligible for statin therapy. The physician overrides it, as clinicians do for the vast majority of alerts. The system verifies what is computationally easy (eg, age, lipid values, and risk score) but ignores what the clinician needs: has this patient been offered statins before? Did they decline, and if so, why (cost concerns, fear of adverse effects, preference for lifestyle modification)? Have they tried statins previously and experienced muscle pain? If they were prescribed a statin, did they ever pick it up from the pharmacy? What did they write in that patient portal message 2 months ago when they mentioned reading online that statins cause memory problems? The answers are scattered across notes, dispensing records, and portal messages. The alert identifies eligibility but not the patient’s decision state or the barriers to action.

Consistent with established definitions, clinical decision support (CDS) includes tools that provide knowledge and patient-specific information to support health decisions and is not limited to guideline adherence.1,2 This Perspective focuses on clinician-facing CDS organized around a defined decision; generic note drafting, inbox management, and open-ended chart summarization are excluded unless they directly support that decision. A prior reason for declining statin therapy is relevant because it changes the next action, not eligibility. Deterministic methods remain preferable when criteria and outputs are explicit; large language models (LLMs) may extend them through flexible synthesis and adaptive presentation.

Early medical LLM applications have focused on drafting replies and summarizing charts.3,4 The larger opportunity is to revisit a long-standing trade-off between clinical fidelity and computational tractability. Health information technology has historically represented complex narratives and knowledge through structured fields and rules because they were computable.5 LLMs do not provide the first access to narrative text; their incremental value is the flexibility to extract, synthesize, and communicate across heterogeneous sources.

Ten‐year cognitive outcomes following epilepsy surgery: Temporal lobectomy versus selective amygdalohippocampectomy

Objective This study aimed to assess long-term memory outcomes in patients with temporal lobe epilepsy (TLE) who underwent anterior temporal lobectomy (ATL) or selective amygdalohippocampectomy (SAH), compared to nonoperated TLE over a 10-year follow-up period.

Cancer Medicine Approvals in the US

We conducted a cross-sectional study of cancer medicines approved by the FDA for adult solid tumors between January 1, 2006, and December 31, 2025, including both original and supplemental indication approvals. Approvals were extracted from the FDA’s oncology (cancer)/hematological malignancies approval notifications website,6 supplemented by the Drugs@FDA website. Nononcology, diagnostic, hematological, pediatric, and biosimilar/generic drugs as well as approvals related to changes in dosages/reformulations were excluded. Approvals for the same drug in the same disease but for multiple indications, such as both first-line and second-line indications or indications for both treatment naive and previously treated populations, were treated as separate approvals.

For each approval, we extracted the following characteristics: date of approval, drug name and type, cancer location treated, regulatory pathway (regular or accelerated approval), primary efficacy end points for approval, and trial design (randomization, phase). Drug types were clinically categorized as chemotherapy, targeted therapy, kinase inhibitors, immunotherapies, antibody-drug conjugates, and others. If an approval was the result of multiple clinical trials or multiple end points from the same trial, it was treated as 1 approval but the data from each trial and each end point were counted separately and credited for overall survival if 1 of those end points was overall survival. Accelerated approvals that were converted into regular approvals were counted as 1 approval. Descriptive statistics were used to examine characteristics related to approval trends.

Cardiovascular Risk Reclassification With the 2026 Dyslipidemia Guideline

This study uses data from the National Health and Nutrition Examination Survey (NHANES) to evaluate the extent of cardiovascular risk reclassification from the 2013 pooled cohort equations (PCEs) to the 2023 Predicting Risk of Cardiovascular EVENTs (PREVENT-ASCVD) equations.

Decoding 3D chromatin architecture reveals distinct enhancer classes underlying hierarchical gene regulation in prostate cancer

The transcription process is controlled by non-coding regulatory elements, more than 70% of which are putative enhancers. These enhancers comprise over 600,000 regions and are marked by histone modifications. However, the mechanisms by which altered enhancers in cancer cooperate within the three-dimensional chromatin architecture to drive oncogenic programs remain poorly understood.

By integrating 201 H3K27ac ChIP-seq datasets from prostate, we identify 3,216 high-confidence prostate cancer-specific putative enhancers. Ultra-high-resolution chromatin interaction profiling by Region Capture Micro-C at a representative chr6q24.1 locus reveals that these enhancers form cancer-specific, highly nested interactions with promoters that coalesce into a multi-connected hub absent in normal prostate cells. CRISPR/Cas9 perturbations of these enhancers, examined one by one, distinguish enhancer classes within the hub. Deletion of a central enhancer collapses hub-wide enhancer activities and architecture, leading to the downregulation of target genes, impaired proliferation, and reduced clonogenic growth. In contrast, deletion of a redundant enhancer results in minimal transcriptional changes, as neighboring enhancers rescue cancer signaling through compensatory architectural rewiring that strengthens alternative enhancer-promoter interactions.

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