AI News Archive: July 20, 2026 — Part 16
Sourced from 500+ daily AI sources, scored by relevance.
- MagicSelector: Joint Optimization for Agent Tool Selection via Counterfactual Decomposition and Progressive Reranking
We present MagicSelector, a joint optimization framework integrating Counterfactual task decomposition, Progressive reranking, and Dynamic Top-K, designed to address the fundamental challenges of tool retrieval in agents. MagicSelector is a specialized framework capable of translating ambiguous user...
- RAMP: Robust Ad Recommendation Under Limited Personalized-Feature Availability via Masking and Alignment Pathways
Click-through rate (CTR) and conversion rate (CVR) prediction are fundamental tasks in online advertising, aiming to estimate the likelihood of user interactions based on various features. While personalized attributes such as age and gender can significantly enhance predictive accuracy, their use i...
- GCM: metric-guided clustering by genetic algorithm for correlation-defined modules
Gene co-expression analyses identify "good" modules by a correlation criterion. However, standard pipelines detect modules with greedy algorithms that optimize other quantities and only measure correlation afterwards. We present a method called Genetic Clustering by Metric (GCM), an open-source Python tool that closes this gap by treating module detection as maximum-likelihood inference and solving it globally. In GCM, the correlation objective is, up to a constant and the sample-size factor, the profile log-likelihood of an explicit generative model: a block-diagonal one-factor Gaussian in which each module is a single regulator with equal-magnitude loadings. This bases model selection on a principled footing through a genuine BIC/AIC in correlation space. GCM maximizes this likelihood with a memetic genetic algorithm: a population-based search hybridized with a greedy local refinement that reassigns genes after the fact, a move the agglomerative clustering at the core of co-expression pipelines cannot make. Across a replicated noise sweep, GCM reproducibly surpasses hierarchical correlation clustering and k-means with the lowest variance, and an ablation shows the local-search step is responsible; the advantage persists when the number of modules is unknown and when unstructured genes must be ignored. GCM faithfully optimizes geometric indices on the Iris benchmark dataset. For a breast-cancer RNA-seq it recovers coherent modules that predict tumor-versus-normal status.GCM depends only on NumPy and SciPy and exposes one swappable-metric interface with single- and multi-objective modes.
- scRepresenter: a workflow for computing, integrating and benchmarking cellular representations in single-cell transcriptomics
Motivation: Single-cell RNA sequencing (scRNA-seq) has become an attractive tool for studying complex diseases, in which transient cell states affecting diverse cell populations characterise disease development and progression. However, due to data sparsity and disease heterogeneity analysis is often challenging. With recent advances in machine learning, two widely used approaches have emerged for learning cellular representations: large-scale foundation models and biological knowledge-guided methods. Despite their complementary strengths, there is currently no unified workflow for systematically comparing and integrating these approaches. Results: Here, we present scRepresenter, an open-source workflow for computing, integrating, and validating cellular embeddings derived from foundation models and biological knowledge-guided methods in the context of complex diseases. It consists of two components: a command-line workflow that computes cellular embeddings and performs downstream analyses, and an interactive Shiny application for visualizing and comparing the computed embeddings. scRepresenter supports four categories of cellular representations: (1) expression-based, (2) knowledge-guided, (3) foundation model-derived, and (4) hybrid embeddings that combine foundation model-derived representations with knowledge-guided representations. This approach takes a cell-by-gene count matrix as input and outputs an integrated object containing the computed embeddings. Then, this object can be uploaded into our interactive Shiny application to compare different embeddings. Availability: The workflow is available at https://github.com/GuilhermePocas/scRepresenter Contact: AL291@cam.ac.uk; MA2129@cam.ac.uk Keywords: single-cell RNA-sequencing, machine learning, representation learning, foundation models, cellular embeddings, complex diseases, amyotrophic lateral sclerosis, human organoid
- Toward routine health phenotyping: High-throughput prediction of metabolic, immune, and inflammatory biomarkers from milk mid-infrared spectroscopy in early-lactation dairy cows
This study evaluated the potential of milk mid-infrared (MIR) spectroscopy, combined with routinely available on-farm variables, for predicting serum metabolic, immune, and inflammatory biomarkers in early-lactation cows. Data included 5,936 blood samples from 4,442 cows across 23 Australian dairy herds, with paired milk MIR spectra and serum measurements for up to 14 biomarkers. Prediction models were developed using partial least squares regression and evaluated using nested 10-fold random cross-validation and leave-one-herd-out validation. The results show that while basic herd-test data, including milk fat, protein, and lactose concentration, as well as on-farm variables, including DIM, calving age, breed, and herd could predict serum biomarkers, combining MIR spectra with these on-farm variables produced the best overall performance. In random cross-validation, blood urea nitrogen (BUN) was predicted most accurately (R2 = 0.78), while {beta}-hydroxybutyrate (BHB) and nonesterified fatty acids (NEFA) showed moderate accuracy (R2 = 0.56 and 0.44, respectively). BUN also showed the strongest external validation performance, with leave-one-herd-out R2 = 0.58 and comparable accuracy for predicting records collected after 70 days in milk (R2 = 0.65). BHB and NEFA had moderate leave-one-herd-out accuracy but did not transfer beyond early lactation. Most other biomarkers showed low or inconsistent external validation performance. Overall, MIR spectroscopy combined with on-farm variables shows promise for routine prediction of BUN, BHB and NEFA, which can be used for monitoring and genetic evaluation of, for example, ketosis and energy deficit. Initial random cross-validation results for glucose, bilirubin and cholesterol were promising, but more data is needed to improve the prediction accuracy and robustness of the predictions.
- Travel health needs in people visiting friends and relatives: a retrospective analysis of the UK National Travel Health Advice Line, 2019-2025
Background: Travellers visiting friends and relatives (VFRs) experience a disproportionate burden of travel-associated infectious diseases, yet little is known about the complexity of pre-travel consultations required to support their care. We compared enquiries relating to VFR travellers and tourists received by the UK National Travel Health Network and Centre (NaTHNaC) specialist Advice Line to identify differences in traveller characteristics, destinations and clinical complexity. Methods: We conducted a retrospective observational study of enquiries to the NaTHNaC Advice Line between 1 January 2019 and 31 December 2025. Enquiries relating to VFR travellers and tourists were compared using descriptive statistics and appropriate statistical tests. Traveller demographics, travel characteristics, destinations and enquiry management were analysed. Results: Of 16,367 enquiries relating to specific travellers, 3,090 (18.9%) concerned VFR travellers and 7,237 (44.2%) concerned tourists. Compared with tourists, VFR travellers were younger (median age 24 vs 52 years, P<0.001), more likely to undertake long-stay (8.4% vs 1.8%, P<0.001) and last-minute travel (5.0% vs 1.1%, P<0.001), and more frequently travelled to the WHO African Region (56.6% vs 29.2%, P<0.001) and Eastern Mediterranean Region (12.7% vs 2.8%, P<0.001). Pregnancy was substantially more common among VFR travellers (11.6% vs 4.3%, P<0.001). Enquiries concerning VFR travellers were more likely to require a call-back (16.1% vs 13.8%, P=0.012) and escalation to a specialist doctor (13.1% vs 10.5%, P<0.001), indicating greater consultation complexity. General practice generated a higher proportion of VFR-related enquiries than tourist enquiries (69.5% vs 63.9%, P<0.001). Conclusions: VFR travellers generate disproportionately complex pre-travel consultations characterised by higher rates of specialist escalation, distinct travel patterns and travel to destinations associated with the greatest burden of imported infectious diseases. These findings highlight the importance of specialist travel medicine support for healthcare professionals managing VFR travellers and reinforce the need for equitable access to timely, high-quality pre-travel healthcare for this high-risk population.
- NUMonomer enables accurate and scalable nucleic acid structure prediction from primary sequence alone
Accurate and efficient prediction of three-dimensional nucleic acid structures can accelerate functional characterization and enable downstream applications. Recent deep-learning methods have substantially improved nucleic acid structure prediction by incorporating auxiliary inputs such as multiple sequence alignments, secondary-structure annotations, and representations from pretrained language models. However, prediction accuracy remains limited, and generating these auxiliary inputs can be computationally expensive. Here we show that learning the hierarchical organization of experimentally determined structures across multiple scales, from recurring local conformations to global fold topologies, together with exploiting representations shared between RNA and single-stranded DNA, improves model generalization. Guided by these findings, we developed NUMonomer, an end-to-end deep-learning framework trained with input sequences spanning thousands of nucleotides on a joint RNA and single-stranded DNA dataset to predict nucleic acid structures directly from sequence. Despite requiring no auxiliary inputs, NUMonomer matches or outperforms leading prediction methods on benchmarks comprising CASP16 RNA targets and non-redundant sets of experimentally determined RNA and single-stranded DNA structures, with particularly pronounced improvements for longer RNAs. Its efficient and scalable architecture also reduces inference costs by approximately two orders of magnitude relative to the evaluated methods, enabling large-scale structure prediction. Together, these findings provide insight into generalization in biomolecular structure learning and establish NUMonomer as a practical framework for nucleic acid structure prediction.
- MedZone Embedder: a framework for representation learning of Japanese secondary medical care areas from a national ICU registry, characterizing intensive care provision structure and regional vulnerability
Background: In Japan, acute inpatient care is divided into approximately 335 secondary medical care areas, which serve as the basic units for planning healthcare delivery systems under the 8th National Health Care Plan. While comparisons between regions and facilities typically rely on a single risk-adjusted metric, this approach confuses differences in patient demographics with differences in the actual infrastructure of intensive care units (ICUs). This paper presents a framework - MedZone Embedder - for deriving data-driven indicators of regional structural vulnerability by mapping secondary medical care areas onto a learned similarity space, together with its working implementation. The paper sets out the concept, the method, a proof of concept, and an explicit staged validation program, rather than national empirical results. Methods: Each area is represented by a feature vector consisting of aggregated values of intensive care provision indicators derived directly from the Japan Intensive Care Patient Database (JIPAD) - specifically, risk-adjusted mortality rates (standardized mortality ratios and an in-hospital composite indicator), technical efficiency, length of stay, readmission rates, case severity, and case composition - with the within-area variance of these indicators also taken into account. No hierarchical processing by facility type is performed. A contrastive autoencoder (multilayer perceptron encoder 32 -> 16 -> 8, symmetric decoder) is trained by self-supervised learning, using an objective function that combines reconstruction and normalized temperature cross-entropy (NT-Xent) on noise-augmented views. The resulting 8-dimensional embedding supports area searches based on cosine similarity and anomaly scoring in the embedding space (using isolation forest, Mahalanobis distance, or k-nearest-neighbor density), which is normalized to a vulnerability score ranging from 0 to 1. If deep learning libraries are unavailable, or if the number of areas is small, an alternative method using deterministic principal component analysis is employed. Results: This method was implemented and deployed within an operational ICU decision support system on a managed cloud platform. The proof of concept (PoC) is structured around five secondary medical care areas within Kyoto Prefecture and runs entirely on synthetic facility-level aggregate data constructed to follow the JIPAD indicator schema; no registry data were accessed. It generated: an aggregate provision profile for each area; an area embedding space equipped with a similar-area search function; and a vulnerability ranking that identifies areas with low patient numbers and low diversity that exhibit overall poor outcomes. At this scale, the contrastive autoencoder falls back to principal component projection. The deep learning pathway has been implemented and unit testing has been completed; training and evaluation on actual registry data are pending data-use approval and the expansion of data integration. Validation is staged: Stage 2 will train the contrastive pathway over JIPAD-covered areas to assess construct validity against public structural indicators (ICU/HCU beds, population, accessibility), and Stage 3 will extend coverage to all areas via National Database (NDB) linkage. Conclusion: MedZone Embedder reframes regional comparison from single-indicator ranking to structural representation: which areas are alike, and which are structural outliers. The contribution of this paper is the framework - the proposal that the intensive care provision structure of Japanese secondary medical care areas can be learned from a national outcomes registry and read through the lens of what we call institutional debt - together with a deployed implementation and a pre-specified validation program. To our knowledge, this is a candidate first application of contrastive representation learning to Japanese secondary medical care areas.
- Diagnostic Accuracy of MRI Radiomics for Predicting KRAS Mutation in Rectal Cancer: A Systematic Review and Meta-analysis
Background: KRAS mutation status is an important biomarker in rectal cancer, with implications for prognosis and treatment response. MRI-based radiomics has emerged as a non-invasive approach for predicting tumor genotypes. However, the diagnostic performance of MRI radiomics for predicting KRAS mutation status remains unclear. This study aimed to evaluate the diagnostic accuracy of MRI radiomics for predicting KRAS mutations in rectal cancer. Methods: A systematic search of PubMed, Cochrane Library, Scopus, and Web of Science was performed through July 2025. Diagnostic test accuracy studies evaluating MRI-based radiomics or artificial intelligence models for predicting KRAS mutation status in adult patients with rectal cancer were included, using molecular testing as the reference standard. Risk of bias was assessed using the QUADAS-2 tool. Pooled sensitivity and specificity were estimated using a bivariate random-effects model. Results: Seven studies involving 1,224 patients were included. The pooled sensitivity was 0.736 (95% CI: 0.697-0.772) and the pooled specificity was 0.645 (95% CI: 0.586-0.701). The false positive rate was 0.355 (95% CI: 0.299-0.414). The area under the hierarchical summary receiver operating characteristic curve was 0.754, with a normalized partial AUC of 0.666. Between-study heterogeneity ranged from low to moderate depending on the estimation method (I2 = 8.4%-53.3%). Conclusion: MRI radiomics demonstrates moderate diagnostic accuracy for predicting KRAS mutation status in rectal cancer and may serve as a promising non-invasive biomarker for preoperative molecular stratification. Further large-scale studies with external validation are required to confirm its clinical utility.
- Diffusion MRI Profiles Map onto Distinct Inflammatory States After Adolescent Concussion: A CARE4Kids Study
Importance: Neuroinflammation is a key component of the response to injury after concussion, but direct links between diffusion MRI metrics and specific plasma inflammatory pathways in human concussion have not been established. Objective: To examine associations between diffusion MRI metrics and pathway-level inflammatory proteomic signatures in adolescents during the subacute period after concussion. Design, Setting, and Participants: Cross-sectional analysis of data from the CARE4Kids Consortium, a six-site prospective study. Participants were English-speaking adolescents ages 11-17.99 with concussion and symptoms at 7-35 days post-injury. Data were collected between 2022-2024. Of 370 enrolled participants, 122 had both diffusion MRI and plasma proteomics available for analysis. Exposure: Advanced diffusion MRI metrics were converted to z-scores and participants were grouped by the spatial extent of outlier values (potholes and peaks) across 15 white matter regions of interest. Nine non-redundant groupings were selected for primary analysis. Main Outcomes and Measures: Pathway-level inflammatory profiles derived from gene set enrichment analysis (GSEA) of ~5,400 plasma proteins measured by Olink proximity extension assay, targeting nine hallmark inflammatory pathways spanning initiation through resolution. Persistent symptoms were assessed 64-115 days post-injury. Results: Diffusion metrics reflecting tissue disorganization were associated with upregulation of the coagulation pathway, consistent with hemostatic-inflammatory signaling. Metrics reflecting reduced tissue complexity and neurite density were associated with upregulation of interferon- and interferon-{gamma} response pathways, consistent with microstructural remodeling driven by cellular immune activation. Elevated free water content was associated with downregulation of most inflammatory pathways and trend-level transforming growth factor - {beta} upregulation, reflecting inflammatory resolution. Time since injury did not differ between groups based on free water (Kolmogorov-Smirnov p = 0.97), suggesting these differences reflect individual variability in recovery pace. Exploratory analyses showed a trend toward lower odds of persistent symptoms in the group with elevated free water content (odds ratio = 0.51, p = 0.18). Conclusions and Relevance: Multiple diffusion MRI metrics are differentially sensitive to distinct neuroinflammatory states in the subacute period after adolescent concussion. These findings suggest that diffusion imaging could serve as a non-invasive tool for inflammatory phenotyping, with potential implications for identifying patients who may benefit from targeted immunomodulatory intervention.
- Classification of scenarios based on the determinants of childhood vaccination coverage in Brazil
ABSTRACT Background: Vaccination coverage in Brazil declined between 2015 and 2022, followed by a recovery in 2023 and 2024. Given this instability, driven by multiple determinants, we sought to identify factors associated with childhood vaccination and group Brazilian municipalities into scenarios that could guide interventions to improve coverage. Methods: In this ecological study of 5,274 Brazilian municipalities, we assessed the rate of children under 1 year unvaccinated with the third dose of the Inactivated Poliovirus Vaccine (IPV) in 2024, chosen for its high correlation with other tracer vaccines such as Diphtheria, Tetanus and Pertussis (DTP) and Measles, Mumps and Rubella (MMR). We applied a hierarchical model with three levels of determinants (socioeconomic, health service structure, and operational), using Poisson regression to estimate standardized Rate Ratios (RR), followed by a K-means cluster analysis to identify municipal profiles. Results: Several determinants were associated with higher rates of unvaccinated children, most notably the proportion of the population not covered by Community Health Workers (CHW) (RR = 1.15; 95% CI 1.14-1.15) and inequality measured by the Gini Index (RR = 1.33; 95% CI 1.32-1.34). We identified six municipal profiles that differed in tracer-vaccine coverage up to four years of age and in the composite Vaccination Needs Index (VNI). Conclusion: Multiple socioeconomic, structural and operational factors were associated with unvaccinated rates, highlighting the relevance of healthcare service organization even in favorable social contexts. Classifying municipalities into risk profiles may support more targeted interventions aligned with local needs. Keywords: Vaccination coverage, Childhood vaccination, Primary Health Care, Determinants, Scenarios, Profiles.
- Genomic insights into the population structure and recent expansion of Coccidioides in the United States
Background Coccidioidomycosis is an emerging fungal disease across the arid Americas and a frequent cause of community-acquired pneumonia. Understanding where Coccidioides populations originate, how they move across space, and whether they are expanding is important for interpreting changing patterns of Valley fever and anticipating future infection risk. Methods We prospectively collected and whole-genome sequenced 186 Coccidioides-positive clinical isolates submitted to a national diagnostic laboratory, and included 126 previously sequenced genomes. We applied genomic clustering, time-calibrated phylogenetic reconstruction, ancestral area reconstruction, mating-type assignment, and demographic inference to identify major populations, infer dispersal patterns, assess evidence for recombination and clonality, and reconstruct historical population dynamics. Findings We analyzed 312 genomes (139 C. immitis; 173 C. posadasii) and identified three major genetic populations within each species. C. immitis included two California-centered populations and one Pacific Northwest population, whereas C. posadasii included two Arizona-centered populations and one Texas-centered population. The most recent common ancestor was estimated at approximately 127,000 years for C. immitis and 234,000 years for C. posadasii. Most populations were not fully monophyletic, consistent with retained ancestral variation and/or ongoing gene flow. Inferred dispersal was largely asymmetric, with most movement originating from California in C. immitis and from Arizona and Texas in C. posadasii. Most populations contained both mating types, but one C. immitis population and a Brazilian subgroup of C. posadasii were clonal. All populations showed recent demographic expansion. Interpretation The evolutionary history of Coccidioides is characterized by strong geographic structure, ongoing gene flow, and recent demographic expansion. These processes are likely to influence future patterns of Valley fever endemicity and supports the use of genomic surveillance to detect shifts in disease risk as environmental conditions change.
- Discordant associations of IGF-binding proteins 1 & 2 with diabetes and cardiovascular disease: insights from UK Biobank
The insulin-like growth factor (IGF)/IGF-binding protein (IGFBP) axis has been implicated in diabetes mellitus and the associated burden of cardiovascular complications. Higher circulating levels of IGFBP-1 and IGFBP-2 have been established as markers of protection from incident type 2 diabetes, yet their associations with cardiovascular disease remain unclear. Utilising the UK Biobank (UKB) resource to integrate disease outcomes, plasma proteomics and MRI data, we examined associations of IGFBP-1 and IGFBP-2 with incident diabetes and cardiovascular disease. Approximately 50,000 UKB participants with plasma proteomic measurements for IGFBP-1 and IGFBP-2 were included. Multivariate Cox regression models revealed that participants in the highest quartiles of IGFBP-1 and IGFBP-2 had a substantially lower risk of incident diabetes (hazard ratio (HR) = 0.31 and 0.32 respectively), but, paradoxically, had increased risks of incident macrovascular disease, all-cause and cardiovascular-related mortality (HR = 1.81 and 2.39). Both proteins were negatively associated with HbA1c levels, triglyceride/HDL ratio and abdominal adiposity, yet positively associated with NT-proBNP, troponin I, cardiac chamber size and aortic dimensions. In summary, negative associations of IGFBP-1 and IGFBP-2 with incident diabetes mellitus did not translate to a reduced cardiovascular risk, suggesting potentially complex actions of IGFBP-1 and IGFBP-2 in the pathophysiology of cardiometabolic disease.
- Early identification of suboptimal responders to metformin in type 2 diabetes using long-term real-world HbA1c trajectories
Aims Metformin remains the primary treatment for type 2 diabetes, yet over 40% of patients fail to maintain glycaemic control. We aimed to identify patients unlikely to respond to metformin prior to treatment initiation and to evaluate whether on-treatment management can improve glycaemic outcomes in suboptimal responders, informing early treatment decisions. Materials and Methods We analyzed 59,881 longitudinal HbA1c measurements from 7,105 patients with type 2 diabetes receiving metformin monotherapy using real-world electronic health records from Kaiser Permanente Northern California with up to six years of follow-up. We integrated demographic, clinical, genetic, and pharmacological factors to characterize metformin responder phenotypes and quantify the impact of adherence and weight control on time to glycaemic failure. Results Three distinct trajectory-based phenotypes were identified: good (63.6%), poor (8.9%), and non-responders (27.5%). Poor responders initially achieved glycaemic targets but lost control within 2.5 years, while non-responders showed minimal HbA1c reduction and failed within 1 year. Five baseline factors-HbA1c, age at diagnosis, body mass index, sex, and estimated glomerular filtration rate-classified phenotypes with good discrimination (area under the receiver operating characteristic curve = 0.84). Incorporating on-treatment HbA1c further enhanced identification of non-responders. Among suboptimal responders, weight control and improved adherence delayed glycaemic failure by approximately 7 months; however, eventual glycaemic failure remained likely. Conclusions We characterized three clinically relevant metformin responder phenotypes and showed that suboptimal responders can be identified early using baseline features. Poor and non-responders are unlikely to achieve durable glycaemic control with metformin alone and may require alternative treatment strategies.
- Analytical Performance and 99th Percentile Upper Reference Limit of the Novel SPINCHIP High-Sensitivity Cardiac Troponin I Point-of-Care Assay
BACKGROUND In line with International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) recommendations for high-sensitivity cardiac troponin assays, analytical validation and reference limit assessments are required to confirm that an assay meets performance criteria. This study evaluated the analytical performance and established the 99th percentile upper reference limit (URL) for the SPINCHIP High-Sensitivity Cardiac Troponin I (SPINCHIP hs-cTnI) point-of-care assay. METHODS Analytical performance characteristics, including the limit of blank (LoB), limit of detection (LoD), and limit of quantification (LoQ), were assessed. Additionally, 1,053 plasma samples and 1,055 whole-blood samples were used to determine the URL. Imprecision around the 99th percentile URL was evaluated as part of the analytical validation. High-sensitivity criteria were assessed by confirming measurable cTnI in [≥]50% of healthy individuals (n=432 plasma; n=431 whole blood) and achieving imprecision <10% at the 99th percentile (plasma, n=960; whole blood, n=480). RESULTS SPINCHIP hs-cTnI demonstrated a LoB of 0.3 ng/L; LoDs of 0.8 ng/L (plasma) and 0.9 ng/L (whole blood); and LoQs of 1.1 ng/L (plasma) and 1.4 ng/L (whole blood). The analytical measuring range was 1.1-9,000 ng/L. Imprecision at the common 99th percentile URL (14 ng/L) was 5.8%; for men (URL=16 ng/L) 5.6% and for women (URL=10 ng/L) 6.3%. Greater than 85.2% (94.0% and 76.1% in men and women, respectively) of healthy individuals showed measurable cTnI above the LoD. CONCLUSIONS The SPINCHIP hs-cTnI assay meets the IFCC high-sensitivity requirements, demonstrating <10% imprecision at the 99th percentile, reliable low-concentration precision and cTnI detection in more than half of healthy individuals.
- An Integrated Anatomic Score for Intraprocedural Risk Stratification in Bicuspid TAVI: Development and External Validation
Background: Bicuspid aortic valve anatomy increases procedural complexity during transcatheter aortic valve implantation, yet outcome-oriented anatomic risk stratification for intraprocedural events remains limited. Aims: We aimed to develop and externally validate an anatomy-driven score to predict a composite intraprocedural endpoint, assessed at exit from the procedure room, in bicuspid transcatheter aortic valve implantation. Methods: Consecutive patients with bicuspid aortic valve undergoing transcatheter aortic valve implantation were analysed in a development cohort (N=793) and a multicentre external validation cohort (N=134). Candidate preprocedural computed tomography and echocardiographic variables were prespecified by expert consensus and refined using penalized regression with bootstrap stability selection within a domain-constrained framework. A five-indicator score (0 to 10 points) was derived from routine imaging metrics spanning the ascending aorta, aortic root, valve complex, annulus-outflow tract unit, and left ventricle, and tested using multivariable logistic regression. Results: The composite intraprocedural endpoint occurred in 101/793 (12.7%) patients in the development cohort, with stepwise increases across risk strata (7.2%, 13.3%, 30.6%; p<0.001). Each 1-point increase was independently associated with higher risk (odds ratio 1.32; 95% confidence interval 1.18-1.47). A similar gradient was observed in external validation (3.1%, 10.8%, 50.0%; p=0.012; odds ratio 1.55 per point), with a C-statistic of 0.725. Higher risk categories were associated with lower early safety and higher 30-day and 1-year mortality. Conclusions: An anatomy-driven score derived from routine preprocedural imaging demonstrates graded discrimination of intraprocedural risk and may inform procedural planning in bicuspid transcatheter aortic valve implantation.
- Human inherited RORgammaT deficiency encompasses genetic heterogeneity, T cell deficiency, and clinical homogeneity
We previously reported inherited RORgammaT deficiency in seven patients from three ancestries (Chilean, Palestinian, Saudi Arabian) with mycobacterial disease and chronic mucocutaneous candidiasis (CMC). We report here five additional patients from different ancestries (Afghan, Indian, Iranian, Japanese, Sri Lankan), each homozygous for a new loss-of-function RORC variant. All but one patient, the exception receiving early prophylaxis, developed mycobacterial disease due to a near-complete depletion of innate-like adaptive T cells, including MAIT and iNKT cells, low counts of adaptive TH1* and CD8+ T cells, and impaired Mycobacterium-induced IFN-gamma production by the remaining cells of these subsets, NK cells, conventional CD4+ T, Vdelta1, and Vdelta2 gamma-delta T cells. Most patients also displayed CMC due to their low counts of TH17 and TH1* cells. One patient died from disseminated Bacille Calmette-Guerin (BCG) vaccine infection, but, unexpectedly, all the other patients are still alive and clinically stable. RORgammaT is essential for protective immunity against mycobacteria and Candida in humans.
- Validation of the SPiRO score for the prediction of ICU admission in patients presenting with leptospirosis in tropical Australia
Objectives: In some resource-limited settings the case-fatality rate of severe leptospirosis can exceed 50%. Early recognition of severe disease can expedite transfer to referral centres for advanced supportive care. The entirely clinical, 3-point SPiRO score can be calculated rapidly at presentation to predict a patients subsequent clinical course. In its derivation study, a SPiRO score of 0 had a negative predictive value (NPV) for intensive care unit (ICU) admission of 98% (95% confidence interval (CI): 96-99). In this validation cohort we sought to confirm the clinical utility of the SPiRO score and to compare its prognostic utility with other leptospirosis-specific and general disease severity scores. Methods: We examined consecutive adults presenting to high-caseload hospitals in tropical Australia with laboratory-confirmed leptospirosis between June 2016 and April 2026. The ability of the SPiRO score to predict requirement for ICU admission before hospital discharge was compared with that of the leptospirosis-specific QuickLepto score and commonly used disease severity scores, namely the SOFA, qSOFA, qSOFA-lactate, NEWS-2, qNEWS, UVA and the SIRS scores. Results: ICU admission was required in 62/309 (20%) episodes of leptospirosis. The SPiRO score performed as well as - or better than - all the other scores in predicting ICU admission. The Area Under the Receiver Operating Characteristic curve for the SPiRO score was 0.83 (95% CI: 0.77-0.89); only the SOFA score had a higher value: 0.84 (0.79-0.90), although the difference was not statistically significant (p=0.08). The SPiRO score had the highest NPV for ICU admission of any of the scores: 95 (95% CI: 91-97)%. Conclusions: The SPiRO score can be calculated easily at the bedside at presentation to expedite the recognition of patients with leptospirosis who are most likely to deteriorate. In resource-limited settings this entirely clinical score can also help reduce unnecessary escalation of care, optimising the use of finite health resources.
- Design tensions in a two-sided marketplace for reusable digital therapeutics software components: a qualitative interview study
Objectives To identify stakeholder-perceived design tensions in a two-sided marketplace for reusable digital therapeutics (DTx) software components and to use these tensions to propose alternative marketplace concepts. Methods We conducted 24 semi-structured interviews with digital health researchers and professionals. Data were analysed using hybrid deductive-inductive codebook thematic analysis. The Magic Triangle provided the initial deductive structure. One researcher coded all transcripts; a second independently applied the developing codebook to five transcripts to refine definitions and consistency. Seventeen parent themes were synthesized into 12 design tensions, which informed three author-generated marketplace concepts. Results Participants described trade-offs concerning target users and host, component scope and customization, quality labels, verification, geographic scope, pricing, interoperability, platform launch, risks and market niche. The resulting concepts emphasized a regional startup ecosystem, a research-oriented hybrid marketplace or a global marketplace with stricter entry requirements. Discussion The concepts combine the tensions in different ways and highlight competing priorities in governance, openness, assurance, scalability and early platform growth. Conclusion Stakeholders identified recurring design choices for a DTx software-component marketplace. The concepts provide hypotheses for prototyping and evaluation; the study did not test technical feasibility, market demand, regulatory acceptability or effects on development cost or time.
- Developing and Prospectively Validating a Reproducible Graph Representation Specification for Clinical Guideline Algorithms: The Measurement Foundation of the Clinical Guideline Complexity Index
Background. Translating a clinical guideline decision algorithm into a computational graph requires judgment, and unconstrained coding yields divergent graphs; any complexity measure computed from such a graph inherits that variation, so its reproducibility must be demonstrated rather than assumed. Objective. To develop, and prospectively test, an empirical method for making graph extraction reproducible, using the Clinical Guideline Complexity Index (CGCI) and four guideline algorithms as a case study. Methods. We built a Graph Representation Specification (an ontology, a motif catalogue, disambiguation conventions, decomposition rules, a deterministic validator, and a scoring engine) and refined it by error-driven grammar induction: measure inter-coder disagreement, localize its dominant class, induce a single grammar rule, and prospectively test whether that rule improves agreement in the anticipated class. Reproducibility was quantified with a pre-specified, topology-based endpoint (Decision Topology Agreement) rather than edge agreement, which is oversensitive to representational choices that do not affect the score. Two trained coders independently coded the diabetes, dyslipidemia, heart-failure, and hypertension algorithms. Results. A rule induced from the diabetes comorbidity panel (assessment topology) generated a pre-specified prediction that heart-failure figures, sharing the same motif, would converge; on a fresh, independently coded pair they did, with an absolute CGCI difference of approximately one. Decision topology reproduced closely (decision-order agreement at or near 1.00 for three of four guidelines), while breadth counting was rule-sensitive: an explicit modifier-counting rule reduced the largest disagreement from 27 to 4 tokens. Residual disagreement was bounded and localizable to specific, nameable representational choices. Conclusions. Graph-extraction reproducibility can be systematically improved through iterative grammar refinement, and a prospectively derived rule can be confirmed to improve agreement. These results establish the measurement foundation (reliability, not construct validity) for a companion study interpreting CGCI as cognitive load, and the method may apply wherever graphs are extracted from structured source artifacts.
- Selective prediction as a triage gate for primary-care depression screening: quantifying and mitigating selection bias in CHARLS-2011
Background Primary care in China lacks structured mental-health assessment, and the machine-learning models that could support such screening are typically developed on heavily selected samples. Cumulative inclusion and exclusion criteria, though usually treated as neutral data-cleaning steps, can create heterogeneity in predictive reliability among retained participants. Using the China Health and Retirement Longitudinal Study (CHARLS) 2011 baseline, we quantified how selection funnels distort epidemiological associations and inflate machine-learning metrics, and tested selective prediction as mitigation. Methods Using the CHARLS 2011 baseline with temporal external validation in CHARLS-2018, we built a four-level selection funnel (L0-L3), evaluated five classifiers with nested cross-validation and SMOTE, and compared model-embedded uncertainty with a decoupled predictor-selector framework; XGBoost cross-validation residuals drove risk stratification and classification and regression tree (CART) rules. Results Sample sizes fell from L0 n=17,705 to L3 n=4,256 (24.0%). The cancer-depression odds ratio attenuated from 1.78 (95% CI 1.32-2.41) to 1.39 (0.74-2.63), losing significance. AUC rose with selection but not after multiple-comparison correction, whereas calibration error increased for four of five models. Model-embedded uncertainty succeeded only for XGBoost; with the decoupled XGBoost residual selector, all five models achieved selective prediction at approximately 20% coverage (test AUC 0.90, 95% CI 0.85-0.95), abstaining on approximately 80% of cases for individual safety. Risk stratification was stable (residual Spearman correlations >0.95; multi-seed Jaccard 0.88), and CART rules used self-rated health, education, pain, and marital status. Conclusions The findings support a deployable primary-care triage pathway: a four-variable rule identifies patients suitable for algorithm-assisted scoring (approximately 20% coverage) and routes the remainder to human evaluation. Methodologically, cumulative selection bias produces a dual distortion: epidemiological associations are compressed and machine-learning metrics inflated. Selective prediction is limited mainly by uncertainty-indicator design. Performance metrics should be reported with selection level, coverage, and calibration trajectory. Decoupled selective prediction with CART rule extraction provides an actionable framework for quality-controlled, tiered-care deployment. Keywords: selective prediction, selection bias, CHARLS, depression, predictor-selector decoupling, uncertainty quantification, classification and regression tree, triage, clinical decision support, health management.
- Mechanism of response to FHD-286 and decitabine combination in patients with advanced myeloid malignancies
Impaired cellular differentiation is a defining characteristic of myeloid malignancies and remains a major therapeutic challenge. The BRG1/Brahma-associated factor (BAF) chromatin remodeling complex, through the ATPases SMARCA4 and SMARCA2, maintains the stemness of leukemic blasts and thus represents a promising target for novel differentiation-based therapies. In a phase 1 study in advanced myeloid malignancies, the first-in-class dual SMARCA4/2 inhibitor FHD-286 combined with decitabine (DAC) was tolerated and produced an objective response rate of 12.8% (6/47) compared with no responses with FHD-286 monotherapy. To understand the basis of this activity, we integrated high-dimensional flow cytometry and single-cell genomic analyses of longitudinal bone marrow samples from responders and nonresponders. While FHD-286 monotherapy was predominantly associated with myeloid differentiation, responders to FHD-286+DAC combination therapy exhibited a range of myeloid and erythroid differentiation trajectories. FHD-286 potentiated the transcriptional impact of DAC, driving tumor clones to fully differentiate out of the immunophenotypically and transcriptionally defined blast compartment. Responders had a baseline transcriptional profile similar to that of CEBPA-mutant acute myeloid leukemia and showed further downregulation of CEBPA upon treatment. These findings reinforce tumor cell differentiation as a mechanism of response to pharmacologic SMARCA4/2 inhibition and support further evaluation of FHD-286+DAC in molecularly defined patient subsets.
- Ceasing oxytocin in the active phase of the first stage of induced labours: A prospective audit at a tertiary hospital.
Introduction: Oxytocin is commonly used in the process of induction of labour and is associated with uterine hyperstimulation and abnormal fetal heart rate patterns that can increase the risk of adverse perinatal outcomes. Cessation of oxytocin in the active phase of induced labour has been shown in randomised trials to reduce uterine tachysystole and abnormal fetal heart rate traces, and may reduce caesarean section. We introduced a policy recommending cessation of oxytocin infusion in the active phase of the first stage of induced labour at a tertiary hospital in Sydney, Australia, and collated both clinical outcomes and maternal satisfaction following implementation. Methods: This was a prospective audit of a policy change at Royal Prince Alfred Hospital, comparing 600 women induced with oxytocin in the 6 months before the policy (November 2019 to May 2020) with 556 women induced in the 6 months after implementation (June to December 2020). Eligible women had a cervix [≥] 5cm, an oxytocin infusion, and regular uterine contractions. The primary clinical outcome was caesarean delivery. The primary patient-centred outcome, maternal satisfaction, measured using the Six Simple Questions questionnaire, was collected in a subset of participants. Secondary outcomes included mode of birth, length of labour, uterine hyperstimulation, and perinatal outcomes. Results: Caesarean delivery occurred in 29% of women before and 28% after policy implementation (p=0.77). Instrumental birth increased from 25% to 27%; and instrumental birth for maternal indications increased from 6.8% to 13% (p=0.0005). Median length of labour increased by one hour (5.4 vs 6.4 hours, p=0.006). Oxytocin was ceased for at least two hours or until birth in 13% of women before the policy versus 35% after. Maternal satisfaction scores were modestly lower after implementation (median 41 vs 38, p=0.03). Perinatal outcomes, including abnormal cord gases, Apgar scores, and NICU admission, were similar between groups. Conclusions: Implementing a policy of recommending cessation of oxytocin in the active phase of induced labour did not reduce caesarean delivery rates in a real-world tertiary hospital setting, despite trial-level evidence supporting the intervention. Poor uptake, negative staff perceptions, and a modest reduction in maternal satisfaction highlight barriers to translating trial efficacy into routine clinical practice. Adequately powered trials are needed to clarify optimal protocols for oxytocin cessation and its effects on maternal and perinatal outcomes.
- Association between Glycemic Traits and Delayed Cerebral Infarction among Non-Diabetic Patients with Aneurysmal Subarachnoid Hemorrhage: A Nested Case-Control Study
ABSTRACT Objective Delayed cerebral infarction (DCIn) is a severe complication following aneurysmal subarachnoid hemorrhage (aSAH). Previous studies suggest that glycemic variability is associated with DCIn. However, whether diabetes status modifies the relationship between glycemic traits and DCIn remains unknown. Methods Clinical data were collected from aSAH patients admitted to the First Affiliated Hospital of Shantou University Medical College between January 2015 and April 2025. The collected data included demographic characteristics, clinical variables, and glycemic traits. Glycemic traits included mean blood glucose (GLU-M), standard deviation of blood glucose (GLU-SD), coefficient of variation of blood glucose (GLU-CV), variance of blood glucose (GLU-Var), range of blood glucose (GLU-R), average real variability of blood glucose (GLU-ARV), and variability independent of the mean (GLU-VIM). After 1:2 case-control matching, conditional logistic regression models were used to evaluate the associations between glycemic traits and DCIn risk, with stratified analyses performed according to diabetes status. Multiplicative interaction terms were additionally included to assess the potential modifying effect of diabetes status. Results A total of 306 patients with aSAH were included. Among them, 102 developed DCIn cases. For each of these 102 cases, two controls were matched by age ({+/-}5 years), sex and year of admission ({+/-}5 years). In the overall population, higher GLU-M and GLU-ARV were associated with increased DCIn risk, with odds ratios (ORs) per 1-SD increase of 1.62 (95% CI, 1.25-2.11) and 1.63 (95% CI, 1.25-2.11), respectively. Among patients without diabetes (n=266), the associations with DCIn per 1-SD were observed for GLU-M (OR, 2.23; 95% CI, 1.56-3.19), GLU-SD (OR, 1.53; 95% CI, 1.13-2.06), GLU-Var (OR, 1.48; 95% CI, 1.04-2.10), and GLU-ARV (OR, 1.88; 95% CI, 1.38-2.55). No significant associations were observed among patients with diabetes. Significant interactions were observed between diabetes status and GLU-SD and GLU-Var, with P for interaction values of 0.033 and 0.032, respectively. Conclusion Higher mean blood glucose and greater glycemic variability are associated with an increased risk of DCIn in aSAH patients, especially in those without diabetes.
- Intravesical Lactobacillus rhamnosus GG reduces symptoms among people with spinal cord injury and disease who use intermittent catheterization: A randomized comparison of two- and four-dose regimens.
Background: Urinary tract infection (UTI) is the most common secondary condition among people with spinal cord injury/disease (SCI/D). Intravesical Lacticaseibacillus rhamnosus GG (LGG) is an antibiotic-sparing approach to managing urinary symptoms. Objective: Determine the optimal number of doses of intravesical LGG for urinary symptom reduction. Design: Prospective, randomized, two-arm dosing trial. Setting: National recruitment with a local subsample providing urine samples in Washington, DC, USA. Participants: Adults with SCI/D and neurogenic lower urinary tract dysfunction (NLUTD) who use intermittent catheterization (IC); 177 enrolled and randomized (intention-to-treat), with 76 compliant instillers (39 low-dose, 37 high-dose) in the per-protocol analytic sample. Interventions: Two (2 doses/24 hours) or four (4 doses/36 hours) intravesical LGG regimens, self-initiated in response to cloudier or malodorous urine per the Self-Management Protocol using Probiotics (SMP-Pro). Main Outcome Measures: Primary: proportion achieving [≥]20% reduction on the Urinary Symptom Questionnaire for Neurogenic Bladder-Intermittent Catheter version (USQNB-IC). Secondary: urinary biomarkers (leukocyte esterase, nitrite, white blood cells, urinary neutrophil gelatinase-associated lipocalin [uNGAL]) and standard urine culture (SUC) in a local subsample. Results: By Day 2, 57.9% (63.8% low-dose; 51.2% high-dose) achieved [≥]20% total symptom reduction; high-dose success rose to 70.0% by Day 4. Thirty percent of high-dose participants did not respond at either time point and could not be distinguished from responders by demographics or urine biomarkers. Urinary biomarkers and SUC were unchanged pre- to post-instillation. No serious adverse events were adjudicated as attributable to intravesical LGG by an independent Data Safety Monitoring Board (DSMB). Conclusions: A two-dose course of intravesical LGG yields clinically meaningful symptom improvement in the majority of people with SCI/D and NLUTD who use IC; four doses benefits a meaningful subgroup of two-day non-responders, while a small cohort remains nonresponsive. These results provide preliminary dosing guidance and support progression to a definitive trial.
- Preconception Health Research Priorities for Adolescents and Young Adults in Australia
Background: Guidelines on pre-pregnancy counselling are primarily clinical, and although recommendations and policy documents on preconception care exist in Australia, they place little or no emphasis on the preconception health of adolescents and young adults. Objective: To identify and prioritise unanswered questions and evidence uncertainties concerning preconception health needs of adolescents and young adults residing in Australia. Design: Research priority exercise Setting and Participants: Participants included young interest-holders (18-24 years) and professional interest-holders from the academics, healthcare, policy, community and government sectors residing in Australia. Methods: We followed the James Lind Alliance (JLA) methodology to identify research priorities for preconception health of adolescents and young adults. The process was led by a multidisciplinary steering committee comprising young interest-holders and professional interest-holders (including academics and clinicians). A rapid literature review was conducted from which 80 research questions were developed across ten domains, which were refined through consultation and prioritised via two rounds of online surveys on Qualtrics using a 9-point Likert scale. Results: The participants included 14 young interest-holders in each survey round, with 22 professional interest-holders in the first round and 33 in the second. Participants from across Australia participated in the survey, but most were from South Australia. In the first survey round, 28 questions across seven domains were prioritised by both professional and young interest-holders. This was followed by a reprioritisation exercise, resulting in the final top 10 research questions spanning five domains. The highest-priority research questions identified by the interest-holders concentrated in the domains of violence and mental health; early intervention and prevention; smoking, tobacco, alcohol, and substance use; access to preconception care and the healthcare system; and priority populations. Conclusion: The study identified the top 10 priority research questions informed by professional and young interest-holders. It promotes new research and collaboration while offering guidance on future research investments and on designing preconception interventions for adolescents and young adults in Australia. Turning these priorities into research could improve the health outcomes for adolescents and their future generations.
- Antidepressant Maintenance Versus Active Monitoring After Depression Remission: A Decision Analysis Stratified by Relapse Risk and Patient Preferences
Importance: Patients who achieve remission from major depressive disorder (MDD) often face a preference-sensitive decision between continued antidepressant maintenance and discontinuation with active monitoring. Quantifying the tradeoff between depression burden and long-term medication exposure may support more individualized shared decision-making. Objective: To quantify tradeoffs between continuous antidepressant maintenance and active monitoring after MDD remission, and to identify preference thresholds favoring each strategy across relapse-risk strata. Design: Individual-level decision-analytic health-state transition model calibrated to randomized maintenance-discontinuation trials and a longitudinal first depressive episode cohort, with a 5-year time horizon. Setting: Outpatient clinical decision after completion of an 8-month continuation phase following remission from MDD. Participants: Adults in remission from MDD, represented across 4 clinically anchored relapse-risk strata ranging from very low risk after a first mild episode to high risk after highly recurrent depression. Exposures: Continuous antidepressant maintenance vs discontinuation with active monitoring and antidepressant restart after detected relapse. Main Outcomes and Measures: Severity-weighted depression-months, antidepressant medication-years, medication-years per depression-month averted, and net benefit across preference thresholds defined as the maximum additional medication-years a patient would be willing to accept to avert 1 depression-month. Results: Continuous maintenance reduced depression burden but required substantially more medication exposure, with efficiency strongly dependent on relapse risk. Medication-years per depression-month averted ranged from 11.8 (95% uncertainty interval [UI], 7.8-19.6) in the very low-risk group to 1.5 (95% UI, 0.8-3.0) in the high-risk group. At a preference threshold of 3 medication-years per depression-month averted, maintenance was preferred for moderate- and high-risk patients; at a threshold of 2, only for high-risk patients; and at a threshold of 1, for no risk group. Conclusions and Relevance: In this decision-analytic model, the value of continuous antidepressant maintenance depended strongly on baseline relapse risk and patient preferences regarding long-term medication exposure. These findings provide a quantitative framework for shared decision-making about antidepressant maintenance after remission from MDD.
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- Alphabet stock pops on report Google is building a Gemini-specific AI chip
The chip, internally dubbed "Frozen v2," could serve 6 to 10 times more tokens per unit of power than Google's latest TPUs
- Trump’s latest AI czar has already resigned
The director role for the Center for AI Standards and Innovation (CAISI) has become a revolving door since David Sacks left his position as czar.
- Head of US AI safety agency resigns
Head of US AI safety agency resigns Reuters
- Trump administration's head of AI safety agency resigns after 3 months on job
Arvind Raman, the director of National Institute of Standards and Technology, will serve as acting director of CAISI, according to a spokesperson
- Top Trump AI safety director leaves job after 3 months
Top Trump AI safety director leaves job after 3 months Business Insider
- Scoop: Trump AI security agency head resigns
Chris Fall, the director of the Center for AI Standards and Innovation, is resigning just three months after taking over the federal AI testing institute, the Commerce Department confirmed on Monday. Why it matters: The abrupt departure comes as the Trump administration grapples with how to deploy AI safely and the agency hashes out standards. Commerce spokesperson Benno Kass confirmed Fall's resignation to Axios. Context: Fall was appointed in April to lead Commerce's Center for AI Standards and Innovation after the administration reorganized the agency formerly known as the U.S. AI Safety Institute. CAISI is responsible for developing AI testing and evaluation capabilities and supporting standards for advanced AI systems. What they're saying: "Following Chris's departure, NIST Director Dr. Arvind Raman will continue to oversee CAISI and will serve as Acting CAISI Director," Commerce spokesperson Kristen Eichamer said in a statement to Axios. Raman, a former Purdue University engineering dean, was sworn in as the director of the National Institute of Standards and Technology on June 30. What we're watching: While Raman will serve as acting director, the office will remain without permanent leadership as the administration debates its next steps on AI standards and oversight. What's next: The Commerce Department expects to announce a new director in the coming weeks. A Commerce official said Fall's appointment was always intended to be temporary and that Raman has been reviewing candidates over the past several weeks. Editor's note: This story has been updated with a statement from the Department of Commerce.
- Head of U.S. federal AI testing institute resigned after just three months
The Commerce Department confirmed Chris Fall's departure and said a new director will be announced in the coming weeks
- Google is working on a new AI chip designed to make Gemini more efficient
Alphabet, Google's parent company, is reportedly working on a new chip designed to make its Gemini models run much more efficiently.
- Google Shares Gain on Report of Chip to Boost AI Efficiency
Shares of Google owner Alphabet Inc. gained on a report that the company is developing a server chip designed to optimize its Gemini artificial intelligence model.
- Google plans new chip to run Gemini models more efficiently, the Information reports
Google plans new chip to run Gemini models more efficiently, the Information reports Reuters
- Alphabet stock pops on report it's developing a more efficient AI chip
The new AI chip, called "Frozen v2," would embed parts of Gemini's architecture directly into the silicon, according to the report.
- Alphabet Stock Jumps After Information Report of New Google Chip
Alphabet Stock Jumps After Information Report of New Google Chip The Information
- Azure touts trio of new AI instances powered by AMD Helios racks
AMD says it will start ramping shipments from Q3
- Google plans new chip to run Gemini models more efficiently: Report
Google expects new chip, dubbed "Frozen v2," to help address an AI computing capacity crunch that has fueled internal tensions and prompted Google Cloud to decline deals with outside customers
- Google's "Frozen v2" chip reportedly bakes Gemini's architecture directly into silicon for efficiency gains
Google is developing "Frozen v2," a server chip that bakes the Gemini architecture directly into hardware. According to internal sources, it could be 6 to 10 times more efficient than current TPUs. Scheduled for 2028, the chip would drastically cut Google's AI inference costs and could give the company a price advantage over OpenAI and Anthropic. The article Google's "Frozen v2" chip reportedly bakes Gemini's architecture directly into silicon for efficiency gains appeared first on The Decoder .
- Google is building a chip with Gemini baked into the silicon
Most AI chips are general-purpose. You load a model onto them, and they run it. Google is reportedly trying something stranger: a chip that is the model, with Gemini’s blueprint etched into the hardware itself. The project, informally called “Frozen v2,” was reported by The Information and picked up by Reuters and Bloomberg Law. Alphabet […] This story continues at The Next Web
- Google plans new chip to run Gemini models more efficiently
Google plans new chip to run Gemini models more efficiently, the Information reports
- Adobe crams multiple AI tools into its experimental camera app
Adobe's Project Indigo app will have new features like removing distractions and offering immediate photo feedback.