AI News Archive: July 19, 2026 — Part 7
Sourced from 500+ daily AI sources, scored by relevance.
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- Learning to read a second language establishes a parallel L2 representation alongside the native one in the VWFA
Learning to read a second language requires the brain to incorporate a new writing system into an already established native-language reading network, yet how this process reshapes the Visual Word Form Area (VWFA) remains poorly understood. Using fMRI with a passive viewing paradigm, we systematically investigated how VWFA responses to Chinese (L1) and English (L2) words evolved across three groups of native Mandarin-speaking children at distinct stages of L2 literacy acquisition: L2 pre-readers, L2 beginning readers, and L2 advanced readers. We combined univariate activation analyses, representational similarity analysis, and supervised machine learning classification to investigate two theoretical accounts of VWFA reorganization: the Overlay Model, which predicts stable L1 responses as L2 responses emerge, and the Competing Model, which predicts competitive reallocation of neural resources from L1 to L2. We found that although the VWFA already exhibited robust selective responses to L1 words, L2-word selectivity was absent in L2 pre-readers but emerged robustly in beginning readers, with L1-word selectivity remaining stable throughout. Representational similarity analysis further revealed that robust L2 word representations within the VWFA emerged only after children began learning to read L2, while L1 word representations remained stable across all three groups. Finally, supervised machine learning analyses successfully discriminated among the three L2 literacy groups on the basis of L2 but not L1 activation patterns, indicating that VWFA responses to L2 alone were sufficient to capture children's stages of L2 literacy acquisition, whereas responses to L1 were insensitive to L2 learning experience, providing no support for the Competing Model's prediction of competitive neural reallocation away from L1. Together, these findings support the Overlay Model, demonstrating that the VWFA incorporates a new writing system within its existing cortical resources without compromising L1 print processing.
- Validating Artificial Intelligence Guidance for Ultrasound Acquisition and Remote Interpretation
Background: Venous thromboembolism (VTE), including deep vein thrombosis (DVT), remains a major global health burden. Diagnostic pathways rely on ultrasound but are limited by availability and prolonged time-to-imaging. Novel artificial intelligence (AI) guidance systems have been designed to enable non-ultrasound-trained operators to acquire proximal lower extremity compression ultrasounds for remote clinician interpretation. Methods: This multicenter, double-blinded, prospective, nonrandomized study evaluated the performance of an AI guidance system (ThinkSono Guidance, ThinkSono, GmbH). Patients underwent AI-guided ultrasound(s) and standard of care ultrasound(s). Primary and secondary endpoints were image quality, sensitivity and specificity for proximal DVT, and prioritization specificity, a measure of specificity in identifying patients requiring standard of care ultrasound after AI-guided scan. Results: Of 634 recruited subjects, 594 were analyzed, with 67 DVTs across 700 scans. 86.83% of AI-guided scans achieved diagnostic image quality. Triage sensitivity was 92.86%, triage specificity 39.12%, prioritization specificity 97.96%. Standard of care ultrasounds could be avoided in 35.32% of patients. Total median AI-guided scan and review time was 7.57 minutes. Conclusions: Clinician-reviewed AI-guided scans were rapid, sensitive for DVT, and specific for prioritizing patients requiring standard of care ultrasounds. These findings suggest AI-guided ultrasound may be a scalable triage strategy to expand DVT evaluation access, particularly in resource-constrained and after-hours settings
- Development and external validation of deep learning models for spontaneous preterm birth prediction from mid-trimester cervical ultrasound
Preterm birth is the leading cause of neonatal death. Despite sustained efforts to identify high-risk women in the mid-trimester, accurate prediction remains difficult. Quantitative cervical ultrasound texture has been proposed as a predictor of spontaneous preterm birth. However, earlier models were developed in small single-centre samples and were not externally validated. We developed image-texture (Local Binary Patterns with a Random Forest), deep-learning (Vision Transformer), clinical-variable, and multimodal models to predict spontaneous preterm birth on the prospective GARBH-Ini cohort. We then externally validated our best models on an independent cohort scanned on a different ultrasound machine. Our best overall model reached an internal-test area under the receiver-operating-characteristic curve of 0.71 (95% CI 0.60, 0.82), but performed modestly at 0.52 (95% CI 0.38, 0.64) externally. The deep-learning and multimodal models did not perform better. Discrimination appeared higher in a clinically high-risk subgroup at the 34-week threshold. These estimates were imprecise because of few cases and need to be confirmed in future studies. Among the several likely reasons for the modest external performance is the heterogeneity of preterm birth. Predicting distinct preterm-birth subtypes separately, and integrating additional biomarkers and data domains, might improve model performance. Keywords: preterm birth; cervical ultrasound; prediction model; external validation; deep learning
- A framework for human-artificial intelligence co-learning for disease activity labeling using electronic health records
Objective To develop and evaluate a framework for human-AI interaction. This approach, SHARE (Synergistic Human-Agent REasoning system) was designed to support scalable phenotyping of complex outcomes accurately, robustly and reproducibly from real-world electronic health record (EHR) data to support real-world evidence (RWE) generation. Methods and Analysis Using rheumatoid arthritis (RA) disease activity as the use-case, we studied a multi-institutional EHR-based RA cohort of 3,167 patients. Expert reviewers and a disease activity agent labeled notes using the same review guideline. The agent combined embedding-based informative-note filtering, structured evidence extraction, and evidence-based integrated reasoning to assign disease activity categories with supporting evidence, rationale, confidence, and ambiguity flags. To support scalable deployment, we evaluated a budget-tiered configuration using GPT-5 Nano for high-volume evidence extraction, o4-mini for final reasoning, benchmarking against a GPT-5.4 high reasoning effort configuration applied at every step. Note-level discrepancies were adjudicated by reviewers into final co-produced labels that were used to refine labels and inform agent development. The main outcome measure was the mean absolute error (MAE) of the initial and final agent vs the final co-produced labels. The agreement between agent- and reviewer-flagged ambiguous notes, per-note cost and compute time across configurations were also tested. Results Expert reviewers labeled 626 notes from 273 patients; human-AI adjudication revised 127 (20%) of these initial labels and added 60 newly labeled notes, yielding a 686-note co-produced reference. Against this reference, the final agent's accuracy improved from a mean absolute error of 0.406 to 0.291 with co-learning, and its ambiguity flag agreed with expert ambiguity designations with 92.1% accuracy. Applied across the cohort, the agent labeled 101,691 notes; the budget tiered configuration matched the accuracy of GPT-5.4 at high reasoning effort while reducing estimated cost by 69% and compute time by 70%. Conclusion Adopting a framework for human-AI co-learning, SHARE, improved the overall quality of gold-standard labels, identified ambiguous cases for further review, and supported accurate and standardized chart reviews of disease activity at a scale infeasible for manual review. SHARE's resource efficiency provides a transferable approach to incorporate complex phenotypes in RWE studies.
- Transient Apical Sparing in Hypertensive Heart Disease Explained by Laplace's Law
Background: Apical sparing of left ventricular longitudinal strain (LS) is an echocardiographic clue to cardiac amyloidosis but may also occur in hypertensive heart disease (HHD). Objectives: To determine whether apical sparing in HHD is associated with regional left ventricular wall stress estimated according to Laplace's law. Methods: We retrospectively studied 1,559 patients with HHD, 47 with light-chain cardiac amyloidosis (ALCA), and 409 normotensive controls. Artificial intelligence-assisted echocardiography quantified segmental LS, wall thickness, and cavity radius at the basal, midventricular, and apical levels. Wall stress was estimated as mean blood pressure (MBP) x radius/(2 x wall thickness). Apical sparing was defined as a relative regional strain ratio (RRSR)[≥]1.0. Results: Apical sparing was present in 14 patients with HHD (0.9%), 13 with ALCA (27.7%), and no controls. Among HHD patients with apical sparing, RRSR decreased from 1.11{+/-}0.13 to 0.72{+/-}0.10 after antihypertensive treatment (P<0.001), accompanied by reduced wall stress and improved basal and midventricular LS, with resolution of apical sparing in all 14 patients. In the overall HHD cohort, changes in MBP and left ventricular mass index were independently associated with changes in RRSR. In an exploratory analysis of HHD patients with apical sparing, a reduction in basal wall stress was associated with a reduction in RRSR ({beta}=0.267 for {bigtriangleup}RRSRx100, 95% CI 0.023-0.511; P=0.036). In ALCA, favorable hematologic response was the only determinant of RRSR reduction. Conclusions: Apical sparing in HHD was uncommon but reversible and may represent a load-sensitive deformation pattern associated with regional wall stress, consistent with Laplace's law.
- Simulation of synthetic health records for assessment of causal inference methods for vaccine efficacy
Background During the COVID-19 pandemic, public health agencies used near real-time observational data to answer questions regarding vaccine effectiveness. However, traditional observational methods do not allow conclusions regarding counterfactual scenarios to be drawn from clinical data. Counterfactuals, which are outcomes that would have occurred under alternative interventions, can be used to formally assess the causal effects of public health interventions on health outcomes while accounting for the effects of confounding. Ideally individual patient data is used for the development of counterfactuals. Low-fidelity synthetic data may be useful for advancing methodological development where governance and privacy constraints prohibit access to sensitive personal data. Methods We simulated synthetic datasets based on the EAVE-II COVID-19 platform which has been limited to use for surveillance purposes. EAVE-II includes almost all resident people in Scotland registered with qualified general medical practitioners. Patient characteristics were simulated to reflect the known distribution of the Scottish population, accounting for dependencies between variables. Each synthetic dataset was encoded to different realistic scenarios for EAVEII 'ground truth' vaccine rollout and effectiveness results, explicitly stating the causal and confounding mechanisms, using a statistically sound method based on marginal structural models. Synthetic datasets of 100,000 individuals were then generated across five confounding scenarios and five severe outcome types. Results In scenarios with weak confounding, both unweighted and inverse probability of treatment weighted (IPTW) logistic regression recovered the true causal parameters. As confounding strength increased, only weighted models recovered the true mechanism. Conclusions Low-fidelity synthetic datasets simulated from EAVE-II data analysts to build and test causal inference pipelines, develop novel analysis pipelines, and train new researchers while awaiting access to real data. We showed how to generate synthetic datasets from a marginal structural model under different confounding scenarios.
- Latent biomarker states underlying disagreement between PET-anchored and distribution-based plasma pTau-217 positivity thresholds
Background: Plasma phosphorylated tau-217 (pTau-217) measurements for use in Alzheimer disease (AD) identification require thresholds to define positivity and there exist different approaches to operationally defining the boundary. We compared amyloid {beta} (AB) PET-anchored and distribution-based positivity cut-off values and explored how these mapped onto latent biomarker states. Methods: We analysed plasma pTau-217 measured in the Bio-Hermes-001 cohort (N = 990) using an immunoassay (Lilly) and mass spectrometry assay (University of Gothenburg). Gaussian mixture models were used to identify latent classes and thresholds were derived in two ways: achieving 90% specificity for AB PET positivity and exceeding the mean + 2SDs of the lowest latent class. We explore classes in reference to AB PET status and clinical diagnosis, as well as agreement between approaches using Cohen kappa for both assays. Results: In both assays, three latent biomarker classes were identified with monotonic increases in AD clinical diagnosis and AB PET positivity. PET-anchored thresholds showed lower specificity but higher sensitivity to amyloid positivity than distribution-based thresholds. Overall agreement between the approaches was acceptable (k = 0.678 for Lilly and 0.575 for University of Gothenburg), with disagreement concentrated in the intermediate latent class. Classes with the lowest and highest pTau-217 concentrations were classified consistently using both thresholds Discussion: The two thresholding approaches yielded similar classifications at both the negative and positive tail of the observed biomarker distribution, but classify intermediate concentrations differently. The boundary definition influenced pTau-217 positivity more than the analytical platform itself. Thresholding approaches may capture different pTau-217 biomarker states, therefore such methodological decisions should be grounded in the context of the intended application.
- Risk Screening in a Medicaid-Managed Pregnancy Medical Home: The Need to Center Maternal Health Outcomes in Public Health Programming
Background: North Carolina Medicaid implemented the Pregnancy Medical Home program to improve access to high-quality maternity care and reduce the risk of adverse perinatal outcomes. Program recipients receive a prenatal risk screening form, originally intended to identify those at high risk of preterm birth and low birth weight, that includes an assessment of social and clinical factors. While prior studies have evaluated whether risk screening can identify pregnancies with higher risk of adverse neonatal outcomes, less is known about the relationship between programmatic risk-stratification and adverse maternal outcomes. Objective: To assess the use of a prenatal risk screen among pregnant Medicaid beneficiaries to identify those at risk of an adverse maternal event. Study design: Linked Medicaid hospital claims, live birth records, and risk screen data from the Pregnancy Medical Home program were used to identify risk factors for adverse maternal events among individuals who gave birth to a liveborn infant in North Carolina between 2014 and 2019. Only those with completed risk screens (75%) were included in the analysis. We used random forest classification to select variables for a multivariable prediction model. We used Poisson regression to model the association between adverse maternal events and selected demographic, psychosocial, clinical, and historical pregnancy characteristics. Adverse maternal events occurring at birth and up to six weeks postpartum included severe maternal morbidity, maternal intensive care unit admission, prolonged birth hospitalization, and postpartum readmissions. Results: A total of 205,916 births met inclusion criteria for this analysis. During the study period, 3.0% of Medicaid beneficiaries had an adverse maternal event occurring between birth and up to six weeks postpartum, including, 0.6% with severe maternal morbidity, 0.9% with an intensive care unit admission at birth, and 1.5% with a prolonged birth hospitalization or postpartum readmission. Maternal age greater than 25 years, Black race, being overweight or obese, smoking, chronic diseases (diabetes, hypertension, mental illness), and pregnancy history characteristics (nulliparity, history of preterm birth, history of hypertensive disorders of pregnancy or gestational diabetes) were associated with an increased risk of adverse maternal events. Modeled together, however, risk factors from the risk form were poorly predictive of the composite outcome. The final model had an Area Under the Curve (AUC) of 0.63 with an optimal sensitivity of 56% and specificity of 63%. Conclusion: Care management during pregnancy is an increasingly relevant topic in public health and prenatal care in the United States. The North Carolina Pregnancy Medical Home is a long-standing and robust Medicaid program that can serve as a model for design and implementation. While this program has effectively designed risk-stratification to identify pregnant people at risk of poor neonatal outcomes who benefit from care management, the risk screen poorly identifies pregnant people at risk of adverse maternal outcomes. Care coordination programs are often designed to optimize neonatal outcomes, and this study highlights the need to center and balance maternal health along with neonatal outcomes to address the needs of a very vulnerable population.
- Predictors of Pregnancy-Related Anemia: A Logistic Regression Study at a Maternity Facility in Ghana.
Background: Anemia during pregnancy remains a major public health concern, particularly in low- and middle-income countries, where it contributes substantially to maternal and neonatal morbidity and mortality. Identifying women at increased risk is essential for timely intervention and improved pregnancy outcomes. Objective: This study aimed to identify the significant predictors of anemia among pregnant women using logistic regression and to evaluate the association between selected clinical and sociodemographic characteristics and anemia. Methods: A cross-sectional study was conducted using secondary data obtained from a community maternity care facility in the Suame Municipality of the Ashanti Region, Ghana. Pregnant women who attended antenatal care during the study period and had complete information on hemoglobin concentration and relevant predictor variables were included. Women with missing hemoglobin measurements at registration or delivery were excluded. Logistic regression analysis was performed to identify independent predictors of anemia. Additional analyses examined the effects of age and weight, as well as the relationship between sickle cell status and blood group. Results: Logistic regression identified diastolic blood pressure, height, hemoglobin concentration at registration, maternal weight and gestational age as significant predictors of anemia during pregnancy (p < 0.05). Although employment status was statistically significant in the model, its direct association with anemia was relatively weak. Maternal age was not significantly associated with anemia. Pregnant women with sickle cell disease had a significantly higher likelihood of anemia. Blood group did not demonstrate a significant relationship with anemia. Effect sizes and confidence intervals were not available in the dataset. Conclusion: Diastolic blood pressure, height, hemoglobin concentration at registration, maternal weight, gestational age, sickle cell status and employment status were identified as important predictors of anemia during pregnancy. These findings highlight the importance of incorporating both clinical and sociodemographic characteristics into antenatal risk assessment and screening programs. Further prospective studies with larger sample sizes and more comprehensive clinical measurements are recommended to validate these findings and strengthen predictive models for anemia during pregnancy.
- Automated Detection of Motor Speech Disorders and Subtype Classification
Motor speech disorders (MSDs) are early markers of neurological disease, but expert perceptual analysis is rarely available outside specialized centers. Automated speech analysis offers a scalable alternative, yet prior studies have not systematically compared modeling approaches or assessed clinically relevant metrics in independent datasets. This study compared static acoustic features, articulatory informed Phonet features, and self-supervised pretrained models for binary and multi label MSD classification. We trained and evaluated models on 583 speech samples using speaker level splits. Baseline models included logistic regression and Gated Recurrent Units (GRUs) trained on eGeMAPS and MFCCs. We extracted three types of Phonet derived features and evaluated pretrained HuBERT and SSAST models in frozen, partially fine-tuned, and fully fine-tuned configurations. Binary classification distinguished MSDs from controls, while multi label classification identified six MSD subtypes. Models were assessed using validation AUC, and cut points were tested on two independent datasets. Pretrained and Phonet based models substantially outperformed static acoustic features. In binary classification, HuBERT achieved the highest AUC (0.95), while compact Phonet derived GRUs achieved comparable performance (up to 0.94). These models generalized well to independent datasets, maintaining high sensitivity (0.94) and specificity (0.97). In multi label classification, Phonet models achieved the highest macro average AUC (0.86), but threshold-based subtype performance declined on unseen data. Automated MSD detection is feasible and clinically promising. Binary classification generalized well, whereas multi label classification showed limited threshold stability across datasets.
- Prediction of acetaminophen-induced hepatotoxicity in acetylcysteine-treated patients using routine admission biomarkers
Study objective. Some acetaminophen-overdose patients develop hepatotoxicity despite acetylcysteine treatment. Established tools struggle to prospectively identify this cohort. We developed a model using only routine admission biomarkers to identify acetylcysteine-treated patients at highest risk, and compared it with the current benchmark, the alanine aminotransferase x acetaminophen product (ALTxAPAP). Methods. Retrospective cohort of all acetaminophen overdose admissions (ICD-10 T39.1) to three UK hospitals (2008-2024) with alanine aminotransferase (ALT) >1000U/L at admission. We fitted elastic-net logistic models stratified by presentation ALT. The outcome was peak ALT >1,000U/L. Performance was assessed on a 25% held-out test set and benchmarked against ALTxAPAP. Results. Of 4,705 admissions, 119 (2.5%) developed hepatotoxicity. The model used seven routine blood tests, four per stratum: acetaminophen, sodium, potassium and lymphocyte count where presentation ALT was <50U/L; ALT, bilirubin, alkaline phosphatase and lymphocyte count where it was 51-1,000U/L. In the test set (n=1,175) it achieved an area under the curve of 0.93 (95% CI 0.89-0.97) versus 0.82 (0.72-0.91) for ALTxAPAP (paired difference 0.11; 95% CI 0.01-0.22; p=0.03), with higher specificity and a higher positive likelihood ratio at every matched sensitivity. Matched to current ALTxAPAP >1,500 practice (sensitivity 89.7%), specificity was 82.5% versus 62.6% and the positive likelihood ratio 5.1 versus 2.4, more than halving false-positive escalations (171 versus 365 per 1,000 patients). Conclusion. A stratified model using only routine admission biomarkers identifies acetylcysteine-treated patients at highest residual hepatotoxicity risk, outperforming the ALTxAPAP rule across decision thresholds, supporting selection for intensified therapy.
- Identification of Persistent Radiomics Feature Co-occurrence Across Diverse Tissue Types and Individuals: A Network-Based Analysis of the RADAPT CT Atlas
Objectives. Radiomics pipelines extract hundreds of quantitative features that are widely known to be redundant, but the structure of this redundancy is usually treated as a per-dataset nuisance to be pruned away. We tested the alternative hypothesis that a substantial number of feature-feature correlations are universal: they persist across patients and across anatomically distinct structures because they reflect shared mathematical and image-statistical properties of how the image is summarised, rather than properties of the tissue being imaged. Materials and Methods. We re-analysed the publicly available Radiomics Atlas Dataset of normal Abdominal and Pelvic CT (RADAPT), restricting the analysis to the 526 non-contrast-enhanced examinations of the 531-subject atlas and to the 107 original (non-filtered) PyRadiomics features. The 53 segmented structures were grouped into four broad anatomical categories -- bones, muscles, vessels, and parenchymal organs. RADAPT is distributed as one Excel file per structure, with patients as rows and features as columns. Within each structure file we z-score-normalised every feature across patients, computed the absolute Spearman correlation matrix, and retained edges with |{rho}| [≥] {tau} for {tau} in {0.70, 0.80, 0.90}. We then intersected the edge sets across all structure files to obtain a "universal" correlation graph, in which an edge survives only if it exceeds the threshold in every structure (each estimated across the full patient sample). Stable feature communities were defined as the maximal cliques of this graph. Robustness to patient sampling was tested by repeating the entire pipeline on five independent random splits of each file into two patient halves (10 sub-cohorts per threshold), and the implementation was independently reproduced in R. Results. Despite the strictness of the global-intersection criterion, 34, 24, and 14 stable feature communities survived at {tau} = 0.70, 0.80, and 0.90 respectively, with the largest cliques containing six members at {tau} = 0.70 and {tau} = 0.80 and five members at {tau} = 0.90. The community structure was clearly interpretable: separate cliques captured (i) variance-like intensity dispersion, (ii) long-run / low-frequency (coarse) texture, (iii) high gray-level texture, (iv) low gray-level texture, (v) volume and surface shape, and (vi) local-homogeneity and energy/entropy duals. On random-half resampling the exact-match recovery rate of these communities was 81.5 %, 86.7 %, and 80.7 % across the three thresholds; departures from exact recovery were almost always a single boundary feature added or dropped, consistent with finite-sample fluctuation of near-threshold edges rather than structural instability. The R re-implementation reproduced the Python results exactly. Conclusion. A substantial portion of radiomics feature collinearity is universal across patients and tissues. We distinguish two layers within it: trivial near-algebraic duals that are universal by construction, and non-trivial cross-matrix-family communities that are the genuine empirical finding. Together they provide an interpretable, definition-grounded basis for aggressive dimensionality reduction, for retrospectively reconciling apparently different feature selections in the literature, and for moving radiomics pipelines toward organ-agnostic, more reproducible models. Clinical relevance statement. Selecting a single representative feature from each universal community shrinks the original-feature space by roughly an order of magnitude without sacrificing biologically distinct information. For example, the five variance-family members (first-order Variance, GLCM SumSquares, GLCM ClusterTendency, GLDM and GLRLM GrayLevelVariance) can be replaced by a single representative, removing redundant degrees of freedom that would otherwise inflate model variance; and labelling each retained feature by its community lets two studies that selected different variance-family names be recognised as having found the same signal, simplifying model development and improving cross-cohort generalisability in clinical CT workflows.
- Privacy-Preserving Matching for Federated Causal Inference in Multicentre Patient Cohorts
Causal effect estimates can often be biased in clinical and epidemiological studies as patient cohorts frequently exhibit substantial covariate imbalances between treated and control groups, often amplified in multicentre studies due to heterogeneous recruitment, clinical practice, and case mix. Covariate balancing methods are therefore essential for valid causal inference. However, their application becomes challenging when data are distributed across cohorts and cannot be pooled because of privacy, legal, or institutional constraints, leaving a gap in practical methods for causal effect estimation in federated and imbalanced clinical data settings. We develop a privacy-preserving framework for covariate balancing and causal effect estimation across distributed data providers, combining federated aggregation with differential privacy to enable propensity score subclassification and matching without sharing individual-level records. Matching relies on non-disclosive quantities and differentially private distance evaluation, and the resulting matched subsets remain local to each server. Balance can be assessed through federated diagnostics and privacy-preserving visualisations, and we provide secure estimators for average treatment effects with associated uncertainty quantification. We implement this framework in the DataSHIELD federated analysis platform via 2 R packages. In simulations, we demonstrate agreement between federated and centralised analyses in the absence of privacy noise and quantify the bias--variance trade-offs induced by differential privacy. We illustrate applicability in two multinational settings-a Long COVID cohort and very preterm birth cohorts-showing that the approach enables practical causal analyses under real-world data protection constraints. The DataSHIELD packages are available on Github. Additional methodological details are provided in the Supplementary Material.
- Gaps Between Willingness and Uptake of Influenza and COVID-19 Vaccines During the 2025-26 Respiratory Virus Season in a U.S. Adult Cohort
During the 2025-26 respiratory virus season, changes to COVID-19 vaccine eligibility, recommendations, and communication may have made vaccination follow-through especially challenging. Under-vaccination may reflect not only lack of willingness, but also breakdowns between willingness and uptake. We analyzed data from 3,390 adults in the CHASING COVID Cohort who completed assessments in August 2025 and March 2026 to examine gaps between vaccine willingness and subsequent influenza and COVID-19 vaccination. Vaccine willingness was defined using prior-season vaccination and stated intention to vaccinate during the 2025-26 respiratory virus season. In August 2025, 73% of participants were influenza vaccine willing and 68% were COVID-19 vaccine willing. Among vaccine-willing participants, 17% and 39% were unvaccinated for influenza and COVID-19, respectively, by March 2026. Absence of prior-season vaccination was the strongest predictor of not vaccinating for influenza and COVID-19, respectively (aRR [95% CI]: 4.04 [3.44-4.74]; 3.01 [2.72-3.34]). Non-vaccination was also associated with food insecurity (1.99 [1.66-2.37]; 1.47 [1.33-1.63]), any healthcare barrier (1.89 [1.57-2.28]; 1.51 [1.36-1.67]), and being not at all confident in vaccine safety (2.95 [2.15-4.05]; 2.21 [1.88-2.59]). Trajectory analyses suggested willingness-uptake gaps reflected incomplete follow-through on intentions and discontinuation among some prior vaccinators. Commonly reported reasons among vaccine-willing non-vaccinators included difficulty finding a convenient time, place, or appointment and, for COVID-19, lack of healthcare provider recommendation. Findings among non-vaccinated adults with prior or stated openness to vaccinate highlight missed opportunities and suggest avenues to improve coverage through strategies that reinforce vaccine confidence, reduce access and logistical barriers, and make vaccination easier to complete.
- Transmission dynamics of Nipah virus in Bangladesh and India, 2001-2026: systematic review and inference on reproduction number, offspring dispersion, and serial interval
Background Nipah virus (NiV) is a priority zoonotic pathogen causing high-fatality outbreaks. Early NiV outbreaks in Malaysia and Singapore had limited transmission beyond spillover events. However, since 2001, NiV outbreaks with person-to-person transmission have occurred in Bangladesh and India, driven by the NiV-Bangladesh genotype and NiV-India genotype. Our study aims to estimate the reproduction number, offspring dispersion, and serial interval governing NiV transmission in Bangladesh and India during 2001-2026. Methods We conducted a systematic review of NiV outbreak investigations in Bangladesh and India, searching PubMed, Embase, Web of Science, and grey literature through 28 February 2026. Case-level offspring counts from 27 eligible sources (323 cases across 67 outbreaks) were used as input to a hierarchical Bayesian negative binomial offspring distribution model. The serial interval was estimated by parametric distribution fitting to 137 transmission pairs. Country-stratified and sensitivity analyses were performed to evaluate the robustness of estimates. Results Pooling across 67 outbreaks, we estimated a median reproduction number of 0.46 (95% CrI: 0.28-0.73), an offspring dispersion parameter of 0.07 (0.05-0.10), and a serial interval of 13.3 days (95% CI: 12.8-13.8). Country-stratified median reproduction numbers were 0.48 (0.23-0.97) for India and 0.35 (0.19-0.59) for Bangladesh, and dispersion parameters were 0.04 (0.02-0.07) and 0.11 (0.06-0.18), respectively, indicating marked overdispersion in both settings. Conclusion NiV transmission is self-limiting on average and highly overdispersed, suggesting that a disproportionate share of onward transmission arises from a small number of cases. This epidemiological profile supports targeted containment measures, including contact tracing and quarantine, for effective NiV outbreak control.
- Hypertension Phenotypes in a National Database: A Three-Axis State Model Integrating Diagnosis, Treatment Intensity, and Blood Pressure Control (The NDB-K7Ps-Study-8)
Hypertension is commonly defined as a binary condition despite substantial heterogeneity in diagnosis, treatment, and blood pressure (BP) control. We propose a three-axis state model integrating diagnosis status, treatment intensity, and BP control to better characterize hypertension phenotypes. The framework generates 27 possible states that can be condensed into seven clinically meaningful groups. We applied the model to 5,129,584 Japanese adults using the National Database of Health Insurance Claims and Specific Health Checkups. Hierarchical cluster analysis, sensitivity analysis excluding patients with cardiovascular diseases other than hypertension, and validation against antihypertensive medication use were performed. Overall, 64% of participants were classified as normotensive, whereas 36% belonged to hypertension-related groups, including 11% with unrecognized hypertension and 7% with diagnosed but untreated hypertension. Agreement with data-driven hierarchical cluster analysis was substantial (weighted {kappa}=0.87). The group distribution remained largely unchanged in the sensitivity analysis, supporting the robustness of the proposed classification. Hypertension diagnosis also showed high validity, with a sensitivity of 96.5%, specificity of 91.8%, and substantial agreement with antihypertensive medication use ({kappa}=0.78). This three-axis framework provides a robust and clinically interpretable approach for characterizing hypertension phenotypes, enabling systematic identification of care gaps and supporting research, clinical decision-making, and population health management.
- Development and external validation of a multivariable regression model for bacteraemia in adults presenting to emergency departments
Background: Bacteraemia is associated with poor outcomes but the diagnostic gold standard, peripheral blood culture, takes up to 24 hours to become clinically actionable, hampering early management decisions in suspected infection. Single predictors and existing sepsis risk scores discriminate poorly, and few multivariable bacteraemia models have been adequately validated in UK populations. Methods: We developed a logistic regression model, using backwards AIC based selection of predefined candidate predictors routinely available within hours of hospital attendance, in a retrospective cohort of 33,874 hospital encounters at University College London Hospitals (UCLH) between 2019 and 2024. Continuous predictors were modelled using restricted cubic splines and missing data handled using multiple imputation. Model performance was assessed via internal external cross validation and prediction instability analysis, before temporal validation in held-out 2024 UCLH data and external validation in 53,669 hospital encounters from the Infections in Oxfordshire Research Database (IORD). Results: Bacteraemia occurred in 5.2% of UCLH and 8.9% of IORD encounters, respectively. Twenty predictors were retained, spanning demographics, comorbidities, vital signs and blood tests. Discrimination was stable across development time periods (pooled c-statistic 0.82, 95%CI 0.81 to 0.84) and was maintained in temporal (0.83, 0.79 to 0.87) and external validation (0.83, 0.82 to 0.83), with excellent calibration in external validation (calibration slope 1.08 (1.05 to 1.11); calibration-in-the-large 0.01 (-0.02 to 0.04)). The model outperformed single predictors, established risk scores, and a reconstructed comparator model, and showed superior net benefit in decision curve analysis. Performance was consistent across age, sex, ethnicity and socioeconomic subgroups but degraded when blood cultures were sampled more than six hours after attendance and varied by likely infection site. Conclusions: This model accurately predicts bacteraemia using routinely collected data available within hours of hospital attendance, with performance maintained in a large, independent external validation cohort. It offers a generalisable, clinically interpretable tool to support early decision-making in suspected infection, pending further work to establish optimal implementation thresholds.
- Healthcare Worker Preparedness for Snakebite Management in Selected Zambian Hospitals: An Exploratory Study
Snakebite envenoming remains a neglected tropical disease and an important public health challenge in Zambia. Effective management of snakebite patients requires healthcare workers who are adequately trained, familiar with treatment protocols, and aware of national management guidelines. However, evidence regarding healthcare worker preparedness for snakebite management in Zambia remains limited. This study explored healthcare worker preparedness for snakebite management in selected hospitals in Zambia. An exploratory cross-sectional study was conducted in seven hospitals in Zambia between collected between May and July 2025. Twenty-one healthcare workers, comprising senior clinicians, junior clinicians, and nurses, were purposively selected to participate. Data were collected using a structured questionnaire assessing training in snakebite management, clinical exposure to snakebite cases, confidence in management, use of local treatment protocols, and awareness of national snakebite management guidelines. Data were analysed using descriptive statistics and presented as frequencies and percentages. Twenty-one healthcare workers participated in the study. Eight participants (38.1%) reported having received no training in snakebite management, while six (28.6%) reported receiving bedside training. Most participants had recent experience managing snakebite patients, with 66.7% reporting management of at least one snakebite case within the preceding year. Fifteen participants (71.4%) reported being very or exceptionally confident in managing snakebite patients. However, only six participants (28.6%) reported using local snakebite treatment protocols, while eight (38.1%) had seen the latest national snakebite management guidelines. Nearly half of participants reported not using local protocols and had never seen national guidelines. The study identified important gaps in healthcare worker preparedness for snakebite management despite high levels of self-reported confidence. Limited formal training, poor guideline awareness, and low utilization of treatment protocols may affect the quality of snakebite care. Strengthening healthcare worker training and improving dissemination of national management guidelines should be prioritized as part of Zambia's snakebite control efforts.
- Assessing electronic health record potential for adaptive learning in multimorbidity care in Sub-Saharan Africa: a mixed-methods study of Zimbabwe's Impilo system
Electronic health records (EHR) are increasingly recognised as critical digital infrastructure for integrated, patient-centred care in the context of rising multimorbidity. In low-resource settings, national EHRs may also support locally driven learning to improve adaptive care across chronic conditions. However, there is limited empirical evidence on whether and how these systems enable learning within routine care in ways that inform broader system adaptation. We conducted a qualitative multi-method assessment of Impilo, Zimbabwe's national EHR, to examine its capacity to support learning for integrated multimorbidity care at primary care level, using HIV-hypertension as a tracer condition pair. Guided by Friedman's socio-technical infrastructure model as the analytical framework and Learning Health Systems (LHS) theory as the interpretive framework, data were drawn from documentary review, ethnographic observation, patient journey mapping, and interviews with frontline health workers and key stakeholders. Frontline learning for person-centred multimorbidity care was actively generated through interpretation of patient trajectories, experiential adjustment, and coordination across HIV and hypertension services using both the EHR and paper-based artefacts such as registers and patient booklets. However, this learning remained largely encounter-bound and weakly stabilised. Impilo did not routinely provide usable longitudinal patient views, practice-facing analytic tools, or institutionalised mechanisms for collective reflection required to support integrated multimorbidity care. Consequently, learning was largely confined to incremental adjustment within existing workflows, with limited capacity to inform broader changes to care pathways, routines, or system design. These findings suggest that the principal barrier to developing LHS is not the absence of data or frontline learning capacity, but the lack of socio-technical arrangements that enable learning to stabilise and inform system adaptation. Digitalisation alone is insufficient to support adaptive multimorbidity care. Co-production with frontline health workers may provide a pathway for aligning digital system design with routine care realities.
- NFIX missense variants that disrupt the β-hairpin loop result in a severe form of Malan syndrome in adolescence with rapidly evolving scoliosis and muscle wasting
Purpose: Pathogenic variants in NFIX cause Marshall-Smith syndrome and Malan syndrome (MALNS). We identified a severe subtype of MALNS characterized by adolescent-onset musculoskeletal deterioration and investigated functional consequences of underlying variants. Methods: Clinical data were collected from seven individuals with pathogenic NFIX variants. Wild-type and mutated recombinant NFIX DNA-binding domains (DBDs) were evaluated using biochemical, structural, and DNA-binding assays. Results: Six individuals carrying R116W, R116P, K125E, or G147E NFIX substitutions developed progressive muscle wasting, markedly reduced body mass index, and rapidly progressive scoliosis after the typical childhood features of MALNS; two died from disease-related complications. A seventh individual with R116G did not develop this severe phenotype. Functional studies on recombinant NFIX DBDs showed complete or near-complete loss of DNA-binding activity for R116W, R116P, K125E, and G147E despite preserved protein folding, consistent with disrupted DNA recognition and a potential dominant-negative mechanism. In contrast, R116G exhibited a 7.7{degrees}C decrease in thermal stability, which may support haploinsufficiency mediated by protein degradation. Conclusion: Specific NFIX missense variants define a severe subtype of MALNS associated with progressive musculoskeletal deterioration. In vitro functional studies support variant-specific disruption of DNA binding, providing a mechanistic basis of genotype-phenotype correlations and informing prognosis, clinical surveillance, and therapy development.
- Identifying and Characterising Common Genetic Differences in Schizophrenia and Bipolar Disorder
Schizophrenia and bipolar disorder are diagnostically distinct categories that overlap substantially in clinical features and genetic aetiology. Understanding genetic variants that contribute liability specifically to each disorder can offer insights into biological processes that differentiate them. Here we used Case-Case GWAS (CC-GWAS) to identify common genetic variants differentially associated with schizophrenia and bipolar disorder, analysing 67,390 schizophrenia cases and 41,917 bipolar disorder cases. We identified 19 genome-wide significant loci, of which 16 (84%) demonstrated divergent genetic effects with risk alleles showing opposite directions of association between disorders. The CC-GWAS summary statistics had detectable disorder-differentiating heritability (10.27%, SE=0.01) and showed genetic correlations indicating that SCZ-differentiating alleles were associated with lower educational attainment, lower cognitive performance, and increased risk of ADHD, anorexia, autism, BD1 (though not BD2), cannabis use disorder, and OCD. Four loci showed divergent effects despite not reaching genome-wide significance in either individual disorder GWAS, demonstrating enhanced power to detect opposite-direction effects. Functional annotation identified 102 mapped genes significantly enriched for expression across all 13 tested brain regions, with no significant enrichment in peripheral tissues, and gene set enrichment analysis implicated neuronal projection and synaptic compartments as the strongest biological themes differentiating the two disorders. Polygenic risk scores derived from these disorder-differentiating variants were associated with earlier age at onset and more severe negative symptoms in schizophrenia, consistent with these variants marking neurodevelopmental dimensions of illness. Our findings provide targets for understanding pathogenic differences between schizophrenia and bipolar disorder and demonstrate that genuine divergent genetic effects exist beyond the substantial shared liability.
- Knowledge and misconceptions of the French population regarding medical genetics: a survey of 3,000 respondents
Advances in high-throughput sequencing and genetic research have expanded the role of genetics in medicine and society. Population-based screening programs, including neonatal and preconception testing, are increasingly implemented globally, alongside the rise of direct-to-consumer (DTC) genetic testing. The "Genetics and the General Public" Ethics Working Group of the French Federation of Human Genetics (FFGH) assessed knowledge and awareness of genetics within the French population through a nationally representative survey (n=3,013) conducted by the polling firm Ipsos bva. Results indicated that 69% of respondents report an interest in genetics, although their level of knowledge remains limited. Most respondents expressed positive attitudes toward genetics, perceiving it as a major source of hope in healthcare. While a majority indicated willingness to undergo genetic testing for medical purposes, they also reported legitimate concerns regarding the potential results. Despite legal restrictions, 12% reported having ordered a DTC genetic test (5% for genealogical; 5% for medical and 2% for both purposes), and 45% of non-users expressed strong interest in this type of test. Notably, there is a substantial lack of awareness regarding the limitations of these tests and the French legal framework governing their use. These findings highlight critical gaps in public knowledge, emphasizing the need for improved genetic education, including incorporating genetics into school curricula and launching targeted awareness campaigns. These initiatives should help clarify the distinctions between clinically validated genetic tests and DTC genetic testing services, addressing both their benefits and their ethical, legal, and scientific limitations, in order to promote informed decision-making.
- Pre-fracture Anemia Is Associated with Nonunion Following Tibia or Femur Fractures: A Retrospective Cohort Study
Fracture nonunion remains a major cause of morbidity, yet patient-specific factors associated with impaired healing remain incompletely characterized. Anemia has been associated with adverse orthopaedic outcomes, but its relationship with fracture nonunion is poorly understood. We examined whether pre-fracture anemia, anemia burden, and clinically relevant anemia subtypes were associated with nonunion following tibial or femoral fractures. Using commercial and Medicare fee-for-service claims from 2016 through 2023, we identified adults aged 19 years or older with a tibial or femoral fracture, continuous enrollment during the preceding year and for at least six months after fracture, and no baseline cancer. Pre-fracture anemia was evaluated as any anemia, the number of distinct anemia diagnoses, and nutritional, hemolytic, aplastic, and other anemia subgroups. Nonunion occurring six to eighteen months after fracture was assessed using incidence rates and multivariable-adjusted hazard models. Among 326,673 adults, 149,704 had pre-fracture anemia and 176,969 did not. The crude incidence of nonunion was 42% higher among individuals with anemia than among those without anemia (incidence rate ratio, 1.42; 95% confidence interval, 1.32 to 1.53) and increased with greater anemia burden. After adjustment for demographic and clinical characteristics, including prior fractures at other anatomical sites, pre-fracture anemia remained associated with nonunion following tibial and femoral fractures, with hazard ratios of 1.83 (95% confidence interval, 1.54 to 2.18) and 1.38 (95% confidence interval, 1.26 to 1.50), respectively. Associations were also observed for nutritional and other anemias, whereas estimates for hemolytic and aplastic anemias were limited by few nonunion events. Within the femur, the association was strongest for distal fractures. These findings demonstrate that pre-fracture anemia is independently associated with nonunion. The increase in risk with greater anemia burden and findings across evaluable subgroups suggest that pre-fracture anemia may help identify patients at increased risk of impaired fracture healing.
- Citrulline and Faecal Elastase 1 as a Combined Diagnostic Biomarker for Pancreatic Ductal Adenocarcinoma
Background: Early detection of pancreatic ductal adenocarcinoma (PDAC) is critical. While faecal elastase-1 (FE-1) is a standard clinical marker for pancreatic function, its diagnostic accuracy for malignancy is limited. We sought to identify plasma metabolites that enhance FE-1 performance in symptomatic "at-risk" patients. Methods: Using the DEPEND cohort (CRUK C45617/A29908), plasma metabolomics was performed on patients with resectable PDAC (n=23) and healthy volunteers (n=24). Predictive modelling included feature selection and cross-validation, with further validation in an independent external cohort. Results: Citrulline was identified as significantly depleted in PDAC patients across discovery and validation cohorts. In isolation, Citrulline achieved an AUC of 0.86 (internal) and 0.88 (external validation). Standalone FE-1 demonstrated an AUC of 0.67. However, combining Citrulline and FE-1 significantly improved diagnostic performance, achieving a combined AUC of 0.96. Stratification revealed distinct metabolomic signatures associated with poorly differentiated tumours, suggesting a link to histological grade. Conclusions: Integrating Citrulline with FE-1 testing substantially improves PDAC detection in symptomatic patients. This non-invasive panel offers high diagnostic potential, though prospective validation is required to establish clinical cut-offs for routine practice.
- Validity and Reliability of the Novel Indonesian Instrument for Aphasia Diagnosis (IDEA)
Aphasia diagnosis in Indonesia remains challenging due to limited culturally and linguistically appropriate instruments. Widely used tools such as the Boston Diagnostic Aphasia Examination (BDAE) and Western Aphasia Battery (WAB) are not adapted to the Indonesian context, while Tes Afasia untuk Diagnosis, Informasi, dan Rehabilitasi (TADIR) provides screening but lacks diagnostic accuracy. To address this gap, we developed the Instrumen Diagnosis dan Evaluasi Afasia (IDEA) for native Indonesian speakers and evaluated its validity, reliability, and normative cutoff values in cognitively healthy Indonesian adults. Eighty-three cognitively normal adults (screened using MoCA-Ina) with no history of neurological disease were assessed using IDEA, which evaluates six language domains. Items were adapted from existing tools and reviewed by experts. Content validity, internal consistency (Cronbachs alpha), and construct validity (Exploratory Factor Analysis) were analyzed using SPSS v25. A total of 83 participants were included (median age = 55.81 years, 54% secondary education). IDEA demonstrated good feasibility, with an average completion time of 45-60 minutes depending on participant engagement. Content validity was established by unanimous expert consensus. Construct validity showed meritorious sampling adequacy (KMO = .872) and significant sphericity (Bartletts test {chi}^2 (15) = 278.523, p<.001), supporting factor analysis. Internal consistency showed good reliability across six domains (Cronbachs = 0.896). IDEA is a valid and reliable tool for assessing aphasia in Indonesian natives. It is a culturally appropriate assessment tool which offers structured, domain-based evaluation and supports differential diagnosis of both classical and progressive aphasia syndromes. Keywords: Aphasia, Language Assessment, Indonesian, IDEA, Validity
- China’s Moonshot AI seeks Hong Kong IPO
Moonshot AI's Kimi K3 model features a 1 million-token context window and 3-trillion-scale open weights.
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- Apple Has Reportedly Started Recording and AI-Summarizing Conversations at the Genius Bar
It's limited in scope; it's opt-in; and managers don't get to see the summaries. Employees are still nervous.
- Apple testing ‘Live Notes’ AI system to record Genius Bar sessions: report
Apple is reportedly piloting a new “Live Notes” system at some of its retail locations. This system uses AI to record and transcribe Genius Bar sessions with customers, according to a new report.
- Korea’s AI chip market is now the opening bell for global stocks
Fund managers in London, New York, and Tokyo have added a new step to their morning routine: checking South Korean stocks. Korea’s $4 trillion equity market is now offering an early read on global AI risk appetite, as swings in SK Hynix and Samsung ripple through chip stocks worldwide. “We are all Korean investors now,” […] This story continues at The Next Web
- China MAZU AI Weather System to Reach 30 Countries: Fengyun Satellites, AI Models, and Radar Unite as Meteorological Infrastructure Goes Global
President Xi announces MAZU intelligent early warning system deployment to 30 countries at WAIC 2026 as China exports integrated meteorological infrastructure combining Fengyun satellites, AI, and radar.
- Spectacular feats of robots at WAIC 2026
Spectacular feats of robots at WAIC 2026
- Using AI makes people less likely to admit they don't know something
Researchers found confidence increased even as accuracy fell
- Googles AI Search is a minefield for schools
A recent report claiming Google AI-powered Search is unsafe for children has led advocates to question the EdTech giant's place in schools.
- Pocket-size AI: Powerful phones star at China show
Pocket-size AI: Powerful phones star at China show The Straits Times
- Pocket-size AI: Powerful phones star at China show
Tech firms are racing to roll out advanced smartphones that use artificial intelligence to do everything from ordering food to composing messages with a simple voice command.
- This AI dashboard gives you ChatGPT, Claude, Gemini, and more — for a flat $60
Pay $59.97 instead of $619 today only and save $559.03 on a lifetime ChatPlayground AI Unlimited subscription with access to 20+ AI models.
- A Robotic Teaching Assistant is Heading to a New York State School
A Robotic Teaching Assistant is Heading to a New York State School PCMag Australia
- A Robotic Teaching Assistant is Heading to a New York State School
A Robotic Teaching Assistant is Heading to a New York State School PCMag UK
- It’s Laughably Easy to Poison Open-Weight AI Models, Researcher Finds
"I did a proper backdoor." The post It’s Laughably Easy to Poison Open-Weight AI Models, Researcher Finds appeared first on Futurism .
- Hidden prompts can plant false memories in AI agents, researchers warn
Large language models (LLMs), the computational algorithms underpinning ChatGPT, Gemini and other artificial intelligence (AI)-powered conversational platforms, are now widely used worldwide. These models can rapidly answer questions, source information online, assist users with specific tasks and produce text tailored for specific purposes.
- Netflix Reveals the Staggering Price of Its AI Deal With Ben Affleck
The streaming service paid $587 million to acquire InterPositive, the AI platform developed by the actor to help filmmakers solve postproduction challenges at a fraction of the cost of reshoots.
- Alibaba’s Qwen Unveils Preview of Flagship AI Model
Alibaba Group Holding Ltd. launched a preview version of its flagship Qwen3.8 Max model, which it described as comparable to leading frontier AI models and second only to Anthropic PBC’s Fable 5.
- Alibaba Previews Qwen3.8-Max, a 2.4 Trillion-Parameter Multimodal Model, Days After Moonshot's Kimi K3 Open-Weight Launch
Alibaba Previews Qwen3.8-Max, a 2.4 Trillion-Parameter Multimodal Model, Days After Moonshot's Kimi K3 Open-Weight Launch MarkTechPost
- Alibaba's Qwen takes on Kimi K3 with open-weight Qwen 3.8, says model is "second only to Fable 5"
Alibaba has unveiled Qwen 3.8, a multimodal AI model with 2.4 trillion parameters that the Qwen team says rivals leading models and trails only Fable 5. A preview is available now. The article Alibaba's Qwen takes on Kimi K3 with open-weight Qwen 3.8, says model is "second only to Fable 5" appeared first on The Decoder .
- Alibaba previews Qwen3.8, claims it’s second only to Claude Fable 5
Alibaba Group Holding Ltd. today previewed the most powerful artificial intelligence model in its Qwen family, Qwen3.8, and claimed the system trails only Anthropic PBC’s Claude Fable 5 among the world’s frontier models. The company revealed the model, offered as a preview build called Qwen3.8-Max-Preview, today at the World Artificial Intelligence Conference in Shanghai. It […] The post Alibaba previews Qwen3.8, claims it’s second only to Claude Fable 5 appeared first on SiliconANGLE .
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