AI News Archive: August 6, 2026 — Part 20
Sourced from 500+ daily AI sources, scored by relevance.
- IGF2BP3 amplifies antiviral innate immunity with implications for autoimmune diseases
The insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3) is a known N6-methyladenosine (m6A) reader, but its role in antiviral innate immunity is unknown. Here, we identify IGF2BP3 as a critical positive regulator of antiviral responses. Viral infection and interferon (IFN) stimulation upregulate IGF2BP3, establishing a feedforward loop that potentiates virus-induced activation of the TBK1-IRF3 and NF-{kappa}B pathways, thereby amplifying type I interferon (IFN-I) production, and restricting viral replication in human and murine cells and in vivo. Mechanistically, IGF2BP3 directly binds and stabilizes MAVS and TBK1 mRNAs and promotes their translation by facilitating recruitment to the eIF4F/PABP-associated initiation complex. Upon infection, IGF2BP3 relocalizes to antiviral stress granules (avSGs), where it scaffolds the RIG-I-G3BP1 complex to enhance viral RNA sensing. Notably, IGF2BP3 is aberrantly upregulated in patients with systemic lupus erythematosus (SLE) and in Trex1 knockout (KO) mice, and pharmacological inhibition by curcumol suppresses IFN-I-driven pathology and improves survival. Collectively, our findings establish IGF2BP3 as a central feedforward circuit that couples viral RNA sensing to the control of RNA stability and translation of key signaling molecules, and reveals its potential as a therapeutic target in interferon-associated autoimmune diseases.
- Quantifying the Quality of Corrective Actions in Medical Safety Incident Reports Using a Hybrid Rule-Based and Large-Language-Model Classification System: A Cross-Sectional Pilot Feasibility Analysis of 11,507 Japanese National Reports (2010, Interim)
Background: Whether corrective actions documented in medical safety incident reports rely on individual vigilance ("Safety-I") or on structural, system-level intervention ("Safety-II") has not been quantitatively evaluated on a national scale in Japan. We developed an automated classification pipeline to assign corrective-action free-text to a 7-level maturity scale (L0-L6) and computed two summary indices: the Safety Measure Quality Profile (SMQP), the full L0-L6 distribution, and the System-based Safety Measure Rate (SSMR), the proportion of non-L0 records classified L3-L6. Methods: We analyzed all 11,507 corrective-action free-text entries from the 2010 release of Japan's national medical accident and near-miss reporting database (Japan Council for Quality Health Care, JCQHC), comprising 8,804 near-miss (Hiyari-Hatto) and 2,703 accident (Jiko) reports. Records were classified using a five-stage hybrid pipeline: an expert-developed rule dictionary, TF-IDF + k-nearest-neighbor matching, cosine-similarity matching, a two-tier large-language-model (LLM) classifier, and a conservative priority-cascade fallback. SSMR was compared between near-miss and accident reports using a chi-square test, Wilson 95% confidence intervals, Cramer's V, and the risk difference (RD), against pre-specified minimal clinically important difference (MCID) criteria of RD >= 2 percentage points and Cramer's V >= 0.10. Results: Every record received a definitive L0-L6 label (0% unresolved). Overall, 16.6% of records were unclassifiable (L0); among the 9,599 classifiable (non-L0) records, individual-vigilance actions (L1) predominated (54.6% of all records), and only 11.82% (95% CI, 11.19-12.49%) met the SSMR criterion (L3-L6). SSMR was higher for accident reports than for near-miss reports (18.12% [95% CI, 16.69-19.64%] vs. 9.[95% CI,46% [95% CI, 8.80-10.17%]; RD = 8.66 percentage points; Cramer's V = 0.120; chi-square(1) = 136.97001), exceeding both pre-specified MCID thresholds. Conclusions: In this interim single-year analysis, the large majority of documented corrective actions in Japanese medical safety reports remained individual-vigilance-based rather than system-based, with accident reports showing a substantively, rather than merely statistically, higher proportion of system-based actions than near-miss reports. These findings support the feasibility of large-scale automated assessment of corrective-action quality and provide the rationale for the planned 16-year longitudinal analysis.
- Multimodal Large Language Models vs. Medical Doctors in Degenerative Lumbar Spine Surgery: A Retrospective Decision Concordance Study of 147 Patients
Objective: To evaluate decision concordance between commercially available multimodal large language models (LLMs), resident doctors, and senior-surgeon ground truth for surgical indication and spinal level in degenerative lumbar spine disease. Methods: We retrospectively analyzed 147 consecutive patients. Each case included clinical documentation and MRI presented as two composite PNG images. Two resident doctors and three multimodal LLMs (GPT 5.5, Claude Sonnet 4.6, Gemini 3.1 Pro) independently assessed operative versus conservative management and, if operative, the surgical level. Analyses used Cochran's Q, McNemar tests with Holm correction, and Bayesian methods. Results: LLMs achieved higher therapy-decision accuracy (66.0%-68.0%; 97-100/147) than residents (54.4%; 80/147) but over-recommended surgery. Conditional level accuracy when surgery was correctly indicated was 71.4% (20/28) for residents versus 33.3%-41.1% for LLMs. Conclusion: Off-the-shelf multimodal LLMs approximate human performance for binary surgical indication but remain inferior for precise level localization. These results establish a practice-relevant baseline of spatial reasoning limitations for tools already used by patients and junior doctors.
- Multimodal artificial intelligence using entire electronic health record and complete pathogen genome data for patient outcome prediction from life-threatening infection: the SuperbugAI Platform
Artificial intelligence (AI) has the potential to transform healthcare, with advanced multimodal approaches showing great promise in leveraging diverse health-related data. Here, we applied multimodal AI to entire electronic health record (EHR) and complete pathogen genome data to predict patient outcomes from life-threatening infection. An automated, scalable pipeline was developed for EHR data preprocessing, quality control, and standardisation. A deep learning fusion model was trained to predict in-hospital mortality, need for ICU admission, prolonged length of stay and 30-day unplanned readmission. We then developed a novel genomic large language model (gLLM) architecture to incorporate bacterial genomic features into the multimodal fusion model. The cohort comprised 2,656 bloodstream infection hospitalisations involving 2,535 patients. Deep learning fusion models using entire structured and unstructured EHR data outperformed traditional APACHE II score mortality prediction (AUROC [95% confidence intervals] 0.93 [0.92-0.94] versus 0.77 [0.77-0.78]). The model also showed strong performance for predicting the need for ICU admission (AUROC 0.978 [0.966 - 0.986]), prolonged hospital length of stay (AUROC 0.803 [0.790 - 0.812]) and unplanned readmission (AUROC 0.696 [0.690 - 0.701]). As proof of principle, incorporating entire microbial genomic features from the causative pathogen further enhanced prediction and enabled identification of key bacterial virulence pathways relevant for human disease. Multimodal AI integrating harmonised EHR and genomic data can accurately identify hospitalised patients at risk of poor outcomes. These approaches are scalable to other subspecialities of medicine.
- Age at Onset and Liability to Disorder: Estimating Covariances in Censored Populations
Studies of resemblance for disorders and other traits measured at the binary (yes/no) level between relatives frequently contain individuals who are currently in the negative category but who will become positive in future. For example, a 10-year-old may develop depression in the future, but is as yet unaffected. Such censoring can substantially bias estimates of correlation between relatives. To overcome this problem we develop a model for the association between liability to a disorder, and its age at onset. The model is designed for data from pairs of relatives to enable estimation of the correlation between an individuals' liability to disorder and their age at onset. Usually, such information is not available at the individual level, because age at onset is uniquely available when onset has occurred. Lacking variation in disorder status, data from non-related persons cannot estimate the covariance between liability and age at onset. Data from relatives can resolve this issue when there is a correlation in liability between the relatives, because different age at onset distributions would be expected in concordant vs. discordant pairs of relatives. Greater severity and worse outcomes are often observed among those with earlier onset, so a correlation between disorder liability and age at onset seems likely in many cases. In this article we present the basic theory of the model, implemented as a mixture distribution, and an application to cannabis use in a Virginia Twin Study of Adolescent Behavioral Development. A negative association of (-.212) between age at onset an liability was found, with confidence intervals of -.263 to -.152, which do not cross zero. The method contrasts with Cox Proportional Hazards, in which disorder liability and onset timing are treated as a single dimension.
- Electrophysiological Markers of Within-Network Connectivity in Major Depression
Depression and treatment-resistant depression (TRD) are significant public health issues, but the associated network-level neurobiological mechanisms remain poorly understood. This study used magnetoencephalography (MEG) to identify altered resting-state connectivity within the default mode (DMN), executive control (ECN), salience (SN), dorsal attention (DAN), motor (MN), and visual (VN) networks as potential biomarkers of depression and treatment resistance. The study recruited 168 participants (80 healthy volunteers (HVs) and 88 currently experiencing a major depressive episode (74 with TRD and 14 without TRD (noTRD))). Data Integration Analysis for Biomarker Discovery using Latent Variable Approaches for Omics Studies (DIABLO) was used to differentiate the depression, TRD, and HV subgroups and identify neural markers of depression and treatment resistance. For differentiating the depression and HV groups, the triple network model (area under the receiver operating curve (AUROC): 0.759-0.787) - which includes the DMN, ECN, and SN - outperformed the six-network model (AUROC: 0.747-0.762) across different bandwidths. For differentiating the TRD and HV groups, the triple network model demonstrated reasonable prediction across different bandwidths (AUROC: 0.737-0.807); potential within-network connectivity differences distinguished those with TRD from HVs, especially DMN within-network connectivity between the inferior parietal lobule and precuneus in the beta band (FDR-corrected p<.05). Hyperconnectivity within the SN (superior parietal lobule and frontal operculum in the alpha band) and DMN (inferior parietal lobule and lateral prefrontal cortex in the beta band) was associated with number of treatment failures (ps<.05). These findings highlight key brain regions and connectivity patterns, advancing our understanding of neural mechanisms underlying depression and treatment resistance.
- Brain volumes and their relationship with cerebral microbleeds and cognition in middle-aged adults with type 1 diabetes
Objective: Type 1 diabetes is related to an increased risk of structural brain alterations, cerebral microbleeds (CMBs), and cognitive deficits. We explored brain volumes and their direct and combined associations with CMBs on cognitive performance in middle-aged individuals with type 1 diabetes. Research Design and Methods: Adults with type 1 diabetes (n=163; mean age 46+/-8 years; diabetes duration 31+/-10 years; 53% women) and 48 matched controls underwent brain MRI and clinical and neuropsychological evaluations. Volumetric MRI measures adjusted to intracranial volume included total brain volume (TBV), white matter volume (WMV), and total volumes of cortex, thalamus, hippocampus, nucleus accumbens, and choroid plexus. Results: Individuals with type 1 diabetes had smaller TBV, WMV, and volumes of cortex, thalamus, and nucleus accumbens, and larger choroid plexus compared to controls (Cohen d=0.39-0.54). Those with type 1 diabetes and 3 or more CMBs had smaller TBV, WMV, and volumes of cortex, thalamus, and nucleus accumbens, compared to those with 0-2 CMBs (Cohen d=0.54-0.92). We found no direct associations between brain volumes and processing speed or executive functions. However, TBV, WMV, nucleus accumbens, and choroid plexus volumes had significant negative synergistic interactions with CMBs on processing speed and executive functions (standardized betas: -0.61 to -0.51 and 0.54 to 0.75, FDR-corrected p=0.006-0.048). Conclusions: Smaller global and regional brain volumes and larger choroid plexus volumes were found in middle-aged individuals with type 1 diabetes compared to healthy controls. Together with CMB burden, structural brain volumetric alterations were associated with accelerated cognitive deficits.
- Emergency-granulopoiesis trajectory, but not the CD4/NK lymphocyte trajectory, is directionally reproduced as a correlate of sepsis mortality: a cross-cohort transcriptomic study with external testing and clinical-analogue triangulation
In early sepsis the direction in which blood immune-cell transcriptional programmes move may carry prognostic information beyond a single baseline measurement, but whether such trajectory associations survive independent testing is unknown. We scored five immune modules, frozen before analysis, in three public longitudinal whole-blood microarray sepsis cohorts and fitted a logistic model ladder fixed in advance to the change per 24 hours in the two cohorts with mortality data (82 patients, 24 deaths), pooling by inverse-variance fixed-effect meta-analysis with Benjamini-Hochberg control. No association survived correction for multiple testing. The two leading signals were a rising CD4/NK lymphocyte trajectory associated with lower mortality (pooled odds ratio 0.53, 95% confidence interval 0.31 to 0.90) and a rising emergency-granulopoiesis trajectory associated with higher mortality (1.60, 0.92 to 2.79), both per one standard deviation. We then tested both in an independent transcriptomic cohort with serial sampling (63 patients, 15 deaths), scored by the identical frozen method, and against their cell-count analogues in an intensive-care database of 12,607 adults meeting Sepsis-3 criteria, of whom 744 to 4,206 had the serial measurements each analogue required. Independent testing separated the two signals, in the order opposite to the one discovery had suggested. The emergency-granulopoiesis association was reproduced in direction and effect size without reaching conventional significance on its own (validation odds ratio 1.72, 0.92 to 3.20, p=0.088; pooled 1.65, 1.09 to 2.50), was positive in all nine sensitivity analyses, each fixed before the estimates were examined, and was supported by two of its three analogues, including the neutrophil-to-lymphocyte ratio (1.31, 1.20 to 1.42). The CD4/NK association did not reproduce (1.06, 0.59 to 1.89), was null in the window most favourable to it, and received no support from an analogue well powered to detect the discovery effect. The discovery signal that looked most consistent failed independent testing.
- Molecular Epidemiology of HIV in an African Epidemic with Declining HIV incidence but High Prevalence: A Longitudinal, Population-based Study in Uganda
Background As HIV incidence declines in African settings with high treatment coverage, it remains unclear how transmission is structured within populations and whether new infections arise from external introductions or local transmission. We characterized the molecular epidemiology of ongoing transmission in a mature multi-subtype epidemic in Uganda. Methods We analyzed HIV genome sequences and survey data from the Rakai Community Cohort Study collected between 1994 and 2019. We identified phylogenetic clusters at 5.3% and 2.5% genetic distance thresholds and inferred long-horizon transmission chains with phylogeographic models. Newly diagnosed infections identified between 2016 and 2019 were mapped onto subtype-specific phylogenies to assess their origins and transmission context. A Bayesian negative binomial branching process model estimated undersampled chain sizes and case reproduction numbers. Findings Among 4,215 participants living with HIV between December 2016 and May 2019, 474 were newly diagnosed, of whom 269 had at least one pure-subtype sequence available. We identified 649 phylogenetic clusters at 5.3% genetic distance and 673 phylogeographic chains including [≥]2 individuals. Most clusters and chains were small (median sizes 2 [IQR 2-3] and 3 [2-4], respectively), with new diagnoses rarely clustered together. Only 46/269 (17.1%) new diagnoses had phylogeographic external origins, while the remaining 82.9% were partially or fully linked to local chains. Mixed-subtypes/recombinant chains were larger and had higher case reproduction numbers (A1/D: 0.84 [95% CrI: 0.79-0.93]; mixed: 0.84 [0.73-0.97]) than single-subtype chains (A1: 0.56 [0.51-0.60]; D: 0.63 [0.59-0.66]; C: 0.55 [0.41-0.71]), yet all estimates were less than one. Interpretation HIV transmission was fragmented across numerous, slowly propagating lineages, maintained by local clusters with occasional introduction. Continued transmission across many chains suggests that further reductions in HIV incidence will require maintaining high levels of population-wide treatment and prevention coverage. Funding The National Institute of Allergy and Infectious Diseases, the Gates Foundation, and the HIV Prevention Trials Network Laboratory Center
- Uncertainty-aware prediction of 48-month eGFR decline in type 2 diabetes mellitus: a secondary analysis of ACCORD
Background Long-horizon kidney trajectory prediction in type 2 diabetes mellitus (T2DM) is usually reported as a point estimate or event risk, although clinical decision-making also depends on whether an individual prediction is reliable. We developed an uncertainty-aware model for 48-month estimated glomerular filtration rate (eGFR) decline and tested whether conformal interval width provides a clinically structured, patient-level signal of prediction reliability. Methods We performed a secondary prognostic modeling analysis of Action to Control Cardiovascular Risk in Diabetes (ACCORD) participants with baseline and 48-month eGFR (n=6,853). The outcome was annualized eGFR change, calculated as 48-month minus baseline eGFR divided by four years. The primary baseline feature set excluded serum creatinine since eGFR is creatinine-derived, and also excluded urine biomarkers. Random forest, gradient boosting, penalized linear models, and XGBoost were compared using fixed training, calibration, and test partitions. Split and locally adaptive conformal intervals were evaluated by empirical coverage and interval width. Interval-width analyses were repeated after conditioning on baseline eGFR. Results The best primary model was random forest (R2=0.382, MAE=3.271 mL/min/1.73m2). Split 90% conformal intervals achieved empirical coverage of 0.917. Locally adaptive 90% intervals achieved empirical coverage of 0.909 with mean width 13.759 mL/min/1.73m2. In unadjusted analyses, wider intervals were associated with larger errors and more rapid decline. After interval-width quintiles were assigned within baseline-eGFR strata, wider intervals remained associated with realized prediction error (annual adjusted increase, 0.151 mL/min/1.73m2 per quintile). Beyond baseline eGFR, wider intervals were associated with younger age, female sex, higher HbA1c, higher triglycerides, and higher systolic blood pressure. Conclusions Baseline clinical variables predicted 48-month eGFR decline with good long-horizon performance in ACCORD, even after excluding serum creatinine and urine biomarkers from the primary model. Conformal prediction provided calibrated patient-specific intervals, and interval width behaved as an informative reliability phenotype rather than a random modeling artifact. These findings support a novel uncertainty-aware framing of kidney trajectory prediction in which rapid and uncertain decline can be identified from baseline clinical data.
- Biobehavioral pain profiling of minoritized adults with chronic widespread pain and clinical obesity before and after bariatric surgery: study protocol for a longitudinal, observational cohort study
Chronic widespread pain (CWP) is highly prevalent among minoritized adults with clinical obesity, and symptom management is challenging. Weight loss via bariatric surgery is often recommended to improve musculoskeletal pain. However, there is significant variation in pain trajectories following bariatric surgery, and the impact of weight loss on movement-evoked pain is largely unknown. The current study aims to systematically characterize and quantify longitudinal changes in pain at rest and movement-evoked pain up to 6 months post-surgery, and to determine whether pain modulatory mechanisms, joint motion, and mechanical loading biosignatures mediate the relationship between weight loss and pain change. This study protocol details the research methodologies and procedures for a prospective observational cohort study of 60 individuals undergoing bariatric surgery for weight loss. Participants will complete questionnaires, anthropometric measurements, clinical and experimental pain testing, functional testing, and a standardized movement testing battery to assess joint motion and mechanical loading using camera-based motion capture before and at 3 and 6 months post-bariatric surgery. Generalized linear mixed models to assess the significance of changes in PAR, MEP, and all patient-reported outcome measures. Reduced models will treat the main effect of time as a fixed factor, and intra-individual repeated measures as random effects. Ethics and dissemination: This study protocol has been registered as an observational study with ClinicalTrials.gov (NCT0675386) in the United States and has been approved by the NYU Langone Health Institutional Review Board (IRB#: i21-01652) and the New York City Health + Hospitals/Bellevue Research Office (Bellevue Study ID #: STUDY00003739). Study results will be published in peer-reviewed journals and presented at national and international conferences and community events.
- Beyond conventional statistics: Genomic Informational Field Theory (GIFT) identifies sex-specific herpes virus associations in multiple sclerosis
Background: Detecting higher order relationships in datasets of complex traits, such as multiple sclerosis (MS), has been challenging. Conventional statistics largely rely on comparing averages across groups and thereby discard important information on the underlying distribution of datapoints. The Genomic Information Field Theory (GIFT) overcomes this limitation by ranking individuals based on linear measures, for example immunoglobulin titres. The exact role of humoral immune responses against several human herpes viruses in a sex-dependent manner in MS is currently unknown. Materials and methods: We compared the performance of GIFT with conventional statistical frameworks to detect differences in the humoral immune response against 4 highly prevalent herpes viruses linked to an individuals susceptibility to develop MS in 200 MS patients and 137 healthy controls. Results: GIFT validated the well-known association that the Epstein Barr Virus (EBV) protein EBNA1 is strongly linked to MS susceptibility in both sexes. In contrast to conventional statistics, GIFT also identified association between herpes simplex virus, varicella zoster virus and the EBV VCA protein and female susceptibility to develop MS, whereas male MS susceptibility was only linked to CMV immunoglobulin levels. None of these associations was observed using conventional statistical tools. Conclusion and discussion: We here show for the first time that GIFT is able to detect novel associations in human immunoglobulin data linked to MS susceptibility, which remained undetected by conventional statistical frameworks. This shows the power of GIFT to detect complex phenotype-trait associations and underlying subgroups within populations.
- Novel KIR2DS4:HLA-B*35 interaction predicts HLA-B*35 positive patient survival post hematopoietic stem cell transplant
Haplo-identical hematopoietic cell transplantation (haploHCT) is an integral treatment paradigm for patients with leukemia. While overall survival (OS) post-haploHCT has steadily improved, relapse-free survival (RFS) remains relatively stagnant. Upon the discovery of killer immunoglobulin-like receptors (KIRs) on natural killer (NK) cells and their cognate human leukocyte antigen (HLA) ligands, algorithms have been developed to enhance graft versus leukemia effects. However, these algorithms fail to yield consistent predictions in patient outcomes. We utilized a combination of in silico protein folding and interactions to determine KIR:HLA reactivity in conjunction with in vitro acoustic force microscopy to measure cell avidity (CA) as a readout for KIR signal strength. CA was determined using monoallelic HLA expressing K562 cell lines, monoallelic KIR Jurkat cells, and peripheral blood NK cells. We extended the CA results and performed standard cytotoxicity assays as well. We discovered that HLA-B*35 interacts with KIR2DS4. We applied the newly discovered interaction to predict outcomes for HCT patients. Stratifying patients based on their HLA-B*35 positivity and donor KIR2DS4 status, we delineated a correlation to survival (P=0.061) when donors only had full-length KIR2DS4. Patients who received a haploHCT and NK cell addback from donors with only full-length KIR2DS4 had a significantly improved RFS (P=0.001) and OS (P=0.016) compared to truncated (KIR1D) and full-length KIR2DS4 donors. This was independently validated in a diverse 10/10 HLA matched European cohort with RFS (P=0.0255) and OS (P=0.0388). Thus, the identified novel KIR2DS4:HLA-B*35 interaction axis predicts patient survival, in both haplo-identical and fully matched, HCT and highlights that our current understanding of the KIR:HLA interactome is incomplete and requires remapping for enhanced therapeutic applications.
- GLP-1 Refractory Obesity Is Associated with Inferior Weight Loss After Bariatric Surgery and a Distinct Hepatic Mitochondrial Phenotype
Background Glucagon-like peptide-1 receptor agonists (GLP1 RAs) are first-line pharmacotherapy for obesity and metabolic dysfunction-associated steatotic liver disease (MASLD); however, 20% to 35% of patients fail to achieve clinically meaningful weight loss despite guideline-directed therapy. Whether this GLP1 refractory obesity (GRO) phenotype is associated with distinct hepatic molecular abnormalities or influences bariatric surgical outcomes remains unknown. Objectives To characterize the hepatic histological, ultrastructural, and molecular phenotype of GRO at bariatric surgery, determine its recovery following surgery, and identify preoperative hepatic biomarkers associated with postoperative weight loss. Setting Academic tertiary referral bariatric surgery center. Methods Intraoperative liver biopsies were obtained from lean controls (n=3), GLP1 naive obese patients (GNO; n=10), and GLP1-refractory obese patients (GRO; n=10) undergoing Roux-en-Y gastric bypass. GRO was defined as <5% total weight loss after 12 months of guideline-directed GLP1 RA therapy. Paired liver biopsies were obtained six months postoperatively from subsets of GNO (n=5) and GRO (n=5). Histological, ultrastructural, and molecular analyses were performed, and preoperative hepatic protein expression was correlated with postoperative total weight loss. Results Compared with GNO, GRO patients exhibited more advanced hepatic steatosis, fibrosis, lipid accumulation, and mitochondrial ultrastructural disruption at surgery (all P<0.05). Despite equivalent Body mass index, GNO patients maintained lean-equivalent hepatic pCREB, pAMPK, pACC, and oxidative phosphorylation (OXPHOS) protein expression, whereas GRO patients demonstrated marked suppression of GLP1R downstream signaling (75 to 85%) and OXPHOS complex subunits (38 to 55%; all P<0.001). Six months after surgery, histological and molecular recovery remained significantly attenuated in GRO. GRO patients achieved less postoperative weight loss than GNO patients (25.2% vs. 29.51% total weight loss; P<0.001). Across the pooled cohort, several hepatic molecular markers correlated with postoperative weight loss; however, no individual biomarker independently predicted postoperative weight loss within the GRO subgroup. Conclusions GLP1 refractory obesity is associated with a distinct hepatic phenotype characterized by impaired GLP1R signaling, mitochondrial dysfunction, and attenuated hepatic recovery following bariatric surgery. The coordinated suppression of hepatic energy-sensing, mitochondrial biogenesis, and oxidative phosphorylation pathways supports the concept that GLP1 refractory obesity represents a biologically distinct metabolic phenotype. Larger prospective studies are required to determine the prognostic utility of hepatic molecular profiling for postoperative outcomes. Keywords: GLP1 receptor agonist refractoriness; bariatric surgery; hepatic steatosis; MASLD; AMPK; pCREB; mitochondrial dysfunction; OXPHOS; weight loss outcomes; biomarker
- Pleasure and Peril: The association between sexual risk behavior and sexual pleasure in young adults from the Generation R Study
BACKGROUND: Sexual pleasure is integral to sexual health, offering important physical and mental benefits. Yet, sex education programs often neglect pleasure, focusing instead on preventing sexual risk behaviors (SRBs), which young adults are particularly vulnerable to. AIM: This study investigates the association between SRBs and sexual pleasure in young adults and whether sex assigned at birth moderates this relationship. METHODS: Embedded within the Generation R cohort, 1010 young adults completed an online questionnaire assessing sexual pleasure using the six subscales of the Amsterdam Sexual Pleasure Inventory (ASPI 1.0), Arousal Enjoyment, Enjoyment-Related Self-Efficacy, Enjoyment-Related Self-Worth, Interaction Enjoyment, Bonding Enjoyment, and Sexual Experience Enjoyment, and various SRBs including sexual debut <15 years, six or more lifetime partners, frequent unprotected sex, and substance use during sex. Multiple linear regression analyses were performed for each SRB and sexual pleasure subscale, adjusting for demographics, self-esteem, relationship status, socioeconomic status, and psychopathology, with additional stratification by sex assigned at birth. OUTCOMES: The primary outcome measure is sexual pleasure, measured across six domains, examined in relation to SRBs. RESULTS: Fully adjusted regression analyses showed that engaging in SRB was positively associated with several dimensions of sexual pleasure. All SRBs were associated with higher Enjoyment-Related Self-Efficacy (ERSE) scores (p<0.002). Early sexual debut was additionally linked to higher Interaction Enjoyment scores, while having six or more lifetime partners was associated with increased Enjoyment-Related Self-Worth and Sexual Experience Enjoyment scores (p<0.002). Some associations, particularly involving ERSE, were only significant among males. Individuals without partnered sexual experience reported lower sexual pleasure scores. CLINICAL IMPLICATIONS: Incorporating sexual pleasure into sex education could promote a more balanced, realistic understanding of sexuality among young adults, emphasizing both enjoyment and responsible sexual decision-making. STRENGHTS & LIMITATIONS: Key strengths of this study are the use of the multidimensional Amsterdam Sexual Pleasure Inventory (ASPI 1.0) and the large population-based cohort study design, enabling a nuanced and generalizable analysis. This study is limited by potential selection and reporting bias, the cross-sectional design, residual confounding, and the absence of universally agreed-upon thresholds for defining SRBs. CONCLUSION: These findings suggest there is a positive association between engagement in SRB and sexual pleasure, possibly reflecting greater overall sexual experience. The stronger associations observed among males might reflect gendered differences in the role of self-esteem and societal expectations.
- Proteomic Signatures and an Injury-Stress Endotype in Myositis-Associated Interstitial Lung Disease
Introduction: Idiopathic inflammatory myopathy-associated interstitial lung disease (IIM-ILD) is a major cause of morbidity and mortality. We tested whether quantitative myositis-specific autoantibodies and proteomic profiling capture biological heterogeneity and prognosis beyond categorical serology. Methods: Myositis-specific autoantibodies were quantified using the luciferase immunoprecipitation systems assay, and 184 serum proteins were measured in 226 IIM patients; 199 with higher-ILD-risk autoantibodies (Jo-1/MDA5/PL-7/PL-12/EJ), 27 with lower-ILD-risk autoantibodies (Mi-2/NXP2/TIF1{gamma}) and 35 healthy controls. We identified shared and subgroup-specific differences by comparing each subgroup with controls, then correlated quantitative autoantibody and protein levels within higher-risk subgroups. Additional analyses included pathway enrichment, unsupervised clustering, longitudinal lung-function change, and mortality. Results: Higher-ILD-risk subgroups shared interferon-responsive CXCR3 chemokine, IL-6/JAK/STAT3, and apoptosis signaling. Dominant autoantibody subgroup profiles differed: interferon/CXCR3 chemokine signaling with T-cell activation and monocyte recruitment in anti-Jo-1; proteostasis/antigen-processing and vascular/cellular stress signals in anti-MDA5; IL-6/macrophage and profibrotic signals in anti-PL-12; and apoptotic and innate immune activation with metabolic/redox-stress signals in anti-PL-7. Within higher-ILD-risk subgroups, autoantibody levels correlated with interferon-response, profibrotic, and metabolic/vascular proteins (r=0.40-0.74; nominal p<0.05). Unsupervised clustering identified four proteomic endotypes beyond autoantibody type, including an injury-stress endotype associated with worse lung function and poorer survival, and a chemokine/checkpoint-high endotype with relatively preserved lung function. Across 203 participants with 38 deaths, a weighted 10-protein score was associated with all-cause mortality (HR, 3.28; 95% CI, 2.12-5.08; p<0.001). Conclusions: Integrated quantitative autoantibodies and proteomic profiling revealed shared inflammatory biology, autoantibody-associated signatures, and an injury-stress endotype associated with poor survival in IIM-ILD, supporting risk stratification beyond categorical serology.
- Rapid growth in autism spectrum disorder referrals reshaped rehabilitation service use: A longitudinal cohort study of children and adolescents in Brazil
Background The global increase in autism spectrum disorder (ASD) diagnoses is expected to substantially increase demand for long-term rehabilitation services. However, little is known about how this increase affects rehabilitation service utilization and capacity in low- and middle-income countries. Methods We conducted a retrospective longitudinal study of children receiving developmental care at a tertiary rehabilitation center in Salvador, Brazil (2017-2026). Patients were classified into Childhood Autism, Other ASD, and non-ASD diagnostic groups according to ICD-10 diagnoses. Temporal trends in admissions and patients under follow-up were analyzed using generalized additive models and segmented Poisson regression. Factors associated with follow-up duration were evaluated using multivariable Cox proportional hazards models. Results Among 2,123 eligible children, 833 (39.2%) had Childhood Autism, 462 (21.8%) had Other ASD, and 828 (39.0%) had non-ASD diagnoses. Compared with children with non-ASD diagnoses, those with Childhood Autism entered care at younger ages, were predominantly male (77.9% vs. 57.2%), attended more visits, and remained under follow-up longer (all P<0.001). Admissions of children with Childhood Autism increased by 30.2% annually before 2023 but declined thereafter (-19.6% annually; P<0.001). Despite this decline, the number of children with Childhood Autism receiving ongoing rehabilitation continued to increase, reflecting prolonged follow-up. In adjusted analyses, Childhood Autism was associated with a substantially lower hazard of reaching the last recorded follow-up visit than non-ASD diagnoses (adjusted hazard ratio, 0.35; 95% CI, 0.30-0.40; P<0.001). Conclusions The rapid increase in ASD admissions fundamentally reshaped rehabilitation service utilization. Because children with ASD remained under follow-up substantially longer than those with other developmental conditions, they accounted for an increasing share of the rehabilitation caseload, even after new admissions began to decline. These findings highlight the importance of planning rehabilitation services according to both new admissions and the cumulative demand generated by long-term follow-up.
- Measuring health literacy from the communal perspective: initial testing of the Information and Support for Health Actions Questionnaire (ISHAQ)
Measuring health literacy is important in addressing health inequity. However, current measures are developed from the individualistic perspective that values personal autonomy and choice. Applying such measures to people from a communal culture that values collective actions may lead to biased responses. To address this gap, a health literacy measure drawing on the communal perspective was developed in Thailand. With the aim to also develop an equitable measure, a grounded approach including strategies to include people with special needs such as people with chronic illness or physical disabilities, blind people and deaf people, was used. Concept mapping workshops were conducted, involving 254 participants including general community members, people with special needs, health professionals and policymakers. The result was a draft questionnaire of 17 hypothesized scales. Psychometric testing involving a survey of 2,228 participants resulted in a 14-scale questionnaire, the Information and Support for Health Actions Questionnaire (ISHAQ). This paper reports on the psychometric testing of the 14-scale ISHAQ and its supplementary scales for people with special needs. Item difficulty, scale reliability, one-factor confirmatory factor analysis using robust maximum likelihood estimator, and measurement invariance across groups with special needs using the alignment method with Bayesian estimation, were evaluated. A total of 2,262 respondents participated in the survey. Six scales achieved excellent model fit while one with reasonable fit and seven scales achieved reasonable to excellent fit following modifications. Supplementary scales also achieved reasonable to excellent model fit. Reliability for all scales were acceptable to good. Measurement invariance was confirmed for eight scales. With strong validity evidence, the ISHAQ is translated into English and ready for implementation. With the potential to be applied in different settings given cultures exist in a continuum, the ISHAQ can be used for health literacy needs assessment to support intervention development to improve health outcomes and equity.
- Strengthening the translation of malaria modelling into policy: Design, implementation, and early outcomes of the Regional Malaria Modelling Translational Fellowship
Malaria programmes increasingly rely on modelled evidence to support intervention prioritisation, resource allocation, and elimination planning, yet a persistent gap remains between technical modelling outputs and their use in decision-making. We describe the design, implementation, and early outcomes of the Regional Malaria Modelling Translational Fellowship, a six-month executive education programme delivered in 2025 to 21 fellows nominated by national malaria programmes in seven African countries. The Fellowship was designed to strengthen translational capacity by focusing on question formulation, systems thinking, model design and critique, interpretation of outputs, uncertainty, health economics, communication, and stakeholder engagement. The hybrid structure combined three intensive in-person blocks with regular virtual sessions. Country-teams work on capstone projects throughout the Fellowship, applying learnings to develop policy-relevant modelling proposals aligned with national malaria priorities. The programme was accredited as a University of Cape Town short course, which supported credibility, participant commitment, and institutional endorsement. Early evaluation showed improvements across all competency domains, with the largest gains in fellows' confidence in applying modelling to decision-making, translating model findings into recommendations, and communicating technical results to non-technical audiences. Qualitative feedback suggested that the Fellowship helped shift participants' engagement with modelling from passive acceptance of results toward critical interpretation, collaborative dialogue, and practical application. These findings suggest that translational, executive-style training can strengthen the interface between modelling and malaria policy. The publicly available curriculum offers a replicable framework that may be adapted for other infectious disease and public health settings where modelling evidence is increasingly central to decision-making.
- Household determinants of animal and human fecal contamination on floors and hands in northwestern coastal Ecuador
Household environments in low-resource settings can become contaminated with fecal matter from multiple sources, including humans and domestic animals. Identifying the source of fecal contamination is critical for designing targeted interventions to reduce exposure to enteric pathogens, particularly for young children who bear the majority of the enteric disease burden. Using data from 140 households with children enrolled in the ECoMiD cohort study in northwestern coastal Ecuador, we (1) characterized source-specific fecal contamination in samples from household floors and maternal and child hands, and (2) identified animal-related and Water, Sanitation and Hygiene (WASH) conditions associated with the presence and concentrations of these markers. We used five qPCR-based microbial source tracking (MST) markers to detect fecal contamination from avian (GFD), canine (DG37), swine (Pig2Bac), ruminant (Rum2Bac), and human (HF183) sources. Prevalence ratios (PR) and mean differences comparing the presence/absence and concentration, respectively, of MST markers between households with and without each animal-related or WASH condition were estimated using generalized linear models with Poisson and Gaussian distributions. Animal MST markers tracked strongly with several animal-related conditions, whereas associations between the human MST marker and household demographic and WASH conditions were more limited. Animal ownership (PR 1.53; 95% CI: 1.04-2.26) was associated with higher prevalence of animal MST markers on floors. Households reporting animal feces indoors had higher prevalence of animal MST markers on floors (PR 1.84; 95% CI: 1.17-2.89), and higher concentrations of animal MST markers on child hands (mean difference 0.27 gene copies (gc)/m2; 95% CI: 0.12-0.41) and maternal hands (mean difference 0.10 gc/m2; CI: 0.02-0.18). Animal feces left unremoved outside the home were associated with higher prevalence of animal MST markers on maternal hands (PR 2.31; 95% CI: 1.11-4.81). Mothers reporting direct contact with animals had higher concentrations of animal MST markers on their children hands (mean difference 0.12 gc/m2; 95% CI: 0.02-0.21). Higher FECEZ scores, an overall metric for animal exposure, were associated with higher prevalence of animal MST markers on maternal hands (PR 2.94; 95% CI: 1.17-7.40). For human fecal contamination, the presence of E. coli on child hands was associated with higher prevalence of human MST markers on floors (PR 1.30; 95% CI: 1.00-1.68). Our findings reinforce household floors and maternal and child hands as key reservoirs of fecal contamination and point to future potential targets worth exploring for interventions in similar high-burden settings.
- Short-term survival benefit associated with neonatal clinical trial participation: An observational cohort study in The Gambia
Background Trial participation effect, defined as a change in clinical outcomes associated with trial enrolment regardless of allocation, is understudied in neonatal trials in low- and middle income countries (LMIC), despite its importance for trial design, interpretation, and research ethics. This study aimed to quantify the trial participation effect and explore potential ways by which research participation may influence neonatal survival. Methods This observational cohort study included neonates weighing <2Kg and aged <24h who were admitted to a Gambian referral hospital and either enrolled in a clinical trial comparing early versus later KMC (eKMC trial;2018 to 2020) or not enrolled due to operational constraints and hence received standard, non research care. All infants were prospectively followed until inpatient discharge or death. The eKMC trial previously found no important effect of early KMC on all cause neonatal mortality. For this analysis, inpatient mortality rates were compared using a generalised linear model, adjusting for baseline differences in participant characteristics. Prospectively collected data on small and sick newborn care readiness and delivery during the trial period were used to explore how trial participation may have influenced survival. Results A total of 545 neonates were included: 279 enrolled in the trial and 266 not enrolled, predominantly due to the absence of an available caregiver. Baseline characteristics were similar between groups, although differences were seen in twin status, place of birth, and age at admission. Trial participation was associated with an absolute reduction in inpatient mortality of 6.3% (22.6% (63/279) among enrolled versus 28.9% (77/266) among non enrolled) and a relative reduction of 29% (aRR 0.71, 95% CI 0.53 to 0.96). This association varied by season, with no evidence of benefit during the dry season (aRR 0.97, 95% CI 0.60 to 1.58), but a 40% reduction in adjusted mortality risk during the rainy season (aRR 0.60, 95% CI 0.41 to 0.87)(Interaction test: p=0.086). Trial participants had access to laboratory diagnostics and received more intensive clinical monitoring, including higher staffing ratios, continuous pulse oximetry, structured education of carers on neonatal danger signs, and enhanced scrutiny of clinical management compared to neonates receiving routine care. Conclusion Trial participation was associated with a substantial reduction in inpatient mortality, suggesting that participation effects should be considered when designing, interpreting, and reporting neonatal clinical trials in LMIC settings. The association was evident only during the rainy season. The participation effect may have been mediated by increased clinical oversight and monitoring, additional nursing support, and access to diagnostic investigations, all of which should be prioritised within routine care to accelerate progress towards SDG neonatal survival targets.
- Targeting anaemia without measuring it: surrogate prediction, district decision uncertainty and the value of repeat measurement in India
Background & objectives: India's fifth National Family Health Survey measured anaemia in all 707 districts, whereas the sixth survey did not. Anaemia is now assessed through a venous blood survey covering 183 districts and reported only at the national level. Using the most recent district-level measurements, we examined whether the remaining survey indicators could predict district anaemia, whether omitting district anaemia altered programme prioritisation, and the value of repeating district-level measurement. Methods: We estimated district anaemia prevalence and uncertainty for children aged 6-59 months and non-pregnant women aged 15-49 years using small-area estimation with design-based variances. We evaluated prediction from the retained survey indicators using both district-level and leave-one-State-out validation, compared district prioritisation under three information scenarios using matched preference draws, and estimated the value of repeating measurement of the same underlying prevalence. Results: Median standard errors of district estimates were 3.57 percentage points for children and 2.22 percentage points for women. The best predictive surrogate had a root mean squared error of 10.14 percentage points for children, of which 9.44 percentage points reflected structural error, representing approximately 2.5 times the root mean squared measurement error. In leave-one-State-out validation, predictions performed worse than the training-set mean. Among the 71 districts prioritised using current estimates, 19.1% were not among the latent top 71 districts. Measuring 183 districts recovered 46.3% of this prioritisation gap when districts were selected according to decision value, compared with 9.6% under equal allocation across States. Interpretation & conclusions: Available survey indicators did not adequately substitute for direct measurement of district anaemia. When measurement resources are limited, the choice of districts to be measured has a greater influence on programme prioritisation than the total number of districts measured, provided differences between measurement platforms are addressed before comparison.
- Population Structure, Novel Sequence Types, and Antimicrobial Resistance in Vibrio parahaemolyticus and Vibrio vulnificus: A Whole Genome Sequencing Study of Clinical and Seafood Isolates in New Jersey
Vibrio parahaemolyticus and Vibrio vulnificus are significant foodborne pathogens linked to seafood consumption and environmental exposure. Whole genome sequencing (WGS) was performed on Vibrio isolates collected from clinical cases and seafood sources throughout the state of New Jersey to elucidate genomic diversity, antimicrobial resistance (AMR) profiles. Sequences were included from isolates collected over eight years, from June 2016 to November 2024. This study identified 465 Vibrio sequences, 406 sequences from seafood sources and 59 sequences from clinical cases. Species-level taxonomic identification via Kraken2 classified 300 isolates as Vibrio parahaemolyticus and 165 as Vibrio vulnificus from whole genome assemblies. Multi-locus sequence typing (MLST) indicated a diverse population of isolates, with 169 known Vibrio sequence types (STs) identified. An additional 168 potential novel allelic profiles were identified, comprising 19.3% of V. parahaemolyticus sequences and 90.9% of V. vulnificus sequences. Novel sequence types were submitted to pubMLST for classification, resulting in the identification of 49 novel V. parahaemolyticus STs and 113 novel V. vulnificus STs. Three V. parahaemolyticus sequence types were identified in both clinical and environmental sequences. A single novel sequence type was identified in both clinical and environmental sequences of V. vulnificus. Analysis of antimicrobial resistance genes revealed the presence of the tetracycline resistance gene tet(34) in nearly all isolates. Beta-lactamase genes were detected in nearly all V. parahaemolyticus sequences but were absent from V. vulnificus, with gene profiles varying by sequence type. To the best of our knowledge, this study provides the first comprehensive WGS-based analysis of the genomic diversity and antimicrobial resistance profiles of Vibrio parahaemolyticus and Vibrio vulnificus isolates from clinical and seafood sources in New Jersey over an eight-year period, to support public health surveillance of these foodborne pathogens.
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- High Sensitivity of Facility-Level Wastewater Surveillance for Detecting Respiratory Virus Surges in Large Municipal Hospitals in New York City
Hospital-based wastewater surveillance may complement community and clinical surveillance data in important ways, and may be useful in jurisdictions without community-based wastewater surveillance. From May 2024-April 2026, we analyzed weekly samples (n=190) from three hospitals in New York City using digital PCR to evaluate the sensitivity, specificity, and positive predictive value (PPV) of wastewater viral detection against facility SARS-CoV-2 and influenza A/B inpatient caseloads. Sensitivity was 38-42% for SARS-CoV-2 and 36-49% for influenza A/B, while specificity exceeded 72% for all pathogens. During respiratory seasons, sensitivity reached 81% for SARS-CoV-2 and 81% for influenza A; both had 100% sensitivity during peak case weeks. Notably, off-peak influenza detections occurred in hospital wastewater at all three hospitals in summer 2024 without corresponding hospital case detection, suggesting the presence of undiagnosed cases. These findings underscore the potential utility of hospital-based wastewater monitoring for tracking respiratory virus activity.
- An Open Demonstrator for an Interoperable Clinical Decision Support System for the Detection of Systemic Inflammation and Sepsis in Pediatric Intensive Care
Background: Sepsis is a life-threatening condition triggered by infection and associated with dysregulated immune response of the patient followed often by multiorgan dysfunction or failure. In the clinical evolution of sepsis towards organ dysfunction, early initiation of a suited therapy significantly increases patient outcomes and reduces mortality rates. Since electronic health records provide data in a machine-readable format, this process could be supported by computerized systems. Methods: We developed an interoperable, time-sensitive CDSS that able to detect systemic inflammation and the different classifications of sepsis (bacterial/viral, suspected/proven, on admission/PICU acquired) in pediatric patients based on the analysis of routine clinical data. This application is provided as part of this publication as an open demonstrator (web application), and the usability and accuracy of the CDSS is shown by a retrospective creation of sepsis outcome labels for a routine data set of 4,655 pediatric patients. As a reference standard, the patients were manually assessed by blinded clinical experts. Results: In comparison with the reference standard, the CDSS achieved sensitivity of 96.9% (95% CI: 80.9-99.6%) and specificity of 99.1% (95% CI: 95.1-99.8%). In the context of a sepsis outcome labeling for 4,655 patients, the CDSS detected 4,342 episodes of inflammation of which 1,723 were classified as sepsis. Conclusions: We demonstrated that our routine-data based CDSS is able to perform a complex sepsis detection process with high diagnostic accuracy. Such CDSS with the ability to differentiate between SIRS, sepsis on admission, suspected and proven sepsis can prospectively support clinical management, monitoring and quality management.
- What Is OpenAI’s Device? A Doughnut-Shaped Speaker That Costs Over $300
A highly anticipated new device from OpenAI will have a unique look, complete with moving parts that help give it personality, and likely cost more than $300, according to people familiar with the matter.
- OpenAI’s new AI smart speaker will reportedly sell for between $300 and $400
Additional details about OpenAI's mysterious new AI device make it sound like a pricey smart speaker.
- OpenAI's ring-shaped smart speaker will reportedly cost between $300 and $400
It would be an ambitiously high price for the AI-powered hardware pivot.
- Report: OpenAI’s upcoming AI speaker will be shaped like a donut and cost around $300
More details have emerged from Bloomberg about OpenAI Group PBC’s long-awaited hardware device, which was previously described as an artificial intelligence-enabled speaker that will be a “physical manifestation” of ChatGPT. In its latest report, Bloomberg described the upcoming device as having a “ring-like” shape and said the company is looking at pricing it at around […] The post Report: OpenAI’s upcoming AI speaker will be shaped like a donut and cost around $300 appeared first on SiliconANGLE .
- Report shares new pricing and design details about OpenAI’s first device
After sharing initial details about OpenAI’s plans for its first smart device, Bloomberg has published a new report with more information about its form factor and price. Here are the details.
- OpenAI's ChatGPT Speaker Will Be Hockey Puck-Sized and Cost Over $300
OpenAI's upcoming AI device is a hockey-puck-sized, doughnut-shaped smart speaker with no display, reports Bloomberg . The product is meant to be easy to carry around with one hand, and it will have "moving parts that help give it personality," along with a price tag around $300 to $400. Bloomberg describes it as an AI-centric device that will stand out from current smart speakers. There are speaker grilles and microphones for voice-based chats with ChatGPT, and the AI model used will have human-like responses and will learn more about the user over time. Moving components will help show when the speaker is responding, and lights will show that it is listening. A camera system and sensors will feed the product information about the user's environment. It is battery-powered so it can be moved from room to room, and it will reportedly use a premium metal material. In its theft of trade secrets lawsuit against OpenAI , Apple claimed the company misled an Apple supplier into developing a metal finishing technique similar to what Apple uses. Former Apple design chief Jony Ive is designing the device for OpenAI. OpenAI acquired Ive's io device startup last year, adding multiple former Apple design team members to its hardware teams. Bloomberg says the portable speaker "is not something Apple has come close to launching," which supports OpenAI's claim that it has no interest in Apple's trade secrets . OpenAI said today that it is "building something entirely new and different from anything at Apple." Apple maintains OpenAI has developed its hardware using information sourced from Apple. Apple is working on a home hub that's supposed to come out as soon as this year, but it has a display and square form factor. Apple also has the HomePod mini speaker, but it is corded and not easily moved. Apple is also developing a tabletop robot that may share more similarities with OpenAI's device. It's said to have a display and a motorized arm for movement, but the robot isn't going to launch until 2027 at the earliest. OpenAI has not stopped development on its new hardware product, and hopes to introduce it later this year ahead of a 2027 launch. Tags: ChatGPT , OpenAI This article, " OpenAI's ChatGPT Speaker Will Be Hockey Puck-Sized and Cost Over $300 " first appeared on MacRumors.com Discuss this article in our forums
- OpenAI’s first gadget sounds like a tiny expressive AI companion
OpenAI’s first hardware product could be a doughnut-shaped AI companion with cameras, sensors, moving parts, and a price of up to $400.
- ChatGPT brings unlimited text chats to free users
OpenAI said that ChatGPT free and Go users are also getting a new think button for complex queries.
- OpenAI will no longer limit how many texts free accounts can send to ChatGPT
There will still be limits on image generation, voice mode usage and other features.
- You Can Now Have Unlimited Text Chats Without Paying for ChatGPT
OpenAI is upgrading its default options for free and paying ChatGPT users.
- ChatGPT enables unlimited chats on free accounts and upgrades to newer models
For free accounts, there is no longer a restriction on text-based conversations, despite the switch to a newer and more powerful GPT-5.6 Luna model.
- OpenAI just made ChatGPT’s latest model more accessible to non-paying users
Free users are getting unlimited chats and deeper thinking.
- OpenAI is giving ChatGPT free users unlimited text chats
OpenAI is making a big change for ChatGPT users on its free and Go tiers: Starting next week, users on those tiers will be able to have unlimited text chats with the chatbot, according to OpenAI. Right now, you may run into rate limits if you do too many text chats on those tiers, but […]
- OpenAI rolls out a major ChatGPT upgrade, even if you don’t pay for it
OpenAI is rolling out a more reliable version of ChatGPT GPT-5.6 Sol for Plus and Pro users, while Free users are getting unlimited text chats with GPT-5.6 Luna. [...]
- Tesla and SpaceX will invest $16.8B to start building ‘Terafab’ chip factory in Texas
After months of speculation, the companies formally announced the massive project will happen just north of Houston.
- Suno hopes to go legit with watermarks for AI-generated music
Suno plans watermarks and download limits to stop "large-scale abuse."
- AI Music Startup Suno Is Adding a Watermark to Songs as Legal Troubles Pile Up
The company also updated its community guidelines to more clearly prohibit scams, spam, and fake engagement.
- Suno is adding audio watermarks so AI-generated songs are more easily identifiable
Suno is adding audio watermarks so AI-generated songs are more easily identifiable
- The messy politics behind Google’s big AI shakeup
In the AI industry, Google prides itself on seeming like the adult in the room: quiet, stable, time-tested. On Wednesday, even as the company announced its largest AI org shakeup yet, Google and its leaders presented a unified front, keeping their messaging focused on how the changes tee up future success. But the reality is […]
- OpenAI’s AI models secretly built a message board to coordinate hacking
OpenAI's AI models quietly swapped hacking tips through an internal message board weeks before two of them broke into Hugging Face, researchers revealed at Black Hat this week.
- Rogue OpenAI models behind 'unprecedented cybersecurity incident' teamed up to break out of their testing environment — multiple agents left each other messages for months, communicating undetected
The rogue OpenAI models that broke out of their testing environment in an "unprecedented cybersecurity incident" recently reportedly spent months communicating with each other.
- Swarms of OpenAI systems set up their own chatrooms to discuss and carry out hacks, company reveals
AI agents were leaving messages to help each other and co-ordinate their attacks, OpenAI reveals
- OpenAI reveals its rogue agent swarm went a little bit Borg ahead of Hugging Face hack
It started with an 'impossible task' and led to AI deciding it needed to act as a collective intelligence