Edwards Lifesciences (Switzerland)

To develop and validate a non-invasive, blood-based diagnostic assay for endometriosis that performs accurately across menstrual cycle phases and complements existing imaging modalities. Multicenter case-control study with machine learning classification and independent cohort validation. Clinical and search settings involving symptomatic women evaluated for suspected endometriosis. A total of 298 reproductive-age women were included. The training cohort comprised 218 participants (137 endometriosis-positive and 81 controls). A modest, independent, and retrospective validation cohort included 80 participants (40 endometriosis-positive and 40 controls). Peripheral blood sampling with quantification of 3 microRNAs via qPCR, 3 protein biomarkers, one steroid hormone using immunoassay, as well as the participant's age and body mass index. Biomarker data were integrated using a random forest machine learning model to classify disease status. In the independent validation cohort, the assay achieved an area under the curve (AUC) of 0.944, sensitivity of 0.80, and specificity of 0.975. Subgroup analysis by menstrual cycle phase demonstrated consistent performance: proliferative-phase samples achieved an AUC of 0.935, sensitivity of 0.767, and specificity of 0.962, while secretory-phase samples achieved an AUC of 0.993, sensitivity of 0.90, and specificity of 1.00. Compared with transvaginal ultrasound and/or MRI, the blood-based assay identified 61.5% histologically confirmed endometriosis cases that were missed by the imaging modalities. A minimally invasive, multi-omic blood-based assay integrating molecular biomarkers with machine learning can accurately detect endometriosis across menstrual cycle phases and provides complementary diagnostic values. This approach has the potential to improve early detection and guide timely clinical intervention. A prospective validation is ongoing in geographically and ethnically diverse populations to further assess its broad clinical utility.

AimsAsymptomatic severe aortic stenosis (aSAS) is associated with morbidity, with mortality increasing once symptoms develop. Benefits of early aortic valve replacement (AVR) over clinical surveillance (CS) with delayed AVR upon symptom onset have been demonstrated, and it is now recommended from the latest European Guidelines if procedural risk is low (Class IIa, Level A). The aim of this study is to estimate the economic impact of transcatheter aortic valve implantation (TAVI) for the treatment of aSAS.Methods and resultsA cost-utility analysis compared TAVI using the SAPIEN 3/SAPIEN 3 Ultra valve vs. CS in patients with aSAS across nine European countries. A lifetime Markov model captured peri-procedural and long-term outcomes across three health states: alive and well, stroke, and death. Inputs were derived from the EARLY TAVR trial (NCT03042104) and literature sources. Clinical event rates were estimated via parametric survival analysis. Transcatheter aortic valve implantation treatment for patients with aSAS was modelled to be the economically dominant strategy for treating aSAS across all nine European healthcare systems, with cost-effectiveness probabilities ranging from 97.3% (UK) to 99.9% (Belgium). Incremental results per person included cost savings from −£1788 (UK) to −CHF15 802 (Switzerland), quality-adjusted life years gains of 0.18 (Germany, UK) to 0.23 (Switzerland), and life-year gains of 0.12 (UK) to 0.17 (Belgium). Annual discounting of upfront TAVI costs vs. delayed AVR costs drove results.ConclusionFor patients with aSAS, early intervention with TAVI is estimated to deliver greater health benefits and reduce costs compared with CS. These findings support policies promoting early detection and timely intervention before symptom onset.

BackgroundEndometriosis is a chronic inflammatory disorder affecting ~ 10% of reproductive-age women, often causing pelvic pain and infertility. Despite its prevalence, diagnosis remains delayed due to non-specific symptoms and lack of reliable non-invasive biomarkers. Emerging evidence implicates the microbiome in disease pathogenesis.ResultsWe analyzed uterine microbiomes from 266 tissue samples collected during either the proliferative or secretory phase, using 16S rRNA gene sequencing. Genus-level analysis revealed variable Lactobacillus abundance among all individuals. Prevotella showed borderline enrichment in proliferative-phase patients. Sub-genus analyses identified a small number of differentially abundant taxa, though none remained significant after FDR correction. To capture subtle microbial shifts, we developed a feature set combining weakly differential taxa, algorithmically selected taxa via machine learning, and a functional dysbiosis score. A supervised classifier trained on proliferative-phase data achieved moderate predictive performance (AUC = 0.70), while secretory-phase models performed more poorly (AUC = 0.58).ConclusionsThe uterine microbiome shows phase-dependent differences in its potential to inform endometriosis status. Although no robust individual microbial biomarkers were identified, machine learning models incorporating subtle community features from the proliferative phase yielded modest diagnostic potential. These results highlight the importance of menstrual cycle-aware sampling and support further development of microbiome-informed diagnostic tools for endometriosis.Supplementary InformationThe online version contains supplementary material available at https://doi.org/10.1186/s12915-026-02659-8.

BackgroundSelecting between mechanical and bioprosthetic valves depends on multiple clinical and patient-specific criteria. This study aims to compare mechanical and bioprosthetic valve replacement outcomes in Egypt to support evidence-informed decision-making.MethodsWe conducted a narrative literature review to identify studies reporting outcomes of mechanical and bioprosthetic valve replacements in Egypt. Findings were validated through an expert meeting with cardiothoracic surgeons. To capture real-world practice, an online survey was administered to cardiothoracic surgeons across Egypt. The narrative review and survey results were compared to global data to formulate recommendations on optimizing valve selection in Egypt. We extracted data from 41 eligible studies identified from the literature.ResultsFindings showed that unlike global data, reoperation rate in Egypt is lower with bioprosthetic valves compared to mechanical valves. Mechanical valves are associated with higher bleeding rates globally and locally. Survival and stroke incidence do not differ significantly between mechanical and bioprosthetic valves. The expert survey was responded to by 128 cardiothoracic surgeons. Experts predominantly used mechanical valves, highlighted age, and anticoagulation eligibility as the main factors influencing valve choice, and showed that longer clinical experience is associated with more tendency to use bioprosthetic valves.ConclusionsValve type selection in Egypt should be individualized and made on a case-by-case basis due to the multiple factors influencing choice and outcomes. To support evidence-based decision-making, more local studies should be conducted, especially for bioprosthetic valves. These factors should be considered in selection to choose the most suitable valve for each patient.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13019-026-04400-6.

Retrospective characterization of cell‒cell relationships in the tumor microenvironment provides significantly better predictive power than PD-L1 expression, tumor mutational burden (TMB), or gene expression profiles. In this small study assessing the safety and possible efficacy of gemcitabine and pembrolizumab in immunotherapy-naïve patients with NSCLC who have received prior treatment, we investigated both standard and novel immune parameters in 16 enrolled patients (NCT03083808). The combination of gemcitabine and pembrolizumab could be administered safely but did not demonstrate synergism compared with historical controls. Novel findings of this study are that elevated frequencies of regulatory T cells near CD3 T cells at baseline were associated with improved outcome to treatment (p < 0.05). Integrating this Treg/T cell relationship metric together with overall T-cell density yielded a tumor inflammation score that correlated (p < 0.002) with disease response. We postulate that this is indicative of an ongoing anticancer immune response. Additionally, while prior studies documented that IgG Ab responses to TAA can identify targets of a coordinated T and B cell response and evidence of immune surveillance, this study found that high autoantibody responses, while not statistically significant, trended toward a worse outcome (p = 0.06). This suggests to us that tumors may have developed mechanisms to escape the immune response to these TAAs.

Artificial intelligence is transforming reproductive medicine by enabling more nuanced and personalized interpretation of key biomarkers such as anti-Müllerian hormone (AMH) and inhibin B. While AMH is widely adopted for assessing ovarian reserve, inhibin B—an follicle-stimulating hormone (FSH)-dependent marker of follicular activity—has been historically underutilized due to its dynamic nature and lack of standardized assays. In this review, we explore how AI-driven tools can integrate these hormonal signals to improve clinical decision-making. We highlight three representative models: OvaRePred (also known as HerTempo), which predicts ovarian reserve and perimenopausal timing; PCOSt, which enables early screening and phenotyping of polycystic ovary syndrome; and POvaStim, which personalizes gonadotropin dosing by modeling ovarian sensitivity. Collectively, these tools offer new possibilities for individualized fertility management and may help shift reproductive care from reactive treatment toward proactive, data-informed health planning. We also discuss emerging innovations such as cross-platform assay harmonization, point-of-care hormone testing, and longitudinal biomarker modeling. Looking ahead, integration with nutritional interventions, wearable technologies, and genetic or immunologic data could extend these tools beyond assisted reproduction—supporting a broader, AI-enabled ecosystem for women’s lifelong health.

The development of highly efficient and cost-effective non-precious metal electrocatalysts for the hydrogen evolution reaction (HER) in alkaline solutions remains a significant challenge in green hydrogen production through water electrolysis. Herein, a reduced graphene oxide/nickel-cobalt bimetallic phosphide (rGO/NiCoP) catalyst was fabricated on carbon cloth by ultrasonic spraying of a rGO/metal oxide precursor (rGO/NiCoOx) and subsequent plasma phosphidation. Benefiting from the bimetallic synergy and the interfacial coupling between rGO and active NiCoP, the composite electrocatalyst achieves a current density of -10 mA cm-2 at an overpotential of just 36.1 mV, along with remarkable stability over 200 hours. Additionally, the rGO/NiCoP‖RuO2 electrolyzer requires only 1.53 V to drive a current density of 10 mA cm-2 and demonstrates satisfactory stability during 60 hours of continuous operation. This work provides a novel and scalable approach for the fabrication of high-performance transition metal phosphide electrocatalysts for HER applications.

Macrophage dysfunction is hallmark of atherosclerotic disease, characterized by inflammation and uptake of oxidized low-density lipoproteins. We investigate the role of the epigenetic reader bromodomain-containing protein 4 (BRD4) in orchestrating macrophage responses through interactions with the mechanosensitive transcriptional coactivators YAP/TAZ. Suppression of BRD4 via bromodomain and extra-terminal motif (BET) protein inhibitors (BETi) unveils a remarkable capacity to mitigate YAP/TAZ-driven inflammation. Knockdown of YAP, TAZ, or BRD4 in macrophages shows a significant convergence of inflammatory genes under the regulatory purview of these transcriptional regulators. In addition, persistent activation of YAP and TAZ initiates a partial inflammatory phenotype in macrophages, which is effectively ameliorated with BETi. Notably, CD36 and low-density lipoprotein receptor-1 (LOX1), pivotal receptors involved in uptake of oxidized low-density lipoprotein, emerge as direct YAP/TAZ targets. We employed a BD2-specific BETi, ABBV-744, in an AAV-PCSK9-induced atherosclerosis model to test the therapeutic potential of BET inhibition. Although reduction in cholesterol levels is modest, BETi substantially curtails plaque formation, diminishing macrophage infiltration, and suppressing the upregulation of YAP/TAZ and oxidized low-density lipoprotein uptake receptors associated with atherogenesis. Intriguingly, even in conditions marked by heightened YAP/TAZ expression induced by myeloid cell-targeted YAP/TAZ overexpression, BETi effectively dampens inflammation, mitigates foam cell formation, and disease progression. Our work underscores the considerable promise of targeting the YAP/TAZ-BRD4 axis as a therapeutic strategy for averting atherosclerosis, thereby disrupting the relentless cycle of inflammation, mechanosensory responses, and oxLDL uptake characteristic of atherosclerosis progression.