Citations

Background and Aims: Liver fibrosis and cirrhosis are major global health burdens with limited treatments. Activated HSCs drive fibrosis through increased proliferation, migration, collagen deposition and glycolysis. The aim of this study is to elucidate the signaling events that lead to enhanced glycolysis, subsequent HSC activation, and liver fibrosis. Approach and Results: Utilizing a phospho-kinase array, we identified with no lysine kinase 1 (WNK1) as a new kinase in primary human HSCs activated by the profibrotic PDGF-B. PDGF-mediated glycolysis in HSCs was attenuated by the WNK1 selective inhibitor, WNK-IN-11, as measured by live-cell metabolic assay, Glifon-300 fluorescence, Glucose-Glo assay, and glucose transporter 1 immunofluorescence. To understand how WNK1 promotes glycolysis, we employed chemical genetics where an engineered WNK1 carrying threonine 301 to glycine mutation (WNK1 T301G ) mutant with a modified ATP pocket accommodated a specific ATP-γ-S analogue to thio-phosphorylate its substrates. Thio-phosphorylated proteins were analyzed by mass spectrometry, which identified the glycolytic protein, triosephosphate isomerase 1 (TPI1), as a novel WNK1 direct substrate. TPI1 interacted with WNK1 in condensates, membrane-less phase separation structures. Condensate disruption and knockdown of TPI1 or WNK1 reduced HSC proliferation and migration in vitro. In vivo, pharmacological or genetic WNK1 inhibition significantly attenuated liver fibrosis in carbon tetrachloride and high-fat, choline-deficient, L -amino acid diet mouse models. Finally, WNK1 inhibition reduced fibrogenesis ex vivo in human precision-cut liver slices. Conclusions: These findings identify WNK1 as a previously unrecognized kinase that promotes HSC activation and liver fibrosis via TPI1-mediated glycolysis, highlighting the WNK1-TPI1 axis as a potential therapeutic target.

We demonstrate the structural and functional similarities of organoid-derived gastric mucus and native mucus collected from human patients. The bioengineered gastric mucus mimics its native counterpart in its proteomic profile and physical architecture. This work highlights the translational potential of organoid-derived mucus for functional investigations of the human gastric mucus layer.

Abstract Aims Trends in upright seated pulmonary artery pressure (PAP) with synchronous measurement of weight, blood pressure (BP), and heart rate (HR) have not been compared prior to heart failure events (HFE) and other causes of hospitalization during remote monitoring. Methods We analysed 24-month data from patients in the PROACTIVE-HF trial. Participants (N = 456) used the CordellaTM HF daily to measure seated PAP, weight, BP, and HR, all taken and transmitted within minutes. Events that were analysed included HFE, those potentially related to HF treatment (HF-TRE), and others (non-HFE). Parameters were trended from 60-days before (D-60) to 30-days after (D + 30) the day of the event (D = 0), and their concurrent changes were compared between D-60 and D = 0, and D = 0 and D + 30 using a mixed effects model. Results There were 196 HFE, 276 non-HFE, and 66 HF-TRE. Prior to HFE, average mean PAP (mPAP) increased from 27.8 ± 10.9 mmHg at D-60 to 32.6 ± 11.2 mmHg at D = 0 (P < .001), and decreased to 30.0 ± 12.4 mmHg at D + 30 (P = .001). Kinetics of mPAP before HFE differed by left ventricular ejection fraction (LVEF); mPAP rose by 3.4 ± 6.8 mmHg in 2 weeks before HFE in those with LVEF >40%, whereas a similar magnitude (3.1 ± 9.1 mmHg) of mPAP rise occurred over 6-weeks before HFE in those with LVEF ≤40%. No consistent changes in weight, SBP, or HR prior to HFE were observed (P > .05). Changes in vital signs but not mPAP were noted prior to HF-TRE and non-HFE. Conclusion Seated mPAP increased weeks in advance of HFE and vital signs did not change consistently before HFE. Differential kinetics of mPAP prior to HFE by the LVEF suggest that an uptrend in seated mPAP may need to be addressed more rapidly in those with LVEF>40%. Changes in vital signs with non-HFE and HF-TRE need further validation.

ABSTRACT The objective of this study is to describe outcomes of rapid exome (rES) and rapid genome sequencing (rGS) in an inpatient setting. This is a retrospective cohort of inpatients with rES or rGS during their hospitalization between April 2016 and November 2023. Medical records were reviewed to measure primary outcomes: diagnostic yield, medical management changes, and anticipatory guidance updates. Descriptive analysis and formal comparisons (Welch's t ‐test with post hoc power analysis, Fischer's exact test) were performed, with rES and rGS comparisons limited to 2023 due to test distribution. Of 95 patients, most (74.7% [71/95]) were < 12 months old and admitted in neonatal (44.2% [42/95]) or pediatric (29.5% [28/95]) intensive care units. Common indications for rapid sequencing included multiple congenital anomalies (24.2% [23/95]), neurological concerns (23.3% [22/95]), and acute illness (16.8% [16/95]). Mean turnaround time of testing significantly shortened for rES from 2021 (13.2 [SD = 5.9] days) to 2023 (8.1 [SD = 4.1] days) ( p ‐value = 0.018; power = 0.74). Diagnostic yield was 29.5% (28/95) and did not significantly differ between rES (13.6% [3/22]) and rGS (35.0% [7/20]) ( p ‐value = 0.15). Sequencing led to medical management changes (86.0% [74/86]) and anticipatory guidance updates (41.4% [39/95]) with similar rates observed in diagnostic and non‐diagnostic tests. Our findings suggest rES or rGS implementation may influence diagnosis and care strategies in critically ill patients.

Abstract Background Computed tomography coronary angiography (CCTA) has emerged as a powerful non-invasive imaging modality for the evaluation of coronary artery disease (CAD), with a global trend towards a "CT-first" strategy in stable patients with suspected CAD. While overall beneficial, this approach requires a shift in resources, straining specific parts of healthcare systems. Pre-test probability (PTP) models stratify risks of obstructive CAD and are intended to result in better utilization of CCTA. However, these models show variability across subpopulations and have modest discriminatory power with low positive predictive values (PPV). Purpose This study aims to leverage explainable artificial intelligence (XAI) for optimization of PTP models. Methods Patients that underwent a CCTA for suspected CAD with a CAD-Reporting and Data System (CAD-RADS) classification and Coronary Artery Calcium Score (CACS) were included. Obstructive CAD was defined as stenosis &gt;50% (CAD-RADS 3 or higher). Missing data was imputed. The Risk Factor Weighted Clinical Likelihood (RF-CL) model based on clinical risk factors (age, sex, type of symptoms, smoking, hypertension, dyslipidemia, diabetes and familial predisposition) was calculated. Two gradient descent-based decision tree models, "AI-ECG" and "AI-CAC" were developed. AI-ECG is built using clinical risk factors and electrocardiogram (ECG) classification. AI-CAC is built using the aforementioned features with addition of CACS. Sensitivity analysis was performed using a 15% threshold, aligning with the threshold for CCTA acquisition. Comparative analysis was performed using Python 3.10.2, with area under the curve (AUC) analysis using the DeLong test and feature importance calculation using shapely values. Preliminary results: We identified 6178 patients with a CCTA between 2022 and 2024 in the Cardiology Centers Netherlands (CCN) and included 2083 patients (mean age 57.4 ± 10.2; 48.1% male). Obstructive CAD was present in 413 (19.8%) patients. The RF-CL model was outperformed by the AI-ECG model (AUC: 0.69 vs 0.80, p&lt;0.001) and the AI-CACS model (AUC: 0.69 vs 0.90, p&lt;0.001). Although sensitivity increased in both the AI-ECG and AI-CACS models from 0.46 to 0.87 and 0.83 respectively, PPV increased only in the AI-CACS model (0.37 to 0.61). Most important features were CACS, age, sex, ECG classification and familial predisposition. Conclusion and Discussion Our study demonstrates that the incorporation of XAI with clinical risk factors, ECG classification and CACS improves the pre-test probability estimation of obstructive CAD. ECG classification appears to be a robust feature. Adopting these models can stratify patients at risk for obstructive CAD, enabling targeted use of CCTA, reducing unnecessary testing, and thus alleviate the burden on healthcare systems. However, further external validation is necessary to assess the model’s performance in prospective real-world settings.Sensitivity analysis feature importance

Abstract Background Coronary Computed Tomography Angiography (CCTA) is the first-line diagnostic tool for suspected stable Coronary Artery Disease (CAD) to rule out obstructive CAD and is recommended for risk stratification. Both obstructive CAD and total plaque burden on CCTA predict cardiovascular (CV) risk. Prior studies demonstrated that type 2 diabetes (T2D) patients exhibit greater plaque burden and higher CV event risk. Purpose This study evaluates whether T2D patients undergoing CCTA for stable CAD symptoms, when matched for traditional cardiovascular risk factors, have a higher prevalence of CAD and greater atherosclerotic plaque burden. Methods A total of 568 patients were included: 284 T2D patients were 1:1 propensity-matched to 284 non-T2D patients based on age, sex, and CV risk factors. All underwent diagnostic CCTA between January 2022 and November 2023. CCTA parameters included CAD-Reporting and Data System (RADS) classification, segment involvement score (SIS), coronary artery calcium (CAC) score, and plaque composition. SIS was defined as the number of segments with at least mild plaque. Coronary segmentation followed the 16-segment model (SCCT). Plaque burden was classified as P1 (mild), P2 (moderate), P3 (severe), and P4 (extensive) per CAD-RADS 2.0, based on SIS, CAC score, and the number and severity of affected vessels. Results The mean age was 60.8 ± 9.97 years (T2D) vs. 61.2 ± 9.80 years (non-T2D), with similar gender distribution (48.6% vs. 48.9% female). BMI was significantly higher in T2D patients (29.2 vs. 26.5, p &lt; 0.05). Smoking was less frequent in T2D patients (56.0% vs. 60.9%, p = 0.03). Other CV risk factors were similar. T2D patients were more likely to use antiplatelet and lipid-lowering agents. CAD prevalence was higher in T2D patients (83.1% vs. 76.4%, p &lt; 0.05), while obstructive CAD was not significantly higher in T2D (33.5% vs. 27.5%, p = 0.13). No significant differences were observed in 1-, 2-, or 3-vessel disease. The SIS was significantly higher in T2D patients (3.85 vs. 3.37, p = 0.03). CAC scores were higher in T2D patients (median 60 [6–369] vs. 31 [0–224]), though not statistically significant. For plaque burden classification, P2 (moderate plaque burden) was more common in non-T2D patients (23.6% vs. 15.5%, p = 0.02), while P4 (extensive plaque burden) was significantly higher in T2D patients (21.1% vs. 14.1%, p = 0.03). Conclusion When matched for cardiovascular risk factors, T2D patients more frequently exhibited CAD on CCTA, with higher SIS scores and a greater prevalence of extensive plaque burden (P4 classification). Despite greater use of preventive medication and a lower smoking prevalence in the T2D group, plaque burden remained higher in these patients. These findings emphasize the need for improved risk stratification and preventive strategies in individuals with T2D.

Abstract Background Coronary computed tomography angiography (CCTA) is a non-invasive diagnostic modality for assessing coronary artery disease (CAD), with a growing global trend favouring a "CT-first" approach. However, this strategy strains healthcare systems as demand for CCTA increases. Pre-test probability (PTP) models for obstructive CAD are used to optimize the usage of CCTA. However, these models perform inconsistently across different subpopulations and offer limited discriminatory accuracy, with low positive predictive values (PPV). This study aims to enhance the PTP models with explainable artificial intelligence (XAI) on electrocardiogram (ECG) data. Methods Patients who underwent CCTA for suspected CAD with both CAD-Reporting and Data System (CAD-RADS) classification and Coronary Artery Calcium Score (CACS) were included. Obstructive CAD was defined as CAD-RADS 3 or above. Missing values were imputed. The Risk Factor Weighted Clinical Likelihood (RF-CL) model, based on clinical variables (age, sex, symptom type, smoking, hypertension, dyslipidaemia, diabetes, and family history) was computed. Two gradient descent decision tree models, "AI-ECG" and "AI-CAC," were developed for the pilot study. AI-ECG used clinical risk factors and ECG classification as abnormal, borderline or normal, while AI-CAC further incorporated CACS. We used a cutoff value of 15%, reflecting current guidelines. Comparative analysis was performed using area under the curve (AUC) analysis, the DeLong test for statistical comparison, and feature importance using SHAP values. We intend to develop and present a model based on ECG signal data during the conference. Pilot results We identified 6 178 patients who underwent CCTA between 2022 and 2024 at the Cardiology Centers Netherlands (CCN), of which 2 083 were included (mean age 57.4 ± 10.2 years; 48.1% male). Obstructive CAD was found in 413 (19.8%) patients. The RF-CL model was outperformed by both AI-ECG (AUC: 0.69 vs. 0.80, p&lt;0.001) and AI-CACS (AUC: 0.69 vs. 0.90, p&lt;0.001). Sensitivity increased in both AI models, from 0.46 to 0.87 for AI-ECG and to 0.83 for AI-CACS, while PPV improved only in the AI-CACS model (from 0.37 to 0.61). Key predictive features were CACS, age, sex, ECG classification, and family history. Conclusion and Discussion Our pilot study shows that integrating XAI with clinical risk factors and ECG classification enhances PTP estimation for obstructive CAD. Implementing this model can better stratify patients at risk of obstructive CAD, support more focused CCTA use and reduce unnecessary testing. Nonetheless, further external validation is needed to assess model performance in real-world, prospective clinical settings. Additionally, models based on raw ECG signals have the potential to extract more detailed information.Reciever Operating Characteristic CurvesFeature Importance

Abstract Background Astroblastomas are rare brain tumors predominantly affecting children and young adults, for which molecular subtypes and clinical management remain undefined. Methods We analyzed tumor samples, molecular profiles, and clinical data from 200 patients, classified as “Astroblastoma, MN1-altered” under WHO criteria, using DNA methylation profiling, DNA/RNA profiling/sequencing, and survival analyses. Results DNA methylation analyses identified 3 groups: Group A (n = 143, characterized by MN1::BEND2 fusions, predominantly supratentorial location, with striking female predominance and favorable survival); Group B (n = 37, epigenetically and transcriptionally closely related to Group A, but characterized by EWSR1::BEND2 fusions, with spinal and infratentorial locations and poor prognosis); and Group C (n = 20, epigenetically and transcriptionally distinct, characterized by MN1::CXXC5 fusions, exclusively supratentorially located, with favorable survival). Progression-free and overall survival were significantly shorter in Group B (5-year PFS 14%; 10-year OS 54%) compared to A (5-year PFS 47%; 10-year OS 89%) and C (5-year PFS 75%; 10-year OS 89%). Radiotherapy improved PFS in Group B (hazard ratio 0.25), while no clear benefit was identified for Groups A and C. Conclusions Astroblastoma, MN1-altered, comprises 3 molecularly and clinically distinct groups, characterized by different fusion genes, including those without MN1. These new insights, including the identification of potential predictive biomarkers like 14q/16q loss, provide a framework for the development of risk-stratified therapeutic approaches. Importantly, we identified a molecularly defined high-risk group that benefits from radiation therapy. Our findings redefine Astroblastoma as a molecularly diverse tumor type, propose a refined classification, support the development of risk-adapted therapeutic strategies and provide a rational standard of care.

Background The efficacy of EVOKE therapy, a closed-loop spinal cord stimulation (SCS) system, has been demonstrated in a double-blinded randomized controlled trial (RCT). RCT evidence, however, may not be generalizable to clinical practice, while limitations of real-world evidence may affect its validity. The aim of this study was to evaluate the effectiveness of EVOKE therapy in routine clinical practice employing a rigorous data collection approach. Methods The ECAP study was a prospective, multicenter, single-arm, pragmatic study that enrolled SCS candidates with chronic, intractable trunk and/or limb pain in 22 US investigational sites. PROMIS-29 questionnaire and objective neurophysiological metrics were collected, and minimal clinically important differences (MCIDs) were used to characterize baseline dysfunction and response to EVOKE therapy both at trial end and maximal analgesic effect post-implant visit. Pre-specified subgroup analyses were conducted based on diagnosis. Results EVOKE therapy observed included a dose ratio of 1.3 and neural dose accuracy of 2.8 μV. Improvements of >1 MCID were observed for all domains and improvements of ≥2 MCIDs were observed for six of the seven domains. Improvements observed at trial end were maintained or improved at post-implant visit, with statistically significant improvement in holistic MCID (MD 0.2, 95% CI 0.1 to 0.4, p=0.004), and a significantly greater proportion of patients considered holistic MCID responders (p=0.047). Subgroup analysis by diagnosis showed similar outcomes. Conclusions EVOKE therapy was associated with clinically meaningful improvements in a real-world chronic pain population across different subgroups and disease pathologies, with effects observed at trial end reproduced at post-implant visit.