Mesoscopic structure graphs for interpreting uncertainty in non-linear embeddings.

Probabilistic-based non-linear dimensionality reduction (PB-NL-DR) methods, such as t-SNE and UMAP, are effective in unfolding complex high-dimensional manifolds, allowing users to explore and understand the structural patterns of data. However, due to the trade-off between global and local structure preservation and the randomness during computation, these methods may introduce false neighborhood relationships, known as distortion errors and misleading visualizations. To address this issue, we first conduct a detailed survey to illustrate the design space of prior layout enrichment visualizations for interpreting DR results, and then propose a node-link visualization technique, ManiGraph. This technique rethinks the neighborhood fidelity between the high- and low-dimensional spaces by constructing dynamic mesoscopic structure graphs and measuring region-adapted trustworthiness. ManiGraph also addresses the overplotting issue in scatterplot visualization for large-scale datasets and supports examining in unsupervised scenarios. We demonstrate the effectiveness of ManiGraph in different analytical cases, including generic machine learning using 3D toy data illustrations and fashion-MNIST, a computational biology study using a single-cell RNA sequencing dataset, and a deep learning-enabled colorectal cancer study with histopathology-MNIST.

A pathology foundation model for cancer diagnosis and prognosis prediction.

Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task1,2. Although such methods have achieved some success, they often have limited generalizability to images generated by different digitization protocols or samples collected from different populations3. Here, to address this challenge, we devised the Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, a general-purpose weakly supervised machine learning framework to extract pathology imaging features for systematic cancer evaluation. CHIEF leverages two complementary pretraining methods to extract diverse pathology representations: unsupervised pretraining for tile-level feature identification and weakly supervised pretraining for whole-slide pattern recognition. We developed CHIEF using 60,530 whole-slide images spanning 19 anatomical sites. Through pretraining on 44 terabytes of high-resolution pathology imaging datasets, CHIEF extracted microscopic representations useful for cancer cell detection, tumour origin identification, molecular profile characterization and prognostic prediction. We successfully validated CHIEF using 19,491 whole-slide images from 32 independent slide sets collected from 24 hospitals and cohorts internationally. Overall, CHIEF outperformed the state-of-the-art deep learning methods by up to 36.1%, showing its ability to address domain shifts observed in samples from diverse populations and processed by different slide preparation methods. CHIEF provides a generalizable foundation for efficient digital pathology evaluation for patients with cancer.

Predicting physical functioning status in older adults: insights from wrist accelerometer sensors and derived digital biomarkers of physical activity.

OBJECTIVE: Conventional physical activity (PA) metrics derived from wearable sensors may not capture the cumulative, transitions from sedentary to active, and multidimensional patterns of PA, limiting the ability to predict physical function impairment (PFI) in older adults. This study aims to identify unique temporal patterns and develop novel digital biomarkers from wrist accelerometer data for predicting PFI and its subtypes using explainable artificial intelligence techniques. MATERIALS AND METHODS: Wrist accelerometer streaming data from 747 participants in the National Health and Aging Trends Study (NHATS) were used to calculate 231 PA features through time-series analysis techniques-Tsfresh. Predictive models for PFI and its subtypes (walking, balance, and extremity strength) were developed using 6 machine learning (ML) algorithms with hyperparameter optimization. The SHapley Additive exPlanations method was employed to interpret the ML models and rank the importance of input features. RESULTS: Temporal analysis revealed peak PA differences between PFI and healthy controls from 9:00 to 11:00 am. The best-performing model (Gradient boosting Tree) achieved an area under the curve score of 85.93%, accuracy of 81.52%, sensitivity of 77.03%, and specificity of 87.50% when combining wrist accelerometer streaming data (WAPAS) features with demographic data. DISCUSSION: The novel digital biomarkers, including change quantiles, Fourier transform (FFT) coefficients, and Aggregated (AGG) Linear Trend, outperformed traditional PA metrics in predicting PFI. These findings highlight the importance of capturing the multidimensional nature of PA patterns for PFI. CONCLUSION: This study investigates the potential of wrist accelerometer digital biomarkers in predicting PFI and its subtypes in older adults. Integrated PFI monitoring systems with digital biomarkers would improve the current state of remote PFI surveillance.

A systematic evaluation of computational methods for cell segmentation.

Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation and instance segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images. We found that general-purpose methods incorporating the attention mechanism exhibit the best overall performance. We identified various factors influencing segmentation performances, including image channels, choice of training data, and cell morphology, and evaluated the generalizability of methods across image modalities. We also provide guidelines for choosing the optimal segmentation methods in various real application scenarios. We developed Seggal, an online resource for downloading segmentation models already pre-trained with various tissue and cell types, substantially reducing the time and effort for training cell segmentation models.

HiCervix: An Extensive Hierarchical Dataset and Benchmark for Cervical Cytology Classification.

Cervical cytology is a critical screening strategy for early detection of pre-cancerous and cancerous cervical lesions. The challenge lies in accurately classifying various cervical cytology cell types. Existing automated cervical cytology methods are primarily trained on databases covering a narrow range of coarse-grained cell types, which fail to provide a comprehensive and detailed performance analysis that accurately represents real-world cytopathology conditions. To overcome these limitations, we introduce HiCervix, the most extensive, multi-center cervical cytology dataset currently available to the public. HiCervix includes 40,229 cervical cells from 4,496 whole slide images, categorized into 29 annotated classes. These classes are organized within a three-level hierarchical tree to capture fine-grained subtype information. To exploit the semantic correlation inherent in this hierarchical tree, we propose HierSwin, a hierarchical vision transformer-based classification network. HierSwin serves as a benchmark for detailed feature learning in both coarse-level and fine-level cervical cancer classification tasks. In our comprehensive experiments, HierSwin demonstrated remarkable performance, achieving 92.08% accuracy for coarse-level classification and 82.93% accuracy averaged across all three levels. When compared to board-certified cytopathologists, HierSwin achieved high classification performance (0.8293 versus 0.7359 averaged accuracy), highlighting its potential for clinical applications. This newly released HiCervix dataset, along with our benchmark HierSwin method, is poised to make a substantial impact on the advancement of deep learning algorithms for rapid cervical cancer screening and greatly improve cancer prevention and patient outcomes in real-world clinical settings.

Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading.

Artificial intelligence (AI) holds significant promise in transforming medical imaging, enhancing diagnostics, and refining treatment strategies. However, the reliance on extensive multicenter datasets for training AI models poses challenges due to privacy concerns. Federated learning provides a solution by facilitating collaborative model training across multiple centers without sharing raw data. This study introduces a federated attention-consistent learning (FACL) framework to address challenges associated with large-scale pathological images and data heterogeneity. FACL enhances model generalization by maximizing attention consistency between local clients and the server model. To ensure privacy and validate robustness, we incorporated differential privacy by introducing noise during parameter transfer. We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19,461 whole-slide images of prostate cancer from multiple centers. In the diagnosis task, FACL achieved an area under the curve (AUC) of 0.9718, outperforming seven centers with an average AUC of 0.9499 when categories are relatively balanced. For the Gleason grading task, FACL attained a Kappa score of 0.8463, surpassing the average Kappa score of 0.7379 from six centers. In conclusion, FACL offers a robust, accurate, and cost-effective AI training model for prostate cancer pathology while maintaining effective data safeguards.

A systematic evaluation of computation methods for cell segmentation.

Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images. We found that general-purpose methods incorporating the attention mechanism exhibit the best overall performance. We identified various factors influencing segmentation performances, including training data and cell morphology, and evaluated the generalizability of methods across image modalities. We also provide guidelines for choosing the optimal segmentation methods in various real application scenarios. We developed Seggal, an online resource for downloading segmentation models already pre-trained with various tissue and cell types, which substantially reduces the time and effort for training cell segmentation models.

Coping with groundwater pollution in high-nitrate leaching areas: The efficacy of denitrification.

The Ningxia Yellow River irrigation area, characterized by an arid climate and high leaching of NO3--N, exhibits complex and unique groundwater nitrate (NO3--N) pollution, with denitrification serving as the principal mechanism for NO3--N removal. The characteristics of N leaching from paddy fields and NO3--N removal by groundwater denitrification were investigated through a two-year field observation. The leaching losses of total nitrogen (TN) and NO3--N accounted for 10.81-27.34% and 7.59-12.74%, respectively, of the N input. The linear relationship between NO3--N leaching and N input indicated that the fertilizer-induced emission factor (EF) of NO3--N leaching in direct dry seeding and seedling-raising and transplanting paddy fields was 8.2% (2021, R2 = 0.992) and 6.7% (2022, R2 = 0.994), respectively. The study highlighted that the quadratic relationship between the NO3--N leaching loss and N input (R2 = 0.999) significantly outperformed the linear relationship. Groundwater denitrification capacity was characterized by monitoring the concentrations of dinitrogen (N2) and nitrous oxide (N2O). The results revealed substantial seasonal fluctuations in excess N2 and N2O concentrations in groundwater, particularly following fertilization and irrigation events. The removal efficiency of NO3--N via groundwater denitrification ranged from 42.70% to 74.38%, varying with depth. Groundwater denitrification capacity appeared to be linked to dissolved organic carbon (DOC) concentration, redox conditions, fertilization, irrigation, and soil texture. The anthropogenic-alluvial soil with limited water retention accelerated the leaching of NO3--N into groundwater during irrigation. This process enhances the groundwater recharge capacity and alters the redox conditions of groundwater, consequently impacting groundwater denitrification activity. The DOC concentration emerged as the primary constraint on the groundwater denitrification capacity in this region. Hence, increasing carbon source concentration and enhancing soil water retention capacity are vital for improving the groundwater denitrification capacity and NO3--N removal efficiency. This study provides practical insights for managing groundwater NO3--N pollution in agricultural areas, optimizing fertilization strategies and improving groundwater quality.

Metabolomics Profiling Predicts Ventricular Arrhythmia in Patients with an Implantable Cardioverter Defibrillator.

Implantable cardioverter defibrillators (ICDs) reduce sudden cardiac death (SCD) when patients experience life-threatening ventricular arrhythmias (LTVA). However, current strategies determining ICD patient selection and risk stratification are inefficient. We used metabolomics to assess whether dysregulated metabolites are associated with LTVA and identify potential biomarkers. Baseline plasma samples were collected from 72 patients receiving ICDs. Over a median follow-up of 524.0 days (range 239.0-705.5), LTVA occurred in 23 (31.9%) patients (22 effective ICD treatments and 1 SCD). After confounding risk factors adjustment for age, smoking, secondary prevention, and creatine kinase MB, 23 metabolites were significantly associated with LTVA. Pathway analysis revealed LTVA associations with disrupted metabolism of glycine, serine, threonine, and branched chain amino acids. Pathway enrichment analysis identified a panel of 6 metabolites that potentially predicted LTVA, with an area under the receiver operating characteristic curve of 0.8. Future studies are necessary on biological mechanisms and potential clinical use.

PhaseFIT: live-organoid phase-fluorescent image transformation via generative AI.

Organoid models have provided a powerful platform for mechanistic investigations into fundamental biological processes involved in the development and function of organs. Despite the potential for image-based phenotypic quantification of organoids, their complex 3D structure, and the time-consuming and labor-intensive nature of immunofluorescent staining present significant challenges. In this work, we developed a virtual painting system, PhaseFIT (phase-fluorescent image transformation) utilizing customized and morphologically rich 2.5D intestinal organoids, which generate virtual fluorescent images for phenotypic quantification via accessible and low-cost organoid phase images. This system is driven by a novel segmentation-informed deep generative model that specializes in segmenting overlap and proximity between objects. The model enables an annotation-free digital transformation from phase-contrast to multi-channel fluorescent images. The virtual painting results of nuclei, secretory cell markers, and stem cells demonstrate that PhaseFIT outperforms the existing deep learning-based stain transformation models by generating fine-grained visual content. We further validated the efficiency and accuracy of PhaseFIT to quantify the impacts of three compounds on crypt formation, cell population, and cell stemness. PhaseFIT is the first deep learning-enabled virtual painting system focused on live organoids, enabling large-scale, informative, and efficient organoid phenotypic quantification. PhaseFIT would enable the use of organoids in high-throughput drug screening applications.

Machine learning for cryosection pathology predicts the 2021 WHO classification of glioma.

BACKGROUND: Timely and accurate intraoperative cryosection evaluations remain the gold standard for guiding surgical treatments for gliomas. However, the tissue-freezing process often generates artifacts that make histologic interpretation difficult. In addition, the 2021 WHO Classification of Tumors of the Central Nervous System incorporates molecular profiles in the diagnostic categories, so standard visual evaluation of cryosections alone cannot completely inform diagnoses based on the new classification system. METHODS: To address these challenges, we develop the context-aware Cryosection Histopathology Assessment and Review Machine (CHARM) using samples from 1,524 glioma patients from three different patient populations to systematically analyze cryosection slides. FINDINGS: Our CHARM models successfully identified malignant cells (AUROC = 0.98 ± 0.01 in the independent validation cohort), distinguished isocitrate dehydrogenase (IDH)-mutant tumors from wild type (AUROC = 0.79-0.82), classified three major types of molecularly defined gliomas (AUROC = 0.88-0.93), and identified the most prevalent subtypes of IDH-mutant tumors (AUROC = 0.89-0.97). CHARM further predicts clinically important genetic alterations in low-grade glioma, including ATRX, TP53, and CIC mutations, CDKN2A/B homozygous deletion, and 1p/19q codeletion via cryosection images. CONCLUSIONS: Our approaches accommodate the evolving diagnostic criteria informed by molecular studies, provide real-time clinical decision support, and will democratize accurate cryosection diagnoses. FUNDING: Supported in part by the National Institute of General Medical Sciences grant R35GM142879, the Google Research Scholar Award, the Blavatnik Center for Computational Biomedicine Award, the Partners' Innovation Discovery Grant, and the Schlager Family Award for Early Stage Digital Health Innovations.

Retracted: Ultra-processed food consumption and risk of dementia and Alzheimer's disease: Long-term results from the Framingham Offspring Study

Retraction: Wang, K, Tang, W, Hao, X, Zhao, J. Ultra-processed food consumption and risk of dementia and Alzheimer's disease: Long-term results from the Framingham Offspring Study. Alzheimer's Dement. 2023; 1­11. https://doi.org/10.1002/alz.13351. The above article, published online on 03 July 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal's Editor-in-Chief Dr. Donna M. Wilcock, the Alzheimer's Association and Wiley Periodicals LLC. The retraction has been agreed as the authors did not have the appropriate approvals in place from the National Heart, Lung and Blood Institute (NHLBI) for use of the data in this article. This contravenes the journal's policy on data use and the journal is issuing this retraction as a result.

Contrast-enhanced image-guided lead deployment for left bundle branch pacing.

BACKGROUND: Left bundle branch pacing (LBBP) is a novel conduction system pacing modality, but pacing lead deployment remains challenging. OBJECTIVES: This study aimed to evaluate the feasibility of visualization-enhanced lead deployment for LBBP implantation and to assess LBBP characteristics on the basis of lead tip location. METHODS: Successful LBBP with a well-defined lead tip location by visualization of the tricuspid value annulus in 20 patients was retrospectively analyzed to develop an image-guided technique to identify the LBBP target site. This technique was then prospectively tested in 60 patients who were randomized into 2 groups, one using the standard approach (the standard group) and the other using the image-guided technique (the visualization group). The procedural details, electrophysiological characteristics, and short-term follow-up were compared between groups. RESULTS: LBBP was successfully achieved in 28 patients in the standard group and in 29 in the visualization group. The procedural and fluoroscopic durations in the visualization group (66.76 ± 14.62 and 7.83 ± 2.05 minutes) were significantly shorter than those in the standard group (85.46 ± 20.19 and 11.11 ± 3.51 minutes) (P < .01). The number of lead deployment attempts in the visualization group was lower than that in the standard group (2.03 ± 1.18 vs 2.96 ± 1.17; P < .01), and the proportion of left bundle branch potential recorded was higher (79.3% vs 46.4%; P = .01). CONCLUSION: Using a visualization technique, the procedural and fluoroscopic durations for LBBP implantation were significantly shortened with fewer lead repositioning attempts.

A Comparison of the Electrophysiological and Anatomic Characteristics of Pacing Different Branches of the Left Bundle Conduction System.

Introduction: Left bundle branch pacing (LBBP) is a rapidly growing conduction system pacing technique. However, little is known regarding the electrophysiological characteristics of different types of LBBP. We aimed to evaluate the electrophysiological characteristics and anatomic lead location with pacing different branches of the left bundle branch. Methods: Consecutive bradycardia patients with successful LBBP were enrolled and classified into groups according to the paced electrocardiogram and the lead location. Electrocardiogram, pacing properties, vectorcardiogram, and lead tip location were analyzed. Results: Ninety-one patients were enrolled, including 48 with the left bundle trunk pacing (LBTP) and 43 with the left bundle fascicular pacing (LBFP). The paced QRS duration in the LBTP group was significantly shorter than that in the LBFP group (108.1 ± 9.9 vs. 112.9 ± 11.2 ms, p = 0.03), with a more rightward QRS transition zone (p = 0.01). The paced QRS area in the LBTP group was similar to that during intrinsic rhythm (35.1 ± 15.8 vs. 34.7 ± 16.6 µVs, p = 0.98), whereas in the LBFP group, the paced QRS area was significantly larger compared to intrinsic rhythm (43.4 ± 15.8 vs. 35.7 ± 18.0 µVs, p = 0.01). The lead tip site for LBTP was located in a small fan-shaped area with the tricuspid valve annulus summit as the origin, whereas fascicular pacing sites were more likely in a larger and more distal area. Conclusions: Pacing the proximal left bundle main trunk produced better electrical synchrony compared with pacing the distal left bundle fascicles. A visualization technique can facilitate achieving LBTP.

Phoenixmap: An Abstract Approach to Visualize 2D Spatial Distributions.

The multidimensional nature of spatial data poses a challenge for visualization. In this paper, we introduce Phoenixmap, a simple abstract visualization method to address the issue of visualizing multiple spatial distributions at once. The Phoenixmap approach starts by identifying the enclosed outline of the point collection, then assigns different widths to outline segments according to the segments' corresponding inside regions. Thus, one 2D distribution is represented as an outline with varied thicknesses. Phoenixmap is capable of overlaying multiple outlines and comparing them across categories of objects in a 2D space. We chose heatmap as a benchmark spatial visualization method and conducted user studies to compare performances among Phoenixmap, heatmap, and dot distribution map. Based on the analysis and participant feedback, we demonstrate that Phoenixmap 1) allows users to perceive and compare spatial distribution data efficiently; 2) frees up graphics space with a concise form that can provide visualization design possibilities like overlapping; and 3) provides a good quantitative perceptual estimating capability given the proper legends. Finally, we discuss several possible applications of Phoenixmap and present one visualization of multiple species of birds' active regions in a nature preserve.

Comprehensive plasma metabolites profiling reveals phosphatidylcholine species as potential predictors for cardiac resynchronization therapy response.

AIMS: This study aimed to identify the plasma metabolite fingerprint in patients with heart failure and to develop a prediction tool based on differential metabolites for predicting the response to cardiac resynchronization therapy (CRT). METHODS AND RESULTS: We prospectively recruited 32 healthy individuals and 42 consecutive patients with HF who underwent CRT between January 2018 and January 2019. Peripheral venous blood samples, clinical data, and echocardiographic signatures were collected before CRT implantation. Liquid chromatography-mass spectrometry was used to perform untargeted metabolites profiling for peripheral plasma under ESI+ and ESI- modes. After 6 month follow-up, patients were categorized as CRT responders or non-responders based on the alterations of echocardiographic characteristics. Compared with healthy individuals, patients with HF had distinct metabolomic profiles under both ESI+ and ESI- modes, featuring increased free fatty acids, carnitine, ß-hydroxybutyrate, and dysregulated lipids with heterogeneous alterations such as phosphatidylcholines (PCs) and sphingomyelins. Disparities of baseline metabolomics profile were observed between CRT responders and non-responders under ESI+ mode but not under ESI- mode. Further metabolites analysis revealed that a group of 20 PCs metabolites under ESI+ mode were major contributors to the distinct profiles between the two groups. We utilized LASSO regression model and identified a panel of four PCs metabolites [including PC (20:0/18:4), PC (20:4/20:0), PC 40:4, and PC (20:4/18:0)] as major predictors for CRT response prediction. Among our whole population (n = 42), receive operating characteristics analysis revealed that the four PCs-based model could nicely discriminate the CRT responders from non-responders (area under the curve = 0.906) with a sensitivity of 83.3% and a specificity of 90.0%. Cross-validation analysis also showed a satisfactory and robust performance of the model with the area under the curve of 0.910 in the training dataset and 0.880 in the testing dataset. CONCLUSIONS: Patients with HF held significantly altered plasma metabolomics profile compared with the healthy individuals. Within the HF group, the non-responders had a distinct plasma metabolomics profile in contrast to the responders to CRT, which was characterized by increased PCs species. A novel predictive model incorporating four PCs metabolites performed well in identifying CRT non-responders. These four PCs might severe as potential biomarkers for predicting CRT response. Further validations are needed in multi-centre studies with larger external cohorts.

DNA methyltransferase 3B deficiency unveils a new pathological mechanism of pulmonary hypertension.

DNA methylation plays critical roles in vascular pathology of pulmonary hypertension (PH). The underlying mechanism, however, remains undetermined. Here, we demonstrate that global DNA methylation was elevated in the lungs of PH rat models after monocrotaline administration or hypobaric hypoxia exposure. We showed that DNA methyltransferase 3B (DNMT3B) was up-regulated in both PH patients and rodent models. Furthermore, Dnmt3b -/- rats exhibited more severe pulmonary vascular remodeling. Consistently, inhibition of DNMT3B promoted proliferation/migration of pulmonary artery smooth muscle cells (PASMCs) in response to platelet-derived growth factor-BB (PDGF-BB). In contrast, overexpressing DNMT3B in PASMCs attenuated PDGF-BB-induced proliferation/migration and ameliorated hypoxia-mediated PH and right ventricular hypertrophy in mice. We also showed that DNMT3B transcriptionally regulated inflammatory pathways. Our results reveal that DNMT3B is a previously undefined mediator in the pathogenesis of PH, which couples epigenetic regulations with vascular remodeling and represents a therapeutic target to tackle PH.

Plasma Metabolomic Profiles Differentiate Patients With Dilated Cardiomyopathy and Ischemic Cardiomyopathy.

Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are common causes of heart failure (HF). Though they share similar clinical characteristics, their differential effects on cardiovascular metabolomics have yet to be elucidated. In this study, we applied a comprehensive metabolomics platform to plasma samples of HF patients with different etiology (38 patients with DCM and 18 patients with ICM) and 20 healthy controls. Significant differences in metabolomics profiling were shown among two cardiomyopathy groups and healthy controls. Two hundred thirty three dysregulated metabolites were identified between DCM vs. healthy controls, and 204 dysregulated metabolites between ICM patients and healthy controls. They have 140 metabolites in common, with fold-changes in the same direction in both groups. Pathway analysis found the commonalities of HF pathways as well as disease-specific metabolic signatures. In addition, we found that a combination panel of 6 metabolites including 1-pyrroline-2-carboxylate, norvaline, lysophosphatidylinositol (16:0/0:0), phosphatidylglycerol (6:0/8:0), fatty acid esters of hydroxy fatty acid (24:1), and phosphatidylcholine (18:0/18:3) may have the potential to differentiate patients with DCM and ICM.

Spermine promotes pulmonary vascular remodelling and its synthase is a therapeutic target for pulmonary arterial hypertension.

Pathological mechanisms of pulmonary arterial hypertension (PAH) remain largely unexplored. Effective treatment of PAH remains a challenge. The aim of this study was to discover the underlying mechanism of PAH through functional metabolomics and to help develop new strategies for prevention and treatment of PAH.Metabolomic profiling of plasma in patients with idiopathic PAH was evaluated through high-performance liquid chromatography mass spectrometry, with spermine identified to be the most significant and validated in another independent cohort. The roles of spermine and spermine synthase were examined in pulmonary arterial smooth muscle cells (PASMCs) and rodent models of pulmonary hypertension.Using targeted metabolomics, plasma spermine levels were found to be higher in patients with idiopathic PAH compared to healthy controls. Spermine administration promoted proliferation and migration of PASMCs and exacerbated vascular remodelling in rodent models of pulmonary hypertension. The spermine-mediated deteriorative effect can be attributed to a corresponding upregulation of its synthase in the pathological process. Inhibition of spermine synthase in vitro suppressed platelet-derived growth factor-BB-mediated proliferation of PASMCs, and in vivo attenuated monocrotaline-mediated pulmonary hypertension in rats.Plasma spermine promotes pulmonary vascular remodelling. Inhibiting spermine synthesis could be a therapeutic strategy for PAH.

Circulating Plasma Metabolomic Profiles Differentiate Rodent Models of Pulmonary Hypertension and Idiopathic Pulmonary Arterial Hypertension Patients.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a severe progressive disease with systemic metabolic dysregulation. Monocrotaline (MCT)-induced and hypoxia-induced pulmonary hypertension (PH) rodent models are the most widely used preclinical models, however, whether or not these preclinical models recapitulate metabolomic profiles of PAH patients remain unclear. METHODS: In this study, a targeted metabolomics panel of 126 small molecule metabolites was conducted. We applied it to the plasma of the 2 preclinical rodent models of PH and 30 idiopathic pulmonary arterial hypertension (IPAH) patients as well as 30 healthy controls to comparatively assess the metabolomic profiles of PAH patients and rodent models. RESULTS: Significantly different metabolomics profiling and pathways were shown among the 2 classical rodent models and IPAH patients. Pathway analysis demonstrated that methionine metabolism and urea cycle metabolism were the most significant pathway involved in the pathogenesis of hypoxia-induced PH model and MCT-induced model, respectively, and both of them were also observed in the dysregulated pathways in IPAH patients. CONCLUSIONS: These 2 models may develop PAH through different metabolomic pathways and each of the 2 classical PH model resembles IPAH patients in certain aspects.