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The lower infrastructure requirements of portable ultra‐low field MRI (ULF‐MRI) systems have enabled their use in diverse settings such as intensive care units and remote medical facilities. The UNITY Project is an international neuroimaging network harnessing this technology, deploying portable ULF‐MRI systems globally to expand access to MRI for studies into brain development. Given the wide range of environments where ULF‐MRI systems may operate, there are external factors that might influence image quality. This work aims to introduce the quality control (QC) framework used by the UNITY Project to investigate how robust the systems are and how QC metrics compare between sites and over time. We present a QC framework using a commercially available phantom, scanned with 64 mT portable MRI systems at 17 sites across 12 countries on four continents. Using automated, open‐source analysis tools, we quantify signal‐to‐noise, image contrast, and geometric distortions. Our results demonstrated that the image quality is robust to the varying operational environment, for example, electromagnetic noise interference and temperature. The Larmor frequency was significantly correlated to room temperature, as was image noise and contrast. Image distortions were less than 2.5 mm, with high robustness over time. Similar to studies at higher field, we found that changes in pulse sequence parameters from software updates had an impact on QC metrics. This study demonstrates that portable ULF‐MRI systems can be deployed in a variety of environments for multi‐center neuroimaging studies and produce robust results. A global study on quality control of portable, ultra‐low field MRI systems within the UNITY network. Cross‐sectional and longitudinal data are presented together with analysis of factors influencing image noise, contrast, and distortions.

Multi-center studies using magnetic resonance imaging facilitate studying small effect sizes, global population variance and rare diseases. The reliability and sensitivity of these multi-center studies crucially depend on the comparability of the data generated at different sites and time points. The level of inter-site comparability is still controversial for conventional anatomical T1-weighted MRI data. Quantitative multi-parameter mapping (MPM) was designed to provide MR parameter measures that are comparable across sites and time points, i.e., 1 mm high-resolution maps of the longitudinal relaxation rate (R1 = 1/T1), effective proton density (PD(*)), magnetization transfer saturation (MT) and effective transverse relaxation rate (R2(*) = 1/T2(*)). MPM was validated at 3T for use in multi-center studies by scanning five volunteers at three different sites. We determined the inter-site bias, inter-site and intra-site coefficient of variation (CoV) for typical morphometric measures [i.e., gray matter (GM) probability maps used in voxel-based morphometry] and the four quantitative parameters. The inter-site bias and CoV were smaller than 3.1 and 8%, respectively, except for the inter-site CoV of R2(*) (<20%). The GM probability maps based on the MT parameter maps had a 14% higher inter-site reproducibility than maps based on conventional T1-weighted images. The low inter-site bias and variance in the parameters and derived GM probability maps confirm the high comparability of the quantitative maps across sites and time points. The reliability, short acquisition time, high resolution and the detailed insights into the brain microstructure provided by MPM makes it an efficient tool for multi-center imaging studies.

INTRODUCTION We aimed to develop risk and prognostic prediction tools for early‐onset dementia (EOD) using health record data shared across five major international cohorts. METHODS More than 400,000 dementia‐free individuals younger than age 65 at baseline were included. Ensemble learning was used to construct the models. Cumulative incidence and Kaplan–Meier curves were used to visualize risk stratification, and subgroup analyses were conducted to evaluate potential disparities. RESULTS The CatBoost‐based risk model achieved an area under the receiver‐operating characteristic curve (AUROC) of 0.814 (<70 years) and 0.892 (<65 years). The Random Survival Forest (RF) prognostic model reached 5‐year AUROC of 0.656. Key predictors included age, employment status, and education. DISCUSSION Based on health record data, this study provides practical and scalable tools for EOD risk screening and prognosis prediction, with potential for implementation in community and primary care settings. Highlights We developed risk and prognostic prediction models for early‐onset dementia (EOD) using indicators shared across five international cohorts. Models showed good discrimination and calibration across internal and external sets, with key predictors including age and work status confirmed by shapley additive explanation (SHAP) analysis. Subgroup analyses supported fairness across sex, age, and comorbidity groups. Our study provides accessible and cost‐effective yet effective tools for the screening, prevention, and prognostic prediction of EOD in large community populations and primary care settings.

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice.

Transportation infrastructure planning is one of the essential ways to achieve a carbon-neutral society in China’s future. With regards to sustainable urban development, the Green Low-Carbon policy for Transportation is set out in the 14th Five-Year Plan Outline 2021–2025. However, there are only a limited number of previous studies that systematically combined land-use planning and urban transportation evolution to clarify the structural issues in urban transportation optimization. In this study, we use traditional analysis and space syntax analysis to examine the relationship between the urban development of Beijing and the evolution of its rail transit transportation. After analyzing Beijing’s multi-center and multi-circle rail transit structure, it was concluded that the current division of labor in Beijing’s rail transit is unclear. Analysis using space syntax shows that connecting suburban centers using suburban railways improves accessibility better than subways. However, after analyzing the synergy between these factors, it is found that the application of space syntax needs to be analyzed in combination with the actual situation.

A growing body of evidence has associated maternal exposure to air pollution with adverse effects on fetal growth; however, the existing literature is inconsistent. We aimed to quantify the association between maternal exposure to particulate air pollution and term birth weight and low birth weight (LBW) across 14 centers from 9 countries, and to explore the influence of site characteristics and exposure assessment methods on between-center heterogeneity in this association. Using a common analytical protocol, International Collaboration on Air Pollution and Pregnancy Outcomes (ICAPPO) centers generated effect estimates for term LBW and continuous birth weight associated with PM(10) and PM(2.5) (particulate matter ≤ 10 and 2.5 µm). We used meta-analysis to combine the estimates of effect across centers (~ 3 million births) and used meta-regression to evaluate the influence of center characteristics and exposure assessment methods on between-center heterogeneity in reported effect estimates. In random-effects meta-analyses, term LBW was positively associated with a 10-μg/m3 increase in PM10 [odds ratio (OR) = 1.03; 95% CI: 1.01, 1.05] and PM(2.5) (OR = 1.10; 95% CI: 1.03, 1.18) exposure during the entire pregnancy, adjusted for maternal socioeconomic status. A 10-μg/m3 increase in PM(10) exposure was also negatively associated with term birth weight as a continuous outcome in the fully adjusted random-effects meta-analyses (-8.9 g; 95% CI: -13.2, -4.6 g). Meta-regressions revealed that centers with higher median PM(2.5) levels and PM(2.5):PM(10) ratios, and centers that used a temporal exposure assessment (compared with spatiotemporal), tended to report stronger associations. Maternal exposure to particulate pollution was associated with LBW at term across study populations. We detected three site characteristics and aspects of exposure assessment methodology that appeared to contribute to the variation in associations reported by centers.

Background Due to the absence of specific symptoms and low survival rate, efficient biomarkers for hepatocellular carcinoma (HCC) diagnosis are urgently required. The purpose of this study was to evaluate the diagnostic performance of protein induced by vitamin K absence or antagonist‐II (PIVKA‐II) and to determine the optimal cutoff values for HBV infection‐related HCC. Methods We conducted a cross‐sectional, multi‐center study in China to ascertain the cutoff value for HCC patients in the context of CHB‐ and HBV‐related cirrhosis. The receiver operating characteristic curve (ROC) and the area under the curve (AUC) were used to evaluate the diagnostic performance of PIVKA‐II. Results This study enrolled 784 subjects and demonstrated that PIVKA‐II had a sensitivity of 84.08% and a specificity of 90.43% in diagnosis HCC from chronic liver diseases. PIVKA‐II at a cutoff of 37.5 mAU/mL yielded an AUC of 0.9737 (sensitivity 91.78% and specificity 96.30%) in discriminating HCC from chronic hepatitis B (CHB) patients. PIVKA‐II at a cutoff of 45 mAU/mL yielded an AUC of 0.9419 (sensitivity 77.46% and specificity 95.12%) in discriminating HCC‐ from HBV‐related cirrhosis patients. Furthermore, using a cutoff value of 40 mAU/mL for PIVKA‐II as an HCC marker, only 4.81% (15/312) was positive in chronic hepatitis and 12.80% (37/289) in cirrhosis patients, revealing the satisfactory specificity of PIVKA‐II in chronic liver disease of different etiologies. Conclusion Our data indicated that PIVKA‐II had satisfactory diagnostic efficiencies and could be used as a screening or surveillance biomarker in HCC high‐risk population. Multi‐center study showed the satisfactory diagnostic performance of PIVKA‐II in hepatocellular carcinoma. PIVKA‐II has excellent specificity in distinguishing benign liver diseases. The distribution of PIVKA‐II among chronic liver diseases.

Large-scale longitudinal multi-site MRI brain morphometry studies are becoming increasingly crucial to characterize both normal and clinical population groups using fully automated segmentation tools. The test–retest reproducibility of morphometry data acquired across multiple scanning sessions, and for different MR vendors, is an important reliability indicator since it defines the sensitivity of a protocol to detect longitudinal effects in a consortium. There is very limited knowledge about how across-session reliability of morphometry estimates might be affected by different 3T MRI systems. Moreover, there is a need for optimal acquisition and analysis protocols in order to reduce sample sizes. A recent study has shown that the longitudinal FreeSurfer segmentation offers improved within session test–retest reproducibility relative to the cross-sectional segmentation at one 3T site using a nonstandard multi-echo MPRAGE sequence. In this study we implement a multi-site 3T MRI morphometry protocol based on vendor provided T1 structural sequences from different vendors (3D MPRAGE on Siemens and Philips, 3D IR-SPGR on GE) implemented in 8 sites located in 4 European countries. The protocols used mild acceleration factors (1.5–2) when possible. We acquired across-session test–retest structural data of a group of healthy elderly subjects (5 subjects per site) and compared the across-session reproducibility of two full-brain automated segmentation methods based on either longitudinal or cross-sectional FreeSurfer processing. The segmentations include cortical thickness, intracranial, ventricle and subcortical volumes. Reproducibility is evaluated as absolute changes relative to the mean (%), Dice coefficient for volume overlap and intraclass correlation coefficients across two sessions. We found that this acquisition and analysis protocol gives comparable reproducibility results to previous studies that used longer acquisitions without acceleration. We also show that the longitudinal processing is systematically more reliable across sites regardless of MRI system differences. The reproducibility errors of the longitudinal segmentations are on average approximately half of those obtained with the cross sectional analysis for all volume segmentations and for entorhinal cortical thickness. No significant differences in reliability are found between the segmentation methods for the other cortical thickness estimates. The average of two MPRAGE volumes acquired within each test–retest session did not systematically improve the across-session reproducibility of morphometry estimates. Our results extend those from previous studies that showed improved reliability of the longitudinal analysis at single sites and/or with non-standard acquisition methods. The multi-site acquisition and analysis protocol presented here is promising for clinical applications since it allows for smaller sample sizes per MRI site or shorter trials in studies evaluating the role of potential biomarkers to predict disease progression or treatment effects. •We implemented a multi-site 3T MRI protocol for brain morphometry on 8EU sites.•We acquired across-session test-retest data on 40 healthy elderly subjects.•We calculated the reproducibility of cortical and volumetric FreeSurfer estimates.•Longitudinal segmentation was more reliable than cross-sectional on all sites.

The gut microbiome plays a crucial role in modulating host health and disease. It serves as a vast reservoir of functional molecules that hold great potential for clinical applications. One specific area of interest is identifying anticancer peptides (ACPs) for innovative cancer therapies. However, ACPs discovery is hindered by a heavy reliance on experimental methodologies. To overcome this limitation, we here employed a novel approach by leveraging the overlap between ACPs and antimicrobial peptides (AMPs). By combining well‐established AMP prediction methods with mining techniques in metagenomic cohorts, a total of 40 potential ACPs is identified. Out of the identified ACPs, 39 demonstrated inhibitory effects against at least one cancer cell line, exhibiting significant differences from known ACPs. Moreover, the therapeutic potential of the two most promising peptides in a mouse xenograft cancer model is evaluated. Encouragingly, the peptides exhibit effective tumor inhibition without any detectable toxic effects. Interestingly, both peptides display uncommon secondary structures, highlighting its distinctive characteristics. This findings highlight the efficacy of the multi‐center mining approach, which effectively uncovers novel ACPs from the gut microbiome. This approach has significant implications for expanding treatment options not only for CRC, but also for other cancer types. A workflow of discovering anticancer peptides (ACPs) from gut microbiome is devised, combining deeplearning based functional peptides identification and differential analysis in metagenomic data of colorectal cancer patients versus healthy. A total of 39 ACPs is found. The two most potent candidates are safe toward normal cells, with no acute toxicity or significantly reduced tumor growthin animals.

Density peak clustering (DPC) is a recently developed density-based clustering algorithm that achieves competitive performance in a non-iterative manner. DPC is capable of effectively handling clusters with single density peak (single center), i.e., based on DPC’s hypothesis, one and only one data point is chosen as the center of any cluster. However, DPC may fail to identify clusters with multiple density peaks (multi-centers) and may not be able to identify natural clusters whose centers have relatively lower local density. To address these limitations, we propose a novel clustering algorithm based on a hierarchical approach, named multi-center density peak clustering (McDPC). Firstly, based on a widely adopted hypothesis that the potential cluster centers are relatively far away from each other. McDPC obtains centers of the initial micro-clusters (named representative data points) whose minimum distance to the other higher-density data points are relatively larger. Secondly, the representative data points are autonomously categorized into different density levels. Finally, McDPC deals with micro-clusters at each level and if necessary, merges the micro-clusters at a specific level into one cluster to identify multi-center clusters. To evaluate the effectiveness of our proposed McDPC algorithm, we conduct experiments on both synthetic and real-world datasets and benchmark the performance of McDPC against other state-of-the-art clustering algorithms. We also apply McDPC to perform image segmentation and facial recognition to further demonstrate its capability in dealing with real-world applications. The experimental results show that our method achieves promising performance.