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The field of metabolomics has witnessed the development of hundreds of computational tools, but only a few have become cornerstones of this field. While MetaboLights and Metabolomics Workbench are two well-established data repositories for metabolomics data sets, Workflows4Metabolomics and MetaboAnalyst are two well-established web-based data analysis platforms for metabolomics. Yet, the raw data stored in the aforementioned repositories lack standardization in terms of the file system format used to store the associated acquisition files. Consequently, it is not straightforward to reuse available data sets as input data in the above-mentioned data analysis resources, especially for non-expert users. This paper presents CloMet, a novel open-source modular software platform that contributes to standardization, reusability, and reproducibility in the metabolomics field. CloMet, which is available through a Docker file, converts raw and NMR-based metabolomics data from MetaboLights and Metabolomics Workbench to a file format that can be used directly either in MetaboAnalyst or in Workflows4Metabolomics. We validated both CloMet and the output data using data sets from these repositories. Overall, CloMet fills the gap between well-established data repositories and web-based statistical platforms and contributes to the consolidation of a data-driven perspective of the metabolomics field by leveraging and connecting existing data and resources.
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http://dx.doi.org/10.1021/acs.jproteome.2c00602 | DOI Listing |
Reprod Biol
September 2025
Department of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Engineering Research Center of Biopreservation and Artificial Organs, Ministry of Education, No 218 Jixi Road, Hefei Anhui230022, China; Key Laboratory of Population Health Across
Current research indicates that polyethylene terephthalate microplastics (PET-MPs) may significantly impair male reproductive function. This study aimed to investigate the potential molecular mechanisms underlying this impairment. Potential gene targets of PET-MPs were predicted via the SwissTargetPrediction database.
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September 2025
Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, PR China; Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, PR China. Electronic address:
Eur J Radiol
September 2025
Department of Radiology, Affiliated Hospital of Hebei University, Baoding 071000, China. Electronic address:
Purpose: The present study aimed to develop a noninvasive predictive framework that integrates clinical data, conventional radiomics, habitat imaging, and deep learning for the preoperative stratification of MGMT gene promoter methylation in glioma.
Materials And Methods: This retrospective study included 410 patients from the University of California, San Francisco, USA, and 102 patients from our hospital. Seven models were constructed using preoperative contrast-enhanced T1-weighted MRI with gadobenate dimeglumine as the contrast agent.
JACC Heart Fail
September 2025
Université de Lorraine, Inserm, Centre d'Investigations Cliniques Plurithématique 1433, Centre Hospitalier Régional Universitaire de Nancy, Nancy, France.
Pathol Res Pract
September 2025
Department of Pathology, Xijing Hospital and School of Basic Medicine, Fourth Military Medical University, Xi'an, China. Electronic address:
Background: Dermal clear cell sarcoma (DCCS) is a rare malignant mesenchymal neoplasm. Owing to the overlaps in its morphological and immunophenotypic profiles with a broad spectrum of tumors exhibiting melanocytic differentiation, it is frequently misdiagnosed as other tumor entities in clinical practice. By systematically analyzing the clinicopathological characteristics, immunophenotypic features, and molecular biological properties of DCCS, this study intends to further enhance pathologists' understanding of this disease and provide a valuable reference for its accurate diagnosis.
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