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After life in utero and birth, the skin is submitted to an important process of adaptation to a relatively dry gaseous environment. Skin surface lipids (SSLs) contribute actively to the protection of the skin barrier. Within this context, our objective was to study the evolution of each lipid compound during the postnatal period. SSLs were collected from six newborns a few days after birth until the age of 6 months. Seventy samples were analyzed using high-temperature gas chromatography coupled to mass spectrometry (HT-GC/MS). The use of separative techniques coupled to mass spectrometry for the analysis of samples containing complex mixtures of lipids generates a large volume of data which renders the results interpretation very difficult. In this study, we propose a new approach to handle the raw data, a clustering-based preprocessing method (CB-PPM), in order to achieve (1) volume reduction of data provided by each chromatogram without loss of information, (2) alignment of time retention shift between different runs, (3) clustering of mass spectra of the same molecule in one qualitative group, (4) and integration of all data into a single matrix to be explored by chemometric tools. This approach allowed us to gather data variations in 256 qualitative groups and therefore enabled us to highlight the variation of compounds including those of low intensity. Moreover, the representation of all data gathered in one matrix rendered reading of the results rapid and efficient. Thus, using this approach, we have demonstrated an increase of cholesterol esterification with epidermal fatty acids (C20 to C25) with age. This epidermis participation in SSL production at a molecular level in the first period of life has not been previously shown. These data can be very interesting for the development and improvement of products destined for the protection of infant skin. Graphical abstract ᅟ.
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http://dx.doi.org/10.1007/s00216-018-1255-8 | DOI Listing |
Heliyon
December 2024
Department of Mechatronics, Aliko Dangote University of Science and Technology, Kano, Nigeria.
Having accurate and effective wind energy forecasting that can be easily incorporated into smart networks is important. Appropriate planning and energy generation predictions are necessary for these infrastructures. The production of wind energy is linked to instability and unpredictability.
View Article and Find Full Text PDFClin Radiol
October 2024
Department of Radiology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu 226361, China. Electronic address:
Aims: This study aims to assess whether consensus clustering, based on computed tomography (CT) radiomics from both intratumoral and peritumoral regions, can effectively stratify the risk of non-small cell lung cancer (NSCLC) patients and predict their postoperative recurrence-free survival (RFS).
Materials And Methods: A retrospective analysis was conducted on the data of surgical patients diagnosed with NSCLC between December 2014 and April 2020. After preprocessing CT images, radiomic features were extracted from a 9-mm region encompassing both the tumor and its peritumoral area.
Heliyon
June 2024
Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, PR China.
Objective: This study aimed to delineate the clear cell renal cell carcinoma (ccRCC) intrinsic subtypes through unsupervised clustering of radiomics and transcriptomics data and to evaluate their associations with clinicopathological features, prognosis, and molecular characteristics.
Methods: Using a retrospective dual-center approach, we gathered transcriptomic and clinical data from ccRCC patients registered in The Cancer Genome Atlas and contrast-enhanced computed tomography images from The Cancer Imaging Archive and local databases. Following the segmentation of images, radiomics feature extraction, and feature preprocessing, we performed unsupervised clustering based on the "CancerSubtypes" package to identify distinct radiotranscriptomic subtypes, which were then correlated with clinical-pathological, prognostic, immune, and molecular characteristics.
Sci Rep
January 2024
Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, 03080, Korea.
Recognizing anatomical sections during colonoscopy is crucial for diagnosing colonic diseases and generating accurate reports. While recent studies have endeavored to identify anatomical regions of the colon using deep learning, the deformable anatomical characteristics of the colon pose challenges for establishing a reliable localization system. This study presents a system utilizing 100 colonoscopy videos, combining density clustering and deep learning.
View Article and Find Full Text PDFComput Methods Biomech Biomed Engin
October 2024
Department of CSE, Dr. N.G.P. Institute of Technology, Coimbatore, Tamil Nadu, India.
Recently, human beings have been affected mainly by dreadful cancer diseases. Predicting cancer risk levels is a major challenge in biomedical research for feature selection and classification at the margins. To resolve this problem, we propose a Subset Clustering-Based Feature Selection using a Multi-Layer Perception Neural Network (SCFS-MLPNN).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!