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Kruger U, Josyula K, Rahul, Kruger M, Ye H, Parsey C, Norfleet J, De S. A statistical machine learning approach linking molecular conformational changes to altered mechanical characteristics of skin due to thermal injury. J Mech Behav Biomed Mater 2023; 141:105778. [PMID: 36965215 DOI: 10.1016/j.jmbbm.2023.105778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 01/22/2023] [Accepted: 03/12/2023] [Indexed: 03/15/2023]
Abstract
This article develops statistical machine learning models to predict the mechanical properties of skin tissue subjected to thermal injury based on the Raman spectra associated with conformational changes of the molecules in the burned tissue. Ex vivo porcine skin tissue samples were exposed to controlled burn conditions at 200 °F for five different durations: (i) 10s, (ii) 20s, (iii) 30s, (iv) 40s, and (v) 50s. For each burn condition, Raman spectra of wavenumbers 500-2000 cm-1 were measured from the tissue samples, and tensile testing on the same samples yielded their material properties, including, ultimate tensile strain, ultimate tensile stress, and toughness. Partial least squares regression models were established such that the Raman spectra, describing conformational changes in the tissue, could accurately predict ultimate tensile stress, toughness, and ultimate tensile strain of the burned skin tissues with R2 values of 0.8, 0.8, and 0.7, respectively, using leave-two-out cross validation scheme. An independent assessment of the resultant models showed that amino acids, proteins & lipids, and amide III components of skin tissue significantly influence the prediction of the properties of the burned skin tissue. In contrast, amide I has a lesser but still noticeable effect. These results are consistent with similar observations found in the literature on the mechanical characterization of burned skin tissue.
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Affiliation(s)
- Uwe Kruger
- Center for Modeling, Simulation & Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY, USA; Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Kartik Josyula
- Center for Modeling, Simulation & Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY, USA; Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Rahul
- Center for Modeling, Simulation & Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY, USA.
| | - Melanie Kruger
- Center for Modeling, Simulation & Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY, USA; Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Hanglin Ye
- Center for Modeling, Simulation & Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Conner Parsey
- U.S. Army Futures Command, Combat Capabilities Development Command Soldier Center STTC, Orlando, FL, USA
| | - Jack Norfleet
- U.S. Army Futures Command, Combat Capabilities Development Command Soldier Center STTC, Orlando, FL, USA
| | - Suvranu De
- Center for Modeling, Simulation & Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY, USA; Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA; Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
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Lunter D, Klang V, Kocsis D, Varga-Medveczky Z, Berkó S, Erdő F. Novel aspects of Raman spectroscopy in skin research. Exp Dermatol 2022; 31:1311-1329. [PMID: 35837832 PMCID: PMC9545633 DOI: 10.1111/exd.14645] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 06/07/2022] [Accepted: 07/12/2022] [Indexed: 11/27/2022]
Abstract
The analytical technology of Raman spectroscopy has an almost 100‐year history. During this period, many modifications and developments happened in the method like discovery of laser, improvements in optical elements and sensitivity of spectrometer and also more advanced light detection systems. Many types of the innovative techniques appeared (e.g. Transmittance Raman spectroscopy, Coherent Raman Scattering microscopy, Surface‐Enhanced Raman scattering and Confocal Raman spectroscopy/microscopy). This review article gives a short description about these different Raman techniques and their possible applications. Then, a short statistical part is coming about the appearance of Raman spectroscopy in the scientific literature from the beginnings to these days. The third part of the paper shows the main application options of the technique (especially confocal Raman spectroscopy) in skin research, including skin composition analysis, drug penetration monitoring and analysis, diagnostic utilizations in dermatology and cosmeto‐scientific applications. At the end, the possible role of artificial intelligence in Raman data analysis and the regulatory aspect of these techniques in dermatology are briefly summarized. For the future of Raman Spectroscopy, increasing clinical relevance and in vivo applications can be predicted with spreading of non‐destructive methods and appearance with the most advanced instruments with rapid analysis time.
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Affiliation(s)
- Dominique Lunter
- University of Tübingen, Department of Pharmaceutical Technology, Institute of Pharmacy and Biochemistry, Eberhard Karls University of Tübingen, Tübingen, Germany
| | - Victoria Klang
- University of Vienna, Department of Pharmaceutical Sciences, Division of Pharmaceutical Technology and Biopharmaceutics, Faculty of Life Sciences, Vienna, Austria
| | - Dorottya Kocsis
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary
| | - Zsófia Varga-Medveczky
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary
| | - Szilvia Berkó
- University of Szeged, Faculty of Pharmacy, Institute of Pharmaceutical Technology and Regulatory Affairs, Szeged, Hungary
| | - Franciska Erdő
- Pázmány Péter Catholic University, Faculty of Information Technology and Bionics, Budapest, Hungary.,University of Tours EA 6295 Nanomédicaments et Nanosondes, Tours, France
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Identification of Key Genes in Severe Burns by Using Weighted Gene Coexpression Network Analysis. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:5220403. [PMID: 35799661 PMCID: PMC9256319 DOI: 10.1155/2022/5220403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 05/12/2022] [Accepted: 05/19/2022] [Indexed: 12/03/2022]
Abstract
The aims of this work were to explore the use of weighted gene coexpression network analysis (WGCNA) for identifying the key genes in severe burns and to provide a reference for finding therapeutic targets for burn wounds. The GSE8056 dataset was selected from the gene expression database of the US National Center for Biotechnology Information for analysis, and a WGCNA network was constructed to screen differentially expressed genes (DEGs). Gene Ontology and pathway enrichment of DGEs were analyzed, and protein interaction network was constructed. A burn mouse model was constructed, and the burn tissue was taken to identify the expression levels of differentially expressed genes. The results showed that the optimal soft threshold for constructing the WGCNA network was 9. 10 coexpressed gene modules were identified, among which the green, brown, and gray modules had the largest number of burn-related genes. The DEGs were mainly related to immune cell activation, inflammatory response, and immune response, and they were enriched in PD-1/PD-L1, Toll-like receptor, p53, and nuclear factor-kappa B (NF-κB) signaling pathways. 5 DEGs were screened and identified, namely, Jun protooncogene (JUN), signal transducer and activator of transcription 1 (STAT1), BCL2 apoptosis regulator (Bcl2), matrix metallopeptidase 9 (MMP9), and Toll-like receptor 2 (TLR2). Compared with skin tissue of normal mouse, the messenger ribose nucleic acid (mRNA) and protein expression levels (PEL) of STAT1 and Bcl2 in burn tissue were greatly decreased, while those of JUN, MMP9, and TLR2 were increased obviously (p < 0.05). In conclusion, STAT1, Bcl2, JUN, MMP9, and TLR2 can be potential biological targets for the treatment of severe burn wounds.
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