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Age-related differences in response to plasma exchange in male rat liver tissues: insights from histopathological and machine-learning assisted spectrochemical analyses

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Abstract

This study aimed to examine the biological effects of blood plasma exchange in liver tissues of aged and young rats using machine learning methods and spectrochemical and histopathological approaches. Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) were the machine learning algorithms employed. Young plasma was given to old male rats (24 months), while old plasma was given to young male rats (5 weeks) for thirty days. LDA (95.83–100%) and SVM (87.5–91.67%) detected significant qualitative changes in liver biomolecules. In old rats, young plasma infusion increased the length of fatty acids, triglyceride, lipid carbonyl, and glycogen levels. Nucleic acid concentration, phosphorylation, and carbonylation rates of proteins were also increased, whereas a decrease in protein concentration was measured. Aged plasma decreased protein carbonylation, triglyceride, and lipid carbonyl levels. Young plasma infusion improved hepatic fibrosis and cellular degeneration and reduced hepatic microvesicular steatosis in aged rats. Otherwise, old plasma infusion in young rats caused disrupted cellular organization, steatosis, and increased fibrosis. Young plasma administration increased liver glycogen accumulation and serum albumin levels. Aged plasma infusion raised serum ALT levels while diminished ALP concentrations in young rats, suggesting possible liver dysfunction. Young plasma increased serum albumin levels in old rats. The study concluded that young plasma infusion might be associated with declined liver damage and fibrosis in aged rats, while aged plasma infusion negatively impacted liver health in young rats. These results imply that young blood plasma holds potential as a rejuvenation therapy for liver health and function.

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All data generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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There is not any custom computer code or algorithm used to generate the results reported in the manuscript.

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Acknowledgements

The authors are grateful to the Department of Chemistry at Bilecik Şeyh Edebali University for providing an FTIR spectrometer and to Adem Kurtcuoğlu for his contributions.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Contributions

HTT: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing—original draft, Writing—review & editing. TC: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization, Writing—original draft, Writing—review & editing. SK: Histopathological analysis, Biochemical and Histopathological interpretation, Visualization, Writing–original draft, Writing—review & editing. GS: Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization. SM: Investigation, Methodology, Resources, Software, Supervision, Validation, Visualization. BB: Resources, Funding acquisition, Supervision, Visualization. HA: Resources, Funding acquisition, Supervision, Visualization. EA: Histopathological and Biochemical interpretation, Visualization, Writing—original draft, Writing—review & editing. Rafig Gurbanov: Investigation, Methodology, Resources, Software, Supervision, Validation, Writing—review & editing, Visualization.

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Correspondence to Rafig Gurbanov.

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The authors have declared that no competing interests exist.

Ethical approval

This study was carried out with the approval of the Ethics Committee (approval number: 2021/03) from the Saki Yenilli Experimental Animal Production and Practice Laboratory.

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Teker, H.T., Ceylani, T., Keskin, S. et al. Age-related differences in response to plasma exchange in male rat liver tissues: insights from histopathological and machine-learning assisted spectrochemical analyses. Biogerontology 24, 563–580 (2023). https://doi.org/10.1007/s10522-023-10032-3

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  • DOI: https://doi.org/10.1007/s10522-023-10032-3

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