Spectrochemical and explainable artificial intelligence approaches for molecular level identification of the status of critically ill patients with COVID-19

Tokgoz G., KIRBOĞA K. K., ÖZEL F., YÜCEPUR S., ARDAHANLI İ., GURBANOV R.

Talanta, cilt.279, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 279
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1016/j.talanta.2024.126652
  • Dergi Adı: Talanta
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, L'Année philologique, Aerospace Database, Analytical Abstracts, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Chemical Abstracts Core, Chimica, Communication Abstracts, Compendex, Food Science & Technology Abstracts, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Anahtar Kelimeler: Biomarker, COVID-19, Explainable artificial intelligence, FTIR, Shapley explanations
  • Bilecik Şeyh Edebali Üniversitesi Adresli: Evet

Özet

This study explores the molecular alterations and disease progression in COVID-19 patients using ATR-FTIR spectroscopy combined with spectrochemical and explainable artificial intelligence (XAI) approaches. Blood serum samples from intubated patients (IC), those receiving hospital services (SC), and recovered patients (PC) were analyzed to identify potential spectrochemical serum biomarkers. Spectrochemical parameters such as lipid, protein, nucleic acid concentrations, and IgG glycosylation were quantified, revealing significant alterations indicative of disease severity. Notably, increased lipid content, altered protein concentrations, and enhanced protein phosphorylation were observed in IC patients compared to SC and PC groups. The serum AGR (Albumin/Globulin Ratio) index demonstrated a distinct shift among patient groups, suggesting its potential as a rapid biochemical marker for COVID-19 severity. Additionally, alterations in IgG glycosylation and glucose concentrations were associated with disease severity. Spectral analysis highlighted specific bands indicative of nucleic acid concentrations, with notable changes observed in IC patients. XAI techniques further elucidated the importance of various spectral features in predicting disease severity across patient categories, emphasizing the heterogeneity of COVID-19's impact. Overall, this comprehensive approach provides insights into the molecular mechanisms underlying COVID-19 pathogenesis and offers a transparent and interpretable prediction algorithm to aid decision-making and patient management.