The coronavirus disease 2019 (COVID-19) epidemic went down in history as a pandemic caused by corona-viruses that emerged in 2019 and spread rapidly around the world. The different symptoms of COVID-19 made it difficult to understand which variables were more influential on the diagnosis, course and mortality of the disease. Machine learning models can accurately assess hidden patterns among risk factors by analyzing large-datasets to quickly predict diagnosis, prognosis and mortality of diseases. Because of this advantage, the use of machine learning models as decision support systems in health services is increasing. The aim of this study is to determine the diagnosis and prognosis of COVID-19 disease with blood-gas data using the Chi-squared Automatic Interaction Detector (CHAID) decision-tree-model, one of the machine learning methods, which is a subfield of artificial intelligence. This study was carried out on a total of 686 patients with COVID-19 (n = 343) and non-COVID-19 (n = 343) treated at Erzincan-Mengücek-Gazi-Training and Research-Hospital between April 1, 2020 and March 1, 2021. Arterial blood gas values of all patients were obtained from the hospital registry system. While the total-accuracyratio of the decision-tree-model was 65.0% in predicting the prognosis of the disease, it was 68.2% in the diagnosis of the disease. According to the results obtained, the low ionized-calcium value (< 1.10 mM) significantly predicted the need for intensive care of COVID-19 patients. At admission, low-carboxyhemoglobin (< 1.00%), high-pH (> 7.43), low-sodium (< 135.0 mM), hematocrit (< 40.0%), and methemoglobin (< 1.30%) values are important biomarkers in the diagnosis of COVID-19 and the results were promising. The findings in the study may aid in the early-diagnosis of the disease and the intensive-care treatment of patients who are severe. The study was approved by the Ministry of Health and Erzincan University Faculty of Medicine Clinical Research Ethics Committee.

Acute lung injury (ALI) is a severe respiratory disorder associated with high morbidity and mortality. Lipopolysaccharide (LPS) is widely used to induce ALI in animal models. D-carvone, a natural monoterpene, has been reported to possess anti-inflammatory and antioxidant properties. This study aimed to investigate the protective effects of D-carvone on LPS-induced ALI in rats. Thirty-six male rats were randomly divided into six groups (n = 6): control, D-carvone (10 mg/kg and 20 mg/kg p.o.), LPS (10 mg/kg E. coli lipopolysaccharide i.p.), and LPS + D-carvone (LPS with either 10 or 20 mg/kg D-carvone). D-carvone was administered orally once daily for 10 days. On day 10, sepsis was induced with LPS administration, and samples were collected after 6 h under deep anesthesia. LPS administration caused significant lung injury, as evidenced by increased histopathological scores, upregulation of pro-inflammatory markers (TLR4, IL-1β, TNF-α), and oxidative stress (increased MDA, decreased GSH and SOD). Treatment with D-carvone at both doses significantly attenuated these changes. D-carvone downregulated pro-inflammatory markers, upregulated anti-inflammatory (NRF2) and anti-apoptotic (Bcl-2) proteins, and reduced the levels of pro-inflammatory cytokines (IL-1β, TNF-α, IL-8) in lung tissues. In conclusion, D-carvone protects against LPS-induced ALI in rats, possibly through its anti-inflammatory and antioxidant properties. These findings suggest that D-carvone could be a potential therapeutic candidate for preventing and treating ALI. Graphical Abstract

Sepsis-induced acute liver injury is a life-threatening condition involving inflammation, oxidative stress, and endothelial dysfunction. In the present study, the preventive effects of resveratrol (RV) alone and RV-loaded silver nanoparticles (AgNPs + RV) against sepsis-induced damage were investigated and compared in a rat model of polymicrobial sepsis induced by cecal ligation and puncture (CLP). Rats were divided into four groups: Sham, CLP, RV, and AgNPs + RV. Pro-inflammatory cytokines (TNF-α, IL-1β, IL-6), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) activation, presepsin, procalcitonin (PCT), 8-hydroxy-2′-deoxyguanosine (8-OHDG), vascular endothelial growth factor (VEGF), and sirtuin-1 (SIRT1) levels were assessed to determine the treatments' effects. AgNPs + RV treatment significantly reduced pro-inflammatory cytokines, NF-κB activation, presepsin, PCT, 8-OHDG, and VEGF levels compared with the CLP group, indicating attenuation of sepsis-induced liver injury. Both RV and AgNPs + RV treatments increased SIRT1 levels, suggesting a potential role of SIRT1 activation in mediating the protective effects. In conclusion, AgNPs + RV treatment demonstrated extremely enhanced efficacy in alleviating sepsis-induced liver injury by modulating inflammation, oxidative stress, and endothelial dysfunction, potentially mediated through SIRT1 activation. In this study, the effect of AgNPs + RV on sepsis was evaluated for the first time, and these findings highlight AgNPs + RV as a promising therapeutic strategy for managing sepsis-induced liver injury, warranting further investigation.