Depression is associated with dysregulation of oxidative stress and inflammatory pathways. Serum biomarkers such as 8-hydroxy-2′-deoxyguanosine (8-OHdG), NLRP3, and kynurenine may reflect these pathological processes and relate to disease severity. This study evaluated the clinical relevance of these biomarkers in patients with depression and their association with symptom severity. A total of 50 patients with depression and 50 healthy controls were assessed. Serum levels of 8-OHdG, NLRP3, and kynurenine were measured. Depression severity was evaluated using the Hamilton Depression Rating Scale (HAM-D) and Patient Health Questionnaire-9 (PHQ-9). Correlations, multiple linear regression, and multivariate logistic regression. Bonferroni and Benjamini–Hochberg corrections were applied. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis. Patients with depression exhibited significantly higher levels of 8-OHdG, NLRP3, and kynurenine (all p < 0.05). Among the evaluated biomarkers, only 8-OHdG demonstrated a weak inverse correlation with PHQ-9 scores and a positive correlation with disease duration, whereas no significant associations were observed for other markers. In multivariable regression analyses, 8-OHdG showed nominal association with PHQ-9 scores; however, this relationship did not remain significant after correction for multiple comparisons. Multivariate logistic regression demonstrated independent associations of all three biomarkers with depression status without evidence of significant multicollinearity. ROC analysis showed moderate diagnostic performance for individual biomarkers, while the combined biomarker model achieved superior discrimination between patients and controls (AUC = 0.857). Serum levels of kynurenine, NLRP3, and 8-OHdG are all markedly increased in depression and together offer helpful diagnostic data. These indicators’ combined performance implies potential utility as complementary biological markers for diagnosing depression and describing its underlying pathophysiology, notwithstanding their modest relationships with symptom severity. Larger prospective trials are necessary for additional validation.
H. Alrasheed, A. A. El-Hanafy, Mostafa M. Bahaa et al.· Scientific Reports· 0 citations
Anaplastic thyroid cancer (ATC) is a rare, aggressive malignancy with poor prognosis. Adherence to guidelines from the National Comprehensive Cancer Network (NCCN), American Thyroid Association (ATA), and European Society for Medical Oncology (ESMO) is critical for optimal patient outcomes. As large language models (LLMs) increasingly enter clinical workflows, rigorous evaluation of their alignment with established guidelines is essential. We evaluated five leading LLMs for their ability to generate guideline-concordant responses to clinical questions about ATC. We conducted a comparative study in 2025 following TRIPOD-LLM (Transparent Reporting of a Multivariable Model for Individual Prognosis or Diagnosis, Large Language Models) guidelines. Seventy clinical questions of varying complexity were developed from ATA, NCCN, and ESMO guidelines. Three surgical oncology experts validated each question and subsequently evaluated responses from five LLMs: ChatGPT 4.1, ChatGPT 5, Gemini 2.5 Pro, Claude Sonnet 4, and DeepSeek R1. Each response was scored for relevancy, clarity, accuracy, and adequacy on a 5-point Likert scale. Inter-rater reliability was assessed using both intraclass correlation coefficients (ICC) and Gwet’s AC2 with ordinal weights. Model comparisons used the Kruskal-Wallis test with Dunn’s post-hoc analysis and Bonferroni correction. A pre-specified sensitivity analysis excluding the unblinded model (ChatGPT 5) was performed to confirm robustness. Significant performance differences emerged across all four metrics: accuracy (p = 0.007), adequacy (p = 0.003), clarity (p = 0.014), and relevance (p < 0.001). Gemini 2.5 Pro achieved the highest median accuracy (4.5), followed by DeepSeek R1 (4.4), while ChatGPT 4.1 scored lowest (4.0). ICC values ranged from 0.34 to 0.44 (poor to moderate), but Gwet’s AC2 yielded substantially higher estimates of 0.61 to 0.73 (moderate to substantial agreement), reflecting the impact of restricted score range on conventional reliability metrics. The sensitivity analysis excluding ChatGPT 5 confirmed the performance hierarchy among blinded models, with significance preserved or strengthened across all four metrics. Leading LLMs show variable capacity to align with ATC clinical guidelines. While top-performing models hold promise as supportive tools, their inconsistencies across domains and complexity levels preclude autonomous clinical use. These models should serve strictly as decision aids under expert supervision.
Mohamed Yasser, Ghada Barakat, S. Awny et al.· Scientific Reports· 0 citations