Introduction: This study aimed to investigate the relationship between the Malnutrition Inflammation Score (MIS) and the C-reactive protein/albumin ratio (CAR) in patients undergoing hemodialysis (HD). Materials and Methods: Sixty-six End-Stage Renal Disease (ESRD) patients on maintenance hemodialysis were recruited for the study. Malnutrition Inflammation Score, biochemical parameters, and C-reactive protein/albumin ratio were analyzed. Results: The mean age of the participants was 56.47 ± 17.3 years. A statistically significant positive correlation was found between the CRP/albumin ratio and MIS ≥ 7 (r = 0.413, p = 0.026), CRP and MIS ≥ 7 (r = 0.388, p = 0.038) and a negative correlation between albumin and MIS (r = − 0.511, p = 0.005). Additionally, MIS was negatively correlated with hemoglobin (r = − 0.412, p = 0.026) and creatinine (r = − 0.568, p = 0.001), while a positive correlation was found between MIS and ferritin (r = 0.584, p = 0.001) for MIS ≥ 7. A multiple regression ANOVA model confirmed a significant association between CAR, CRP, albumin, and MIS (F = 6.432, p = 0.002), with significant contributions from CAR (p = 0.003), albumin (p = 0.008), and CRP (p = 0.003). Conclusion: Our study is the first to show an independent association between CAR and MIS in hemodialysis patients. The CRP/albumin ratio can serve as a valuable indicator of malnutrition in this population, providing a reliable tool for assessing nutritional status.

Background: Accurate and rapid detection of COVID-19 remains critical for clinical management, especially in resource-limited settings. Current diagnostic methods face challenges in terms of speed and reliability, creating a need for complementary AI-based models that integrate diverse data sources. Objectives: This study aimed to develop and evaluate a multi-modal machine learning model that combines clinical biomarkers and chest X-ray images to enhance diagnostic accuracy and provide interpretable insights. Methods: We used a dataset of 250 patients (180 COVID-19 positive and 70 negative cases) collected from clinical settings. Biomarkers such as CRP, ferritin, NLR, and albumin were included alongside chest X-ray images. Random Forest and Gradient Boosting models were used for biomarkers, and ResNet and VGG CNN architectures were applied to imaging data. A late-fusion strategy integrated predictions from these modalities. Stratified k-fold cross-validation ensured robust evaluation while preventing data leakage. Model performance was assessed using AUC-ROC, F1-score, Specificity, Negative Predictive Value (NPV), and Matthews Correlation Coefficient (MCC), with confidence intervals calculated via bootstrap resampling. Results: The Gradient Boosting + VGG fusion model achieved the highest performance, with an AUC-ROC of 0.94, F1-score of 0.93, Specificity of 93%, NPV of 96%, and MCC of 0.91. SHAP and LIME interpretability analyses identified CRP, ferritin, and specific lung regions as key contributors to predictions. Discussion: The proposed multi-modal approach significantly enhances diagnostic accuracy compared to single-modality models. Its interpretability aligns with clinical understanding, supporting its potential for real-world application.

Aim: The phase angle obtained from the maximum reactance (PA max) is a better potential indicator than the phase angles obtained from multiple frequencies. Our aim in this study is to investigate the correlation of PA max with fasting glucose and hemoglobin A1c in Type-2 Diabetes Mellitus (T2 DM). Material and Methods: The study was conducted prospectively, two groups were formed as T2 DM (n=75) and healthy controls (n=32) and their demographic variables were examined. Right hand, left hand, right leg, left leg, hand leg and leg leg segment measurements were taken with electrical impedance method and phase angle was obtained at maximum reactance. Correlations with fasting glucose, hemoglobin A1c and other variables were examined. Results: Fasting glucose, glycated hemoglobin A1c, age, body mass index, body fat percentage were found to be significantly higher in the T2 DM group compared with the healthy group. However, RH PA max, RL PA max, LL PA max, H_L PA max, and L_L PA max values were found to be significantly lower than in the healthy group. In T2 DM group, hemoglobin A1c was found significantly negative correlated with PA max in all segments, while fasting glucose is negatively correlated with all segments except LH Pamax. Conclusion: PA max is significantly reduced by the impaired glycemic index in T2 DM and is a potential marker reflecting metabolic status.

Amaç: Bu çalışmada inflamatuar ve metabolik belirteçlerin akne şiddeti ve birbirleri ile olan ilişkilerini incelemek, bu belirteçlerin akne mekanizmasındaki farklı inflamatuar ve metabolik yollardaki etkinliklerini bir bütün olarak araştırmak amaçlanmıştır. Gereç ve Yöntemler: Çalışmamız akne tanısı almış 203 hasta ile prospektif kesitsel bir çalışma olarak yürütüldü. Akne şiddeti “International Consensus Conference on Acne Classification System” ’e göre hafif, orta ve ağır olarak üç sınıfa ayrıldı. Hastaların inflamatuar ve metabolik belirteçlerinden fibrinojen/albümin oranı (FAO), D-dimer/albümin oranı (DAO), ürik asit/HDL oranı (UHO), CRP, sedimantasyon (ESH), lökosit sayısı (WBC), D-vitamini, trigliserid (TG) ve HOMA-IR değerlerine bakıldı. Elde edilen değerlerin akne şiddeti ve birbirleri ile ilişkileri incelendi. İstatiksel test olarak Mann- Whitney U testi, Kruskal-Wallis H testi ve Spearmans korelasyon analizinden faydalanıldı. Bulgular: Çalışmamızda CRP (p=0.008), WBC (p=0.006) ve yeni inflamatuar belirteçlerden UHO’nun (p

ObjectiveAt this study we aimed to investigate relationship between Malnutrition Inflammation Score (MIS), C reactive protein /albumin ratio (CAR), albumin, CRP and other biochemical parameters at hemodialysis patients.Design and Methods66 ESRD patients on maintenance hemodialysis has been recruited to the study. MIS, biochemical parameters and CAR were analyzed.ResultsStatistically significant positive correlation was found between CAR and MIS≥7 (r=0.413, p=0.026), with CRP and MIS≥7 (r=0.388, p=0.038) and negative correlation was found with albumin and MIS (r=-0.511, p=0.005). For MIS, CAR, albumin and CRP a multiple regression ANOVA model was conducted to examine that relation of CAR, CRP, albumin on MIS (F=6.432, p=0.002). ANOVA model was found statistically significant (r=0.660, p=0.002). Variables CAR (p=0.003), albumin (p=0.008) and CRP(p=0.003) was found significant in the ANOVA model.ConclusionOur study showed first time in the literature that CAR is independently associated with MIS in HD patients.