Search Results (4,837)

Background: Ulcerative colitis (UC) management relies on accurately assessing disease activity. Fecal calprotectin (FC) is a promising non-invasive biomarker, but method-specific differences in measurement can affect interpretation. Objective: To compare the performance of Calprest ELISA and DiaSorin Liaison CLIA in measuring FC concentrations and their correlation with endoscopic findings in UC. Methods: Stool samples from 40 UC patients were analyzed using both methods, with 138 samples collected across three clinical timepoints. Spearman’s correlation, Wilcoxon test, Bland–Altman analysis, and ROC curves were used to evaluate method agreement and diagnostic performance relative to Mayo endoscopic scores. Results: A total of 135 paired results showed strong correlation (ρ = 0.795, p < 0.001) but significant inter-method differences (p = 0.039). Liaison tended to yield higher FC values. ROC analysis established optimal cut-offs for detecting endoscopic remission and active disease: 47.95/69.55 µg/g (Liaison) and 65/125 µg/g (Calprest). Calprest demonstrated slightly better diagnostic accuracy. Conclusions: Both methods are reliable for monitoring UC activity. Calprest offers greater dynamic range, while Liaison excels in automation and speed. Method-specific thresholds should guide clinical interpretation to ensure accurate disease monitoring. Full article

The unique natural environment and climate of tropical island regions present significant challenges to construction. Under these variable natural conditions and complex construction processes, identifying and analyzing potential risks that could lead to vulnerabilities in construction safety systems and clarifying their transmission pathways remains a pressing issue. To fill this research gap, a GV-IB model for vulnerability analysis of construction safety systems in tropical island building projects (CSSTIBPs) was established. This model constructs a vulnerability analysis index system for tropical island construction safety systems based on the Grey Relational Analysis (GRA) and Vulnerability Scoping Diagram (VSD), considering exposure, sensitivity, and adaptability. By combining the artificial fish swarm algorithm with the K2 algorithm and the EM algorithm, an Improved Bayesian Network (IBN) is constructed to analyze and infer the influencing factors and disaster chains of vulnerability in tropical island construction safety systems. The IBN can effectively overcome the dependence on node order and data gaps in traditional Bayesian Network construction methods. The effectiveness of the model is verified by analyzing Hainan Island, China. The research results show that (a) The IBN stability verification showed an Area Under ROC Curve (AUC) of 0.783 > 0.7, indicating high effectiveness in identifying vulnerability factors. (b) Within the vulnerability measurement nodes of the CSSTIBPs, the influence on the system decreases in the following order is exposure (0.41), sensitivity (0.31), and adaptability (0.03). (c) Emergency response time, safety training, hazard identification time, accident response time, and duration of severe weather are key factors affecting the vulnerability of CSSTIBPs. Full article

Silicosis is an irreversible and progressive pulmonary fibrotic disease caused by the long-term inhalation of silica dust. The precise molecular mechanisms underlying the disease remain incompletely understood, and effective early diagnostic biomarkers are still lacking. In this study, we used a silicosis mouse model and transcriptomic sequencing to identify 2950 mRNAs, 461 lncRNAs, 81 miRNAs, and 44 circRNAs that were differentially expressed in lung tissue. Enrichment analysis revealed that these differentially expressed genes were significantly enriched in the phosphatidylinositol 3-kinase (PI3K)–protein kinase B (Akt) signaling pathway, nuclear factor kappa-light-chain-enhancer of activated B cell (NF-κB) signaling pathway, and tumor necrosis factor (TNF) signaling pathway. The constructed competing endogenous RNA (ceRNA) network highlighted extensive regulatory interactions among lncRNAs/circRNAs, miRNAs, and mRNAs. Human validation showed that the expression levels of hsa-miR-215-5p and hsa-miR-146b-5p were significantly upregulated in the peripheral blood of early-stage pneumoconiosis patients, while hsa-miR-485-5p was downregulated. Logistic regression analysis revealed that hsa-miR-215-5p (OR = 1.966, 95% CI: 1.6938–2.2796, p < 0.001) and hsa-miR-146b-5p (OR = 1.9367, 95% CI: 1.697–2.201, p < 0.001) were independent risk factors for pneumoconiosis (p < 0.001). ROC curve analysis showed that both miRNAs demonstrated good diagnostic efficacy for pneumoconiosis, with AUC values of 0.9563 and 0.8876, respectively. These results provide novel insights into the complex ceRNA regulatory network involved in silicosis pathogenesis and suggest potential early, non-invasive diagnostic biomarkers. Full article

Acute lymphoblastic leukemia (ALL) is a genetically heterogeneous disease where current clinical practice guidelines remain focused on traditional cytogenetic markers. Despite recent advances demonstrating excellent diagnostic accuracy for gene expression signatures, a discontinuity exists between biomarker validation and clinical implementation. This study aimed to develop and validate a multiparametric gene expression signature using digital PCR (dPCR) to accurately diagnose pediatric ALL, with potential utility for monitoring measurable residual disease (MRD). We analyzed 130 bone marrow aspirates from pediatric patients from four clinical groups: non-leukemia, MRD-negative, MRD-positive and leukemia characterized by immunophenotype. Gene expression of an 8-gene panel (JUP, MYC, NT5C3B, GATA3, PTK7, CNP, ICOSLG, and SNAI1) was quantified by dPCR. The diagnostic performance of individual markers was assessed, and a Random Forest machine learning model was trained to classify active disease. The model was validated using a 5-fold stratified cross-validation approach. Individual markers, particularly JUP, MYC, and NT5C3B, showed good diagnostic accuracy for distinguishing leukemia from non-leukemia. However, integrating all eight markers into a multivariate Random Forest model significantly enhanced performance. The model achieved a mean cross-validated area under the curve (AUC) of 0.908 (±0.041) on receiver operator characteristic (ROC) analysis and 0.961 (±0.019) on Precision–Recall (PR) analysis, demonstrating high reliability and a favorable balance between sensitivity and precision. The integrated model achieved high sensitivity (88.9%) for detecting active disease, particularly at initial diagnosis. Although specificity was moderate (65.0%), the high positive predictive value (PPV 85.1%) and accuracy (81.5%) confirm the clinical utility of a positive result. While the panel showed promising performance for distinguishing MRD-positive from MRD-negative samples, the limited MRD-positive cohort size (n = 11) indicates that validation in larger MRD-focused studies is required before clinical implementation for treatment monitoring. This dPCR-based platform provides accessible, quantitative detection without requiring knowledge of clonal shifts or specific genomic landscape, offering potential advantages for resource-limited settings such as those represented in our Mexican pediatric cohort. Full article

Background/Objectives: Lactate, traditionally considered a byproduct of anaerobic metabolism, is increasingly recognized as a biomarker of tissue perfusion and systemic stress. While hyperlactatemia is frequent after pediatric cardiac surgery, evidence regarding its prognostic role remains controversial. This study aimed to evaluate whether serial lactate measurements predict mortality in children undergoing surgery for congenital heart disease in Southeast Mexico. Methods: We conducted a retrospective cohort study including children aged 0–210 weeks with confirmed congenital heart disease who underwent first-time cardiac surgery between January 2022 and December 2024. Serum lactate was measured intraoperatively, at intensive care unit (ICU) admission, and at 12 and 24 h postoperatively using a Gem^® Premier™ 3500 analyzer. Sociodemographic, clinical, and surgical data were recorded. Associations between lactate levels and mortality were analyzed with Cox regression, adjusting for RACHS-2 category and intraoperative complications. Predictive performance was assessed with ROC curves and Harrell’s C-index. Results: 103 patients were included (median age 49.2 weeks; 60% female). Lactate levels overlapped intraoperatively but significantly discriminated against survivors from non-survivors thereafter. ICU admission lactate ≥ 4.2 mmol/L predicted mortality with 100% sensitivity and 60% specificity (AUC = 0.84). Hazard ratios confirmed that lactate at ICU admission (HR 2.17, 95% CI 1.16–4.06; p = 0.015), 12 h (HR 6.37, 95% CI 1.02–39.6; p = 0.047), and 24 h (HR 1.81, 95% CI 1.07–3.09; p = 0.028) were significant predictors of mortality. The model showed excellent discrimination (Harrell’s C = 0.986), though optimism due to the limited number of deaths should be considered. Conclusions: Serial lactate monitoring, particularly upon ICU admission, provides strong prognostic information for in-hospital mortality in pediatric cardiac surgery patients. Incorporating early postoperative lactate into routine monitoring may allow timely therapeutic adjustments. Preoperative lactate assessment warrants further evaluation as a potential risk stratification tool. Full article

Background/Objectives: Pediatric trauma systems require accurate metrics for evaluating triage decisions. Undertriage occurs when an injured child requires pediatric trauma center resources but is treated at a center lacking those resources. Current undertriage definitions utilize mortality-based scores, including the Injury Severity Score (ISS) > 15 or the International Classification of Disease (ICD) Injury Severity Score (ICISS) > 10. However, resource-based metrics like the ICD Critical Care Severity Score (ICASS) may be preferable in children. This study evaluated the relationship of ISS, ICISS and ICASS to the need for pediatric trauma resources (NFPTCR) to derive a more empiric definition of undertriage. Methods: The American College of Surgeons Trauma Quality Improvement Program database was queried for patients aged ≤ 15 years old. NFPTCR was defined as blood product transfusion within 4 h, invasive procedure for cardiopulmonary stabilization/contamination/bleeding within 72 h, initial admission to intensive care unit (ICU) or ICU stay ≥ 3 days, intubation, mechanical ventilation and general anesthesia ≤ 5 years old, or physical child abuse. ICASS and ICISS were derived from 2014 to 2018 datasets and applied to the 2019 dataset. The ability of ISS, ICISS and ICASS to distinguish NFPTCR patients was assessed using multivariable logistic regression and receiver–operator characteristic (ROC) analysis. Results: Out of 97,773 children, 15,985 (16%) were NFPTCR+. ISS, ICISS and ICASS had areas under the curve of 0.760, 0.701 and 0.812 for NFPTCR+, respectively (all p < 0.001). ISS had 36% sensitivity at 15; whereas ICASS had 95%, 93% and 89% sensitivity at 5, 10 and 15, respectively. Conclusions: ICASS was superior to ISS and ICISS for identifying NFPTCR. Consideration should be given to redefining pediatric trauma undertriage based on resource-based metrics, like ICASS. Full article

Objectives: To develop and validate a transthoracic lung ultrasound (TLUS)-integrated clinical nomogram for predicting pulmonary arterial hypertension (PAH) in patients with connective tissue disease-associated interstitial lung disease (CTD-ILD). Methods: This multicenter retrospective study included 550 patients with CTD-ILD from the Second Affiliated Hospital of Fujian Medical University and 169 external cases from the Xijing Hospital, Fourth Military Medical University. Patients were randomly divided into a training cohort (n = 385) and an internal validation cohort (n = 165); the external dataset served as a testing cohort. Demographic, physiological, laboratory, pulmonary function, and TLUS data were collected. Univariate and multivariate logistic regression analyses identified independent predictors of PAH, which were used to construct a nomogram model. Discrimination was assessed using receiver operating characteristic (ROC) curves and area under the curve (AUC) values. Calibration, decision curve analysis (DCA), and clinical impact curves (CIC) were performed to evaluate model accuracy and clinical utility. Results: Five independent predictors were identified: respiratory rate, diffusing capacity of the lung for carbon monoxide (DLCO% predicted), TLUS score, red blood cell (RBC) count, and brain natriuretic peptide (BNP). The model achieved excellent discrimination with AUCs of 0.952 (95% confidence interval [CI]: 0.927–0.977) in the training cohort, 0.935 (95% CI: 0.885–0.985) in the validation cohort, and 0.874 (95% CI: 0.806–0.942) in the testing cohort, outperforming individual predictors. Calibration plots showed close agreement between predicted and observed probabilities, while DCA and CIC confirmed strong clinical benefit and applicability across all thresholds. Conclusions: This TLUS-integrated nomogram provides a noninvasive and reliable tool for individualized PAH risk assessment in CTD-ILD patients. By combining ultrasound findings with physiological and laboratory markers, the model enables accurate detection of high-risk cases and may assist clinicians in optimizing surveillance and management strategies. Full article

Background/Objectives: Hypertensive disorders of pregnancy (HDP) remain a significant cause of maternal and perinatal morbidity worldwide. In some healthcare settings, access to angiogenic testing is limited, underscoring the need for affordable biomarkers to guide risk assessment. NT-proBNP, a marker of myocardial wall stress and cardio-renal dysfunction, may offer complementary prognostic value to the angiogenic sFlt-1/PlGF ratio. Methods: In this prospective multicenter observational study, we enrolled 180 pregnant women and categorized them into preeclampsia (PE, n = 95), non-PE HDP (gestational or chronic hypertension, n = 25), and healthy controls (n = 60). NT-proBNP and sFlt-1/PlGF levels were measured at enrollment, after 20 weeks of gestation, predominantly during the second and third trimesters. Associations with proteinuria, uric acid, creatinine, and maternal–fetal complications were examined using multivariable logistic regression adjusted for maternal age, BMI, and gestational age. Discrimination was assessed using receiver operating characteristic (ROC) curve analysis, and the incremental value of NT-proBNP beyond the sFlt-1/PlGF ratio was evaluated using ΔAUC and net reclassification improvement (NRI). Results: Median NT-proBNP levels were significantly higher in PE compared with non-PE HDP and controls (p < 0.01). NT-proBNP ≥200 pg/mL independently predicted maternal–fetal complications (adjusted OR 3.12, 95% CI 1.41–6.90, p = 0.005) and correlated with proteinuria (r = 0.47), creatinine (r = 0.43), and uric acid (r = 0.40) (all p < 0.001). sFlt-1/PlGF alone yielded an AUC of 0.84 (95% CI 0.77–0.89), while NT-proBNP alone demonstrated an AUC of 0.78 (0.71–0.84). Combining both biomarkers improved discrimination (AUC 0.88, 95% CI 0.82–0.92), with a ΔAUC of 0.04 (p = 0.02) and a continuous NRI of 0.21 (p = 0.03). The 200 pg/mL threshold for NT-proBNP achieved 80% sensitivity and 71% specificity (p < 0.001). Conclusions: NT-proBNP provides independent and complementary prognostic value to the sFlt-1/PlGF ratio in predicting maternal–fetal complications in HDP. A practical threshold of 200 pg/mL aids risk assessment, and integrating NT-proBNP into angiogenic models improves prediction. Further multicenter studies are needed to validate multimarker strategies and their cost-effectiveness. Full article

Background: In patients with ST-segment elevation myocardial infarction (STEMI), apelin is upregulated and exerts cardioprotective effects against ischemia–reperfusion injury (IRI). The present study aimed to investigate serum apelin-36 levels in STEMI patients and their relationship with the no-reflow phenomenon. Methods: In this study, 161 patients presenting with STEMI within 12 h of symptom onset and undergoing primary percutaneous coronary intervention (pPCI) were enrolled. Biochemical parameters, including apelin-36, troponin T, creatine kinase (CK), the MB fraction of creatine kinase (CK-MB), total cholesterol, triglycerides, and other routine laboratory parameters, were measured. Two-dimensional echocardiography was performed in all patients. Thereafter, patients were divided into two groups according to their level of aaapelin-36. Results: Among the 161 consecutive STEMI patients, 115 (71.42%) had Apelin-36 levels ≤ 0.58 ng/mL (group 1), whereas 46 (28.57%) had Apelin-36 levels > 0.58 ng/mL (group 2). In total, 51 (31.67%) STEMI patients experienced no-reflow phenomenon after PCI: 29 (25.21%) of patients with apelin-36 ≤ 0.58 ng/mL and 22 (47.82%) of those with a value > 0.58 ng/mL (p < 0.001). In terms of Gensini score, the mean value in group 1 was 70.29 (±28.76), while in group 2, it was 81.95 (±23.82) (p = 0.004). Overall, a positive correlation between apelin-36 and Gensini score was observed in both groups using Kendall’s correlation analysis (group 1: p = 0.05; group 2: p < 0.0001). Binary logistic regression analysis identified apelin-36 and diabetes mellitus as significant predictors at the 5% level, with p-values of 0.045 and 0.036, respectively. Patients with apelin-36 levels ≤ 0.58 ng/mL had troponin T levels of 290.0 (8.5–9510.0), while those with a value > 0.58 ng/mL had troponin T levels of 132.15 (9.4–5190.0) (p < 0.012). The receiver operating characteristics (ROC) curve of apelin-36 was used to plot the true positive rate against the false positive rate at different cut-off points, with AUC = 0.77 (95% CI, 0.69–0.84), and the cut-off value for apelin-36 was 0.58 ng/mL, with p = 0.001. Conclusions: Significant associations were observed between apelin-36 and the no-reflow phenomenon in patients with STEMI. An apelin-36 cut-off value of 0.58 ng/mL, measured at admission, could be used to identify patients who were at increased risk of no-reflow phenomenon/reperfusion injury. Full article

Background: Diabetes mellitus (DM) is a condition that may increase the severity of acute pancreatitis (AP) through chronic inflammation and disturbances in immune responses. However, the independent effect of DM on clinical outcomes in AP has not yet been fully elucidated. Methods: In this retrospective cohort study, 492 patients diagnosed with acute pancreatitis at the Gastroenterology Clinic of Ankara Bilkent City Hospital between January 2022 and March 2025 were included. Patients were divided into two groups based on the presence of diabetes, and outcomes were compared using statistical methods. Results: Of the total 492 patients (mean age 58.6 ± 17.2 years; 50.2% female) included, 98 (19.9%) had DM. Moderate-to-severe AP occurred in 67.3% of diabetic versus 37.8% of non-diabetic patients (p < 0.0001), and severe disease developed more frequently in the diabetic group (6.1% vs. 1.0%, p = 0.0057). Systemic complications were significantly more common in patients with diabetes (45.9% vs. 26.9%, p = 0.0004). Hospital mortality was higher among patients with diabetes (9.2% vs. 4.6%, p = 0.0344), and Kaplan–Meier analysis demonstrated numerically lower overall survival in patients with diabetes (log-rank p = 0.095), with early divergence in survival curves. Cox proportional hazards analysis confirmed diabetes as an independent predictor of in-hospital mortality (adjusted HR 2.64, 95% CI 1.17–5.97; p = 0.019). After adjustment for confounders, diabetes remained independently associated with the development of moderate/severe pancreatitis (adjusted OR 2.00, 95% CI 1.24–3.22; p = 0.004). Diabetes also independently predicted in-hospital mortality (adjusted OR 3.36, 95% CI 1.35–8.34; p = 0.009), along with APACHE II score. ROC analysis demonstrated that adding diabetes mellitus to the APACHE II score significantly improved mortality prediction compared with APACHE II alone (AUC 0.785 vs. 0.724). The retrospective and single-center design of this study may limit its generalizability and create potential selection bias. There were insufficient data on the type of diabetes, its duration, and glycemic control (e.g., HbA1c), and therefore, we could not assess these factors, all of which may influence risk estimates. Although the survival curves showed early divergence, the borderline log-rank significance (p = 0.095) highlights the limited statistical power to detect long-term survival differences in this cohort. Conclusions: DM is associated with substantially increased severity and in-hospital mortality in AP, primarily through an elevated risk of systemic organ failure. Incorporation of diabetes status into early severity stratification may improve prognostic accuracy and guide closer monitoring and timely interventions in this high-risk population. Full article

Acrolein, a highly reactive environmental toxicant widely present in urban air and tobacco smoke, has been implicated in the development of multiple malignancies. In oral tissues, chronic acrolein exposure induces oxidative stress, inflammation, and genetic mutations, all of which are closely linked to the development of oral squamous cell carcinoma (OSCC). Although accumulating evidence indicates a strong association between acrolein exposure and OSCC, its prognostic significance remains poorly understood. In this study, we analyzed transcriptome data to identify differentially expressed genes (DEGs) between tumor and adjacent normal tissues, and screened acrolein-related candidates by intersecting DEGs with previously identified acrolein-associated gene sets. Functional alterations of these genes were assessed using Gene Set Variation Analysis (GSVA), and a protein–protein interaction (PPI) network was constructed to identify key regulatory genes. A prognostic model was developed using Support Vector Machine–Recursive Feature Elimination (SVM-RFE) combined with LASSO-Cox regression and validated in an independent external cohort. Among the acrolein-related DEGs, four key genes (PLK1, AURKA, CTLA4, and PPARG) were ultimately selected for model construction. Kaplan–Meier analysis showed significantly worse overall survival in the high-risk group (p < 0.0001). Receiver operating characteristic (ROC) curve analysis further confirmed the strong predictive performance of the model, with area under the curve (AUC) values of 0.72 at 1 year, 0.72 at 3 years, and 0.75 at 5 years. Furthermore, the high risk score was significantly correlated with a ‘cold’ immune microenviroment, suggesting that acrolein-related genes may modulate the tumor immune microenvironment. Collectively, these findings highlight the role of acrolein in OSCC progression, suggesting the importance of reducing acrolein exposure for cancer prevention and public health, and call for increased attention to the relationship between environmental toxicants and disease initiation, providing a scientific basis for public health interventions and cancer prevention strategies. Full article

Background: Radiomics can provide quantitative descriptors of tumor phenotype, but translation is often limited by feature instability across scanners and protocols. We aimed to develop and internally validate a protocol-specific CT-radiomics model using preoperative imaging to predict 5-year recurrence in patients with stage I lung adenocarcinoma after complete surgical resection. Methods: The retrospective study included 270 patients with completely resected stage I lung adenocarcinoma from January 2010–December 2021, among whom 23 (8.5%) experienced recurrence within five years. Radiomic features were extracted from routine preoperative CT scans. After preprocessing to remove highly constant and highly correlated features, the Synthetic Minority Over-sampling Technique addressed class imbalance in the training set. Recursive Feature Elimination identified the most predictive radiomic features. An XGBoost classifier was trained using optimized hyperparameters identified through RandomizedSearchCV with cross-validation. Model performance was evaluated using the ROC curve and predictive metrics. Results: Five radiomic features differed significantly between recurrence groups (p = 0.007 to <0.001): Shape Sphericity, first-order 90Percentile, GLCM Autocorrelation, GLCM Cluster Shade, and GLDM Large Dependence Low Gray Level Emphasis. The radiomics model showed excellent discriminatory ability with AUC values of 0.99 (95% CI: 0.98–1.00), 0.97 (95% CI: 0.91–1.00), and 0.96 (95% CI: 0.85–1.00) on the training, validation, and test sets, respectively. On the test set, the model achieved sensitivity of 100% (95% CI: 51–100%), specificity of 94% (95% CI: 81–98%), PPV of 67% (95% CI: 30–90%), NPV of 100% (95% CI: 90–100%), and overall accuracy of 95% (95% CI: 83–99%). Conclusions: Under protocol-homogeneous imaging conditions, CT radiomics accurately predicted recurrence in patients with completely resected stage I lung adenocarcinoma. External multi-vendor validation is needed before broader deployment. Full article

Background/Objectives: Our aim was to investigate the diagnostic accuracy of prenatal ultrasound (US) in fetal limb abnormalities. As a secondary target, we wanted to correlate various predictors for the diagnosis accuracy. Methods: We prospectively enrolled cases with routine prenatal US performed in five participating centers. Subsequently, we selected and processed all cases with limb abnormalities: suspected, diagnosed, and missed on the prenatal diagnosis scans. We collected data on the type of anomaly, the US equipment and probes used, the operator’s expertise, the gestational age at the diagnosis, the length of the examination, and the use of US reporting form. SPSS 22.0 software was applied to perform the analyses using non-parametric statistical methods. Associations between categorical variables were evaluated using Fisher’s exact test and Chi-square tests. For correlations between the gestational age and the anomaly severity, we used Spearman’s rank-order correlation. Predictive performance of operator- and scan-related variables for diagnostic accuracy was assessed using receiver operating characteristic (ROC) curve analysis, with area under the curve (AUC) estimates, standard errors (SE), confidence intervals (95% CI), and p-values reported. Results: Our data showed that most US examinations were performed as part of routine screening (79.7%), and the most frequent anomaly diagnosed was clubfoot. Operators’ expertise demonstrated the highest predictive performance, while technical parameters—scan duration (AUC = 0.20, p = 0.1188) and US equipment (AUC = 0.30, p = 0.3478)—did not significantly predict diagnostic accuracy. Conclusions: The overall diagnostic accuracy of prenatal US was 85.5%. Our findings indicate that diagnostic performance is driven primarily by operator expertise and training, rather than by gestational age at scan and technical parameters. Full article

Background: Psoriasis is a chronic, immune-mediated skin disorder characterized by accelerated epidermal turnover. Galectins are a family of carbohydrate-binding proteins that play crucial roles in various biological processes. Methods: This study aimed to assess the plasma concentrations of galectin 7 and 8 (gal-7 and 8) in 60 psoriatic patients compared to the control group of 30 individuals without dermatoses. Results: The median gal-7 plasma concentration in patients was 188.8 (11.43–1406) pg/mL, and it was significantly higher than in controls (p < 0.001). There was a positive correlation between gal-7 concentration and psoriasis area and severity index (PASI; R = 0.3, p = 0.0199), and a negative with RBC (R = −0.41, p < 0.001), hemoglobin concentration (R = −0.34, p < 0.01), total cholesterol (R = −0.38, p < 0.01) and LDL concentration (R = −0.36, p < 0.05). In contrast, gal-7 was not correlated with psoriasis duration or patients’ age or sex (p > 0.05). The median gal-8 plasma concentration in patients was 0.07 (0.02–0.5) ng/mL, and was significantly higher in patients than controls (p < 0.05). There was a positive correlation between gal-8 concentration and glucose concentration (R = 0.26, p < 0.05). Gal-8 concentration was not correlated with PASI, BMI, age or sex of patients (p > 0.05). We also analyzed the receiver operating characteristic (ROC) curve to evaluate the predictive power of gal-7 and 8 for psoriasis. Gal-7 achieved statistical significance in predicting psoriasis and had an area under the curve (AUC) value of 0.842 (p < 0.001), a sensitivity of 80%, and a specificity of 86.7%, whereas gal-8 had an AUC value of 0.644 (p = 0.025), a sensitivity of 81%, and a specificity of 47%. Conclusions: Gal-7 and gal-8 could potentially serve as psoriasis biomarkers, whereby gal-7 could also serve as a marker of its severity. Future studies are needed to clarify their actual role or potential as therapeutic targets in psoriasis. Understanding their precise functions may open new perspectives for personalized treatment strategies in psoriatic patients. Full article

Background and Objectives: Healthcare-associated infections (HAIs) significantly increase morbidity and mortality in critically ill patients, and their burden became more pronounced during the COVID-19 pandemic. However, data describing the temporal evolution of HAIs, pathogen distribution, and associated risk factors across consecutive pandemic waves remain limited. This study aimed to characterize the epidemiology, microbiology, and outcomes of HAIs in COVID-19 intensive care units (ICU) patients and to identify clinical and laboratory predictors of mortality. Materials and Methods: This retrospective observational study included adult patients with RT-PCR–confirmed COVID-19 who developed at least one HAI ≥ 48 h after ICU admission between March 2020 and December 2020, encompassing the first three pandemic waves in Türkiye, in a tertiary-care ICU. Demographic, clinical, laboratory, and microbiological data were collected. Inflammatory markers and severity scores (SAPS-II, MCCI, and NLR) were analyzed. Receiver operating characteristic (ROC) curve analysis was used to determine optimal cut-off values for mortality prediction. Results: Among the 1656 ICU admissions, 145 patients (8.7%) developed HAIs; after exclusions, 136 patients were included in the final analysis. Bloodstream infections were the most frequent HAI (57%), followed by urinary tract infections (31%), ventilator-associated pneumonia (9%), and surgical site infections (1%). Klebsiella pneumoniae was the predominant pathogen, followed by Candida albicans and Acinetobacter baumannii. Multidrug-resistant organisms, including MRSA and VRE, showed variable distribution across pandemic periods. Overall in-hospital mortality was 74.3%. Non-survivors had significantly higher SAPS-II, MCCI, and NLR values. ROC analysis identified NLR > 38.8 and SAPS-II > 35.5 as mortality-predictive thresholds. Dynamic inflammatory marker patterns correlated with infection timing, and early peaks of CRP, WBC, and IL-6 were associated with worse outcomes. Conclusions: HAIs imposed a substantial clinical burden on critically ill COVID-19 patients, with high mortality driven predominantly by multidrug-resistant bloodstream infections. Severity indices and inflammation-based biomarkers demonstrated strong prognostic value. Temporal shifts in pathogen ecology across pandemic waves underscore the need for adaptive infection-prevention strategies, continuous microbiological surveillance, and strengthened antimicrobial stewardship in critical care settings. Full article