Background/Objectives: Postoperative delirium (POD) and postoperative cognitive dysfunction (POCD) are prevalent neurological complications following cardiac surgery, significantly affecting patient recovery and long-term outcomes, including increased risk of persistent cognitive impairment, functional decline, and mortality. Understanding the underlying mechanisms and risk factors for POD/POCD is crucial for improving perioperative management. This study aimed to investigate the relationship between postoperative systemic inflammation, assessed through inflammatory markers, and the occurrence of POD and POCD in patients undergoing cardiac surgery. Methods: We prospectively enrolled 88 patients aged 18–79 years undergoing open-heart surgery. Patients with preoperative cognitive impairment or high surgical risk (based on EuroSCORE and SOFA scores) were excluded to focus on the impact of inflammation in a relatively unselected cohort. Postoperative inflammatory responses (CRP, NLR, IL-6, IL-17A, SII, and SIRI) were measured, and patients were assessed for POD (CAM-ICU) and POCD (neuropsychological testing) during hospitalization and at 3 months follow-up. Statistical comparisons were performed between patients who developed POD/POCD and those who did not. Results: Postoperative inflammation was confirmed across the cohort, with significant increases in CRP, NLR, IL-6, SII, and SIRI. While correlational analyses between changes in individual inflammatory markers and POD/POCD were not statistically significant in the entire cohort, patients who developed POD/POCD exhibited significantly higher levels of IL-6 and NLR at 48 h postoperatively (p < 0.05). Established clinical risk factors significantly associated with POD/POCD included older age, prolonged cardiopulmonary bypass (CPB) duration, extended mechanical ventilation, vasopressor support duration, blood transfusion, renal dysfunction, and elevated postoperative creatine kinase (CK) and lactate dehydrogenase (LDH) (p < 0.05). Ejection fraction (EF) < 45% and atrial fibrillation (AF) were also more prevalent in the POD/POCD group. Conclusions: Our findings emphasize the significant role of the postoperative inflammatory response, particularly IL-6 and NLR, in conjunction with established clinical risk factors, in the development of POD and POCD after cardiac surgery. Postoperative IL-6 and NLR levels, readily measurable and cost-effective markers, may contribute to identifying patients at higher risk. Comprehensive perioperative management strategies targeting inflammation, modifiable clinical risk factors, and organ function are crucial for mitigating POD and POCD and improving cognitive outcomes in this vulnerable population.

Background/Objectives: Cytokines play a fundamental role in the tumor microenvironment, influencing breast cancer progression, metastasis, and therapeutic resistance. The objective of this systematic review and meta-analysis was to evaluate the prognostic impact and therapeutic relevance of key cytokines in breast cancer, based on human studies published between 2015 and 2025. Methods: We systematically searched PubMed, Web of Science, and Scopus for eligible studies reporting on cytokine expression and clinical outcomes in breast cancer. Inclusion criteria were based on the PRISMA framework, focusing on human cohorts and excluding in vitro or animal models. Data were extracted on cytokine types, measurement methods, patient population, and outcomes. Meta-analyses were performed using random-effects models for cytokines with sufficient data, notably IL-6 and TNF-α. Results: Twenty-three studies were included. Elevated IL-6 was consistently associated with poor overall survival (pooled HR = 2.25, 95% CI 1.83–2.76), while high TNF-α levels showed a trend toward worse outcomes but without statistical significance. IL-1β, IL-8, and IL-10 were also linked to increased metastasis and reduced response to therapy. Immunosuppressive cytokines such as IL-10 and TGF-β facilitated tumor immune evasion, while IL-17 promoted inflammation and angiogenesis. Cytokines such as IL-12 and IFN-γ were associated with improved immune responses and a favorable prognosis. Conclusions: Cytokines are central mediators of breast cancer progression and immune regulation. Elevated levels of pro-inflammatory and immunosuppressive cytokines correlate with poor outcomes and may serve as prognostic biomarkers and therapeutic targets. Their integration into personalized treatment strategies holds significant clinical potential but requires further prospective validation and biomarker standardization.

(1) Background: This study evaluates the predictive effectiveness of biomarkers in diagnosing newborn sepsis. (2) Methods: This was a case–control study conducted on neonates hospitalized at the Clinical Hospital “Louis Turcanu”, Timisoara, Romania, from October 2018 to July 2023. Using a vacutainer collection device, venous blood was collected at admission for complete blood tests, including ferritin, hemoglobin, LDH, and blood culture analysis. Neonates were divided into two groups: sepsis-positive and sepsis-negative. The outcome of interest was a diagnosis of sepsis. (3) Results: Data from 86 neonates, 51 of whom had been confirmed to have sepsis, were analyzed. This study found no significant difference in gestational age, infant weight, fetal growth restriction, or APGAR score between neonates with and without sepsis. However, there was a higher incidence of sepsis among neonates delivered via cesarean section. Neonatal patients with sepsis showed significantly higher levels of neonatal serum ferritin and LDH compared to those without sepsis. Ferritin and LDH biomarkers demonstrated excellent discriminatory capabilities in diagnosing neonatal sepsis. Logistic regression analysis revealed a significant association between elevated ferritin and LDH levels and the likelihood of neonatal sepsis, while anemia did not show a significant association. (4) Conclusions: LDH and ferritin concentrations are found to be predictive biomarkers for neonatal sepsis, indicating a potential role in detecting susceptible neonates and implementing prompt interventions to improve patient outcomes.

Background and Objectives: Breast cancer is a leading global health challenge, where early detection is essential for improving survival outcomes. Two-dimensional (2D) mammography is the established standard for breast cancer screening; however, its diagnostic accuracy is limited by factors such as breast density and inter-reader variability. Recent advances in artificial intelligence (AI) have shown promise in enhancing radiological interpretation. This study aimed to assess the utility of AI in improving lesion detection and classification in 2D mammography. Materials and Methods: A retrospective analysis was performed on a dataset of 578 mammographic images obtained from a single radiology center. The dataset consisted of 36% pathologic and 64% normal cases, and was partitioned into training (403 images), validation (87 images), and test (88 images) sets. Image preprocessing involved grayscale conversion, contrast-limited adaptive histogram equalization (CLAHE), noise reduction, and sharpening. A convolutional neural network (CNN) model was developed using transfer learning with ResNet50. Model performance was evaluated using sensitivity, specificity, accuracy, and area under the receiver operating characteristic (AUC-ROC) curve. Results: The AI model achieved an overall classification accuracy of 88.5% and an AUC-ROC of 0.93, demonstrating strong discriminative capability between normal and pathologic cases. Notably, the model exhibited a high specificity of 92.7%, contributing to a reduction in false positives and improved screening efficiency. Conclusions: AI-assisted 2D mammography holds potential to enhance breast cancer detection by improving lesion classification and reducing false-positive findings. Although the model achieved high specificity, further optimization is required to minimize false negatives. Future efforts should aim to improve model sensitivity, incorporate multimodal imaging techniques, and validate results across larger, multicenter prospective cohorts to ensure effective integration into clinical radiology workflows.

Background/Objectives: Breast cancer remains a leading cause of cancer-related morbidity worldwide, and refining prognostic tools is essential for individualized patient management. Recent evidence suggests that the systemic immune–inflammation index (SII), derived from routine blood tests, may offer valuable prognostic insights. This study aimed to evaluate whether SII can reliably predict clinical outcomes for patients undergoing curative resection. Methods: We retrospectively analyzed patients with histologically confirmed breast cancer who underwent surgical intervention at a single tertiary center. Preoperative complete blood counts were used to calculate SII. Using receiver operating characteristic (ROC) curve analysis, we identified an optimal SII cutoff. Using statistical tests, including t-tests and ANOVA, we examined differences in clinicopathological factors between low- and high-SII groups. Using univariate and multivariate analyses, we explored associations between SII and variables such as tumor stage and hormone receptor status. Results: Patients with elevated SII levels showed significant associations with more advanced tumor stage and systemic inflammatory profiles. The identified SII cutoff separated patients into distinct risk groups, and high SII values correlated with poorer prognostic features. Multivariate models indicated that SII provided additional predictive value beyond standard markers. Conclusions: Our findings suggest that SII may provide prognostic insights into breast cancer, particularly in stratifying patients based on inflammatory profiles. However, the current study does not support the use of SII as a clinical tool for tailoring treatment strategies. Further preclinical and randomized controlled trials are required to determine its predictive utility and to assess its potential integration into personalized management approaches.

Background/Objective: Preeclampsia is a hypertensive disorder associated with pregnancy that has a significant impact on maternal and neonatal health and has the potential to result in significant perinatal adverse outcomes. Maternal education has been proposed as a protective factor during pregnancy; however, its role in preeclamptic pregnancies remains unclear. This study aimed to explore the relationship between maternal education level, as defined by ISCED classification, and neonatal outcomes (birth weight, gestational age, and APGAR score) in pregnancies complicated by preeclampsia. Methods: A retrospective case-control analysis was conducted on 674 deliveries at a single tertiary center in Western Romania between January 2022 and August 2024. Neonatal outcomes, specifically birth weight, gestational age, and APGAR scores were studied and stratified into three ISCED-based maternal education subgroups. Statistical analyses, including ANOVA, chi-square tests, and logistic regression, were used to analyze the effect of maternal education, with confounders such as maternal age and chronic hypertension being controlled for. Results: Preeclampsia was associated with lower birth weight (p < 0.001), gestational age at birth (p < 0.001), and APGAR scores (p < 0.001) than the control group. Maternal level of education was associated with better neonatal outcomes in the preeclamptic group, with lower odds of fetal growth restriction (OR = 0.68, p = 0.03) and preterm birth; however, the effect was less pronounced in the control group. Conclusions: Maternal education partially mitigates the adverse effects of preeclampsia on neonatal well-being, birth weight, and gestational age at birth. These findings underscore the importance of incorporating maternal education into prenatal care programs to improve perinatal outcomes, with a special focus on high-risk pregnancies.