Search Results (4,907)

Background/Objectives: The objective of this study was to assess the connection between the systemic inflammation response index (SIRI) values and failure patterns of patients with IDH wild-type glioblastoma (GB) who underwent radiotherapy (RT) with FLAIR-based gross tumor volume (GTV) delineation. Methods: Seventy-one patients who received RT at a dose of 60 Gy to the GTV and 50 Gy to the clinical target volume (CTV) and had documented recurrence were retrospectively analyzed. Each patient’s maximum distance of recurrence (MDR) from the GTV was documented in whichever plane it extended the farthest. The failure patterns were described as intra-GTV, in-CTV/out-GTV, distant, and intra-GTV and distant. For analytical purposes, the failure pattern was categorized into two groups, namely Group 1, intra-GTV or in-CTV/out-GTV, and Group 2, distant or intra-GTV and distant. The SIRI was calculated before surgery and corticosteroid administration. A receiver operating characteristic (ROC) curve analysis was used to determine the optimal SIRI cut-off that distinguishes between the different failure patterns. Results: Failure occurred as follows: intra-GTV in 40 (56.3%), in-CTV/out-GTV in 4 (5.6%), distant in 18 (25.4%), and intra-GTV + distant in 9 (12.7%) patients. The mean MDR was 13.5 mm, and recurrent lesions extended beyond 15 mm in only seven patients. Patients with an SIRI score ≥ 3 demonstrated a significantly higher incidence of Group 1 failure patterns than their counterparts with an SIRI score < 3 (74.3% vs. 50.0%; p = 0.035). Conclusions: The present results show that using the SIRI with a cut-off value of ≥3 significantly predicts failure patterns. Additionally, the margin for the GTV can be safely reduced to 15 mm when using FLAIR-based target delineation in patients with GB. Full article

Background: Coronary artery disease (CAD) is the leading cause of death worldwide. High-Density lipoprotein (HDL) is a well-established marker associated with CAD. The current research goes beyond the conventional HDL-C measurement in previous studies and dives into the functional intricacies of HDL. By understanding how HDL works, rather than just how much of it exists, we can better tailor diagnostic and therapeutic strategies for CAD and related conditions. Hence, the current study quantifies the serum levels of two novel HDL-associated markers, Paraoxonase-1 (PON-1) and Scavenger Receptor Class B Type 1 (SRB-1), in CAD cases vs. controls. Methods: A total of 92 subjects, including 69 CAD and 23 healthy controls, were included, based on the prevalence of the disease. Further, based on the severity of the disease, CAD cases were subcategorized as CAD-I, -II, and -III. Serum PON-1 and SRB-1 levels were measured and compared between patient and control groups. Results: The levels of PON-1 and SRB-1 (32.6 ng/mL and 12.49 ng/mL) were significantly lower in CAD patients vs. the healthy control, at 60.36 ng/mL and 15.85 ng/mL, respectively (p < 0.000). A further intergroup comparison showed a statistically significant difference between the CAT-I and -III for PON-1 (p < 0.025), the CAT-I and -III, and CAT-II and -III for SRB-1 (p < 0.000). The receiver operating characteristics (ROC) curve showed cutoff values of 48.20 ng/mL and 14.90 ng/mL for PON-1 and SRB-1. Conclusions: The current study found that serum levels of HDL-associated PON-1 and SRB-1 are significantly lower in CAD cases, and were also inversely related to the increasing severity of coronary artery disease. This inference implies that serum PON-1 and SRB-1 could be used as non-invasive tools for the identification of coronary atherosclerosis and risk assessment in CAD cases. Full article

The aim of this study was to optimize a basophil activation test in the detection of allergy to the house dust mite Dermatophagoides pteronyssinus in children with allergic respiratory diseases. This study involved 32 cases, 13 girls and 19 boys aged 4–17 years, with perennial asthma or allergic rhinitis caused by D. pteronyssinus. The control group consisted of 13 girls and 19 boys aged 4–17 years with seasonal allergic asthma or rhinitis provoked by Timothy or birch pollen. House dust mite (HDM) allergy was excluded in the controls based on their medical history, skin prick test (SPT) results and sIgE determination. In all patients, a basophil activation test (BAT) was performed with five dilutions of D. pteronyssinus allergen (the dilution series ranged from 22.5 to 0.00225 ng/mL). The results were analyzed by using the receiver operating characteristics (ROC) to determine the optimal allergen concentrations, outcome measures and cut-off points that would differentiate most accurately between HDM-allergic and non-allergic patients. As a “gold standard”, criteria for allergen-specific immunotherapy with D. pteronyssinus or respective pollens were applied by an experienced pediatric allergist following the guidelines of the European Academy of Allergy and Clinical Immunology. The highest diagnostic efficiency was yielded by the protocol assuming a cut-off value of 9.76% activated basophils after activation with a single allergen concentration of 2.25 ng/mL (sensitivity 90.6%, specificity 100%). This protocol yielded 3 (4.7%) misclassifications, all false negative, when compared with the “gold standard”. There was a strong correlation with the BAT results at 22.5, 2.25 and 0.225 ng/mL (respectively r = 0.90 and r = 0.78, p < 0.001), as well as between the BAT at 2.25 ng/mL and SPT (r = 0.82, p < 0.001) and between the SPT and sIgE levels (r = 0.78, p < 0.001). High cross-reactivity between D. pteronyssinus and D. farinae was confirmed based on the BAT at 22.5 ng/mL (r = 0.82, p < 0.001). In conclusion, the BAT showed very good concordance with the result of a meticulous process of decision-making that combined validated allergy tests (SPT, sIgE) with expert guidelines, specialist knowledge and experience. Facing the risk of the incorrect qualification of patients for costly, long-lasting and potentially risky allergen-specific immunotherapy, the inclusion of a basophil activation test into diagnostic process seems fully justified. Full article

Road traffic accidents result in numerous fatalities and injuries annually. Advanced driving assistance systems (ADASs) have garnered significant attention to mitigate these harms. An accurate safety assessment can significantly improve the effectiveness and credibility of ADASs. However, a real-time safety assessment remains a key challenge due to the complex interactions among humans, vehicles, and the road environment. Traditional safety assessment methods, relying on crash data and surrogate safety measures (SSMs), face limitations in real-time applicability and scenario coverage, especially in freeway ramp areas with frequent merging and lane changing. To address these gaps, this paper develops a driving safety field based on the uncertainty of vehicle movements, which integrates the characteristics of driving behaviors, vehicles, and the road environment. The proposed method is validated with a simulation of driving scenarios and ROC curves obtained from the NGSIM dataset. The results demonstrate that our proposed driving safety field effectively quantifies the real-time risk in ramp influence areas and outperforms Time to Collision (TTC), making it suitable for integration into collision warning systems of ADASs. Full article

Background: Aortic valve stenosis (AS) is the most common degenerative valvular heart disease, significantly impacting the outcome. Current guidelines recommend valve replacement only for symptomatic patients, but advanced cardiovascular imaging, particularly cardiac magnetic resonance (CMR), may refine these recommendations. Feature-tracking CMR (FT-CMR) effectively assesses left ventricular (LV) strain and shows promise in predicting major adverse cardiovascular events (MACEs), though data on AS are limited. This study explored the role of CMR-derived LV strain in predicting MACEs occurrence in patients with severe AS. Method: We prospectively assessed 84 patients with severe AS and 84 matched controls. Global longitudinal (GLS), circumferential (GCS), and radial strain (GRS) were evaluated using FT-CMR. A composite endpoint—cardiac death, ventricular tachyarrhythmias, and heart failure hospitalization—was analyzed over a median follow-up of 31 months. Results: GLS was considerably reduced in AS patients (−15.8% vs. −19.7%, p < 0.001) and significantly predicted MACEs (HR = 1.24, p = 0.002) after adjusting for LVEF, 6 min walk distance, native T1, and late gadolinium enhancement. This underscores GLS’s unique and robust predictive capability for MACEs in severe AS patients. Kaplan–Meier curves and ROC analysis both showed that GLS had the highest predictive performance for MACEs, with an AUC of 0.857. Conclusions: GLS provided independent incremental predictive value for outcome. Full article

Background and Objectives: Acute cardiorespiratory failure disrupts the delicate balance of energy supply, demand, and consumption, with elevated lactate levels and decreased blood pH serving as crucial indicators. Acute cardiogenic pulmonary edema (ACPO), a common cause of acute respiratory failure, poses a substantial mortality risk. Lactate, a byproduct of pyruvate reduction, is a pertinent marker in perfusion assessment. Lactate clearance (LC) has proven prognostic efficacy in various conditions but lacks consensus on its predictive power in acute cardiogenic pulmonary edema. Materials and Methods: This prospective observational study, conducted in a metropolitan area’s third-level emergency department, involved patients with cardiogenic pulmonary edema from May 2021 to August 2023. The inclusion criteria specified acute cardiogenic pulmonary edema, excluding patients with incomplete data or other respiratory conditions. Lactate clearance, calculated at presentation and after 6 h, served as the primary outcome predictor. Our data analysis employed logistic regression, the ROC curve, and statistical tests. Results: The cohort of 106 patients revealed that a lactate clearance below 14.29% was significantly associated with mortality. While 51.6% of survivors were discharged, LC’s predictive success for discharge was inconclusive. Logistic regression underscored the significance of lactate clearance, with a one-unit increase yielding a 5.55-fold probability of survival. The AUC for LC was 0.759. Conclusions: This study pioneers the exploration of lactate clearance in patients with acute cardiogenic pulmonary edema. LC below 14.29% signifies a poor prognosis, emphasizing its potential as an early treatment initiation marker. While acknowledging this study’s limitations, we advocate for further multicenter research to refine the understanding of lactate clearance in this context. Full article

Cardiometabolic syndrome (CMS) is a growing concern in children and adolescents, marked by obesity, hypertension, insulin resistance, and dyslipidemia. This study aimed to predict CMS using machine learning based on data from the CASPIAN-V study, which involved 14,226 participants aged 7–18 years, with a CMS prevalence of 82.9%. We applied the XGBoost algorithm to analyze key noninvasive variables, including self-rated health, sunlight exposure, screen time, consanguinity, healthy and unhealthy dietary habits, discretionary salt and sugar consumption, birthweight, and birth order, father and mother education, oral hygiene behavior, and family history of dyslipidemia, obesity, hypertension, and diabetes using five-fold cross-validation. The model achieved high sensitivity (94.7% ± 4.8) and specificity (78.8% ± 13.7), with an area under the ROC curve (AUC) of 0.867 ± 0.087, indicating strong predictive performance and significantly outperformed triponderal mass index (TMI) (adjusted paired t-test; p < 0.05). The most critical selected modifiable factors were sunlight exposure, screen time, consanguinity, healthy and unhealthy diet, dietary fat type, and discretionary salt consumption. This study emphasizes the clinical importance of early identification of at-risk individuals to implement timely interventions. It offers a promising tool for CMS risk screening. These findings support using predictive analytics in clinical settings to address the rising CMS epidemic in children and adolescents. Full article

Recent studies have uncovered intriguing connections between Parkinson’s disease (PD) and cancer, two seemingly distinct disease categories. Disulfidptosis has garnered attention as a novel form of regulated cell death that is implicated in various pathological conditions, including neurodegenerative disorders and cancer. Disulfidptosis involves the dysregulation of intracellular redox homeostasis, leading to the accumulation of disulfide bonds and subsequent cell demise. This has sparked our interest in exploring common molecular mechanisms and genetic factors that may be involved in the relationship between neurodegenerative diseases and tumorigenesis. The Gene4PD database was used to retrieve PD differentially expressed genes (DEGs), the biological functions of differential expression disulfidptosis-related genes (DEDRGs) were analyzed, the ROCs of DEDRGs were analyzed using the GEO database, and the expression of DEDRGs was verified by an MPTP-induced PD mouse model in vivo. Then, the DEDRGs in more than 9000 samples of more than 30 cancers were comprehensively and systematically characterized by using multi-omics analysis data. In PD, we obtained a total of four DEDRGs, including ACTB, ACTN4, INF2, and MYL6. The enriched biological functions include the regulation of the NF-κB signaling pathway, mitochondrial function, apoptosis, and tumor necrosis factor, and these genes are rich in different brain regions. In the MPTP-induced PD mouse model, the expression of ACTB was decreased, while the expression of ACTN4, INF2, and MYL6 was increased. In pan-cancer, the high expression of ACTB, ACTN4, and MYL6 in GBMLGG, LGG, MESO, and LAML had a poor prognosis, and the high expression of INF2 in LIHC, LUAD, UVM, HNSC, GBM, LAML, and KIPAN had a poor prognosis. Our study showed that these genes were more highly infiltrated in Macrophages, NK cells, Neutrophils, Eosinophils, CD8 T cells, T cells, T helper cells, B cells, dendritic cells, and mast cells in pan-cancer patients. Most substitution mutations were G-to-A transitions and C-to-T transitions. We also found that miR-4298, miR-296-3p, miR-150-3p, miR-493-5p, and miR-6742-5p play important roles in cancer and PD. Cyclophosphamide and ethinyl estradiol may be potential drugs affected by DEDRGs for future research. This study found that ACTB, ACTN4, INF2, and MYL6 are closely related to PD and pan-cancer and can be used as candidate genes for the diagnosis, prognosis, and therapeutic biomarkers of neurodegenerative diseases and cancers. Full article

Autonomous vehicles (AVs) face critical decisions in pedestrian interactions, necessitating ethical considerations such as minimizing harm and prioritizing human life. This study investigates machine learning models to predict human decision making in simulated driving scenarios under varying pedestrian configurations and time constraints. Data were collected from 204 participants across 12 unique simulated driving scenarios, categorized into young (24.7 ± 3.5 years, 38 males, 64 females) and older (71.0 ± 5.7 years, 59 males, 43 females) age groups. Participants’ binary decisions to maintain or change lanes were recorded. Traditional logistic regression models exhibited high precision but consistently low recall, struggling to identify true positive instances requiring intervention. In contrast, the AdaBoost algorithm demonstrated superior accuracy and discriminatory power. Confusion matrix analysis revealed AdaBoost’s ability to achieve high true positive rates (up to 96%) while effectively managing false positives and negatives, even under 1 s time constraints. Learning curve analysis confirmed robust learning without overfitting. AdaBoost consistently outperformed logistic regression, with AUC-ROC values ranging from 0.82 to 0.96. It exhibited strong generalization, with validation accuracy approaching 0.8, underscoring its potential for reliable real-world AV deployment. By consistently identifying critical instances while minimizing errors, AdaBoost can prioritize human safety and align with ethical frameworks essential for responsible AV adoption. Full article

Background and Objectives: Systemic inflammatory indices have been largely investigated for their potential predictive value in multiple inflammatory, infectious, and oncological diseases; however, their value in colorectal cancer is still a subject of research. This study investigates the dynamics of pre- and postoperative values of NLR, PLR, SII, and MLR in patients with colorectal cancer and their predictive value for early postoperative outcomes. Materials and Methods: A 2-year retrospective cohort study was performed on 200 patients operated for colorectal adenocarcinoma. Systemic inflammatory indices were calculated based on complete blood count preoperatively and on the first and sixth postoperative days. The patients were divided into two groups based on their emergency or elective presentation. The pre- and postoperative values of serum inflammatory biomarkers and their correlations with postoperative outcomes were separately analyzed for the two study subgroups. Results: There were no significant differences in sex distribution, addressability, associated comorbidities, or types of surgery between the two groups. Patients in the emergency group presented higher preoperative and postoperative values of WBC, neutrophils, NLR, and SII compared to elective patients. The postsurgery hospital stays correlated well with pre- and postoperative day one and day six values of NLR (p = 0.001; 0.02; and <0.001), PLR (p < 0.001), SII (p = 0.037; <0.001; <0.001), and MLR (p = 0.002; p = 0.002; <0.001). In a multivariate analysis, reintervention risk was higher for emergency presentation and anemia, and lower in right colon cancer. In the emergency group, a multivariate model including age, MLR PO1, and pTNM stage was predictive for severe postoperative complications (AUC ROC 0.818). First-day postoperative inflammatory indices correlated well with sepsis, with the best predictive value being observed for the first postoperative day NLR (AUC 0.836; sensibility 88.8%; specificity 66.7%) and SII (AUC 0.796; sensitivity 66.6%; specificity 90%). For elective patients, the first postoperative day PLR and anemia were included in a multivariate model to predict Clavien–Dindo complications graded 3 or more (AUC ROC 0.818) and reintervention (AUC ROC 0.796). Conclusions: Easy-to-calculate and inexpensive systemic inflammatory biomarkers could be useful in predicting early postoperative outcomes in colorectal cancer for both elective and emergency surgery. Full article

The COVID-19 pandemic underscores the need for accurate prognostic tools to predict patient outcomes. This study evaluates the effectiveness of four prominent COVID-19 prediction scores—PAINT, ISARIC4C, CHIS, and COVID-GRAM—at two critical time points: at admission and seven days post-symptom onset, to assess their utility in predicting mortality among hospitalized patients. Conducted at the Clinical Emergency Hospital Pius Brînzeu in Timișoara, this retrospective analysis included adult patients hospitalized with confirmed SARS-CoV-2 infection. Eligible patients had complete data for the scores at both time points. Statistical analysis involved ROC curves and logistic regression to assess the scores’ predictive accuracy for mortality. The study included 215 patients, split into 139 survivors and 76 non-survivors. At admission, the PAINT, ISARIC4C, CHIS, and COVID-GRAM scores significantly differentiated between the survival outcomes (p < 0.0001). The best cutoff values at admission were 6.26 for PAINT, 7.95 for ISARIC4C, 5.58 for CHIS, and 0.63 for COVID-GRAM, corresponding to sensitivities of 85.47%, 80.56%, 88.89%, and 83.33% and specificities of 77.34%, 82.12%, 75.01%, and 78.45%, respectively. By day seven, the cutoff values increased, indicating deteriorating conditions in patients who eventually succumbed to the virus. The hazard ratios at admission for exceeding these cutoffs were significant: PAINT (HR = 3.45), ISARIC4C (HR = 2.89), CHIS (HR = 4.02), and COVID-GRAM (HR = 3.15), highlighting the scores’ abilities to predict severe outcomes. One week post symptom onset, these scores’ predictive values and corresponding hazard ratios increased, further validating their prognostic significance over time. The evaluated COVID-19 prediction scores robustly predict mortality at admission and become more predictive by the seventh day of symptom onset. These findings support the use of these scores in clinical settings to facilitate early identification and intervention for high-risk patients, potentially improving patient outcomes during the ongoing global health crisis. Full article

Background/Objectives: In this study, we aimed to compare the predictive power of non-conventional (neutrophil/lymphocyte ratio—NLR; platelet/lymphocyte ratio—PLR) and conventional markers (C-reactive protein—CRP; procalcitonin—PCT; interleukin-6—IL-6) in terms of disease progression and mortality in severe SARS-CoV-2 patients. Methods: In this prospective observatory study, blood samples were collected daily, focusing on the established inflammatory markers. Critically ill COVID-19 patients who required ICU admission were included. Patient treatment followed established COVID-19 protocols, and the data analysis was performed using SPSS with non-normal distribution methods. The study cohort primarily included patients infected with the delta variant. Results: A mortality rate of 76.6% was observed among 167 patients during the study period. Significant differences in conventional and non-conventional markers between survivor and non-survivor groups were observed. The PCT levels were significantly elevated (p < 0.005) in the deceased group. Among the non-conventional markers, the NLR was consistently higher in non-survivors and emerged as a significant predictor of mortality, whereas the PLR was not elevated among the non-survivors. ROC analyses indicated that PCT and the NLR were the markers with the highest predictive power for mortality. The multivariate logistic regression analysis identified NLR, PCT, CRP, and IL-6 as significant predictors of mortality across different days. The NLR showed a consistent, though not always statistically significant, association with increased mortality risk, particularly on Days 2 and 5. Conclusions: The NLR’s accessibility and simplicity of determination make it a valuable and practical tool for monitoring inflammatory processes in viral infections. Our findings suggest that incorporating NLR analysis into routine clinical practice could enhance the early identification of high-risk patients, thereby improving patient management and outcomes. Full article

Detecting abnormal ECG patterns is a crucial area of study aimed at enhancing diagnostic accuracy and enabling early identification of Chronic Kidney Disease (CKD)-related abnormalities. This study compares a unique strategy for abnormal ECG patterns using the LADTree model to standard machine learning (ML) models. The study design includes data collection from the MIT-BIH Arrhythmia dataset, preprocessing to address missing values, and feature selection using the CfsSubsetEval method using Best First Search, Harmony Search, and Particle Swarm Optimization Search approaches. The performance assessment consists of two scenarios: percentage splitting and K-fold cross-validation, with several evaluation measures such as Kappa statistic (KS), Best First Search, recall, precision-recall curve (PRC) area, receiver operating characteristic (ROC) area, and accuracy. In scenario 1, LADTree outperforms other ML models in terms of mean absolute error (MAE), KS, recall, ROC area, and PRC. Notably, the Naïve Bayes (NB) model has the lowest MAE, but the Support Vector Machine (SVM) performs badly. In scenario 2, NB has the lowest MAE but the highest KS, recall, ROC area, and PRC area, closely followed by LADTree. Overall, the findings indicate that the LADTree model, when optimized for ECG signal data, delivers promising results in detecting abnormal ECG patterns potentially related with CKD. This study advances predictive modeling tools for identifying abnormal ECG patterns, which could enhance early detection and management of CKD, potentially leading to improved patient outcomes and healthcare practices. Full article

To explore the effects of altered amino acids (AAs) and the carnitine metabolism in non-pregnant women with infertility (NPWI), pregnant women without infertility (PWI) and infertility-treated pregnant women (ITPW) compared with non-pregnant women (NPW, control), and develop more efficient models for the diagnosis of infertility and pregnancy, 496 samples were evaluated for levels of 21 AAs and 55 carnitines using targeted high-performance liquid chromatography with tandem mass spectrometry (HPLC-MS/MS). Three methods were used to screen the biomarkers for modeling, with eight algorithms used to build and validate the model. The ROC, sensitivity, specificity, and accuracy of the infertility diagnosis training model were higher than 0.956, 82.89, 66.64, and 82.57%, respectively, whereas those of the validated model were higher than 0.896, 77.67, 69.72, and 83.38%, respectively. The ROC, sensitivity, specificity, and accuracy of the pregnancy diagnosis training model were >0.994, 96.23, 97.79, and 97.69%, respectively, whereas those of the validated model were >0.572, 96.39, 93.03, and 94.71%, respectively. Our findings indicate that pregnancy may alter the AA and carnitine metabolism in women with infertility to match the internal environment of PWI. The developed model demonstrated good performance and high sensitivity for facilitating infertility and pregnancy diagnosis. Full article

Background: Platelets and lymphocytes levels are important in assessing systemic disorders, reflecting inflammatory and immune responses. This study investigated the relationship between blood parameters (platelet count (PLT), mean platelet volume (MPV), lymphocyte count (LINF), and platelet-to-lymphocyte ratio (PLR)) and osteoarthritis (OA) severity, considering age, sex, and body mass index (BMI). Methods: Patients aged ≥40 years were included in this cross-sectional study and divided into groups based on knee OA severity using the Kellgren–Lawrence (KL) grading system. A logistic regression model, adjusted for confounders, evaluated the ability of PLT, MPV, LINF, and PLR to categorize OA severity. Model performance in terms of accuracy, sensitivity, and specificity was assessed using ROC curves. Results: The study involved 245 OA patients (51.4% female, 48.6% male) aged 40–90 years, 35.9% with early OA (KL < 3) and 64.1% moderate/severe OA (KL ≥ 3). Most patients (60.8%) were aged ≥60 years, and BMI was <25 kg/m² in 33.9%. The model showed that a 25-unit increase in PLR elevates the odds of higher OA levels by 1.30 times (1-unit OR = 1.011, 95% CI [1.004, 1.017], p < 0.005), while being ≥40 years old elevates the odds by 4.42 times (OR 4.42, 95% CI [2.46, 7.95], p < 0.0005). The model’s accuracy was 73.1%, with 84% sensitivity, 52% specificity, and an AUC of 0.74 (95% CI [0.675, 0.805]). Conclusions: Higher PLR increases the likelihood of moderate/severe OA, suggesting that monitoring these biomarkers could aid in early detection and management of OA severity. Further research is warranted to cross-validate these results in larger populations. Full article