Search Results (5,350)

Cholangiocarcinoma (CCA) is an aggressive malignancy with limited methods for early detection, necessitating the development of reliable biomarkers for diagnosis and management. However, conventional tumor markers, such as CA19-9 and CEA, exhibit insufficient diagnostic accuracy. Recent advancements in molecular genetics have identified several actionable mutations in CCA, enabling molecularly targeted therapies that improve survival in patients harboring these genetic alterations. Cancer panels, which facilitate multiplex genetic profiling, are critical for identifying these mutations. Studies indicate that several actionable mutations are detected in CCA cases, with patients receiving mutation-guided therapies achieving markedly better outcomes. Liquid biopsies, including cell-free DNA and circulating tumor DNA, offer real-time, non-invasive approaches to monitoring tumor dynamics, heterogeneity, and treatment responses. Furthermore, numerous studies have identified non-coding RNAs in serum and bile as promising biomarkers for the diagnosis and management of CCA. On the other hand, immunotherapy, particularly immune checkpoint inhibitors, has shown efficacy in subsets of CCA patients. However, the success of these therapies is often affected by the status of the tumor immune microenvironment (TME), underscoring the need for comprehensive TME analysis to predict responses to immune checkpoint inhibitors. Despite these advances, no single biomarker currently demonstrates sufficient sensitivity or specificity for clinical application. The integration of multi-omics approaches with cutting-edge technologies holds promise for enhancing diagnostic accuracy, optimizing treatment stratification, and advancing precision medicine in CCA. These developments highlight the transformative potential of biomarkers to improve early detection, prognostic assessment, and personalized therapeutic interventions for CCA. Full article

Interstitial cells of Cajal (ICCs) play a key role in gastrointestinal smooth muscle contractions, but their relationship with anal canal function in advanced haemorrhoidal disease (HD) remains poorly understood. This study uses deep neural network (DNN) models to estimate ICC presence and quantity in anal canal tissues affected by HD. Haemorrhoidectomy specimens were collected from patients undergoing surgery with the LigaSure device. A YOLOv11-based machine learning model, trained on 376 immunohistochemical images, automated ICC detection using the CD117 marker, achieving a mean average precision (mAP50) of 92%, with a recall of 86% and precision of 88%. The DNN model accurately identified ICCs in whole-slide images, revealing that one-third of grade III HD patients and 60% of grade IV HD patients had a high ICC density. Preoperatively, pain was reported in 35% of grade III HD patients and 41% of grade IV patients, with a significant reduction following surgery. A significant decrease in bleeding (p < 0.0001) was also noted postoperatively. Notably, patients with postoperative bleeding, diagnosed with stage IV HD, had high ICC density in their anorectal tissues (p = 0.0041), suggesting a potential link between ICC density and HD severity. This AI-driven model, alongside clinical data, may enhance outcome prediction and provide insights into HD pathophysiology. Full article

Background: to date, there are no specific markers available for diagnosing sepsis. Diagnosis is, indeed, mainly determined by clinical suspicion and the evaluation of the patient’s overall condition. This evaluation involves assessing various inflammatory markers, such as C-reactive protein (CRP) and procalcitonin (PCT), along with markers of tissue hypoxia, such as serum lactate. Additionally, it includes scores that account for complete blood count (CBC), organ function markers, and the patient’s vital parameters, including SOFA, qSOFA, SIRS, and NEWS. Over the years, various potential biomarkers have been studied; among these presepsin appears to offer some significant advantages. Objective: Presepsin, which is the N-terminal fragment of the soluble component of CD14, is primarily elevated in infectious conditions. Its levels rise much earlier in the context of infection compared to currently used biomarkers. As a result, Presepsin shows promise for the early identification of septic patients and could aid in prognostic assessment, allowing clinicians to prioritize care for critically ill individuals. Methods: this study aims to evaluate the role of serum presepsin in the early diagnosis of sepsis in patients who present to the emergency room with a clinical suspicion of sepsis. The secondary objectives include comparing the diagnostic performance of presepsin with traditional biomarkers currently used for sepsis diagnosis and assessing its utility as a prognostic biomarker for mortality risk stratification, in comparison with validated severity prediction scores. Result: Presepsin had valuable diagnostic utility for sepsis (AUC 0.946, p < 0.001) comparable to PCT (AUC 0.905, p < 0.001). Conclusions: the combination of Presepsin, PCT, and EWS yielded the highest diagnostic accuracy for sepsis. Full article

Background: In this prospective study, the prognostic role of ADAMTS13 activity and von Willebrand (VWF) antigen (VWF: Ag) levels in ischemic stroke outcomes was investigated. Methods: Patients diagnosed with acute ischemic stroke were prospectively enrolled in this study, while samples for ADAMTS13 activity and VWF: Ag level measurements were collected upon their admission to our unit. The National Institutes of Health Stroke Scale (NIHSS) score was estimated upon admission and at discharge. The modified Rankin scale for neurologic disability (Rankin) score was estimated based on the patient’s history before the stroke onset, during admission (RankinAdm), and at discharge (RankinDis). Results: In the study, 29 patients with a median age of 82.5 (51, 92) were included. In univariate analysis, ADAMTS13 activity during admission was associated with platelet values at the same time point (r = 0.12, p = 0.01) and VWF: Ag levels were associated with age (r = 0.439, p = 0.04), previous ischemic stroke (r = 0.9176, p = 0.031), and glucose levels (r = 0.64, p = 0.049). Associations between ADAMTS13/VWF: Ag Ratio with RankinDis (r = 0.3253, p = 0.03), and the change between RankinDis and RankinAdm (r = 0.1589, p = 0.014) were identified. Additionally, VWF: Ag levels during admission were correlated with RankinDis (r = 0.0072, p = 0.049). Conclusions: These markers might be useful as biomarkers for the prediction of poor outcomes after stroke. Full article

Background/Objectives: The aberrant expression and activation of HER family members is a known major oncogenic pathway for the proliferation, progression, and metastasis of a wide range of human malignancies. In this study, our aim was to examine the relative expression and prognostic significance of all members of the HER family, the type III EGFR mutant (EGFRvIII), and the putative stem cell markers CD44 and CD109 in patients with glioblastoma. Methods: The expression levels of wild-type EGFR (wtEGFR), HER2, HER3, HER4, EGFRvIII, CD44, and CD109 were determined in tumour specimens from 80 patients by immunohistochemistry. The staining was scored based on the percentage of positive tumour cells, the intensity, and the cellular location of immunostaining. The association between the expression level of the biomarkers and patient overall survival was evaluated using Chi-squared, Kaplan–Meier survival curves, and log-rank tests. Results: At a cut-off value of ≥5% with positive staining, 46% (wtEGFR), 75% (HER2), 19% (HER3), 71% (HER4), 85% (EGFRvIII), 95% (CD44), and 16% (CD109) of the cases were positive for these biomarkers. Interestingly, at the same cut-off value, the expression of wtEGFR in these patients was accompanied by co-expression with HER2 (35%), HER3 (0%), HER4 (30%), EGFRvIII (36%), CD44 (44%), HER2/EGFRvIII (28%), HER2/CD44 (31%), and EGFRvIII/CD44 (36%). In addition, the expression of EGFRvIII was accompanied by co-expression with HER2 (65%), HER3 (15%), HER4 (63%), CD44 (83%), CD109 (16%), wtEGFR/HER2 (28%), and 55% of the cases had co-expression of EGFRvIII/HER2/HER4/CD44. With the exception of HER2 expression, at cut-off values of ≥5% of tumour cells with positive staining, which was associated with better overall survival [HR = 0.57 (p = 0.038), HR = 0.56 (p = 0.034)], there was no significant association between the expression of other members of the HER family, EGFRvIII, CD44, and CD109 on the overall survival in both univariate and multivariate analysis. Conclusions Our results suggest that the co-expression of different members of the HER family, with EGFRvIII, CD44, and CD109, occurs in patients with glioblastoma. As the results of therapy with EGFR inhibitors have not been encouraging in patients with a brain tumour, further investigation should determine whether the co-expression of such biomarkers can be of predictive value for the response to the therapy with various types of HER inhibitors and their potential as therapeutic targets for co-targeted therapy. Full article

Human mesenchymal stem cells (hMSCs) are widely used in regenerative medicine, but large-scale in vitro expansion alters their function, impacting proliferation and differentiation potential. Currently, a predictive marker to assess these changes is lacking. Here, we used dielectrophoresis (DEP) to characterize the electrical phenotype of hMSCs derived from bone marrow (BM), adipose tissue (AT), and umbilical cord (UC) as they aged in vitro from passage 4 (P4) to passage 9 (P9). The electrical phenotype was defined by the DEP spectra, membrane capacitance, and cytoplasm conductivity. Cell morphology and size, growth characteristics, adipogenic differentiation potential, and osteogenic differentiation potential were assessed alongside label-free biomarker membrane capacitance and cytoplasm conductivity. Differentiation was confirmed by histological staining and RT-qPCR. All hMSCs exhibited typical morphology, though cell size varied, with UC-hMSCs displaying the largest variability across all size metrics. Growth analysis revealed that UC-hMSCs proliferated the fastest. The electrical phenotype varied with cell source and in vitro age, with high passage hMSCs showing noticeable shifts in DEP spectra, membrane capacitance, and cytoplasm conductivity. Correlation analysis revealed that population doubling level (PDL) correlated with membrane capacitance and cytoplasm conductivity, indicating PDL as a more precise marker of in vitro aging than passage number. Additionally, we demonstrate that membrane capacitance correlates with the osteogenic marker COL1A1 and that cytoplasm conductivity correlates with the adipogenic markers ADIPOQ and FABP4, suggesting that DEP-derived electrical properties serve as label-free biomarkers of differentiation potential. While DEP has previously been applied to BM-hMSCs and AT-hMSCs, and more recently to UC-hMSCs, few studies have provided a direct comparison across all three sources or tracked changes across continuous expansion. These findings underscore the utility of DEP as a label-free approach for assessing hMSC aging and function, offering practical applications for optimizing stem cell expansion and stem cell banking in clinical settings. Full article

Background/Objectives: Isolated nocturnal hypertension (INH) represents a unique phenotype that can only be identified through ambulatory blood pressure monitoring (ABPM). An increasing body of evidence suggests a significant association between INH and heightened cardiovascular morbidity, mortality, and, more recently, kidney disease progression. Considering these findings, this study aims to retrospectively assess the prevalence of INH and its relationship with glomerular filtration rate (GFR) and albumin excretion rate (AER) in a large cohort of hypertensive patients. Methods: A total of 1340 subjects selected from the patients of our European Hypertension Excellence Centre of the University of Palermo were enrolled. Biochemical tests, urinalysis, 24 h ambulatory blood pressure monitoring, and collection of anamnestic and anthropometric data were performed on each patient. Results: In our cohort, the prevalence of INH was 11%. Logistic regression analyses revealed that male sex, AER, and eGFR were significantly associated with the INH phenotype. AER ≥ 5.8 µg/min predicted the presence of INH with 73.7% sensitivity and 58.4% specificity. An eGFR < 60 mL/min/1.73 m² was also correlated with INH, although its predictive value was less prominent. Multivariable regression models confirmed that AER and eGFR, along with male sex, were independent predictors of INH. In patients with normal blood pressure, AER and metabolic syndrome were also associated with INH. CKD (AER < 30 mg/day and eGFR < 60 mL/min/1.73 m²) was significantly linked to INH. Conclusions: Our research confirms the direct relationship between AER and INH and the inverse relationship between GFR and INH, thus underlining the leading role of renal function in the onset of INH, as widely observed in the literature. The confirmed association between renal markers and INH in the subgroup of subjects with a clinically normal blood pressure could help us to identify the subjects who should undergo ABPM. Full article

Background: Patients with advanced breast cancer (BC) may be treated with stereotactic ablative radiotherapy (SABR) for tumor control. Variable treatment responses are a clinical challenge and there is a need to predict tumor radiosensitivity a priori. There is evidence showing that tumor infiltrating lymphocytes (TILs) are markers for chemotherapy response; however, this association has not yet been validated in breast radiation therapy. This pilot study investigates the computational analysis of TILs to predict SABR response in patients with inoperable BC. Methods: Patients with inoperable breast cancer (n = 22) were included for analysis and classified into partial response (n = 12) and stable disease (n = 10) groups. Pre-treatment tumor biopsies (n = 104) were prepared, digitally imaged, and underwent computational analysis. Whole slide images (WSIs) were pre-processed, and then a pre-trained convolutional neural network model (CNN) was employed to identify the regions of interest. The TILs were annotated, and spatial graph features were extracted. The clinical and spatial features were collected and analyzed using machine learning (ML) classifiers, including K-nearest neighbor (KNN), support vector machines (SVMs), and Gaussian Naïve Bayes (GNB), to predict the SABR response. The models were evaluated using receiver operator characteristics (ROCs) and area under the curve (AUC) analysis. Results: The KNN, SVM, and GNB models were implemented using clinical and graph features. Among the generated prediction models, the graph features showed higher predictive performances compared to the models containing clinical features alone. The highest-performing model, using computationally derived graph features, showed an AUC of 0.92, while the highest clinical model showed an AUC of 0.62 within unseen test sets. Conclusions: Spatial TIL models demonstrate strong potential for predicting SABR response in inoperable breast cancer. TILs indicate a higher independent predictive performance than clinical-level features alone. Full article

Inflammatory bowel diseases (IBDs), comprising Crohn’s disease (CD) and ulcerative colitis (UC), are chronic immune-mediated inflammatory diseases of the gastrointestinal (GI) tract with still-elusive etiopathogeneses and an increasing prevalence worldwide. Despite the growing availability of more advanced therapies in the last two decades, there are still a number of unmet needs. For example, the achievement of mucosal healing has been widely demonstrated as a prognostic marker for better outcomes and a reduced risk of dysplasia and cancer; however, the accuracy of endoscopy is crucial for both this aim and the precise and reproducible evaluation of endoscopic activity and the detection of dysplasia. Artificial intelligence (AI) has drastically altered the field of GI studies and is being extensively applied to medical imaging. The utilization of deep learning and pattern recognition can help the operator optimize image classification and lesion segmentation, detect early mucosal abnormalities, and eventually reveal and uncover novel biomarkers with biologic and prognostic value. The role of AI in endoscopy—and potentially also in histology and imaging in the context of IBD—is still at its initial stages but shows promising characteristics that could lead to a better understanding of the complexity and heterogeneity of IBDs, with potential improvements in patient care and outcomes. The initial experience with AI in IBDs has shown its potential value in the differentiation of UC and CD when there is no ileal involvement, reducing the significant amount of time it takes to review videos of capsule endoscopy and improving the inter- and intra-observer variability in endoscopy reports and scoring. In addition, these initial experiences revealed the ability to predict the histologic score index and the presence of dysplasia. Thus, the purpose of this review was to summarize recent advances regarding the application of AI in IBD endoscopy as there is, indeed, increasing evidence suggesting that the integration of AI-based clinical tools will play a crucial role in paving the road to precision medicine in IBDs. Full article

Background/Objectives: Donor-derived cell-free DNA (dd-cfDNA) is a marker of graft injury that increases in acute rejection and has excellent negative predictive value. Left ventricular global longitudinal strain (LVGLS) and strain-derived myocardial work indices are novel echocardiographic parameters with growing applications. Still, they have been poorly investigated in heart transplant (HTx) recipients so far. We sought to examine the diagnostic impact of left ventricular longitudinal strain-derived indices in diagnosing myocardial injury as assessed by dd-cfDNA after HTx. Methods: Since October 2022, HTx recipients have been shifted from our endomyocardial biopsy (EMB)-based rejection surveillance protocol to a monthly dd-cfDNA-led rejection assessment. We analysed the percentage of donor-derived to total cell-free DNA. For echocardiographic analysis, patient selection was restricted to those transplanted ≥ 6 months. We used 2D speckle-tracking echocardiography to assess LVGLS and strain-derived myocardial work parameters. Results: We analysed four hundred and forty-nine dd-cfDNA samples from seventy-one patients until November 2024. The mean dd-cfDNA fraction remained very low (0.13 ± 0.06%). Eighty-eight percent of surveillance EMBs that would have otherwise been performed were avoided. The mean LVGLS was lower than the literature reference values. We found no correlation between dd-cfDNA and LVGLS. Transplanted hearts had different myocardial work indices than the reference values reported in the literature. Conclusions: dd-cfDNA effectively rules out clinically significant acute rejection and decreases the need for invasive surveillance EMBs. LVGLS seems less sensitive than dd-cfDNA for the identification of myocardial injury in the early stages of HTx rejection in patients at low risk for rejection. Full article

Adulteration detection or authentication is considered a type of one-class classification (OCC) in chemometrics. An effective OCC model requires representative samples. However, it is challenging to collect representative samples from all over the world. Moreover, it is also very hard to evaluate the representativeness of collected samples. In this study, we blazed a new trail to propose an authentication method to identify adulterated edible oils without building a prediction model beforehand. An authentication method developed by real-time one-class classification modeling, and model population analysis was designed to identify adulterated oils in the market without building a classification model beforehand. The underlying philosophy of the method is that the sum of the absolute centered residual (ACR) of the good model built by only authentic samples is higher than that of the bad model built by authentic and adulterated samples. In detail, a large number of OCC models were built by selecting partial samples out of inspected samples using Monte Carlo sampling. Then, adulterated samples involved in the test of these good models were identified. Taking the inspected samples of avocado oils as an example, as a result, 6 out of 40 avocado oils were identified as adulterated and then validated by chemical markers. The successful identification of avocado oils adulterated with soybean oil, corn oil, or rapeseed oil validated the effectiveness of our method. The proposed method provides a novel idea for oils as well as other high-value food adulteration detection. Full article

Experimental autoimmune encephalomyelitis (EAE) is a preclinical animal model widely used to study multiple sclerosis (MS). Blood-based analytes, including cytokines and neural biomarkers are the predictors of neurodegeneration, disease activity, and disability in patients with MS. However, understudied confounding factors cause variation in reports on EAE across animal strains/studies, limiting the utility of these biomarkers for predicting disease activity. In this study, we investigated blood-based analyte profiles, including neural markers (NFL and GFAP) and cytokines (IL-6, IL-17, IL-12p70, IL-10, and TNF-α), in two clinically distinct EAE models: relapsing-remitting (RR)-EAE and chronic-EAE. Ultrasensitive single-molecule array technology (SIMOA, Quanterix) was used to profile the analytes in the blood plasma of mice at the acute, chronic, and progressive phases of disease. In both models, NFL was substantially increased during post-disease onset across all phases, with a pronounced increase observed in chronic-EAE. The leakage of GFAP into peripheral blood was also greater after disease onset in both EAE models, especially in the acute phase of chronic-EAE. Among all cytokines, only IL-10 had consistently lower levels in both EAE models throughout the course of disease. This study suggests NFL, GFAP, and IL-10 as potential translational predictors of disease activity in EAE, making them potential candidates as surrogate markers for the preclinical testing of therapeutic interventions in animal models of MS. Full article

Background and Objectives: This study aimed to determine whether albumin levels and the ratios of key biochemical markers, including the hemoglobin, albumin, lymphocyte, and platelet (HALP score) and lymphocyte/C-reactive protein ratio (LCR), can predict survival and recurrence in patients with pancreatic adenocarcinoma. Materials and Methods: A total of 87 patients who underwent surgery for pancreatic adenocarcinoma in our clinic between January 2017 and December 2021 were included. Preoperative albumin levels, HALP scores, and LCR values were calculated and analyzed to evaluate their predictive value for pathological findings in the early postoperative period. Results: The mean age of the study population was 64.8 ± 9.6 years; 59 patients (67.8%) were male, and 28 (32.2%) were female. The cut-off values for HALP, LCR, and albumin were 34.4, 0.61, and 38.55, respectively. Patients with low HALP scores had significantly shorter overall survival than those with high scores (15.8 vs. 19.3 months; p < 0.01). Similarly, patients with low LCR scores showed shorter survival than those with high scores (17.8 vs. 18.5 months; p < 0.01). High albumin levels were associated with significantly longer survival compared to low albumin levels (16.3 vs. 14.6 months; p < 0.01). Conclusions: Low HALP scores and low albumin levels were identified as significant independent prognostic factors for both disease-free and overall survival in patients with pancreatic adenocarcinoma. Full article

Diabetic cardiomyopathy (DCM), a critical complication of type 2 diabetes mellitus (T2DM), is marked by metabolic dysfunction, oxidative stress, and chronic inflammation, ultimately progressing to heart failure. This study investigated the synergistic therapeutic potential of Hippophae rhamnoides L. (sea buckthorn, SBU) extract and metformin in a mouse model of T2DM-induced DCM. T2DM was induced using a 45% high-fat-AGEs-enriched diet, followed by treatment with SBU, metformin, or their combination. Treatment effects were monitored through bioinformatic analysis, chemoinformatic prediction, behavioral testing, biochemical assays, histopathological evaluations and gene expression profiles. Based on bioinformatic analysis, we identified key hub genes involved in the diabetic cardiomyopathy including SERPINE1, NRG1, MYH11, PTH, NR4A2, NRF2, PGC1α, GPX4, ATF1, ASCL2, NOX1, NLRP3, CCK8, COX2, CCL2, PTGS2, EGFR, and oncostatin, which are pivotal in modulating the ferroptosis pathway. Furthermore, the expression of long non-coding RNAs (lncRNAs) NEAT1 and MALAT1, critical regulators of inflammation and cell death, was effectively downregulated, correlating with decreased levels of the pro-inflammatory marker oncostatin. The combined therapy significantly improved glucose regulation, reduced systemic inflammation and protected the heart from oxidative damage. Histopathological analysis revealed notable reductions in cardiac necrosis and fibrosis. Particularly, the combination therapy of SBU and metformin demonstrated a synergistic effect, surpassing the benefits of individual treatments in preventing cardiac damage. These findings highlight the potential of integrating SBU with metformin as a novel therapeutic strategy for managing DCM by targeting both metabolic and ferroptosis-related pathways. This dual intervention opens promising avenues for future clinical applications in diabetic heart disease management, offering a comprehensive approach to mitigating the progression of DCM. Full article

Background/Objectives: The diagnosis of fetal nutritional status is of great importance for the accurate evaluation and monitoring of these pregnancies. The objective of the present study is to develop a model that allows for the prenatal assessment of fetal body mass index and to evaluate its diagnostic efficacy in predicting neonatal nutritional status. Methods: A retrospective cohort study was conducted to develop and evaluate a new model in the diagnosis of alterations in fetal nutritional status based on the customized fetal body mass index. By establishing the relationship between weight and length, we can calculate the fetal body mass index, which could correlate more effectively with nutritional status. Results: A total of 12,633 subjects were recruited, and 9499 were included in our study. Capacities to predict both neonatal malnourishment and overnutrition were calculated for each of the three methods analyzed (BMI, GROW, and IG21st). The receiver operating characteristic curve for each method was developed. The sensitivity and specificity for the assessment of malnutrition were 0.83 and 0.90, respectively. The area under the ROC curve of our method was 0.95 for malnutrition, while for IG21st and GROW, it was 0.80 and 0.79, respectively. Conclusions: This study demonstrates a superior diagnostic capacity for alterations in fetal and neonatal nutritional status of this new fetal BMI curve compared to the previously used fetal weight percentile curves. Full article