Biomarkers最新文献

Introduction. Elevated fluoride (F^-) exposure during childhood produces dental fluorosis (DF). Nails have been used for monitoring systemic F^- in relation to DF. The aim of this study was to evaluate F^- levels in toenails in association with DF severity in Mexican schoolchildren. Materials and methods. 120 schoolchildren from nonendemic areas (NEAs) and endemic F^- areas (EAs) were screened for DF via the Thylstrup and Fejerskov index (TFI). Toenails were collected to quantify systemic F^-. The associations between the biomarker, DF severity, tap water intake, sex, and age were analyzed. Results. The mean F^- in toenails in the NEAs and EAs were 0.63 ± 0.43 and 2.72 ± 1.38 mg/kg, respectively (p < 0.001). A positive correlation was observed between the biomarker and DF severity (r_s = 0.755, p < 0.001). Tap water consumption and the biomarker were associated with DF severity (p < 0.001). Within TFI7-8 the mean F^- level was higher in those ages 10-11 than in those ages 8-9 (p < 0.05). Conclusion. Systemic F^- levels in toenails are associated with DF severity in Mexican schoolchildren from both the NEA and the EA, which reflects the ability of the biomarker to accurately record the exposure to the compound in relation to clinical damage.

Background: Ischemia and associated hypoxemia-induced oxidative stress play an important role in hyperemesis gravidarum (HG) pathogenesis.

Objective: The aim was to investigate the role of ischemia-modified albumin (IMA) in predicting HG.

Methods: A prospective cohort study was conducted with 138 participants with singleton pregnancies who had experienced HG in previous pregnancies. Blood samples were taken at or before 5 weeks, provided they had no symptoms of nausea and vomiting at that time. The samples were stored under appropriate conditions to be analyzed for IMA. All participants were then followed to determine whether they would develop HG.

Results: HG occurred in 42 participants (HG group), while the remaining 96 participants did not develop HG (control group). Baseline characteristics showed no significant differences. IMA levels were significantly higher in the HG group (p < 0.001). IMA levels did not correlate with body mass index or maternal age. IMA with a cut-off of >74.74 ng/mL (95% sensitivity, 67% specificity) had a discriminatory power with an AUC value of 0.791 (95% CI: 0.714-0.856; p < 0.001) for predicting HG.

Conclusion: Our results show an association between high IMA levels in early pregnancy and an increased risk of HG. IMA can be used as a predictive tool for HG.

Background: The role of systemic inflammation in the development and progression of cardiovascular diseases has been attractive, but its association with incident dilated cardiomyopathy (DCM) is rarely investigated. This study aimed to systematically investigate the association between various inflammatory markers and DCM incidence.

Methods: The data were derived from the UK Biobank database. Systemic inflammation markers in this study encompassed peripheral immune cell counts and their ratios and the low-grade inflammation score (INFLA-score). The Cox proportional hazards regression, restricted cubic splines model, and segmented regression were adopted to assess the association between systemic inflammation markers and DCM incidence. Additionally, the subgroup Cox analysis stratified across sex was also performed.

Results: A total of 351,148 participants were enrolled in this study, and 377 subjects developed DCM during a mean follow-up of 12.21 years. The positive association between C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), and CRP-to-lymphocyte ratio (CLR) and DCM incident risk was found (CRP: HR = 1.190, P = 0.001; NLR: HR = 1.315, P = 0.033; CLR: HR = 1.206, P < 0.001), while the lymphocyte-to-monocyte ratio (LMR) was negatively associated with DCM incident risk (HR = 0.756; P = 0.033). Furthermore, the increased risk of DCM incidence was significantly and nonlinearly correlated with the reduction of platelet count (HR = 0.543; P = 0.002). In the subgroup analysis, sex-specific inflammation markers related to DCM development were noticed.

Conclusions: The study has underlined that multiple inflammation markers were significantly associated with the risk of incident DCM, which would provide evidence for the aetiological study of DCM.

Background: Lung cancer is the cancer with the highest morbidity and mortality in the world. With the increasing diagnosis rate of patients with early-stage lung cancer, surgery treatment becomes an option for more patients. However, there is a lack of effective indicators to assess the risk of recurrence after lung cancer surgery.

Methods: We collected levels of serum autoantibodies and evaluated their roles as biomarkers especially for postoperative recurrence of lung cancer. In vitro experiments including antibody-dependent cellular cytotoxicity (ADCC), antibody-dependent cellular phagocytosis (ADCP) and complement-dependent cytotoxicity (CDC) were performed to explore the functions of serum autoantibodies.

Results: Our study demonstrated that serum autoantibody-positive patients with early-stage lung cancer had a longer postoperative progression period. The levels of serum autoantibodies in patients with lung cancer were higher than that in patients with benign lung diseases. But all the serum autoantibodies had no difference between patients with stage I and II. In addition, the results of in vitro experiments indicated that serum autoantibodies can mediate immune responses and enhance anti-tumour effects.

Conclusion: This study proposed effective biomarkers for prognosis in lung cancer patients after surgery which is critical to reduce the recurrence.

Purpose: Chimeric antigen receptor (CAR) T-cell CD19 therapy has changed the treatment paradigm for patients with relapsed/refractory B-cell acute lymphoblastic leukemia. It is frequently associated with potentially severe toxicities: cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS), and admission to PICU is often required. Some biomarkers seem to correlate with CRS severity. Our goal is to elucidate the role of procalcitonin (PCT), C-reactive protein (CRP) and ferritin in the context of CRS following CAR T-cell infusion to predict its severity and PICU admission.

Methods: Prospective observational study (2016-2022) in children and young adult who received CAR T-cell therapy (Tisagenlecleucel/ARI-0001). We collected epidemiologic data, specific CAR T-cell toxicities, PICU admission, biomarker results (PCT, CRP and ferritin), length of stay and mortality. Biomarkers were analyzed considering two values: the highest value during ward admission, and the highest overall value including PICU admission.

Results: Seventy-seven patients were included. Median age at infusion was 9.1 years (IQR 6-13), 49.4% were females. Before CAR T-cell infusion, the median bone marrow blast was 9% (IQR 0-59). The most frequent toxicity was CRS in 62 patients (80.5%), it was severe in 18 cases (23.4%). Fourteen patients (18.1%) had ICANS. Thirty-one patients (40.3%) required admission to the PICU. PCT and ferritin were higher in patients admitted to PICU (PCT 0.8 ng/mL vs 0.15 ng/mL, p < 0.001, ferritin 5490 vs. 2900 µg/L, p < 0.019). The proposed cut-off for PCT to predict admission to PICU is 0.55 ng/mL, presenting a sensitivity of 67.7% and a specificity of 86.7%. The maximum value of three biomarkers was higher in those who presented any primary outcome: development of severe CRS, the need for admission to PICU, and in-hospital mortality. Biomarkers were higher in those who needed inotropic or respiratory support.

Conclusions: PCT levels increase after CAR-T cell therapy in the setting of systemic inflammation and could be a predictor of PICU admission and evolution to death. Further research studying its role in the context of CRS and the differential diagnosis between infection and CRS is needed to better understand the biology of this biomarker and to define its value in clinical practice.

Background: Lung cancer is a primary global health concern, responsible for a considerable portion of cancer-related fatalities worldwide. Understanding its molecular complexities is crucial for identifying potential targets for treatment. The goal is to slow disease progression and intervene early to prevent the development of advanced lung cancer cases. Hence, there's an urgent need for new biomarkers that can detect lung cancer in its early stages.

Methods: The study conducted RNA-Seq analysis of lung cancer samples from the publicly available SRA database (NCBI SRP009408), including both control and tumour samples. The genes with differential expression between tumour and healthy tissues were identified using R and Bioconductor. Machine learning (ML) techniques, Random Forest, Lasso, XGBoost, Gradient Boosting and Elastic Net were employed to pinpoint significant genes followed by classifiers, Multilayer Perceptron (MLP), Support Vector Machines (SVM) and k-Nearest Neighbours (k-NN). Gene ontology and pathway analyses were performed on the significant differentially expressed genes (DEGs). The top genes from DEG and machine learning analyses were combined for protein-protein interaction (PPI) analysis, identifying 10 hub genes essential for lung cancer progression.

Results: The integrated analysis of ML and DEGs revealed the significance of specific genes in lung cancer samples, identified the top 5 upregulated genes (COL11A1, TOP2A, SULF1, DIO2, MIR196A2) and the top 5 downregulated genes (PDK4, FOSB, FLYWCH1, CYB5D2, MIR328), along with their associated genes implicated in pathways or co-expression networks were identified. Among the various algorithms employed, Random Forest and XGBoost proved effective in identifying common genes, underscoring their potential significance in lung cancer pathogenesis. The MLP exhibited the highest accuracy in classifying samples using all genes. Additionally, the protein-protein interaction (PPI) analysis identified 10 hub genes that are pivotal in lung cancer pathogenesis: COL1A1, SOX2, SPP1, THBS2, POSTN, COL5A1, COL11A1, TIMP1, TOP2A and PKP1.

Conclusion: The study contributes to the early prediction of lung cancer by identifying potential biomarkers that could enhance early diagnosis and pave the way for practical clinical applications in the future. Integrating DEGs and machine learning-derived significant genes for PPI analysis offers a robust approach to uncovering critical molecular targets for lung cancer treatment.

Background: Testing for Staphylococcus aureus (SA) colonization in emergency department (ED) patients may guide prevention strategies against hospital acquired infections (HAIs). This study determined the prevalence of SA carriers in a general ED population, characterized the population, and identified predictors for SA colonization.

Methods: A prospective monocentric observational cohort study in a tertiary care hospital collected nasopharyngeal swabs in 1000 adult patients. Polymerase chain reaction (PCR) testing for methicillin resistant and methicillin sensitive SA (MRSA/MSSA) was performed. Risk factor questionnaires and routine data from the clinical information system were captured. Descriptive statistics and binary logistic regression models were applied to report prevalence and outcomes and to identify predictors.

Results: The prevalence for SA was 33.7% (n = 328; 95%-CI: 30.7-36.8): MSSA 30.9% (n = 301; 95%-CI: 28.0-34.0) and MRSA 2.8% (n = 27; 95%-CI: 1.8-4.0). Key predictors of SA colonization included having a catheter (OR 2.0, 95%-CI 1.0-4.0, p = 0.044) and requiring nursing care (OR 1.9, 95%-CI: 1.2-2.9, p = .007), even after adjusting for age and sex.

Conclusion: Testing strategies for SA detection in ED need to focus on vulnerable populations with an elevated risk for HAIs and associated adverse outcomes. Individuals requiring nursing care could be a key target population for screening efforts.

Background: Radon, a radioactive gas, is a significant risk factor for lung cancer, especially in non-smokers. This study examines the expression of exosomal microRNAs (miRNAs) as potential biomarkers for radon-induced effects.

Methods: A total of 109 participants from high- and low-radon areas in Kazakhstan were included. Exosomal hsa-miR-125b-5p and hsa-miR-320c levels were quantified using real-time PCR.

Results: Results revealed a 25.4-fold increase in hsa-miR-125b-5p and a 12.5-fold decrease in hsa-miR-320c in participants exposed to high-radon levels compared to controls. Bioinformatic analysis identified key target genes, such as PRDM1 and IRF4, which are implicated in cancer development.

Conclusion: These findings suggest that exosomal miRNAs could serve as non-invasive biomarkers for radon exposure, offering potential for early diagnosis and monitoring of radon-induced lung cancer. The study underscores the need for further research to validate these miRNAs as reliable diagnostic tools.

Background and objectives: Prior studies have shown that small non-coding RNAs (sncRNAs) are associated with cancer occurrence or development. Recently, a newly discovered class of small ncRNAs known as PIWI-interacting RNAs (piRNAs) have been found to play a vital role in physiological processes and cancer initiation. This study aims to utilize piRNAs as innovative, noninvasive diagnostic biomarkers for breast cancer. Our objective is to develop computational methods that leverage piRNA attributes for breast cancer prediction and its application in diagnostics.

Methods: We created a set of piRNA sequence descriptors using information extracted from the piRNA sequences. To ensure accuracy, we found a path to convert non-standard piRNA to standard names to enable precise identification of these sequences. Using these descriptors, we applied machine-learning (ML) techniques in WEKA (Waikato Environment for Knowledge Analysis) to a dataset of piRNA to assess the predictive accuracy of the following classifiers: Logistic Regression model, Sequential Minimal Optimization (SMO), Random Forest classifier, and Logistic Model Tree (LMT). Furthermore, we performed Shapley additive explanations (SHAP) Analysis to understand which descriptors were the most relevant to the prediction accuracy. The ML models were then validated on an independent dataset to evaluate their effectiveness in predicting breast cancer.

Results: The top three performing classifiers in WEKA were Logistic Regression, SMO, and LMT. The Logistic Regression model achieved an accuracy of 90.7% in predicting breast cancer, while SMO and LMT attained 89.7% and 85.65%, respectively.

Conclusions: Our study demonstrates the effectiveness of using ML-based piRNA classifiers in diagnosing breast cancer and contributes to the growing body of evidence supporting piRNAs as biomarkers in cancer diagnosis. However, additional research is needed to validate these findings and further assess the clinical applicability of this approach.