Area Under The ROC Curve Research Articles

Comparison of 2D and 3D lung lobe quantification with Ventilation/Perfusion Ratio.

In this study, standard 2D lung lobe quantification is compared with two 3D lung lobe quantification software tools to investigate the clinical benefit of a 3D approach. The accuracy of 2D versus 3D lung lobe quantification is evaluated based on the calculated numerical ventilation-perfusion ratio (VQR) using a receiver operating curve (ROC) analysis.A study group of 50 consecutive patients underwent a planar lung scintigraphy (anterior/posterior) as well as ventilation/perfusion single photon emission computed tomography (SPECT/CT) to exclude acute pulmonary embolism. All data were acquired with SPECT OPTIMA NM/CT 640 (GE Healthcare). 2D analysis was performed for all ventilation/perfusion scans using a lung analysis tool (Syngo Workstation, Siemens Healthineers). 3D quantification analysis was performed using QLUNG (Q. Lung, Xeleris 4.0, GE Healthcare) and LLQ (Hermes Hybrid 3D Lung Lobar Quantification, Hermes Medical Solutions). The area under the ROC curve (AUC) served as a decision criterion to find the best agreement between clinical PE findings and calculated PE candidates of the 2D and 3D methods. The significance of the ROC curves was evaluated using the DeLong comparison.A significant difference between 2D/3D could be determined. Both 3D approaches showed robust and comparable results. The AUC range of [0.10, 0.67] was given for 2D lobar analysis, QLUNG AUC range revealed in [0.39,0.74] and LLQ AUC range was [0.42,0.72]. Averaged over all lung lobes an AUC=0.39 was given for 2D analysis and AUC=0.58 was given for LLQ/QLUNG.We could demonstrate the better performance of 3D analysis compared to 2D analysis. Consequently, is recommended to use a 3D approach in clinical practice.

AI-enabled thermal monitoring of commercial (PHEV) Li-ion pouch cells with Feature-Adapted Unsupervised Anomaly Detection

Distributed temperature profiling of lithium-ion batteries provides valuable insights, aiding thermal management and minimising risk of battery failures. Highlighted by Batteries Europe as crucial for battery safety, advances in thermal monitoring are imperative to continuous safe adoption of battery technology. Deep Learning techniques have recently emerged as powerful tools for anomaly detection (AD) in many thermal mapping applications. These data-driven methods can handle common challenges like data unavailability or environment variations. Our study devises a methodology to leverage Deep Learning with thermal data from commercially available pouch cells and an infrared camera. We explain the building blocks of FAUAD (Feature-Adapted Unsupervised Anomaly Detection), which models the normality of the input data and synthesizes anomalies in its feature space. The resulting model is benchmarked against some of the latest state-of-the-art methods and achieves high anomaly detection capability; Area Under the ROC Curve (AUROC) score of 0.971 on simulated data, 0.990 on contaminated real data, and a perfect score of 1.0 on real clean data. While maintaining a compact size of 15 MB. FAUAD offers a notable advancement in unsupervised anomaly detection for battery thermal monitoring. The proposed method is cell chemistry agnostic and open to usage scenarios beyond this works’ scope.

Utilization and validation of dried blood spot-based metabolomics in plasma-derived diagnostic models

Metabolomics has rapidly advanced in life sciences, enhancing our understanding of physiological conditions. Plasma stands out as a predominant sample type in metabolomics studies. Dried blood spot (DBS) has been recognized as a valuable strategy due to its unique characteristics, such as minimal invasiveness, small sample volume, and easy transport. Most large cohort studies construct the diagnostic model using plasma-based metabolomics rather than DBS. However, the comparability of metabolite measurements between DBS and plasma samples, as well as the integration of DBS-based metabolomics into established plasma-derived diagnostic models, remains unclear. We collected plasma and DBS samples from the same 12 healthy controls under fasting and non-fasting conditions and performed ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS)-based metabolomics. We identified 277 metabolites present in both plasma and DBS samples, with 229 (82.7 %) showing a strong correlation between the two sample types. Furthermore, pre-processing datasets from plasma and DBS samples before combining them effectively diminished inconsistencies between plasma- and DBS-based metabolomics without compromising the inherent classification within the data. For the clinical application, the plasma samples were obtained from the gastric cancer patients (n = 204) and the healthy controls (n = 128) to serve as the training cohorts. Additionally, DBS samples were collected from different participants of gastric cancer (n = 19) and healthy controls (n = 12) to validate the diagnostic model. Finally, DBS-based metabolomics were integrated into established plasma-derived gastric cancer diagnostic models, yielding an area under the ROC curve (AUC) of 0.934. Overall, incorporating DBS-based metabolomics into an established plasma-derived diagnostic model holds promise, offering valuable opportunities and insights for diagnostic research and clinical practice.

A Novel Prognostic Risk Score Model Based on RNA Editing Level in Lower-Grade Glioma

BackgroundLower-grade glioma (LGG) refers to WHO grade 2 and 3 gliomas. Surgery combined with radiotherapy and chemotherapy can significantly improve the prognosis of LGG patients, but tumor progression is still unavoidable. As a form of posttranscriptional regulation, RNA editing (RE) has been reported to be involved in tumorigenesis and progression and has been intensively studied recently. MethodsSurvival data and RE data were subjected to univariate and multivariate Cox regression analysis and lasso regression analysis to establish an RE risk score model. A nomogram combining the risk score and clinicopathological features was built to predict the 1-, 3-, and 5-year survival probability of patients. The relationship among ADAR1, SOD2 and SOAT1 was verified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) ResultsA risk model associated with RE was constructed and patients were divided into different risk groups based on risk scores. The model demonstrated strong prognostic capability, with the area under the ROC curve (AUC) values of 0.882, 0.938, and 0.947 for 1-, 3-, and 5-year survival predictions, respectively. Through receiver operating characteristic curve (ROC) curves and calibration curves, it was verified that the constructed nomogram had better performance than age, grade, and risk score in predicting patient survival probability. Apart from this functional analysis, the results of correlation analyses between risk differentially expressed genes (RDEGs) and RE help us to understand the underlying mechanism of RE in LGG. ADAR may regulate the expression of SOD2 and SOAT1 through gene editing. ConclusionIn conclusion, this study establishes a novel and accurate 17-RE model and a nomogram for predicting the survival probability of LGG patients. ADAR may affect the prognosis of glioma patients by influencing gene expression.

Validation of the Node Reporting and Data System (Node-RADS) for standardized CT evaluation of regional lymph nodes in esophageal squamous cell carcinoma patients.

The accurate identification of positive lymph nodes in esophageal squamous cell carcinoma (ESCC) influences patient risk assessment and treatment decisions, but there is no standardized approach for radiological evaluation. The aim of this study was to verify the diagnostic performance of the new Node Reporting and Data System 1.0 (Node-RADS) in the assessment of lymph node metastasis in patients with ESCC, as verified by final histopathology. Node-RADS is a scoring system composed of different criteria for evaluating lymph node metastasis, with scores ranging from 1 to 5, corresponding to the degree of suspicion of lymph node involvement. In this single-center study, Node-RADS was used to retrospectively evaluate regional lymph nodes in 173 ESCC patients who underwent computed tomography (CT) before radical resection. In addition, the area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the Node-RADS score and individual criteria. Node-RADS outperformed the individual assessment criteria (AUC: 94.3%, sensitivity: 96.5%, specificity: 92.0%), with scores ≥ 3 indicating the maximum diagnostic effectiveness. The diagnostic efficiency of the highest Node-RADS score surpassed that of the short axis score (AUC: 94.3% vs. 81.9%, p < 0.001). Our results indicated that the best diagnostic cut-off points for the short axis, long axis and short axis/long axis ratio were 9 mm, 11 mm, and 0.74, respectively. Node-RADS has emerged as a practical, repetitive method for the early identification of high-risk metastatic lymph nodes, providing therapeutic guidance and predicting disease prognosis in ESCC patients. Question How does the Node Reporting and Data System 1.0 (Node-RADS) perform in the assessment of lymph node metastasis in patients with esophageal squamous cell carcinoma (ESCC)? Findings The maximum diagnostic efficiency was achieved with a Node-RADS score of ≥ 3. Clinical relevance The Node-RADS has improved diagnostic efficiency for distinguishing lymph node metastasis in patients with ESCC.

A nomogram to predict the risk of insulin resistance in Chinese women with polycystic ovary syndrome

BackgroundInsulin resistance (IR) is considered a major driver of the pathophysiology of polycystic ovary syndrome (PCOS), mediating the progression of hyperandrogenism and metabolic and reproductive dysfunction in patients with PCOS. Early detection of the risk of concurrent IR is essential for women with PCOS. To address this need, this study developed a predictive nomogram for assessing the risk of IR in women with PCOS, aiming to provide a tool for risk stratification and assist in clinical decision-making.MethodsPatients with untreated PCOS-IR diagnosed in a single-center retrospective cohort study from January 2023 to December 2023 were included for nomogram construction and validation. The area under the ROC curve (AUC), calibration curve, Hosmer–Lemeshow (H-L) goodness-of-fit test, and decision curve analysis (DCA) were used to evaluate the nomogram’s discrimination, calibration, and clinical decision performance. A risk stratification model based on the nomogram was then developed.ResultsA total of 571 patients were included in the study; 400 patients enrolled before September 2023 were divided into the training and validation sets, and 171 patients enrolled later were used as the external validation set. The variables identified by logistic regression and the random forest algorithm—body mass index (BMI, OR 1.43), triglycerides (TG, OR 1.22), alanine aminotransferase (ALT, OR 1.03), and fasting plasma glucose (FPG, OR 5.19)—were used to build the nomogram. In the training, internal validation, and external validation sets, the AUCs were 0.911 (95% CI 0.878–0.911), 0.842 (95% CI 0.771–0.842), and 0.901 (95% CI 0.856–0.901), respectively. The nomogram showed good agreement between predicted and observed outcomes, and patients were categorized into low-, medium-, and high-risk groups based on their scores.ConclusionsIndependent predictors of untreated PCOS-IR risk were incorporated into a nomogram that effectively classifies patients into risk groups, providing a practical tool for guiding clinical management and early intervention.

Identification of a machine learning-based diagnostic model for axial spondyloarthritis in rheumatological routine care using a random forest approach

ObjectivesIn axial spondyloarthritis (axSpA), early diagnosis is crucial, but diagnostic delay remains long and diagnostic criteria do not exist. We aimed to identify a diagnostic model that distinguishes patients with...

Clinical characteristics and early identification of augmented renal clearance in PICU patients with severe sepsis associated with MRSA infection

ObjectivesTo investigate the epidemiological characteristics of Augmented Renal Clearance (ARC) in severe sepsis children with MRSA infection and find risk factors to establish a model predicting ARC onset in PICU.DesignRetrospective study, in which ARC was defined by estimated glomerular filtration rate (eGFR) measured by the modified Schwartz formula above 130 ml/min/1.73 m2. Univariable and multivariable logistic regression analyses were performed to find the predictor for ARC. Multi-strategy modeling was used to form an early prediction model for ARC, which was evaluated by the area under the ROC curve (AUC), accuracy (ACC) and other indicators.SettingOne China PICU.PatientsSevere sepsis children with MRSA infection admitted to PICU from May 2017 to June 2022 at Children's Hospital of Nanjing Medical University.InterventionsNone.Measurements and main results125 of 167 (74.9%) patients with severe sepsis with MRSA infection have occurred ARC during the hospitalization of PICU, of which 44% have an absolute decrease in vancomycin trough level (VTL), patients with ARC have a longer length of stay in both hospital and PICU, lower VTL and require longer anti-infective treatment. 20 different models were established for the early recognition of ARC. Among them, the best performer had an AUC of 0.746 and a high application prospect.ConclusionARC is a phenomenon significantly underestimated in pediatric patients with severe sepsis associated with MRSA infection, which can affect 74.9% of these patients and affects the process of anti-infection treatment and clinical outcomes. To achieve early prediction only by specific risk factors is unreliable, a model based on Multivariate Logistic Regression in this study was chosen to be used clinically.

Predicting ICU Admission for Hospitalized COVID-19 Patients by Artificial Neural Network Combined with Elastic Net

Background: Machine learning models could assist physicians in identifying high-risk COVID-19 patients. This study aimed to predict the Intensive Care Unit (ICU) admission in COVID-19 hospitalized patients by the Artificial Neural Network (ANN) model combined with Elastic Net algorithm. Methods: In this prospective study, the data of 139 COVID-19 patients admitted to Imam Reza Hospital in Tabriz between 20 March and 5 April 2020, were analyzed. The Elastic Net method was used to choose features with high importance. The chosen variables were standardized and ANN was fitted to the data with one hidden layer based on the descending gradient algorithm. To validate the model, the training and test group method with a ratio of 70 to 30 was used. The model’s predictive power was reported by calculating the overall accuracy, sensitivity, specificity, and Area Under the ROC curve (AUC). Results: According to the results of the Elastic Net, the ANN model was constructed based on age, sex, body mass index, diabetes mellitus status, history of heart disease, and vital signs including systolic and diastolic blood pressure, saturated oxygen level, pulse rate, respiration rate, and body temperature. The overall accuracy of this model was 93.15%, sensitivity 80%, specificity 95.8%, and AUC 0.90. Saturated oxygen level, pulse rate, and age were the most important and predictive variables. Conclusion: In the investigated sample of patients admitted with COVID-19, the fitted ANN model had acceptable performance to predict the ICU admission. This finding could be useful for physicians and policy makers.

Serum homocysteine and left ventricular hypertrophy in adults with chronic kidney disease: A case–control study

Hyperhomocysteinemia (serum homocysteine concentration &gt; 15 μmol/L) is of high prevalence in chronic kidney disease (CKD). And myocardial hypertrophy is a common complication of CKD. Given that both hyperhomocysteinemia and cardiac hypertrophy have an association with CKD, we hypothesized that high level of plasma homocysteine (Hcy) is associated with a higher prevalence of ventricular hypertrophy(LVH) in adults with CKD. The registration number of the case-control study is ChiCTR2200064834. The information of inpatients with CKD including Echocardiograms and analysis of plasma Hcy concentrations were collected. We performed linear and logistic regression to investigate the association of plasma Hcy with left ventricular hypertrophy (LVH) (LVMI ≥ 95th percentile), adjusted for levels of hemoglobin, ferritin, cystatin C and β-adrenergic blocker therapy. Further, a stratified analysis of the relationship between plasma Hcy and LVH was carried out according to eGFR. The case records for 1068 inpatients with CKD were collected. After data soring and case-control matching, there were 374 samples screened for statistical analysis. Univariate logistic regression indicated a high level of serum Hcy had an association with LVH (OR, 1.16; 95% CI, 1.11–1.20). Finally, multivariable logistic regression suggested that hyperhomocysteinemia was independently associated with LVH (OR, 1.14; 95% CI, 1.10–1.19) after adjustment for hemoglobin, ferritin, cystatin C, and β-adrenergic receptor blocker therapy. We constructed a predicting model including the variable of Hcy for cardiac hypertrophy in CKD. The model had an area under the ROC curve (AUC) of 0.86 (95% CI: 0.82–0.89, P &lt; .001). The decision curve analysis (DCA) showed a superior net clinical benefit of model with Hcy over model without Hcy. Elevated level of serum Hcy is closely associated with LVH in adults with CKD.

A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway

BackgroundExosome small RNAs are believed to be involved in the pathogenesis of cancer, but their role in breast cancer is still unclear. This study utilized machine learning models to screen for key exosome small RNAs and analyzed and validated them.MethodPeripheral blood samples from breast cancer screening positive and negative people were used for small RNA sequencing of plasma exosomes. The differences in the expression of small RNAs between the two groups were compared. We used machine learning algorithms to analyze small RNAs with significant differences between the two groups, fit the model through training sets, and optimize the model through testing sets. We recruited new research subjects as validation samples and used PCR-based quantitative detection to validate the key small RNAs screened by the machine learning model. Finally, target gene prediction and functional enrichment analysis were performed on these key RNAs.ResultsThe machine learning model incorporates six small RNAs: piR-36,340, piR-33,161, miR-484, miR-548ah-5p, miR-4282, and miR-6853-3p. The area under the ROC curve (AUC) of the machine learning model in the training set was 0.985 (95% CI = 0.948-1), while the AUC in the test set was 0.972 (95% CI = 0.882–0.995). RT-qPCR was used to detect the expression levels of these key small RNAs in the validation samples, and the results revealed that their expression levels were significantly different between the two groups (P < 0.05). Through target gene prediction and functional enrichment analysis, it was found that the functions of the target genes were enriched mainly in the chemokine signaling pathway.ConclusionThe combination of six plasma exosome small RNAs has good prognostic value for women with positive breast cancer by imaging screening. The chemokine signaling pathway may be involved in the early stage of breast cancer. It is worth further exploring whether small RNAs mediate chemokine signaling pathways in the pathogenesis of breast cancer through the delivery of exosomes.

Risk factors and nomogram predictive models for postsurgical progression/hyperprogression recurrence in hepatocellular carcinoma with macroscopic vascular invasion

PurposeThis study aimed to develop postsurgical progression/hyperprogression recurrence (type III-IV recurrence) prediction models for hepatocellular carcinoma (HCC) patients with macroscopic vascular invasion (MaVI) and to guide treatment strategies in the accurate healthcare era.Patients and methods393 HCC patients with MaVI from two central hospitals made up the entire study population. In developmental (290 patients) and validation (103 patients) cohorts, all patients were randomized into one or the other. Two prediction models for type III-IV recurrence were developed, based on the findings of univariate and multivariate analysis in the development cohort, and multidimensional verification was carried out in both cohorts.ResultsThe postoperative recurrence rate of type III-IV in 393 HCC patients with MaVI was 70.9%. Young age, large tumor size (≥ 10 cm), node number, incomplete tumor capsule, postoperative complications, and high Ki67 index were the independent risk factors for relapse of type III-IV. In the development cohort, two nomograms (pre- and postoperative) had the Area Under the ROC curve (AUC) of 0.827 and 0.891, respectively. The two nomograms performed well, according to multidimensional verification methods such as clinical impact curves, decision curve analysis (DCA), and calibration curves. The validation cohort saw similar encouraging results. Both nomograms could separate patients into two distinct prognosis subgroups with ideal cutoff values of 170.3 presurgery and 175.0 postsurgery (both P < 0.05).ConclusionWe constructed two novel and potentially clinically valuable models for predicting type III-IV recurrence. These two models can develop strategies for treating those suffering from HCC with MaVI owing to their strong prediction performance and availability.

Combining interferon-γ release assays and metagenomic next-generation sequencing for diagnosis of pulmonary tuberculosis: a retrospective study

BackgroundRapid diagnosis of pulmonary tuberculosis (PTB) is urgently needed. We aimed to improve diagnosis rates by combining tuberculosis-interferon (IFN)-γ release assays (TB-IGRA) with metagenomic next-generation sequencing (mNGS) for PTB diagnosis.MethodsA retrospective study of 29 PTB and 32 non-TB patients from our hospital was conducted between October 2022 and June 2023. Samples were processed for TB-IGRA and mNGS tests according to the manufacturer’s protocol.ResultsThe levels of IFN-γ release in PTB patients were significantly higher than those in non-TB patients (604.15 ± 112.18 pg/mL, and 1.04 ± 0.38 pg/mL, respectively; p < 0.0001). Regarding presenting symptoms or signs, cough and thoracalgia were less common in PTB patients than in non-TB patients (p = 0.001 and p = 0.024, respectively). Total protein and albumin levels in the sera of PTB patients were significantly elevated compared to non-TB patients (p = 0.039 and p = 0.004, respectively). The area under the ROC curve (AUC) for TB-IGRA in PTB diagnosis was 0.939. With an optimal IFN-γ cut-off value of 14.3 pg/mL (Youden’s index 0.831), sensitivity was 86.2% and specificity was 96.9%. ROC curve analysis for mNGS and TB-IGRA combined with mNGS showed AUCs of 0.879 and 1, respectively. The AUC of TB-IGRA combined with mNGS was higher than that of TB-IGRA and mNGS alone.ConclusionsTB-IGRA combined with mNGS may be an effective method for diagnosing tuberculosis, and can be used in the clinical diagnosis of PTB.

Using logistic regression model to predict the future coronary heart disease

Abstract. Coronary Heart Disease (CHD) has become a significant concern due to the global rise in cardiovascular diseases, prompting a need for effective predictive tools. This study employs logistic regression modeling, a machine learning technique, to predict the risk of CHD and identify key influencing factors. Initially, complex data was preprocessed to handle missing values and other issues. The accuracy of the logistic regression model was then evaluated using confusion matrices and classification reports. Additionally, the model's performance was validated through the calculation of odds ratios and the analysis of ROC curves. The results showed that logistic regression performed well in the prediction of CHD, with a model prediction accuracy of 0.86 and an area under the ROC curve (AUC) of 0.73. The model identified the main factors that affect the development of CHD and pointed out potential room for improvement in predictive accuracy. However, although the model has achieved a high accuracy, there is still room for further improvement. In addition, focusing on improving data preprocessing techniques, especially in dealing with missing values, may improve the discriminatory ability of the model.

Predicting Mortality in Severe Burns: A Comparison of Four Mortality Prediction Scores and the Role of Organizational Changes in the Croatian Burn Center

Background: The primary aim of this study was to evaluate the performance of four burn prognostic scores—Abbreviated Burn Severity Index (ABSI), Ryan, Belgium Outcome Burn Injury (BOBI), and revised Baux score (rBaux) in a Croatian burn center. A secondary aim was to compare patient outcomes before and after the organizational and protocol changes. Methods: A retrospective study and comparison of four prediction scores was conducted over a nine-year period in burn patients with ≥20% total body surface area (TBSA) burned. Additionally, outcomes before and after organizational changes were compared. Results: A total of 149 patients were included, with the mean patient age of 54.62 ± 19.38 years, the mean of TBSA of 42.98 ± 19.90, and an overall mortality rate of 48.99%. The area under the ROC curve (AUROC) was 0.79 for the rBaux and ABSI score, 0.77 for the BOBI score, and 0.76 for the Ryan score. The duration of mechanical ventilation and length of stay (LOS) in burn intensive care units (BICU) decreased after the organizational changes, though survival rates remained similar. Conclusions: Prognostic scores are good predictors of mortality but with moderate predictive accuracy. Continuity of care in intensive care could be important for better outcomes.

Investigating the diagnostic and prognostic significance of genes related to fatty acid metabolism in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) is one of the most prevalent and lethal cancers worldwide, with death rates increasing by approximately 2–3% per year. The high mortality and poor prognosis of HCC are primarily due to inaccurate early diagnosis and lack of monitoring when liver transplantation is not feasible. Fatty acid (FA) metabolism is a critical metabolic pathway that provides energy and signaling factors in cancer, particularly in HCC, and promotes malignancy. Therefore, it is essential to explore specific FA metabolism-related diagnostic and prognostic signatures that can enable the effective early diagnosis and monitoring of HCC.MethodsIn this study, we used genes associated with FA metabolism pathway and weighted gene co-expression network analysis (WGCNA) to establish a gene co-expression network and identify hub genes related to HCC (disease WGCNA) and FA clusters (cluster WGCNA). A diagnostic model was constructed using data downloaded from the Gene Expression Omnibus database (GSE25097), and a prognostic model was established using The Cancer Genome Atlas cohort, in which Univariate Cox regression analysis, multivariate Cox risk model, and LASSO Cox regression analysis were applied. The immune infiltration of HCC cells was evaluated using CIBERSORT. The function of the key SLC22A1 gene was experimentally verified in vitro and in vivo.ResultsTwelve overlapping genes (CPEB3, ASPDH, DEPDC7, ETFDH, UGT2B7, GYS2, F11, ANXA10, CYP2C8, GLYATL1, C6, and SLC22A1) from disease and cluster WGCNA were identified as key genes and used in the construction of the diagnostic and prognostic models. The RF model had the highest area under the ROC curve (AUC) of 0.994 was identified as the most effective for distinguishing patients with HCC with different features. The top five important genes (C6, UGT2B7, SLC22A1, F11, and CYP2C8) from the RF model were selected as diagnostic genes for further analysis (ROC curves: AUC value = 0.986, 95% confidence interval [95% CI] = 0.967–0.999). Moreover, a risk score formula consisting of four genes (GYS2, F11, ANXA10 and SLC22A1) was established and its independent prognostic ability was further demonstrated (univariate Cox regression analysis: hazard ratio [HR] = 3.664%, 95% CI = 2.033–6.605, P < 0.001; multivariate Cox regression analysis: HR = 2.801%, 95% CI = 1.553–5.049, P < 0.001). Additionally, in vitro and in vivo experiments demonstrated that SLC22A1 inhibits HCC tumor development, suggesting it may be a potential therapeutic target for HCC.ConclusionsThese findings indicate a considerable value of specific FA metabolism-related genes in the diagnostic and prognostic evaluation of HCC, which provide novel insights into the disease’s management, as well as has potential implications for personalized treatment strategies. However, further investigation of the effects of these model genes on HCC is required.

Circulating CCRR serves as potential novel biomarker for predicting acute myocardial infarction

Abstract Objective Cold regions exhibit a high prevalence of cardiovascular disease, particularly acute myocardial infarction (AMI), which is one of the leading causes of death associated with cardiovascular conditions. Cardiovascular disease is closely linked to the abnormal expression of long non-coding RNA (lncRNA). This study investigates whether circulating levels of lncRNA cardiac conduction regulatory RNA (CCRR) could serve as a biomarker for AMI. Materials and methods We measured circulating CCRR from whole blood samples collected from 68 AMI patients and 69 non-AMI subjects. An AMI model was established using C57BL/6 mice. Quantitative reverse transcription PCR (qRT-PCR) was used to assess CCRR expression. Exosomes were isolated from cardiomyocytes, and their characteristics were evaluated using electron microscope and nanoparticle tracking analysis. The exosome inhibitor GW4869 was employed to examine the effect of exosomal CCRR on cardiac function using echocardiography. Protein expression was detected using Western blot and immunofluorescence staining. Results The circulating level of CCRR was significantly higher in AMI patients (1.93 ± 0.13) than in non-AMI subjects (1.00 ± 0.05, P &lt; 0.001). The area under the ROC curve (AUC) of circulating CCRR was 0.821. Similar changes in circulating CCRR levels were consistently observed in an AMI mouse model. Exosomal CCRR derived from hypoxia-induced cardiomyocytes and cardiac tissue after AMI were increased, a change that was reversed by GW4869. Additionally, CCRR-overexpressing exosomes improved cardiac function in AMI. Conclusion Circulating lncRNA CCRR is a potential predictor of AMI. Exosomal CCRR plays a role in the communication between the heart and other organs through circulation.

Random Forest Analysis of Out-of-Pocket Health Expenditures Associated with Cardiometabolic Diseases, Lifestyle, Lipid Profile, and Genetic Information in São Paulo, Brazil.

Background/Objectives: There is a lack of empirical studies of out-of-pocket health expenditures associated with dyslipidemias, which are major cardiovascular risk factors, especially in underrepresented admixed populations. The study investigates associations of health costs with lipid traits, GWAS-derived genetic risk scores (GRSs), and other cardiometabolic risk factors. Methods: Data from the observational cross-sectional 2015 ISA-Nutrition comprised lifestyle, environmental factors, socioeconomic and demographic variables, and biochemical and genetic markers related to the occurrence of cardiometabolic diseases. GWAS-derived genetic risk scores were estimated from SNPs previously associated with lipid traits. There was phenotypic and genetic information available for 490 independent individuals, which was used as inputs for random forests and logistic regression to explain private quantitative and categorical health costs. Results: There were significant correlations between GRSs and their respective lipid phenotypes. The main relevant variables across techniques and outcome variables comprised income per capita, principal components of ancestry, diet quality, global physical activity, inflammatory and lipid markers, and LDL-c GRS and non-HDL-c GRS. The area under the ROC curve (AUC) of quartile-based categorical health expenditure without GRSs was 0.76. GRSs were not significant for this categorical outcome. Conclusions: We present an original contribution to the investigation of determinants of private health expenditures in a highly admixed population, providing insights on associations between genetic and socioeconomic dimensions of health in Brazil. Ancestry information was also among the main factors contributing to health expenses, providing a novel view of the role of genetic ancestry on cardiometabolic risk factors and its potential impact on health costs.

Construction of Immune-Related circRNA-miRNA-mRNA Network and Identification of circRNAs as Biomarkers in Coronary Atherosclerotic Heart Disease.

Circular RNAs (circRNAs) play crucial roles in the immune and inflammatory responses of many diseases by acting as competing endogenous RNAs (ceRNAs). However, the role of circRNAs as ceRNAs in the immune and inflammatory processes of coronary atherosclerosis heart disease (CHD) remains unclear. This study aimed to identify and validate the potential immune-related circRNAs as biomarkers for CHD. Firstly, we constructed a ceRNA regulatory network including 14 circRNAs, 24 miRNAs, and 15 genes through bioinformatics analysis. Four hub genes were identified and five candidate immune-related circRNAs were screened. Subsequently, the expression levels of these candidate circRNAs were detected by qRT-PCR. Notably, hsa_circRNA_101069 and hsa_circRNA_406053 showed significant up-regulation in CHD patients (p < 0.001). The value of these circRNAs as biomarkers for CHD was evaluated by the area under the ROC curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) indexes. Adding circRNAs to a traditional CHD model significantly enhanced classification performance, with an IDI of 0.058 and an NRI of 0.280 for hsa_circRNA_101069 and an IDI of 0.051 and an NRI of 0.480 for hsa_circRNA_406053. Furthermore, hsa_circRNA_101069 was up-regulated in ox-LDL-induced THP-1 macrophages, and silencing hsa_circRNA_101069 significantly inhibited the apoptosis rates and the inflammatory cytokines levels. This study constructed an immune-related circRNA-miRNA-mRNA network and identified two circRNAs as biomarkers for CHD, with hsa_circRNA_101069 potentially contributing to the pathological basis of CHD.

A novel platelets-related gene signature for predicting prognosis, immune features and drug sensitivity in gastric cancer.

Platelets can dynamically regulate tumor development and progression. Nevertheless, research on the predictive value and specific roles of platelets in gastric cancer (GC) is limited. This research aims to establish a predictive platelets-related gene signature in GC with prognostic and therapeutic implications. We downloaded the transcriptome data and clinical materials of GC patients (n=378) from The Cancer Genome Atlas (TCGA) database. Prognostic platelets-related genes screened by univariate Cox regression were included in Least Absolute Shrinkage and Selection Operator (LASSO) analysis to construct a risk model. Kaplan-Meier curves and receiver operating characteristic curves (ROCs) were performed in the TCGA cohort and three independent validation cohorts. A nomogram integrating the risk score and clinicopathological features was constructed. Functional enrichment and tumor microenvironment (TME) analyses were performed. Drug sensitivity prediction was conducted through The Cancer Therapeutics Response Portal (CTRP) database. Finally, the expression of ten signature genes was validated by quantitative real-time PCR (qRT-PCR). A ten-gene (SERPINE1, ANXA5, DGKQ, PTPN6, F5, DGKB, PCDH7, GNG11, APOA1, and TF) predictive risk model was finally constructed. Patients were categorized as high- or low-risk using median risk score as the threshold. The area under the ROC curve (AUC) values for the 1-, 2-, and 3-year overall survival (OS) in the training cohort were 0.670, 0.695, and 0.707, respectively. Survival analysis showed a better OS in low-risk patients in the training and validation cohorts. The AUCs of the nomogram for predicting 1-, 2-, and 3-year OS were 0.708, 0.763, and 0.742, respectively. TME analyses revealed a higher M2 macrophage infiltration and an immunosuppressive TME in the high-risk group. Furthermore, High-risk patients tended to be more sensitive to thalidomide, MK-0752, and BRD-K17060750. The novel platelets-related genes signature we identified could be used for prognosis and treatment prediction in GC.