OBJECTIVE: Glycolysis and immune metabolism play important roles in acute myocardial infarction (AMI). Therefore, this study aimed to identify and experimentally validate the glycolysis-related hub genes in AMI as diagnostic biomarkers, and further explore the association between hub genes and immune infiltration.
METHODS: Differentially expressed genes (DEGs) from AMI peripheral blood mononuclear cells (PBMCs) were analyzed using R software. Glycolysis-related DEGs (GRDEGs) were identified and analyzed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) for functional enrichment. A protein-protein interaction network was constructed using the STRING database and visualized using Cytoscape software. Immune infiltration analysis between patients with AMI and stable coronary artery disease (SCAD) controls was performed using CIBERSORT, and correlation analysis between GRDEGs and immune cell infiltration was performed. We also plotted nomograms and receiver operating characteristic (ROC) curves to assess the predictive accuracy of GRDEGs for AMI occurrence. Finally, key genes were experimentally validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting using PBMCs.
RESULTS: A total of 132 GRDEGs and 56 GRDEGs were identified on the first day and 4-6 days after AMI, respectively. Enrichment analysis indicated that these GRDEGs were mainly clustered in the glycolysis/gluconeogenesis and metabolic pathways. Five hub genes (HK2, PFKL, PKM, G6PD, and ALDOA) were selected using the cytoHubba plugin. The link between immune cells and hub genes indicated that HK2, PFKL, PKM, and ALDOA were significantly positively correlated with monocytes and neutrophils, whereas G6PD was significantly positively correlated with neutrophils. The calibration curve, decision curve analysis, and ROC curves indicated that the five hub GRDEGs exhibited high predictive value for AMI. Furthermore, the five hub GRDEGs were validated by RT-qPCR and western blotting.
CONCLUSIONS: We concluded that HK2, PFKL, PKM, G6PD, and ALDOA are hub GRDEGs in AMI and play important roles in AMI progression. This study provides a novel potential immunotherapeutic method for the treatment of AMI.

Neutrophil extracellular trap (NET) is released by neutrophils to trap invading pathogens and can lead to dysregulation of immune responses and disease pathogenesis. However, systematic evaluation of NET-related genes (NETRGs) for the diagnosis of pediatric sepsis is still lacking. Three datasets were taken from the Gene Expression Omnibus (GEO) database: GSE13904, GSE26378, and GSE26440. After NETRGs and differentially expressed genes (DEGs) were identified in the GSE26378 dataset, crucial genes were identified by using LASSO regression analysis and random forest analysis on the genes that overlapped in both DEGs and NETRGs. These crucial genes were then employed to build a diagnostic model. The diagnostic model\'s effectiveness in identifying pediatric sepsis across the three datasets was confirmed through receiver operating characteristic curve (ROC) analysis. In addition, clinical pediatric sepsis samples were collected to measure the expression levels of important genes and evaluate the diagnostic model\'s performance using qRT-PCR in identifying pediatric sepsis in actual clinical samples. Next, using the CIBERSORT database, the relationship between invading immune cells and diagnostic markers was investigated in more detail. Lastly, to evaluate NET formation, we measured myeloperoxidase (MPO)-DNA complex levels using ELISA. A group of five important genes (MME, BST1, S100A12, FCAR, and ALPL) were found among the 13 DEGs associated with NET formation and used to create a diagnostic model for pediatric sepsis. Across all three cohorts, the sepsis group had consistently elevated expression levels of these five critical genes as compared to the normal group. Area under the curve (AUC) values of 1, 0.932, and 0.966 indicate that the diagnostic model performed exceptionally well in terms of diagnosis. Notably, when applied to the clinical samples, the diagnostic model also showed good diagnostic capacity with an AUC of 0.898, outperforming the effectiveness of traditional inflammatory markers such as PCT, CRP, WBC, and NEU%. Lastly, we discovered that children with high ratings for sepsis also had higher MPO-DNA complex levels. In conclusion, the creation and verification of a five-NETRGs diagnostic model for pediatric sepsis performs better than established markers of inflammation.

UNASSIGNED: Pediatric sepsis has a very high morbidity and mortality rate. The purpose of this study was to evaluate diagnostic biomarkers and immune cell infiltration in pediatric sepsis.
UNASSIGNED: Three datasets (GSE13904, GSE26378, and GSE26440) were downloaded from the gene expression omnibus (GEO) database. After identifying overlapping genes in differentially expressed genes (DEGs) and modular sepsis genes selected via a weighted gene co-expression network (WGCNA) in the GSE26378 dataset, pivotal genes were further identified by using LASSO regression and random forest analysis to construct a diagnostic model. Receiver operating characteristic curve (ROC) analysis was used to validate the efficacy of the diagnostic model for pediatric sepsis. Furthermore, we used qRT-PCR to detect the expression levels of pivotal genes and validate the diagnostic model\'s ability to diagnose pediatric sepsis in 65 actual clinical samples.
UNASSIGNED: Among 294 overlapping genes of DEGs and modular sepsis genes, five pivotal genes (STOM, MS4A4A, CD177, MMP8, and MCEMP1) were screened to construct a diagnostic model of pediatric sepsis. The expression of the five pivotal genes was higher in the sepsis group than in the normal group. The diagnostic model showed good diagnostic ability with AUCs of 1, 0.986, and 0.968. More importantly, the diagnostic model showed good diagnostic ability with AUCs of 0.937 in the 65 clinical samples and showed better efficacy compared to conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%).
UNASSIGNED: We developed and tested a five-gene diagnostic model that can reliably identify pediatric sepsis and also suggest prospective candidate genes for peripheral blood diagnostic testing in pediatric sepsis patients.

This study aimed to elucidate the clinical diagnostic value of plasma catecholamines and their metabolites for pheochromocytoma and paraganglioma (PPGL)-induced secondary hypertension using ultraperformance liquid chromatography-mass spectrometry (UPLC-MS/MS). The study population included 155 patients with PPGL that were divided into the PPGL with hypertension (n = 79) and a PPGL without hypertension (n = 76) groups, and 90 healthy volunteers and 90 patients with primary hypertension as the control groups. UPLC-MS/MS was performed to detect plasma levels of catecholamines and their metabolites, including dopamine, vanillylmandelic acid (VMA), norepinephrine, metanephrine, and normetanephrine. Receiver operating characteristic curves were generated to analyze the diagnostic value of the plasma levels of catecholamines and their metabolites in PPGL-induced secondary hypertension. Patients in the primary hypertension and PPGL without hypertension groups had higher levels of dopamine, VMA, norepinephrine, metanephrine, and normetanephrine than patients in the normal group (all p < .05). On the other hand, patients in the PPGL with hypertension group had higher levels of dopamine, VMA, norepinephrine, metanephrine, and normetanephrine than patients in the normal, primary hypertension, and PPGL without hypertension groups (all p < .05). Collectively, our findings showed that dopamine, VMA, norepinephrine, metanephrine, and normetanephrine are all effective biomarkers for the diagnosis of PPGL and PPGL-induced secondary hypertension.

Metagenomic next-generation sequencing (mNGS) of plasma cell-free DNA (cfDNA) shows promising application for complicated infections that cannot be resolved by conventional microbiological tests (CMTs). The criteria for cfDNA sequencing are currently in need of agreement and standardization.
We performed a retrospective cohort observation of 653 patients who underwent plasma cfDNA mNGS, including 431 with suspected bloodstream infections (BSI) and 222 with other suspected systemic infections. Plasma mNGS and CMTs were performed simultaneously in clinical practice. The diagnostic efficacy of plasma mNGS and CMTs in the diagnosis of blood-borne and other systemic infections was evaluated using receiver operating characteristic (ROC) curves. The sensitivity and specificity of the two methods were analyzed based on the final clinical outcome as the gold standard.
The mNGS test showed an overall positive rate of 72.3% (472/653) for detecting microorganisms in plasma cfDNA, with a range of 2 to 6 different microorganisms detected in 171 patient specimens. Patients with positive mNGS results were more immunocompromised and had a higher incidence of severe disease (P<0·05). The sensitivity of mNGS was higher for BSI (93·5%) and other systemic infections (83·6%) compared to CMTs (37·7% and 14·3%, respectively). The mNGS detected DNA from a total of 735 microorganisms, with the number of microbial DNA reads ranging from 3 to 57,969, and a higher number of reads being associated with clinical infections (P<0·05). Of the 472 patients with positive mNGS results, clinical management was positively affected in 203 (43%) cases. Negative mNGS results led to a modified clinical management regimen in 92 patients (14.1%). The study also developed a bacterial and fungal library for plasma mNGS and obtained comparisons of turnaround times and detailed processing procedures for rare pathogens.
Our study evaluates the clinical use and analytic approaches of mNGS in predicting bloodstream and local infections in clinical practice. Our results suggest that mNGS has higher positive predictive values (PPVs) for BSI and systemic infections compared to CMTs, and can positively affect clinical management in a significant number of patients. The standardized whole-process management procedure for plasma mNGS developed in this study will ensure improved pre-screening probabilities and yield clinically valuable data.

This study aimed to investigate the diagnostic value of luteinizing hormone (LH) basal values and sex hormone-binding globulin (SHBG) for rapidly progressive central precocious puberty (RP-CPP).
A total of 121 girls presenting with secondary sexual characteristics were selected from the Department of Pediatric Endocrinology, Lianyungang Clinical Medical College of Nanjing Medical University, from May 2021 to June 2023. The children were followed up for 6 months and were divided into three groups: RP-CPP group (n=40), slowly progressive central precocious puberty (SP-CPP) group (n=40), and premature thelarche (PT) group (n=41). The differences in LH basal values and SHBG among girls in the three groups were compared. ROC curves were drawn to analyze the value of LH basal values and SHBG in identifying RP-CPP.
Significant differences were observed in age, height, predicted adult height (PAH), weight, body mass index (BMI), bone age (BA), BA-chronological age (CA), LH basal, LH peak, FSH basal, LH peak/FSH peak, estradiol (E2), testosterone, and SHBG levels between the RP-CPP group and the SP-CPP and PT groups (P < 0.05). The LH basal value in the RP-CPP group was higher than that in the SP-CPP group and the PT group, while SHBG levels were lower than in the latter two groups, and these differences were statistically significant (P < 0.05). When the LH basal value was ≥0.58 IU/L and SHBG was ≤58.79 nmol/L, the sensitivity for diagnosing RP-CPP was 77.5% and 67.5%, and the specificity was 66.7% and 74.1%.
Detection of basal LH and SHBG levels allows for early diagnosis of the progression of central precocious puberty.

The receiver operating characteristics (ROC) analysis is commonly used in clinical settings to check the performance of a single threshold for distinguishing population-wise bimodal-distributed test results. However, for population-wise three-modal distributed test results, a single threshold ROC (stROC) analysis showed poor discriminative performance. The purpose of this study is to use a double-threshold ROC analysis for the three-modal distributed test results to provide better discriminative performance than the stROC analysis. A double-threshold receiver operating characteristic plot (dtROC) is constructed by replacing the single threshold with a double threshold. The sensitivity and specificity coordinates are chosen to maximize sensitivity for a given specificity value. Besides a simulation study assuming a mixture of lognormal, Poisson, and Weibull distributions, a clinical application is examined by a secondary data analysis of palpation test results of the C7 spinous process using the modified thorax-rib static technique. For the assumed mixture models, the discrimination performance of dtROC analysis outperforms the stROC analysis (area under ROC (AUROC) increased from 0.436 to 0.983 for lognormal distributed test results, 0.676 to 0.752 for the Poisson distribution, and 0.674 to 0.804 for Weibull distribution).

The comparison of Receiver Operating Characteristic (ROC) curves is frequently used in the literature to compare the discriminatory capability of different classification procedures based on diagnostic variables. The performance of these variables can be sometimes influenced by the presence of other covariates, and thus they should be taken into account when making the comparison. A new non-parametric test is proposed here for testing the equality of two or more dependent ROC curves conditioned to the value of a multidimensional covariate. Projections are used for transforming the problem into a one-dimensional approach easier to handle. Simulations are carried out to study the practical performance of the new methodology. The procedure is then used to analyse a real data set of patients with Pleural Effusion to compare the diagnostic capability of different markers.

UNASSIGNED: There is currently no biomarker that can reliably identify sepsis, despite recent scientific advancements. We systematically evaluated the value of lysosomal genes for the diagnosis of pediatric sepsis.
UNASSIGNED: Three datasets (GSE13904, GSE26378, and GSE26440) were obtained from the gene expression omnibus (GEO) database. LASSO regression analysis and random forest analysis were employed for screening pivotal genes to construct a diagnostic model between the differentially expressed genes (DEGs) and lysosomal genes. The efficacy of the diagnostic model for pediatric sepsis identification in the three datasets was validated through receiver operating characteristic curve (ROC) analysis. Furthermore, a total of 30 normal samples and 35 pediatric sepsis samples were gathered to detect the expression levels of crucial genes and assess the diagnostic model\'s efficacy in diagnosing pediatric sepsis in real clinical samples through real-time quantitative PCR (qRT-PCR).
UNASSIGNED: Among the 83 differentially expressed genes (DEGs) related to lysosomes, four key genes (STOM, VNN1, SORT1, and RETN) were identified to develop a diagnostic model for pediatric sepsis. The expression levels of these four key genes were consistently higher in the sepsis group compared to the normal group across all three cohorts. The diagnostic model exhibited excellent diagnostic performance, as evidenced by area under the curve (AUC) values of 1, 0.971, and 0.989. Notably, the diagnostic model also demonstrated strong diagnostic ability with an AUC of 0.917 when applied to the 65 clinical samples, surpassing the efficacy of conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%).
UNASSIGNED: A four-gene diagnostic model of lysosomal function was devised and validated, aiming to accurately detect pediatric sepsis cases and propose potential target genes for lysosomal intervention in affected children.

BACKGROUND: The test tradeoff curve helps investigators decide if collecting data for risk prediction is worthwhile when risk prediction is used for treatment decisions. At a given benefit-cost ratio (the number of false-positive predictions one would trade for a true positive prediction) or risk threshold (the probability of developing disease at indifference between treatment and no treatment), the test tradeoff is the minimum number of data collections per true positive to yield a positive maximum expected utility of risk prediction. For example, a test tradeoff of 3,000 invasive tests per true-positive prediction of cancer may suggest that risk prediction is not worthwhile. A test tradeoff curve plots test tradeoff versus benefit-cost ratio or risk threshold. The test tradeoff curve evaluates risk prediction at the optimal risk score cutpoint for treatment, which is the cutpoint of the risk score (the estimated risk of developing disease) that maximizes the expected utility of risk prediction when the receiver-operating characteristic (ROC) curve is concave.
METHODS: Previous methods for estimating the test tradeoff required grouping risk scores. Using individual risk scores, the new method estimates a concave ROC curve by constructing a concave envelope of ROC points, taking a slope-based moving average, minimizing a sum of squared errors, and connecting successive ROC points with line segments.
RESULTS: The estimated concave ROC curve yields an estimated test tradeoff curve. Analyses of 2 synthetic data sets illustrate the method.
CONCLUSIONS: Estimating the test tradeoff curve based on individual risk scores is straightforward to implement and more appealing than previous estimation methods that required grouping risk scores.
CONCLUSIONS: The test tradeoff curve helps investigators decide if collecting data for risk prediction is worthwhile when risk prediction is used for treatment decisions.At a given benefit-cost ratio or risk threshold, the test tradeoff is the minimum number of data collections per true positive to yield a positive maximum expected utility of risk prediction.Unlike previous estimation methods that grouped risk scores, the method uses individual risk scores to estimate a concave ROC curve, which yields an estimated test tradeoff curve.