Receiver Operating Characteristic Curve Research Articles

Predictive value of IGF-1/IGFBP-3 ratio for thyroid nodules in type 2 diabetic mellitus.

To explore the predictive value of the IGF-1/IGFBP-3 ratio for the presence of thyroid nodules in patients with type 2 diabetes mellitus (T2DM). This observational study prospectively enrolled patients with T2DM at the Second Hospital of Jilin University between May 2021 and January 2022. Thyroid nodule (TN) status was determined by ultrasonography. Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive value of the serum IGF-1/IGFBP-3 molar ratio for TNs. Multivariable logistic regression analysis was conducted to identify risk factors for thyroid nodules in patients with T2DM. A total of 122 patients (mean age ± standard deviation: 52.57 ± 11.71 years; 74 males) were enrolled. 37.7% (n=46) of patients did not have TNs, while 62.3% (n=76) had TNs. The duration of diabetes, age, and HDL-C level were significantly higher in the T2DM group with TNs compared to the group without TNs (all P < 0.05). The area under the ROC curve (AUC) for the combination of IGF-1, IGFBP-3, and the serum IGF-1/IGFBP-3 molar ratio in predicting TNs in T2DM patients was 0.619 (P < 0.001). Additionally, multivariable logistic regression analysis revealed that the duration of diabetes, age, fasting plasma glucose (FPG), fasting insulin (FINS), thyroid-stimulating hormone (TSH), IGF-1, and IGFBP-3 levels were independent risk factors for thyroid nodules, while the serum IGF-1/IGFBP-3 molar ratio level was an independent protective factor for thyroid nodules in patients with T2DM (all P < 0.05). The combination of IGF-1, IGFBP-3, and the serum IGF-1/IGFBP-3 molar ratio may have a better predictive value for TNs in T2DM patients than using any single marker alone. The duration of diabetes, age, FPG, FINS, TSH, IGF-1, IGFBP-3, and the serum IGF-1/IGFBP-3 molar ratio levels were independently associated with thyroid nodules in patients with T2DM.

Cerebrospinal fluid L-lactate as a diagnostic marker for infectious-inflammatory disorders in the central nervous system of cattle.

Bacterial infection of the central nervous system (CNS) poses a clinical challenge and is a leading cause of neurological disorders in cattle. Human studies have demonstrated an increase in cerebrospinal fluid (CSF) L-lactate levels in bacterial meningitis. The aims of this study were to establish a Reference Interval (RI) for L-lactate in bovine CSF and assess its potential as a biomarker for detecting infectious-inflammatory disorders. CSF L-lactate was measured in the field using a commercially available lactate monitor. The RI for CSF L-lactate was calculated on healthy animals; univariate and receiver operating characteristic (ROC) analyses were performed to disclose an association between CSF L-lactate levels and interpretation of CSF in sick animals. Twenty-seven healthy cattle and 86 sick cattle with either CNS infectious-inflammatory disorders (47/86) or CNS disorders of other etiology (39/86) were included in this prospective study. The RI for CSF L-lactate was 1.1-2.4 mmol/L. The concentration was higher in the cattle with neutrophilic pleocytosis and the area under the ROC curve was 0.92 compared to other animals. Based on a cut-off of 3.15 mmol/L, CSF L-lactate had diagnostic sensitivity and specificity for neutrophilic pleocytosis of 93 and 80%, respectively. This is the first study to determine a RI for CSF L-lactate in cattle. Elevated CSF L-lactate levels indicated neutrophilic pleocytosis, which is often manifested in acute bacterial infection. The present findings may aid in diagnosis and correct use of antimicrobial drugs.

Metabolic profiling of blood plasma in depression in pregnant women during early pregnancy.

The study aimed to perform metabolic profiling of serum samples using liquid chromatography with mass spectroscopy (LC-MS) and to explore potential biomarkers of early trimester depression. Using the Edinburgh Postnatal Depression Scale (EPDS), participants were randomly divided into study and control groups. Serum metabolic profiles of the two groups were analysed by using LC-MS. Differential metabolite and pathway analysis were identified by using orthogonal projections to latent structure-discriminant analysis (OPLS-DA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Additionally, least absolute shrinkage and selection operator (LASSO) logistic and receiver operating characteristic (ROC) curve analyses were also conducted to explore potential biomarkers of antenatal depression (AD). The study included 41 participants, consisting of 16 subjects with AD and 25 controls. A total of 22 different metabolites were identified (p < .005), mainly affecting glycerophospholipid metabolism, linoleic acid metabolism, synthesis and degradation of ketone bodies, phenylalanine metabolism, and butanoate metabolism. The area under the ROC curve (AUC) for the LysoPC (24:0) was 0.858. This suggests that LysoPC (24:0) may be a potentially effective predictor of risk factors for AD. The study suggests that LysoPC (24:0) may be an effective and specific lipid biomarker for early trimester depression.

Diagnostic Performance for Detection of Glaucomatous Structural Damage Using Pixelwise Analysis of Retinal Thickness Measurements.

To compare the diagnostic accuracy of thickness measurements of individual and combined macular retinal layers to discriminate 188 glaucomatous and 148 glaucoma suspect eyes from 362 healthy control (HC) eyes on a pixel-by-pixel basis. For this retrospective study, we manually corrected the segmentations of posterior pole optical coherence tomography (OCT) scans to determine the thickness of the nerve fiber layer (NFL), ganglion cell layer (GCL), inner plexiform layer (IPL), the ganglion cell complex (GCC), and the total neural retina (TR). For each eye, the total number of pixels with thickness values less than the fifth percentile of the HC distribution was used to create a receiver operating characteristic (ROC) curve for each layer and for layer combinations. Using total abnormal pixel count criteria to discriminate glaucoma from HC eyes, the individual layers with the highest area under the ROC curve (AUC) were the NFL and GCL; IPL performance was significantly lower (P < 0.05). GCC had a significant higher AUC (94.3%) than individual the AUC of the NFL (92.3%) (P = 0.0231) but not higher than AUC of the GCL (93.4%) (P = 0.3487). The highest AUC (95.4%) and sensitivity (85.1%) at 95% specificity was found for the Boolean combination of NFL or GCL. The highest AUC is not significantly higher (P = 0.0882) than the AUC of the GCC but the highest sensitivity is significantly higher than the sensitivity of the GCC. This pattern was similar for discriminating between suspect and HC eyes (P = 0.0356). Using pixel-based methods, the diagnostic accuracy of NFL and GCL exceeded that of IPL and TR. GCC had equivalent performance as NFL and GCL. The specific spatial locations within the posterior pole that exhibit best performance vary depending on which layer is being assessed. Recognizing this dependency highlights the importance of considering multiple layers independently, as they offer complementary information for effective and comprehensive diagnosis.

Tree-based classification model for Long-COVID infection prediction with age stratification using data from the national COVID cohort collaborative (N3C)

Abstract Objectives We propose and validate a domain knowledge-driven classification model for diagnosing Post-acute sequelae of SARS-CoV-2 infection (PASC), also known as Long COVID, using Electronic Health Records (EHR) data. Materials and Methods We developed a robust model that incorporates features strongly indicative of PASC or associated with the severity of COVID-19 symptoms as identified in our literature review. The XGBoost tree-based architecture was chosen for its ability to handle class-imbalanced data and its potential for high interpretability. Using the training data provided by the Long COVID Computation Challenge (L3C), which was a sample of the National COVID Collaborative (N3C), our models were fine-tuned and calibrated to optimize Area Under the Receiver Operating Characteristic curve (AUROC) and the F1 score, following best practices for the class-imbalanced N3C data. Results Our age-stratified classification model demonstrated strong performance with an average 5-fold cross-validated AUROC of 0.844 and F1 score of 0.539 across the young adult, mid-aged, and older-aged populations in the training data. In an independent testing dataset, which was made available after the challenge was over, we achieved an overall AUROC score of 0.814 and F1 score of 0.545. Discussion The results demonstrated the utility of knowledge-driven feature engineering in a sparse EHR data and demographic stratification in model development to diagnose a complex and heterogeneously presenting condition like PASC. The model's architecture, mirroring natural clinician decision-making processes, contributed to its robustness and interpretability, which are crucial for clinical translatability. Further, the model’s generalizability was evaluated over a new cross-sectional data as provided in the later stages of the L3C challenge. Conclusion The study proposed and validated the effectiveness of age-stratified, tree-based classification models to diagnose PASC. Our approach highlights the potential of machine learning in addressing the diagnostic challenges posed by the heterogeneity of Long COVID symptoms.

Characterizing myocardial edema and fibrosis in hypertensive crisis with cardiovascular magnetic resonance imaging.

A hypertensive crisis is associated with an increased risk of cardiovascular events. Although altered cardiac structure, function, and myocardial architecture on cardiovascular magnetic resonance (CMR) have been associated with increased adverse events in hypertensive patients, the studies did not include patients with hypertensive crisis. Our study aimed to determine myocardial tissue characteristics in patients with hypertensive crisis using CMR imaging. Participants underwent comprehensive CMR imaging at 1.5T. The imaging protocol included cine-, T2-weighted-, contrasted- and multi-parametric mapping images. Blood and imaging biomarkers were compared in hypertensive emergency and hypertensive urgency. Predictors of myocardial edema was assessed using linear regression. The predictive value of T1- and T2 mapping for identifying hypertensive emergency (from urgency) was assessed with receiver operator characteristics curves. Eighty-two patients (48.5 ± 13.4 years, 57% men) were included. Hypertensive emergency constituted 78%. Native T1 was higher in patients with LVH compared to those without (1056 ± 33 vs. 1013 ± 40, P < 0.001), and tended to be higher in hypertensive emergency than urgency (1051 ± 37 vs. 1033 ± 40, P = 0.077). T2-w signal intensity (SI) ratio and T2 mapping values were higher in hypertensive emergency (1.5 ± 0.2 vs. 1.4 ± 0.1, P = 0.044 and 48 ± 2 vs. 47 ± 2, P = 0.004), and in patients with than without LVH (1.5 ± 0.2 vs. 1.4 ± 0.1, P = 0.045 and P = 0.030). A trend for higher extracellular volume was noted in hypertensive emergency compared to urgency (25 ± 4 vs. 22 ± 3, P = 0.050). Native T1 correlated with T2 mapping (rs = 0.429, P < 0.001), indexed LV mass (rs = 0.493, P < 0.001), cardiac troponin (rs = 0.316, P < 0.001) and NT-proBNP (rs = 0.537, P < 0.001), while T2 correlated with cardiac troponin (rs = 0.390, P < 0.001), and NT-proBNP (rs = 0.348, P < 0.001). Non-ischemic LGE pattern occurred in 59% and was 21% more prevalent in the hypertensive emergency group (P = 0.005). Our findings demonstrate that hypertensive crisis is associated with distinct myocardial tissue alterations, including increased myocardial edema and fibrosis, as detected on CMR. Patients with hypertensive emergency had a higher degree of myocardial oedema than hypertensive urgency. Further research is necessary to explore the prognostic value of these findings.

The value of electroencephalography features in the prognostic evaluation of large hemispheric infarction patients at different time intervals.

Large Hemispheric Infarction (LHI) is a devastating disease with high mortality. This study aimed to use electroencephalography (EEG) to evaluate the death risk of LHI patients and identify suitable evaluation time. This study retrospectively collected clinical and EEG data from 73 LHI patients, dividing them into death and survival group at discharge. EEG data was classified as 1-5 days and 6-14 days after onset according to the time intervals of cerebral edema. Regression and receiver operator characteristic curve (ROC) analysis were applied to explore the impact of temporal changes in various EEG and clinical features on death. The areas under ROC curve (AUC) of death prediction for non-α frequency on non-infarct side at 6-14 days after onset was significantly higher than that at 1-5 days (p = 0.004). And there was no significant difference between the AUC of seizure activity for death prediction at 1-5 days and 6-14 days (p = 0.418). Multivariate regression analysis revealed that non-α frequency on non-infarct side and seizure activity at 6-14 days after onset were the independent risk factors for the death of LHI patients. Additionally, above two EEG features significantly improved the death predictive efficacy of clinical features in LHI patients with the integrated discrimination improvement index (IDI) of 0.174 (p = 0.015) and the net reclassification improvement (NRI) of 1.314 (p<0.001). Non-α frequency on non-infarct side and seizure activity were reliable indicators for death prediction. 6-14 days after onset was the better time window for death evaluation of LHI patients through EEG.

Development and validation of a predictive model for failure of ureteral access sheath placement in patients with ureteral calculi

ObjectiveThe Ureteral Access Sheath (UAS) has notable benefits but may fail to traverse the ureter in some cases. Our objective was to develop and validate a dynamic online nomogram for patients with ureteral stones who experienced UAS placement failure during retrograde intrarenal surgery (RIRS).MethodsThis study is a retrospective cohort analysis using medical records from the Second Hospital of Tianjin Medical University. We reviewed the records of patients with ureteral stones who underwent RIRS in 2022 to identify risk factors associated with UAS placement failure. Lasso combined logistic regression was utilized to identify independent risk factors associated with unsuccessful UAS placement in individuals with ureteral stones. Subsequently, a nomogram model was developed to predict the likelihood of failed UAS placement in this patient cohort. The model’s performance was assessed through Receiver Operating Characteristic Curve (ROC) analysis, calibration curve assessment, and Decision Curve Analysis (DCA).ResultsSignificant independent risk factors for unsuccessful UAS placement in patients with ureteral stones included age (OR = 0.95, P < 0.001), male gender (OR = 2.15, P = 0.017), body mass index (BMI) (OR = 1.12, P < 0.001), history of stone evacuation (OR = 0.35, P = 0.014), and ureteral stone diameter (OR = 0.23, P < 0.001). A nomogram was constructed based on these variables. Model validation demonstrated an area under the ROC curve of 0.789, indicating good discrimination. The calibration curve exhibited strong agreement, and the decision curve analysis revealed a favorable net clinical benefit for the model.ConclusionsYoung age, male sex, high BMI, no history of stone evacuation, and small diameter of ureteral stones were independent risk factors for failure of UAS placement in patients with ureteral stones, and the dynamic nomogram established with these 5 factors was clinically effective in predicting the outcome of UAS placement.

The furosemide stress test predicts successful discontinuation of continuous renal replacement therapy in critically ill patients with acute kidney injury

PurposeThere is still no good method for predicting renal recovery and successful discontinuation of continuous renal replacement therapy (CRRT). This study assessed the ability of the furosemide stress test (FST) to predict successful discontinuation of CRRT. Materials and methodsThis prospective single-center study included patients with acute kidney injury who underwent an initial attempt at discontinuation of CRRT. Successful discontinuation was defined as alive without renal replacement therapy for 7 days after discontinuation. Furosemide 1.0 mg/kg was administered intravenously within 2 h after discontinuation of CRRT. Urine output was recorded for the next 2 h. Receiver-operating characteristic curve and logistic regression analyses were performed to determine the best discriminative variable and to identify independent risk factors. ResultsDiscontinuation of CRRT was successful in 30 of 55 patients. The area under the curve for prediction of successful discontinuation was significantly greater for urine output in the 2 h following the FST (0.913) than for 24-h urine output on the previous day (0.739, P = 0.003) and urine neutrophil gelatinase-associated lipocalin (0.725, P = 0.020). A 2-h urine output of 188 mL had optimal sensitivity (0.800) and specificity (0.920). Multivariate analysis showed that 2-h urine output independently predicted successful discontinuation. ConclusionsA urine output >188 mL in the first 2 h after FST predicted successful discontinuation of CRRT.

Development and validation of a prognostic model for colon cancer based on mitotic gene signatures and immune microenvironment analysis

BackgroundMitotic processes play a pivotal role in tumor progression and immune responses. However, the correlation between mitosis-related genes, clinical outcomes, and the tumor microenvironment (TME) in colon cancer remains unclear. This study aims to develop a prognostic and therapeutic significance model for colon cancer based on mitosis-related genes.MethodsRNA expression profiles and clinical data of 453 colon cancer patients were downloaded from The Cancer Genome Atlas (TCGA). Mitosis-related genes were selected from the MsigDb database. The gene model was constructed using differential analysis, univariate and multivariate Cox regression, and Lasso regression analyses. The predictive model was validated using data from the GSE17536, GSE17537, and GSE39582 datasets. Predictive accuracy was evaluated via Receiver Operating Characteristic (ROC) curves, while nomograms were developed by integrating clinical and pathological features. Gene set enrichment analysis explored biological processes and pathways linked to the model. TME was assessed using ESTIMATE, and the proportion and function of immune cells were analyzed through CIBERSORT. Drug sensitivity analysis was conducted using the CTRP database.ResultsA predictive model based on 17 mitosis-related genes (KIFC1, CCNF, EME1, CDC25C, ORC1, CCNJL, ANKRD53, MEIS2, FZD3, TPD52L1, MAPK3, CDKN2A, EDN3, NPM2, PSRC1, INHBA, BIRC5) was created. The model exhibited robust predictive performance across both training and validation cohorts. Nomograms for predicting 3-, 5-, and 7-year survival rates in colon cancer (COAD) patients were generated. The model's correlation with immune cell infiltration and function was highlighted.ConclusionThe mitosis-related gene model serves as a valuable indicator for predicting survival outcomes in colon cancer patients.

Urban Flooding Disaster Risk Assessment Utilizing the MaxEnt Model and Game Theory: A Case Study of Changchun, China

This research employs the maximum entropy (MaxEnt) model alongside game theory, integrated with an extensive framework of natural disaster risk management theory, to conduct a thorough analysis of the indicator factors related to urban flooding. This study conducts an assessment of the risks associated with urban flooding disasters using Changchun city as a case study. The validation outcomes pertaining to urban flooding hotspots reveal that 88.66% of the identified flooding sites are situated within areas classified as high-risk and very high-risk. This finding is considered to be more reliable and justifiable when contrasted with the 77.73% assessment results derived from the MaxEnt model. Utilizing the methodology of exploratory spatial data analysis (ESDA), this study applies both global and local spatial autocorrelation to investigate the disparities in the spatial patterns of flood risk within Changchun. This study concludes that urban flooding occurs primarily in the city center of Changchun and shows a significant agglomeration effect. The region is economically developed, with a high concentration of buildings and a high percentage of impervious surfaces. The Receiver Operating Characteristic (ROC) curve demonstrates that the MaxEnt model achieves an accuracy of 90.3%. On this basis, the contribution of each indicator is analyzed and ranked using the MaxEnt model. The primary determinants affecting urban flooding in Changchun are identified as impervious surfaces, population density, drainage density, maximum daily precipitation, and the Normalized Difference Vegetation Index (NDVI), with respective contributions of 20.6%, 18.1%, 13.1%, 9.6%, and 8.5%. This research offers a scientific basis for solving the urban flooding problem in Changchun city, as well as a theoretical reference for early warnings for urban disaster, and is conducive to the realization of sustainable urban development.

Serum metabolomics analysis reveals potential biomarkers of penicillins-induced fatal anaphylactic shock in rats

Immunoglobulin E (IgE)-mediated immediate hypersensitivity reactions are the most concerning adverse events after penicillin antibiotics (PENs) administration because of their rapid progression and potential for fatal outcome. However, the diagnosis of allergic death is a forensic challenge because it mainly depends on nonspecific characteristic morphological changes, as well as exclusion and circumstantial evidence. In this study, an untargeted metabolomics approach based on liquid chromatography-mass spectrometry (LC-MS) was used to screen potential forensic biomarkers of fatal anaphylactic shock induced by four PENs (benzylpenicillin (BP), amoxicillin (AMX), oxacillin (OXA), and mezlocillin (MEZ)), and analyzed the metabolites, metabolic pathway and the mechanism which were closely related to the allergic reactions. The metabolomics results discovered that a total of 24 different metabolites in all four anaphylactic death (AD) groups, seven of which were common metabolites. A biomarker model consisting of six common metabolites (linoleic acid, prostaglandin D2, lysophosphatidylcholine (18:0), N-acetylhistamine, citric acid and indolelactic acid) AUC value of Receiver Operating Characteristic (ROC) curve was 0.978. Metabolism pathway analysis revealed that the pathogenesis of PENs-induced AD is closely related to linoleic acid metabolism. Our results revealed that the metabolomic profiling has potential in PENs-induced AD post-mortem diagnosis and metabolic mechanism investigations.

Metabolomic profiling reveals the step-wise alteration of bile acid metabolism in patients with diabetic kidney disease

BackgroundDiabetic kidney disease (DKD) is the major complication of diabetes concomitant with gut dysbiosis and glycometabolic disorder, which are strongly associated with bile acid (BA) metabolism. Yet studies investigating the BA metabolism involving in DKD pathogenesis are limited. This study aimed to explore the metabolomic profiling of BAs in DKD and analyze its association with DKD progression.MethodsAn ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method was established to quantify BAs in the plasma, fecal and urine samples of patients with DKD or T2DM and healthy individuals (n = 30 for each group). The key BAs associated with DKD were identified by orthogonal partial least-squares discriminant analysis (OPLS-DA) and receiver-operating characteristic (ROC) curve. Polynomial regression and Pearson’s correlation analyses were performed to assess the correlation between the key BAs and the clinical indicators reflecting DKD progression.ResultsMetabolomic profiling of 50 kinds of BAs presented the markedly step-wise alterations of BAs in plasma and feces as well as the little in urine of patients with DKD. Eight kinds of BAs in the plasma, eight kinds in the feces and three kinds in the urine were abnormally expressed, accompanying with the increased conjugated/unconjugated ratios of cholic acid, deoxycholic acid, chenodeoxycholic acid, ursodeoxycholic acid and hyocholic acid in the plasma, and of cholic acid, chenodeoxycholic acid and lithocholic acid in the feces. Moreover, the increased plasma level of glycochenodeoxycholic acid, and the increased fecal levels of glycolithocholic acid, 7-ketodeoxycholic acid and chenodeoxycholic acid-3-β-D-glucuronide are strongly correlated with the clinical indicators reflecting DKD progression, including eGFR, 24 h urinary protein and 24 h urinary microalbumin.ConclusionsOur study for the first time disclosed the specific alterations of BA metabolism reflecting the step-wise progression of DKD, providing the basis for early identification and therapeutical strategies for DKD.

Exploration of plasma biomarkers for Alzheimer's disease by targeted lipid metabolomics based on nuclear magnetic resonance (NMR) spectroscopy.

Alzheimer's disease (AD) is the most common cause of dementia, but the disease lacks convenient and cost-effective alternative biomarkers currently. We utilized targeted lipid metabolomics based on nuclear magnetic resonance (NMR) spectroscopy to identify plasma biomarkers in AD patients. Our study was a cross-sectional study that enrolled 58 AD patients and 40 matched health controls (HCs). Firstly, we identified plasma lipid metabolites that were significantly different between the two groups based on P < 0.05 and variable importance in the projection (VIP) > 1. Then we examined the correlation between the lipid metabolites and cognitive function using partial correlation analysis and assessed the diagnostic ability of the lipid metabolites using receiver operating characteristic (ROC) curves. Seventeen lipoproteins showed significant differences between AD patients and HCs among 114 lipid metabolites. All 17 lipoproteins were subtypes of low-density lipoprotein (LDL). Among them, LDL-3 particle number, LDL-3 apolipoprotein-B, LDL-3 phospholipids, LDL free cholesterol and LDL phospholipids were significantly correlated with cognitive function. The ROC curves showed that LDL-2 triglycerides (TG) and LDL-3 TG could significantly distinguish AD patients from HCs, with the area under the curve (AUC) above 0.7. In addition, we explored a strategy of combined diagnosis that significantly improved the diagnostic efficacy for AD (AUC = 0.879). Our study provides insight into the lipoprotein alterations associated with AD and potential biomarkers for its diagnosis and cognitive function assessment.

Improving agreement in assessing subtrochanteric fracture healing among orthopedic surgeons using the Radiographic Union Score for Hip (RUSH)

BackgroundPrompt identification of fracture healing is crucial for medical decision-making and clinical research. This study aims to assess the effectiveness of the Radiographic Union Score for Hip (RUSH) in subtrochanteric fractures and determine the optimal scoring threshold for fracture healing.MethodsWe obtained 94 sets of X-ray films from the postoperative follow-up of 35 patients who underwent intramedullary nail fixation for subtrochanteric fractures. Six orthopedic trauma surgeons evaluated the imaging data and determined fracture healing based on subjective judgment. They then scored the X-ray images using the RUSH form. After four weeks, the same observers re-evaluated 47 randomly selected sets of radiographs. Subsequently, the observers and study designer conducted a consensus meeting where they agreed on whether the fractures had healed. We used Fleiss kappa to evaluate agreement among observers based on subjective impressions, and the intraclass correlation coefficient assessed RUSH score consistency. Regression analysis examined the relationship between scores and fracture healing consensus using a receiver operator characteristic (ROC) curve.ResultsBased on overall impressions, the agreement level among orthopedic trauma surgeons for determining the healing status of subtrochanteric fractures was moderate (Kappa = 0.564, 95% CI: 0.511–0.616). However, utilizing the RUSH scoring system can improve the consistency to a substantial level of agreement (ICC = 0.748, 95% CI: 0.663–0.817). Regarding intraobserver reliability, there were significant differences among orthopedic trauma surgeons in judging fracture healing based on overall impressions. However, using the RUSH scoring form, the attending group achieved better intraobserver consistency than the resident group. Regression analysis and ROC curve analysis revealed a strong correlation between the RUSH total score, cortical bone score, cancellous bone score, and consensus on fracture healing. The AUC values are 0.769 (95% CI: 0.670–0.868), 0.779 (95% CI: 0.681–0.876), and 0.771 (95% CI: 0.674–0.867), respectively.ConclusionsOur study highlights the effectiveness of the RUSH scoring system in enhancing interobserver agreement and intraobserver consistency when evaluating subtrochanteric fracture healing in orthopedic trauma surgeons. We propose setting 21 points as the threshold for the simplified RUSH scoring system to determine fracture healing. This system includes only eight items related to cortical bone, with a total score of 24 points.

TFE3 and TP53 were novel diagnostic biomarkers related to mitochondrial autophagy in chronic rhinosinusitis with nasal polyps.

Chronic rhinosinusitis with nasal polyps (CRSwNP) belongs to a subtype of Chronic rhinosinusitis which is a heterogeneous inflammatory condition. It has been reported that mitophagy may provide a new therapeutic option for CRSwNP. The GSE136825 (training dataset) and GSE179265 (validation dataset) were scoured from the Gene Expression Omnibus database. The candidate genes related to mitophagy were identified by differential expression analysis. Subsequently, the biomarkers were selected from the machine learning, Receiver Operating Characteristic curves, and expression level verification. A backpropagation (BP) neural network was generated to evaluate the diagnostic ability of biomarkers. In addition, the infiltration abundance of immune cells, potential drugs, and related ear-nose-throat (ENT) diseases were analyzed based on the biomarkers. Finally, qPCR analysis was performed to verify these biomarkers. A total of 8 candidate genes were identified by overlapping 3,400 differentially expressed genes (DEGs) and 72 mitophagy-related genes Subsequently, TFE3 and TP53 were identified as biomarkers of CRSwNP, and the area under the curves (AUC) of the BP neural network was 0.74, which indicated that the biomarkers had excellent abilities. TFE3 and TP53 were co-enriched in the cancer pathway, cell cycle, endocytosis, etc. What's more, Macrophage and Immature dendritic cells had significant correlations with biomarkers. The drugs (Doxorubicin, Tetrachlorodibenzodioxin, etc.) and the ear-nose-throat diseases (hearing loss, sensorineural, tinnitus, etc.) related to biomarkers were predicted. Ultimately, qPCR results showed that the expression levels of TFE3 and TP53 in polyp tissue of CRSwNP were increased. Overall, TFE3 and TP53 could be used as biomarkers or potential therapeutic targets to diagnose and treat CRSwNP.

Nomogram for predicting early neurological deterioration in patients with mild large and medium vessel occlusion stroke intended for medical management: a multicenter retrospective study

BackgroundAccurately forecasting early neurological deterioration of ischemic origin (ENDi) following medical management may aid in identifying candidates for thrombectomy. We aimed to develop and validate a nomogram to predict ENDi...

Diffusion MRI is superior to quantitative T2-FLAIR mismatch in predicting molecular subtypes of human non-enhancing gliomas.

This study compared the classification performance of normalized apparent diffusion coefficient (nADC) with percentage T2-FLAIR mismatch-volume (%T2FM-volume) for differentiating between IDH-mutant astrocytoma (IDHm-A) and other glioma molecular subtypes. A total of 105 non-enhancing gliomas were studied. T2-FLAIR digital subtraction maps were used to identify T2FM and T2-FLAIR non-mismatch (T2FNM) subregions within tumor volumes of interest (VOIs). Median nADC from the whole tumor, T2FM, and T2NFM subregions and %T2FM-volume were obtained. IDHm-A classification analyses using receiver-operating characteristic curves and multiple logistic regression were performed in addition to exploratory survival analyses. T2FM subregions had significantly higher nADC than T2FNM subregions within IDHm-A with ≥ 25% T2FM-volume (P < 0.0001). IDHm-A with ≥ 25% T2FM-volume demonstrated significantly higher whole tumor nADC compared to IDHm-A with < 25% T2FM-volume (P < 0.0001), and both IDHm-A subgroups demonstrated significantly higher nADC compared to IDH-mutant oligodendroglioma and IDH-wild-type gliomas (P < 0.05). For classification of IDHm-A vs. other gliomas, the area under curve (AUC) of nADC was significantly greater compared to the AUC of %T2FM-volume (P = 0.01, nADC AUC = 0.848, %T2FM-volume AUC = 0.714) along with greater sensitivity. In exploratory survival analyses within IDHm-A, %T2FM-volume was not associated with overall survival (P = 0.2), but there were non-significant trends for nADC (P = 0.07) and tumor volume (P = 0.051). T2-FLAIR subtraction maps are useful for characterizing IDHm-A imaging characteristics. nADC outperforms %T2FM-volume for classifying IDHm-A amongst non-enhancing gliomas with preserved high specificity and increased sensitivity, which may be related to inherent diffusivity differences regardless of T2FM. In line with previous findings on visual T2FM-sign, quantitative %T2FM-volume may not be prognostic.

Correlation study of NF-κB, IER3, and Recurrence of Ovarian Endometrioid Cysts.

The study aimed to investigate the expression of nuclear actor-k-gene binding(NF-κB) and immediate early response 3(IER3) in ovarian endometrioid cysts and its correlation with the recurrence of the ovarian endometrioid cyst. From January 2018 to March 2019, a total of 88 patients who underwent laparoscopic ovarian cyst excision due to ovarian endometrioid cyst in Changzhou Maternity and Child Health Care Hospital were selected. Clinical data of the patients were collected. The patient's Revised American Fertility Society (R-AFS) score, least function(LF) score, and endometriosis fertility index (EFI) were calculated. Immunohistochemistry was performed to detect the expression of IER3 and NF-κB. The receiver-operating characteristic (ROC) curve was used to evaluate the predictive value of IER3 and NF-κB expression on postoperative recurrence. Cox regression was fitted to analyze the influencing factors of ovarian endometrioid cyst recurrence. The expression of NF-κB was positively correlated with IER3 (P < 0.001). ROC curve showed that NF-κB combined with IER3 had higher predictive value for disease recurrence. Multivariate Cox regression showed that the IER3 expression intensity > 4.5 (HR = 3.418,95%CI: 1.227 ~ 9.523, P = 0.019) and the NF-κB expression intensity > 4.5 (HR = 5.491,95%CI: 1.600 ~ 18.838, P = 0.007) were independent risk factors for recurrence, and EFI score (HR = 0.791,95%CI: 0.637 ~ 0.983, P = 0.035) was a protective factor for recurrence. Our results suggested that EFI score is a protective factor for recurrence. The expression levels of NF-κB and IER3 > 4.5 are correlated with the recurrence of ovarian endometrioid cysts and independent risk factors for recurrence.

The value of quantitative analysis of radionuclide bone SPECT/CT imaging in vertebral compression fracture: a retrospective study

BackgroundMost patients with osteoporosis experience vertebral compression fracture (VCF), which significantly reduces their quality of life. These patients are at a high risk of secondary VCF regardless of treatment. Thus, accurate diagnosis of VCF is important for treating and preventing new fractures. We aimed to investigate the diagnostic and predictive value of quantitative bone imaging techniques for fresh VCF.MethodsFrom November 2021 to March 2023, 34 patients with VCF were enrolled in this study, all of whom underwent routine 99mTc-MDP whole-body bone planar scan and local SPECT/CT imaging. The maximum standard uptake value (SUVmax) of 57 fresh VCF, 57 normal adjacent vertebrae, and 19 old VCF were measured. Based on the site of the fracture, fresh VCFs were regrouped into the intervertebral-type group and the margin-type group. Meanwhile, 52 patients who had no bone metastasis or VCFs in their bone scan were assigned to the control group. The SUVmax of 110 normal vertebral bodies and 10 old VCFs in the control group were measured.ResultsThe median SUVmax of fresh VCF was 19.80, which was significantly higher than the SUVmax of other groups. The receiver operator characteristic (ROC) curve showed that the cut-off value of SUVmax was 9.925 for diagnosing fresh VCF. The SUVmax in the intervertebral-type group was significantly higher than that in the margin-type group (P = 0.04). The SUVmax of normal vertebrae was higher among patients than among the control group (P<0.01), but the CT HU value showed no significant difference.ConclusionThe quantitative technique of bone SPECT/CT has a significant value in diagnosing fresh VCF. It can also determine the severity of fractures. In addition, whether the SUVs of the vertebrae adjacent to the fractured vertebra can predict re-fracture deserves further studies.