Receiver Operating Characteristic Research Articles

Development and Validation of a Fusion Model Based on Carotid Plaques and White Matter Lesion Burden Imaging Characteristics to Evaluate Ischemic Stroke Severity in Symptomatic Patients.

The diagnostic value of carotid plaque characteristics based on higher-resolution vessel wall MRI (HRVW-MRI) combined with white matter lesion (WML) burden for the risk of ischemic stroke is unclear. To combine carotid plaque features and WML burden to construct a hybrid model for evaluating ischemic stroke severity and prognosis in patients with symptomatic carotid artery stenosis. Retrospective. One hundred and ninty-three patients with least one confirmed carotid atherosclerotic stenosis ≥30% and cerebrovascular symptoms within the last 2 weeks (136 in the training cohort and 57 in the test cohort). 3.0T, T2-weighted fluid attenuated inversion recovery (T2-FLAIR) and diffusion-weighted imaging (DWI); HRVW-MRI: 3D T1-weighted variable flip angle fast spin-echo sequences (VISTA), T2-weighted VISTA, simultaneous noncontrast angiography and intraplaque hemorrhage (SNAP), and contrast-enhanced T1-VISTA. The following features of the plaques or vessel wall were assessed by three MRI readers independently: calcification (CA), intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC), ulceration, plaque enhancement (PE), maximum vessel diameter (Max VD), maximum wall thickness (Max WT), total vessel area (TVA), lumen area (LA), plaque volume, and lumen stenosis. WMLs were graded visually and categorized as absent-to-mild WMLs (Fazekas score 0-2) or moderate-severe WMLs (Fazekas score 3-6). WML volumes were quantified using a semiautomated volumetric analysis program. Modified Rankin scores (mRS) were assessed at 90 days, following an outpatient interview, or by telephone. LASSO-logistic regression analysis was performed to construct a model. The performance of the model was evaluated using receiver operating characteristic (ROC) curve analyses, calibration curves, decision curve analyses, and clinical imaging curves. Conditional logistic regression analysis was used to explore the associations between the hybrid model-derived score and the modified Rankin Scale (mRS) score at 90 days. The model was constructed using five selected features, including IPH, plaque enhancement, ulceration, NWI, and total Fazekas score in deep WMLs (DWMLs). The hybrid model yielded an area under the curve of 0.92 (95% confidence interval [CI] 0.87-0.97) in the training cohort and 0.88 (0.80-0.96) in the test cohort. Furthermore, the hybrid model-derived score (odds ratio = 1.28; 95% CI 1.06-1.53) was independently associated with the mRS score 90 days after stroke. The hybrid model constructed using MRI plaque characteristics and WML burden has potential to be an effective noninvasive method of assessing ischemic stroke severity. The model-derived score has promising utility in judging neurological function recovery. Stage 2.

Diagnostic Value of Single LH and LH/FSH Ratio at 60-minute after GnRHa Stimulation Test for Central Precocious Puberty.

To evaluate the diagnostic value of luteinizing hormone (LH) and LH/follicle stimulating hormone (FSH) ratio at 60 min after gonadotropin-releasing hormone analogs (GnRHa) stimulation test for central precocious puberty (CPP) in girls. Two hundred and fifty-seven girls, aged 3 to 7.5 y, suspected of precocious puberty at authors' hospital from April 2020 through November 2023 were enrolled in the study. The blood was taken at 0, 30, 60 min after GnRHa stimulation test, and LH and LH/FSH were detected by chemiluminescence assay. The diagnostic efficacy was analysed by Mann-Whitney U test, spearman's correlation analysis and receiver operating characteristic (ROC) analysis. The proportion of obesity was analysed by Chi-square test. LH and LH/FSH at different times were statistically significantly different (P <0.05) between the CPP and non-CPP groups. Spearman's correlation analysis showed that the level of LH and LH/FSH at 60 min had the strongest consistency with the peak of LH (r = 0.9988, P <0.001) and LH/FSH (r = 0.9981, P <0.001). ROC curve analysis showed that the area under the ROC curves at 60 min of LH and LH/FSH were 0.975 and 0.997 with a cut-off value of 5.70 IU/L and 0.609, respectively. The peak of LH and LH/FSH in the diagnosis of CPP can be determined by LH and LH/FSH at 60 min after the triptorelin acetate is injected. This will reduce the number of blood draws required compared with the traditional stimulation test, which is more effective and acceptable for children.

Assessing hepatic steatosis by magnetic resonance in potential living liver donors.

To determine the accuracy of magnetic resonance imaging-proton density fat fraction (MRI-PDFF) measurements for detecting liver fat content in potential living liver donors and to compare these results using liver biopsy findings. A total of 139 living liver donors (men/women: 83/56) who underwent MRI between January 2017 and September 2021 were included in this analysis retrospectively. The PDFFs were measured using both MR spectroscopy (MRS) and chemical shift-based MRI (CS-MRI) for each donor in a blinded manner. Significant positive correlations were found between liver biopsy and MRS-PDFF and CS-MRI PDFF in terms of hepatic steatosis detection [r = 0.701, 95% confidence interval (CI): 0.604-0.798, r = 0.654, 95% CI: 0.544-0.765, P < 0.001, respectively). A weak level correlation was observed between liver biopsy, MRI methods, and vibration-controlled transient elastography attenuation parameters in 42 available donors. Based on receiver operating characteristic (ROC) analysis, MRS-PDFF and CS-MRI PDFF significantly distinguished >5% of histopathologically detected hepatic steatosis with an area under the ROC curve (AUC) of 0.837 ± 0.036 (P < 0.001, 95% CI: 0.766-0.907) and 0.810 ± 0.036 (P < 0.001, 95% CI: 0.739-0.881), respectively. The negative predictive values (NPVs) of MRS-PDFF and CS-MRI PDFF were 88.3% and 81.3%, respectively. In terms of distinguishing between clinically significant hepatic steatosis (≥10% on histopathology), the AUC of MRS-PDFF and CS-MRI were 0.871 ± 0.034 (P < 0.001 95% CI: 0.804-0.937) and 0.855 ± 0.036 (P < 0.001, 95% CI: 0.784-0.925), respectively. The NPVs of MRS-PDFF and CS-MRI were 99% and 92%, respectively. The methods of MRS-PDFF and CS-MRI PDFF provide a non-invasive and accurate approach for assessing hepatic steatosis in potential living liver donor candidates. These MRI PDFF techniques present a promising clinical advantage in the preoperative evaluation of living liver donors by eliminating the requirement for invasive procedures like liver biopsy.

Development and validation of a novel criterion of histologic healing in ulcerative colitis defined by inflammatory cell enumeration in lamina propria mucosae: A multicenter retrospective cohort in China.

Histological healing is closely associated with improved long-term clinical outcomes and lowered relapses in patients with ulcerative colitis (UC). Here, we developed a novel diagnostic criterion for assessing histological healing in UC patients. We conducted a retrospective cohort study in UC patients, whose treatment was iteratively optimized to achieve mucosal healing at Shanghai Tenth People's Hospital of Tongji University from January 2017 to May 2022. We identified an inflammatory cell enumeration index (ICEI) for assessing histological healing based on the proportions of eosinophils, CD177+ neutrophils, and CD40L+ T cells in the colonic lamina propria under high power field (HPF), and the outcomes (risks of symptomatic relapses) of achieving histological remission vs. persistent histological inflammation using Kaplan-Meier curves. Intrareader reliability and inter-reader reliability were evaluated by each reader. The relationships to the changes in the Nancy index and the Geboes score were also assessed for responsiveness. The ICEI was further validated in a new cohort of UC patients from other nine university hospitals. We developed an ICEI for clinical diagnosis of histological healing, i.e., Y=1.701X1 + 0.758X2 + 1.347X3 - 7.745 (X1, X2, and X3 represent the proportions of CD177+ neutrophils, eosinophils, and CD40L+ T cells, respectively, in the colonic lamina propria under HPF). The receiver operating characteristics curve (ROC) analysis revealed that Y <-0.391 was the cutoff value for the diagnosis of histological healing and that an area under the curve (AUC) was 0.942 (95% confidence interval [CI]: 0.905-0.979) with a sensitivity of 92.5% and a specificity of 83.6% (P<0.001). The intraclass correlation coefficient (ICC) for the intrareader reliability was 0.855 (95% CI: 0.781-0.909), and ICEI had good inter-reader reliability of 0.832 (95% CI: 0.748-0.894). During an 18-month follow-up, patients with histological healing had a substantially better outcome compared with those with unachieved histological healing (P<0.001) using ICEI. During a 12-month follow-up from other nine hospitals, patients with histological healing also had a lower risk of relapse than patients with unachieved histological healing. ICEI can be used to predict histological healing and identify patients with a risk of relapse 12 months and 18 months after clinical therapy. Therefore, ICEI provides a promising, simplified approach to monitor histological healing and to predict the prognosis of UC. Chinese Clinical Trial Registry, No. ChiCTR2300077792.

Neutral to slightly undercorrected mechanical leg alignment provides superior long-term results in patients undergoing matrix-associated autologous chondrocyte implantation.

The aim of this study was to evaluate the role of leg alignment on long-term clinical outcomeafter matrix-associated autologous chondrocyte implantation (M-ACI) and to define an individualized target range to optimize clinical outcome. The present study examined patients who underwent M-ACI of the femoral condyle. The Knee Injury and Osteoarthritis Outcome Score (KOOS) and Magnetic Resonance Observation of Cartilage Repair Tissue (MOCART) were used to assess the results. Clinical outcomes were related to Patient Acceptable Symptomatic State (PASS). For intra- and interobserver reliability of mechanical tibiofemoral angle, mechanical medial proximal tibial angleand mechanical lateral distal femoral angle, we calculated intraclass correlation coefficientsusing a two-way mixed model with absolute agreement. A regression model and receiver-operating characteristics curve were used to identify an individual range of alignment where a favourable clinical outcome could be expected in the long term. Additional osteotomy was performed in 50% of patients with similar clinical outcomes as physiologically aligned patients (p > 0.05). The curve-fitting regression model identified a target range of -2.5° valgus to 4.5° varus for ideal postoperative alignment (R2 = 0.12, p = 0.01). Patients within this range were more likely to achieve PASS (70% vs. 27%, p = 0.001). In medially treated defects, a refined range of -2.5° valgus to 4° varus alignment was found (R2 = 0.15, p = 0.01). These patients were more likely to achieve PASS (67% vs. 30%, p = 0.01) and showed favourable postoperative KOOS and MOCART scores (p = 0.02). Patients with lateral defects were more likely to achieve PASS within a range of -2° valgus and 0.5° varus (90% vs. 45%, p = 0.03) and showed favourable postoperative KOOS and MOCART scores (p = not significant). An individual range of leg alignment-whether achieved by osteotomy or physiologic alignment-should be respected in M-ACI treatment. A neutral to slightly undercorrected alignment favours the postoperative outcome after M-ACI. When planning surgery for patients with focal cartilage defects of the femoral condyle, these ranges should be recognized as critical factors. Level III.

Establishing a machine learning model based on dual-energy CT enterography to evaluate Crohn’s disease activity

ObjectivesThe simplified endoscopic score of Crohn’s disease (SES-CD) is the gold standard for quantitatively evaluating Crohn’s disease (CD) activity but is invasive. This study aimed to develop and validate a machine learning (ML) model based on dual-energy CT enterography (DECTE) to noninvasively evaluate CD activity.MethodsWe evaluated the activity in 202 bowel segments of 46 CD patients according to the SES-CD score and divided the segments randomly into training set and testing set at a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) was used for feature selection, and three models based on significant parameters were established based on logistic regression. Model performance was evaluated using receiver operating characteristic (ROC), calibration, and clinical decision curves.ResultsThere were 110 active and 92 inactive bowel segments. In univariate analysis, the slope of spectral curve in the venous phases (λHU-V) has the best diagnostic performance, with an area under the ROC curve (AUC) of 0.81 and an optimal threshold of 1.975. In the testing set, the AUC of the three models established by the 7 variables to differentiate CD activity was 0.81–0.87 (DeLong test p value was 0.071–0.766, p > 0.05), and the combined model had the highest AUC of 0.87 (95% confidence interval (CI): 0.779–0.959).ConclusionsThe ML model based the DECTE can feasibly evaluate CD activity, and DECTE parameters provide a quantitative analysis basis for evaluating specific bowel activities in CD patients.Critical relevance statementThe machine learning model based on dual-energy computed tomography enterography can be used for evaluating Crohn’s disease activity noninvasively and quantitatively.Key PointsDual-energy CT parameters are related to Crohn’s disease activity.Three machine learning models effectively evaluated Crohn’s disease activity.Combined models based on conventional and dual-energy CT have the best performance.Graphical

Development and validation of a nomogram for predicting high-burnout risk in nurses.

To develop a predictive model for high-burnout of nurses. A cross-sectional study. This study was conducted using an online survey. Data were collected by the Chinese Maslach Burnout Inventory-General Survey (CMBI-GS) and self-administered questionnaires that included demographic, behavioural, health-related, and occupational variables. Participants were randomly divided into a development set and a validation set. In the development set, multivariate logistic regression analysis was conducted to identify factors associated with high-burnout risk, and a nomogram was constructed based on significant contributing factors. The discrimination, calibration, and clinical practicability of the nomogram were evaluated in both the development and validation sets using receiver operating characteristic (ROC) curve analysis, Hosmer-Lemeshow test, and decision curve analysis, respectively. Data analysis was performed using Stata 16.0 software. A total of 2750 nurses from 23 provinces of mainland China responded, with 1925 participants (70%) in a development set and 825 participants (30%) in a validation set. Workplace violence, shift work, working time per week, depression, stress, self-reported health, and drinking were significant contributors to high-burnout risk and a nomogram was developed using these factors. The ROC curve analysis demonstrated that the area under the curve of the model was 0.808 in the development set and 0.790 in the validation set. The nomogram demonstrated a high net benefit in the clinical decision curve in both sets. This study has developed and validated a predictive nomogram for identifying high-burnout in nurses. The nomogram conducted by our study will assist nursing managers in identifying at-high-risk nurses and understanding related factors, helping them implement interventions early and purposefully. The study adhered to the relevant EQUATOR reporting guidelines: TRIPOD Checklist for Prediction Model Development and Validation. No patient or public contribution.

Extracellular volume-based scoring system for tracking tumor progression in pancreatic cancer patients receiving intraoperative radiotherapy

ObjectivesTo investigate the value of extracellular volume (ECV) derived from portal-venous phase (PVP) in predicting prognosis in locally advanced pancreatic cancer (LAPC) patients receiving intraoperative radiotherapy (IORT) with initial stable disease (SD) and to construct a risk-scoring system based on ECV and clinical-radiological features.Materials and methodsOne hundred and three patients with LAPC who received IORT demonstrating SD were enrolled and underwent multiphasic contrast-enhanced CT (CECT) before and after IORT. ECV maps were generated from unenhanced and PVP CT images. Clinical and CT imaging features were analyzed. The independent predictors of progression-free survival (PFS) determined by multivariate Cox regression model were used to construct the risk-scoring system. Time-dependent receiver operating characteristic (ROC) curve analysis and the Kaplan–Meier method were used to evaluate the predictive performance of the scoring system.ResultsMultivariable analysis revealed that ECV, rim-enhancement, peripancreatic fat infiltration, and carbohydrate antigen 19-9 (CA19-9) response were significant predictors of PFS (all p < 0.05). Time-dependent ROC of the risk-scoring system showed a satisfactory predictive performance for disease progression with area under the curve (AUC) all above 0.70. High-risk patients (risk score ≥ 2) progress significantly faster than low-risk patients (risk score < 2) (p < 0.001).ConclusionECV derived from PVP of conventional CECT was an independent predictor for progression in LAPC patients assessed as SD after IORT. The scoring system integrating ECV, radiological features, and CA19-9 response can be used as a practical tool for stratifying prognosis in these patients, assisting clinicians in developing an appropriate treatment approach.Critical relevance statementThe scoring system integrating ECV fraction, radiological features, and CA19-9 response can track tumor progression in patients with LAPC receiving IORT, aiding clinicians in choosing individual treatment strategies and improving their prognosis.Key PointsPredicting the progression of LAPC in patients receiving IORT is important.Our ECV-based scoring system can risk stratifying patients with initial SD.Appropriate prognostication can assist clinicians in developing appropriate treatment approaches.Graphical

Identification and Verification of Endoplasmic Reticulum Stress-Related Genes as Novel Signatures for Osteoarthritis Diagnosis and Therapy: A Bioinformatics Analysis-Oriented Pilot Study.

Endoplasmic reticulum stress (ERS) has been reported to be closely associated with the development of osteoarthritis (OA), but the underlying mechanisms are not fully delineated. The present study was designed to investigate the involvement of ERS-related genes in regulating OA progression. The expression profiles of OA patients and normal people were downloaded from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) in datasets GSE55457 and GSE55235 were screened and identified by R software with the construction of the protein-protein interaction (PPI) networks. Through the STRING and Venn diagram analysis, hub ERS-related genes were obtained. Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses were performed. Biomarkers with high diagnostic values of osteoarthritis (OA) were studied. The hematoxylin and eosin (H&E) staining and micro-CT were applied to evaluate the establishment of the OA model. The expression levels of biomarkers were validated with the use of reverse transcription‑quantitative polymerase chain reaction (RT-qPCR) and western blot. Finally, we evaluated the correlations of hub ERS-related genes with the immune infiltration cells via the CIBERSORT algorithm. A total of 60 downregulated and 52 upregulated DEGs were identified, and the following GO and KEGG pathway analyses verified that those DEGs were mainly enriched in biological process (BP), cellular component (CC), molecular function (MF), and inflammation-associated signal pathways. Interestingly, among all the DEGs, six ER stress-associated genes, including activating transcription factor 3 (ATF3), DEAD-Box Helicase 3 X-Linked (DDX3X), AP-1 transcription factor subunit (JUN), eukaryotic initiation factor 4 (EIF4A1), KDEL endoplasmic reticulum protein retention receptor 3 (KDELR3), and vascular endothelial growth factor A (VEGFA), were found to be closely associated with OA progression, and the following RT-qPCR and Western Blot analysis confirmed that DDX3X, JUN, and VEGFA were upregulated, whereas KDELR3, EIF4A1, and ATF3 were downregulated in OA rats tissues compared to the normal tissues, which were in accordance with our bioinformatics findings. Furthermore, our receiver operating characteristic (ROC) curve analysis verified that the above six ER stress-associated genes could be used as ideal biomarkers for OA diagnosis and those genes also potentially regulated immune responses by influencing the biological functions of mast cells and macrophages. Collectively, the present study firstly identified six ER stress-associated genes (ATF3, DDX3X, JUN, EIF4A1, KDELR3, and VEGFA) that may play critical role in regulating the progression of OA.

Validation of the Cognitive Assessment Instrument for Obsessions and Compulsions (CAIOC-13) in an Indian Sample

Background: Brief self-report measures of cognition are advantageous for flagging significant cognitive dysfunction without extensive neuropsychological assessments. The Cognitive Assessment Instrument for Obsessions and Compulsions (CAIOC-13) is a recently developed self-report that assesses everyday cognitive dysfunction in obsessive-compulsive disorder (OCD), for example, difficulties with reading, slowness, and decision-making. This study was undertaken to validate the CAIOC-13 in an Indian sample of OCD. Material and Methods: 75 subjects with OCD and 81 non-clinical controls completed CAIOC-13, Perceived Deficits Questionnaire (PDQ), and Dysfunctional Attitude Scale-Short Form (DAS-SF1). Convergent and divergent validity with PDQ and DAS-SF1 were established with Pearson’s correlation; the Receiver Operating Characteristic (ROC) curve was used to analyze discriminant validity; and factorial structure was evaluated using the principal component analysis (PCA). Results: CAIOC-13 scores showed a strong significant correlation ( r = 0.56; p &lt; .001) with PDQ and a moderate correlation with DAS-SF1 scores ( r = 0.33; p = .003). CAIOC-13 could accurately discriminate between OCD and controls (area under curve = 0.92). PCA revealed strong loading on a single component. Conclusion: CAIOC-13 is a valid tool for briefly assessing OCD-related cognitive dysfunction in Indian samples. Future studies may examine the correlation of CAIOC-13 with standardized neuropsychological assessments.

Drug Burden Index is a Modifiable Predictor of 30-Day-Hospitalization in Community-Dwelling Older Adults with Complex Care Needs: Machine Learning Analysis of InterRAI Data.

Older adults (≥ 65 years) account for a disproportionately high proportion of hospitalization and in-hospital mortality, some of which may be avoidable. Although machine learning (ML) models have already been built and validated for predicting hospitalization and mortality, there remains a significant need to optimise ML models further. Accurately predicting hospitalization may tremendously impact the clinical care of older adults as preventative measures can be implemented to improve clinical outcomes for the patient. In this retrospective cohort study, a dataset of 14,198 community-dwelling older adults (≥ 65 years) with complex care needs from the Inter-Resident Assessment Instrument database was used to develop and optimise three ML models to predict 30-day-hospitalization. The models developed and optimized were Random Forest (RF), XGBoost (XGB), and Logistic Regression (LR). Variable importance plots were generated for all three models to identify key predictors of 30-day-hospitalization. The area under the receiver operating characteristics curve for the RF, XGB and LR models were 0.97, 0.90 and 0.72, respectively. Variable importance plots identified the Drug Burden Index and alcohol consumption as important, immediately potentially modifiable variables in predicting 30-day-hospitalization. Identifying immediately potentially modifiable risk factors such as the Drug Burden Index and alcohol consumption is of high clinical relevance. If clinicians can influence these variables, they could proactively lower the risk of 30-day-hospitalization. ML holds promise to improve the clinical care of older adults. It is crucial that these models undergo extensive validation through large-scale clinical studies before being utilized in the clinical setting.

Analysis and validation of biomarkers of immune cell-related genes in postmenopausal osteoporosis: An observational study.

Postmenopausal osteoporosis (PMOP) is a common metabolic inflammatory disease. In conditions of estrogen deficiency, chronic activation of the immune system leads to a hypo-inflammatory phenotype and alterations in its cytokine and immune cell profile, although immune cells play an important role in the pathology of osteoporosis, studies on this have been rare. Therefore, it is important to investigate the role of immune cell-related genes in PMOP. PMOP-related datasets were downloaded from the Gene Expression Omnibus database. Immune cells scores between high bone mineral density (BMD) and low BMD samples were assessed based on the single sample gene set enrichment analysis method. Subsequently, weighted gene co-expression network analysis was performed to identify modules highly associated with immune cells and obtain module genes. Differential analysis between high BMD and low BMD was also performed to obtain differentially expressed genes. Module genes are intersected with differentially expressed genes to obtain candidate genes, and functional enrichment analysis was performed. Machine learning methods were used to filter out the signature genes. The receiver operating characteristic (ROC) curves of the signature genes and the nomogram were plotted to determine whether the signature genes can be used as a molecular marker. Gene set enrichment analysis was also performed to explore the potential mechanism of the signature genes. Finally, RNA expression of signature genes was validated in blood samples from PMOP patients and normal control by real-time quantitative polymerase chain reaction. Our study of PMOP patients identified differences in immune cells (activated dendritic cell, CD56 bright natural killer cell, Central memory CD4 T cell, Effector memory CD4 T cell, Mast cell, Natural killer T cell, T follicular helper cell, Type 1 T-helper cell, and Type 17 T-helper cell) between high and low BMD patients. We obtained a total of 73 candidate genes based on modular genes and differential genes, and obtained 5 signature genes by least absolute shrinkage and selection operator and random forest model screening. ROC, principal component analysis, and t-distributed stochastic neighbor embedding down scaling analysis revealed that the 5 signature genes had good discriminatory ability between high and low BMD samples. A logistic regression model was constructed based on 5 signature genes, and both ROC and column line plots indicated that the model accuracy and applicability were good. Five signature genes were found to be associated with proteasome, mitochondria, and lysosome by gene set enrichment analysis. The real-time quantitative polymerase chain reaction results showed that the expression of the signature genes was significantly different between the 2 groups. HIST1H2AG, PYGM, NCKAP1, POMP, and LYPLA1 might play key roles in PMOP and be served as the biomarkers of PMOP.

Optimizing diagnosis and surgical decisions for chronic osteomyelitis through radiomics in the precision medicine era

Introduction: Chronic osteomyelitis is a complex clinical condition that is associated with a high recurrence rate. Traditional surgical interventions often face challenges in achieving a balance between thorough debridement and managing resultant bone defects. Radiomics is an emerging technique that extracts quantitative features from medical images to reveal pathological information imperceptible to the naked eye. This study aims to investigate the potential of radiomics in optimizing osteomyelitis diagnosis and surgical treatment.Methods: Magnetic resonance imaging (MRI) scans of 93 suspected osteomyelitis patients were analyzed. Radiomics features were extracted from the original lesion region of interest (ROI) and an expanded ROI delineated by enlarging the original by 5 mm. Feature selection was performed and support vector machine (SVM) models were developed using the two ROI datasets. To assess the diagnostic efficacy of the established models, we conducted receiver operating characteristic (ROC) curve analysis, employing histopathological results as the reference standard. The model’s performance was evaluated by calculating the area under the curve (AUC), sensitivity, specificity, and accuracy. Discrepancies in the ROC between the two models were evaluated using the DeLong method. All statistical analyses were carried out using Python, and a significance threshold of p &amp;lt; 0.05 was employed to determine statistical significance.Results and Discussion: A total of 1,037 radiomics features were extracted from each ROI. The expanded ROI model achieved significantly higher accuracy (0.894 vs. 0.821), sensitivity (0.947 vs. 0.857), specificity (0.842 vs. 0.785) and AUC (0.920 vs. 0.859) than the original ROI model. Key discriminative features included shape metrics and wavelet-filtered texture features. Radiomics analysis of MRI exhibits promising clinical translational potential in enhancing the diagnosis of chronic osteomyelitis by accurately delineating lesions and identifying surgical margins. The inclusion of an expanded ROI that encompasses perilesional tissue significantly improves diagnostic performance compared to solely focusing on the lesions. This study provides clinicians with a more precise and effective tool for diagnosis and surgical decision-making, ultimately leading to improved outcomes in this patient population.

The prognostic value of preoperative systemic inflammatory response index in predicting outcomes of acute type A aortic dissection patients underwent surgical treatment

BackgroundThe systemic inflammatory response index (SIRI) is a novel inflammatory-immune biological marker that has prognostic value in various cardiovascular diseases. This study aims to investigate the relationship between SIRI and short-term and long-term prognosis in patients with acute type A aortic dissection (AAAD) underwent surgical treatment.MethodsWe conducted a retrospective analysis of patients with AAAD who underwent emergency surgical treatment at our center. Through multifactorial logistics regression analysis and cox proportional hazards regression analysis, we identified SIRI as an independent risk factor for major adverse events (MAEs) and long-term aorta-related adverse events (ARAEs) post-surgery. The optimal cutoff value of preoperative SIRI was determined using receiver operating characteristic (ROC) curve analysis, and patients were divided into low SIRI group and high SIRI group. The prognostic outcomes at different time points post-surgery for the two groups of patients were analyzed using Kaplan-Meier survival analysis, and the significance was determined by log-rank test.ResultsA total of 691 AAAD patients were included in this study. Among them, 50 patients (7.2%) died within 30 days post-surgery, and 175 patients (25.3%) experienced MAEs. A total of 641 patients were followed up, with an average follow-up time of 33.5 ± 17.5 months, during which 113 patients (17.6%) experienced ARAEs. The results of multifactorial logistics regression analysis and cox proportional hazards regression analysis showed that SIRI was an independent risk factor for postoperative MAEs (OR=3.148, 95%CI[1.650-6.006], p&amp;lt;0.001) and ARAEs (HR=2.248, 95%CI[1.050-4.809], p&amp;lt;0.037). Kaplan-Meier analysis demonstrated that the MAEs-free survival in the high SIRI group was significantly lower than that in the low SIRI group, and a similar trend was observed in the ARAEs-free survival during follow-up (log-rank test, p&amp;lt;0.001).ConclusionPreoperative SIRI is significantly associated with the short-term and long-term prognosis of AAAD patients underwent emergency open surgery, demonstrating its valuable prognostic value. Therefore, preoperative SIRI is a reliable biological marker that can serve as a valuable tool for preoperative risk stratification and decision management.

Diagnostic performance of lactate dehydrogenase as a potential biomarker in predicting preeclampsia and associated factors

BackgroundPreeclampsia (PE), a pregnancy specific syndrome, is defined as new-onset hypertension (≥140/90 mmHg) and proteinuria diagnosed after gestational week 20 or new-onset pre-eclampsia associated signs in the absence of proteinuria, and it may tend to present as late as 4–6 weeks’ postpartum period. It is a leading cause of maternal mortality in both developed and developing countries. In order to prevent PE, the disease must be diagnosed at its earliest stage, however, the triads of high blood pressure, edema and albuminuria is neither specific nor sensitive enough for diagnosing the disease. Lactate dehydrogenase (LDH) is useful biochemical marker reflecting the occurrence of complications associated with preeclampsia. Besides, it has been suggested as potential biomarker to predict the severity of preeclampsia and as indicator of multi-organ involvement. The aim of this study was to investigate the diagnostic accuracy of LDH, which is affordable and easy to test, as a potential clinical biomarker to predict onset of preeclampsia.MethodsA hospital based cross-sectional study was conducted as of September 9 to December 24, 2022 at Debre Birhan Comprehensive Specialized Hospital (DBCSH). A total of 132 study subjects (66 preeclamptic and 66 normotensive controls) were enrolled in the study. A receiver operating characteristics (ROC) curve was used to calculate the area under the curve (AUC) and determine diagnostic accuracy of LDH. Youden’s index was used to identify an optimal cut-off point for LDH in detecting preeclampsia associated complications.ResultAUC for LDH was found to be 0.963 (95% CI, 0.91, 1.0; p = 0.000) from ROC curve analysis. An optimal cut-off point for LDH was 376.5 U/L having a sensitivity and specificity of 87.5 and 90.8%, respectively.ConclusionSerum LDH had an AUC of greater than 0.8 and showed good diagnostic accuracy in predicting development of preeclampsia. Disease duration, gestational age, systolic and diastolic blood pressure among enormous number of predictor variables had association with serum level of LDH.

Identification of potential landfill site suitability for urban solid waste disposal of Balurghat Municipality of Dakshin Dinajpur District using GIS and multi-criteria decision-making approach

The production of urban solid waste is one of the most dangerous environmental health risks on the planet. By selecting suitable landfill sites, it is possible to resolve the issue of health risks and environmental pollution driven by the unscientific disposal of solid waste. The current research is centered on determining suitable landfill locations for solid waste disposal within the Balurghat municipality through the integration of Geographic Information System (GIS) and Multi-Criteria Decision-Making (MCDM) approach. Ten factors were taken into consideration according to their weightage including elevation (0.022), Lithology (0.027), Slope (0.032), Land use and land cover LULC (0.040), Depth of water (0.057), Population density (0.075), Distance from sensitive places (0.107), Distance from road (0.154), Distance from river (0.205) and Land value (0.282). After conducting thorough field visits, an analytical hierarchy process (AHP) under the MCDM approach using GIS reveals acceptable sites that were noted and referenced. Final results are validated through a receiver operating characteristic (ROC) curve using randomly selected 80 potential landfill-suitable site location points with higher accuracy, and a total of 93% of the area under the curve (AUC) has been found worthy to validate the spatial result. The present investigation revealed that only 0.65% of land is very highly suitable, 31.06% of land is highly suitable, 52.54% of land is moderately suitable and 14.74% of land is not suitable for landfilling in Balurghat Municipal area and surroundings respectively. The highly suitable site belongs to the Mamna, Katna, Kuaran, Amtali, Noksha, Bannapara, and Malancha villages of the study area which is a very negligible area. So, the planners should adopt alternative strategies like source minimization, composting, sanitary landfills and awareness programmes for sustainable solid waste management. Hence, the Balurghat Municipality administration, urban planners, and administrative authorities stand to gain valuable insights from the findings of this study as they endeavour to enhance the management of urban solid waste in the area.

Interpretable Machine Learning for Geochemical Anomaly Delineation in the Yuanbo Nang District, Gansu Province, China

Machine learning (ML) has shown its effectiveness in handling multi-geoinformation. Yet, the black-box nature of ML algorithms has restricted their widespread adoption in the domain of mineral prospectivity mapping (MPM). In this paper, methods for interpreting ML model predictions are introduced to aid ML-based MPM, with the goal of extracting richer insights from the ML modeling of an exploration geochemical dataset. The partial dependence plot (PDP) and accumulated local effect (ALE) plot, along with the SHAP value analysis, were utilized to demonstrate the application of random forest (RF) modeling within both regression and classification frameworks. Initially, the random forest regression (RFR) model established the relationship between the concentrations of Au and those of elements such as As, Sb, and Hg in the study area, and from this model, the most important geochemical elements and their quantitative relationships with Au were revealed by their contributions in the modeling through PDP and ALE analyses. Secondly, the RF classification modeling established the relationships of mineralization occurrences (i.e., known mineral deposits) with geochemical elements (i.e., Au, As, Sb, Hg, Cu, Pb, Zn, and Ag), as did RFR modeling. The most important geochemical elements for indicating regional Au mineralization and the trajectories of PDP and ALE reached a consensus that As and Sb contributed the most, both in the regression and classification modeling, with regard to Au mineralization. Finally, the SHAP values illustrated the behavior of the training samples (i.e., known mineral deposits) in RF modeling, and the resulting prospectivity map was evaluated using receiver operating characteristics.

The Putti Sign Following Residual Brachial Plexus Birth Injury: Prevalence and Significance.

The Putti sign, a common deformity and complaint in children with brachial plexus birth injury (BPBI), stems from a glenohumeral (GH) abduction contracture. Despite recent clinical studies offering insights into this deformity, none have explored the prevalence of the Putti sign or its correlation with GH abduction contractures. We conducted a prospective analysis of 238 patients (median age 7.5 years; range, 4.1-16.2) with residual BPBI seen in the clinic from December 2019 to December 2022. Epidemiological data, including demographics, palsy levels, modified Mallet scale sum, surgical history, and presence/absence of the Putti sign and glenohumeral adduction angle (GHADD), were collected. Patients were categorized into 4 age groups: 0 to 5 years (n=67), 6 to 10 years (n=102), 11 to 15 years (n=53), and 16 years and older (n=16). Results were expressed as medians (minimum-maximum), with frequency comparisons done using Pearson's chi-square analysis. Mann-Whitney U and Kruskal-Wallis tests were used for quantitative variable comparison, and receiver operating characteristic (ROC) analysis determined the threshold GHADD angle for Putti sign appearance. Main findings included: (1) 27% of patients with residual BPBI exhibited the Putti sign, (2) confirmed correlation between the Putti sign and GH adduction contractures, (3) Putti sign manifestation with GHADD angle measuring less than -5° because to abduction contracture, and (4) association between this deformity and reduced activities requiring external rotation. No significant differences in Putti sign prevalence were found across age groups. Our study underscores the common occurrence of the Putti sign in children with residual BPBI. It is important to note that we highlight its functional significance beyond cosmetic concerns. Contrary to prior literature, our analysis reveals functional impairment associated with the Putti sign. Although no age-based differences in Putti sign prevalence were observed, patients aged 0 to 5 years and 11 to 15 years showed more severe glenohumeral abduction contractures, possibly due to growth spurts. Diagnosis IV.

Hot rolled steel surface defect detection and classification using an automatic ensemble approach

Abstract This study introduces an ensemble-based Deep Neural Network (DNN) model for detecting defects on steel surfaces. The method suggested in this study classifies steel surface conditions into six possible fault categories, namely, crazing, inclusion, rolled in, pitted surface, scratches, and patches. The images undergo preprocessing and extraction of features in spatial and frequency domains using image segmentation techniques such as grey level difference method (GLDM), fast Fourier Transform (FFT), grey level co-occurrence matrix (GLCM), texture analysis and discrete wavelet transform (DWT). The ensembling of image features into a fused feature pool is carried out after the preprocessing of input images that are provided as input to a light-weight neural network model for training and testing. The performance of the model is comprehensively evaluated via an ablation study both before and after ensembling. In addition, the model capability is effectively analyzed using receiver operating characteristics (ROC) curve, confusion matrix from which classification accuracy of the model could be obtained and other parameters including precision and f1-score. It was observed that the proposed deep learning network presents phenomenally high accuracy of 99.72% for detection and classification of steel surface faults. This result was found to be superior when compared with the performance of the same neural network over each feature type individually. This study also compares the classification results of the model built based on the ensembled feature set with the results of various other classification approaches available in literature. The ensemble-based model could potentially be integrated into existing inspection systems for real-time, efficient and robust condition monitoring of steel surfaces.

Construction of ferroptosis-related prediction model for pathogenesis, diagnosis and treatment of ruptured abdominal aortic aneurysm.

Abdominal aortic aneurysm (AAA) is a dangerous cardiovascular disease, which often brings great psychological burden and economic pressure to patients. If AAA rupture occurs, it is a serious threat to patients' lives. Therefore, it is of clinical value to actively explore the pathogenesis of ruptured AAA and prevent its occurrence. Ferroptosis is a new type of cell death dependent on lipid peroxidation, which plays an important role in many cardiovascular diseases. In this study, we used online data and analysis of ferroptosis-related genes to uncover the formation of ruptured AAA and potential therapeutic targets. We obtained ferroptosis-related differentially expressed genes (Fe-DEGs) from GSE98278 dataset and 259 known ferroptosis-related genes from FerrDb website. Enrichment analysis of differentially expressed genes (DEGs) was performed by gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG). Receiver Operating characteristic (ROC) curve was employed to evaluate the diagnostic abilities of Fe-DEGs. Transcription factors and miRNAs of Fe-DEGs were identified through PASTAA and miRDB, miRWalk, TargetScan respectively. Single-sample gene set enrichment analysis (ssGSEA) was used to observe immune infiltration between the stable group and the rupture group. DGIdb database was performed to find potential targeted drugs of DEGs. GO and KEGG enrichment analysis found that DEGs mainly enriched in "cellular divalent inorganic cation homeostasis," "cellular zinc ion homeostasis," "divalent inorganic cation homeostasis," "Mineral absorption," "Cytokine - cytokine receptor interaction," "Coronavirus disease - COVID-19." Two up-regulated Fe-DEGs MT1G and DDIT4 were found to further analysis. Both single and combined applications of MT1G and DDIT4 showed good diagnostic efficacy (AUC = 0.8254, 0.8548, 0.8577, respectively). Transcription factors STAT1 and PU1 of MT1G and ARNT and MAX of DDIT4 were identified. Meanwhile, has_miR-548p-MT1G pairs, has_miR-53-3p/has_miR-181b-5p/ has_miR-664a-3p-DDIT4 pairs were found. B cells, NK cells, Th2 cells were high expression in the rupture group compared with the stable group, while DCs, Th1 cells were low expression in the rupture group. Targeted drugs against immunity, GEMCITABINE and INDOMETHACIN were discovered. We preliminarily explored the clinical significance of Fe-DEGs MT1G and DDIT4 in the diagnosis of ruptured AAA, and proposed possible upstream regulatory transcription factors and miRNAs. In addition, we also analyzed the immune infiltration of stable and rupture groups, and found possible targeted drugs for immunotherapy.