Receiver Operating Characteristic Research Articles

Evaluation of AccuBrain-based MRI quantitative analysis in diagnosing Alzheimer's disease and assessing behavioral and psychological symptoms of dementia.

Alzheimer's disease (AD) is a major cause of dementia, marked by cognitive decline and behavioral and psychological symptoms of dementia (BPSD). Early differentiation between AD, mild cognitive impairment (MCI), and healthy controls (HC) is essential for improving diagnostic accuracy and guiding effective treatment strategies. This retrospective study included 120 participants divided into AD (n = 40), MCI (n = 40), and HC (n = 40) groups. Brain MRI data were analyzed using the AccuBrain system to quantify AD Resemblance Atrophy Index (AD-RAI), Quantitative Medial Temporal Atrophy (QMTA), hippocampal volume, and white matter hyperintensities. Correlation analyses were conducted between imaging biomarkers and cognitive function scores (Mini-Mental State Examination, MMSE; Neuropsychiatric Inventory, NPI). Receiver operating characteristic (ROC) curve analysis was used to evaluated the diagnostic performance of the biomarkers. AD patients had significantly higher AD-RAI (0.91 ± 0.25) and more pronounced hippocampal atrophy (0.36 ± 0.09) compared to MCI and HC (P < 0.001). Correlation analyses showed that AD-RAI and QMTA were negatively correlated with MMSE scores (r = -0.718, P < 0.001; r = -0.463, P < 0.001), while hippocampal volume was positively correlated with MMSE (r = 0.408, P < 0.001). ROC analysis revealed that AD-RAI had an AUC of 0.777 for distinguishing AD from MCI, while QMTA had an AUC of 0.938 for distinguishing AD from HC. BPSD patients exhibited higher AD-RAI (1.09 ± 0.18) and greater hippocampal atrophy, with ROC AUC > 0.9 for distinguishing BPSD from non-BPSD patients. The AccuBrain MRI system demonstrated high sensitivity and diagnostic value in distinguishing AD from MCI and HC, as well as in identifying patients with BPSD. Correlation and ROC analyses support the use of these imaging biomarkers for early diagnosis and personalized treatment strategies in AD.

Based on disulfidptosis, unveiling the prognostic and immunological signatures of Asian hepatocellular carcinoma and identifying the potential therapeutic target ZNF337-AS1.

Disulfidptosis is a newly discovered programmed cell death pathway that may be connected to tumorigenesis and development, showing promise as a novel treatment strategy for cancer. This study aims to construct a prognostic model of disulfidptosis-related Long non-coding RNAs (DRLRs) within the Asian HCC population and to investigate the impact of DRLRs on HCC. Utilising a combination of univariate Cox, Lasso-Cox, and multivariate Cox analyses, five pivotal DRLRs (AC099850.3, ZNF337-AS1, LINC01138, AL031985.3, AC131009.1) were identified, forming a robust prognostic signature. Subsequent validations included Receiver Operating Characteristic (ROC) and Concordance Index analyses, alongside Principal Component Analysis. Comprehensive bioinformatics analysis was performed on the hub DRLRs, followed by experimental validation using quantitative real-time polymerase chain reaction and cellular functional assays. The risk score independently predicted prognosis, outperforming traditional clinical-pathological factors across varying ages, tumour stages, and pathological classifications in the cohort. A nomogram integrating these variables demonstrated capability in forecasting survival. Multivariate analysis confirmed that the risk score and AJCC TNM staging are independent prognostic factors for predicting overall survival (OS) in Asian HCC patients (both P < 0.001). The prognostic model's ROC area under the ROC values for 1-, 3-, and 5-year predictions were 0.837, 0.794, and 0.783, respectively, indicating its strong diagnostic and prognostic value. Pathway and immune landscape analyses elucidated the biological underpinnings and immune modulations associated with the high-risk group. Immune landscape analysis indicated that both immunescore (P < 0.001) and estimatescore (P < 0.05) were significantly decreased in the high-risk group, with both specific and non-specific immune responses being significantly suppressed, while the tumour immune dysfunction and exclusion score was notably increased (P < 0.001). Tumour mutational burden (TMB) analysis revealed a significantly higher TMB in the high-risk group (P = 0.033) and shorter OS for HCC patients in the high TMB subgroup (P = 0.002). Notably, Potential chemotherapeutic agents (PFI3, 5-Fluorouracil, BPD-00008900, GDC0810, and AZ6102) were identified for high-risk group. Experimental validations through quantitative PCR and in vitro assays confirmed the deregulation of these DRLRs in HCC, with functional studies highlighting the potential of ZNF337-AS1 silencing in curtailing tumour invasiveness. Our investigations validate a DRLR-based risk scoring model as an effective prognostic tool for Asian HCC. This model not only enhances understanding of disulfidptosis's role in HCC but also facilitates personalised treatment strategies, potentially improving patient outcomes.

Prognostic role of interim PET-CT demonstrating partial metabolic response in diffuse large B-Cell lymphoma: a retrospective study.

Interim 18F-FDG PET/CT (iPET/CT) imaging demonstrates potential in assessing the early therapeutic response in lymphoma. Nevertheless, the prognostic significance of interim PET-CT in diffuse large B-cell lymphoma (DLBCL) remains controversial. This study aimed to evaluate whether semi-quantitative PET/CT metabolic parameters and other metrics could enhance the prognostic value of interim PET/CT in DLBCL patients exhibiting partial metabolic remission (PMR). A retrospective analysis was performed from January 2018 to December 2023, focusing on patients with DLBCL who achieved PMR on interim PET-CT. Patient demographics, clinical characteristics, and semi-quantitative PET/CT metabolic parameters were extracted from the medical records. Multivariate analyses were conducted to identify the risk factors associated with failure to achieve complete metabolic remission (CMR) at the end of treatment (EOT). Receiver operating characteristic (ROC) curve analysis was employed to determine the optimal cut-off values for continuous predictive variables. Progression-free survival (PFS) and overall survival (OS) were estimated using Kaplan-Meier methods, and risk factors were evaluated using Cox regression models. In a cohort consisting of 80 newly diagnosed DLBCL cases that exhibited PMR on interim PET scans, 50 cases ultimately achieved CMR, while the remaining 30 cases still had positive PET findings at EOT. Analysis revealed that the interim lesion-to-liver maximum standardized uptake value ratio (RLL) and bone marrow involvement were independent prognostic factors for positive PET-CT outcomes at EOT. Notably, an interim RLL threshold greater than 1.66 emerged as a reliable predictor with a sensitivity of 73.3% and a specificity of 72.0%. Additionally, the International Prognostic Index (IPI) and interim RLL were identified as independent prognostic indicators for both progression-free survival (PFS) and overall survival (OS). Our study revealed that within the cohort of DLBCL patients exhibiting PMR on interim PET scans, an interim RLL greater than 1.66 and bone marrow involvement emerged as independent risk factors for positive PET results at the end of treatment. Additionally, the IPI and interim RLL were identified as independent prognostic markers for both progression-free survival and overall survival. The integration of clinical characteristics with semi-quantitative PET/CT parameters has the potential to enhance the prognostic role of interim PET/CT exhibiting PMR in DLBCL cases.

Effect of Spinal Cord Compression and Deformation on Postoperative Neurological Deficits During Spinal Deformity Correction Surgery.

Study DesignRetrospective comparative analysis.ObjectiveTo evaluate the impact of the spinal cord deformation rate (SCDR) on postoperative neurological deficits (PNDs) in spinal deformity surgery and to determine the optimal SCDR threshold for risk stratification.MethodsPatients undergoing corrective surgery between October 2010 and March 2022 were included. Spinal cord morphology at the apex was assessed using axial T2-weighted magnetic resonance imaging, categorizing spinal cords into type-A and type-B. Differences between the two groups were analyzed and the odds ratio (OR) for PNDs in patients with type-B was calculated. For patients classified as type-B, univariate analysis was conducted to identify variables, including SCDR, that may influence PNDs risk. Statistically significant variables were further analyzed using multivariate analysis. The receiver operating characteristic (ROC) curve and area under the curve (AUC) analyses were performed to evaluate the discriminative ability of risk factors and determine the optimal cut-off value.ResultsAmong 200 patients included, PNDs occurred in 17 cases (8.5%), predominantly in type-B (15/82 in type-B vs 2/118 in type-A, P < 0.001). Multivariate analysis revealed SCDR as an independent predictor of PNDs (OR = 1.076, P = 0.001). ROC analysis determined an optimal SCDR cutoff of 42.15% (AUC: 0.867), with 82.4% sensitivity, 88.5% specificity, and 98.2% negative predictive value (NPV). SCDR demonstrated superior predictive performance compared to the major Cobb angle (AUC: 0.776) and T-DAR (AUC: 0.769).ConclusionsFor patients undergoing corrective surgery, an SCDR of ≥42.15% serves as a strong predictor of PNDs. Its high NPV effectively excludes low-risk patients, offering improved risk stratification.

Regional Liver Function Assessment Using 99mTc-GSA SPECT/CT Scintigraphy in Malignant Perihilar Biliary Tumor Undergoing Major Liver Resection: A Dual-Center Cohort Study.

Cholestasis can lead to unreliable results of routine liver function assessment tests in clinical practice and the functional cutoff value of hepatectomy is still unclear. The aim of this study was to determine which 99mTc-GSA scintigraphy functional indicators can predict post-hepatectomy liver failure (PHLF) in patients before major liver resection due to malignant perihilar biliary disease. In addition, it aimed to assess the efficiency of functional future liver remnant (FLR) assessment of 99mTc-GSA scintigraphy indicators. A 99mTc-GSA scintigraphy was performed prior to planned surgery in 187 patients, including 81 patients with major liver resection. The 99mTc-GSA scintigraphy parameters including functional liver volume (FLV), ratio of the FLR functional volume to body weight (FLVFLR-BWR), and predictive residual index (PRI) were calculated from radioactive count in regions of FLR and total liver (TOTAL). Morphological liver volume (MLV) was calculated from computed tomography and standardized by standard liver volume (SLV). The efficacy of these parameters in predicting PHLF was compared using generalized linear mixed models and receiver operating characteristic (ROC) curve analysis. PHLF occurred in 22 patients, who showed lower MLVFLR/SLV, FLVFLR, FLVFLR/FLVTOTAL, FLVFLR-BWR, and PRI and higher resection rate (P < 0.05 for all) than patients without PHLF. After adjusting for clinical parameters, a decreased FLVFLR-BWR (odds ratio, OR 0.17; 95% confidence intervals, CI 0.05-0.53) was found to be an independently significant indicator in the model of GLMM. FLVFLR-BWR (0.835) had the highest ROC among all liver functional indicators. The FLR functional parameter preoperatively estimated from preoperative 99mTc-GSA scintigraphy protocol is a promising tool for regional liver function assessment, and it can distinguish high-risk patients who may develop PHLF with malignant perihilar biliary tumor undergoing major liver resection.

Approaches to optimally target frailty screening among people with HIV in clinical care: findings from the Centers for AIDS Research Network of Integrated Clinical Systems (CNICS).

Frailty screening in HIV care has been recommended, however, screening adds burden to busy clinics. We compared criteria that predict concurrent frailty to identify approaches to optimally target frailty screening. The development cohort included people with HIV (PWH) at six U.S. sites. Frailty was assessed based on four components of a modified Fried phenotype: fatigue, unintentional weight loss, low mobility, and poor physical activity. We evaluated demographic and clinical characteristics, comorbidities, and substance use as predictors of who should be screened using selection approaches for simple and complex frailty screening tools, including machine learning approaches. We compared discrimination and calibration including area under the receiver operator characteristic (ROC) curve (AUC), sensitivity, and specificity in a validation cohort (7th site). Among the 9,592 PWH in the development cohort, 11% were frail. AUC ranged from 0.52 for simple screening approaches such as age-based to 0.84 for complex approaches in the development and validation cohorts. Using an age cutoff >50 years reduced the percentage of PWH needing screening by over half but also reduced the sensitivity to 58% in the validation cohort. Complex approaches required 47% to be screened and had a sensitivity of 89%. Age-based frailty screening approaches (e.g., >50 years) miss many frail PWH. Complex tools had marginally better testing characteristics but would be more difficult to implement in clinical care. Simple targeted screening based on three characteristics (age, gender, and depressive symptoms) identified 89% of frail PWH and reduced the number who needed screened by 52%.

The role of urine microbiota in culture-negative patients with pyuria.

Pyuria is usually caused by bacteria and usually results in antibiotic prescriptions. However, traditional urine culture is time-consuming and has a high false negative possibility. Additionally, the role of urine viruses in pyuria is unclear. Metagenomics can enhance the precision and efficiency of diagnosis by directly sequencing the microbiota in urine. We aimed to determine the association of urine microbiota in patients with or without pyuria and culture negative. In this retrospective study, we screened urine samples from patients who received whole genome sequencing (WGS) and had a negative urine culture from October 2021 to May 2024. We compared differences in the top 10 detected genera of urine microbiota between the pyuria group and the non-pyuria group. Multivariable analysis was used for correlation analysis and performed to odds ratio (OR) and OR with 95% confidence interval (CI). The receiver operating characteristic (ROC) curve analyses tested the predictive ability of associated microbiota to pyuria. We found 29 microbial genera including 2 viral genera. Escherichia [OR 11.688 (95%CI 2.190-62.362), p = 0.004], Gardnerella [OR 9.904 (95%CI 2.180-45.005), p = 0.003] or Polyomavirus [OR 5.205 (95%CI 1.295-20.919), p = 0.020] was associated with the independent risk factors of pyuria, while Lactobacillus was associated with a decreased risk of pyuria [OR 17.273 (95%CI 1.297-230.061), p = 0.031]. An integrated logistic regression model of Escherichia, Gardnerella, Polyomavirus, and Lactobacillus exhibited a predictive power for pyuria with the area under curve (AUC) of 0.8132 [95%CI (0.7098-0.9167), p < 0.001]. Urine microbiota is diverse. Escherichia, Gardnerella, or Polyomavirus are independently associated with pyuria, while Lactobacillus is a positive factor against pyuria.

Estimated glucose disposal rate outperforms other insulin resistance surrogates in predicting incident cardiovascular diseases in cardiovascular-kidney-metabolic syndrome stages 0–3 and the development of a machine learning prediction model: a nationwide prospective cohort study

Background The American Heart Association recently introduced the concept of cardiovascular-kidney-metabolic (CKM) syndrome, highlighting the increasing importance of the complex interplay between metabolic, renal, and cardiovascular diseases (CVD). While substantial evidence supports a correlation between the estimated glucose disposal rate (eGDR) and CVD events, its predictive value compared with other insulin resistance (IR) indices, such as triglyceride–glucose (TyG) index, TyG-waist circumference, TyG-body mass index, TyG-waist-to-height ratio, triglyceride-to-high density lipoprotein cholesterol ratio, and the metabolic score for insulin resistance, remains unclear.Methods This prospective cohort study utilized data from the China Health and Retirement Longitudinal Study (CHARLS). The individuals were categorized into four subgroups based on the quartiles of eGDR. The associations between eGDR and incident CVD were evaluated using multivariate logistic regression analyses and restricted cubic spline. Seven machine learning models were utilized to assess the predictive value of the eGDR index for CVD events. To assess the model’s performance, we applied receiver operating characteristic (ROC) and precision-recall (PR) curves, calibration curves, and decision curve analysis.Results A total of 4,950 participants (mean age: 73.46 ± 9.93 years), including 50.4% females, were enrolled in the study. During follow-up between 2011 and 2018, 697 (14.1%) participants developed CVD, including 486 (9.8%) with heart disease and 263 (5.3%) with stroke. The eGDR index outperformed six other IR indices in predicting CVD events, demonstrating a significant and linear relationship with all outcomes. Each 1-unit increase in eGDR was associated with a 14%, 14%, and 19% lower risk of CVD, heart disease, and stroke, respectively, in the fully adjusted model. The incorporation of the eGDR index into predictive models significantly improved prediction performance for CVD events, with the area under the ROC and PR curves equal to or exceeding 0.90 in both the training and testing sets.Conclusions The eGDR index outperforms six other IR indices in predicting CVD, heart disease, and stroke in individuals with CKM syndrome stages 0–3. Its incorporation into predictive models enhances risk stratification and may aid in the early identification of high-risk individuals in this population. Further studies are needed to validate these findings in external cohorts.Graphical abstract

The Prognostic Value of the Systemic Immune-Inflammation Index in Glioblastoma Patients and the Establishment of a Nomogram.

The systemic immune-inflammation index (SII) has recently attracted significant interest as a new biomarker for predicting the prognosis of patients with glioblastoma (GBM). However, the predictive significance of it is still a subject of debate. This study intended to assess the clinical effectiveness of the SII in GBM and establish a nomogram. Receiver operating characteristic (ROC) curves were utilized to determine the optimal cut-off values of the SII. Kaplan-Meier (KM) survival curves were used to analyze the median overall survival (OS). Cox regression analysis was carried out to evaluate the associations between OS and different clinical factors. Based on the SII and clinical characteristics, a nomogram was constructed, and its value in clinical application was evaluated by means of decision curve analysis. The optimal SII cut-off value was 610.13. KM analysis revealed that GBM patients with higher SII values had shorter OS (15.0 vs. 34.0 months, P = 0.044). Multivariate analysis demonstrated that a high SII was an independent predictor of poor outcome in GBM (HR = 1.79, P = 0.029). The nomogram incorporating the preoperative SII showed good predictive accuracy for GBM patient prognosis (C-index = 0.691). The SII is an independent predictive indicator for GBM. Patients with elevated SII levels tend to have a poorer prognosis. A nomogram combining the SII with clinical and molecular pathological features can assist clinicians in assessing the risk of death in GBM patients, providing a basis for individualized treatment decisions.

Validation of a Mathematical Model for Rupture Status of Spherical Intracranial Aneurysms.

An accurate mathematical model of intracranial aneurysm (IA) mechanics is of great value for its potential utility in assessing probability of IA rupture. Such a model for spherical IAs has been developed which predicts a wall-thickness-to-IA-radius ratio (WTR) of 6.1×10-3 at which IAs rupture. To our knowledge, no further work has been done to validate this model with clinical data. We aim to assess the accuracy and utility of this model of spherical IA rupture mechanics. Aneurysm height, width, neck diameter, and vessel radius were measured on radiologic images of IAs of the basilar terminus, anterior communicating, and posterior communicating arteries. Geometric modeling was used to approximate IA wall thickness. Calculations were performed with and without accounting for changes in IA morphology which have been shown to occur post-rupture. Receiver operating characteristic (ROC) curves and positive likelihood ratios (LR) were produced for WTR, aspect ratio (AR), bottleneck factor (BF), and size ratio (SR). Logistic regression analysis was performed to determine the WTR where there is a 50% chance of presentation as a ruptured aneurysm in our cohort. 52 unruptured and 28 ruptured spherical IAs were included. ROC curve analysis revealed similar areas under the curve for WTR, AR, BF, and SR, ranging from 0.636 to 0.773 with overlapping confidence intervals. LRs ranged from 1.34 (95% CI 1.09-1.65) for AR calculated with post-rupture dimensional adjustments to 2.14 (95% CI 1.45-3.14) for WTR and BF calculated without post-rupture adjustments. Logistic regression revealed a strong association between decreased WTR and rupture status. The point at which there is a 50% chance of presentation as ruptured was found to be WTR = 7.9×10-3 when calculated without post-rupture adjustments and WTR' = 6.2×10-3 when calculated with post-rupture adjustments, from which the proposed 6.1×10-3 differs by 23% and 1.4%, respectively. The model for IA rupture mechanics assessed in this study agrees reasonably well with clinical data and could serve as a foundation for further investigation. It additionally performs well in discriminating between ruptured and unruptured aneurysms, though its performance in this dataset is similar to more conventional, simpler parameters. Most importantly, this study demonstrates that biomathematical models can provide valuable insight into the nature of aneurysmal lesions despite their simplifying assumptions.

Evaluation of Serum Level of Chemokine (C-C Motif) Ligand 2 (CCL2) in Canine Urothelial Carcinomas, Mast Cell Tumours, and Insulinomas, and Its Correlation With Circulating Monocytes.

Chemokine (C-C motif) ligand 2 (CCL2) is a strong monocyte chemoattractant that has been shown to be increased in humans and dogs with neoplasia. Although in human urothelial carcinoma (UCa) CCL2 levels increase with metastatic disease, a previous study showed an opposite trend in dogs. The aim of this study was to assess serum CCL2 levels in dogs with mast cell tumours (MCT) and insulinoma with or without metastasis. The second aim was to evaluate any correlation between CCL2 serum levels and circulating monocyte counts in UCa, MCT and insulinoma. Serum CCL2 levels were significantly higher in dogs affected with MCT or insulinoma compared to healthy dogs (p = 0.002 and p = 0.017, respectively). Furthermore, low levels of serum CCL2 were associated with metastasis in insulinoma-affected dogs (p = 0.04) similarly to UCa-affected dogs. However, in the MCT group, low levels of serum CCL2 were associated with non-metastatic disease (p = 0.0045). Based on receiver operating characteristic (ROC) curves, optimal cut-off values were determined for CCL2 concentration in order to calculate sensitivity and specificity of the test. Good accuracy was reached for CCL2 as a diagnostic marker, but it was poor as a staging marker. In conclusion, a dual role of CCL2 has been shown in different tumour types. A low level of CCL2 was associated with metastatic disease in dogs with insulinoma similarly to UCa-affected dogs. In contrast, a low level of CCL2 was associated with a non-metastatic disease in dogs with MCT. There was only a moderate correlation between CCL2 and circulating monocyte count in our study in both Uca and MCT, and a fair opposite correlation in insulinoma, leading to hypothesise that the influx of monocytes may be better observed in the tumour itself rather than in the circulation. These results could help to further understand CCL2 in the tumour microenvironment and its possibility as a therapeutic target.

Association of weight-adjusted waist index (WWI) with overactive bladder (OAB): a cross-sectional study from NHANES 2005–2018

The overactive bladder (OAB) is associated with obesity and inflammation. The weight-adjusted waist index (WWI) has emerged as a novel and more accurate measure of obesity compared to traditional indices, while the Dietary Inflammatory Index (DII) quantifies the inflammatory potential of one’s diet. However, the relationship between WWI, DII, and OAB remains unclear. This study aims to elucidate the association between WWI and OAB and to determine whether this relationship is mediated by dietary inflammation. Data from the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2018, comprising 10,176 participants, were analyzed in this study. The association between WWI and OAB was examined using multivariate logistic regression models, smooth curve fitting, and subgroup analysis. Furthermore, the predictive capabilities of various anthropometric indices—including WWI, body roundness index (BRI), body mass index (BMI), waist circumference, body fat percentage (BF%), and weight—on OAB incidence were assessed using Receiver Operating Characteristic (ROC) curve analysis. Finally, a mediation analysis was conducted to explore whether the DII mediates the relationship between WWI and OAB. There was a significant positive association between WWI and OAB. After adjusting for covariates, for each unit increase in WWI, there was a 40% increase in the prevalence of OAB (OR 1.40, 95% CI 1.25–1.58, P < 0.001). This positive correlation persisted when WWI was categorized into quartiles. The dose–response curve showed a significant linear positive correlation between WWI and OAB. The correlation between WWI and OAB persisted in subgroup analysis. The ROC curve analysis revealed that WWI had a superior predictive capability compared to traditional obesity indices, with an area under the curve (AUC) of 0.661 (95% CI 0.648–0.674, all P < 0.001). The results of the mediation analysis showed that 5.02% of the association between WWI and OAB was mediated by DII (P = 0.016). Our findings suggest that individuals with higher WWI may have an increased risk of OAB. Additionally, an anti-inflammatory diet may be beneficial in preventing OAB. Given the cross-sectional nature of this study, we cannot establish a causal relationship. Future longitudinal studies are needed to validate this association and investigate the potential biological mechanisms underlying this relationship.

Bioinformatics analysis of oxidative phosphorylation-related differentially expressed genes in osteoporosis

BackgroundOsteoporosis (OP) is a common metabolic bone disease characterized by decreased bone mass and increased fracture risk. Recent studies suggest that oxidative phosphorylation (OXPHOS) plays a crucial role in the pathogenesis of OP. This study aims to investigate the differential expression and potential functional roles of OXPHOS-related genes in OP.MethodsWe downloaded gene expression data from two OP-related datasets, GSE56815 and GSE7429, using the GEOquery package. We also collected OXPHOS-related genes from the GeneCards and MsigDB databases. The limma package was used for differential expression analysis of GSE56815, and differentially expressed genes (DEGs) were identified. We intersected these DEGs with OXPHOS-related genes to identify OXPHOS-related differentially expressed genes. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), were conducted using the clusterProfiler package. Additionally, we performed gene set enrichment analysis (GSEA). The Mann–Whitney U test analyzed differences in the expression of OXPHOSRDEGs, and their diagnostic potential was assessed by Receiver Operating Characteristic (ROC) curves. Correlation analysis, Protein–Protein Interaction (PPI) network construction, mRNA-miRNA, mRNA-TF interaction network construction, and immune infiltration analysis using CIBERSORT were also conducted. RAW264.7 cells were induced in vitro for 3 days to differentiate towards osteoblasts, and RT-PCR assay was used to verify the differentiation and detect the differential expression of target genes.ResultsOur results identified 31 DEGs in GSE56815, with 26 upregulated and 5 downregulated genes. Among these, we identified 10 OXPHOSRDEGs: VPS35, TBC1D2, UBQLN2, SH3GLB2, WWP1, NFKBIA, MFSD10, SLC2 A3, RP2, and ZNF91. GO and KEGG enrichment analyses revealed significant involvement of these genes in mechanisms such as the positive regulation of the protein catabolic process and the endocytosis pathway. ROC analysis demonstrated high diagnostic accuracy for VPS35 (AUC = 0.832) and TBC1D2 (AUC = 0.751). Correlation analysis indicated strong relationships between certain OXPHOSRDEGs. The PPI network highlighted 8 hub genes with significant functional similarity among them.ConclusionThis study systematically elucidates the differential expression and potential mechanisms of OXPHOS-related genes in OP through comprehensive bioinformatics analyses. The identified key genes offer valuable insights into the molecular underpinnings of OP and present potential diagnostic biomarkers and therapeutic targets for further investigation.

Predicting cerebral infarction and transient ischemic attack in healthy individuals and those with dysmetabolism: a machine learning approach combined with routine blood tests

Ischemic cerebral infarction is the most prevalent type of stroke, causing significant disability and death worldwide. Transient ischemic attack (TIA) is a strong predictor of subsequent stroke. Individuals with dysmetabolism, such as hypertension, hypercholesterolemia, and diabetes, are at increased risk for cerebral infarction (CI) and TIA. In resource-limited settings, diagnosing CI and TIA can be particularly difficult due to a lack of advanced imaging and specialized expertise. Therefore, we aim to develop a simple, convenient, blood-based approach that could assist clinicians in diagnosing CI and TIA, especially in regions where advanced imaging or stroke-specific expertise is limited. All study subjects were patients admitted to the First Hospital of Xiamen University and healthy check-up populations between January 2018 and September 2023. This study employed five machine learning methods alongside 21 blood routine indicators, 30 blood biochemical indicators, age, and gender to construct predictive models for CI and TIA in both healthy individuals and those with dysmetabolism. The Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) served as the metric to assess the comprehensive predictive capability of the models. Subsequently, the SHAP package was employed for model interpretation. Extreme Gradient Boosting (XGBoost) outperforms other models in all predictive models. In the models predicting CI and TIA among healthy, the AUC is 0.9958 (0.9947–0.9969) and 0.9928 (0.9899–0.9951), respectively. Among the nine shared key features of the two models are indicators of glucose metabolism, lipid metabolism, and liver metabolism. In the models for predicting CI and TIA among patients with hypertension, hypercholesterolemia, diabetes, and those with all three metabolic disorders combined, the AUCs ranged from 0.6990 to 0.8591. We found that the indicators K significantly contributed to predict CI and TIA from those with dysmetabolism. Additionally, metabolic-related indicators, such as glucose (GLU) and high-density lipoprotein cholesterol (HDL-C), are ranked highly among the top ten contributing features. XGBoost performed the best in all models. It can effectively differentiate CI and TIA from healthy and dysmetabolic patients by combining blood routine and blood biochemical indicators. Moreover, it can also differentiate CI from TIA. Although any suspicious findings from this model would still require confirmatory imaging, the simplicity and low cost of blood-based testing may offer a practical adjunct for clinicians—particularly in areas lacking advanced imaging or extensive stroke expertise—and could facilitate earlier diagnostic decision-making.

Cross-Cultural Validation and Application of the EDE-Q 6.0 for Screening and Assessment of Eating Disorders in Chinese Females.

This study aims to evaluate the cross-cultural validity and utility of the Chinese version of the Eating Disorder Examination Questionnaire 6.0 (EDE-Q 6.0) as a screening and assessment tool for eating disorders (EDs) among Chinese females. Participants included 652 female patients diagnosed with EDs and 311 healthy female controls, with data collected from five separate research projects conducted between 2018 and 2024. Receiver operating characteristic (ROC) curve analysis determined diagnostic accuracy and optimal cut-off scores. Correlation analyses, Wilcoxon signed-rank tests, and regression analyses were used to evaluate the relationship between changes in EDE-Q 6.0 scores and other clinical measures following treatment in 55 patients with anorexia nervosa (AN) and 74 patients with bulimia nervosa (BN). The Chinese version of the EDE-Q 6.0 demonstrated strong diagnostic accuracy in distinguishing EDs from the healthy population, with an optimal cut-off global score of 2.54 (area under ROC curve = 88.5%, sensitivity = 77.5%, specificity = 91.8%), significantly higher than in previous reports. Changes in EDE-Q 6.0 scores significantly correlated with clinical measures such as BMI, binge-eating frequency, Beck Depression Inventory, Beck Anxiety Inventory, and Eating Disorder Inventory subscales in both patients with AN and patients with BN (p < 0.05). Wilcoxon tests and stepwise regression confirmed its validity in reflecting clinical changes, capturing symptom variability related to BMI and depression in AN (p < 0.01) and binge-eating frequency and body dissatisfaction in BN (p < 0.01). The Chinese version of the EDE-Q 6.0 is a valid and effective screening and assessment tool for EDs in Chinese females. The updated cut-off score, now closer to international standards, reflects progress in the cultural adaptation of the tool and a potential shift in acceptance to report psychological problems among Chinese patients.

Factors associated with Mycoplasma pneumoniae-induced Lobar pneumonia with mucus plugging and the optimal timing for bronchoalveolar lavage: a retrospective study

BackgroundLobar pneumonia with mucus plugging (LPMP) caused by Mycoplasma pneumoniae (MP) is a severe form of community-acquired pneumonia in children, often leading to prolonged disease courses and complications. Identifying clinical factors associated with delayed radiographic resolution and determining the optimal timing for bronchoalveolar lavage (BAL) intervention are crucial for improving clinical outcomes.MethodsWe conducted a retrospective analysis of 151 children aged 2–14 years diagnosed with LPMP between November 2023 and July 2024. Patients were divided into two groups based on radiographic resolution at one month: common resolution (n = 83) and delayed resolution (n = 68). Clinical data, laboratory results, and timing of BAL intervention were compared between groups. Multivariate logistic regression identified independent risk factors of delayed resolution, and receiver operating characteristic (ROC) curves assessed predictive performance.ResultsChildren in the delayed resolution group had significantly longer fever durations before intervention (P < 0.001), higher C-reactive protein levels (P < 0.001), and elevated lactate dehydrogenase levels (P < 0.001) compared to the common resolution group. Multivariate analysis identified elevated D-dimer levels (P = 0.010), delayed BAL intervention (P < 0.001), and prolonged hospital stay (P = 0.044) as independent risk factors of delayed resolution. ROC analysis showed that BAL intervention within 6 days had excellent predictive accuracy. Early BAL intervention was associated with shorter hospital stays (P < 0.001), faster cough resolution (P < 0.001), lower incidence of atelectasis at one month (P = 0.024), and higher rates of lung consolidation absorption (P < 0.001).ConclusionElevated D-dimer levels, delayed BAL intervention, and prolonged hospital stay are significant associated with delayed radiographic resolution in children with LPMP. Early BAL intervention within 6 days of admission improves clinical outcomes by accelerating recovery and reducing complications. These findings support prompt BAL as a key component in the management of pediatric LPMP.

The hypoperfusion volume has a strong predictive value for hemorrhagic transformation in acute ischemic stroke patients with anterior circulation occlusion after endovascular thrombectomy

ObjectiveIt remains unclear whether hypoperfusion volume elevates the risk of hemorrhagic transformation (HT) after endovascular thrombectomy (EVT) in patients with acute ischemic stroke (AIS). This study aims to investigate the association between hypoperfusion volume and HT after EVT.Materials and methodsWe retrospectively recruited AIS patients with anterior circulation occlusion after receiving EVT from January 2021 to May 2024. The pre-EVT hypoperfusion volume was assessed using computed tomography perfusion, with a hypoperfusion area defined as time-to-maximum > 6s. Multivariable analysis determined whether the hypoperfusion volume served as an independent predictor of symptomatic intracerebral hemorrhage (sICH) or intracerebral hemorrhage (ICH), and its predictive value was evaluated using receiver operating characteristic (ROC) curves.ResultsA total of 115 patients were analyzed, with 55 (47.8%) having ICH and 16 (13.9%) experiencing sICH. The median age was 67, and 28.6% were female. The median core infarct volume was 4.3 ml, and the median hypoperfusion volume was 112.8 ml. After adjusting for potential confounding factors, the hypoperfusion volume remained independently correlated with sICH (OR = 1.008, 95% CI = 1.001–1.015, P = 0.018) and ICH (OR = 1.006, 95% CI = 1.001–1.012, P = 0.033). ROC curve analysis demonstrated that the hypoperfusion volume effectively predicted sICH [(area under the curve (AUC) = 0.702] or ICH (AUC = 0.643).ConclusionThe hypoperfusion volume has a strong predictive value for sICH and ICH in AIS patients with anterior circulation occlusion after EVT. This underscores the necessity of assessing the hypoperfusion volume before EVT, particularly for patients with smaller core infarct volumes in AIS.

A novel nomogram based on PET/CT to predict lymph nodal metastasis for lung adenocarcinoma with normal size lymph node

BackgroundA precise assessment of lymph nodal status is essential for guiding an individualized treatment plan in lung adenocarcinoma patients. A novel nomogram using easily accessible indicators was developed and validated in this study to predict CT-negative lymph nodal metastasis.MethodsBetween September 2020 and December 2023, data from 132 consecutive patients diagnosed with lung adenocarcinoma who underwent lung resection with systemic lymph node dissection or sampling were retrospectively reviewed. Risk factors associated with lymph nodal metastasis were identified using univariable and multivariable logistic regression analyses. Subsequently, a nomogram was developed on basis of these identified parameters. The performance and validity of the nomogram were evaluated using the area under the receiver operating characteristic (ROC) curve, calibration curve, and bootstrap resampling techniques.ResultsFour predictors (primary tumor location, primary tumor SUVmax value, N1 lymph node SUVmax, and N2 lymph node SUVmax) were identified and incorporated into the nomogram. The nomogram exhibited notable discrimination, evidenced by an area under the ROC curve of 0.825 (95% CI: 0.749–0.886, P < 0.001). Excellent concordance between the predicted and observed probabilities of lymph nodal involvement was demonstrated by the calibration curve. Furthermore, decision curve analysis indicated a net benefit associated with the use of our nomogram.ConclusionThe nomogram demonstrated efficacy and practicality in predicting CT-negative lymph node metastasis for lung adenocarcinoma patients. It holds potential to offer valuable treatment guidance for clinicians.

Right ventricular strain predicts outcome in patients receiving sacubitril/valsartan: A sub-analysis of DISCOVER-ARNI.

Angiotensin receptor/neprilysin inhibitors (ARNI) have emerged as a pivotal medical treatment considerably improving the clinical outcome of patients with heart failure and reduced ejection fraction (HFrEF). Identifying individuals who stand to benefit the most from ARNI could markedly enhance patient management strategies. The aim of this sub-analysis of DISCOVER-ARNI register was to evaluate the prospective prognostic significance of speckle tracking echocardiography (STE) parameters in patients undergoing ARNI therapy. DISCOVER-ARNI multicentre Italian register retrospectively enrolled 341 patients with HFrEF referred for treatment with ARNI. These patients underwent clinical, biohumuoral, and echocardiographic assessment at baseline. Subsequently, among those with available right ventricular STE data, a prospective long-term follow-up was conducted by telephone interview or on-site visits. The primary endpoint encompassed a composite of outcomes, including all-cause or cardiovascular mortality, heart failure hospitalization, heart transplantation, and left ventricular assist device (LVAD) implantation. Overall, 136 HFrEF patients were included in this sub-analysis (mean age 65±10years, 82% male). The mean follow-up was 40±18months, during which 32 patients reached the primary endpoint (14 deaths of which 10 due to cardiovascular reasons, 22 hospitalization, 3 heart transplantation, 1 LVAD implantation). Baseline assessment revealed that patients with events had higher LV volumes and EF (LV end-diastolic volume 212±65 vs.174±57mL, P=0.002; LV end-systolic volume 156±52 vs. 122±49mL, P=0.001; LV EF=26±5 vs. 29±5mL, P=0.006, respectively), lower but preserved tricuspid annular plane systolic excursion (TAPSE, 17±3 vs.19±3, P=0.008), and higher systolic pulmonary artery pressures (38±11 vs. 31±8mmHg, P=0.001) compared to those who did not experience events. LV, left atrial (LA), and free wall right ventricular longitudinal strain (fwRVLS) were reduced in patients with events (-7±2 vs. -8±2%, P=0.002; 11±3 vs. 15±7%, P=0.001 and -15±5 vs. -22±5%, P=0.007, respectively). Employing Cox proportional hazard model including LVEF, TAPSE, RVFAC, LV strain, LA strain, and fwRVLS, the latest emerged as the sole independent predictor of the combined endpoint (hazard ratio=1.15 [1.05;1.26], P=0.002). Receiver operating characteristic (ROC) curves determined that fwRVLS=-20% was the optimal cut-off for predicting the combined endpoint (area under curve [AUC]=0.70). This threshold was used for constructing Kaplan-Meier survival curves, demonstrating effective risk stratification of fwRVLS over long-term follow-up for the primary endpoint. fwRVLS by STE holds promise as a valuable parameter to assess response to ARNI therapy in terms of overall survival, heart failure hospitalizations, heart transplantation, or LVAD implantation.

Combining machine learning and dynamic system techniques to early detection of respiratory outbreaks in routinely collected primary healthcare records

BackgroundMethods that enable early outbreak detection represent powerful tools in epidemiological surveillance, allowing adequate planning and timely response to disease surges. Syndromic surveillance data collected from primary healthcare encounters can be used as a proxy for the incidence of confirmed cases of respiratory diseases. Deviations from historical trends in encounter numbers can provide valuable insights into emerging diseases with the potential to trigger widespread outbreaks.MethodsUnsupervised machine learning methods and dynamical systems concepts were combined into the Mixed Model of Artificial Intelligence and Next-Generation (MMAING) ensemble, which aims to detect early signs of outbreaks based on primary healthcare encounters. We used data from 27 Brazilian health regions, which cover 41% of the country’s territory, from 2017-2023 to identify anomalous increases in primary healthcare encounters that could be associated with an epidemic onset. Our validation approach comprised (i) a comparative analysis across Brazilian capitals; (ii) an analysis of warning signs for the COVID-19 period; and (iii) a comparison with related surveillance methods (namely EARS C1, C2, C3) based on real and synthetic labeled data.ResultsThe MMAING ensemble demonstrated its effectiveness in early outbreak detection using both actual and synthetic data, outperforming other surveillance methods. It successfully detected early warning signals in synthetic data, achieving a probability of detection of 86%, a positive predictive value of 85%, and an average reliability of 79%. When compared to EARS C1, C2, and C3, it exhibited superior performance based on receiver operating characteristic (ROC) curve results on synthetic data. When evaluated on real-world data, MMAING performed on par with EARS C2. Notably, the MMAING ensemble accurately predicted the onset of the four waves of the COVID-19 period in Brazil, further validating its effectiveness in real-world scenarios.ConclusionIdentifying trends in time series data related to primary healthcare encounters indicated the possibility of developing a reliable method for the early detection of outbreaks. MMAING demonstrated consistent identification capabilities across various scenarios, outperforming established reference methods.