Receiver Operating Characteristic Curve Analysis Research Articles

Relationship of VDR and VDBP gene polymorphisms with sepsis susceptibility and prognosis

ObjectiveThis research focused on the association of vitamin D receptor (VDR) and vitamin D binding protein (VDBP) gene polymorphisms with sepsis susceptibility and prognosis.Methods110 septic patients were selected as the sepsis group, and another 100 patients with common infections who did not develop sepsis as the control group. 28 days death of patients in the sepsis group were counted. Within 24 h of admission, patients were scored by Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) and Sequential Organ Failure Assessment (SOFA). Serum lactate (Lac), C-reactive protein (CRP), procalcitoninogen (PCT) and vitamin D levels were evaluated. All patient DNAs were extracted. The polymorphisms of VDR and VDBP genes and vitamin D genes Fok Ⅰ (rs2228570) and VDBP rs4588 locus were tested and compared in both groups; and the receiver operating characteristic (ROC) curves were plotted to calculate the area under the area under the curve (AUC) and assess the diagnostic value of each indicator for sepsis. Patients in the sepsis group were categorized into a death group and a survival group, the above indicators were compared in the two groups and the factors affecting the prognosis of sepsis patients were analyzed.ResultsCompared to the control group, the sepsis group exhibited higher APACHE II scores, SOFA scores, serum Lac, CRP, PCT levels, VDR Fok Ⅰ (rs2228570) locus f allele and VDBP rs4588 locus A allele frequencies, and lower vitamin D levels (P < 0.05). The ROC curve analysis showed that the AUC for the diagnosis of sepsis using the AA genotype at the VDBP gene rs4588 locus was 0.579 (95% CI: 0.501–0.656) (sensitivity: 52.70%; specificity: 63.00%, P < 0.05). APACHE II and SOFA scores and serum levels of Lac, CRP, and PCT in the death group were raised and vitamin D levels were diminished than those in the survival group (P < 0.05). Raised APACHE II and SOFA scores were independent risk factors affecting sepsis prognosis.ConclusionThe f allele at the VDR Fok Ⅰ (rs2228570) locus and the A allele at the VDBP rs4588 locus significantly raise the risk of sepsis in patients.

Prognostic value of the preoperative systemic immune-inflammation nutritional index in patients with gastric cancer

BACKGROUND Gastric cancer (GC) is the fifth most common cancer and the third leading cause of cancer-related deaths in China. Many patients with GC frequently experience symptoms related to the disease, including anorexia, nausea, vomiting, and other discomforts, and often suffer from malnutrition, which in turn negatively affects perioperative safety, prognosis, and the effectiveness of adjuvant therapeutic measures. Consequently, some nutritional indicators such as nutritional risk index (NRI), prognostic nutritional index (PNI), and systemic immune-inflammatory-nutritional index (SIINI) can be used as predictors of the prognosis of GC patients. AIM To examine the prognostic significance of PNI, NRI, and SIINI in postoperative patients with GC. METHODS A retrospective analysis was conducted on the clinical data of patients with GC who underwent surgical treatment at the Guangxi Medical University Cancer Hospital between January 2010 and December 2018. The area under the receiver operating characteristic (ROC) curve was assessed using ROC curve analysis, and the optimal cutoff values for NRI, PNI, and SIINI were identified using the You-Review-HTMLden index. Survival analysis was performed using the Kaplan-Meier method. In addition, univariate and multivariate analyses were conducted using the Cox proportional hazards regression model. RESULTS This study included a total of 803 patients. ROC curves were used to evaluate the prognostic ability of NRI, PNI, and SIINI. The results revealed that SIINI had superior predictive accuracy. Survival analysis indicated that patients with GC in the low SIINI group had a significantly better survival rate than those in the high SIINI group (P < 0.05). Univariate analysis identified NRI [hazard ratio (HR) = 0.68, 95% confidence interval (CI): 0.52-0.89, P = 0.05], PNI (HR = 0.60, 95%CI: 0.46-0.79, P < 0.001), and SIINI (HR = 2.10, 95%CI: 1.64-2.69, P < 0.001) as prognostic risk factors for patients with GC. However, multifactorial analysis indicated that SIINI was an independent risk factor for the prognosis of patients with GC (HR = 1.65, 95%CI: 1.26-2.16, P < 0.001). CONCLUSION Analysis of clinical retrospective data revealed that SIINI is a valuable indicator for predicting the prognosis of patients with GC. Compared with NRI and PNI, SIINI may offer greater application for prognostic assessment.

Explainable artificial intelligence in forensic DNA analysis: Alleles identification in challenging electropherograms using supervised machine learning methods.

Explainable artificial intelligence in forensic DNA analysis: Alleles identification in challenging electropherograms using supervised machine learning methods.

How to view the effectiveness of spinal deformity surgery for adult degenerative scoliosis in octogenarian population? A comprehensive analysis and judgment.

This study aims to explore the connections between risk factors and clinical outcomes in octogenarian patients diagnosed with adult degenerative scoliosis (ADS). A total of 394 patients participated in this study, with 206 individuals in the younger group and 188 in the octogenarian group. We meticulously gathered patient demographics, including basic information, comorbidities, radiographic data, clinical scoring systems, and details on medical and surgical complications as well as revision surgeries. Univariable linear regression analysis revealed positive correlations between age and several postoperative outcomes: Sagittal vertical axis (SVA) (B = -0.572, P = 0.001), Oswestry Disability Index (ODI) (B = 0.145, P < 0.001), and Japanese Orthopaedic Association (JOA) score (B = 0.035, P < 0.001). Additionally, a positive relationship was observed between age and both proximal junctional kyphosis (PJK) (B = 0.055, P = 0.039) and internal fixation loosening (B = 0.253, P < 0.001). Receiver operator characteristic (ROC) curve analysis indicated that the age threshold values for predicting PJK and internal fixation loosening were 71.5 and 79.5, respectively. In octogenarian patients with ADS, the cost associated with spinal deformity surgery is accompanied by a greater incidence of surgical complications. Compared to younger patients, surgical intervention tends to be more effective at enhancing objective motor functions while having a lesser impact on the subjective experiences of octogenarian patients.

Serum alkaline phosphatase/creatinine ratio serving as a screening tool for low lumbar bone mineral density in patients with degenerative lumbar scoliosis

Purpose(1) To figure out a simple and effective indicator that could assist in the assessment of bone mineral density (BMD) based on big data and (2) to verify its predictive value for low BMD among patients with degenerative lumbar scoliosis (DLS).MethodsA total of 6,167 participants from the National Health and Nutrition Examination Survey (NHANES) database (2009–2010, 2013–2014, 2017–2018) and 166 patients who were diagnosed with DLS and hospitalized in our center between June 2019 and April 2023 were enrolled in the study. Cases were divided into two groups based on whether the T-score was below − 1. The Osteopenia Index (OI) was defined as the ratio of alkaline phosphatase (ALP) (IU/L) to creatinine (mg/dL). Multivariable logistic regression was performed to identify risk factors, while restricted cubic spline (RCS) analysis was applied to explore the potential non-linear relationship. Patients with DLS from our center were used to validate the diagnostic value of OI through receiver operating characteristic curve (ROC) analysis.ResultsParticipants from NHANES were divided into three subgroups according to the tertiles of OI: subgroup 1 (OI < 68), subgroup 2 (68 ≤ OI < 93), and subgroup 3 (OI ≥ 93). A multivariable logistic regression model adjusted for age, gender, and race revealed that elevated OI was a significant risk factor for osteopenia (subgroup 2 vs. subgroup 1: odds ratio [OR] = 1.473, 95% confidence interval [CI] = 1.173–1.849; subgroup 3 vs. subgroup 1: OR = 2.092, 95% CI = 1.566–2.796). Moreover, the RCS plot showed that the risk of osteopenia gradually increased with the elevation of OI. In patients with DLS, OI showed a significant correlation with lumbar T-score (ρ = − 0.392) and HU value (ρ = − 0.373) (both P < 0.001). ROC analysis revealed that the area under the curve of OI was 0.757, and the cut-off value was set at 124.73 according to the Youden index. A nomogram based on a logistic regression model adjusted for age, gender, and blood urea nitrogen was plotted, with a McFadden R² of 0.212.ConclusionOI correlated significantly with lumbar BMD and HU value. Logistic regression and RCS analysis demonstrated that OI could serve as a simple, economical, and effective screening tool for low lumbar BMD in DLS patients, with its predictive ability further enhanced when adjusted for age and gender.

Dissecting the genetic association between abnormal sperm parameters and depression: a transcriptome-wide analysis of 157 participants.

Depression often occurs in the males with semen abnormalities. Evidence suggested a genetic correlation between depression and the pathogenesis of abnormal sperm parameters, whereas the mechanisms remained unclear. Genomic datasets of major depressive disorder (MDD) and abnormal sperm parameters were obtained from the Gene Expression Omnibus database. After screening the datasets, differentially expressed genes (DEGs) were identified. GO and pathway enrichment analyses, a protein-protein interaction network, and receiver operator characteristic curve analysis were conducted. Then, MDD-related DEGs (MDRGs), the external validation, immunological, and translational regulation analysis were performed. Moreover, tissue expression of MDRGs was explored. A total of 249 overlapped MDRGs were discovered in the MDD and abnormal sperm parameters gene sets. MDRGs had a tight relationship with adhesion-associated and PI3 K-Akt-associated biological signaling. The protein-protein interaction module showed the enriched pathways involved in neuron differentiation and cell adhesion. Drug prediction revealed ten pharmacologic candidates. Finally, two hub MDRGs were identified and validated with good diagnostic values. Immunological and translational results showed three closely correlated kinds of CD8 + T lymphocytes, neutrophils, and macrophages, 19 transcription factor-MDRGs, and 71 miRNA-MDRGs interactions. Furthermore, expression signatures of Carnosine Dipeptidase 2 (CNDP2) and Galectin 3 Binding Protein (LGALS3BP) were displayed in cortex and testis. Our study discovered the genetic profiles in abnormal sperm parameters and MDD and elucidated enriched pathways and molecular associations between hub genes and immune infiltration. These findings provide novel insights into the common pathogenesis of both diseases as well as the potential biomarkers for MDD-associated abnormal sperm parameters.

Application of Metabolomics and the Discovery of Potential Serum Biomarkers for Diuretic Resistance in Heart Failure.

Diuretic resistance (DR) is characterized by insufficient fluid and sodium excretion enhancement despite maximum loop diuretic doses, indicating a phenotype of refractory heart failure (HF). Recently, metabolomics has emerged as a crucial tool for diagnosing and understanding the pathogenesis of various diseases. This study aimed to differentiate diuretic-resistant patients from non-resistant HF to identify biomarkers linked to the emergence of DR. Serum samples from HF patients, both with and without DR, were subjected to non-targeted metabolomic analysis using liquid chromatography-tandem mass spectrometry. Metabolite variations between groups were identified using principal component analysis and orthogonal partial least-square discriminant analysis. Metabolic pathways were assessed through the Kyoto Encyclopedia of Genes and Genomes database enrichment analysis, and potential biomarkers were determined using receiver operating characteristic curves (ROCs). In total, 192 metabolites exhibited significant differences across the two sample groups. Among these, up-regulation was observed in 164 metabolites, while 28 metabolites were down-regulated. A total of 28 pathways involving neuroactive ligand-receptor interaction and amino acid biosynthesis were affected. The top five metabolites identified by ROC analysis as potential DR biomarkers were hydroxykynurenine, perillic acid, adrenic acid, 5-acetamidovalerate, and adipic acid. Significant differences in metabolite profiles were observed between the diuretic-resistant and non-diuretic-resistant groups among patients with HF. The top five differentially expressed endogenous metabolites were hydroxykynurenine, perillic acid, adrenic acid, 5-acetamidovalerate, and adipic acid. The metabolic primary pathways implicated in DR were noted as amino acid, energy, and nucleotide metabolism. This study was registered with the China Clinical Trials Registry (https://www.chictr.org.cn/hvshowproject.html?id=197183&v=1.7, ChiCTR2100053587).

Effects of high-dose glucocorticoids on gut microbiota in the treatment of Graves' ophthalmopathy.

Many studies indicate the gut microbiome is associated with diseases caused by administering high-dose glucocorticoids (GCs), such as hypertension, hyperglycemia, and osteoporosis. However, the association between intestinal flora and the use of high-dose GCs remains elusive. We aimed to characterize gut microbiome in Graves' ophthalmopathy (GO) patients after administering high-dose GCs. In this study, 20 primary GO patients were recruited. The differences in gut microbiota of GO patients before and after administering high-dose GCs were analyzed by 16S rDNA sequencing technology. Untargeted metabolomic analysis was used to examine the differences in gut metabolites between two groups. There were significant differences in α and β diversities of gut microbiota in GO patients before and after administering high-dose GCs. The random forest analysis indicated that three intestinal bacteria (Faecalibacterium, Streptococcus, and Prevotella) could distinguish the two groups with the highest accuracy, which was proven by receiver operator characteristic curve and linear discriminant analysis effect size analysis. The short-chain fatty acid-producing ability in GO patients' gut after high-dose GC administration was significantly decreased. The 5-hydroxytryptamine levels significantly increased in the gut of GO patients after administering high-dose GCs. Our study suggests that high-dose GC administration causes the changes in gut microbiome and metabolites. Moreover, the altered flora and metabolites are related to hypertension, hyperglycemia, and osteoporosis. These findings can help understand the development of side effects caused by high-dose GCs and can be further used to develop potential probiotics to facilitate the prevention for those side effects.IMPORTANCEFor the first time, we revealed that gut microbiome and metabolome in Graves' ophthalmopathy patients after high-dose glucocorticoid (GC) administration significantly changed, and the altered flora and metabolites are related to hypertension, hyperglycemia, and osteoporosis. These findings can help understand the development of side effects caused by high-dose GCs and can be further used to develop potential probiotics to facilitate the prevention for those side effects.

Identification of Gully-Type Debris Flow Shapes Based on Point Cloud Local Curvature Extrema

The identification of gully-type debris flow remains a challenging task due to the irregularity of terrain, which causes significant fluctuations in local curvature and hinders accurate feature extraction using traditional methods. To address this issue, this study proposes a novel identification approach based on point cloud local curvature extrema. The methodology involves collecting image data of debris flow and landslide areas using DJI Matrice 300 RTK (M300RTK), planning control points and flight routes, and generating three-dimensional point cloud data through image matching and point cloud reconstruction techniques. A quadratic surface fitting method was employed to calculate the curvature of each point in the point cloud, while a topological k-neighborhood algorithm was introduced to establish spatial relationships and extract extreme curvature features. These features were subsequently used as inputs to a convolutional neural network (CNN) for landslide identification. Experimental results demonstrated that the CNN architecture used in this method achieved rapid convergence, with the loss value decreasing to 0.0032 (cross-entropy loss) during training, verifying the model’s effectiveness. The introduction of early stopping and learning rate decay strategies effectively prevented overfitting. Receiver-operating characteristic (ROC) curve analysis revealed that the proposed method achieved an area under the ROC curve (AUC) of 0.92, significantly outperforming comparative methods (0.78–0.85).

To establish and validate autophagy related biomarkers for the diagnosis of IgA nephropathy

IgA nephropathy (IgAN) is one of the most common immune-related primary glomerular diseases. The pathological mechanism of this disease is complex, and the specific pathogenesis is still unclear. To obtain a comprehensive understanding of its molecular mechanism and to provide new perspectives regarding the detection and treatment of the disease, this study investigated the role of immune cells in IgAN, as well as the role of autophagy-related biomarkers in IgAN development. The original datasets GSE93798, GSE35487, GSE58539, GSE116626 and GSE115857 were downloaded from Gene Expression Omnibus (GEO) and were further integrated and analyzed. The differentially expressed genes (DEGs) between IgAN and healthy control (HC) group were identified by the “limma” R package. The gene ontology (GO) function, Kyoto Encyclopedia of Genes and Genome (KEGG) pathway, GeneSet Enrichment Analysis (GSEA) and DisGeNet enrichment were adopted to analyze the genes from the intersection of DEGs. The hub genes were screened by the square least absolute shrinkage and selection operator (LASSO) and cross validation. Immune cell infiltration was analyzed using CIBERSORT. The correlation between hub genes and infiltrating immune cells was calculated by R software. For the purpose of exploring the value of hub genes for diagnosing IgAN, a receiver operating characteristic (ROC) curve was constructed. Finally, Real-time quantitative polymerase chain reaction (qRT-PCR) was used to verify the relative mRNA level of the AT-DEGs. 12 DEGs were screened out. Enrichment analysis revealed that autophagy-related DEGs (AT-DEGs) were mainly related to intrinsic apoptotic signaling pathway, cellular response to external stimulus, transcription repressor complex and other cellular functions, KEGG pathways enriched by AT-DEGs mainly included biological metabolic pathways related to autophagy, while DisGeNET analysis showed that these AT-DEGs were mainly related to immunological diseases. The optimal six hub genes were obtained by lasso analysis as potential biomarkers for IgAN. ROC curve analysis showed that 4 of the 6 HUB genes had great diagnostic value. Immune infiltration results showed B cells memory, macrophages M2, NK cells activated, T cells CD4+ memory resting, and monocytes are the predominant immune cells with the development of IgAN. The qRT-PCR results showed that, compared to the NC group, SIRT1 mRNA expression in PBMCs from IgAN patients was significantly reduced, while BAG3, CDKN1A, and FOS mRNA levels were markedly elevated. SIRT1, BAG3, COKN1A and FOS can be considered as effective biomarkers related to autophagy for the diagnosis of IgAN. These findings suggest some potential new serum biomarkers for IgAN diagnosis.

Metagenomic NGS reveals determinants of polymicrobial spinal infection pathogenesis

To explore the influencing factors of spinal mixed infection under mNGS-assisted detection. A retrospective analysis was conducted on the general clinical data of patients diagnosed with spinal infections at Guilin People’s Hospital, covering the period from October 2022 to October 2024, to evaluate the effectiveness of different treatment modalities including conservative, pharmacological, and surgical interventions. In the end, a total of 45 cases were included, including 18 cases of mixed infection and 27 cases of single infection. The receiver operating characteristic (ROC) curve was utilized to evaluate the predictive efficacy of various indices for the occurrence of mixed infection in patients with spinal infections, with the curve’s proximity to the top left corner indicating higher diagnostic accuracy. Multivariate Logistic regression was used to analyze the independent risk factors affecting the development of mixed infection in patients with spinal infection. No significant differences were found between the two groups regarding gender, smoking, alcohol consumption, hypertension, albumin levels, liver function, malignancy, or rheumatic immune disease history (P > 0.05). However, the mixed infection group had significantly higher proportions of patients aged > 60 years (78% (14/18) vs. 48% (13/27)), diabetes mellitus (44% (8/18) vs. 15% (4/27)), chronic kidney disease (17% (3/18) vs. 0.00 (0/27)), and previous spinal surgery (39% (7/18) vs. 11% (3/27)), along with lower BMI (20.70 ± 2.15 vs. 24.04 ± 3.76) and hemoglobin levels (105.17 ± 14.05 g/L vs. 117.48 ± 18.08 g/L). The results of the ROC curve analysis showed that the area under the curve for BMI and hemoglobin in predicting the occurrence of mixed infection in patients was 0.787 and 0.704, respectively, with optimal cutoff values of 21.12 kg/m2 and 119 g/L. Multivariate logistic regression identified BMI < 21.12 kg/m2, hemoglobin < 119 g/L, and diabetes as independent risk factors. Lower BMI, diabetes and hemoglobin are independent risk factors for spinal mixed infection. Increasing BMI, effectively controlling blood glucose and improving anemia are helpful to reduce the occurrence of spinal mixed infection.

Abstract 1915: A plasma biomarker panel of microRNAs miR-133-3p and miR-153-3p for diagnosis of prostate cancer

Abstract Prostate cancer is highly prevalent and the second-leading cause of cancer-related death among men in the United States. Its incidence rate has been rising by 3% per year overall and by about 5% per year for advanced-stage disease. Current diagnostic methods, particularly the PSA test, remain controversial due to variable sensitivity and limited specificity, highlighting the need for more precise and minimally invasive diagnostic tools. MicroRNAs (miRNAs)—19-24-nucleotide, non-coding RNAs—have emerged as critical regulators of gene expression and are frequently deregulated in cancer. These miRNAs exhibit cancer-specific dysregulation, high sensitivity, and stability, positioning them as strong candidates for blood-based biomarkers in early cancer detection and prognosis. Using computational miRNA target prediction analysis, we identified several miRNAs—namely, miR-133a/b, miR-144, miR-153, and miR-590—that could serve as highly sensitive and specific biomarkers for early prostate cancer detection. Our bioinformatics analysis further revealed that these miRNAs regulate glutathione S-transferase P1 (GSTP1), a gene often epigenetically silenced in early prostate cancer stages, suggesting their potential diagnostic value. To evaluate this, we analyzed miRNA levels in 84 human plasma samples from 56 prostate cancer patients and 28 non-cancer individuals served as controls using quantitative real-time PCR. Sensitivity and specificity were assessed through Receiver Operating Characteristic (ROC) curve analysis. The expression of miR-133-3p and miR-153-3p were significantly elevated in the plasma of prostate cancer patients compared to the control group, with p-values of 0.021 and 0.0006, respectively. Binary logistic regression and ROC analyses comparing these miRNAs to PSA showed significantly higher sensitivity and specificity, with p-values of 0.0001 and 0.0003. This two-miRNA panel exhibited near-perfect diagnostic accuracy, with an area under the curve (AUC) ranging from 0.98 to 1.0. Compared with previously identified miRNA biomarkers, this 2-miRNA panel demonstrated greater diagnostic stability and superior performance across public dataset cohorts. Our findings suggest that miR-133-3p and miR-153-3p could serve as reliable blood-based biomarkers for diagnosis of prostate cancer. Their association with GSTP1—a gene implicated in early prostate cancer through epigenetic silencing—underscores their biological relevance to the disease. The high sensitivity and specificity of this panel represent a significant improvement over PSA-based screening and indicate its promise for clinical application in prostate cancer diagnostics. Overall, this study supports the development of miRNA-based diagnostic tests to enable earlier detection, reduce unnecessary treatments, and ultimately improve outcomes for prostate cancer patients. Citation Format: Shiv Verma, Cheryl L. Thompson, Pingfu Fu, Gregory T. MacLennan, Sanjay Gupta. A plasma biomarker panel of microRNAs miR-133-3p and miR-153-3p for diagnosis of prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1915.

The predictive value of miR-28-5p and miR-424-5p in metabolic syndrome and their mechanism of action through regulation of Fras-1-related extracellular matrix protein 2.

The prevalence of metabolic syndrome (MS) is rising due to lifestyle changes. To investigate the pathogenesis of MS and identify potential biomarkers, bioinformatics tools were used to screen for MS-related genes, such as Fras-1-related extracellular matrix protein 2 (FREM2), and miRNAs, including miR-28-5p and miR-424-5p. An insulin resistance (IR) cell model was established by treating human liver cells with insulin. The roles of FREM2, miR-28-5p, and miR-424-5p in IR were examined through overexpression and silencing experiments. Transfection of miR-28-5p/miR-424-5p mimics and a dual-luciferase assay were performed to explore their regulation of FREM2. The diagnostic value of miR-28-5p/miR-424-5p in MS was assessed using the receiver operating characteristic (ROC) curve. Increased expression of FREM2 and suppression of miR-28-5p/miR-424-5p enhanced glucose uptake in IR cells. Transfection with miR-28-5p or miR-424-5p mimics reduced luciferase activity in cells transfected with the wild-type FREM2 reporter vector and suppressed FREM2 expression. The ROC curve analysis indicated that miR-28-5p and miR-424-5p serve as effective classifiers for MS, with their combined use offering higher reliability. In conclusion, miR-28-5p and miR-424-5p exacerbated IR progression in human liver cells (HHL-5) through the negative regulation of FREM2, and they are potential biomarkers for MS.

Histopathological biomarkers in squamous cell carcinoma of the vulva: the prognostic relevance of tumor-infiltrating lymphocytes (TILs)—a retrospective study of 157 cases

BackgroundThe prognostic relevance of tumor-infiltrating lymphocytes (TILs) has so far been recognized in several solid tumors like in breast, endometrial and ovarian cancer—nonetheless, the immune contexture of squamous cell carcinomas of the vulva, analyzed by means of stromal (s) and intratumoral (i) TILs, remains yet to be elucidated.Material and methodsIn this study, we examined the immunooncological biomarkers sTILs and iTILs in 157 vulvectomy specimens with histologically diagnosed vulvar squamous cell carcinoma (VSCC) according to the standardized methodology proposed by the International Immunooncology Biomarkers Working Group in 2017. In a next step, we evaluated the association of infiltrating lymphocytes to traditional histopathological parameters such as infiltration depth and HPV related tumorigenesis. After determining optimal cut-off values using Receiver Operating Characteristic (ROC) curve analysis, we assessed the prognostic relevance of sTILs and iTILs with regard to overall survival, local recurrence, and metastasis using the Log-rank (Mantel–Cox) test and Fisher's exact test.ResultsWe propose optimal cut-off values of 5% for iTILs and 20% for sTILs analysis, which identify patients with a distinct superior survival rate (sTILs: p = 0.0137; iTILs: p = 0.0226). Furthermore, a low number of iTILs was associated with a higher risk of local recurrence in our study collective (p = 0.0432).ConclusionThe fast and cost-effective determination of the histological biomarkers iTILs and sTILs yields prognostic relevance in vulvar cancer. A potential integration within the routine diagnostic workflow could be globally feasible, even in resource-poor settings.

Upper Instrumented Vertebra Hounsfield Unit Assessment and Association With Mechanical Complications Following Posterior Spinal Fusion.

Bone mineral density is often considered in preoperative surgical planning for spinal fusions. CT imaging-based measurement of Hounsfield units (HU) has been suggested as a surrogate marker for dual-energy x-ray absorptiometry scans. However, research establishing a threshold for HU at the uppermost instrumented vertebrae (UIV) that can mitigate the risk of mechanical and junctional complications following posterior spinal fusion is limited. Adults who underwent posterior spinal fusion and had 1-year follow-up data available were included in this retrospective cohort study. HU measurements were collected at various vertebral levels, including at the UIV. Inter- and intrarater reliability were calculated using the using intraclass correlation coefficients (ICCs). HU thresholds for mechanical and junctional complications were established using receiver operating characteristic (ROC) curve analyses, while accounting for age, sex, UIV level, and osteoporosis. Across 70 patients, mean age was 64.4 years, 65.3% were female, and 10.0% had osteoporosis. The intrarater reliability ICCs were classified as excellent (ie, greater than 0.9) for all vertebral levels, whereas the interrater reliability ICCs were classified as good (ie, greater than 0.8) for all vertebral levels. ROC curve analyses identified a HU cutoff of 105. Patients below the HU threshold had markedly higher rates of mechanical and junctional complications (52.0% vs. 8.9%, P < 0.001), specifically adjacent segment disease (32.0% vs. 2.2%, P < 0.001). CT-based measurement of HU at the upper instrumented vertebra has excellent intrarater and good interrater reliability. In this cohort of lumbar and thoracolumbar spinal fusion patients, patients with HU under 105 at the UIV had an increased risk of mechanical and junctional complications following spinal fusions. Future studies may work to refine HU assessment and the association with postoperative complications, as using HU to guide surgical candidacy and UIV selection may be beneficial.

Evaluation of blood-count-derived inflammatory markers in patients with idiopathic epiretinal membrane

BackgroundTo assess the role of inflammation in the pathogenesis of idiopathic epiretinal membrane (iERM) by evaluating blood-count-derived inflammatory marker levels.MethodsThe medical records of patients diagnosed with iERM and cataract patients with normal fundus examinations were analyzed retrospectively. Levels of neutrophils, monocytes, lymphocytes, and thrombocytes were obtained from blood samples. Systemic inflammatory markers, including neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and systemic inflammatory response index (SIRI) were calculated and compared between the two groups. The receiver operating characteristic curve (ROC) analysis was performed to determine the best cutoff value of NLR, PLR, SII, and SIRI in iERM.ResultsIn total, 91 iERM cases and 95 controls were included in the study. iERM patients had significantly higher NLR (2.25 vs. 1.91, p = 0.003), PLR (117.22 vs. 113.33, p = 0.042), SII (529.45 vs. 472.57, p = 0.003), and SIRI (1.25 vs. 0.90, p < 0.001). The area under the curve of NLR, PLR, SII, and SIRI in differentiating patients with iERM and controls was 0.637, 0.608, 0.645 and 0.660, respectively, according to ROC analysis. The best cutoff values (with sensitivity and specificity) were 1.95 (60.4% and 52.6%) for NLR, 116.7 (54.9% and 55.7%) for PLR, 498.03 (58.2% and 58.9%) for SII, and 1.07 (62.6% and 64.6%) for SIRI.No significant differences in inflammatory markers were found across iERM stages.ConclusionPatients with iERM exhibit higher levels of blood-count-derived inflammatory markers, suggesting a link between systemic subclinical inflammation and iERM development. However, these markers do not correlate with iERM severity. Further research with larger cohorts and broader inflammatory marker analysis is needed to elucidate the role of systemic inflammation in iERM pathogenesis.

Diagnostic and prognostic performance of serum GPC3 and PIVKA-II in AFP-negative hepatocellular carcinoma and establishment of nomogram prediction models

ObjectiveA significant proportion, ranging from 20 to 40%, of individuals with hepatocellular carcinoma (HCC) do not exhibit elevated Alpha-fetoprotein (AFP) levels. This study aimed to evaluate the utility of serum glypican-3 (GPC3) and protein induced by vitamin K absence or antagonist II (PIVKA-II) in an AFP-negative HCC (N-HCC) population, and to develop nomogram diagnostic and prognostic prediction models utilizing GPC3 and PIVKA-II.MethodsSerum GPC3 and PIVKA-II levels were measured in this case-control study, followed by the establishment of a receiver operating characteristic (ROC) curve, restricted cubic spline (RCS), and Kaplan-Meier survival curve. Additionally, a diagnostic prediction nomogram was constructed using univariate and multivariate logistic regression. Furthermore, we utilized least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression to develop a prognostic prediction nomogram. The performance of these models was evaluated using ROC curve analysis and decision curve analysis (DCA).ResultsSerum GPC3 and PIVKA-II expression levels were significantly elevated in untreated patients with N-HCC (especially stageI and tumor size < 3 cm) compared to those with AFP-negative benign liver disease (N-BLD). Derived from ROC analysis, the diagnostic cutoff points for GPC3 and PIVKA-II were set at 0.100 ng/mL and 40.00 mAU/mL, respectively. PIVKA-II demonstrated sensitivity and specificity of 84.62% and 90.38%, surpassing GPC3’s 76.92% and 73.08%. The area under the ROC curve (AUC) for a diagnostic prediction nomogram incorporating GPC3, PIVKA-II, and gamma-glutamyltransferase (GGT) was 0.943 (95% CI: 0.912–0.974), superior to models using GPC3 or PIVKA-II alone. This model showed 95.20% sensitivity and 81.70% specificity in differentiating N-HCC from N-BLD. Stratifying patients into high-risk and low-risk groups using cutoff values established by RCS for GPC3 (0.124 ng/mL) and PIVKA-II (274 mAU/mL) revealed significant associations between these risk stratifications and patient survival. Finally, the use of GPC3-highrisk, cirrhosis, albumin (ALB), portal venous thrombosis (PVT), and surgical treatment as five parameters in the nomogram prognostic prediction model effectively differentiated between high- and low-risk prognostic patients with N-HCC with relatively high accuracy.ConclusionsSerum GPC3 and PIVKA-II demonstrate clinical significance in the timely detection and prognosis assessment of N-HCC. The application of nomogram prediction models based on GPC3 and PIVKA-II stands as an important adjunctive tool for diagnosing and prognosticating N-HCC.

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.

The potential crosstalk genes and molecular mechanisms between systemic lupus erythematosus and periodontitis.

Several studies have demonstrated an increased risk of periodontitis (PD) among patients diagnosed with systemic lupus erythematosus (SLE). However, the underlying common mechanism between them remains incompletely understood. Accordingly, the aim of this study is to examine diagnostic biomarkers and potential therapeutic targets for SLE and PD by leveraging publicly accessible microarray datasets and transcriptome analysis. Datasets pertaining to SLE and PD were retrieved from the Gene Expression Omnibus (GEO) database, and subsequently analyzed for differentially expressed genes (DEGs). Key gene modules were identified through weighted gene co-expression network analysis (WGCNA), and shared genes were obtained by overlapping key genes between DEGs and WGCNA. These shared genes were subsequently subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, leading to the establishment of a Protein-Protein Interaction (PPI) network. Random forest (RF) and Least Absolute Shrinkage and Selection Operator (Lasso) regression were employed to identify key hub genes. Receiver operating characteristic (ROC) curves were generated using a new validation dataset to evaluate the performance of candidate genes. Finally, levels of immune cell infiltration in SLE and PD were assessed using CIBERSORTx. A total of 50 core genes were identified between the genes screened by WGCNA and DEGs. Functional enrichment analysis revealed that these genes are primarily associated with the PI3K-Akt and B-cell receptor signaling pathways. Additionally, using machine learning algorithms and ROC curve analysis, a total of 8 key genes (PLEKHA1, CEACAM1, TNFAIP6, TCN2, GLDC, GNG7, LY96, VCAN) were identified Finally, immune infiltration analysis highlighted the significant roles of neutrophils, monocytes, plasma cells, and gammadelta T cells (γδ T cells) in the pathogenesis of both SLE and PD. This study identifies 8 hub genes that could potentially serve as diagnostic markers for both SLE and PD, highlighting the importance of VCAN and LY96 in diagnosis. Moreover, the involvement of the PI3K-Akt signaling pathway in both diseases suggests its significant role. These identified key genes and signaling pathways lay the groundwork for deeper comprehension of the interplay between SLE and PD.

Association between the plasma atherogenic index and type 2 diabetes in Chinese population: prospective cohort study based on 4C study.

Currently, the plasma atherogenic index (AIP) is mainly used to predict atherosclerosis and cardiovascular diseases. Therefore, we aim to investigate the potential association between AIP and type 2 diabetes through a prospective cohort study. The 4C study, a multicenter prospective cohort investigation, targets the Chinese population and initially enrolled 10,008 participants. Baseline data encompassing lifestyle, metabolic status, and various other factors were collected in 2011. A 10-year follow-up survey was subsequently conducted, ultimately including 9,092 participants. AIP, defined as the logarithmic transformation of the triglycerides to high-density lipoprotein ratio, was divided into quartiles. To explore the potential association between AIP and the risk of type 2 diabetes, Cox regression, restricted cubic spline, receiver operating characteristic curve(ROC), and subgroup analysis were employed. Over a 10-year follow-up period, 693 new cases of type 2 diabetes were identified. In a fully adjusted model, AIP demonstrated a positive association with type 2 diabetes (HR: 4.40; 95% CI: 3.21, 6.04). Compared to the Q1 group, the risk of type 2 diabetes increased progressively across the Q2, Q3, and Q4 groups, with a significant trend (p-value < 0.05). Restricted cubic spline (RCS) analysis revealed an inverse L-shaped association between AIP and the risk of type 2 diabetes, with a turning point at 0.45. The ROC analysis indicates that incorporating the AIP into the base model enhances its diagnostic performance for type 2 diabetes. Furthermore, similar patterns were observed in the subgroup analyses. Among the Chinese population, elevated AIP levels are positively correlated with an increased risk of type 2 diabetes, indicating that AIP could potentially serve as a biomarker for assessing the risk of developing type 2 diabetes.