ROC Curve Analysis Research Articles

Evaluation of the analytic performance and macroprolactin sensitivity of a new prolactin immunoassay.

Prolactin measurement is essential in endocrine diagnostics. Challenges such as the hook effect and reactivity to macroprolactin, which varies according to the reagent, complicate accurate measurement. The present study evaluated a newly marketed reagent to detect prolactin, IDS Prolactin, comparing it to an established reagent, Roche Elecsys Prolactin, assessing its behavior toward macroprolactin and polyethylene glycol (PEG) treatment, and establishing reference intervals. The IDS Prolactin and Roche Elecsys Prolactin assays were compared using 44 samples containing macroprolactin confirmed on gel filtration chromatography (macroprolactin: BBPRL) and 104 samples for which the diagnosis of macroprolactin was excluded (monomeric prolactin: MNPRL). Analytic performance of the IDS Prolactin assay was also assessed. The new reagent showed satisfactory analytic performance, meeting EFLM standards for repeatability and intermediate imprecision. Comparison between the two methods found robust correlation for monomeric samples (y=1.060x-18.28; r2=0.993). Compared to the Roche assay, which is particularly low in its reaction to macroprolactin, the IDS assay displayed a higher level of detection. PEG precipitation effectively separated monomeric and macroprolactin samples when a cut-off of 65% recovery was used or at the threshold of 444 mIU/L (20.9 µg/L) for post-PEG monomeric prolactin upper limit of normal. Reference intervals were established for women, with ROC curve analysis demonstrating high sensitivity and specificity. The IDS Prolactin assay showed excellent analytic performance and satisfactory characteristics on macroprolactinemic samples.

Upregulation of Peripheral Blood NLRP3 and IL-18 in Patients With Acute Kidney Injury in Sepsis and Its Clinical Significance.

Sepsis-associated acute kidney injury (SA-AKI) is a common complication that can lead to renal failure in patients, significantly affecting the prognosis and survival of patients. In this study, we aimed to evaluate the predictive value of NOD-like receptor protein 3 (NLRP3) and interleukin 18 (IL-18) in peripheral blood mononuclear cells (PBMCs) of SA patients for the occurrence of SA-AKI. We screened AKI-related data sets using the Gene Expression Omnibus (GEO) database and identified differentially expressed genes (DEGs) associated with AKI. KEGG and GO analysis were used to identify enriched molecular functions and pathways. The study included 62 SA patients admitted to the Department of Intensive Care Medicine of our hospital from February 2021 to June 2022, including 34 non-AKI cases and 28 AKI cases, and 25 healthy volunteers were used as the control group. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect the levels of NLRP3 and IL-18 in PBMCs of the subjects. Bioinformatics analysis and experimental validation showed that the expression levels of NLRP3 and IL-18 were significantly upregulated in SA-AKI patients. In addition, the expressions of NLRP3 and IL-18 were positively correlated with APACHE II scores. ROC curve analysis revealed that NLRP3 and IL-18 have the potential to diagnose SA-AKI. This study provides preliminary evidence for NLRP3 and IL-18 as potential diagnostic biomarkers for SA-AKI.

The diagnostic validity of the cervical side bend-rotation test for C 1/2 dysfunction

ABSTRACT Introduction Neck pain and headaches are common, with a reported lifetime prevalence of up to 66%. Upper cervical segmental dysfunction has been implicated as meaningful in neck pain and multiple headache types. Several tests have been described to assess upper cervical joint dysfunction, including the flexion-rotation test (FRT), the side bend-rotation test (SBRT), and joint play assessment (PA). The purpose of this study was to determine the diagnostic validity of the SBRT to detect C1–2 dysfunction in a sample of people with medically diagnosed sinus headaches and controls. Methods Design: prospective diagnostic accuracy study, occurring during an observational case-control study in a sample of individuals with medically diagnosed sinus headaches. All participants were assessed using the SBRT, FRT, and C1–2 joint play assessments. The diagnostic accuracy of the SBRT was assessed using a reference standard of concurrent positive FRT (a loss of at least 10° from expected ROM (≤34°)) and restriction of C1–2 joint play. Cut-off scores for the SBRT were determined using ROC curve analysis, and tests of diagnostic accuracy were calculated using 2 × 2contingency tables. Results A total of 80 individuals (40 headache, 64 female, mean age 32.9 ± 13.8 yrs.) were included in the study. Mean ROM for the tests was: SBRT 31.4 ± 9.4°, FRT 44.9 ± 9.5°, and C1–2 mobility 22 hypomobile/58 normal. An SBRT cutoff score of <25° was confirmed using ROC curves. Using this cutoff score, the SBRT demonstrated 100% sensitivity and 62% specificity to detect C1–2 hypomobility. Discussion/conclusion The SBRT, using a cutoff score of ≤25°, appears to be a sensitive test to detect C1–2 dysfunction. Based on the strong sensitivity and negative predictive values, scores greater than 25° may effectively rule-out C1–2 dysfunction. The SBRT should be considered as part of a sequential clinical decision-making process when screening for C1–2 dysfunction, although further research is required to improve generalizability of these findings.

Delays in the final stages of fertilization are strongly associated with trichotomous cytokinesis and cleavage arrest.

Recent evidence showed that the phase between pronuclear fading and the first cleavage is a perilous bridge connecting the zygote and the embryo. Indeed, delay in the short interval between pronuclear breakdown (PNBD) and the first cytokinesis may result in chromosome segregation errors. We tested the hypothesis that delays in this final phase of fertilization are associated with a detrimental impact on embryo development. This is a retrospective study of 1315 zygotes cultured using time lapse technologies generated in 205 first ICSI-cycles. We observed an association between increasing times of the pronuclear fading-first cleavage interval (t2-tPNf) and the rates of trichotomous/direct unequal cleavage at the first (DUC-1) and second (DUC-2) mitotic cycle. Moreover, we observed a reduced blastulation rate. No significant associations were observed between rates of direct unequal cleavage at the third mitotic cycle (DUC-3) and top-quality blastocysts, euploidy, and live births. To evaluate whether the interval t2-tPNf could have a predictive value for the onset of DUC-1 and DUC-2, ROC curve analyses were performed. The area under the curve values obtained for DUC-1 showed a significant prediction accuracy. The best cut-offs to identify zygotes with a high risk of DUC-1 and DUC-2 occurrence were t2-tPNf > 2.78 (hours) and t2-tPNf > 2.50 (hours), respectively. Delay in the short interval between PNBD and the first cytokinesis result in trichotomous cleavage and early developmental arrest. However, if the embryos reach the blastocyst stage, rates of euploidy and live birth do not appear to be compromised.

Analysis of four long non-coding RNAs for hepatocellular carcinoma screening and prognosis by the aid of machine learning techniques

Hepatocellular carcinoma (HCC) represents a significant health burden in Egypt, largely attributable to the endemic prevalence of hepatitis B and C viruses. Early identification of HCC remains a challenge due to the lack of widespread screening among at-risk populations. The objective of this study was to assess the utility of machine learning in predicting HCC by analyzing the combined expression of lncRNAs and conventional laboratory biomarkers. Plasma levels of four lncRNAs (LINC00152, LINC00853, UCA1, and GAS5) were quantified in a cohort of 52 HCC patients and 30 age-matched controls. The individual diagnostic performance of each lncRNA was assessed using ROC curve analysis. Subsequently, a machine learning model was constructed using Python’s Scikit-learn platform to integrate these lncRNAs with additional clinical laboratory parameters for HCC diagnosis. Individual lncRNAs exhibited moderate diagnostic accuracy, with sensitivity and specificity ranging from 60 to 83% and 53–67%, respectively. In contrast, the machine learning model demonstrated superior performance, achieving 100% sensitivity and 97% specificity. Notably, a higher LINC00152 to GAS5 expression ratio significantly correlated with increased mortality risk. The integration of lncRNA biomarkers with conventional laboratory data within a machine learning framework demonstrates significant potential for developing a precise and cost-effective diagnostic tool for HCC. To enhance the model’s robustness and prognostic capabilities, future studies should incorporate larger cohorts and explore a wider array of lncRNAs.

Sanguinarine identified as a natural dual inhibitor of AURKA and CDK2 through network pharmacology and bioinformatics approaches

Cervical cancer (CA) continues to be a female malignant tumor with limited therapeutic options, resulting in a high mortality rate. Sanguinarine (SANG), a naturally occurring alkaloid, has demonstrated notable efficacy in preclinical treatment of CA. However, the mechanism through which SANG acts against CA is not fully understood. To address this, utilizing nine drug target prediction databases, we have successfully identified 379 potential targets for SANG. Venn diagram analysis compared 2367 CA-related targets from the GeneCards disease database, 2618 CA-closely related targets derived from multiple datasets in GEO through WGCNA analysis, and the 379 potential targets of SANG, resulting in 35 shared targets. Subsequently, by employing PPI network analysis, the Cytohubba plugin, the Human Protein Atlas, TCGA database data, and ROC curve analysis, we have identified AURKA and CDK2 as key targets of SANG in combating CA. Single-gene GSEA results suggest that the overexpression of AURKA and CDK2 is closely correlated with DNA replication, cell cycle progression, and various DNA repair pathways in CA. Molecular docking and molecular simulation dynamics analyses have confirmed the stable binding of both AURKA and CDK2 to SANG. In summary, by integrating diverse methodological approaches, this study discovered that SANG potentially inhibits the malignant features of CA by targeting AURKA and CDK2, thereby regulating DNA replication, cell cycle progression, and multiple DNA repair pathways. This lays a solid foundation for further exploring the pharmacological role of SANG in CA therapy. However, further in-depth in vitro and in vivo experiments are required to corroborate our findings.

Immunoinformatic predictions and characterization of Schistosoma mansoni peptides as candidates for immunodiagnostic

Schistosoma mansoni represents a significant etiological agent of schistosomiasis, a neglected tropical disease with a global distribution. Although the Kato-Katz technique is an effective diagnostic tool in areas with a high prevalence of the disease, it lacks sensitivity in regions with lower prevalence. The objective of this study was to identify and validate novel immunogenic peptide targets derived from the S. mansoni proteome. The initial set of 14,499 predicted sequences were obtained from the WormBase database, and it was filtered to 8,308 by removing proteins lacking start or stop codons, shorter than 100 amino acids, or with undetermined amino acids. The sequences were then cross-referenced for cross-reactivity and ranked based on B-cell immunogenicity, resulting in the selection of 442 peptides for synthesis and screening. Immunoblotting revealed 22 reactive peptides, with 15 exhibiting specificities for sera from individuals at the initial infection (T0) stage and five reactive to both T0 and post-treatment (30D) sera. Subsequently, 19 peptides were subjected to further validation through molecular weight assessment and synthesized for ELISA testing. The multi-peptide pool demonstrated a reactivity frequency of 54.5 % in infected individuals, which surpassed the reactivity frequencies observed for individual peptides. The six peptides exhibiting the highest reactivity were subsequently analyzed according to infection intensity. The multi-peptide pool exhibited the highest reactivity (65.2 %) in low-intensity cases. ROC curve analysis indicated that Peptide 15 demonstrated the highest sensitivity (78.79 %) and specificity (87.5 %), while the multi-peptide pool exhibited 67.65 % sensitivity and 81.82 % specificity. These findings highlight the potential of peptide-based diagnostics to enhance the detection and control of schistosomiasis.

Constructing and identifying an eighteen-gene tumor microenvironment prognostic model for non-small cell lung cancer

BackgroundThe tumor microenvironment (TME) plays a crucial role in tumorigenesis and tumor progression. This study aimed to identify novel TME-related biomarkers and develop a prognostic model for patients with non-small-cell lung cancer (NSCLC).MethodsAfter downloading and preprocessing data from The Cancer Genome Atlas (TCGA) data portal and Gene Expression Omnibus (GEO) datasets, we classified the molecular subtypes using the “NMF” R package. We performed survival analysis and quantified immune scores between clusters. A Cox proportional hazards model was then constructed, and its formula was produced. We assessed model performance and clinical utility. A prediction nomogram was also constructed and validated. Additionally, we explored the potential regulatory mechanisms of our TME gene signature using Gene Set Enrichment Analysis (GSEA).ResultsFrom data processing and univariate Cox regression analysis, 57 TME-related prognostic genes were identified, and two significantly distinct clusters were established. Using Cox regression and Lasso regression, an 18-gene TME-related prognostic model was developed. Patients were stratified into high- and low-risk groups based on the risk score, with survival analysis showing that the low-risk group had significantly better outcomes than the high-risk group (P < 0.01). ROC curve analysis demonstrated strong predictive performance, with 1-year, 3-year, and 5-year AUC values ranging from 0.654 to 0.702 across different cohorts. The model accurately predicted survival outcomes across subgroups with varying clinical features, and its predictive accuracy was validated through a nomogram.ConclusionsWe developed a prognostic model based on TME-related genes in NSCLC. Our 18-gene TME signature can effectively predict the prognosis of NSCLC with high accuracy.

HIPK3 hypomethylation as a potential epigenetic biomarker in rheumatic immune diseases with emphasis on rheumatoid arthritis.

This study aimed to explore the potential of HIPK3 DNA methylation as a biomarker for rheumatoid arthritis (RA) by examining its methylation levels in various rheumatic immune diseases and analyzing its correlation with clinical indicators. We recruited 368 participants, including patients with RA, ankylosing spondylitis (AS), GOUT, psoriatic arthritis (PSA), sjogren's syndrome (SS), systemic lupus erythematosus (SLE), dermatomyositis (DM), and healthy controls (HC), and MethylTargetTM targeted region methylation sequencing technology was employed to analyze the DNA methylation levels of HIPK3 (cg15692052). We analyzed the HIPK3 methylation levels and correlated them with common clinical indicators. Transcriptome data from the GEO dataset (GSE93272) were also analyzed to assess HIPK3 mRNA expression levels in RA patients. Statistical analyses included Spearman correlation, One-Way ANOVA, Kruskal-Wallis tests, and ROC curve analysis. HIPK3 methylation levels were significantly lower in the RA, SLE, and DM groups compared to the HC group (P < 0.05). RA subgroups based on RF and CCP statuses also showed lower HIPK3 methylation levels compared to the HC group (P < 0.05). Analysis of the GEO dataset revealed significantly elevated HIPK3 mRNA expression in RA patients (P < 0.05). A negative correlation was found between HIPK3 methylation levels and ESR (r = -0.14, P = 7.1e-3), CRP (r = -0.25, P = 4.1e-7), RF (r = -0.18, P = 5.2e-4), and DAS28-CRP (r = -0.12, P = 0.04) in the RA group. ROC analysis indicated that HIPK3 methylation levels had high diagnostic value for RA (AUC = 0.742), SLE (AUC = 0.701), and DM (AUC = 0.948), especially in distinguishing seronegative RA (AUC = 0.612, 0.747, 0.836) and differentiating RA from AS (AUC = 0.788). The study suggests that HIPK3 hypomethylation in peripheral blood is associated with RA and correlated with inflammatory markers, such as CRP. HIPK3 methylation levels have potential as a novel biomarker for RA diagnosis and predicting inflammation trends, showing particular promise in differentiating RA from other types of arthritis. Key Points • MethylTargetTM targeted region methylation sequencing technology was employed to analyze the DNA methylation levels of HIPK3 (cg15692052). • HIPK3 methylation levels have potential as a novel biomarker for RA diagnosis and predicting inflammation trends.

Development and Validation of a Frailty Risk Prediction Model for Community-Dwelling Elderly in Shanghai

Background: China is rapidly aging, increasing the burden on families, society, and public health services. The health of elderly individuals tends to deteriorate with age, and chronic conditions like frailty become more prevalent, driving up the use of healthcare services. Early screening and intervention for frailty are crucial in managing this demographic shift. While tools like the Fried Frailty Phenotype and Frailty Index assess frailty in communities, they are resource-intensive and only indicate frailty status without predicting risk or providing management recommendations. This study aims to develop a risk prediction model for frailty using real-world data, which can support the early detection of high-risk individuals in community settings. Objectives: To analyze the prevalence of frailty and its influencing factors in community-dwelling elderly, to construct a frailty risk prediction model and develop a nomogram, and to validate the model and assess its clinical utility. Methods: A cross-sectional survey of 420 elderly individuals in a Shanghai community health center was conducted (August 2022–March 2023). Data from various assessment tools were used to build a frailty prediction model through logistic regression, with validation conducted on 180 additional participants. The model’s predictive performance was evaluated using the ROC, AUC, calibration curves, and decision curve analysis (DCA). Results: The frailty prevalence was 7.4%. Independent risk factors included social support, malnutrition, fatigue, sarcopenia, reduced grip strength, and sleep duration. The prediction model achieved an AUC of 0.968 in the training set and 0.939 in the validation set, indicating high discrimination and calibration. DCA confirmed the model’s clinical utility. Conclusion: This study highlights a frailty prevalence rate of 7.4% among elderly individuals in Shanghai, with key risk factors identified. The validated frailty risk prediction model provides accurate and clinically effective frailty risk assessment, supporting targeted early interventions to prevent frailty in community settings.

Retrospective analysis of prognostic factors in HICH patients after neuroendoscopic hematoma evacuation

The objective of this study is to investigate key prognostic factors of clinical data and prognostic factors in patients with hypertensive intracerebral hemorrhage (HICH) who have undergone neuroendoscopic hematoma evacuation, specifically focusing on those with a hemorrhage volume of 20-40 mL, to identify the determinants influencing their prognosis. In this study, a total of 113 patients were ultimately included in the analysis. Variables such as age, preoperative Glasgow Coma Scale (GCS) scores, and hemorrhage locations were assessed. LASSO logistic regression was employed to select pertinent variables, which were then incorporated into a multivariate logistic regression model. The model’s performance was evaluated using ROC and calibration curves, along with clinical utility curves, and the recovery times of patients were analyzed using Kaplan–Meier curves, complemented by COX regression analysis. These three variables—Age (OR: 0.811; 95% CI 0.711–0.925), GCS score (OR: 25.923; 95% CI 4.108–163.598), and ICH location (OR: 7.345; 95% CI 1.811–29.783)—are strong predictors of intracerebral hemorrhage prognosis. Among the patients analyzed, 85.84% experienced a favorable prognosis. Younger age, higher preoperative GCS scores, and hemorrhages located in the basal ganglia and cerebral lobes were associated with better outcomes (mRS score of 0–3) . The nomogram, validated by an ROC curve analysis yielding an AUC of 0.9417 and the Hosmer–Lemeshow test, demonstrated accurate predictive and calibration capabilities for postoperative prognosis in patients with hypertensive intracerebral hemorrhage. Kaplan–Meier intervals and COX regression analysis indicated that age is a significant factor affecting the recovery time of these patients. Age, GCS score, and ICH location are significant prognostic factors for patients undergoing neuroendoscopic hematoma evacuation following hypertensive intracerebral hemorrhage, with age being a particularly important determinant of recovery time. Younger age, higher GCS scores, and lobar hemorrhage are associated with better prognosis.

Assessment of groundwater potential zones for hard rock area of sabi river basin using an integrated approach of remote sensing, GIS and AHP techniques

The study was conducted to assess groundwater potential zones (GWPZs) for hard rock area of Sabi river basin for the sustainable development. Remote Sensing (RS) and Geographic Information System (GIS) have emerged as crucial methods in retrieval, monitoring and conserving groundwater resources were used along with Analytical Hierarchy Process (AHP) to demarcate the GWPZs. In order to assess GWP in the study area, eight thematic maps: drainage density, geomorphology, geology, rainfall, land use/land cover, lineament density, slope and soil were prepared using RS and conventional data. The AHP was applied to assign the weightages for each thematic map and its sub classes. Then, the thematic layers were overlaid using the weighted overlay analysis means of ArcGIS 10.4 to assess the GWPZ map of Sabi basin. The Sabi basin was categorized into five classes of GWPZ as very good (0.01%), good (24.72%), moderate (56.54%), poor (10.45%) and very poor (8.28%). The accuracy of produced GWPZ map was assessed with well yield data by agreement scheme using ground water prospects map and by using ROC curve analysis through R-studio software which indicates the satisfactory GWPZ map prediction accuracy. This study provides valuable database in planning and development of groundwater recharging and to delineate locations of potential artificial recharge sites in the hard rock regions and semi -arid areas so that appropriate structures could be planned and constructed to manage aquifer recharge for sustainable groundwater management in the future.

Diagnostic Value of Abnormal Prothrombin for Primary Liver Cancer

Objective: To investigate the diagnostic value of abnormal serum prothrombin levels in hepatocellular carcinoma (HCC). Methods: A total of 298 patients were diagnosed with HCC at Hunan Provincial People’s Hospital between January 1, 2019, and December 31, 2024, through imaging or liver biopsy, along with 100 patients with cirrhosis, 100 patients with chronic hepatitis B virus infection, and 89 healthy controls, were included in the study. Basic demographic information, as well as levels of abnormal serum prothrombin and alpha-fetoprotein (AFP), were collected. The levels of abnormal serum prothrombin and AFP across the four groups were compared, and their diagnostic efficacy for HCC was analyzed using ROC curve analysis. Results: Abnormal prothrombin levels were significantly higher in the liver cancer group compared to the cirrhosis, hepatitis, and healthy control groups (P &lt; 0.05). No significant differences in abnormal serum prothrombin levels were observed among the cirrhosis, hepatitis, and healthy control groups (P &gt; 0.05). Serum AFP levels were significantly higher in the liver cancer group compared to the cirrhosis, hepatitis, and healthy control groups (P &lt; 0.05) and were higher in the hepatitis group compared to the cirrhosis and healthy control groups (P &lt; 0.05). However, no significant difference in AFP levels was found between the cirrhosis and healthy control groups (P &gt; 0.05). ROC curve analysis indicated that the area under the curve (AUC) for abnormal serum prothrombin and AFP in diagnosing HCC was 0.925 (95% CI: 0.901–0.949) and 0.810 (95% CI: 0.775–0.845), respectively, with sensitivities and specificities of 84% and 75% for abnormal prothrombin and 94% and 76% for AFP. For the diagnosis of AFP-negative HCC, the AUC for abnormal serum prothrombin was 0.838 (95% CI: 0.774–0.901), with a sensitivity and specificity of 65% and 95%, respectively. Conclusion: Serum abnormal prothrombin levels are highly expressed in HCC patients and demonstrate strong diagnostic efficacy for HCC.

Effect of The Duration of The Diagnosis on DNA Methylation Changes in Three Different Diabetic Genes in Type 2 Diabetes Mellitus

The primary epigenetic alteration is DNA methylation. This study examined variations in DNA methylation in patients with type 2 diabetes at various time intervals, concentrating on the Calpain-10 (CAPN10), ATP binding cassette subfamily C member 8 (ABCC8), and Transcription factor 7-like 2 (TCF7L2) genes. Since persistent diabetes can lead to aberrant methylation patterns, it is interested in how long these changes may last. The blood samples from 140 individuals with Type 2 Diabetes Mellitus (T2DM) were collected, and the patients were grouped according to how long they had been diagnosed. Four groups of individuals were created based on their time on the disease: those with T2DM &lt;1 year, those with T2DM 1-3 years, and those with T2DM 3-5 years. The genders and ages of the participants were also noted. Using the Promega technique, bisulfite conversion and DNA extraction were completed. Methylation-specific PCR amplification was used to detect DNA methylation. In the study, ROC curve analysis, Chi-square and Spearman's correlation coefficients, as well as non-parametric tests, were employed to analyze the methylation percentage variation and methylation patterns among groups. A significant threshold of p &lt; 0.05 was established. The results of the study indicated that the DNA methylation rates of T2DM patients and the control group differed significantly. Patients with type 2 diabetes, particularly those who had just received a diagnosis, showed higher levels of methylation than the control group. The study also found that the length of diagnosis may have an impact on the discriminative strength of the DNA methylation status of the ABCC8 gene, resulting in varying degrees of T2DM prediction.

Exploring acetylation-related gene markers in polycystic ovary syndrome: insights into pathogenesis and diagnostic potential using machine learning

Objective Polycystic ovary syndrome (PCOS) is a prevalent cause of menstrual irregularities and infertility in women, impacting quality of life. Despite advancements, current understanding of PCOS pathogenesis and treatment remains limited. This study uses machine learning-based data mining to identify acetylation-related genetic markers associated with PCOS, aiming to enhance diagnostic precision and therapeutic efficacy. Methods Advanced machine learning techniques were used to improve the precision of key gene identification and reveal their biological mechanisms. Validation on an independent dataset (GSE48301) confirmed their diagnostic value, assessed through ROC curves and nomograms for PCOS risk prediction. Molecular mechanisms of acetylation-related gene regulation in PCOS were further examined through clustering, immune-environmental, and gene network analyses. Results Our analysis identified 15 key acetylation-regulated genes differentially expressed in PCOS, including SGF29, NOL6, KLF15, and INO80D, which are relevant to PCOS pathogenesis. ROC curve analyses on training and validation datasets confirmed the model’s high diagnostic accuracy. Additionally, these genes were associated with immune cell infiltration, offering insights into the inflammatory aspect of PCOS. Conclusion The identified acetylation gene markers offer novel insights into the molecular mechanisms underlying PCOS and hold promise for enhancing the development of precise diagnostic and therapeutic strategies.

An observational study of ultrasound semiquantitative scoring for predicting the risk of gout flare.

An observational study was conducted to determine whether semiquantitative scoring of ultrasound signs of gout predicted flare over 12months. Gout patients were enrolled consecutively in this 12-month prospective observational single-center study. Ultrasound evaluation and clinical assessment were performed at baseline. All patients were examined bilaterally evaluating 14joints (knee, ankle, metatarsophalangeal joints 1-5) and 10tendons (posterior tibial, quadriceps, peroneus longus and brevis scored as one, patellar, and Achilles tendons). The following ultrasound features were examined and semiquantitative scoring was performed: DC sign, aggregates, tophi, bone erosion, synovial hypertrophy, PD activity, and tenosynovitis. Patients were divided into two groups, one with flares during the follow-up period and the other without flares. Atotal of 119 participants completed the study; 61(51.3%) participants experienced at least one flare over 12months, with amedian of 2.0flares. The ultrasound findings indicative of DC sign, aggregate, tophi, bone erosion, and PD activity at baseline were significantly correlated with the development of gout flares over 12months. Logistic regression analysis suggested that DC sign score (OR:2.41, 95% CI: 0.92-4.37; P = 0.02), tophi score (OR:1.87, 95% CI: 0.65-2.28; P = 0.04), and PD activity score (OR:1.93, 95% CI: 0.58-3.26; P = 0.03) were independent predictors of flare. ROC curve analysis to assess ultrasound semiquantitative scoring has good sensitivity and specificity for the prediction of gout flares. Ultrasound semiquantitative scoring can predict the risk of flare, and ultrasound findings indicative of DC sign, tophi, and PD activity are independent predictors of gout flares over 12months.

The predictive value of CK-MB on the curative effect of urokinase combined with LMWH calcium in AMI.

This study investigates the predictive value of CK-MB in treating acute myocardial infarction (AMI) with urokinase combined with low molecular weight heparin (LMWH) calcium. AMI patients treated at our hospital from April 2020 to August 2022 were included in this prospective, multi-center cohort study. Patients were randomly assigned to a urokinase group or a combined treatment group (urokinase plus LMWH). CK-MB levels were measured using ELISA, and adverse reactions were recorded. The accuracy of CK-MB as a predictor was evaluated via ROC curve analysis and AUC. Before treatment, CK-MB levels were similar in both groups (p > 0.05). Post-treatment, levels were significantly lower in the combined group (p < 0.05). The combined group had a higher proportion of mild CK-MB elevation and lower moderate/severe elevations (p < 0.05). Adverse reaction rates were similar (p > 0.05). The combined treatment group showed higher effectiveness (p < 0.05), with better sensitivity (94%) and specificity (82%) than the urokinase group (sensitivity 89%, specificity 78%). CK-MB is a valuable predictor for evaluating the efficacy of urokinase plus LMWH in AMI treatment, offering advantages in reducing CK-MB levels and improving outcomes.

Association of serum HIF-1α levels with coronary artery calcification in patients of non-dialysis chronic kidney disease

Vascular calcification is the main cause of death in patients with chronic kidney disease (CKD). The study aims to investigate the risk factor affecting coronary artery calcification (CAC), and the relationship between serum hypoxia-inducible factor-1α (HIF-1α) levels and CAC in non-dialysis CKD patients. One hundred and twenty-three patients were selected in the final analysis. Spearman rank correlation analysis assessed the correlation between HIF-1α levels and CAC scores. Logistic regression analysis was utilized to assess potential risk variables associated with CAC. A ROC curve was drawn to analyze the diagnostic value of HIF-1α in non-dialysis CKD patients with CAC. Compared to the non-CAC group, the CAC group was older age, with a higher proportion of males, smokers, hypertension and diabetes, lower eGFR and HDL-C, and higher SBP and HIF-1α levels. As the HIF-1α levels went up, the proportion of patients with VC increased, as well as Agatston scores. Spearman rank correlation analysis showed that HIF-1α levels were positively correlated with VC and Agatston scores. Logistic regression analysis indicated that being male (OR = 0.242, P = 0.036), older age (OR = 1.093, P < 0.001), and increased HIF-1α levels (OR = 1.002, P = 0.025) were risk factors for the occurrence of CAC. Furthermore, ROC curve analysis determined that a HIF-1α cutoff value of 451.177 pg·mL-1 provided the best diagnostic value for non-dialysis CAC, with a sensitivity of 65.8% and a specificity of 80.9%. The prevalence of CAC is high in non-dialysis CKD patients, with old age and male gender being traditional risk factors for CAC occurrence. HIF-1α positively correlate with both CAC and VC, which can provide certain diagnostic values for non-dialysis CAC.

Effectiveness of Systemic Immune-Inflammatory Index in Predicting Bone Metastasis in Newly Diagnosed Prostate Cancer Patients

Objective To explore the effectiveness of systemic immune inflammation index (Systemic immune-inflammation index, SII) in predicting bone metastasis in patients with newly diagnosed prostate cancer (Prostatic cancer, PCa). Methods The retrospective analysis of 200 cases of prostate cancer from January 2020 to June 2023 and 70 cases of bone metastasis compared the expression difference of SII between the two groups and analyzed the clinical diagnostic value of SII in prostate cancer bone metastasis. The resulting SII in the bone metastasis group was 500.63(351.24-696.57) 109/ L, the SII in the no-bone metastasis group was 350.12(258.41-529.40) 109/ L, statistically significant, P &lt;0.05. The ROC curve analysis revealed that its sensitivity was 68.63% and its specificity was 60.95%. Conclusion System immune inflammation index (SII) has certain application value for bone metastasis in patients with newly diagnosed prostate cancer (PCa), and high level of SII indicates poor prognosis, which is worth further clinical study.

Prognostic Prediction Model for Endovascular Treatment of Acute Anterior Circulation Ischemic Stroke Based on RAPID Software

To construct a universally applicable nomogram using computed tomography perfusion (CTP) parameters based on Rapid processing of PerfusIon and Diffusion (RAPID) software for evaluating the prognosis of acute anterior circulation large vessel occlusion (LVO) treated by endovascular therapy. A total of 130 patients were divided into two groups: a group with a good prognosis [Rankin Rating Scale (mRS) score ≤ 2 points] and a group with a poor prognosis (mRS score >2 points). The least absolute shrinkage and selection operator (LASSO) logistic regression method was used to screen the predictive factors, and a nomogram chart prediction model was established. There were 52 patients in the good prognosis group and 78 patients in the poor prognosis group. Logistic regression analysis showed that age, baseline National Institute of Health Stroke Scale score, and volumes of cerebral blood flow (VCBF)<30% were independent influencing factors for the prognosis of patients [OR=1.065, 95% CI: 1.007-1.126; OR=1.097, 95% CI: 1.008-1.193; OR=1.107, 95% CI: 1.024-1.197; P all<0.05]. Our nomogram model showed good discrimination and calibration. The ROC curve analysis showed that the area under the curve of the prediction model was 0.809 (95% CI: 0.733-0.885, P<0.001), and the calibration curve was close to the ideal curve. Our nomogram can be used as a simple and reasonable tool to predict the prognosis of acute anterior circulation LVO treated by endovascular therapy. The CTP parameter VCBF<30% obtained by RAPID software was an important predictive factor in this model.