Area Under Curve Research Articles

The value of computed tomography angiography for evaluation of left atrial enlargement in patients with persistent atrial fibrillation

BackgroundThe post-processing technology of CTA offers significant advantages in evaluating left atrial enlargement (LAE) in patients with persistent atrial fibrillation (PAF). This study aims to identify parameters for rapidly and accurately diagnosing LAE in patients with PAF using CT cross-sections.MethodsLeft atrial pulmonary venous (PV) CT was performed to 300 PAF patients with dual-source CT, and left atrial volume (LAV), left atrial anteroposterior diameter (LAD1), left atrial transverse diameter (LAD2), and left atrial area (LAA) were measured in the ventricular end systolic (ES) and middle diastolic (MD). LA index (LAI) = LA parameter/body surface area (BSA). Left atrial volume index (LAVIES) > 77.7 ml/m2 was used as the reference standard for the LAE diagnosis.Results227 patients were enrolled in the group, 101 (44.5%) of whom had LAE. LAVES and LAVMD (r = 0.983), LAVIES and LAVIMD (r = 0.984), LAAES and LAVIES (r = 0.817), LAAMD and LAVIES (r = 0.814) had strong positive correlations. The area under curve (AUC) showed that all measured parameters were suitable for diagnosing LAE, and the diagnostic efficacy was compared as follows: LAA/LAAI> LAD> the relative value index of LAD, LAD2> LAD1. LAA and LAAI demonstrated comparable diagnostic efficacy, with LAA being more readily available than LAAI.ConclusionsThe axial LAA measured by CTA can be served as a parameter for the rapid and accurate diagnosis of LAE in patients with PAF.

Utilizing machine learning algorithms for predicting risk factors for bone metastasis from right-sided colon carcinoma after complete mesocolic excision: a 10-year retrospective multicenter study

BackgroundBone metastasis (BM) occurs when colon cancer cells disseminate from the primary tumor site to the skeletal system via the bloodstream or lymphatic system. The emergence of such bone metastases typically heralds a significantly poor prognosis for the patient. This study's primary aim is to develop a machine learning model to identify patients at elevated risk of bone metastasis among those with right-sided colon cancer undergoing complete mesocolonectomy (CME).Patients and methodsThe study cohort comprised 1,151 individuals diagnosed with right-sided colon cancer, with a subset of 73 patients presenting with bone metastases originating from the colon. We used univariate and multivariate regression analyses as well as four machine learning algorithms to screen variables for 38 characteristic variables such as patient demographic characteristics and surgical information. The study employed four distinct machine learning algorithms, namely, extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and k-nearest neighbor algorithm (KNN), to develop the predictive model. Additionally, the model was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA), while Shapley additive explanation (SHAP) was utilized to visualize and analyze the model.ResultsThe XGBoost algorithm performed the best performance among the four prediction models. In the training set, the XGBoost algorithm had an area under curve (AUC) value of 0.973 (0.953–0.994), an accuracy of 0.925 (0.913–0.936), a sensitivity of 0.921 (0.902–0.940), and a specificity of 0.908 (0.894–0.922). In the validation set, the XGBoost algorithm had an AUC value of 0.922 (0.833–0.995), an accuracy of 0.908 (0.889–0.926), a sensitivity of 0.924 (0.873–0.975), and a specificity of 0.883 (0.810–0.956). Furthermore, the AUC value of 0.83 for the external validation set suggests that the XGBoost prediction model possesses strong extrapolation capabilities. The results of SHAP analysis identified alkaline phosphatase (ALP) levels, tumor size, invasion depth, lymph node metastasis, lung metastasis, and postoperative neutrophil-to-lymphocyte ratio (NLR) levels as significant risk factors for BM from right-sided colon cancer subsequent to CME.ConclusionThe prediction model for BM from right-sided colon cancer developed using the XGBoost machine learning algorithm in this study is both highly precise and clinically valuable.

Blood-based HYAL2 methylation as a potential marker for the preclinical detection of coronary heart disease and stroke

BackgroundCoronary heart disease (CHD) and stroke have become the leading cause of premature mortality and morbidity worldwide. Therefore, sensitive and accurate biomarkers for early detection of CHD and stroke are urgently needed for effective prevention and treatment. We aim to investigate the association between blood-based HYAL2 methylation and the risk of CHD and stroke in Chinese population.MethodsIn a prospective nested case–control study comprising 171 CHD cases, 139 stroke cases, who developed the diseases after recruitment and 356 controls who remained healthy during the 2.5 years of follow-up time, the methylation level of HYAL2 in the peripheral blood was quantified using mass spectrometry, and the association was calculated by logistic regression adjusted for covariant.ResultsSignificant association between HYAL2 methylation in the peripheral blood and increased risk of preclinical CHD and stroke were identified [odds ratios (ORs) per − 10% methylation: 1.35–1.64, p ≤ 0.045 for HYAL2_CpG_1, HYAL2_CpG_2 and HYAL2_CpG_3 in CHD; ORs per − 10% methylation: 0.76–1.64, p ≤ 0.033 for HYAL2_CpG_2 and HYAL2_CpG_4 in stroke]. The association in CHD was further enhanced by female gender, younger age (< 70 years old), without the history of hypertension and cancer. The combination of four HYAL2 methylation sites showed an effective discrimination of CHD and stroke cases without hypertension from controls [area under curve (AUC) = 0.78 and 0.75, respectively].ConclusionsThis study presents a strong association of altered HYAL2 methylation in peripheral blood with preclinical CHD and stroke, providing a novel biomarker for risk assessment and early detection of cardiovascular diseases.

Study on pharmacokinetic and tissue distribution of hyperin, astragalin, kaempferol-3-O-β-D-glucuronide from rats with multiple administrations of Semen Cuscutae processed with salt solution with effect of treating recurrent spontaneous abortion.

Semen Cuscutae is a traditional Chinese medicine (TCM) that tonifies the kidneys and prevents miscarriage. According to Chinese medicine theory, kidney deficiency is one of the main causes of recurrent spontaneous abortion (RSA). The previous studies showed that raw product of Semen Cuscutae (SP) and Semen Cuscutae processed with salt solution (YP) have ameliorative effects on RSA, and that YP is superior to SP. However, the active components of YP to ameliorate RSA remain unclear and require further studies. The objective of this study is to investigate the active components of YP in ameliorating RSA. First, a rat model of RSA was established using hydroxyurea in combination with mifepristone. Aqueous decoction of YP was given by gavage to rats. Second, pregnant rats were sampled on days 5, 7, 9, 10 and 12 during the modelling period. The content of Hyperin (HY), astragalin (AS) and kaempferol-3-O-β-D-glucuronide (KA) in blood and liver, heart, spleen, lung and kidney tissues were detected by liquid chromatography-mass spectrometry (LC-MS). The pharmacodynamic indicators including progesterone (P), chorionic gonadotropin β (β-HCG), estradiol (E2), tumor necrosis factor-α (TFN-α), interleukin 4 (IL-4), and tryptophan (TRP) were measured by enzyme-linked immunosorbent assay (ELISA) Pearson's correlation analysis and grey relational analysis were used to establish the relationship between the pharmacodynamic indexes and chemical constituents. The pharmacokinetic results showed that the area under curve (AUC) value of KA was the largest. The tissue distribution results showed that astragalin was widely distributed in liver, heart, spleen, lung and kidney in the RSA model rats, while HY was detected only in the uterus, and KA was detected only in the kidney. The pearson correlationl analysis showed that KA was significantly and positively correlated with the contents of E2, P, β-HCG and TRP. Both AS and HY were significantly negatively correlated with the content of TNF-α, respectively. This study reveals the pharmacokinetics and tissue distribution of KA, AS and HY in rats with RSA. It was elucidated that all three were involved in the regulation of progesterone levels and immune function. It initially revealed the mechanism of action of YP in enhancing the improvement of RSA, and it provided a theoretical basis for the quality assessment of YP.

Quantitative estimation of doxylamine succinate by zero-order derivative area under curve Spectrophotometric methods in bulk and in-tablet dosage form

The purpose of this study is to establish Zero-order UV-Spectrophotometric - absorbance and Zero Order- Area under curve (AUC) methods for estimation of Doxylamine succinate in bulk and in pharmaceutical dosage form . Doxylamine succinate is Histamine H1 antagonist with pronounced sedative properties. It is used in allergies and as an antitussive, antiemetic, and hypnotic. Doxylamine has also been administered in veterinary applications and was formerly used in parkinsonism, distilled water was used as solvent for solubilization of Doxylamine succinate. Maximum absorption for Doxylamine succinate was found to be at wavelength 260nm when dissolved in Distilled water. The methods are based upon measurement of absorbance at 260nm and integration of area under curve for analysis of Doxylamine succinate in the wavelength range of 251.20-267.20 nm. The drug followed linearity in the concentration range of 10 - 60µg/mL with correlation coefficient value r&amp;#62; 0.99 . The proposed methods were validated for accuracy (% recovery), precision, repeatability and ruggedness, as per ICH guidelines. The proposed methods were applied for qualitative and quantitative estimation of Doxylamine succinate in tablet and results were found in good agreement with the label claimed. Developed methods can be used for routine analysis of Doxylamine succinate in bulk and pharmaceutical tablet.

Neurite Orientation Dispersion and Density Imaging (NODDI) reveals white matter microstructural changes in Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) patients with cognitive impairment

PurposeThis study aimed to assess changes in white matter microstructure among patients undergoing obstructive sleep apnea hypopnea syndrome (OSAHS) complicated by cognitive impairment through neurite orientation dispersion and density imaging (NODDI), and evaluate the relationship to cognitive impairment as well as the diagnostic performance in early intervention. MethodsTotally 23 OSAHS patients, 43 OSAHS patients complicated by cognitive impairment, and 15 healthy controls were enrolled in OSA, OSACI and HC groups of this work. NODDI toolbox and FMRIB's Software Library (FSL) were used to calculate neurite density index (NDI), Fractional anisotropy (FA), volume fraction of isotropic water molecules (Viso), and orientation dispersion index (ODI). Tract-based spatial statistics (TBSS) were carried out to examine the above metrics with one-way ANOVA. This study explored the correlations of the above metrics with mini-mental state examination (MMSE), and montreal cognitive assessment (MoCA) scores. Furthermore, receiver operating characteristic (ROC) curves were plotted. Meanwhile, area under curve (AUC) values were calculated to evaluate the diagnostic performance of the above metrics. ResultsNDI, ODI, Viso, and FA were significantly different among different brain white matter regions, among which, difference in NDI showed the greatest statistical significance. In comparison with HC group, OSA group had reduced NDI and ODI, whereas elevated Viso levels. Conversely, compared to the OSA group, the OSACI group displayed a slight increase in NDI and ODI values, which remained lower than HC group, viso values continued to rise. Post-hoc analysis highlighted significant differences in these metrics, except for FA, which showed no notable changes or correlations with neuropsychological tests. ROC analysis confirmed the diagnostic efficacy of NDI, ODI, and Viso with AUCs of 0.6908, 0.6626, and 0.6363, respectively, whereas FA's AUC of 0.5042, indicating insufficient diagnostic efficacy. ConclusionsThis study confirmed that NODDI effectively reveals microstructural changes in white matter of OSAHS patients with cognitive impairment, providing neuroimaging evidence for early clinical diagnosis and intervention.

IMPLEMENTATION OF SVM-PSO IN SENTIMENT ANALYSIS OF GOOGLE PLACE REVIEW USERS AT CAFE HEADQUARTERS

Sentiment analysis plays an important role in understanding customer perceptions of businesses, allowing companies to respond more effectively to customer needs and satisfaction. This study aims to evaluate the performance of a Support Vector Machine (SVM) model optimized with Particle Swarm Optimization (PSO) in classifying the sentiment of user reviews on Markas Cafe. The dataset consists of 1,533 user reviews categorized into three sentiment classes: positive, neutral, and negative. The optimization process using PSO is used to find the optimal SVM parameters. The results showed that the SVM-PSO model achieved an accuracy of 87.7% and an Area Under Curve (AUC) of 0.85, with the best performance on positive sentiment (94.7% precision and 92.8% recall). Although the model showed good ability in detecting positive sentiments, the results for neutral and negative sentiments indicated the need for further improvement. This study confirms the effectiveness of SVM-PSO in sentiment analysis and suggests this approach can be utilized by businesses to improve marketing and customer service strategies based on user feedback.

Habitat Suitability Modeling of Endemic Genus Chimonanthus in China under Climate Change

Climate change has significantly altered in the distribution of plant and animal species, potentially resulting in either species expansion or extinction. The genus Chimonanthus, an economically significant and endemic group in China, holds substantial medicinal and ornamental value; however, its wild resources are limited. There is a paucity of knowledge regarding the distribution characteristics of the genus Chimonanthus species and the critical ecological factors influencing habitat suitability. Utilizing species distribution data and environmental variables, we performed simulations and analyses to identify suitable habitats, evaluate the primary environmental factors influencing distribution, determine potential distribution areas and centroids, and predict changes in suitable areas under three future climate scenarios (SSP-126, SSP-245, SSP-585) employing MaxEnt and ArcGIS. These findings indicated that the Area Under Curve (AUC) values exceeded 0.97 for all five Chimonanthus species, suggesting that the model predictions are highly accurate. The primary environmental variables influencing the distribution of C. grammatus are temperature, particularly isothermality (BIO3), and the mean temperature of warmest quarter (BIO10). However, precipitation of warmest quarter (BIO18) was an important environmental factor limiting the distributions of C. praecox, C. nitens, C. salicifolius and C. zhejiangensis. In the current period, the area of habitat suitability for C. praecox is the largest, at 2,498,600 km2, while that for C. zhejiangensis is the smallest, at 700,400 km2. The five Chimonanthus species are distributed mainly in southern China under the current climate scenario and will migrate to higher latitudes under future climate scenarios. C. nitens had high niche overlap and range overlap with C. zhejiangensis and C. salicifolius, respectively. A similar situation occurs between C. zhejiangensis and C. salicifolius. These findings suggest that there may be strong interspecific competition among adjacent species. Our results indicate that the establishment of nature reserves at current distribution sites within optimal areas is crucial for the conservation of germplasm resources, particularly for C. grammatus and C. salicifolius. The data generated from this research can serve as a valuable reference for the selection of in situ conservation sites, the determination of appropriate planting locations, the scientific introduction of species, and the development of long-term conservation and management strategies for Chimonanthus.

Comparison of Global Registry of Acute Coronary Events and Rapid Emergency Medicine Scores in In-Hospital Mortality of Patients Admitted to the Emergency Service and Diagnosed with Non-ST-Segment Elevation Myocardial Infarction.

Although there are many scoring systems for acute coronary syndromes, there is no suitable score for early risk stratification during initial medical contact with non-ST-elevation myocardial infarction (NSTEMI) patients. The present study compared the Rapid Emergency Medicine Score (REMS), an easy-to-use scoring system in emergency departments, with the Global Registry of Acute Coronary Events (GRACE) score used for in-hospital mortality risk stratification of NSTEMI patients. The results were: (i) the REMS score outperformed the GRACE score in predicting the in-hospital mortality; (ii) in estimating in-hospital mortality, the sensitivity of the GRACE score was 88%, the specificity was 65%, while the sensitivity of the REMS score was 100% and the specificity was 76%; (iii) the AUC (Area Under Curve) value of the REMS score (AUC 0.89) was superior to the GRACE score (AUC 0.79) in the data obtained from Receiver operating characteristic (ROC) descriptive analysis, but not statistically significant (P > .05). We suggest that the REMS score can be used to predict in-hospital mortality in patients with NSTEMI.

Establishment and validation of circulating cell-free DNA signatures for nasopharyngeal carcinoma detection

Early detection of nasopharyngeal carcinoma (NPC) poses a significant challenge. The absence of highly sensitive and specific diagnostic biomarkers for nasopharyngeal carcinoma contributes to the unfavourable prognosis of NPC patients. Here, we aimed to establish a non-invasive approach for detecting NPC using circulating cell-free DNA (cfDNA). We investigated the potential of next-generation sequencing (NGS) of peripheral blood cells as a diagnostic tool for NPC. We collected data on genome-wide nucleosome footprint (NF), 5'-end motifs, fragmentation patterns, CNV information, and EBV content from 553 Chinese subjects, including 234 NPC patients and 319 healthy individuals. Through case-control analysis, we developed a diagnostic model for NPC, and validated its detection capability. Our findings revealed that the frequencies of NF, fragmentation, and motifs were significantly higher in NPC patients compared to healthy controls. We developed an NPC score based on these parameters that accurately distinguished NPC from non-NPC cases according to the American Joint Committee on Cancer staging system from non-NPC (validation set: area under curve (AUC)=99.9% (95% CI: 99.8%-100%), se: 98.15%, sp: 100%). This model showed superior performance over plasma EBV DNA. Additionally, the NPC score effectively differentiated between NPC patients and healthy controls, even after clinical treatment. Furthermore, the NPC score was found to be independent of potential confounders such as age, sex, or TNM stage. We have developed and verified a non-invasive approach with substantial potential for clinical application in detecting NPC. A full list of funding bodies that contributed to this study can be found in Funding section.

Predictive indicators in peripheral blood and left atrium blood for left atrial spontaneous echo contrast in atrial fibrillation patients

ObjectivesThe purpose of this study was to demonstrate the discriminating predictive indicators in peripheral blood and left atrium blood for predicting the risk of left atrial spontaneous echo contrast (LASEC) in atrial fibrillation patients underwent catheter ablation.MethodsA total of 108 consecutive AF patients treated with radiofrequency ablation between July 2022 and July 2023 were enrolled and divided into two groups based on preprocedural transesophageal echocardiography: the non LASEC group (n = 71) and the LASEC group (n = 37). Circulating platelet and endothelial- derived MPs (PMPs and EMPs) in peripheral blood and left atrial blood were detected. Plasma soluble P-selectin (sP-selectin) and von Willebrand factor (vWF) were observed. Diagnostic efficiency was measured using receiver operating characteristic (ROC) curve.ResultsPeripheral sP-selectin, vWF and EMPs expressions elevated in all subjects when compared to those in left atrium blood. Levels of sP-selectin and vWF were significantly higher in peripheral blood of LASEC group than those of non LASEC group (p = 0.0018,p = 0.0271). Significant accumulations of peripheral PMPs and EMPs were documented in LASEC group by comparison with non LASEC group (p = 0.0395,p = 0.018). The area under curve(AUC) of combined PMPs and sP-selectin in predicting LASEC was 0.769 (95%CI: 0.678–0.845, sensitivity: 86.49%, specificity: 59.15%), significantly larger than PMPs or sP-selectin alone.ConclusionsExpressions of PMPs, sP-selectin, EMPs and vWF Increased in NVAF patients with LASEC and that might be potential biomarkers for LASEC prediction.

Role of Artificial intelligence model in prediction of low back pain using T2 weighted MRI of Lumbar spine

Background Low back pain (LBP), the primary cause of disability, is the most common musculoskeletal disorder globally and the primary cause of disability. Magnetic resonance imaging (MRI) studies are inconclusive and less sensitive for identifying and classifying patients with LBP. Hence, this study aimed to investigate the role of artificial intelligence (AI) models in the prediction of LBP using T2 weighted MRI image of the lumbar spine. Methods This was a prospective case-control study. A total of 200 MRI patients (100 cases and controls each) referred for lumbar spine and whole spine screening were included. The scans were performed using 3.0 Tesla MRI (United Imaging Healthcare). T2 weighted images of the lumbar spine were segmented to extract radiomic features. Machine learning (ML) models, such as random forest, decision tree, logistic regression, K-nearest neighbors, adaboost, and deep learning methods (DL), such as ResNet and GoogleNet, were used, and performance measures were calculated. Results Our study showed that Random forest and AdaBoost are the most reliable ML models for predicting LBP. Random forest showed high performance with area under curve (AUC) values from 0.83 to 0.88 across all lumbar vertebrae and L2-L3, L3-L4, and L4-L5 intervertebral discs (IVDs), with AUCs of 0.88 the highest at L5-S1 IVD (0.92). Adaboost demonstrated high performance at the L2-L5 vertebrae with AUC values of 0.82 to 0.90, with the highest AUC (0.97) at the L5-S1 IVD. Among the DL models, GoogleNet outperformed the other models at 30 epochs with an accuracy of 0.85, followed by ResNet 18 (30 epochs) with an accuracy of 0.84. Conclusion The study demonstrated that ML and DL models can effectively predict LBP from MRI T2 weighted image of the lumbar spine. ML and DL models could also enhance the diagnostic accuracy of LBP, potentially leading to better patient management and outcomes.

Application of a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram in peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancer.

This study aims to use a combined clinical prediction model based on enhanced T1-weighted image(T1WI) full volume histogram to predict preoperative peripheral nerve invasion (PNI) and lymphatic vessel invasion (LVI) in rectal cancer. We included a total of 68 PNI patients and 80 LVI patients who underwent surgical resection and pathological confirmation of rectal cancer. According to the PNI/LVI status, patients were divided into PNI positive group (n = 39), the PNI negative group (n = 29), LVI positive group (n = 48), and the LVI negative group (n = 32). External validation included a total of 42 patients with nerve and vascular invasion in patients with surgically resected and pathologically confirmed rectal cancer at another healthcare facility, with a PNI positive group (n = 32) and a PNI-negative group (n = 10) as well as an LVI positive group (n = 35) and LVI-negative group (n = 7). All patients underwent 3.0T magnetic resonance T1WI enhanced scanning. We use Firevoxel software to delineate the region of interest (ROI), extract histogram parameters, and perform univariate analysis, LASSO regression, and multivariate logistic regression analysis in sequence to screen for the best predictive factors. Then, we constructed a clinical prediction model and plotted it into a column chart for personalized prediction. Finally, we evaluate the performance and clinical practicality of the model based on the area under curve (AUC), calibration curve, and decision curve. Multivariate logistic regression analysis found that variance and the 75th percentile were independent risk factors for PNI, while maximum and variance were independent risk factors for LVI. The clinical prediction model constructed based on the above factors has an AUC of 0.734 (95% CI: 0.591-0.878) for PNI in the training set and 0.731 (95% CI: 0.509-0.952) in the validation set; The training set AUC of LVI is 0.701 (95% CI: 0.561-0.841), and the validation set AUC is 0.685 (95% CI: 0.439-0.932). External validation showed an AUC of 0.722 (95% CI: 0.565-0.878) for PNI; and an AUC of 0.706 (95% CI: 0.481-0.931) for LVI. This study indicates that the combination of enhanced T1WI full volume histogram and clinical prediction model can be used to predict the perineural and lymphovascular invasion status of rectal cancer before surgery, providing valuable reference information for clinical diagnosis.

Utilizing Radiomics of Peri-Lesional Edema in T2-FLAIR Subtraction Digital Images to Distinguish High-Grade Glial Tumors From Brain Metastasis.

Differentiating high-grade glioma (HGG) and isolated brain metastasis (BM) is important for determining appropriate treatment. Radiomics, utilizing quantitative imaging features, offers the potential for improved diagnostic accuracy in this context. To differentiate high-grade (grade 4) glioma and BM using machine learning models from radiomics data obtained from T2-FLAIR digital subtraction images and the peritumoral edema area. Retrospective. The study included 1287 patients. Of these, 602 were male and 685 were female. Of the 788 HGG patients included in the study, 702 had solitary masses. Of the 499 BM patients included in the study, 112 had solitary masses. Initially, the model was developed and tested on solitary masses. Subsequently, the model was developed and tested separately for all patients (solitary and multiple masses). Axial T2-weighted fast spin-echo sequence (T2WI) and T2-weighted fluid-attenuated inversion recovery sequence (T2-FLAIR), using 1.5-T and 3.0-T scanners. Radiomic features were extracted from digitally subtracted T2-FLAIR images in the area of peritumoral edema. The maximum relevance-minimum redundancy (mRMR) method was then used for dimensionality reduction. The naive Bayes algorithm was used in model development. The interpretability of the model was explored using SHapley Additive exPlanations (SHAP). Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant. The performance metrics include area under curve (AUC), sensitivity (SENS), and specificity (SPEC). The mean age of HGG patients was 61.4 ± 13.2 years and 61.7 ± 12.2 years for BM patients. In the external validation cohort, the model achieved AUC: 0.991, SENS: 0.983, and SPEC: 0.922. The external cohort results for patients with solitary lesions were AUC: 0.987, SENS: 0.950, and SPEC: 0.922. The artificial intelligence model, developed with radiomics data from the peritumoral edema area in T2-FLAIR digital subtraction images, might be able to differentiate isolated BM from HGG. 3 TECHNICAL EFFICACY: Stage 2.

Risk factors for complications in patients undergoing gastrointestinal endoscopy under acupuncture anesthesia.

To identify risk factors for complications in patients undergoing gastrointestinal endoscopy under acupuncture anesthesia and to construct a nomogram predictive model. This retrospective study included 292 patients who underwent gastrointestinal endoscopy under acupuncture anesthesia at the Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine from June 2020 to May 2023. Logistic regression analysis was used to identify risk factors for complications in patients undergoing gastrointestinal endoscopy under acupuncture anesthesia. A nomogram prediction model was constructed using the RMS package of R4.1.2 software based on the independent risk factors identified. The predictive performance of the model was assessed using consistency index (C-index), calibration curve, and receiver operating characteristic (ROC) curve. Seventy-five patients (25.68%) had complications. Body mass index (BMI), history of cardiovascular diseases, fasting time, history of respiratory diseases, and Sedation-Agitation Scale (SAS) score were identified as risk factors for complications. Based on this risk, a nomogram predictive model was constructed. The C-index of the nomogram model was 0.927. Calibration curve showed a good consistency between actual observations and nomogram predictions. The ROC curve area under curve (AUC) was 0.927 (95% CI: 0.895-0.959), indicating a certain predictive value for the occurrence of complications. When the optimal cut-off value was selected, the sensitivity and specificity of the model were 77.0% and 92.0%, respectively, indicating that the predictive model was effective. BMI, history of cardiovascular disease, fasting time, history of respiratory disease, and SAS score are independent risk factors for complications in patients undergoing gastrointestinal endoscopy under acupuncture anesthesia. The constructed nomogram predictive model has a good performance in predicting the occurrence of complications in patients undergoing gastrointestinal endoscopy with under acupuncture anesthesia.

Comparison of obesity indicators for predicting cardiovascular risk factors and multimorbidity among the Chinese population based on ROC analysis

To date, the best obesity-related indicators (ORIs) for predicting hypertension, dyslipidaemia, Type 2 diabetes mellitus (T2DM) and multimorbidity are still controversial. This study assessed the ability of 17 ORIs [body mass index (BMI), body fat percentage (BF%), c-index, Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE), a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), waist-hip ratio (WHR), waist-to-height ratio (WHtR), body roundness index (BRI), abdominal volume index (AVI), triglyceride glucose index (TYG), lipid accumulation product (LAP), visceral adiposity index (VAI), Chinese visceral adiposity index (CVAI), waist triglyceride index (WTI) and cardiometabolic index (CMI)] to predict hypertension, dyslipidemia, T2DM, and multimorbidity in populations aged 40–69 years. From November 2017 to December 2022, 10,432 compliant residents participated in this study. Receiver operating characteristic curves were used to assess the ability of ORIs to predict target diseases across the whole population and genders. The DeLong test was used to analyse the heterogeneity of area under curves (AUCs). Multivariable logistic regression was used to analyse the association of ORIs with hypertension, dyslipidaemia, T2DM, and multimorbidity. The prevalence of hypertension, dyslipidaemia, T2DM, and multimorbidity was 67.46%, 39.36%, 12.54% and 63.58%, respectively. After excluding ORIs associated with the target disease components, in the whole population, CVAI (AUC = 0.656), BMI (AUC = 0.655, not significantly different from WC and AVI), CVAI (AUC = 0.645, not significantly different from LAP, CMI, WHR, and WTI), and TYG (AUC = 0.740) were the best predictor of hypertension, dyslipidemia, T2DM, and multimorbidity, respectively (all P < 0.05). In the male population, BF% (AUC = 0.677), BMI (AUC = 0.698), CMI (AUC = 0.648, not significantly different from LAP and CVAI), and TYG (AUC = 0.741) were the best predictors (all P < 0.05). In the female population, CVAI (AUC = 0.677), CUN-BAE (AUC = 0.623, not significantly different from BF%, WC, WHR, WHtR, BRI and BMI), CVAI (AUC = 0.657, not significantly different from WHR), TYG (AUC = 0.740) were the best predictors (all P < 0.05). After adjusting for all covariates, all ORIs were significantly associated with hypertension, dyslipidaemia, T2DM, and multimorbidity (all P < 0.05), except for ABSI and hypertension and BAI and T2DM, which were insignificant. Ultimately, after considering the heterogeneity of prediction of ORIs among different populations, for hypertension, BF% was the best indicator for men and CVAI for the rest of the population. The best predictors of dyslipidaemia, T2DM, and multimorbidity were BMI, CVAI and TYG, respectively. Screening for common chronic diseases in combination with these factors may help to improve the effectiveness.

Typing diagnostic value of 68Ga-pentixafor PET/CT for patients with primary aldosteronism and unilateral nodules.

Our goal was to compare the lateralization of 68Ga-pentixafor PET/CT with adrenal vein sampling (AVS) in primary aldosteronism (PA) patients with unilateral lesions. We retrospectively enrolled 61 patients with PA and all patients showed unilateral nodular lesions on CT and underwent 68Ga-Pentixafor PET/CT. The general clinical data, imaging and AVS results were collected. The diagnostic efficiency of 68Ga-Pentixafor PET/CT imaging in PA patients was calculated by visual and semi-quantitative analysis to compare the consistency with AVS, and the correlation between CXCR4 express and 68Ga-Pentixafor uptake was performed. The study included 42 unilateral PA (UPA) and 19 bilateral PA (BPA). The area under curve (AUC) of 68Ga-Pentixafor PET/CT to diagnosis UPA with 10 min maximum standardized uptake value (SUVmax) > 8.17 was 0.82 ([0.70-0.90], P < 0.001), and the sensitivity and specificity were 0.64 and 0.90, respectively. The maximal AUC of 68Ga-pentixafor PET/CT for the diagnosis UPA in patients with nodules with a diameter ≥1 cm was 0.87 ([0.73-0.95],P both <0.001,[10 min SUVmax=8.17 and 10 min mean standardized uptake value (SUVmean)=5.57]), and the sensitivity and specificity were 0.73 and 0.93, respectively. Unilateral adrenalectomy and significant CXCR4 expression were present in 32 UPA, including 27 aldosterone-producing adenoma and 5 idiopathic adrenal hyperplasia. Additionally, 68Ga-pentixafor uptake in adrenal lesions was significantly correlated with CXCR4 expression, and statistical differences in 68Ga-pentixafor uptake among IRS subgroups. 68Ga-Pentixafor PET/CT can be helpful for subtyping diagnosis of PA patients with unilateral adrenal nodular, showing significant potential in non-invasive PA classification.

Radiomic Analysis of Treatment Effect for Patients with Radiation Necrosis Treated with Pentoxifylline and Vitamin E.

The combination of oral pentoxifylline (Ptx) and vitamin E (VitE) has been used to treat radiation-induced fibrosis and soft tissue injury. Here, we review outcomes and perform a radiomic analysis of treatment effects in patients prescribed Ptx + VitE at our institution for the treatment of radiation necrosis (RN). A total of 48 patients treated with stereotactic radiosurgery (SRS) had evidence of RN and had MRI before and after starting Ptx + VitE. The radiation oncologist's impression of the imaging in the electronic medical record was used to score response to treatment. Support Vector Machine (SVM) was used to train a model of radiomics features derived from radiation necrosis on pre- and 1st post-treatment T1 post-contrast MRIs that can classify the ultimate response to treatment with Ptx + VitE. A total of 43.8% of patients showed evidence of improvement, 18.8% showed no change, and 25% showed worsening RN upon imaging after starting Ptx + VitE. The median time-to-response assessment was 3.17 months. Nine patients progressed significantly and required Bevacizumab, hyperbaric oxygen therapy, or surgery. Patients who had multiple lesions treated with SRS were less likely to show improvement (p = 0.037). A total of 34 patients were also prescribed dexamethasone, either before (7), with (16), or after starting (11) treatment. The use of dexamethasone was not associated with an improved response to Ptx + VitE (p = 0.471). Three patients stopped treatment due to side effects. Finally, we were able to develop a machine learning (SVM) model of radiomic features derived from pre- and 1st post-treatment MRIs that was able to predict the ultimate treatment response to Ptx + VitE with receiver operating characteristic (ROC) area under curve (AUC) of 0.69. Ptx + VitE appears safe for the treatment of RN, but randomized data are needed to assess efficacy and validate radiomic models, which may assist with prognostication.

5-Hydroxymethylcytosines in circulating cell-free DNA as a diagnostic biomarker for nasopharyngeal carcinoma

ObjectiveTo evaluate the diagnostic value of 5-hydroxymethylcytosines (5hmC) in circulating cell-free DNA (cfDNA) for nasopharyngeal carcinoma (NPC) and to develop a diagnostic model. MethodsGenome-wide 5hmC profiles in cfDNA from 174 NPC patients and 146 non-cancer individuals were analyzed using the 5hmC-Seal technique. A cfDNA 5hmC-based diagnostic model to identify NPC patients was developed using least absolute shrinkage and selection operator (LASSO) logistic regression, and performance was evaluated with receiver operating characteristic (ROC) curves and confusion matrices. ResultsThe 5hmC-Seal data from patients with NPC showed a different genome-wide distribution than non-tumor samples. Our initial analysis revealed a 12-gene-based 5hmC marker panel to be an accurate diagnostic model effectively distinguishing between NPC samples and non-cancerous samples (training set: area under curve (AUC)= 0.97 [95 % CI: 0.94–0.99]; and test set: AUC= 0.93 [95 % CI: 0.88–0.98]) superior to EBV DNA testing. The diagnostic score performed well in differentiating the non-cancer subjects from early-stage NPC (training set: AUC=0.99 [95 % CI: 0.98–1]; test set: AUC=0.98 [95 % CI: 0.95–1]), and advanced-stage NPC (training set: AUC=0.96 [95 % CI: 0.93–0.99]; test set: AUC=0.93 [95 % CI: 0.88–0.98]). Notably, in EBV-negative patients, the diagnostic scores showed excellent capacity for distinguishing EBV-negative patients with NPC from non-cancer subjects in both the training set (AUC= 0.94 [95 % CI: 0.88–1]) and test set (AUC=0.91 [95 % CI: 0.81–1]). Conclusion5hmC modifications in cfDNA are promising noninvasive biomarkers for NPC, offering high sensitivity and specificity, particularly for early-stage and EBV-negative NPC.

Prevalence of cognitive impairment and its associated factors in middle-aged and elderly people in Anhui Province, China: An observational study.

To understand the prevalence of cognitive impairment (CI) in middle-aged and elderly individuals in Anhui Province and to develop a CI risk prediction model. From May to June 2022, a multistage, stratified cluster-sampling method was used to select 3200 middle-aged and elderly people over 45 years old in Anhui Province for a questionnaire survey, and the Chinese version of the Mini-Mental State Examination (MMSE) was used to assess cognitive function. SPSS 25.0 was used for univariate and multivariate analyses, and R software was used to establish and validate the nomogram. A total of 3059 valid questionnaires were included, of which 384 were from participants who were diagnosed with CI, and the prevalence rate was 12.6%. Multivariate logistic analysis showed that female sex, advanced age, family history, etc., were closely related to the occurrence of CI. The area under curve (AUC) values in the modeling and validation groups were 0.845 (95% CI: 0.822-0.868) and 0.868 (95% CI: 0.835-0.902), respectively, indicating that the predictive ability of the model was good. The Hosmer-Lemeshow test suggested that the model had good goodness-of-fit, and the decision-curve evaluation nomogram had a high benefit within the threshold, which had a certain clinical importance. The prevalence rate of CI among middle-aged and elderly individuals in Anhui Province was 12.6%. Female sex, elderly age, family history, low educational status, current smoking status, sleep disorders, hypertension, stroke, and diabetes were shown to be risk factors for CI, while exercise was shown to be a protective factor.