Curve Analysis Research Articles

Predictive models for cholesterol crystals and plaque vulnerability in acute myocardial infarction: Insights from an optical coherence tomography study

BackgroundCholesterol crystals (CCs) are recognized as a risk factor for vulnerable atherosclerotic plaque rupture (PR) and major adverse cardiovascular events. However, their predictive factors and association with plaque vulnerability in patients with acute myocardial infarction (AMI) remain insufficiently explored. Therefore, This study aims to investigate the association between CCs and plaque vulnerability in culprit lesions of AMI patients, identify the factors influencing CCs formation, and develop a predictive model for CCs. MethodsA total of 431 culprit lesions from AMI patients who underwent pre-intervention optical coherence tomography (OCT) imaging were analyzed. Patients were divided into groups based on the presence or absence of CCs and PR. The relationship between CCs and plaque vulnerability was evaluated. A risk nomogram for predicting CCs was developed using the least absolute shrinkage and selection operator method and logistic regression analysis. ResultsCCs were identified in 64.5 % of patients with AMI. The presence of CCs was associated with a higher prevalence of vulnerable plaque features, such as thin-cap fibroatheroma (TCFA), PR, macrophage infiltration, neovascularization, calcification, and thrombus, compared to patients without CCs. The CCs model demonstrated an area under the curve (AUC) of 0.676 for predicting PR. Incorporating CCs into the TCFA model (AUC = 0.656) significantly enhanced predictive accuracy, with a net reclassification improvement index of 0.462 (95 % CI: 0.263–0.661, p < 0.001) and an integrated discrimination improvement index of 0.031 (95 % CI: 0.013–0.048, p = 0.001). Multivariate regression analysis identified the atherogenic index of plasma (odds ratio [OR] = 2.417), TCFA (OR = 1.759), macrophage infiltration (OR = 3.863), neovascularization (OR = 2.697), calcification (OR = 1.860), and thrombus (OR = 2.430) as independent risk factors for CCs formation. The comprehensive model incorporating these factors exhibited reasonable discriminatory ability, with an AUC of 0.766 (95 % CI: 0.717–0.815) in the training set and 0.753 (95 % CI: 0.704–0.802) in the internal validation set, reflecting good calibration. Decision curve analysis suggested that the model has potential clinical utility within a threshold probability range of approximately 18 % to 85 %. ConclusionsCCs were associated with plaque vulnerability in the culprit lesions of AMI patients. Additionally, this study identified key factors influencing CCs formation and developed a predictive model with potential clinical applicability.

Exploring the potential mechanism of Radix Bupleuri in the treatment of sepsis: a study based on network pharmacology and molecular docking

AimTo explore, using network pharmacology and RNA-seq technologies, potential active targets and mechanisms underpinning Radix Bupleuri’s effectiveness during sepsis treatment.MethodsFollowing the Sepsis-3.0 criteria, the research cohort, comprising 23 sepsis patients and 10 healthy participants, was obtained from public databases. Peripheral blood samples were collected and subjected to RNA-seq analysis. Active ingredients and potential targets of Radix Bupleuri were identified using the Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine 2.0 (BATMAN-TCM 2.0) database and TCMSP database. Subsequently, protein-protein interaction (PPI) network construction, Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted to explore cross-targets between disease and drugs. Survival analysis of key targets was performed using the GSE65682 dataset, and single-cell RNA-seq was employed for cellular localization analysis of key genes. Finally, molecular docking and Molecular dynamics simulation of the core target was conducted.ResultsDifferential expression analysis revealed 4253 genes associated with sepsis. Seventy-six active components and 1030 potential targets of Radix Bupleuri were identified. PPI, GO, and pathway enrichment analyses indicated involvement in the regulation of transmembrane transport, monatomic ion transport, and MAPK signaling. Survival curve analysis identified PIK3CD, ARRB2, SUCLG1, and SPI1 as key targets associated with lower mortality in the high expression group, while higher mortality was observed in the high PNP and FURIN expression groups. Single-cell RNA sequencing unveiled the cellular localization of PIK3CD, PNP, SPI1, and FURIN within macrophages, while ARRB2 and SUCLG1 exhibited localization in both macrophages and T-cells. Subsequent molecular docking and Molecular dynamics simulation indicated a potential binding interaction for Carvone-PIK3CD, Encecalin-ARRB2, Lauric Acid-SUCLG1, Pulegone-FURIN, Nootkatone-SPI1, and Saikogenin F-PNP.ConclusionRadix Bupleuri could modulate immune function by affecting PIK3CD, ARRB2, SUCLG1, FURIN, SPI1, and PNP, thereby potentially improving the prognosis of sepsis.

Breast cancer promotes the expression of neurotransmitter receptor related gene groups and image simulation of prognosis model

Breast cancer (BC), a prevalent and severe malignancy, detrimentally affects women globally. Its prognostic implications are profoundly influenced by gene expression patterns. This study retrieved 509 BC-associated oncogenes and 1,012 neurotransmitter receptor-related genes from the GSEA and KEGG databases, intersecting to identify 98 relevant genes. Clinical and transcriptomic expression data related to BC were downloaded from the TCGA, and differential genes were identified based on an FDR value <0.05 & |log2FC| ≥ 0.585. Univariate analysis of these genes revealed that high expression of NSF and low expression of HRAS, KIF17, and RPS6KA1 are closely associated with BC survival prognosis. A prognostic model constructed for these four genes demonstrated significant prognostic relevance for BC-TCGA patients (P < 0.001). Subsequently, an immunofunctional analysis of the BC oncogene-neurotransmitter receptor-related gene cluster revealed the involvement of immune cells such as T cells CD8, T cells CD4 memory resting, and Macrophages M2. Further analysis indicated that immune functions were primarily concentrated in APC_co_inhibition, APC_co_stimulation, CCR, and Check-point, among others. Lastly, a prognostic nomogram model was established, and ROC curve analysis revealed that the nomogram is a vital indicator for assessing BC prognosis, with 1-year, 3-year, and 5-year survival rates of 0.981, 0.897, and 0.802, respectively. This model demonstrates high calibration, clinical utility, and predictive capability, promising to offer an effective preliminary tool for clinical diagnostics.

U-Net-based computed tomography quantification of viral pneumonia can predict fibrotic interstitial lung abnormalities at 3-month follow-up

BackgroundGiven the high prevalence of fibrotic interstitial lung abnormalities (ILAs) post-COVID-19, this study aims to evaluate the effectiveness of quantitative CT features in predicting fibrotic ILAs at 3-month follow-up.MethodsThis retrospective study utilized cohorts from distinct clinical settings: the training dataset comprised individuals presenting at the fever clinic and emergency department, while the validation dataset included patients hospitalized with COVID-19 pneumonia. They were classified into fibrotic group and nonfibrotic group based on whether the fibrotic ILAs were present at follow-up. A U-Net-based AI tool was used for quantification of both pneumonia lesions and pulmonary blood volumes. Receiver operating characteristic (ROC) curve analysis and multivariate analysis were used to assess their predictive abilities for fibrotic ILAs.ResultsAmong the training dataset, 122 patients (mean age of 68 years ±16 [standard deviation], 73 men), 55.74% showed fibrotic ILAs at 3-month follow-up. The multivariate analysis identified the pneumonia volume [PV, odd ratio (OR) 3.28, 95% confidence interval (CI): 1.20–9.31, p = 0.02], consolidation volume (CV, OR 3.77, 95% CI: 1.37–10.75, p = 0.01), ground-glass opacity volume (GV, OR 3.38, 95% CI: 1.26–9.38, p = 0.02), pneumonia mass (PM, OR 3.58, 95% CI: 1.28–10.46, p = 0.02), and the CT score (OR 12.06, 95% CI: 3.15–58.89, p &amp;lt; 0.001) as independent predictors of fibrotic ILAs, and all quantitative parameters were as effective as CT score (all p &amp;gt; 0.05). And the area under the curve (AUC) values were PV (0.79), GV (0.78), PM (0.79), CV (0.80), and the CT score (0.77). The validation dataset, comprising 45 patients (mean age 67.29 ± 14.29 years, 25 males) with 57.78% showing fibrotic ILAs at follow-up, confirmed the predictive validity of these parameters with AUC values for PV (0.86), CV (0.90), GV (0.83), PM (0.88), and the CT score (0.85). Additionally, the percentage of blood volume in vessels &amp;lt;5mm2 relative to the total pulmonary blood volume (BV5%) was significantly lower in patients with fibrotic ILAs (p = 0.048) compared to those without.ConclusionU-Net based quantification of pneumonia lesion and BV5% on baseline CT scan has the potential to predict fibrotic ILAs at follow-up in COVID-19 patients.

Clinical utility of novel anthropometric indices in identifying type 2 diabetes mellitus among South African adult females.

To assess the clinical utility of novel anthropometric indices and other traditional anthropometric indices in identifying the risk of type 2 diabetes mellitus (T2D) among South African adult females. In the first South African National Health and Nutrition Examination Survey (SANHANES-1), traditional [body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR)] and novel [a-body shape index (ABSI), abdominal volume index (AVI), body adiposity index (BAI), body roundness index (BRI), conicity index (CI), and Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE)] anthropometric indices were assessed. T2D was diagnosed using glycated haemoglobin (HbA1c) ≥ 6.5% among participants without known T2D. Basic statistics and multiple regression analyses were explored the association between anthropometric indices and newly diagnosed T2D. Receiver operating characteristic (ROC) curve analysis was used to measure the predictive ability of both traditional and novel indices. Among 2 623 participants, 384 (14.6%) had newly diagnosed T2D. All anthropometric indices mean values were significantly higher among participants with T2D (most p < 0.001). Higher mean values increased T2D odds e.g., in the model adjusted for age, employment, residence, and population group, odds ratio (OR) and 95% confidence interval (CI) for T2D with some of anthropometric indices were: 1.86 (1.60-2.15) for WC, 1.84 (1.59-2.13) for WHtR, 1.73 (1.51-1.99) for AVI, 1.71 (1.49-1.96) for BRI and 1.86 (1.57-2.20) for CUN-BAE. The top quartile for all indices had the highest T2D odds (p < 0.05). These outcomes were the highest for WC, AVI, and CUN-BAE and remained so even after removing the confounding effects of age, employment, population group, and residence. Based on the ROC analysis, none of the anthropometrical indices performed excellently (i.e., had an area under the curve [AUC] > 0.80). The WC, WHtR, AVI, BRI, and CUN-BAE, however, performed acceptably (AUCs 0.70-0.79), while also exhibiting corresponding cutoff values of 86.65cm, 0.57, 15.52, 3.83, and 38.35, respectively. The data shows that traditional and novel anthropometric indices similarly identifying newly diagnosed T2D among adult South African females. We recommend the continuing the use of traditional indices, as they are affordable and easy to use in our setting.

Abnormally narrow peaks in TPR-H2 over Pt/CeO2: Experiment and mathematical modelling

The application and development of methods using hydrogen as a gas for testing active surface sites is of great interest in the study of oxide catalysts. In this work a systematic study of Pt/CeO2 catalysts, depending on the platinum loading and calcination temperature was carried out using the method of temperature-programmed reduction by hydrogen (TPR-H2). Experimental TPR-H2 curves demonstrated a complex structure over a wide temperature range, including the presence of abnormally narrow consumption peaks (ANCP-H2) at low temperatures. To describe the kinetics, mathematical modeling of H2 consumption by various platinum active centers has been used, including isolated platinum ions (single atoms) and PtOx clusters of 2D and 3D structures on the surface of ceria nanoparticles. We proposed a criterion that makes it possible to extend the analysis of the TPR-H2 curves for reducible metal-supported oxide systems and unambiguously establish the action of the autocatalytic mechanism in hydrogen consumption. The kinetics of ANCP-H2 by cluster PtOx centers with a half-width of several degrees on the base of the autocatalysis mechanism was described on the base of the proposed model. The simulations show that ANCP-H2 are described by synchronous fast formation of metallic centers that provide a sharp acceleration of H2 consumption. In general, the obtained results establish the fundamental aspects of the H2 interaction with heterogeneous catalysts and are of particular interest in the light of various practical applications of hydrogen energy.

Learning curves of novice residents on cataract surgery simulator: the E3CAPS pedagogic study

IntroductionTraining novice ophthalmology residents on the EyeSi® simulator increases cataract surgery safety. However, there is no consensus regarding how much training residents should perform before their first time on patients. We evaluated the French national training program through the analysis of the learning curves of novice residents.MethodsThis prospective multicentric pedagogic study was conducted with French novice residents. Each resident completed the recommended four two-hour training sessions and performed a standardized assessment simulating standard cataract surgery before the first session (A0), at the end of the first (A1), second (A2), third (A3) and fourth (A4) sessions. For each surgical step of each attempt, the following data were collected: score, odometer, completion time, posterior capsular rupture and cumulative energy delivered (ultrasounds) during phacoemulsification. A performance threshold was set at a score of 80/100 for each surgical step, 400/500 for the overall procedure. Only descriptive statistics were employed.ResultsSixteen newly nominated ophthalmology residents were included. Median score progressively increased from 95 [IQR 53; 147]) at A0 to 425 [IQR 411; 451] at A4. Despite a significant progression, the “emulsification” step had the lowest A4 scores 86 [IQR 60; 94] without reduction in completion time, odometer or ultrasounds delivered. The rate of posterior capsular rupture decreased linearly from 75% at A0 to 13% at A4 during “emulsification” and from 69 to 0% during “irrigation and aspiration”. At A4, only 25% [8; 53] of residents had > 80 at each step and only 75% [47; 92] had > 400/500 overall.ConclusionA training program consisting of four two-hour sessions on the EyeSi simulator over four consecutive days effectively enhances the surgical skills of novice ophthalmology residents. Undergoing more training sessions may improve scores and decrease the incidence of surgical complications, particularly at the emulsification step of cataract surgery. The learning curves presented here can reassure residents who are progressing normally and help identify those who need a further personalized training program. Trial registrationClinicalTrials registration number: NCT05722080 (first submitted 22/12/2022, first posted 10/02/2023).

ПРИМЕНЕНИЕ КОЛИЧЕСТВЕННОГО МЕТОДА В ИССЛЕДОВАНИИ ОПОЛЗНЕВОЙ ВОСПРИИМЧИВОСТИ СЕЛЕОПАСНОГО БАССЕЙНА РЕКИ АГСУЧАЙ

The article uses a quantitative method to analyze dangerous landslide processes occurring in the mudflow-prone Agsuchay River basin, taking into account the active development of tourism and recreational activities in the Shamakhi-Ismayilli region. In order to identify landslide susceptibility and the potential manifestation of landslides, the “weight” of 9 factors associated with landslides was determined, including hypsometry, slope angles (slope steepness), slope exposure, geological structure (lithology), distance from faults, average annual precipitation, distance to the erosion networks, distance to roads and land use. By summing up all the landslide formation factors without exception and multiplying them by their “weight”, a map of landslide susceptibility of the mudflow-prone Agsuchay River basin was compiled. The reliability of the obtained models was assessed using the AUC ROC (area under the error curve) analysis, which showed a fairly high efficiency (up to 72 %) of the applied method.

Machine learning model outperforms the ACS Risk Calculator in predicting non-home discharge following primary total knee arthroplasty.

Despite the increase in outpatient total knee arthroplasty (TKA) procedures, many patients are still discharged to non-home locations following index surgery. The ability to accurately predict non-home discharge (NHD) following TKAs has the potential to promote a reduction in associated adverse events and excess healthcare costs. This study aimed to evaluate whether a machine learning (ML) model could outperform the American College of Surgeons (ACS) Risk Calculator in predicting NHD following TKA, using the same set of clinical variables. We hypothesised that the ML model would outperform the ACS Risk Calculator. Data from 365,240 patients who underwent a primary TKA between 2013 and 2020 were extracted from the ACS-National Surgical Quality Improvement Program database and used to develop an artificial neural network (ANN) to predict discharge disposition following primary TKA. The ANN and ACS calculator were assessed and compared using discrimination, calibration and decision curve analysis. Age (>68 years), BMI (>35.5 kg/m2) and ASA Class (≥2) were found to be the most important variables in predicting NHD following TKA. When compared to the ACS calculator, the ANN model demonstrated a significantly superior ability to distinguish the area under the receiver operating characteristic curve (AUC) among NHD patients and provided probability predictions well aligned with the true outcomes (AUCANN = 0.69, AUCACS = 0.50, p = 0.002, slopeANN = 0.85, slopeACS = 4.46, interceptANN = 0.04, and interceptACS = 0.06). Our findings support the hypothesis that machine learning models outperform the ACS Risk Calculator in predicting non-home discharge after TKA, even when constrained to the same clinical variables. Our findings underscore the potential benefits of integrating machine learning models into clinical practice for improving preoperative patient risk identification, optimisation, counselling and clinical decision-making. III.

Retrospective Analysis of Radiofrequency Ablation in Patients with Small Solitary Hepatocellular Carcinoma: Survival Outcomes and Development of a Machine Learning Prognostic Model.

The effectiveness of radiofrequency ablation (RFA) in improving long-term survival outcomes for patients with a solitary hepatocellular carcinoma (HCC) measuring 5 cm or less remains uncertain. This study was designed to elucidate the impact of RFA therapy on the survival outcomes of these patients and to construct a prognostic model for patients following RFA. This study was performed using the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2017, focusing on patients diagnosed with a solitary HCC lesion ≤5 cm in size. We compared the overall survival (OS) and cancer-specific survival (CSS) rates of these patients with those of patients who received hepatectomy, radiotherapy, or chemotherapy or who were part of a blank control group. To enhance the reliability of our findings, we employed stabilized inverse probability treatment weighting (sIPTW) and stratified analyses. Additionally, we conducted a Cox regression analysis to identify prognostic factors. XGBoost models were developed to predict 1-, 3-, and 5-year CSS. The XGBoost models were evaluated via receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA) curves and so on. Regardless of whether the data were unadjusted or adjusted for the use of sIPTWs, the 5-year OS (46.7%) and CSS (58.9%) rates were greater in the RFA group than in the radiotherapy (27.1%/35.8%), chemotherapy (32.9%/43.7%), and blank control (18.6%/30.7%) groups, but these rates were lower than those in the hepatectomy group (69.4%/78.9%). Stratified analysis based on age and cirrhosis status revealed that RFA and hepatectomy yielded similar OS and CSS outcomes for patients with cirrhosis aged over 65 years. Age, race, marital status, grade, cirrhosis status, tumor size, and AFP level were selected to construct the XGBoost models based on the training cohort. The areas under the curve (AUCs) for 1, 3, and 5 years in the validation cohort were 0.88, 0.81, and 0.79, respectively. Calibration plots further demonstrated the consistency between the predicted and actual values in both the training and validation cohorts. RFA can improve the survival of patients diagnosed with a solitary HCC lesion ≤5 cm. In certain clinical scenarios, RFA achieves survival outcomes comparable to those of hepatectomy. The XGBoost models developed in this study performed admirably in predicting the CSS of patients with solitary HCC tumors smaller than 5 cm following RFA.

Study on medical dispute prediction model and its clinical-application effectiveness based on machine learning

BackgroundMedical dispute is a global public health issue, which has been garnering increasing attention. In this study, we used machine learning (ML) method to establish a dispute prediction model and explored the clinical-application efficiency of this model in effectively reducing the occurrence of medical disputes.MethodsRetrospective study of All disputes filed by Gansu Medical Mediation Committee from 2019 to 2021 and patients with the same hospital level as that of the dispute group and hospitalization year were randomly selected as the control group in 1:1 ratio. SPSS software was used for univariate feature selection of the 14 factors that may cause disputes, and factors with statistical differences were selected. The data were divided into training and test sets in a 7:3 ratio. Six ML models were selected, and Python was used to establish a dispute prediction model. The area under the curve (AUC) of the receiver operating characteristic curve (ROC), sensitivity, specificity, accuracy, precision, average precision (AP), and F1 score were used to characterize the fitting and accuracy of the models, while decision curve analysis (DCA) was used to evaluate their clinical utility.ResultsA total of 1189 patients in the dispute and control groups were extracted. Following 11 influencing factors were selected: the inpatient department, doctor title, patient age, patient gender, patient occupation, payment method, hospitalization days, hospitalization times, discharge method, blood transfusion volume, and hospitalization espenses. Compared to other models, the AUC (0.945, 95% CI 0.913–0.981), Sensitivity (0.887), Accuracy (0.887), AP (0.834), and F1 score (0.880) of the random forest model were higher than those of other models, while the DCA curve indicated its high clinical benefits.ConclusionsInpatient department, hospitalization expenses, and discharge type are the primary influencing factors of dispute. Random forest exhibited high dispute prediction and clinical-application value and is expected to be promoted for offline dispute prediction.

Monocyte-to-Albumin Ratio Is Associated With Hematoma Expansion in Spontaneous Intracerebral Hemorrhage.

Hematoma expansion (HE) after spontaneous intracerebral hemorrhage (ICH) is a severe complication that independently predicts poor prognosis. In this study, we aimed to investigate whether monocyte-to-albumin ratio (MAR), a novel marker of systemic inflammation, could predict HE in patients with ICH. We retrospectively assessed the data of patients with ICH. The clinical, imaging, and laboratory test data including, the MAR on admission, were analyzed. A multivariate logistic regression analysis was carried out to explore the relationship between MAR and hematoma growth. The receiver operating characteristic (ROC) curve was employed to investigate the predictive value of MAR for HE after ICH. A total of 246 patients were included in the present study. Multivariate logistic regression analysis demonstrated that the MAR was associated with HE (odds ratio [OR]=1.179; 95% confidence interval, 1.093-1.272; p=0.000). ROC curve analysis showed that MAR could predict HE, with an area under the curve of 0.802 (95% CI: 0.744-0.859, p<0.001). The optimal predictive cutoff value of MAR for HE was 10.01 (sensitivity: 72.43%, specificity: 77.05%). Our results suggested that a high MAR on admission was associated with an increased risk of HE in ICH patients, and MAR can become an independent predictor of HE in ICH patients.

Diagnostic Value of Serum Apolipoprotein B100 Combined With Hippocampal Volume in Alzheimer's Disease.

To explore the diagnostic value of serum apolipoprotein B100 (Apo B100) combined with hippocampal volume in Alzheimer's disease (AD). A total of 59 AD patients and 59 healthy subjects were selected. The Mini-Mental State Examination (MMSE) was used for neuropsychological assessment. Blood glucose and serum lipid levels were detected by biochemical analyzer. Polymerase chain reaction (PCR) was used to detect apolipoprotein E (Apo E) ε3/ε4 genotypes in the plasma. Hippocampal volume was calculated using Slicer software. Independent-sample t test or Mann-Whitney U test were used to compare the levels of various indicators between the two groups. Spearman's correlation analysis was used to analyze the correlation between each level. The receiver operating characteristic curve (ROC) was plotted, and the area under the curve (AUC) was calculated to compare the diagnostic efficacy of individual and combined detection of serum Apo B100 levels and hippocampal volume in AD. Compared with the healthy control group, the levels of serum total cholesterol (TC), low-density lipoprotein (LDL), Apo B100, and plasma Apo E ε3/ε4 were higher in the AD group, and serum high-density lipoprotein (HDL) level was lower in the AD group (both p<0.05). The hippocampal volume in the AD group was lower than in the control group (p<0.01). The serum Apo B100 level was negatively correlated with MMSE score (r=-0.646), whereas hippocampal volume was positively correlated with MMSE score (r=0.630). ROC curve analysis showed that the AUC of the combined serum Apo B100 level and hippocampal volume for AD was higher than that of either alone (AUC=0.821, p<0.01). Serum Apo B100 level is elevated, and the hippocampal volume is reduced in AD patients. The combined detection of the two has a higher diagnostic efficiency for AD than other alone and has the potential to become an important indicator for the diagnosis of AD in the future.

Utility of Radiographic Parameter in Assessing Bone Density and Subsequent Fractures in Patients With Osteoporotic Vertebral Compression Fracture.

To investigate the ability of radiological parameter canal bone ratio (CBR) to assess bone mineral density and to differentiate between patients with primary and multiple osteoporotic vertebral compression fracture (OVCF). A retrospective analysis was conducted on OVCF patients treated at our hospital. CBR was measured through full-spine x-rays. Patients were categorized into primary and multiple fracture groups. Receiver operating characteristic curve analysis and area under the curve (AUC) calculation were used to assess the ability of parameters to predict osteoporosis and multiple fractures. Predictors of T values were analyzed by multiple linear regression, and independent risk factors for multiple fractures were determined by multiple logistic regression analysis. CBR showed a moderate negative correlation with dual-energy x-ray absorptiometry T values (r = -0.642, p < 0.01). Higher CBR (odds ratio [OR], -6.483; 95% confidence interval [CI], -8.234 to -4.732; p < 0.01) and lower body mass index (OR, 0.054; 95% CI, 0.023-0.086; p < 0.01) were independent risk factors for osteoporosis. Patients with multiple fractures had lower T values (mean ± standard deviation [SD]: -3.76 ± 0.73 vs. -2.83 ± 0.75, p < 0.01) and higher CBR (mean ± SD: 0.54 ± 0.07 vs. 0.46 ± 0.06, p < 0.01). CBR had an AUC of 0.819 in predicting multiple fractures with a threshold of 0.53. T values prediction had an AUC of 0.816 with a threshold of -3.45. CBR > 0.53 was an independent risk factor for multiple fractures (OR, 14.66; 95% CI, 4.97-43.22; p < 0.01). CBR is negatively correlated with bone mineral density (BMD) and can be a novel opportunistic BMD assessment method. It is a simple and effective measurement index for predicting multiple fractures, with predictive performance not inferior to T values.

End-to-end [18F]PSMA-1007 PET/CT radiomics-based pipeline for predicting ISUP grade group in prostate cancer.

To develop an end-to-end radiomics-based pipeline for the prediction of International Society of Urological Pathology grade group (ISUP GG) in prostate cancer (PCa). This retrospective study includes 356 patients (241 in training set and 115 in independent test set) with histopathologically confirmed PCa who underwent [18F]PSMA-1007 PET/CT scan. Patients were classified into two groups according to their ISUP GG (1-3 vs. 4-5). Radiomics features were extracted from the whole, automatically segmented prostate on PET/CT images, 30 models were constructed by combining 6 feature selection algorithms and 5 machine learning classifiers. The clinical model incorporated age, total prostate-specific antigen (tPSA), maximum standardized uptake value (SUVmax), and prostate volume. The predictive performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC), balanced accuracy (bAcc), and decision curve analysis (DCA). The best-performing radiomics model significantly outperformed clinical model (AUC 0.879 ± 0.041 vs. 0.799 ± 0.051, bAcc 0.745 ± 0.074 vs. 0.629 ± 0.045). On an external independent test set, best-performing radiomics model perform better than clinical model, with an AUC of 0.861 vs. 0.750, p = 0.002 (Delong), and bAcc of 0.764 vs. 0.582, p = 0.043 (McNemar). The learning curve, calibration curve and DCA demonstrated goodness-of-fit and improved benefits in clinical practice. The end-to-end radiomics-based pipeline is an effective non-invasive tool to predict ISUP GG in PCa.

Relation between peripheral blood inflammatory indices and severity of central retinal artery occlusion.

Central retinal artery occlusion (CRAO) has been identified as an acute emergency resulting in vision loss, with its pathogenesis potentially involving systemic inflammation and abnormal lipid metabolism. Over recent years, it has been established that peripheral blood inflammatory indices, including the neutrophil-to-lymphocyte ratio (NLR), the systemic immunoinflammatory index (SII), and the monocyte-to-high-density lipoprotein ratio (MHR), play significant roles in assessing systemic inflammation and lipid metabolism. However, the role of these indices in assessing the severity of CRAO has rarely been explored. This study aimd to investigate the relationship between these inflammatory indices and the severity of CRAO. This was a retrospective clinical study with a total of 49 CRAO patients and 50 age- and sex-matched controls involved. The patients with CRAO were divided into three groups (13 with incomplete CRAO, 16 with subtotal CRAO and 20 with total CRAO). Data were compared across these groups, and additionally, correlation analysis, restricted cubic spline plots, and receiver operating characteristic curve analysis were performed. The values of NLR, SII and MHR were significantly higher in the CRAO group compared to controls (NLR: 2.49(1.71,3.44) vs 1.60(1.24,1.97), P<0.001; SII: 606.46(410.25,864.35) vs 403.91(332.90,524.31), P=0.001; MHR: 0.33(0.26,0.44) vs 0.25(0.21,0.34), P<0.001). MHR was also significantly higher in total CRAO than in incomplete CRAO and subtotal CRAO (0.41(0.32,0.60) vs 0.29(0.21,0.43), P=0.036; 0.41(0.32,0.60) vs 0.29(0.23,0.38), P=0.017). Significant positive associations were found between MHR, NLR, SII and both the incidence (all P<0.001) and severity (P<0.001, P<0.001, P=0.003, respectively) of CRAO. MHR had a linear relationship with both the occurrence and severity of CRAO (P-overall=0.013, P-non-linear=0.427 and P-overall=0.013, P-non-linear=0.825). Combining MHR and NLR significantly improved diagnostic efficacy for CRAO and total CRAO, with area under the curve of 0.816 and 0.827, respectively, compared to using MHR alone (0.705 and 0.816). Elevated levels of peripheral blood NLR, SII, and MHR are positively associated with CRAO incidence, highlighting their potential as early predictive markers. The combined NLR and MHR index further enhances diagnostic accuracy and may facilitate timely assessment of CRAO severity by ophthalmologists and internists.

P-wave terminal force is related to left pulmonary vein reconnection in patients with atrial fibrillation recurrence after pulmonary vein isolation.

In patients with atrial fibrillation (AF) recurrence after pulmonary vein (PV) isolation, noninvasive markers predicting PV reconnection or PV reconnection sites have not been fully elucidated. This study investigated the relationship between the P-wave terminal force in lead V1 (PTFV1) and the PV reconnection or reconnection site in patients with AF recurrence. We retrospectively studied consecutive patients who underwent second AF ablation between April 1, 2018, and June 1, 2023. PTFV1 was investigated before the first AF ablation (pre-ablation PTFV1) and before the second AF ablation (post-ablation PTFV1). In addition, we examined the ratio of the post-ablation to pre-ablation PTFV1 (PTFV1 ratio). These values were compared between patients with and without PV reconnection, with and without left PV (LPV) reconnection, and with and without right PV (RPV) reconnection. The analysis included 56 patients. PTFV1 was reduced because of the first AF ablation. In addition, the values were more decreased in patients without PV reconnection than with PV reconnection. The PTFV1 ratio was significantly smaller in the patients without LPV reconnection than with LPV reconnection; no significant difference was observed between the patients with and without RPV reconnection. Receiver operating characteristic curve analysis showed that a PTFV1 ratio > 0.69 predicted LPV reconnection with 70.0% sensitivity and 66.7% specificity. In conclusion, the PTFV1 ratio may be a noninvasive marker predicting LPV reconnection in patients with AF recurrence.

Distal Junctional Failure After Fusion Stopping at L5 in Patients With Adult Spinal Deformity: Incidence, Risk Factors, and Radiographic Criteria.

To identify risk factors and establish radiographic criteria for distal junctional failure (DJF) in patients with adult spinal deformity (ASD), who underwent fusion surgery stopping at L5. This retrospective study was undertaken from January 2016 to December 2020. Patients with ASD who underwent fusion surgery (≥5 levels) stopping at L5 were analyzed. DJF was defined as symptomatic adjacent segment pathology at the lumbosacral junction necessitating consideration for revision surgery. Demographic data and radiographic measurements were compared between the DJF and non-DJF groups. Receiver operating characteristic curve analysis was performed to identify the radiographic cutoff value for DJF. Among 76 patients, 16 (21.1%) experienced DJF. DJF was associated with older age, antidepressant/anxiolytic medication, longer level of fusions, and worse preoperative sagittal alignment. Antidepressant/anxiolytic medication (odds ratio, 5.60) and preoperative pelvic incidence (PI)-lumbar lordosis (LL) mismatch>40° (odds ratio, 5.87) were independent risk factors for DJF. Without both factors, the incidence of DJF has been greatly reduced (9.1%). Two radiographic criteria were determined for DJF: last distal junctional angle (DJA)>-5° and Δ last DJA-post DJA>5°. When both criteria were met, the sensitivity and specificity of the DJF were 93.3% and 91.7%, respectively. Use of antidepressant/anxiolytic medication and preoperative PI-LL mismatch >40° were independent risk factors for DJF. DJF could be diagnosed using postoperative changes in the DJA. If both criteria were met, DJF could be strongly suggested.

Risk factors and predictive model for nosocomial infections by extensively drug-resistant Acinetobacter baumannii

BackgroundExtensively drug-resistant Acinetobacter baumannii (XDRAB) has become a significant pathogen in hospital environments, particularly in intensive care units (ICUs). XDRAB’s resistance to conventional antimicrobial treatments and ability to survive on various surfaces pose a substantial threat to patient health, often resulting in severe infections such as ventilator-associated pneumonia (VAP) and bloodstream infections (BSI).MethodsWe retrospectively analyzed clinical data from 559 patients with XDRAB infections admitted to Jinhua Central Hospital between January 2021 and December 2023. Patients were randomly divided into a training set (391 cases) and a testing set (168 cases). Variables were selected using Lasso regression and logistic regression analysis, and a predictive model was constructed and validated internally and externally. Model performance and clinical utility were evaluated using the Hosmer-Lemeshow test, C-index, ROC curve, decision curve analysis (DCA), and clinical impact curve (CIC).ResultsLasso regression analysis was used to screen 35 variables, selecting features through 10-fold cross-validation. We chose lambda.1se=0.03450 (log(lambda.1se)=-3.367), including 10 non-zero coefficient features. These features were then included in a multivariate logistic regression analysis, identifying 8 independent risk factors for XDRAB infection: ICU stay of 1-7 days (OR=3.970, 95%CI=1.586-9.937), ICU stay &amp;gt;7 days (OR=12.316, 95%CI=5.661-26.793), hypoproteinemia (OR=3.249, 95%CI=1.679-6.291), glucocorticoid use (OR=2.371, 95%CI=1.231-4.564), urinary catheterization (OR=2.148, 95%CI=1.120-4.120), mechanical ventilation (OR=2.737, 95%CI=1.367-5.482), diabetes mellitus (OR=2.435, 95%CI=1.050-5.646), carbapenem use (OR=6.649, 95%CI=2.321-19.048), and β-lactamase inhibitor use (OR=4.146, 95%CI=2.145-8.014). These 8 factors were used to construct a predictive model visualized through a nomogram. The model validation showed a C-index of 0.932 for the training set and 0.929 for the testing set, with a Hosmer-Lemeshow test p-value of 0.47, indicating good calibration. Furthermore, the DCA curve demonstrated good clinical decision-making performance, and the CIC curve confirmed the model’s reliable clinical impact.ConclusionRegression analysis identified ICU stay duration, hypoproteinemia, glucocorticoid use, urinary catheterization, mechanical ventilation, diabetes mellitus, carbapenem use, and β-lactamase inhibitor use as independent risk factors for XDRAB infection. The corresponding predictive model demonstrated high accuracy and stability.

Hemodynamic profiles in idiopathic intracranial hypertension: Doppler ultrasonography as a diagnostic tool for pulsatile tinnitus

Purpose: This study aims to evaluate the role of ophthalmic Doppler ultrasonography (USG) in differentiating IIH patients with and without bilateral PT by analyzing hemodynamic changes in the orbital vessels. Materials and Methods: A prospective, single-center, case-control study was conducted at Harran University from November 2023 to June 2024. The study included 20 IIH patients. Participants underwent clinical assessments, ophthalmic evaluations, brain imaging, lumbar puncture, and transorbital sonography. IIH patients were categorized into PT (n=8) and non-PT (n=12) groups. Doppler USG measured end-diastolic velocity (EDV), peak systolic velocity (PSV), time-averaged velocity (TAV), resistive index (RI), pulsatility index (PI), and systolic to the diastolic ratio (S/D) of the central retinal artery. Results: Significant differences were observed in left eye EDV and TAV, with lower values in the PT group. Although not statistically significant, right-eye EDV and TAV also trended lower in the PT group. Additionally, both eyes' PSV values showed a trend toward statistical significance, being lower in the PT group. ROC curve analysis identified optimal thresholds for left eye EDV (5.35; sensitivity 75%, specificity 83.33%) and TAV (9; sensitivity 50%, specificity 91.67%). Conclusion: Orbital Doppler USG reveals distinct hemodynamic profiles in IIH patients with PT, suggesting EDV and TAV as potential biomarkers. Larger cohort studies are needed to validate and clarify IIH-PT mechanisms.