Discriminatory Capability Research Articles

Rapid and Accurate Detection of Marssonina coronariae in Apple Trees using Loop-Mediated Isothermal Amplification (LAMP)

Marssonina blotch of apple is a well-known plant disease caused by Marssonina coronariae, which can cause severe economic consequences. Due to the importance of early diagnosis for effective plant disease management, we aimed to develop a loop-mediated isothermal amplification (LAMP) assay that could rapidly detect M. coronariae in apple plants. The ribosomal DNA internal transcribed spacer (rDNA-ITS) sequence of M. coronariae was selected as the target for primer design. Our results showed optimal conditions for the LAMP reaction at 62℃ for 50 min, as indicated by color change and gel electrophoresis. The LAMP assay demonstrated specific discriminatory capability in differentiating M. coronariae from other pathogenic fungi in apple plants. In addition, the sensitivity tests revealed a detection limit of 100 fg μL-1 genomic DNA and 100 spores of M. coronariae for the LAMP assay. Finally, we successfully applied the LAMP assay to detect M. coronariae in apple leaf samples from the field. In general, our study provided a straightforward and efficient method for rapid diagnosis of apple blotch caused by M. coronariae, which could be applied in field condition and early occurrence of disease caused by M. coronariae could be detected.

Prediction algorithm for gastric cancer in a general population: A validation study.

Worldwide, gastric cancer is a leading cause of cancer incidence and mortality. This study aims to devise and validate a scoring system based on readily available clinical data to predict the risk of gastric cancer in a large Chinese population. We included a total of 6,209,697 subjects aged between 18 and 70 years who have received upper digestive endoscopy in Hong Kong from 1997 to 2018. A binary logistic regression model was constructed to examine the predictors of gastric cancer in a derivation cohort (n = 4,347,224), followed by model evaluation in a validation cohort (n = 1,862,473). The algorithm's discriminatory ability was evaluated as the area under the curve (AUC) of the mathematically constructed receiver operating characteristic (ROC) curve. Age, male gender, history of Helicobacter pylori infection, use of proton pump inhibitors, non-use of aspirin, non-steroidal anti-inflammatory drugs (NSAIDs), and statins were significantly associated with gastric cancer. A scoring of ≤8 was designated as "average risk (AR)". Scores at 9 or above were assigned as "high risk (HR)". The prevalence of gastric cancer was 1.81% and 0.096%, respectively, for the HR and LR groups. The AUC for the risk score in the validation cohort was 0.834, implying an excellent fit of the model. This study has validated a simple, accurate, and easy-to-use scoring algorithm which has a high discriminatory capability to predict gastric cancer. The score could be adopted to risk stratify subjects suspected as having gastric cancer, thus allowing prioritized upper digestive tract investigation.

Distinct clusters of movement entropy in children's exploration of a virtual reality balance beam.

Although assessing motor competence is vital to advancing current understandings of motor development and its significance in various fields, no consensus exists on how the construct should be operationalised and measured. Existing approaches to assessing motor competence in children typically involve applying qualitative and/or quantitative scoring procedures in which children's performance is evaluated according to certain levels of assessment-specific task performance dependent upon predefined sets of instructions and procedures. Building upon ecological dynamics as a framework, different levels of motor competence can be identified in children's attempts to coordinate their degrees of freedom while trying to complete the interactive task and environmental constraints. Given the dynamic, nonlinear features of that coordinating process, assessments need to consider the inherit structure of inter- and intra-individual variability in patterns of movement. Against that background, we investigated 7-10-year-old children's (n = 58) whole-body joint kinematics as they freely explored a balance beam in a virtual reality playground. Specifically, we used exploratory cluster analysis to examine the discriminatory capability of utilising joint-specific sample entropy as a window into individual differences in movement coordination that emerged from children's exploration of the constraints embedded in the virtual task. Among the results, three clusters of children with distinct profiles of movement variability emerged, all of which showed heterogeneous levels of repeatability in joint movements in combination with the level of spatiotemporal exploration on the balance beam that could not be explained by between-cluster differences in age and gender distributions. Those findings suggest that entropy from whole-body movements can be used to cluster children into distinct groups with different profiles regarding the structure of movement variability, which can inform new understandings and the development of gross motor competence assessments for children.

Development and validation of a nomogram for predicting pulmonary complications after video-assisted thoracoscopic surgery in elderly patients with lung cancer.

Postoperative pulmonary complications (PPCs) significantly increase the morbidity and mortality in elderly patients with lung cancer. Considering the adverse effects of PPCs, we aimed to derive and validate a nomogram to predict pulmonary complications after video-assisted thoracoscopic surgery in elderly patients with lung cancer and to assist surgeons in optimizing patient-centered treatment plans. The study enrolled 854 eligible elderly patients with lung cancer who underwent sub-lobectomy or lobectomy. A clinical prediction model for the probability of PPCs was developed using univariate and multivariate analyses. Furthermore, data from one center were used to derive the model, and data from another were used for external validation. The model's discriminatory capability, predictive accuracy, and clinical usefulness were assessed using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis, respectively. Among the eligible elderly patients with lung cancer, 214 (25.06%) developed pulmonary complications after video-assisted thoracoscopic surgery. Age, chronic obstructive pulmonary disease, surgical procedure, operative time, forced expiratory volume in one second, and the carbon monoxide diffusing capacity of the lung were independent predictors of PPCs and were included in the final model. The areas under the ROC curves (AUC) of the training and validation sets were 0.844 and 0.796, respectively. Ten-fold cross-validation was used to evaluate the generalizability of the predictive model, with an average AUC value of 0.839. The calibration curve showed good consistency between the observed and predicted probabilities. The proposed nomogram showed good net benefit with a relatively wide range of threshold probabilities. A nomogram for elderly patients with lung cancer can be derived using preoperative and intraoperative variables. Our model can also be accessed using the online web server https://pulmonary-disease-predictor.shinyapps.io/dynnomapp/. Combining both may help surgeons as a clinically easy-to-use tool for minimizing the prevalence of pulmonary complications after lung resection in elderly patients.

Early detection of Parkinson's disease: Systematic analysis of the influence of the eyes on quantitative biomarkers in resting state electroencephalography

While resting state electroencephalography (EEG) provides relevant information on pathological changes in Parkinson's disease, most studies focus on the eyes-closed EEG biomarkers. Recent evidence has shown that both eyes-open EEG and reactivity to eyes-opening can also differentiate Parkinson's disease from healthy aging, but no consensus has been reached on a discriminatory capability benchmark. The aim of this study was to determine the resting-state EEG biomarkers suitable for real-time application that can differentiate Parkinson's patients from healthy subjects under both eyes closed and open. For this, we analysed and compared the quantitative EEG analyses of 13 early-stage cognitively normal Parkinson's patients with an age and sex-matched healthy group. We found that Parkinson's disease exhibited abnormal excessive theta activity in eyes-closed, which was reflected by a significantly higher relative theta power, a higher time percentage with a frequency peak in the theta band and a reduced alpha/theta ratio, while Parkinson's patients showed a significantly steeper non-oscillatory spectral slope activity than that of healthy subjects. We also found considerably less alpha and beta reactivity to eyes-opening in Parkinson's disease plus a significant moderate correlation between these EEG-biomarkers and the MDS-UPDRS score, used to assesses the clinical symptoms of Parkinson's Disease. Both EEG recordings with the eyes open and reactivity to eyes-opening provided additional information to the eyes-closed condition.We thus strongly recommend that both eyes open and closed be used in clinical practice recording protocols to promote EEG as a complementary non-invasive screening method for the early detection of Parkinson's disease, which would allow clinicians to design patient-oriented treatment and improve the patient's quality of life.

The number of metastatic lymph nodes is more predictive of prognosis than location-based N stage for nonsmall cell lung cancer: a retrospective cohort study.

The eighth edition of nodal classification is defined only by the anatomical location of metastatic lymph nodes and has limited prognostic discrimination power. The authors aimed to evaluate the prognostic significance and discriminatory capability of the number of metastatic lymph nodes (nN) in resected nonsmall cell lung cancer. Patients with stage IA to IIIB resected nonsmall cell lung cancer between 1 January 2009 and 31 December 2013 were analyzed as a Chinese cohort. The optimal thresholds for the nN classification were determined by the X-tile. The receiver operating characteristic curve, net reclassification improvement and standardized net benefit calculated by decision curve analysis was estimated to quantify the nN classification's performance in prognostic stratification. External validation in the surveillance, epidemiology, and end results database was performed to test the robustness of the nN classification. Both cohorts showed a stepwise prognosis deterioration with increasing nN. One to three, four to six, and more than six were selected as optimal thresholds of nN classification in the Chinese cohort, which included 4432 patients, then validated in the SEER cohort ( n =28022 patients). Multivariate Cox analysis showed the nN classification was an independent predictive factor for overall survival in both cohorts (Chinese cohort and SEER cohort: N 0 vs. N 1-3 , P <0.001; N 0 vs. N 3-6 , P <0.001; N 0 vs. N >6 , P <0.001). And prognostic discriminatory capability was improved in the nN classification compared with location-based N classification [5-year NRI score, 0.106 (95% CI: 0.049-0.132) and 5-year time-independent AUC, 0.593 (95% CI: 0.560-0.625) vs. 0.554 (95% CI: 0.520-0.588), P <0.001]. The nN classification tended to be a superior prognostic indicator than the location-based N classification. The number of metastatic lymph nodes should be considered in the future revision of the TNM system.

Conjunctive use of mineralogy and elemental composition for empirical forensic provenancing of topsoil from Canberra, Australia

The capability to spatially triage geographical areas as low and high interest has the potential to provide valuable information as forensic intelligence to law enforcement operations, and related provenancing applications. Among others, our previously published work has largely been based on the elemental composition of topsoil samples, omitting other potentially useful compositional characteristics, such as mineralogy, that have proven valuable in forensic casework discriminations. In this contribution, a total of 334 topsoil (0–5 cm sampling depth; 0–75 µm fraction) samples collected from the Canberra region in Australia, were selected from a larger collection (n = 685) and their bulk mineralogy determined using X-ray powder diffraction (XRPD). Utilising an existing casework technique for discriminating soils by mineralogy, a total of twelve diagnostic peaks were selected representing commonly occurring minerals. Peak intensities were normalised relative to the sum of their intensities and used to create an indicative mineralogy dataset for the study region. Based on an existing algorithm for assigning investigative analytical similarities from overlapping areas between two Cauchy distributions, the provenance was estimated for thirteen blind topsoil samples. Provenance maps based on the mineralogy were subsequently combined with earlier elemental-based predictions, incorporating contrasting discriminatory capabilities from both techniques. Results indicate the mineralogical component of topsoils can provide accurate provenance predictions, and when combined with those based on the elemental composition, can further delineate areas as low interest that otherwise would not necessarily be differentiated from one technique alone.

Risk prediction of bladder cancer among person with diabetes: A derivation and validation study.

This study aimed to devise and validate a clinical scoring system for risk prediction of bladder cancer to guide urgent cystoscopy evaluation among people with diabetes. People with diabetes who received cystoscopy from a large database in the Chinese population (2009-2018). We recruited a derivation cohort based on random sampling from 70% of all individuals. We used the adjusted odds ratios (aORs) for independent risk factors to devise a risk score, ranging from 0 to 5: 0-2 'average risk' (AR) and 3-5 'high risk' (HR). A total of 5905 people with diabetes, among whom 123 people with BCa were included. The prevalence rate in the derivation (n = 4174) and validation cohorts (n = 1731) was 2.2% and 1.8% respectively. Using the scoring system constructed, 79.6% and 20.4% in the derivation cohort were classified as AR and HR respectively. The prevalence rate in the AR and HR groups was 1.57% and 4.58% respectively. The risk score consisted of age (18-70: 0; >70: 2), male sex (1), ever/ex-smoker (1) and duration of diabetes (≥10 years: 1). Individuals in the HR group had 3.26-fold (95% CI = 1.65-6.44, p = 0.025) increased prevalence of bladder than the AR group. The concordance (c-) statistics was 0.72, implying a good discriminatory capability of the risk score to stratify high-risk individuals who should consider earlier cystoscopy. The risk prediction algorithm may inform urgency of cystoscopy appointments, thus allowing a more efficient use of resources and contributing to early detection of BCa among people planned to be referred.

LC/MS-based untargeted lipidomics reveals lipid signatures of nonpuerperal mastitis

BackgroundNonpuerperal mastitis (NPM) is a disease that presents with redness, swelling, heat, and pain during nonlactation and can often be confused with breast cancer. The etiology of NPM remains elusive; however, emerging clinical evidence suggests a potential involvement of lipid metabolism.MethodLiquid chromatography‒mass spectrometry (LC/MS)-based untargeted lipidomics analysis combined with multivariate statistics was performed to investigate the NPM lipid change in breast tissue. Twenty patients with NPM and 10 controls were enrolled in this study.ResultsThe results revealed significant differences in lipidomics profiles, and a total of 16 subclasses with 14,012 different lipids were identified in positive and negative ion modes. Among these lipids, triglycerides (TGs), phosphatidylethanolamines (PEs) and cardiolipins (CLs) were the top three lipid components between the NPM and control groups. Subsequently, a total of 35 lipids were subjected to screening as potential biomarkers, and the chosen lipid biomarkers exhibited enhanced discriminatory capability between the two groups. Furthermore, pathway analysis elucidated that the aforementioned alterations in lipids were primarily associated with the arachidonic acid metabolic pathway. The correlation between distinct lipid populations and clinical phenotypes was assessed through weighted gene coexpression network analysis (WGCNA).ConclusionsThis study demonstrates that untargeted lipidomics assays conducted on breast tissue samples from patients with NPM exhibit noteworthy alterations in lipidomes. The findings of this study highlight the substantial involvement of arachidonic acid metabolism in lipid metabolism within the context of NPM. Consequently, this study offers valuable insights that can contribute to a more comprehensive comprehension of NPM in subsequent investigations.Trial registrationShuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine (Number: 2019-702-57; Date: July 2019).

Prognostic nomogram for predicting lower extremity deep venous thrombosis in ruptured intracranial aneurysm patients who underwent endovascular treatment.

Lower extremity deep vein thrombosis (DVT) is one of the major postoperative complications in patients with ruptured intracranial aneurysms (RIA) who underwent endovascular treatment (EVT). However, patient-specific predictive models are still lacking. This study aimed to construct and validate a nomogram model for estimating the risk of lower extremity DVT for RIA patients who underwent EVT. This cohort study enrolled 471 RIA patients who received EVT in our institution between 1 January 2020 to 4 February 2022. Perioperative information on participants is collected to develop and validate a nomogram for predicting lower extremity DVT in RIA patients after EVT. Predictive accuracy, discriminatory capability, and clinical effectiveness were evaluated by concordance index (C-index), calibration curves, and decision curve analysis. Multivariate logistic regression analysis showed that age, albumin, D-dimer, GCS score, middle cerebral artery aneurysm, and delayed cerebral ischemia were independent predictors for lower extremity DVT. The nomogram for assessing individual risk of lower extremity DVT indicated good predictive accuracy in the primary cohort (c-index, 0.92) and the validation cohort (c-index, 0.85), with a wide threshold probability range (4-82%) and superior net benefit. The present study provided a reliable and convenient nomogram model developed with six optimal predictors to assess postoperative lower extremity DVT in RIA patients, which may benefit to strengthen the awareness of lower extremity DVT control and supply appropriate resources to forecast patients at high risk of RIA-related lower extremity DVT.

An increase in calculated small dense low-density lipoprotein cholesterol predicts new onset of hypertension in a Japanese cohort.

A disorder of lipid metabolism is involved in cardiovascular diseases including hypertension. A high level of small dense low-density lipoprotein cholesterol (sdLDL-C) is a strong risk factor for atherosclerotic cardiovascular disease. However, the association between sdLDL-C and hypertension has not been fully investigated. We investigated the associations between the development of hypertension during a 10-year period and levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), non-HDL-C, triglycerides (TG), and LDL-C and sdLDL-C calculated by using the Sampson equations in 28,990 Japanese subjects who received annual health examinations. After exclusion of subjects with missing data, those with hypertension, and those with TG ≥ 800 mg/dL at baseline, a total of 15,177 subjects (men/women: 9374/5803, mean age: 46 years) were recruited. During the 10-year period, 2379 men (25.4%) and 724 women (12.5%) had new onset of hypertension. Multivariable Cox proportional hazard model analyses showed that levels of HDL-C, non-HDL-C, TG and sdLDL-C, but not levels of TC and LDL-C, were independent risk factors for the development of hypertension after adjustment of age, sex, family history of hypertension, systolic blood pressure, obesity, current smoking habit, alcohol drinking habit, estimated glomerular filtration rate, diagnosis of diabetes mellitus and use of lipid-lowering drugs and that the adjusted risk of sdLDL-C (per 1-standard deviation) was highest (hazard ratio [95% confidence interval: 1.09 [1.05-1.13]). The addition of sdLDL-C to traditional risk factors for hypertension significantly improved the discriminatory capability, which was better than that of other lipid fractions. In conclusion, a high level of calculated sdLDL-C predicts the development of hypertension.

Coexistence of Metabolic Dysfunction-Associated Fatty Liver Disease and Chronic Kidney Disease Is a More Potent Risk Factor for Ischemic Heart Disease.

Background Metabolic dysfunction-associated fatty liver disease (MAFLD), defined as fatty liver with overweight/obesity, type 2 diabetes, or metabolic abnormalities, is a newly proposed disease. However, it remains unclear whether the coexistence of MAFLD and chronic kidney disease (CKD) is a more potent risk factor for ischemic heart disease (IHD). Methods and Results We investigated the risk of the combination of MAFLD and CKD for development of IHD during a 10-year follow-up period in 28 990 Japanese subjects who received annual health examinations. After exclusion of subjects without data for abdominal ultrasonography or with the presence of IHD at baseline, a total of 14 141 subjects (men/women: 9195/4946; mean age, 48 years) were recruited. During the 10-year period (mean, 6.9 years), 479 subjects (men/women, 397/82) had new onset of IHD. Kaplan-Meier survival curves showed significant differences in rates of the cumulative incidence of IHD in subjects with and those without MAFLD (n=4581) and CKD (n=990; stages 1/2/3/4-5, 198/398/375/19). Multivariable Cox proportional hazard model analyses showed that coexistence of MAFLD and CKD, but not MAFLD or CKD alone, was an independent predictor for development of IHD after adjustment for age, sex, current smoking habit, family history of IHD, overweight/obesity, diabetes, hypertension, and dyslipidemia (hazard ratio, 1.51 [95% CI, 1.02-2.22]). The addition of the combination of MAFLD and CKD to traditional risk factors for IHD significantly improved the discriminatory capability. Conclusions The coexistence of MAFLD and CKD predicts new onset of IHD better than does MAFLD or CKD alone.

Development and validation of a predictive model for spinal fracture risk in osteoporosis patients.

Spinal osteoporosis is a prevalent health condition characterized by the thinning of bone tissues in the spine, increasing the risk of fractures. Given its high incidence, especially among older populations, it is critical to have accurate and effective predictive models for fracture risk. Traditionally, clinicians have relied on a combination of factors such as demographics, clinical attributes, and radiological characteristics to predict fracture risk in these patients. However, these models often lack precision and fail to include all potential risk factors. There is a need for a more comprehensive, statistically robust prediction model that can better identify high-risk individuals for early intervention. To construct and validate a model for forecasting fracture risk in patients with spinal osteoporosis. The medical records of 80 patients with spinal osteoporosis who were diagnosed and treated between 2019 and 2022 were retrospectively examined. The patients were selected according to strict criteria and categorized into two groups: Those with fractures (n = 40) and those without fractures (n = 40). Demographics, clinical attributes, biochemical indicators, bone mineral density (BMD), and radiological characteristics were collected and compared. A logistic regression analysis was employed to create an osteoporotic fracture risk-prediction model. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the model's performance. Factors significantly associated with fracture risk included age, sex, body mass index (BMI), smoking history, BMD, vertebral trabecular alterations, and prior vertebral fractures. The final risk-prediction model was developed using the formula: (logit [P] = -3.75 + 0.04 × age - 1.15 × sex + 0.02 × BMI + 0.83 × smoking history + 2.25 × BMD - 1.12 × vertebral trabecular alterations + 1.83 × previous vertebral fractures). The AUROC of the model was 0.93 (95%CI: 0.88-0.96, P < 0.001), indicating strong discriminatory capabilities. The fracture risk-prediction model, utilizing accessible clinical, biochemical, and radiological information, offered a precise tool for the evaluation of fracture risk in patients with spinal osteoporosis. The model has potential in the identification of high-risk individuals for early intervention and the guidance of appropriate preventive actions to reduce the impact of osteoporosis-related fractures.

Prediction of advanced colorectal neoplasia based on metabolic parameters among symptomatic patients.

Worldwide, colorectal cancer (CRC) is the third most common cancer and ranks second among the leading causes of cancer death. This study aims to devise and validate a scoring system based on metabolic parameters to predict the risk of advanced colorectal neoplasia (ACN) in a large Chinese population. This was a cohort study of 495584 symptomatic subjects aged 40years or older who have received colonoscopy in Hong Kong from 1997 to 2017. The algorithm's discriminatory ability was evaluated as the area under the curve (AUC) of the mathematically constructed receiver operating characteristic curve. Age, male gender, inpatient setting, abnormal aspartate transaminase/alanine transaminase, white blood cell, plasma gamma-glutamyl transferase, high-density lipoprotein cholesterol, triglycerides, and hemoglobin A1c were significantly associated with ACN. A scoring of <2.65 was designated as "low risk (LR)." Scores at 2.65 or above had prevalence higher than the overall prevalence and hence were assigned as "high risk (HR)." The prevalence of ACN was 32% and 11%, respectively, for HR and LR groups. The AUC for the risk score in the derivation and validation cohort was 70.12%. This study has validated a simple, accurate, and easy-to-use scoring algorithm, which has a high discriminatory capability to predict ACN in symptomatic patients. Future studies should examine its predictive performance in other population groups.

A three-gene signature marks the time to locoregional recurrence in luminal-like breast cancer

Gene expression profiling (GEP)-based prognostic signatures are being rapidly integrated into clinical decision making for systemic management of breast cancer patients. However, GEP remains relatively underdeveloped for locoregional risk assessment. Yet, locoregional recurrence (LRR), especially early after surgery, is associated with poor survival. GEP was carried out on two independent luminal-like breast cancer cohorts of patients developing early (≤5 years after surgery) or late (>5 years) LRR and used, by a training and testing approach, to build a gene signature able to intercept women at risk of developing early LRR. The GEP data of two in silico datasets and of a third independent cohort were used to explore its prognostic value. Analysis of the first two cohorts led to the identification of three genes, CSTB, CCDC91 and ITGB1, whose expression, derived by principal component analysis, generated a three-gene signature significantly associated with early LRR in both cohorts (P value <0.001 and 0.005, respectively), overcoming the discriminatory capability of age, hormone receptor status and therapy. Remarkably, the integration of the signature with these clinical variables led to an area under the curve of 0.878 [95% confidence interval (CI) 0.810-0.945]. In in silico datasets we found that the three-gene signature retained its association, showing higher values in the early relapsed patients. Moreover, in the third additional cohort, the signature significantly associated with relapse-free survival (hazard ratio 1.56, 95% CI 1.04-2.35). Our three-gene signature represents a new exploitable tool to aid treatment choice in patients with luminal-like breast cancer at risk of developing early recurrence.

Skeletal-Related Events in Renal Cell Carcinoma: Prediction With Alkaline Phosphatase (ALP), C-reactive Protein (CRP), Haemoglobin (Hb) and Erythrocyte Sedimentation Rate (ESR) (A.C.H.E.) Score for Risk Stratification.

Introduction Skeletal metastasis is catastrophic in patients with renal cell carcinoma (RCC), leading to skeletal-related events (SRE) such as nerve entrapment, hypercalcemia and even pathological fractures, which may require surgical intervention. The nature of the bone metastasis in advanced RCC is large, destructive, hyper-vascular and mostly lytic. The present retrospective analysis aims to identify potential risk factors for predicting SREs in advanced RCC with bone metastasis. Methods The clinical data of 42 patients with RCC and bone metastasis and at least one episode of SRE were reviewed, and the correlations between erythrocyte sedimentation rate (ESR), alkaline phosphatase (ALP), C-reactive protein (CRP), haemoglobin (Hb), carcinoembryonic antigen (CEA) and bone metastases were analysed. Risk factors were identified by multivariate logistic regression analysis. Bone metastasis was diagnosed on a bone scan. The receiver operating characteristic (ROC) curve calculated the cut-off value of the independent correlation factors. Results The areas under the ROC curve for ALP, Hb, CRP, and ESR were 0.84, 0.76, 0.86 and 0.88, respectively, suggesting excellent discriminatory capability of ALP, CRP, ESR and sufficient discriminative ability of Hb in predicting bone metastasis. Multivariate logistic regression analysis showed ALP, CRP, Hb and ESR associated with SRE and skeletal metastasis. Conclusion We propose that an A.C.H.E. score encompassing ALP, CRP, Hb, and ESR are potential risk factors for developing SRE and concomitant bone metastasis in advanced RCC patients. For new RCC patients, if values of ALP >128 U/L, CRP ≥74 mg/L, Hb <11.5 g/L, and ESR ≥55 mm/hr are detected, intensive monitoring and bone scanning are warranted as these cases are at a higher risk of skeletal events.

Unique combinations of shape morphometric features improves discriminability of ftld phenotypes

AbstractBackgroundFrontotemporal lobar degeneration (FTLD) is associated with diverse clinical phenotypes underlain by multiple disease pathologies and genetic mutations. As such, traditional structural MRI analyses lack sensitivity and specificity for discriminating FTLD syndromes. Here, we use data‐driven methods to extract a concise set of MRI‐derived shape morphometric features and examine the discriminatory capability of their unique combinations in four FTLD clinical phenotypes.Method190 patients with sporadic or familial FTLD spectrum disorders (i.e., behavioral variant (bvFTD, n = 107), non‐fluent variant primary progressive aphasia (nfvPPA, n = 27), semantic variant primary progressive aphasia (svPPA, n = 12) and progressive supranuclear palsy (PSP, n = 44)) and 27 controls without pathogenic mutations from the ALLFTD cohort were evaluated. MRI data were preprocessed with FreeSurfer software and four cortical measures were extracted and indexed to a normalized surface atlas: cortical thickness (CT), surface area (SA), surface curvature (SC) and jacobian white matter surface metric distortion (JW). For all phenotypes, each shape feature was contrasted with controls using linear models adjusted for age and gender. Principal component analysis (PCA) was then applied to each individual structural measure and the discriminatory power based on individual and combined measures was assessed using logistic regression and 10‐fold cross‐validation.ResultFigure 1 displays FDR‐corrected T‐scores of differences from controls in each morphometric feature in bvFTD. Results reveal complementary patterns of CT, SA, SC and JW for each phenotype, with greatest differences observed in CT across groups. In Figure 2, we display the features derived from the PCA, weighted by eigenvector coefficients, in bvFTD. In Figures 3‐6, receiver operating characteristic curves for individual and combined measures alongside areas under the curve (AUCs) are shown for each phenotype. In bvFTD, nfvPPA and PSP, the superior model included CT and SC at AUCs of 88.3, 87, and 78.6, respectively. For svPPA, the superior model included CT, SC and JW at an AUC of 85.6.ConclusionIntegrating additional MRI‐derived surface morphometric features improved classification performance in all FTLD phenotypes. The principal component analysis‐based approach indicated distinct brain regions contribute to discrimination for each shape feature, suggesting they may reflect unique aspects of neurodegeneration across groups.

Power Grid Fault Diagnosis Method Based on Inception Network

With the continuous evolution of grid intelligence, the traditional grid fault diagnosis model can not satisfied the development needs of smart grid in terms of accuracy and diagnosis speed, and how to apply the fault information provided by the supervisory control and data acquisition (SCADA) system to achieve fast and accurate grid fault diagnosis is the main problem at present. In this paper, we propose an Inception network-based fault diagnosis method, in which the alarm information of different fault types is filtered and transformed into a grayscale map as model input to avoid the interference of redundant alarm information to the fault diagnosis, and the Inception network with excellent image feature extraction capability is employed to obtain the grayscale map of the alarm information to achieve fast fault diagnosis. This method has been proven to have excellent fault-type discriminatory capability and is effective in achieving fast and accurate fault diagnosis based on deep learning algorithms.

Volume under the ROC surface for high-dimensional independent screening with ordinal competing risk outcomes.

We propose a screening method for high-dimensional data with ordinal competing risk outcomes, which is time-dependent and model-free. Existing methods are designed for cause-specific variable screening and fail to evaluate how a biomarker is associated with multiple competing events simultaneously. The proposed method utilizes the Volume under the ROC surface (VUS), which measures the concordance between values of a biomarker and event status at certain time points and provides an overall evaluation of the discrimination capacity of a biomarker. We show that the VUS possesses the sure screening property, i.e., true important covariates can be retained with probability tending to one, and the size of the selected set can be bounded with high probability. The VUS appears to be a viable model-free screening metric as compared to some existing methods in simulation studies, and it is especially robust to data contamination. Through an analysis of breast-cancer gene-expression data, we illustrate the unique insights into the overall discriminatory capability provided by the VUS.

Quantitative myocardial T2 mapping adds value to Japanese circulation society diagnostic criteria for active cardiac sarcoidosis.

Noninvasive identification of active myocardial inflammation in patients with cardiac sarcoidosis plays a key role in management but remains elusive. T2 mapping is a proposed solution, but the added value of quantitative myocardial T2 mapping for active cardiac sarcoidosis is unknown. Retrospective cohort analysis of 56 sequential patients with biopsy-confirmed extracardiac sarcoidosis who underwent cardiac MRI for myocardial T2 mapping. The presence or absence of active myocardial inflammation in patients with CS was defined using a modified Japanese circulation society criteria within one month of MRI. Myocardial T2 values were obtained for the 16 standard American Heart Association left ventricular segments. The best model was selected using logistic regression. Receiver operating characteristic curves and dominance analysis were used to evaluate the diagnostic performance and variable importance. Of the 56 sarcoidosis patients included, 14 met criteria for active myocardial inflammation. Mean basal T2 value was the best performing model for the diagnosis of active myocardial inflammation in CS patients (pR2 = 0.493, AUC = 0.918, 95% CI 0.835-1). Mean basal T2 value > 50.8ms was the most accurate threshold (accuracy = 0.911). Mean basal T2 value + JCS criteria was significantly more accurate than JCS criteria alone (AUC = 0.981 vs. 0.887, p = 0.017). Quantitative regional T2 values are independent predictors of active myocardial inflammation in CS and may add additional discriminatory capability to JCS criteria for active disease.