Characteristic Curve Model Research Articles

Seismic active earth pressure of unsaturated soils with a crack using pseudo-dynamic approach

Abstract The analyses of active earth pressure subjected to earthquake shaking are routinely explored concerning dry or saturated soils. However, the fact often neglected in previous literature is that soils are mostly unsaturated in nature. This work puts forward a kinematical approach for the assessment of active resistance of a rigid retaining structure against unsaturated backfills with pseudo-dynamic approach, and the effect of a crack presented in backfills is also considered. A layer-wise summation method is employed to compute the non-uniformly distributed seismic forces due to earthquake accelerations considering time and spatial effects and corresponding work rates. Several nonlinear formulae as well as different soil-water characteristic curve models are employed to determine the additional cohesion caused by matric suction. To verify the validity of the proposed approach, a comparison is conducted with its simplified modality and conventional pseudo-static approach based on identical input conditions. The comparison results show good agreement. The lateral earth pressure can be derived from the principle of work rate balance using a numerical optimization technique. A parametric analysis is implemented to investigate the influences of crack depth, water level, dynamic factors of seismic effects and nonlinear evaluation models on the critical seismic active earth pressure.

Wide-ranging analysis of survival-related alternative splicing events in invasive breast carcinoma.

Invasive breast carcinoma (BRCA) is a serious disease that threatens the survival time of those affected. Alternative splicing (AS) involved in BRCA pathogenesis may be a potential therapeutic target. However, to the best of our knowledge, a systematic analysis of survival-related alternative splicing events (SREs) has not yet been reported. The aim of the present study was to identify SREs and analyze their potential biological functions as BRCA prognostic biomarkers. An UpSet plot demonstrated AS global characteristics. Cox's proportional hazards regression model quantitatively demonstrated the prognostic relevance of AS events. Functional enrichment analysis investigated the potential pathways through which AS events affect BRCA progression. The receiver operating characteristic curve model determined the clinical significance of AS events represented using percent-spliced-in (PSI) values. The regulatory network of splicing factors (SFs) and AS events laid the foundation for studying the role of SFs in BRCA. The present study identified 1,215 SREs and their distribution characteristics, suggesting that AS events in exon skipping (ES) primarily exerted normal physiological functions, while AS events in alternative terminator sites had the most significant prognostic effect. The present study demonstrated that survival-associated genes are involved primarily in certain biological processes of ribosomal proteins. In the diagnostic model, the alternative acceptor site, alternative donor site, alternative promoter site and ES performed well. ELAVL4 was the key gene associated with prognosis and SREs. In conclusion, a number of AS events affect BRCA initiation, progression and prognosis. The PSI value of AS events has the potential to diagnose BRCA and predict a prognosis; however, this must be confirmed in additional studies.

Whole-Body Characterization of Estrogen Receptor Status in Metastatic Breast Cancer with 16α-18F-Fluoro-17β-Estradiol Positron Emission Tomography: Meta-Analysis and Recommendations for Integration into Clinical Applications.

Estrogen receptor (ER) status by immunohistochemistry (IHC) of cancer tissue is currently used to direct endocrine therapy in breast cancer. Positron emission tomography (PET) with 16α-18F-fluoro-17β-estradiol (18 F-FES) noninvasively characterizes ER ligand-binding function of breast cancer lesions. Concordance of imaging and tissue assays should be established for 18 F-FES PET to be an alternative or complement to tissue biopsy for metastatic lesions. We conducted a meta-analysis of published results comparing 18 F-FES PET and tissue assays of ER status in patients with breast cancer. PubMed and EMBASE were searched for English-language manuscripts with at least 10 patients and low overall risk of bias. Thresholds for imaging and tissue classification could differ between studies but had to be clearly stated. We used hierarchical summary receiver-operating characteristic curve models for the meta-analysis. The primary analysis included 113 nonbreast lesions from 4 studies; an expanded analysis included 327 total lesions from 11 studies. Treating IHC results as the reference standard, sensitivity was 0.78 (95% confidence region 0.65-0.88) and specificity 0.98 (0.65-1.00) for the primary analysis of nonbreast lesions. In the expanded analysis including non-IHC tissue assays and all lesion sites, sensitivity was 0.81 (0.73-0.87) and specificity 0.86 (0.68-0.94). These results suggest that 18 F-FES PET is useful for characterization of ER status of metastatic breast cancer lesions. We also review current best practices for conducting 18 F-FES PET scans. This imaging assay has potential to improve clinically relevant outcomes for patients with (historically) ER-positive metastatic breast cancer, including those with brain metastases and/or lobular histology. IMPLICATIONS FOR PRACTICE: 16α-18F-fluoro-17β-estradiol positron emission tomography (18 F-FES PET) imaging assesses estrogen receptor status in breast cancer in vivo. This work reviews the sensitivity and specificity of 18 F-FES PET in a meta-analysis with reference tissue assays and discusses best practices for use of the tracer as an imaging biomarker. 18 F-FES PET could enhance breast cancer diagnosis and staging as well as aid in therapy selection for patients with metastatic disease. Tissue sampling limitations, intrapatient heterogeneity, and temporal changes in molecular markers make it likely that 18 F-FES PET will complement existing assays when clinically available in the near future.

Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set

During the perioperative period patients often suffer complications, including acute kidney injury (AKI), reintubation, and mortality. In order to effectively prevent these complications, high-risk patients must be readily identified. However, most current risk scores are designed to predict a single postoperative complication and often lack specificity on the patient level. In other fields, machine learning (ML) has been shown to successfully create models to predict multiple end points using a single input feature set. We hypothesized that ML can be used to create models to predict postoperative mortality, AKI, reintubation, and a combined outcome using a single set of features available at the end of surgery. A set of 46 features available at the end of surgery, including drug dosing, blood loss, vital signs, and others were extracted. Additionally, six additional features accounting for total intraoperative hypotension were extracted and trialed for different models. A total of 59,981 surgical procedures met inclusion criteria and the deep neural networks (DNN) were trained on 80% of the data, with 20% reserved for testing. The network performances were then compared to ASA Physical Status. In addition to creating separate models for each outcome, a multitask learning model was trialed that used information on all outcomes to predict the likelihood of each outcome individually. The overall rate of the examined complications in this data set was 0.79% for mortality, 22.3% (of 21,676 patients with creatinine values) for AKI, and 1.1% for reintubation. Overall, there was significant overlap between the various model types for each outcome, with no one modeling technique consistently performing the best. However, the best DNN models did beat the ASA score for all outcomes other than mortality. The highest area under the receiver operating characteristic curve (AUC) models were 0.792 (0.775–0.808) for AKI, 0.879 (0.851–0.905) for reintubation, 0.907 (0.872–0.938) for mortality, and 0.874 (0.864–0.866) for any outcome. The ASA score alone achieved AUCs of 0.652 (0.636–0.669) for AKI, 0.787 (0.757–0.818) for reintubation, 0.839 (0.804–0.875) for mortality, and 0.76 (0.748–0.773) for any outcome. Overall, the DNN architecture was able to create models that outperformed the ASA physical status to predict all outcomes based on a single feature set, consisting of objective data available at the end of surgery. No one model architecture consistently performed the best.

Novel preoperative predictive score to evaluate lymphovascular space involvement in endometrial cancer: an aid to the sentinel lymph node algorithm

IntroductionSentinel lymph node (SLN) dissection has been recognized as a valid tool for staging in patients with endometrial cancer. Several factors are predictors of recurrence and survival in endometrial cancer,...

CURB-65 may serve as a useful prognostic marker in COVID-19 patients within Wuhan, China: a retrospective cohort study.

A recently developed pneumonia caused by SARS-CoV-2 has quickly spread across the world. Unfortunately, a simplified risk score that could easily be used in primary care or general practice settings has not been developed. The objective of this study is to identify a simplified risk score that could easily be used to quickly triage severe COVID-19 patients. All severe and critical adult patients with laboratory-confirmed COVID-19 on the West campus of Union Hospital, Wuhan, China, from 28 January 2020 to 29 February 2020 were included in this study. Clinical data and laboratory results were obtained. CURB-65 pneumonia score was calculated. Univariate logistic regressions were applied to explore risk factors associated with in-hospital death. We used the receiver operating characteristic curve and multivariate COX-PH model to analyse risk factors for in-hospital death. A total of 74 patients (31 died, 43 survived) were finally included in the study. We observed that compared with survivors, non-survivors were older and illustrated higher respiratory rate, neutrophil-to-lymphocyte ratio, D-dimer and lactate dehydrogenase (LDH), but lower SpO2 as well as impaired liver function, especially synthesis function. CURB-65 showed good performance for predicting in-hospital death (area under curve 0.81, 95% confidence interval (CI) 0.71-0.91). CURB-65 ⩾ 2 may serve as a cut-off value for prediction of in-hospital death in severe patients with COVID-19 (sensitivity 68%, specificity 81%, F1 score 0.7). CURB-65 (hazard ratio (HR) 1.61; 95% CI 1.05-2.46), LDH (HR 1.003; 95% CI 1.001-1.004) and albumin (HR 0.9; 95% CI 0.81-1) were risk factors for in-hospital death in severe patients with COVID-19. Our study indicates CURB-65 may serve as a useful prognostic marker in COVID-19 patients, which could be used to quickly triage severe patients in primary care or general practice settings.

Bounding surface constitutive model for unsaturated soils considering microscopic pore structure and bonding effect

This article presents a bounding surface model for unsaturated soils by using skeleton stress and bonding variable based on microcosmic pore structure as constitutive variables. A Hydraulic hysteresis soil-water characteristic curve model considering deformation and hydraulic hysteresis is combined to achieve hydraulic coupling. The proposed model can capture the change of the inter-particles bonding effect in the deformation process of unsaturated soils and accurately predict the hydraulic mechanical behavior of unsaturated soils under complicated loading paths and wetting-drying cycles. The validity of the proposed model is confirmed by the results of unsaturated isotropic compression tests, wetting-drying cycles tests and unsaturated triaxial shear tests reported in the literature.

Diagnostic utility of CT for small bowel obstruction: Systematic review and meta-analysis.

BackgroundTo perform a systematic review and meta-analysis evaluating the diagnostic performance of computed tomography (CT) for small bowel obstruction (SBO), including diagnostic accuracy, ischemia, predicting surgical intervention, etiology and transition point.MethodsPubMed/MEDLINE and related databases were searched for research articles published from their inception through August 2018. Findings were pooled using bivariate random-effects and summary receiver operating characteristic curve models. Meta-regression and subgroup analyses were performed to evaluate whether publication year, patient age, enhanced CT, slice thickness and pathogenesis affected classification accuracy.ResultsIn total, 45 studies with a total of 4004 patients were included in the analysis. The pooled sensitivity and specificity of CT for SBO were 91% (95% confidence interval [CI]: 84%, 95%) and 89% (95% CI: 81%, 94%), respectively, and there were no differences in the subgroup analyses of age, publication year, enhanced CT and slice thickness. For ischemia, the pooled sensitivity and specificity was 82% (95% CI: 67%, 91%) and 92% (95% CI: 86%, 95%), respectively. No difference was found between enhanced and unenhanced CT based on subgroup analysis; however, high sensitivity was found in adhesive SBO compared with routine causes (96% vs. 78%, P = 0.03). The pooled sensitivity and specificity for predicting surgical intervention were 87% and 73%, respectively. The accuracy for etiology of adhesions, hernia and tumor was 95%, 70% and 82%, respectively. In addition, the pooled sensitivity and specificity for transition point was 92% and 77%, respectively.ConclusionsCT has considerable accuracy in diagnosis of SBO, ischemia, predicting surgical intervention, etiology and transition point.

Intratumor Heterogeneity Assessed by 18F-FDG PET/CT Predicts Treatment Response and Survival Outcomes in Patients with Hodgkin Lymphoma

Radiomic analysis of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images enables the extraction of quantitative information of intratumour heterogeneity. This study investigated whether the baseline 18F-FDG PET/CT radiomics can predict treatment response and survival outcomes in patients with Hodgkin lymphoma (HL). Thirty-five patients diagnosed with HL who underwent 18F-FDG PET/CT scans before and during chemotherapy were retrospectively enrolled in this investigation. For each patient, we extracted 709 radiomic features from pretreatment PET/CT images. Clinical variables (age, gender, B symptoms, bulky tumor, and disease stage) and radiomic signatures (intensity, texture, and wavelet) were analyzed according to response to therapy, progression-free survival (PFS), and overall survival (OS). Receiver operating characteristic curve, logistic regression, and Cox proportional hazards model were used to examine potential predictive and prognostic factors. High-intensity run emphasis (HIR) of PET and run-length nonuniformity (RLNU) of CT extracted from gray-level run-length matrix (GLRM) in high-frequency wavelets were independent predictive factors for the treatment response (odds ratio [OR] = 36.4, p = 0.014; OR = 30.4, p = 0.020). Intensity nonuniformity (INU) of PET and wavelet short run emphasis (SRE) of CT from GLRM and Ann Arbor stage were independently related to PFS (hazard ratio [HR] = 9.29, p = 0.023; HR = 18.40, p = 0.012; HR = 7.46, p = 0.049). Zone-size nonuniformity (ZSNU) of PET from gray-level size zone matrix (GLSZM) was independently associated with OS (HR = 41.02, p = 0.001). Based on these factors, a prognostic stratification model was devised for the risk stratification of patients. The proposed model allowed the identification of four risk groups for PFS and OS (p < 0.001 and p < 0.001). HIR_GLRMPET and RLNU_GLRMCT in high-frequency wavelets serve as independent predictive factors for treatment response. ZSNU_GLSZMPET, INU_GLRMPET, and wavelet SRE_GLRMCT serve as independent prognostic factors for survival outcomes. The present study proposes a prognostic stratification model that may be clinically beneficial in guiding risk-adapted treatment strategies for patients with HL.

Probabilistic Risk Assessment of unsaturated Slope Failure Considering Spatial Variability of Hydraulic Parameters

Probabilistic risk assessment of slope failure evaluates the slope safety in a quantitative manner, which considers the failure probability and failure consequence simultaneously. However, risk assessment of unsaturated slope accounting for spatially variable soil-water characteristic curve (SWCC) model parameter and saturated hydraulic conductivity has been rarely reported. A probabilistic risk assessment approach is proposed in current study for rationally quantifying the unsaturated slope failure risk with the aid of Monte Carlo (MC) simulation. The SEEP/W and SLOPE/W modules contained in Geostudio software are applied to carry out deterministic analysis, where factor of safety (FS) of the unsaturated slope is calculated by Morgenstern–Price method. The spatially variable hydraulic parameters are characterized by their respective random fields that are transferred from the random void ratio field in this study, rather than generating them separately. The proposed approach is subsequently employed to an unsaturated slope example for exploring the influences of spatially variable void ratio. Results show that the unsaturated slope failure risk is considerably affected by the spatially variable void ratio, and the single exponential autocorrelation function (ACF) popularized in geotechnical engineering tends to underestimate the failure risk in the unsaturated slope risk assessment.

Predicting incident dementia 3-8 years after brief cognitive tests in the UK Biobank prospective study of 500,000 people

IntroductionProspective studies reporting associations between cognitive performance and subsequent incident dementia have been subject to attrition bias. Furthermore, the extent to which established risk factors account for such associations requires further elucidation. MethodsWe used UK Biobank baseline cognitive data (n ≤ 488,130) and electronically linked hospital inpatient and death records during three- to eight-year follow-up, to estimate risk of total dementia (n = 1051), Alzheimer's disease (n = 352), and vascular dementia (n = 169) according to four brief cognitive tasks, with/without adjustment for constitutional and modifiable risk factors. ResultsWe found associations of cognitive task performance with all-cause and cause-specific dementia (P < .01); these were not accounted for by established risk factors. Cognitive data added up to 5% to the discriminative accuracy of receiver operating characteristic curve models; areas under the curve ranged from 82% to 86%. DiscussionThis study offers robust evidence that brief cognitive testing could be a valuable addition to dementia prediction models.

Factors Associated With Long-term Risk of Recurrence After Percutaneous Radiofrequency Thermocoagulation of the Gasserian Ganglion for Patients With Trigeminal Neuralgia: A Multicenter Retrospective Analysis.

This study aimed to estimate risk factors associated with recurrence after radiofrequency thermocoagulation (RFT) of the Gasserian ganglion among a large sample of patients with trigeminal neuralgia (TN) during a long-term follow-up. We performed a multicenter retrospective analysis of data from 1481 patients with TN who underwent RFT from 2005 through 2017. Recurrence-free survival (RFS) was assessed by the Kaplan-Meier method. Risk factors of all patient characteristics were determined by using univariate and multivariate Cox regression analysis. Prognostic value was determined by prognostic index (PI) with regression coefficients and receiver-operating characteristic curve model. The median of RFS was 136 months (95% confidence interval [CI]: 123.5-148.5). The rate of RFS was 85.3% (95% CI: 83.5%-87.1%) at 1 year, 74.6% (95% CI: 72.2%-77.0%) at 3 years, 68.0% (95% CI: 65.5%-70.5%) at 5 years, and 54.9% (95% CI: 51.6%-58.2%) at 10 years. Multivariate analysis showed that atypical facial pain (hazard ratio [HR]=16.914, 95% CI: 13.117-21.808, P<0.001), Barrow Neurological Institute (BNI) Class II/III facial hypesthesia before undergoing RFT (HR=2.47, 95% CI: 1.52-4.016, P<0.001)/(HR=3.288, 95% CI: 1.035-10.433, P=0.044), and history of previous microvascular decompression/RFT/neurosurgeries≥2 (HR=1.642, 95% CI: 0.941-2.863, P=0.041)/(HR=2.808, 95% CI: 1.819-4.334, P<0.001)/(HR=3.83, 95% CI: 1.802-8.146, P<0.001) were independently associated with RFS. Patients with PI>0.764 were identified as high-risk patients for TN recurrence with a median RFS of 36 months (95% CI: 23.9-48.1) compared with those with PI<0.764 (HR=6.785, 95% CI: 5.371-8.573, P<0.001). Our results indicated the patients with a higher risk for recurrence after RFT for the treatment of TN. In addition, our findings might provide support for clinical decision-making before the RFT procedure.

Risk thresholds of levodopa dose for dyskinesia in Chinese patients with Parkinson's disease: a pilot study.

Levodopa is widely used to treat Parkinson's disease (PD), and its long-term therapy may induce dyskinesia in a dose-dependent manner. However, the threshold dose with a relatively low risk for dyskinesia has not been determined. Demographic, clinical profiles and detailed information of dopaminergic drugs were recorded for 403 PD patients in treatment with levodopa. Variables were compared between dyskinesia and non-dyskinesia groups. Logistic regression analysis was used to assess the association between levodopa dose-related variables and dyskinesia. Receiver operating characteristic curve and decision tree classification model were used to investigate the cut-off value of levodopa dose to best separate the dyskinesia group from the non-dyskinesia group. Patients with dyskinesia tended to have a lower weight and age at onset, higher percentage of female and wearing-off, longer duration of disease and levodopa treatment, higher H-Y stage and MDS-UPDRS Part III score, and higher levodopa dose and levodopa equivalent dose than those without dyskinesia. After adjusted for demographical and clinical variables, levodopa dose-related factors (daily dose, cumulative dose, and weight-adjusted dose) were still associated with dyskinesia. Both the receiver operating characteristic and decision tree classification analysis indicated that patients who have taken levodopa dose ≤ 400mg per day may be associated with a reduced risk for dyskinesia. In conclusion, we evaluated the thresholds of levodopa treatment with a relatively low risk for dyskinesia. These data should be considered for prevention and management of dyskinesia in patients with PD.

Diagnostic value of endoscopic ultrasonography, fibroscan, acoustic radiation pulse imaging, serological index, and their combination for early stage liver cirrhosis

Objective: To investigate the diagnostic value of endoscopic ultrasonography (EUS), Fibroscan, acoustic radiation force impulse (ARFI), and aspartate aminotransferase-to-platelet ratio (APRI) and their combination for early stage liver cirrhosis. Methods: Three hundred and twenty-two hospitalized patients who had been diagnosed with chronic viral liver disease from March 2016 to April 2018 were included. According to the clinical diagnosis, patients were divided into chronic hepatitis and the early stage liver cirrhosis group (Child-Pugh A grade). All patients were examined by Fibroscan to detect liver stiffness measurement (LSM), ARFI to detect liver virtual touch tissue quantification (VTQ) value, esophagogastroduodenoscopy and EUS to detect esophagogastric varices, laboratory and imaging examination. The index of EUS, Fibroscan, ARFI, and APRI was analyzed and the regression model was established by binary logistic regression, and the diagnostic efficacy of the above index and regression model for early stage of cirrhosis was evaluated by the area under a receiver operating characteristic curve (AUROCs). Results: An early stage cirrhosis group had significantly higher detection rate with EUS (esophagogastric varices), Fibroscan (LSM), ARFI (VTQ) and APRI than chronic hepatitis group [76.7% vs. 10.7%, 10.4 (7.8, 17.3) vs. 6.1 (5.2, 8.4) kPa, 1.71(1.48, 2.07) m/s vs. 1.25(1.14, 1.43) m/s and 0.65 (0.38, 1.15) vs. 0.38(0.26, 0.62), respectively]. The corresponding chi-square test were 140.86, Z = -9.069, Z = -9.948 and Z = -5.764, respectively and the differences were statistically significant (P < 0.01). The areas under the receiver operating characteristic curve and regression model were 0.830 (0.783 ~ 0.877), 0.793 (0.744 ~ 0.841), 0.821 (0.775 ~ 0.868), 0.686 (0.628 ~ 0.744) and 0.947 (0.925 ~ 0.969) for the diagnosis of early stage cirrhosis, respectively. Among them, the regression model of three indices (EUS, LSM and VTQ) had the largest AUROCs (0.947) and the corresponding sensitivity and specificity were 0.878 and 0.867, respectively. Conclusion: The combination of EUS, LSM and ARFI had a superior diagnostic value for early stage liver cirrhosis, and may improve the diagnosis rate and reduce the misdiagnosis rate.

A Novel Score System of Routine Blood Measurements for Predicting Kawasaki Disease in Febrile Children

Background: Kawasaki disease (KD) is the most concerning acquired heart disease among febrile children (FC) under the age of 5 years old. In particular, KD is a clinically diagnosed disease with no effective laboratory examination that can serve as diagnostic tool. In this study, we derived and assessed a novel score system of routine blood measurements without any clinical inspection data for predicting KD in young FC patients in the pediatric emergency department. Methods: We enrolled 6310 FC and 874 KD subjects in this study. We collected data of biological parameters (total complete cell counts with complete differential count (DC)), C-reactive protein (CRP), aspartate aminotransferase (AST), and alanine aminotransferase (ALT). For model training, we used the receiver operating characteristic (ROC) curve and logistic regression model to identify potential independent predictors, then generated the candidate score system by using Youden's index criterion for KD based on the rounded estimated coefficients. Blind tests from two independent cohorts were used for confirmation. Results: We obtained eight independent predictors (platelets, eosinophil, ALT, CRP, hemoglobin, MCH, MCHC, monocyte) from the logistic regression model to generate the candidate score system. In this score system, the top three scores were eosinophil>1.5% (score: 7), GPT>30 U/L (score: 6), and CRP>25 mg/L (score: 6). A score of 14 has the best sensitivity value plus specificity prediction rate for KD. The sensitivity, specificity, and accuracy for the testing set were 0.824, 0.839, 0.838, respectively. The blind testing of two independent cohorts of KD patients (N =103 and 170) from two difference institutes had a 0.780 (213/273) sensitivity that is compatible with the performance of this model. Conclusion: KD shares many clinical signs with other childhood febrile illnesses, which poses a diagnostic challenge for first-line clinicians. Our findings demonstrate a novel score system, which combines CBC/DC, GPT, and CRP, has a good discriminatory ability for predicting KD in febrile children. Using this novel score system with artificial intelligence (AI) will help distinguish KD early and prevent coronary artery lesions. Funding Statement: This study received funding from the following grants: MOST 108-2314-B-182 -037 -MY3 and MOST 103-2410-H-264-004 from the Ministry of Science and Technology of Taiwan and 8E0212 from Chang Gung Memorial Hospital in Taiwan. Declaration of Interests: All of the authors declared to have no financial interests to disclose with regard to this article. Ethics Approval Statement: This study was approved by the Chang Gung Medical Foundation’s Institutional Review Board.

Overexpression levels of cripto-1 predict poor prognosis in patients with prostate cancer following radical prostatectomy.

Overexpression of cripto-1 (CR-1), an epidermal growth factor-cripto-1/FRL-1/Cryptic family protein, has been reported in multiple types of malignancy. However, the clinical functions of CR-1 in prostate cancer (PCa) remain largely unclear. The objective of the present study was to investigate the association between CR-1 expression and the clinicopathological features and prognosis of PCa. CR-1 expression was evaluated in 138 PCa tissues and 67 benign prostate hyperplasia (BPH) tissues using immunohistochemistry. The association between the clinicopathological features of patients with PCa and CR-1 expression was analyzed using a χ2 test. Receiver operating characteristic (ROC) curve and Cox regression model were used to analyze the association between CR-1 expression and biochemical recurrence (BCR)-free survival. It was revealed that the protein expression of CR-1 was markedly higher in PCa tissues than in BPH tissues. The mRNA expression of CR-1 in PCa tissue and cells was also significantly higher than in BPH tissue and the normal RWPE-1 prostate cell line (P<0.05). In addition, high CR-1 expression was significantly associated with prostate-specific antigen level (P=0.008), Gleason score (P=0.011) and lymph node metastasis (P=0.025) in patients with PCa. ROC curve indicated that patients with elevated expression of CR-1 exhibited shorter BCR-free survival (P<0.001). Furthermore, multivariate statistical analysis demonstrated that overexpression of CR-1 may be a novel predictor for prognosis of patients with PCa. Accordingly, the present study considered CR-1 to be a valuable predictor of poor prognosis and progression in PCa, and a potential therapeutic target for patients with PCa.

Prediction of type 2 diabetes mellitus using noninvasive MRI quantitation of visceral abdominal adiposity tissue volume.

The correlation between visceral adipose tissue volume (VATV), hepatic proton-density fat fraction (PDFF), and pancreatic PDFF has been previously studied to predict the presence of type 2 diabetes mellitus (T2DM). This study investigated VATV quantitation in patients with T2DM, prediabetes, and normal glucose tolerance (NGT) using MRI to assess the roles of VATV, hepatic, and pancreatic PDFF in predicting the presence of T2DM. Forty-eight patients with a new clinical diagnosis of T2DM (n=15), prediabetes (n=17), or NGT (n=16) were included and underwent abdominal magnetic resonance imaging (MRI) scanning with the iterative decomposition of water and fat with echo asymmetry and least square estimation image quantification (IDEAL-IQ) sequencing. VATV was obtained at the level of the 2nd and 3rd lumbar vertebral bodies (VATV L2 and VATV L3) where the sum of VATV L2 and VATV L3 (total VATV) were computed, respectively. Also, pancreatic and hepatic fat content was quantified by measuring the PDFF. The receiver operating characteristic (ROC) curve and binary logistics regression model analysis were employed to evaluate their ability to predict the presence of T2DM. The VATV L2, VATV L3, and total VATV values of the T2DM group were significantly higher than the prediabetes and NGT groups (P<0.05). There was no statistically significant difference between the values of VATV L2, VATV L3, and total VATV between the prediabetes and NGT groups (P>0.05). The ROC curve showed the areas under the curve for VATV L2, VATV L3, total VATV, hepatic PDFF, and pancreatic PDFF were 0.76, 0.80, 0.80, 0.79, and 0.75, respectively, in predicting the presence of T2DM (P<0.01). The ROC curves of VATV L2, VATV L3, total VATV, hepatic PDFF, and pancreatic PDFF failed to predict the presence of prediabetes and NGT (P>0.05). The binary logistics regression model analysis revealed that only VATV L3 was independently associated with the incidence of T2DM (P=0.01 and OR =1.01). The sensitivity, specificity, and total accuracy were 80.00%, 88.20%, and 84.40%, respectively. Compared with hepatic PDFF, pancreatic PDFF, VAVT L2, and total VATV, VAVT L3 was the better predictor of T2DM.

Fecal Lactoferrin for Assessment of Inflammatory Bowel Disease Activity: A Systematic Review and Meta-Analysis.

Persistent disease activity is associated with a poor prognosis in inflammatory bowel disease (IBD) patients. Therefore, monitoring of IBD activity can avoid the poor prognosis. Serum biomarkers reflect a summation of systemic host responses rather than being specific for intestinal inflammation. And endoscopic monitoring is invasive, costly, and time consuming. The objective of our study was to perform a meta-analysis evaluating the diagnostic accuracy of fecal lactoferrin (FL) in assessing IBD activity. We systematically searched the databases from inception to May 2018 that evaluated IBD activity. The methodological quality of each study was assessed according to the Quality Assessment of Diagnostic Accuracy Studies checklist. The extracted data were pooled using a summary receiver operating characteristic curve model. Random-effects model was used to summarize the diagnostic odds ratio, sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio. Ten studies comprising 773 IBD patients were included in the meta-analysis. The pooled sensitivity and specificity values for assessing ulcerative colitis (UC) activity were 0.81 [95% confidence interval (CI), 0.64-0.92] and 0.82 (95% CI, 0.61-0.93), respectively. And the pooled sensitivity and specificity values for assessing Crohn's disease (CD) activity were 0.82 (95% CI, 0.73-0.88) and 0.71 (95% CI, 0.63-0.78), respectively. The diagnostic performance of the FL assay in the UC patients appeared to be superior to that in the CD patients. Our meta-analysis has found that FL is an inexpensive, simple, stable, and useful screening marker with high sensitivity and modest specificity for assessing IBD activity, appearing to have greater ability to evaluate UC rather than CD.

A New Soil-Water Characteristic Curve Model for Unsaturated Loess Based on Wetting-Induced Pore Deformation

The soil-water characteristic curve (SWCC) is the basis for describing seepage, strength, and constitutive model of unsaturated soil. The existing SWCC models do not work accurately for evaluating loess, because they do not consider the pore deformation that is induced by wetting. The present study develops a new SWCC model for unsaturated loess. The model considers the effect of wetting-induced pore deformation (WIPD) on the SWCC. The new model includes 6 parameters, which could be confirmed by laboratory tests. The pore volume function (PVF) was described by the WIPD. The shift factor ξ1i and the compression factor ξ2i were introduced into the model. The relationship between the void ratio e and ξ1i and ξ2i was established using the average pore radius. The new SWCC model for saturated loess was improved based on the classical van Genuchten (V-G) model. If the WIPD had not been considered, the new model would regress into the classical V-G model. SWCC tests of unsaturated loess with different void ratios were carried out to verify the new model. The model parameters were calibrated in the original state, and the SWCCs of different void ratios were predicted by the new model and found to be in good agreement with the test results.

Risk Factors for Contralateral Hematoma Progression after Unilateral Evacuation of Bilateral Chronic Subdural Hematomas

To find risk factors for contralateral hematoma progression (CHP) in bilateral chronic subdural hematomas after initial unilateral evacuation. We retrospectively analyzed 53 patients with bilateral chronic subdural hematomas who underwent unilateral surgical evacuation in our department. Risk factors for CHP were identified by univariate analysis, a P value <0.05 were entered into multivariate logistic regression model and a predictive receiver operating characteristic curve model. The progression rate was 32.08%, the average progression interval was 2.32 months. The progression rate of the homogeneous hypodense group was significantly higher than that of the other density group (P= 0.017). The limited type of contralateral hematoma had a significantly lower progression rate than that of the widespread type (P= 0.001). Both pre- and postoperative volume of contralateral hematoma were significantly more in the CHP group compared with the contralateral hematoma without progression group (P= 0.031 and P= 0.001, respectively). Of the 4 risk factors, only postoperative volume of contralateral hematoma was an independent risk factor in multivariate logistic regression model (P= 0.033; 95% confidence interval, 1.005-1.124). The cut-off values of contralateral hematoma volume before and after operation were 29.27 cm3 and 37.84 cm3, respectively. Contralateral hematoma volume after operation is an independent risk predictor for CHP after unilateral evacuation. An additional surgery on contralateral hematoma or medical treatment should be taken into consideration if the volume is >37.84 cm3 in the first cranial computed tomography scan after surgery.