Amide proton transfer imaging has added value for predicting extraprostatic extension in prostate cancer patients.

Background: Prostate cancer invades the capsule is a key factor in selecting appropriate treatment methods. Accurate preoperative prediction of extraprostatic extension (EPE) can help achieve precise selection of treatment plans. Purpose: The aim of this study is to verify the diagnostic efficacy of tumor size, length of capsular contact (LCC), apparent diffusion coefficient (ADC), and Amide proton transfer (APT) value in predicting EPE. Additionally, the study aims to investigate the potential additional value of APT for predicting EPE. Method: This study include 47 tumor organ confined patients (age, 64.16 ± 9.18) and 50 EPE patients (age, 61.51 ± 8.82). The difference of tumor size, LCC, ADC and APT value between groups were compared. Binary logistic regression was used to screen the EPE predictors. The receiver operator characteristic curve analysis was performed to assess the diagnostic performance of variables for predicting EPE. The diagnostic efficacy of combined models (model I: ADC+LCC+tumor size; model II: APT+LCC+tumor size; and model III: APT +ADC+LCC+tumor size) were also analyzed. Results: APT, ADC, tumor size and the LCC were independent predictors of EPE. The area under the curve (AUC) of APT, ADC, tumor size and the LCC were 0.752, 0.665, 0.700 and 0.756, respectively. The AUC of model I, model II, and model III were 0.803, 0.845 and 0.869, respectively. The cutoff value of APT, ADC, tumor size and the LCC were 3.65%, 0.97×10-3mm2/s, 17.30mm and 10.78mm, respectively. The sensitivity/specificity of APT, ADC, tumor size and the LCC were 76%/89.4.0%, 80%/59.6%, 54%/78.9%, 72%/66%, respectively. The sensitivity/specificity of model I, Model II and Model III were 74%/72.3%, 82%/72.5% and 84%/80.9%, respectively. Data conclusion: Amide proton transfer imaging has added value for predicting EPE. The combination model of APT balanced the sensitivity and specificity.

Hippocampal amide proton transfer values are associated with cerebral small vessel disease imaging markers and total burden: a community-based cross-sectional study.

Background: Neurodegeneration has been suggested to be associated with cerebral small vessel disease (CSVD). The association between different CSVD imaging markers and the extent of neurodegeneration could be indirectly confirmed by examining the relationship between CSVD imaging markers and the hippocampal amide proton transfer (APT) values. The associations between hippocampal APT values with CSVD imaging markers and CSVD total load need to be further validated. The aim of this study was to investigate potential variations in hippocampal APT values among individuals with CSVD imaging markers and varying degrees of CSVD total burden. Methods: A cross-sectional study (retrospective analysis of prospectively-acquired data) was conducted at Nanxishan Hospital of Guangxi Zhuang Autonomous Region. From May 2020 to June 2021, 165 individuals (age, 40-76 years; male/female, 103/62) were included in this study. The inclusion criteria for the participants were as follows: The presence of lacunar infarction (LI), and/or cerebral microbleed (CMB); moderate-to-severe enlarged perivascular space (EPVS) (>20); deep white matter hyperintensity (WMH) > Fazekas 2 or periventricular WMH > Fazekas. The exclusion criteria comprised the following: History of craniocerebral operation; Cases with significant pathology incidentally identified during magnetic resonance (MR) scan; Drug or alcohol abuse. The differences of hippocampal APT values between CSVD imaging makers presence or absence groups and different CSVD total burden groups were compared using independent t-test and one-way analysis of variance (ANOVA). The correlations between APT values and CSVD imaging markers were analyzed using Pearson correlation analysis. A mediation analysis model was used to investigate the mediating effect of the hippocampal APT values in the association between CSVD total loads and Montreal Cognitive Assessment (MoCA) score was assessed. Results: The hippocampal APT values among different CSVD total load groups were significantly different (P<0.001). The hippocampal APT values were significantly different between the imaging markers presence and absence groups. The P values for the LI, WMH EPVS, and CMB presence or absence groups were <0.001, <0.001, 0.034, and 0.002, respectively. The hippocampal APT values were significantly correlated with CMB (P<0.01), LI (P<0.01) and WMH (P<0.01). The mediation models demonstrated that the APT values of the hippocampus partially mediated the association between CSVD total load and MoCA score, the proportion of mediation attributable was calculated as 17.50%. Conclusions: Hippocampal APT values were associated with CSVD imaging markers and total burden. Hippocampal APT values may serve as a biomarker for the early detection of neurodegeneration in CSVD patients.

Correlation between symptoms and cognitive function changes in patients with primary insomnia and pathways in gut microbiota.

Background: Primary insomnia (PI) refers to syndromes of difficulty falling asleep, poor sleep quality, early awakening, and difficulty falling asleep after waking up. Although there have been numerous studies, the specific etiology and pathogenesis of PI are still misunderstanding. In recent years, the gut microbiota has been proved to be involved in the metabolism of many mental disorders. But the specific mechanisms of its involvement in PI have not been fully elucidated. This study aims to explore the relationship between the gut microbiota and the symptoms, cognitive function changes in PI. Methods: In this study, the gut microbiota of PI patients and healthy controls was profiled by performing stool 16s rRNA gene sequencing. The co-occurrence network was constructed by using Weight Gene Co-expression Network Analysis (WGCNA) algorithm. The correlation between gut microbiota associated pathways and traits in PI were predicted. Results: WGCNA results demonstrated several Operational Taxonomic Units (OTU) modules are correlated to symptoms. By using PICRUSt2 software, we predicted the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of microbiota in modules. For instance, sleep efficiency may be correlated with the presence of Insulin signaling pathway, Flavonoid biosynthesis, Ascorbate and aldarate metabolism, Nitrotoluene degradation, Biotin metabolism, RNA polymerase and Chlorocyclohexane and chlorobenzene degradation. Total sleep time may be correlated with the presence of Tyrosine metabolism, Propanoate metabolism, Carbon fixation pathways in prokaryotes, Carotenoid biosynthesis, Systemic lupus erythematosus, Nitrotoluene degradation and Biosynthesis of unsaturated fatty acids. The severity of insomnia may be correlated with Insulin signaling pathway, Flavonoid biosynthesis, Ascorbate and aldarate metabolism, Nitrotoluene degradation, Biotin metabolism and RNA polymerase. Change of name score in Montreal Cognitive Assessment (MoCA) may be correlated with Tyrosine metabolism, Propanoate metabolism, Carbon fixation pathways in prokaryotes, Carotenoid biosynthesis, Systemic lupus erythematosus, Nitrotoluene degradation, Biosynthesis of unsaturated fatty acids, Apoptosis, Steroid hormone biosynthesis, Geraniol degradation, Protein digestion and absorption and Bisphenol degradation in Gut Microbiota (GM). Conclusion: This study revealed the potential relationships between gut microbiota and PI. By using pathway prediction and enrichment analysis, we concluded many metabolic pathways may associated with some important traits of insomnia patients, including sleep efficiency, severe insomnia, total sleep time and change of name score in MoCA. The metabolic pathways include Insulin signaling pathway, Flavonoid biosynthesis, Ascorbate and aldarate metabolism, Nitrotoluene degradation, Biotin metabolism, RNA polymerase and Chlorocyclohexane, chlorobenzene degradation, Tyrosine metabolism, Propanoate metabolism, Carbon fixation pathways in prokaryotes, Carotenoid biosynthesis, Systemic lupus erythematosus, Biosynthesis of unsaturated fatty acids, Apoptosis, Steroid hormone biosynthesis, Geraniol degradation, Protein digestion and absorption and Bisphenol degradation.Our study demonstrated that PI patients demonstrate significant changes in gut microbiota, which will help delineate the relationship between gut microbiota and syndromes of PI.

Amide proton transfer could be a surrogate imaging marker for predicting vascular cognitive impairment.

BACKGROUD: Emerging evidence suggests an overlap in the underlying pathways contributing to both cerebral small vessel disease (CSVD) and the neurodegenerative disease. Studies investigating the progression of CSVD should incorporate markers that reflect neurodegenerative lesions. OBJECTIVE: We aim to investigate whether Amide proton transfer (APT) can serve as a potential marker for reflecting vascular cognitive impairment (VCI). METHOD: Participants were categorized into one of three groups based on their Montreal Cognitive Assessment (MoCA) scores: normal control group (age,54.9 ± 7.9; male, 52.9%), mild cognitive impairment (MCI) group (age,55.7 ± 6.9; male, 42.6%), or vascular dementia (VaD) group (age,57.6 ± 5.5, male, 58.5%). One way analysis of variance was performed to compare the demographic and APT variables between groups. Multiple logistic regression analysis wwas constructed to examine the relationship between APT values and VCI grouping. A hierarchical linear regression model was employed to examine the associations between patients' demographic factors, imaging markers, APT values, and MoCA. RESULTS: The APT values of frontal white matter, hippocampus, amygdala, and thalamus were significantly different among different groups (p < 0.05). The APT values of frontal white matter, amygdala, and thalamus indicate a significant positive effect on MCI grouping. the APT values of frontal white matter, amygdala, and thalamus indicate a significant positive effect on VaD grouping. The demographic data, CSVD imaging markers and APT values can account for 5.1%, 20.1% and 27.7% of the variation in MoCA, respectively. CONCLUSION: APT imaging can partially identifying and predicting the occurrence of VCI.

Spectral computed tomography parameters could be surrogate imaging markers to detect early perfusion changes in diabetic kidneys.

Background: Kidney microvasculopathy is the baseline pathophysiological feature of diabetic kidney disease (DKD). We aimed to evaluate the spectral computed tomography (CT) parameters for detecting renal perfusion changes among diabetic patients. Methods: From August 2020 to June 2022, 34 patients (age, 57.7±10.7 years; male, 20) clinically diagnosed with type 2 diabetes mellitus (DM) and 19 DM-free individuals (age, 48.1±16.9 years; male, 12) were selected for analysis. The series participants formed the DM group and control group, respectively. Spectral parameters, including effective atomic number (Zeff), iodine density (ID), normalized iodine density (NID) and the slope of the energy spectrum curves (λ), between the 2 groups were analyzed using independent samples t-test. Receiver operator characteristic (ROC) curves were used to evaluate the diagnostic performance of spectral parameters for detecting renal perfusion changes. Results: The results indicate that in both cortical and medullary phases, the values of Zeff, ID, NID, and λ40-70 for the renal cortex of the DM group were significantly higher than those in the control group (P<0.05). In the cortex phase, the diagnostic efficacy of cortical spectral CT parameters discriminating DM patients from controls was as follows: the area under ROC curve (AUC) of ID value was 0.816 [95% confidence interval (CI): 0.679-0.921] at the optimal cutoff value 4.14, the AUC of Zeff value was 0.800 (95% CI: 0.668-0.901) at the optimal cutoff value 9.26, the AUC of λ40-70 value was 0.822 (95% CI: 0.675-0.918) at the optimal cutoff value 8.26, and the AUC of NID value was 0.851 (95% CI: 0.684-0.926) at the optimal cutoff value 0.37. In medullary phase: the AUC of ID value was 0.769 (95% CI: 0.617-0.846) at the optimal cutoff value 5.08, the AUC of Zeff value was 0.763 (95% CI: 0.614-0.837) at the optimal cutoff value 9.58, the AUC of λ40-70 value was 0.766 (95% CI: 0.617-0.839) at the optimal cutoff value 10.07, and the AUC of NID value was 0.79 (95% CI: 0.623-0.855) at the optimal cutoff value 1.37. Conclusions: Spectral CT could serve as an alternative protocol for the early identification of kidney injury in diabetic patients.

Textural features of the frontal white matter could be used to discriminate amnestic mild cognitive impairment patients from the normal population.

OBJECTIVE: We aim to develop a radiomics model based on 3-dimensional (3D)-T1WI images to discriminate amnestic mild cognitive impairment (aMCI) patients from the normal population by measuring changes in frontal white matter. METHODS: In this study, 126 patients with aMCI and 174 normal controls (NC) were recruited from the local community. All subjects underwent routine magnetic resonance imaging examination (including 3D-T1WI ). Participants were randomly divided into a training set (n = 242, aMCI:102, NC:140) and a testing set (n = 58, aMCI:24, NC:34). Texture features of the frontal lobe were extracted from 3D-T1WI images. The least absolute shrinkage and selection operator (LASSO) was used to reduce feature dimensions and develop a radiomics signature model. Diagnostic performance was assessed in the training and testing sets using the receiver operating characteristic (ROC) curve analysis. The area under the ROC curve (AUC), sensitivity, and specificity were also calculated. The efficacy of the radiomics model in discriminating aMCI patients from the normal population was assessed by decision curve analysis (DCA). RESULTS: A total of 108 frontal lobe texture features were extracted from 3D-T1WI images. LASSO selected 58 radiomic features for the final model, including log-sigma (n = 18), original (n = 8), and wavelet (n = 32) features. The performance of radiomic features extracted from 3D T1 imaging for distinguishing aMCI patients from controls was: in the training set, AUC was 1.00, and the accuracy, sensitivity, and specificity were 100%, 98%, and 100%, respectively. In the testing set, AUC was 0.82 (95% CI:0.69-0.95), and the accuracy, sensitivity, and specificity were 69%, 92%, and 55%, respectively. The DCA demonstrated that the model had favorable clinical predictive value. CONCLUSIONS: Textural features of white matter in the frontal lobe showed potential for distinguishing aMCI from the normal population, which could be a surrogate protocol to aid aMCI screening in clinical setting.

Matrine Protects Endothelial Progenitor Cells against Apoptosis and Promotes Their Migration, Invasion, and Tube Formation Abilities via Modulating miR-126b/FOXO4 Axis.

INTRODUCTION AND OBJECTIVE: Endothelial progenitor cells (EPCs) have been proven to exhibit a therapeutic effect in deep vein thrombosis, but are susceptible to microenvironment. Besides, Matrine has promotive effects on EPCs, but its effects on microRNA (miR)-126 remain obscure, which are therefore discussed in the study. METHODS: The cultured EPCs were extracted from Sprague-Dawley rats and identified by immunofluorescence assay. After being treated with Matrine or transfected with miR-126b inhibitor and small interfering RNA targeting forkhead box (FOXO) 4, the viability and apoptosis of EPCs were determined by cell counting kit-8 assay and flow cytometry. The migration, invasion, and tube formation abilities were detected by scratch, Transwell, and tube formation assays. The target genes of miR-126b were predicted by TargetScan, and verified by dual-luciferase reporter assay. The expressions of miR-126b, FOXO4, matrix metalloproteinase (MMP) 2, MMP9, and vascular endothelial growth factor (VEGF) A were determined by quantitative real-time polymerase chain reaction and Western blot. RESULTS: The EPCs were successfully extracted and cultured, as evidenced by positive reaction cluster of differentiation (CD) 34 and CD133. Matrine promoted the viability, migration, invasion, and tube formation while inhibiting the apoptosis of EPCs, and upregulated the expression of miR-126b. Besides, miR-126b inhibitor reversed the effects of Matrine on EPCs and downregulated the expression levels of MMP2, MMP9, and VEGFA. MiR-126b targeted the FOXO4, and siFOXO4 reversed the abovementioned effects of miR-126b inhibitor on EPCs. CONCLUSION: Matrine protects EPCs from apoptosis and promotes their migration, invasion, and tube formation abilities via regulating miR-126b/FOXO4 axis.

Comparison and combination of amide proton transfer magnetic resonance imaging and the apparent diffusion coefficient in differentiating the grades of prostate cancer.

Background: More effective risk stratification of prostate cancer (PCa) than that possible with current methods can reduce undertreatment and guard against overtreatment. The aim of this study is to validate the differences and combined effects of amide proton transfer (APT) imaging and apparent diffusion coefficient (ADC) in discriminating the PCa grade group (GG) ≤2 from GG ≥3 PCa. Methods: This is an ongoing prospective study conducted in the radiology department of Nanxishan Hospital of Guangxi Zhuang Autonomous Region. Patients pathologically diagnosed with PCa were enrolled consecutively according to the eligibility criteria. A total of 180 patients (age range, 42-92 years) were included in this study. Using histopathology as the reference standard, we placed 71 cases in GG ≤2 (mean age 67.03±8.696 years) and 109 cases in GG ≥3 (age 69.60±9.638 years). Magnetic resonance imaging (MRI) parameters, including APT and ADC values, were analyzed using an independent samples t-test and binary logistic regression analysis stratified with GG. Receiver operating characteristic curve was used to analyze the diagnostic performance for different parameters distinguishing GG ≤2 and GG ≥3. Results: APT [odds ratio (OR) for the transitional zone (TZ) PCa: 3.20, 95% CI: 1.14-8.98, P=0.02; OR for the peripheral zone (PZ) PCa: 86.32, 95% CI: 13.24-562.88, P=0.003] and ADC values (OR for TZ PCa: 89.79; 95% CI: 2.85-2,827.99, P=0.01; OR for PZ PCa: 39.92; 95% CI: 3.22-494.18, P=0.004) were independent predictors that differentiated the GG of patients. The sensitivity and specificity of the APT values were 61.1% and 81.0%, respectively, while the sensitivity and specificity of the ADC values were 83.3% and 61.9%, respectively. The optimal cutoff value of APT was 3.35% and which of ADC was 1.25×10-3 mm2/s in TZ origin PCa. At the optimal cutoff values of 3.31% (APT) and 0.79×10-3 mm2/s (ADC) in PZ PCa, the sensitivity and specificity of the APT values were 74.0% and 83.6%, respectively, while the sensitivity and specificity of the ADC values were 94.0% and 53.4%, respectively. The area under the curve of the combination of APT and ADC was significantly higher than either of APT or ADC alone in Delong test (TZ: P=0.002 and P=0.020; PZ: P=0.033 and P<0.001). Conclusions: APT and ADC have complementary effects on the sensitivity and specificity for identifying different PCa GGs. A combination model of APT and ADC could improve the diagnostic efficacy of PCa differentiation.

Diagnostic value of dual-layer spectral detector CT in differentiating lung adenocarcinoma from squamous cell carcinoma.

Background and objective: The pathological type of non-small cell lung cancer is considered to be an important factor affecting the treatment and prognosis. The purpose of this study was to investigate the diagnostic value of spectral parameters of dual-layer spectral detector computed tomography (DLCT) in determining efficacy to distinguish adenocarcinoma (AC) and squamous cell carcinoma (SC), and their combined diagnostic efficacy was also analyzed. Methods: This is a single-center prospective study, and we collected 70 patients with lung SC and 127 patients with lung AC confirmed by histopathological examination. Morphological parameters, plain scan CT value, biphasic enhanced CT value, and spectral parameters were calculated. The diagnostic efficiency of morphological parameters, spectral parameters, and spectral parameters combined with morphological parameters was obtained by statistical analysis. Results: In univariate analysis, seven morphological CT features differed significantly between SC and AC: tumor location (distribution), lobulation, spicule, air bronchogram, vacuole sign, lung atelectasis and/or obstructive pneumonia, and vascular involvement (all p < 0.05). In the arterial phase and the venous phase, the spectral parameters of AC were higher than those of SC (AP-Zeff: 8.07 ± 0.23 vs. 7.85 ± 0.16; AP-ID: 1.41 ± 0.47 vs. 0.94 ± 0.28; AP-NID: 0.13 ± 0.04 vs. 0.09 ± 0.03; AP-λ: 3.42 ± 1.10 vs. 2.33 ± 0.96; VP-Zeff: 8.26 ± 0.23 vs. 7.96 ± 0.16; VP-ID: 1.18 ± 0.51 vs. 1.16 ± 0.30; VP-NID: 0.39 ± 0.13 vs. 0.29 ± 0.08; VP-λ: 4.42 ± 1.28 vs. 2.85 ± 0.72; p < 0.001). When conducting multivariate analysis combining CT features and DLCT parameters with the best diagnostic efficacy, the independent predictors of AC were distribution on peripheral (OR, 4.370; 95% CI, 1.485-12.859; p = 0.007), presence of air bronchogram (OR, 5.339; 95% CI, 1.729-16.484; p = 0.004), and presence of vacuole sign ( OR, 7.330; 95% CI, 1.030-52.184; p = 0.047). Receiver operating characteristic curves of the SC and AC showed that VP-λ had the best diagnostic performance, with an area under the curve (AUC) of 0.864 and sensitivity and specificity rates of 85.8% and 74.3%, respectively; the AUC was increased to 0.946 when morphological parameters were combined, and sensitivity and specificity rates were 89.8% and 87.1%, respectively. Conclusion: The quantitative parameters of the DLCT spectrum are of great value in the diagnosis of SC and AC, and the combination of morphological parameters and spectral parameters is helpful to distinguish SC from AC.

Dual-layer spectral detector computed tomography parameters can improve diagnostic efficiency of lung adenocarcinoma grading.

Background: It is difficult to distinguish the pathological grade of lung adenocarcinoma (LUAD) with traditional computed tomography (CT). The aim of this study was to assess tumor differentiation by dual-layer spectral detector CT combined with morphological parameters. Methods: In this prospective study, a total of 67 patients with pathologically diagnosed LUAD were enrolled: 39 patients in the well- and moderately-differentiated group (14 and 25 patients, respectively) and 28 patients in the poorly-differentiated group. Morphological parameters, non-enhanced CT number, double-enhanced CT number, effective atomic number, monoenergetic CT images (40 and 70 keV), iodine density, and thoracic aorta iodine density of tumors were measured. The slope of the spectral curve and normalized iodine density were calculated. The diagnostic efficiency of the spectral parameters alone, and the combined spectral and morphological parameters were obtained by statistical analysis. Results: The morphological signs of LUAD (the vessel convergence sign, bronchus encapsulated air sign, and liquefactive necrosis) were higher in the poorly-differentiated group than in the well-moderately-differentiated group (57.1% vs. 30.8%, 40.0%; 60.7% vs. 28.2%, 32.0%; 64.3% vs. 28.2%, 24.0%; all P<0.05). There were significant differences in normalized iodine density (arterial phase: 0.10±0.04 vs. 0.12±0.05, 0.13±0.04; venous phase: 0.31±0.07 vs. 0.39±0.17, 0.40±0.17) among the poorly-differentiated group and moderately-differentiated group as well as the well-differentiated group (all P<0.05). Receiver operating characteristic (ROC) curves of the poorly-differentiated group and well-moderately-differentiated group showed that the normalized iodine density had the best diagnostic efficiency in the arterial phase, with an area under the curve (AUC) of 0.817, sensitivity of 92.9%, and specificity of 82.1% (P<0.001). The AUC increased to 0.916 when the morphological parameters were included, and sensitivity and specificity were 96.4% and 82.1% (P<0.001), respectively. Conclusions: The parameters of dual-layer spectral detector CT can help discriminate the pathological grade of LUAD. Among the spectral parameters, the normalized iodine density in the arterial phase has the best diagnostic efficiency, and the combination of spectral and morphological parameters improves the pathological grading of LUAD.

Amide Proton Transfer Could Provide More Accurate Lesion Characterization in the Transition Zone of the Prostate.

BACKGROUND: There is an overlap comparing transition zone prostate cancer (TZ PCa) and benign prostatic hyperplasia (BPH) on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI), creating additional challenges for assessment of TZ tumors on MRI. PURPOSE: To evaluate whether amide proton transfer-weighted (APTw) imaging provides new diagnostic ideas for TZ PCa. STUDY TYPE: Prospective. POPULATION: A total of 51 TZ PCa patients (age, 49-89), 44 stromal BPH (age, 57-92), and 45 glandular BPH patients (age, 56-92). FIELD STRENGTH/SEQUENCE: A 3 T; T2WI turbo spin echo (TSE), quantitative T2*-weighted imaging, DWI echo planar imaging, 3D APTw TSE. ASSESSMENT: Differences in APTw, apparent diffusion coefficient (ADC), and T2* among three lesions were compared by one-way analysis of variance (ANOVA). Regions of interest were drawn by two radiologists (X.Q.Z. and X.Y.Q., with 21 and 15 years of experience, respectively). STATISTICAL TESTS: Multivariable logistic regression analyses; ANOVA with post hoc testing; receiver operator characteristic curve analysis; Delong test. Significance level: P < 0.05. RESULTS: APTw among TZ PCa, stromal BPH, and glandular BPH (3.48% ± 0.83% vs. 2.76% ± 0.49% vs. 2.72% ± 0.45%, respectively) were significantly different except between stromal BPH and glandular BPH (P > 0.99). Significant differences were found in ADC (TZ PCa 0.76 ± 0.16 × 10-3  mm2 /sec vs. stromal BPH 0.91 ± 0.14 × 10-3  mm2 /sec vs. glandular BPH 1.08 ± 0.18 × 10-3  mm2 /sec) among three lesions. APTw (OR = 12.18, 11.80, respectively) and 1/ADC (OR = 703.87, 181.11, respectively) were independent predictors of TZ PCa from BPH and stromal BPH. The combination of APTw and ADC had better diagnostic performance in the identification of TZ PCa from BPH and stromal BPH. DATA CONCLUSION: APTw imaging has the potential to be of added value to ADC in differentiating TZ PCa from BPH and stromal BPH. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Parameters of Dual-layer Spectral Detector CT Could be Used to Differentiate Non-Small Cell Lung Cancer from Small Cell Lung Cancer.

BACKGROUND AND OBJECTIVE: Differentiating non-small cell lung cancer (NSCLC) from small cell lung cancer (SCLC) remains a substantial challenge. This study aimed at evaluating the performance of dual-layer spectral detector CT (DLCT) in differentiating NSCLC from SCLC. METHODS: Spectral images of 247 cancer patients confirmed by pathology were retrospectively analyzed in both the arterial phase (AP) and the venous phase (VP), including 197 cases of NSCLC and 50 cases of SCLC. Effective atomic number (Z-eff), Spectral CT-Mono Energetic (MonoE [40keV~90keV]), iodine density (ID) and thoracic aorta iodine density (IDaorta) in contrast-enhanced images were measured and compared between the SCLC and NSCLC subgroups of tumors. The slope of the spectral curve (λ, interval of 10 keV) and normalized iodine density (NID) were also calculated between the SCLC and NSCLC. Through the statistical analysis, the diagnostic efficiency of each spectral parameter was calculated, and the difference in their efficiency was analyzed. RESULTS: Both in NSCLS and SCLC, all parameters in VP were significantly higher than those in AP (p<0.001), except for λ90. There were significant differences in all spectral parameters between NSCLS and SCLC, both in AP and VP (p < 0.001). Except for VP-λ90, there was no significant difference in ROC curves of all spectral parameters. VP-NID exhibited the best diagnostic performance with an AUC value of 0.917 (95%[CI]: 0.870~0.965), sensitivity and specificity of 92.9% and 80%, and a diagnostic threshold of 0.217. CONCLUSION: All parameters of DLCT have high diagnostic efficiency in differentiating NSCLC from SCLC except for VP-λ90, and VP-NID has the highest diagnostic efficiency.

Prevalence and Consequences of Cerebral Small Vessel Diseases: A Cross-Sectional Study Based on Community People Plotted Against 5-Year Age Strata.

Purpose: To study the variation tendency of cerebral small vessel disease (CSVD) imaging markers and total burden with aging and to research the relationship between aging, CSVD markers and cognitive function. Methods: Participants in local urban communities were recruited for neuropsychological and magnetic resonance imaging assessments. Montreal Cognitive Assessment (MoCA), Mini-mental State Examination (MMSE), Number Connection Test A (NCT-A) and Digital Symbol Test (DST) were adopted as neuropsychological scale. Age was stratified at 5-year intervals, and the variation tendency of imaging markers and variables of neuropsychological scales in different age groups was studied. We further studied the relationship between aging, image markers and neuropsychological scales by multi-linear regression. Results: Finally, a total of 401 stroke-free participants (age, 54.83±7.74y; 45.9% were male) were included in the present analysis. With the increase of age, the incidence of imaging markers of CSVD were increased with aging except cerebral microbleeds. The performance results of NCT-A and DST were significant difference in 6 age groups (P < 0.001). In addition, linear decline of the neuropsychological function reflected by NCT-A and DST variables was observed. Linear regression found that age was an independent factor affecting the neuropsychological function reflected by NCT-A and DST variables, and the standard correction coefficients among different age groups increased gradually with age. In addition, brain atrophy is an independent factor affecting neuropsychological variables (odds ratio: -2.929, 95% CI: [-5.094 to -0.765]). There was no correlation between the number of neuroimaging markers and neuropsychological variables after full adjustment. Conclusion: There are many CVSD markers even in younger people, the incidence rate and CVSD marker numbers increase with age. Aging and CSVD may eventually affect cognitive function through brain atrophy.

Amide Proton Transfer MRI Could Be Used to Evaluate the Pathophysiological Status of White Matter Hyperintensities.

BACKGROUND: The pathophysiology of white matter hyperintensities (WMH) remains unclear, investigations of amide proton transfer (APT) signals in WMH disease may provide relevant pathophysiological information. PURPOSE: To evaluate the APT signals differences and heterogeneity of WMH and adjacent normal-appearing white matter (NAWM) at different Fazekas grades and different locations. STUDY TYPE: Prospective. POPULATION: In all, 180 WMH patients (age, 40-76; male/female, 77/103) and 59 healthy controls (age, 42-70; male/female, 23/36). FIELD STRENGTH/SEQUENCE: A 3 T; 3D fluid-attenuated inversion recovery (FLAIR), 3D APT-weighted (APTw). ASSESSMENT: The mean APTw values (APTwmean ) and the APTw signals heterogeneity (APTwmax-min ) among different grades WMH and NAWM and the APTwmean of the same grade deep WMH (DWMH) and paraventricular WMH (PWMH) were calculated and compared. Regions of interests were delineated on WMH lesions, NAWM and healthy white matter. STATISTICAL TESTS: One-way analysis of variance (ANOVA); independent sample t test; Chi-square test. Significance level: P < 0.05. RESULTS: APTwmean among different grade WMH (from grade 0 to 3, 0.58 ± 0.14% vs. 0.29 ± 0.23% vs. 0.37 ± 0.24% vs. 0.61 ± 0.22%, respectively) were significantly different except between grade 1 and 2 (P = 0.27) and between grade 0 and 3 (P = 0.97). The differences in APTwmean between WMH and NAWM were significant (WMH vs. NAWM from grade 1 to 3, 0.29% ± 0.23% vs. 0.55% ± 0.27%; 0.37% ± 0.24% vs. 0.59% ± 0.22%; 0.61% ± 0.22% vs. 0.42% ± 0.24%, respectively). Lower APTwmean values were found only in grade 3 NAWM than other grades NAWM and controls. The APTwmax-min values of grade 1-3 WMH (0.38% ± 0.27% vs. 0.51% ± 0.31% vs. 0.67% ± 0.34%, respectively) were significantly different. Higher APTmean values were found only in grade 2 PWMH (0.47% ± 0.22% vs. 0.32% ± 0.24%). DATA CONCLUSION: Significant differences of APT signals were found in WMH of different Fazekas grades and different locations. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.

Image Mosaic Algorithm-Based Analysis of Pathological Characteristics of Gastric Polyp Patients Using Computed Tomography Images.

The application value of image mosaic algorithm (IMA) based CT imaging technology in the analysis of pathological characteristics of gastric polyp (GP) patients was explored in this work. 588 cases of GP patients in the hospital were selected as the research objects, and CT images based on IMA were adopted for examination. The patient's basic information, image performance, and gastroscopy results were recorded. The results showed that the absolute mean bright error (AMBE) index and information entropy of the IMA are 0.0625 and 7.0385, respectively. The clinical symptoms of patients were mostly abdominal pain (21.4%), abdominal distension (15.6%), and sour regurgitation (17.8%). The common size of GP was no more than 0.5 cm, and the common type was Yamada type II. There were notable differences between single and multiple GPs of different pathological types (P < 0.05). Proliferative polyps were mostly found in the stomach and antrum, while fundus gland polyps were mostly in the stomach and fundus. There was significant difference between the growth location of the hyperplastic polyp and basal gland polyp (P < 0.05). In summary, the CT images of IMA proposed in this paper can not only realize image splicing effectively but also were superior to the traditional SIFT method in the quality of splicing image and were conducive to the analysis of the pathological characteristics of GP patients, which had significant clinical promotion value.

Amide Proton Transfer-Weighted MRI Might Help Distinguish Amnestic Mild Cognitive Impairment From a Normal Elderly Population.

Objectives: To evaluate whether 3D amide proton transfer weighted (APTw) imaging based on magnetization transfer analysis can be used as a novel imaging marker to distinguish amnestic mild cognitive impairment (aMCI) patients from the normal elderly population by measuring changes in APTw signal intensity in the hippocampus and amygdala. Materials and Methods: Seventy patients with aMCI and 74 age- and sex-matched healthy volunteers were recruited for routine MRI and APT imaging examinations. Magnetic transfer ratio asymmetry (MTRasym) of the amide protons (at 3.5 ppm), or APTw values, were measured in the bilateral hippocampus and amygdala on three consecutive cross-sectional APT images and were compared between the aMCI and control groups. The independent sample t-test was used to evaluate the difference in APTw values of the bilateral hippocampus and amygdala between the aMCI and control groups. Receiver operator characteristic analysis was used to assess the diagnostic performance of the APTw. The paired t-test was used to assess the difference in APTw values between the left and right hippocampus and amygdala, in both the aMCI and control groups. Results: The APTw values of the bilateral hippocampus and amygdala in the aMCI group were significantly higher than those in the control group (left hippocampus 1.01 vs. 0.77% p < 0.001; right hippocampus 1.02 vs. 0.74%, p < 0.001; left amygdala 0.98 vs. 0.70% p < 0.001; right amygdala 0.94 vs. 0.71%, p < 0.001). The APTw values of the left amygdala had the largest AUC (0.875) at diagnosis of aMCI. There was no significant difference in APTw values between the left and right hippocampus and amygdala, in either group. (aMCI group left hippocampus 1.01 vs. right hippocampus 1.02%, p = 0.652; healthy control group left hippocampus 0.77 vs. right hippocampus 0.74%, p = 0.314; aMCI group left amygdala 0.98 vs. right amygdala 0.94%, p = 0.171; healthy control group left amygdala 0.70 vs. right amygdala 0.71%, p = 0.726). Conclusion: APTw can be used as a new imaging marker to distinguish aMCI patients from the normal elderly population by indirectly reflecting the changes in protein content in the hippocampus and amygdala.

Temporal changes of CT findings between non-severe and severe cases of COVID-19 pneumonia: a multi-center, retrospective, longitudinal Study.

Background and aim: To perform a longitudinal analysis of serial CT findings over time in patients with COVID-19 pneumonia. Methods: From February 5 to March 8, 2020, 73 patients (male to female, ratio of 43:30; mean age, 51 years) with COVID-19 pneumonia were retrospectively enrolled and followed up until discharge from three institutions in China. The patients were divided into the severe and non-severe groups according to treatment option. The patterns and distribution of lung abnormalities, total CT scores, single ground-glass opacity (GGO) CT scores, single consolidation CT scores, single reticular CT scores and the amounts of zones involved were reviewed by 2 radiologists. These features were analyzed for temporal changes. Results: In non-severe group, total CT scores (median, 9.5) and the amounts of zones involved were slowly increased and peaked in disease week 2. In the severe group, the increase was faster, with scores also peaking at 2 weeks (median, 20). In both groups, the later parameters began to decrease in week 4 (median values of 9 and 19 in the non-severe and severe groups, respectively). In the severe group, the dominant residual lung lesions were reticular (median single reticular CT score, 10) and consolidation (median single consolidation CT score, 7). In the non-severe group, the dominant residual lung lesions were GGO (median single GGO CT score, 7) and reticular (median single reticular CT score, 4). In both non-severe and severe groups, the GGO pattern was dominant in week 1, with a higher proportion in the severe group compared with the non-severe group (72% vs. 65%). The consolidation pattern peaked in week 2, with 9 (32%) and 19 (73%) in the non-severe and severe groups, respectively; the reticular pattern became dominant from week 4 (both group >40%). Conclusion: The extent of CT abnormalities in the severe and non-severe groups peaked in disease week 2. The temporal changes of CT manifestations followed a specific pattern, which might indicate disease progression and recovery.

Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection.

In this retrospective study, chest CTs of 121 symptomatic patients infected with coronavirus disease-19 (COVID-19) from four centers in China from January 18, 2020 to February 2, 2020 were reviewed for common CT findings in relationship to the time between symptom onset and the initial CT scan (i.e. early, 0-2 days (36 patients), intermediate 3-5 days (33 patients), late 6-12 days (25 patients)). The hallmarks of COVID-19 infection on imaging were bilateral and peripheral ground-glass and consolidative pulmonary opacities. Notably, 20/36 (56%) of early patients had a normal CT. With a longer time after the onset of symptoms, CT findings were more frequent, including consolidation, bilateral and peripheral disease, greater total lung involvement, linear opacities, "crazy-paving" pattern and the "reverse halo" sign. Bilateral lung involvement was observed in 10/36 early patients (28%), 25/33 intermediate patients (76%), and 22/25 late patients (88%).

Predictive models of minimal hepatic encephalopathy for cirrhotic patients based on large-scale brain intrinsic connectivity networks.

We aimed to find the most representative connectivity patterns for minimal hepatic encephalopathy (MHE) using large-scale intrinsic connectivity networks (ICNs) and machine learning methods. Resting-state fMRI was administered to 33 cirrhotic patients with MHE and 43 cirrhotic patients without MHE (NMHE). The connectivity maps of 20 ICNs for each participant were obtained by dual regression. A Bayesian machine learning technique, called Graphical Model-based Multivariate Analysis, was applied to determine ICN regions that characterized group differences. The most representative ICNs were evaluated by the performance of three machine learning methods (support vector machines (SVMs), multilayer perceptrons (MLP), and C4.5). The clinical significance of these potential biomarkers was further tested. The temporal lobe network (TLN), and subcortical network (SCN), and sensorimotor network (SMN) were selected as representative ICNs. The distinct functional integration patterns of the representative ICNs were significantly correlated with behavior criteria and Child-Pugh scores. Our findings suggest the representative ICNs based on GAMMA can distinguish MHE from NMHE and provide supplementary information to current MHE diagnostic criteria.

Cullin-1 promotes cell proliferation in human breast cancer and is related to diabetes.

AIM: Breast carcinoma (BCA) and diabetes mellitus (DM) are two major health problems in women and the general population. Cullin-1 is reported to be an important tumor-related protein involved in cell-cycle progression, signal transduction and transcription. The aim of this work is to investigate the role of Cullin-1 in the development of BCA and to find potential relationships between Cullin-1 and diabetes in BCA patients. METHODS: To evaluate the function of Cullin-1, we entered 168 patients with primary invasive BCA in this study. Pairs of BCA tissues and adjacent noncancerous tissues from these patients were collected between 2006 and 2008. We used immunohistochemistry to analyze the correlation between Cullin-1 expression and clinicopathological variables and patient survival. In addition, we investigated the role of Cullin-1 in BCA cell proliferation. RESULTS: Cullin-1 expression was upregulated in BCA tissues. Enhanced immunoreactivity for Cullin-1 in BCA tissues was inversely correlated with overall survival and disease-free survival, which suggested a poor prognosis in BCA patients. Strong expression of Cullin-1 was more frequently observed in patients with estrogen receptor negativity and HER2 positivity. We also found that Cullin-1 expression was increased in BCA patients with a previous diagnosis of diabetes. CONCLUSIONS: Our results demonstrate that increased Cullin-1 expression is significantly correlated with poor prognosis in patients with BCA. Cullin-1 might regulate BCA cell proliferation through the ubiquitin-proteasome system. Thus, Cullin-1 might be an important marker and a therapeutic target in BCA.