Ketogenic Diet Modulates Neuroinflammation via Metabolites from Lactobacillus reuteri After Repetitive Mild Traumatic Brain Injury in Adolescent Mice.

Repetitive mild traumatic brain injury (rmTBI) is associated with a range of neural changes which is characterized by axonal injury and neuroinflammation. Ketogenic diet (KD) is regarded as a potential therapy for facilitating recovery after moderate-severe traumatic brain injury (TBI). However, its effect on rmTBI has not been fully studied. In this study, we evaluated the anti-neuroinflammation effects of KD after rmTBI in adolescent mice and explored the potential mechanisms. Experimentally, specific pathogen-free (SPF) adolescent male C57BL/6 mice received a sham surgery or repetitive mild controlled cortical impacts consecutively for 7 days. The uninjured mice received the standard diet, and the mice with rmTBI were fed either the standard diet or KD for 7 days. One week later, all mice were subjected to behavioral tests and experimental analysis. Results suggest that KD significantly increased blood beta-hydroxybutyrate (ß-HB) levels and improved neurological function. KD also reduced white matter damage, microgliosis, and astrogliosis induced by rmTBI. Aryl hydrocarbon receptor (AHR) signaling pathway, which was mediated by indole-3-acetic acid (3-IAA) from Lactobacillus reuteri (L. reuteri) in gut and activated in microglia and astrocytes after rmTBI, was inhibited by KD. The expression level of the toll-like receptor 4 (TLR4)/myeloid differentiation primary response 88 (MyD88) in inflammatory cells, which mediates the NF-κB pathway, was also attenuated by KD. Taken together, our results indicated that KD can promote recovery following rmTBI in adolescent mice. KD may modulate neuroinflammation by altering L. reuteri in gut and its metabolites. The inhibition of indole/AHR pathway and the downregulation of TLR4/MyD88 may play a role in the beneficial effect of KD against neuroinflammation in rmTBI mice.

A three-component multi-b-value diffusion-weighted imaging might be a useful biomarker for detecting microstructural features in gliomas with differences in malignancy and IDH-1 mutation status.

OBJECTIVES: The purpose of the study was to explore the performance of a three-component diffusion model in evaluating the degree of malignancy and isocitrate dehydrogenase 1 (IDH-1) gene type of gliomas. METHODS: Overall, 60 patients with gliomas were enrolled. The intermediate and perfusion-related diffusion coefficients (Dint and Dp) and fractions of strictly limited, intermediate, and perfusion-related diffusion (Fvery-slow, Fint, and Fp) were obtained with a three-component diffusion model. Parameters were also obtained from a diffusion kurtosis model and mono- and biexponential models. All parameters were compared between different tumor grades and IDH-1 gene types. Diagnostic performance and logistic regression analyses were performed. RESULTS: High-grade gliomas (HGGs) had significantly higher Fint, Fvery-slow, and Dp values but significantly lower Fp and Dint values than low-grade gliomas (LGGs), and Fint and Fp differed significantly among grade II, III, and IV gliomas (p < 0.05 for all). Fint achieved the highest AUC of 0.872 in differentiating between LGGs and HGGs. Logistic regression analysis revealed that in each model, Fint, diffusion coefficient (D), apparent diffusion coefficient (ADC), mean diffusivity (MD), and mean kurtosis (MK) were associated with glioma grading. After multiple regression analysis, Fint remained the only differentiator. Additionally, Fint and Fp showed significant differences between IDH-1 mutated and IDH-1 wild-type gliomas (p = 0.007 and 0.01, respectively). CONCLUSIONS: The three-component DWI model served as a useful biomarker for detecting microstructural features in gliomas with different grades and IDH-1 mutation statuses. KEY POINTS: • The three-component model enables the estimation of an intermediate diffusion component. • The three-component model performed better than the other models in glioma grading and genotyping. • Fint was useful in evaluating the grade and genotype of gliomas.

Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas.

OBJECTIVES: Pituitary adenomas can exhibit aggressive behavior, characterized by rapid growth, resistance to conventional treatment, and early recurrence. This study aims to evaluate the clinical value of shape-related features combined with textural features based on conventional MRI in evaluating the aggressiveness of pituitary adenomas and develop the best diagnostic model. METHODS: Two hundred forty-six pituitary adenoma patients (84 aggressive, 162 non-aggressive) who underwent preoperative MRI were retrospectively reviewed. The patients were divided into training (n = 193) and testing (n = 53) sets. Clinical information, shape-related, and textural features extracted from the tumor volume on contrast-enhanced T1-weighted images (CE-T1WI), were compared between aggressive and non-aggressive groups. Variables with significant differences were enrolled into Pearson's correlation analysis to weaken multicollinearity. Logistic regression models based on the selected features were constructed to predict tumor aggressiveness under fivefold cross-validation. RESULTS: Sixty-five imaging features, including five shape-related and sixty textural features, were extracted from volumetric CE-T1WI. Forty-seven features were significantly different between aggressive and non-aggressive groups (all p values < 0.05). After feature selection, four features (SHAPE_Sphericity, SHAPE_Compacity, DISCRETIZED_Q3, and DISCRETIZED_Kurtosis) were put into logistic regression analysis. Based on the combination of these features and Knosp grade, the model yielded an area under the curve value of 0.935, with a sensitivity of 94.4% and a specificity of 82.9%, to discriminate between aggressive and non-aggressive pituitary adenomas in the testing set. CONCLUSION: The radiomic model based on tumor shape and textural features study from CE-T1WI might potentially assist in the preoperative aggressiveness diagnosis of pituitary adenomas. KEY POINTS: • Pituitary adenomas with aggressive behavior exhibit rapid growth, resistance to conventional treatment, and early recurrence despite gross resection and may require multiline treatments. • Shape-related features and texture features based on CE-T1WI were significantly correlated with the Ki-67 labeling index, mitotic count, and p53 expression, and the proposed model achieved a favorable prediction of the aggressiveness of PAs with an AUC value of 0.935. • The prediction model might provide valuable guidance for individualized treatment in patients with PAs.

Diffusion-weighted MRI for predicting pathologic response to neoadjuvant chemotherapy in breast cancer: evaluation with mono-, bi-, and stretched-exponential models.

BACKGROUND: To investigate the performance of diffusion-weighted (DW) MRI with mono-, bi- and stretched-exponential models in predicting pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT) for breast cancer, and further outline a predictive model of pCR combining DW MRI parameters, contrast-enhanced (CE) MRI findings, and/or clinical-pathologic variables. METHODS: In this retrospective study, 144 women who underwent NACT and subsequently received surgery for invasive breast cancer were included. Breast MRI including multi-b-value DW imaging was performed before (pre-treatment), after two cycles (mid-treatment), and after all four cycles (post-treatment) of NACT. Quantitative DW imaging parameters were computed according to the mono-exponential (apparent diffusion coefficient [ADC]), bi-exponential (pseudodiffusion coefficient and perfusion fraction), and stretched-exponential (distributed diffusion coefficient and intravoxel heterogeneity index) models. Tumor size and relative enhancement ratio of the tumor were measured on contrast-enhanced MRI at each time point. Pre-treatment parameters and changes in parameters at mid- and post-treatment relative to baseline were compared between pCR and non-pCR groups. Receiver operating characteristic analysis and multivariate regression analysis were performed. RESULTS: Of the 144 patients, 54 (37.5%) achieved pCR after NACT. Overall, among all DW and CE MRI measures, flow-insensitive ADC change (ΔADC200,1000) at mid-treatment showed the highest diagnostic performance for predicting pCR, with an area under the receiver operating characteristic curve (AUC) of 0.831 (95% confidence interval [CI]: 0.747, 0.915; P < 0.001). The model combining pre-treatment estrogen receptor and human epidermal growth factor receptor 2 statuses and mid-treatment ΔADC200,1000 improved the AUC to 0.905 (95% CI: 0.843, 0.966; P < 0.001). CONCLUSION: Mono-exponential flow-insensitive ADC change at mid-treatment was a predictor of pCR after NACT in breast cancer.

Blood oxygenation level-dependent cardiovascular magnetic resonance of the skeletal muscle in healthy adults: Different paradigms for provoking signal alterations.

PURPOSE: Stress blood oxygenation level-dependent (BOLD) cardiovascular magnetic resonance allows for quantitative evaluation of blood flow reserve in skeletal muscles. This study aimed to prospectively compare three commonly used skeletal BOLD cardiovascular magnetic resonance paradigms in healthy adults: gas inhalation, cuff compression-induced ischemia and postocclusive reactive hyperemia, and exercise. METHODS: Twelve young (22 ± 0.9 years) and 10 elderly (58 ± 5.0 years) healthy subjects underwent BOLD cardiovascular magnetic resonance under the three paradigms. T2∗ signal intensity time curves were generated and quantitative parameters were calculated. Meanwhile, stress transcutaneous oxygen pressure measurements were obtained as comparison. Measurement reproducibility was assessed with intraclass correlation coefficients. Differences in the T2∗ BOLD variation, the correlation with transcutaneous oxygen pressure, and the age-related change between paradigms were statistically analyzed. RESULTS: Minimum ischemic value and maximum hyperemic peak value showed the highest interobserver and interscan reproducibilities (intraclass correlation coefficient >0.90). The plantar dorsiflexion exercise paradigm elicited the largest T2∗ BOLD variation (15.48% ± 10.56%), followed by ischemia (8.30% ± 6.33%). Negligible to weak changes were observed during gas inhalation. Correlations with transcutaneous oxygen pressure measurements were found in the ischemic phase (r = 0.966; P < .001) and in the postexercise phase (r = -0.936; P < .001). Minimum ischemic value, maximum hyperemic peak value, maximum postexercise value, and slope of postexercise signal decay showed significant differences between young and elderly subjects (P < .01). CONCLUSION: Ischemia and reactive hyperemia have superior reproducibility, and exercise could induce the largest T2∗ variation. Key parameters from the two paradigms show age-related differences.

Brain T1ρ mapping for grading and IDH1 gene mutation detection of gliomas: a preliminary study.

INTRODUCTION: The longitudinal relaxation time in the rotating frame (T1ρ) has proved to be sensitive to metabolism and useful in application to neurodegenerative diseases. However, few literature exists on its utility in gliomas. Thus, this study was conducted to explore the performance of T1ρ mapping in tumor grading and characterization of isocitrate dehydrogenase 1 (IDH1) gene mutation status of gliomas. METHODS: Fifty-seven patients with gliomas underwent brain MRI and quantitative measurements of T1ρ and apparent diffusion coefficient (ADC) were recorded. Parameters were compared between high-grade gliomas (HGG) and low-grade gliomas (LGG) and between IDH1 mutant and wildtype groups. RESULTS: HGG showed significantly higher T1ρ values in both the solid and peritumoral edema areas compared with LGG (P < 0.001 and P = 0.005, respectively), whereas no significant differences in the two areas were found for ADC (both P > 0.05). Receiver operating characteristic (ROC) curve analysis showed that T1ρ value in the solid area achieved the highest area under the ROC curve (AUC, 0.841) in grading with a sensitivity of 80.6% and a specificity of 81.0%. In the grade II/III glioma group, multivariate logistic regression showed that both tumor frontal lobe location (odds ratio [OR] 526.608; P = 0.045) and T1ρ value of the peritumoral edema area (OR 0.863; P = 0.037) were significant predictors of IDH1 mutation. Using the combination, the diagnostic sensitivity and specificity for IDH1 mutated gliomas were 93.3% and 88.9%, respectively. CONCLUSIONS: Our study shows the feasibility of applying T1ρ mapping in assessing the histologic grade and IDH1 mutation status of gliomas.

Added value of mean and entropy of apparent diffusion coefficient values for evaluating histologic phenotypes of invasive ductal breast cancer with MR imaging.

OBJECTIVES: To study the added value of mean and entropy of apparent diffusion coefficient (ADC) values at standard (800 s/mm2) and high (1500 s/mm2) b-values obtained with diffusion-weighted imaging in identifying histologic phenotypes of invasive ductal breast cancer (IDC) with MR imaging. METHODS: One hundred thirty-four IDC patients underwent diffusion-weighted imaging with b-values of 800 and 1500 s/mm2, and corresponding ADC800 and ADC1500 maps were generated. Mean and entropy of volumetric ADC values were compared with molecular markers (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67). Associations among morphologic features, ADC metrics, and phenotypes (luminal A, luminal B [HER2 negative], luminal B [HER2 positive], HER2 positive, and triple negative) were evaluated. RESULTS: Mean ADC values were significantly decreased in ER-positive, PR-positive, and HER2-negative tumors (p < 0.01). Ki-67 ≥ 20% tumors demonstrated significantly higher ADC entropy values compared with Ki-67 < 20% tumors (p < 0.001). Luminal A subtype tended to display lower ADC entropy values compared with other subtypes, while HER2-positive subtype tended to display higher mean ADC values. ADC1500 entropy provided superior diagnostic performance over ADC800 entropy (p = 0.04). Independent risk factors were ADC1500 entropy (p = 0.002) associated with luminal A, irregular mass shape (p = 0.018) and ADC1500 entropy (p = 0.022) with luminal B (HER2 positive), mean ADC1500 (p = 0.018) with HER2 positive, and smooth mass margin (p = 0.012) and rim enhancement (p = 0.003) with triple negative. CONCLUSIONS: Mean and entropy of ADC values provided complementary information and added value for evaluating IDC histologic phenotypes. High-b-value ADC1500 may facilitate better phenotype discrimination. KEY POINTS: • ADC metrics are associated with molecular marker status in IDC. • ADC 1500 improves differentiation of histologic phenotypes compared with ADC 800 . • ADC metrics add value to morphologic features in IDC phenotyping.

Difference in the functional connectivity of the dorsolateral prefrontal cortex between smokers with nicotine dependence and individuals with internet gaming disorder.

BACKGROUND: It has been reported that internet gaming disorder (IGD) and smokers with nicotine dependence (SND) share clinical characteristics, such as over-engagement despite negative consequences and cravings. This study is to investigate the alterations in the resting-state functional connectivity (rsFC) of the dorsolateral prefrontal cortex (DLPFC) observed in SND and IGD. In this study, 27 IGD, 29 SND, and 33 healthy controls (HC) underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scan. DLPFC connectivity was determined in all participates by investigating the synchronized low-frequency fMRI signal fluctuations using a temporal seed-based correlation method. RESULTS: Compared with the HC group, the IGD and SND groups showed decreased rsFC with DLPFC in the right insula and left inferior frontal gyrus with DLPFC. Compared with SND group, the IGD subjects exhibited increased rsFC in the left inferior temporal gyrus and right inferior orbital frontal gyrus and decreased rsFC in the right middle occipital gyrus, supramarginal gyrus, and cuneus with DLPFC. CONCLUSION: Our results confirmed that SND and IGD share similar neural mechanisms related to craving and impulsive inhibitions. The significant difference in rsFC with DLPFC between the IGD and SND subjects may be attributed to the visual and auditory stimulation generated by long-term internet gaming.

Multiparametric diffusion-weighted imaging in breast lesions: Association with pathologic diagnosis and prognostic factors.

PURPOSE: To determine the utility of multiparametric diffusion-weighted imaging (DWI) including monoexponential (apparent diffusion coefficient [ADC]), biexponential (Df , Ds , and f), stretched-exponential (distributed diffusion coefficient [DDC] and α), and kurtosis (mean diffusivity [MD] and mean kurtosis [MK]) models in the differentiation and characterization of breast lesions, and assess their associations with prognostic factors in invasive breast cancer. MATERIALS AND METHODS: This study included 101 patients (44 benign and 57 malignant lesions) who underwent 3T breast multi-b-value DWI. Diffusion model selection was investigated in benign and malignant lesions using the Akaike information criteria (AIC). Mann-Whitney U-test and receiver operating characteristic (ROC) curves were used for statistical analysis. RESULTS: Goodness-of-fit analysis showed that most benign lesion voxels (50.5%) were preferred by the kurtosis model, and most malignant lesion voxels (51.2%) by the stretched-exponential model. All diffusion measures showed significant differences between benign and malignant lesions (P < 0.05), and between in situ and invasive cancers (P < 0.05) except MD (P = 0.103). There were no significant differences in areas under the ROC curves (AUCs) between ADC and non-monoexponential diffusion parameters (P > 0.05), except Df and α, whose AUCs were significantly lower than AUC of ADC for differentiating benign from malignant lesions (P = 0.03 and P < 0.01, respectively). In patients with invasive breast cancer, α was significantly correlated with tumor size (P = 0.007) and Ki-67 expression (P = 0.012), Df was significantly correlated with lymph node metastasis (P = 0.021) and Ki-67 expression (P = 0.042), and ADC, Ds , f, DDC, and MD were significantly correlated with estrogen receptor status (all P < 0.05). CONCLUSION: Multiparametric DWI shows relationships with pathologic outcomes and prognostic factors of breast lesions. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:740-750.

Stroke assessment with intravoxel incoherent motion diffusion-weighted MRI.

Intravoxel incoherent motion (IVIM) diffusion-weighted MRI can simultaneously measure diffusion and perfusion characteristics in a non-invasive way. This study aimed to determine the potential utility of IVIM in characterizing brain diffusion and perfusion properties for clinical stroke. The multi-b-value diffusion-weighted images of 101 patients diagnosed with acute/subacute ischemic stroke were retrospectively evaluated. The diffusion coefficient D, representing the water apparent diffusivity, was obtained by fitting the diffusion data with increasing high b-values to a simple mono-exponential model. The IVIM-derived perfusion parameters, pseudodiffusion coefficient D*, vascular volume fraction f and blood flow-related parameter fD*, were calculated with the bi-exponential model. Additionally, the apparent diffusion coefficient (ADC) was fitted according to the mono-exponential model using all b-values. The diffusion parameters for the ischemic lesion and normal contralateral region were measured in each patient. Statistical analysis was performed using the paired Student t-test and Pearson correlation test. Diffusion data in both the ischemic lesion and normal contralateral region followed the IVIM bi-exponential behavior, and the IVIM model showed better goodness of fit than the mono-exponential model with lower Akaike information criterion values. The paired Student t-test revealed significant differences for all diffusion parameters (all P < 0.001) except D* (P = 0.218) between ischemic and normal areas. For all patients in both ischemic and normal regions, ADC was significantly positively correlated with D (both r = 1, both P < 0.001) and f (r = 0.541, P < 0.001; r = 0.262, P = 0.008); significant correlation was also found between ADC and fD* in the ischemic region (r = 0.254, P = 0.010). For all pixels within the region of interest from a representative subject in both ischemic and normal regions, ADC was significantly positively correlated with D (both r = 1, both P < 0.001), f (r = 0.823, P < 0.001; r = 0.652, P < 0.001) and fD* (r = 0.294, P < 0.001; r = 0.340, P < 0.001). These findings may have clinical implications for the use of IVIM imaging in the assessment and management of acute/subacute stroke patients. Copyright © 2016 John Wiley & Sons, Ltd.

Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient.

PURPOSE: To investigate the utility of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in capturing breast lesion heterogeneity and determine which ADC metric may help best differentiate benign from malignant breast mass lesions at 3.0T magnetic resonance imaging (MRI). MATERIALS AND METHODS: We retrospectively included 101 women with breast mass lesions (benign:malignant = 36:65) who underwent 3.0T diffusion-weighted imaging (DWI) and subsequently had histopathologic confirmation. ADC histogram parameters, including the mean, minimum, maximum, 10th/25th/50th/75th/90th percentile, skewness, kurtosis, and entropy ADCs, were derived for the whole-lesion volume in each patient. Mann-Whitney U-test, univariate and multivariate logistic regression, area under the receiver-operating characteristic curve (Az ), intraclass correlation coefficient (ICC), and Bland-Altman test were used for statistical analysis. RESULTS: Mean, minimum, maximum, and 10th/25th/50th/75th/90th percentile ADCs were significantly lower (all P < 0.0001), while skewness and entropy ADCs were significantly higher (P < 0.001 and P = 0.001, respectively) in malignant lesions compared with benign ones. The Az values of minimum and 25th percentile ADCs were significantly higher than that of mean ADC (P = 0.0194 and P = 0.0154, respectively) or that of median ADC (P = 0.0300 and P = 0.0401, respectively), indicating that minimum and 25th percentile ADCs may be more accurate for lesion discrimination. Multivariate logistic regression showed that the minimum ADC was the unique independent predictor of breast malignancy. Minimum and 25th percentile ADCs had excellent interobserver agreement (ICC = 0.943 and 0.989, respectively; narrow width of 95% limits of agreement). CONCLUSION: These results suggest that whole-lesion ADC histogram analysis may facilitate the differentiation between benign and malignant breast mass lesions.

Differentiation of pyogenic hepatic abscesses from malignant mimickers using multislice-based texture acquired from contrast-enhanced computed tomography.

BACKGROUND: Pyogenic hepatic abscess may mimic primary or secondary carcinoma of the liver on contrast-enhanced computed tomography (CECT). The present study was to explore the usefulness of the analysis of multislice-based texture acquired from CECT in the differentiation between pyogenic hepatic abscesses and malignant mimickers. METHODS: This retrospective study included 25 abscesses in 20 patients and 33 tumors in 26 subjects who underwent CECT. To make comparison, we also enrolled 19 patients with hepatic single simple cyst. The images from CECT were analyzed using a Laplacian of Gaussian band-pass filter (5 filter levels with sigma weighting ranging from 1.0 to 2.5). We also quantified the uniformity, entropy, kurtosis and skewness of the multislice-based texture at different sigma weightings. Statistical significance for these parameters was tested with one-way ANOVA followed by Tukey honestly significant difference (HSD) test. Diagnostic performance was evaluated using the receiver operating characteristic (ROC) curve analysis. RESULTS: There were significant differences in entropy and uniformity at all sigma weightings (P<0.001) among hepatic abscesses, malignant mimickers and simple cysts. The significant difference in kurtosis and skewness was shown at sigma 1.8 and 2.0 weightings (P=0.002-0.006). Tukey HSD test showed that the abscesses had a significantly higher entropy and lower uniformity compared with malignant mimickers (P=0.000-0.004). Entropy (at a sigma 2.0 weighting) had the largest area under the ROC curve (0.888) in differentiating abscesses from malignant mimickers, with a sensitivity of 81.8% and a specificity of 88.0% when the cutoff value was set to 3.64. CONCLUSION: Multislice-based texture analysis may be useful for differentiating pyogenic hepatic abscesses from malignant mimickers.

Exploring "Equigenesis" in the Associations Between Green Space and Kidney Health Among Middle-Aged and Older Adults Using Street View Data.

Background and Objectives: This study systematically explores the association between community green space and preventing kidney failure among middle-aged and older adults in China, using street view data. Research Design and Methods: The 33 Chinese Community Health Study was used to conduct the analysis. We used street view data to assess street view green space (SVG) exposure and clearly distinguished the difference between grass (SVG-grass) and trees (SVG-tree). The normalized difference vegetation index (NDVI) was also used. Kidney failure was defined as a serum creatinine concentration of above 177 mol/L. We used multilevel logistic regression models (controlled for a series of covariates) to examine the associations between SVG and the odds of middle-aged and older adults having kidney failure. We also tested whether middle-aged and older adults from socioeconomically disadvantaged groups are likely to derive greater benefits from the effects of green space ("equigenesis"). Results: The results showed that both SVG (OR = 0.353; 95% CI = 0.171-0.731) and SVG-trees (OR = 0.327; 95% CI = 0.146-0.736) were negatively associated with the likelihood of middle-aged and older adults experiencing kidney failure, but there was no significant evidence of any links between either SVG-grass (OR = 0.567; 95% CI = 0.300-1.076) or the NDVI (OR = 0.398; 95% CI = 0.237-1.058) and kidney failure. Furthermore, the moderation analysis indicated that income and educational attainment have a moderating effect on the association between green space and the improvement of kidney health, which suggests that green space has greater positive effects on the kidney health of disadvantaged groups. Discussion and Implications: To reduce inequalities in relation to kidney disease through urban planning, policymakers are advised to provide more visual green space-especially trees-within the community and to focus in particular on socioeconomically disadvantaged population groups.

Deep learning reconstruction for lumbar spine MRI acceleration: a prospective study.

BACKGROUND: We compared magnetic resonance imaging (MRI) turbo spin-echo images reconstructed using a deep learning technique (TSE-DL) with standard turbo spin-echo (TSE-SD) images of the lumbar spine regarding image quality and detection performance of common degenerative pathologies. METHODS: This prospective, single-center study included 31 patients (15 males and 16 females; aged 51 ± 16 years (mean ± standard deviation)) who underwent lumbar spine exams with both TSE-SD and TSE-DL acquisitions for degenerative spine diseases. Images were analyzed by two radiologists and assessed for qualitative image quality using a 4-point Likert scale, quantitative signal-to-noise ratio (SNR) of anatomic landmarks, and detection of common pathologies. Paired-sample t, Wilcoxon, and McNemar tests, unweighted/linearly weighted Cohen κ statistics, and intraclass correlation coefficients were used. RESULTS: Scan time for TSE-DL and TSE-SD protocols was 2:55 and 5:17 min:s, respectively. The overall image quality was either significantly higher for TSE-DL or not significantly different between TSE-SD and TSE-DL. TSE-DL demonstrated higher SNR and subject noise scores than TSE-SD. For pathology detection, the interreader agreement was substantial to almost perfect for TSE-DL, with κ values ranging from 0.61 to 1.00; the interprotocol agreement was almost perfect for both readers, with κ values ranging from 0.84 to 1.00. There was no significant difference in the diagnostic confidence or detection rate of common pathologies between the two sequences (p ≥ 0.081). CONCLUSIONS: TSE-DL allowed for a 45% reduction in scan time over TSE-SD in lumbar spine MRI without compromising the overall image quality and showed comparable detection performance of common pathologies in the evaluation of degenerative lumbar spine changes. RELEVANCE STATEMENT: Deep learning-reconstructed lumbar spine MRI protocol enabled a 45% reduction in scan time compared with conventional reconstruction, with comparable image quality and detection performance of common degenerative pathologies. KEY POINTS: • Lumbar spine MRI with deep learning reconstruction has broad application prospects. • Deep learning reconstruction of lumbar spine MRI saved 45% scan time without compromising overall image quality. • When compared with standard sequences, deep learning reconstruction showed similar detection performance of common degenerative lumbar spine pathologies.

Development of a composite regional vulnerability index and its relationship with the impacts of the COVID-19 pandemic.

The interactions between vulnerability and human activities have largely been regarded in terms of the level of risk they pose, both internally and externally, for certain groups of disadvantaged individuals and regions/areas. However, to date, very few studies have attempted to develop a comprehensive composite regional vulnerability index, in relation to travel, housing, and social deprivation, which can be used to measure vulnerability at an aggregated level in the social sciences. Therefore, this research aims to develop a composite regional vulnerability index with which to examine the combined issues of travel, housing and socio-economic vulnerability (THASV index). It also explores the index's relationship with the impacts of the COVID-19 pandemic, reflecting both social and spatial inequality, using Greater London as a case study, with data analysed at the level of Middle Layer Super Output Areas (MSOAs). The findings show that most of the areas with high levels of composite vulnerability are distributed in Outer London, particularly in suburban areas. In addition, it is also found that there is a spatial correlation between the THASV index and the risk of COVID-19 deaths, which further exacerbates the potential implications of social deprivation and spatial inequality. Moreover, the results of the multiscale geographically weighted regression (MGWR) show that the travel and socio-economic indicators in a neighbouring district and the related vulnerability indices are strongly associated with the risk of dying from COVID-19. In terms of policy implications, the findings can be used to inform sustainable city planning and urban development strategies designed to resolve urban socio-spatial inequalities and the potential related impacts of COVID-19, as well as guiding future policy evaluation of urban structural patterns in relation to vulnerable areas.

Tuning the Local Coordination of CoP1-xSx between NiAs- and MnP-Type Structures to Catalyze Lithium-Sulfur Batteries.

The slow conversion and rapid shuttling of polysulfides remain major challenges that hinder the practical application of lithium-sulfur (Li-S) batteries. Efficient catalysts are needed to accelerate the conversion and suppress the shuttling. However, the lack of a rational understanding of catalysis poses obstacles to the design of catalysts, thereby limiting the rapid development of Li-S batteries. Herein, we theoretically analyze the modulation of the electronic structure of CoP1-xSx caused by the NiAs-to-MnP-type transition and its influence on catalytic activity. We found that the interacting d-orbitals of the active metal sites play a determining role in adsorption and catalysis, and the optimal dz2-, dxz-, and dyz-orbitals in an appropriately distorted five-coordinate pyramid enable higher catalytic activity compared with their parent structures. Finally, rationally designed catalysts and S were electrospun into carbonized nanofibers to form nanoreactor chains for use as cathodes. The resultant Li-S batteries exhibited superior properties over 1000 cycles with only a decay rate of 0.031% per cycle and demonstrated a high capacity of 887.4 mAh g-1 at a high S loading of 10 mg cm-2. The structural modulation and bonding analyses in this study provide a powerful approach for the rational design of Li-S catalysts.

Spatial risk for a superspreading environment: Insights from six urban facilities in six global cities across four continents.

Introduction: This study sets out to provide scientific evidence on the spatial risk for the formation of a superspreading environment. Methods: Focusing on six common types of urban facilities (bars, cinemas, gyms and fitness centers, places of worship, public libraries and shopping malls), it first tests whether visitors' mobility characteristics differ systematically for different types of facility and at different locations. The study collects detailed human mobility and other locational data in Chicago, Hong Kong, London, São Paulo, Seoul and Zurich. Then, considering facility agglomeration, visitors' profile and the density of the population, facilities are classified into four potential spatial risk (PSR) classes. Finally, a kernel density function is employed to derive the risk surface in each city based on the spatial risk class and nature of activities. Results: Results of the human mobility analysis reflect the geographical and cultural context of various facilities, transport characteristics and people's lifestyle across cities. Consistent across the six global cities, geographical agglomeration is a risk factor for bars. For other urban facilities, the lack of agglomeration is a risk factor. Based on the spatial risk maps, some high-risk areas of superspreading are identified and discussed in each city. Discussion: Integrating activity-travel patterns in risk models can help identify areas that attract highly mobile visitors and are conducive to superspreading. Based on the findings, this study proposes a place-based strategy of non-pharmaceutical interventions that balance the control of the pandemic and the daily life of the urban population.

Exploring the relationship between the COVID-19 pandemic and changes in travel behaviour: A qualitative study.

During the COVID-19 crisis, a series of measures were taken to restrict travel and social activities outside the home in order to curb the pandemic and ameliorate its negative effects. These unprecedented measures have had a profound impact on the number and purposes of trips and modes of travel. In China, although the pandemic is now generally under control and transport availability has returned to nearly normal, the extent of the changes in travel behaviour wrought during and after the pandemic still remains unclear. Therefore, the aim of this paper is to investigate the differences in individual travel behaviours during and after the COVID-19 pandemic, using Huzhou as an example. Semi-structured interviews were used to examine the influence of COVID-19 on the travel behaviour and perceptions of different groups. The results indicate that, initially, travel demand was greatly reduced. Second, decreased travel reduced participation in activities, which can have adverse effects on people's health as well as their subjective well-being. Third, the degree and duration of such impacts varied from person to person. Students, lower income cohorts, groups living in small communities with insufficient green spaces, and those working in tourism, catering, informal businesses and transport-related sectors were more vulnerable than others. Policymakers, urban and transport planners should therefore pay attention to the social inequities that arise from unequal access to transport and heterogeneity between individuals. Additionally, public transport systems require further development to promote social cohesion.

Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach.

PURPOSE: Preoperative prediction of isocitrate dehydrogenase 1 (IDH1) mutation in lower-grade gliomas (LGGs) is crucial for clinical decision-making. This study aimed to examine the predictive value of a machine learning approach using qualitative and quantitative MRI features to identify the IDH1 mutation in LGGs. MATERIALS AND METHODS: A total of 102 LGG patients were allocated to training (n = 67) and validation (n = 35) cohorts and were subject to Visually Accessible Rembrandt Images (VASARI) feature extraction (23 features) from conventional multimodal MRI and radiomics feature extraction (56 features) from apparent diffusion coefficient maps. Feature selection was conducted using the maximum Relevance Minimum Redundancy method and 0.632+ bootstrap method. A machine learning model to predict IDH1 mutation was then established using a random forest classifier. The predictive performance was evaluated using receiver operating characteristic (ROC) curves. RESULTS: After feature selection, the top 5 VASARI features were enhancement quality, deep white matter invasion, tumor location, proportion of necrosis, and T1/FLAIR ratio, and the top 10 radiomics features included 3 histogram features, 3 gray-level run-length matrix features, and 3 gray-level size zone matrix features and one shape feature. Using the optimal VASARI or radiomics feature sets for IDH1 prediction, the trained model achieved an area under the ROC curve (AUC) of 0.779 ± 0.001 or 0.849 ± 0.008 on the validation cohort, respectively. The fusion model that integrated outputs of both optimal VASARI and radiomics models improved the AUC to 0.879. CONCLUSION: The proposed machine learning approach using VASARI and radiomics features can predict IDH1 mutation in LGGs.

Simplified perfusion fraction from diffusion-weighted imaging in preoperative prediction of IDH1 mutation in WHO grade II-III gliomas: comparison with dynamic contrast-enhanced and intravoxel incoherent motion MRI.

Background Effect of isocitr ate dehydrogenase 1 (IDH1) mutation in neovascularization might be linked with tissue perfusion in gliomas. At present, the need of injection of contrast agent and the increasing scanning time limit the application of perfusion techniques. We used a simplified intravoxel incoherent motion (IVIM)-derived perfusion fraction (SPF) calculated from diffusion-weighted imaging (DWI) using only three b-values to quantitatively assess IDH1-linked tissue perfusion changes in WHO grade II-III gliomas (LGGs). Additionally, by comparing accuracy with dynamic contrast-enhanced (DCE) and full IVIM MRI, we tried to find the optimal imaging markers to predict IDH1 mutation status. Patients and methods Thirty patients were prospectively examined using DCE and multi-b-value DWI. All parameters were compared between the IDH1 mutant and wild-type LGGs using the Mann-Whitney U test, including the DCE MRI-derived Ktrans, ve and vp, the conventional apparen t diffusion coefficient (ADC0,1000), IVIM-de rived perfusion fraction (f), diffusion coefficient (D) and pseudo-diffusion coefficient (D*), SPF. We evaluated the diagnostic performance by receive r operating characteristic (ROC) analysis. Results Significant differences were detected between WHO grade II-III gliomas for all perfusion and diffusion parameters (P < 0.05). When compared to IDH1 mutant LGGs, IDH1 wild-type LGGs exhibited significantly higher perfusion metrics (P < 0.05) and lower diffusion metrics (P < 0.05). Among all parameters, SPF showed a higher diagnostic performance (area under the curve 0.861), with 94.4% sensitivity and 75% specificity. Conclusions DWI, DCE and IVIM MRI may noninvasively help discriminate IDH1 mutation statuses in LGGs. Specifically, simplified DWI-derived SPF showed a superior diagnostic performance.