Enhanced choroid plexus segmentation with 3D UX-Net and its association with disease progression in multiple sclerosis.

BACKGROUND: The choroid plexus (CP) is suggested to be closely associated with the neuroinflammation of multiple sclerosis (MS). Segmentation based on deep learning (DL) could facilitate rapid and reproducible volume assessment of the CP, which is crucial for elucidating its role in MS. PURPOSE: To develop a reliable DL model for the automatic segmentation of CP, and further validate its clinical significance in MS. METHODS: The 3D UX-Net model (3D U-Net used for comparison) was trained and validated on T1-weighted MRI from a cohort of 216 relapsing-remitting MS (RRMS) patients and 75 healthy subjects. Among these, 53 RRMS with baseline and 2-year follow-up scans formed an internal test set (dataset1b). Another 58 RRMS from multi-center data served as an external test set (dataset2). Dice coefficient was computed to assess segmentation performance. Compare the correlation of CP volume obtained through automatic and manual segmentation with clinical outcomes in MS. Disability and cognitive function of patients were assessed using the Expanded Disability Status Scale (EDSS) and Symbol Digit Modalities Test (SDMT). RESULTS: The 3D UX-Net model achieved Dice coefficients of 0.875 ± 0.030 and 0.870 ± 0.044 for CP segmentation on dataset1b and dataset2, respectively, outperforming 3D U-Net's scores of 0.809 ± 0.098 and 0.601 ± 0.226. Furthermore, CP volumes segmented by the 3D UX-Net model aligned consistently with clinical outcomes compared to manual segmentation. In dataset1b, both manual and automatic segmentation revealed a significant positive correlation between normalized CP volume (nCPV) and EDSS scores at baseline (manual: r = 0.285, p = 0.045; automatic: r = 0.287, p = 0.044) and a negative correlation with SDMT scores (manual: r = -0.331, p = 0.020; automatic: r = -0.329, p = 0.021). In dataset2, similar correlations were found with EDSS scores (manual: r = 0.337, p = 0.021; automatic: r = 0.346, p = 0.017). Meanwhile, in dataset1b, both manual and automatic segmentation revealed a significant increase in nCPV from baseline to follow-up (p < 0.05). The increase of nCPV was more pronounced in patients with disability worsened than stable patients (manual: p = 0.023; automatic: p = 0.018). Patients receiving disease-modifying therapy (DMT) exhibited a significantly lower nCPV increase than untreated patients (manual: p = 0.004; automatic: p = 0.004). CONCLUSION: The 3D UX-Net model demonstrated strong segmentation performance for the CP, and the automatic segmented CP can be directly used in MS clinical practice. CP volume can serve as a surrogate imaging biomarker for monitoring disease progression and DMT response in MS patients.

Cross-sectional and longitudinal evaluation of white matter microstructure damage and cognitive correlations by automated fibre quantification in relapsing-remitting multiple sclerosis patients.

The purpose of this study was to characterize whole-brain white matter (WM) fibre tracts by automated fibre quantification (AFQ), capture subtle changes cross-sectionally and longitudinally in relapsing-remitting multiple sclerosis (RRMS) patients and explore correlations between these changes and cognitive performance A total of 114 RRMS patients and 71 healthy controls (HCs) were enrolled and follow-up investigations were conducted on 46 RRMS patients. Fractional anisotropy (FA), mean diffusion (MD), axial diffusivity (AD), and radial diffusivity (RD) at each node along the 20 WM fibre tracts identified by AFQ were investigated cross-sectionally and longitudinally in entire and pointwise manners. Partial correlation analyses were performed between the abnormal metrics and cognitive performance. At baseline, compared with HCs, patients with RRMS showed a widespread decrease in FA and increases in MD, AD, and RD among tracts. In the pointwise comparisons, more detailed abnormalities were localized to specific positions. At follow-up, although there was no significant difference in the entire WM fibre tract, there was a reduction in FA in the posterior portion of the right superior longitudinal fasciculus (R_SLF) and elevations in MD and AD in the anterior and posterior portions of the right arcuate fasciculus (R_AF) in the pointwise analysis. Furthermore, the altered metrics were widely correlated with cognitive performance in RRMS patients. RRMS patients exhibited widespread WM microstructure alterations at baseline and alterations in certain regions at follow-up, and the altered metrics were widely correlated with cognitive performance in RRMS patients, which will enhance our understanding of WM microstructure damage and its cognitive correlation in RRMS patients.

Interpretable and Intuitive Machine Learning Approaches for Predicting Disability Progression in Relapsing-Remitting Multiple Sclerosis Based on Clinical and Gray Matter Atrophy Indicators.

RATIONALE AND OBJECTIVES: To investigate whether clinical and gray matter (GM) atrophy indicators can predict disability in relapsing-remitting multiple sclerosis (RRMS) and to enhance the interpretability and intuitiveness of a predictive machine learning model. MATERIALS AND METHODS: 145 and 50 RRMS patients with structural MRI and at least 1-year follow-up Expanded Disability Status Scale (EDSS) results were retrospectively enrolled and placed in the discovery and external test cohorts, respectively. Six clinical and radiomics feature-based machine learning classifiers were trained and tested to predict disability progression in the discovery cohort and validated in the external test set. Partial dependence plot (PDP) analysis and a Shiny web application were conducted to enhance the interpretability and intuitiveness. RESULTS: In the discovery cohort, 98 patients had disability stability, and 47 patients were classified as having disability progression. In the external test set, 35 patients were disability stable, and 15 patients had disability progression. Models trained with both clinical and radiomics features (area under the curve (AUC), 0.725-0.950) outperformed those trained with clinical (AUC, 0.600-0.740) or radiomics features only (AUC, 0.615-0.945). Among clinical+ radiomics feature models, the logistic regression (LR) classifier-based model performed best, with an AUC of 0.950. Only the radiomics feature-only models were applied in the external test set due to the data collection problem and showed fair performance, with AUCs ranging from 0.617 to 0.753. PDP analysis showed that female patients and those with lower volume, surface area, and symbol digit modalities test (SDMT) scores; greater mean curvature and age; and no disease modifying therapy (DMT) had increased probabilities of disease progression. Finally, a Shiny web application (https://lauralin1104.shinyapps.io/LRshiny/) was developed to calculate the risk of disability progression. CONCLUSION: Interpretable and intuitive machine learning approaches based on clinical and GM atrophy indicators can help physicians predict disability progression in RRMS patients for clinical decision-making and patient management.

Aberrant white matter microstructure detected by automatic fiber quantification in pediatric myelin oligodendrocyte glycoprotein antibody-associated disease.

BACKGROUND AND OBJECTIVES: Myelin oligodendrocyte glycoprotein antibody-associated diseases (MOGAD) is an idiopathic inflammatory demyelinating disorder in children, for which the precise damage patterns of the white matter (WM) fibers remain unclear. Herein, we utilized diffusion tensor imaging (DTI)-based automated fiber quantification (AFQ) to identify patterns of fiber damage and to investigate the clinical significance of MOGAD-affected fiber tracts. METHODS: A total of 28 children with MOGAD and 31 healthy controls were included in this study. The AFQ approach was employed to track WM fiber with 100 equidistant nodes defined along each tract for statistical analysis of DTI metrics in both the entire and nodal manner. The feature selection method was used to further screen significantly aberrant DTI metrics of the affected fiber tracts or segments for eight common machine learning (ML) to evaluate their potential in identifying MOGAD. These metrics were then correlated with clinical scales to assess their potential as imaging biomarkers. RESULTS: In the entire manner, significantly reduced fractional anisotropy (FA) was shown in the left anterior thalamic radiation, arcuate fasciculus, and the posterior and anterior forceps of corpus callosum in MOGAD (all p < 0.05). In the nodal manner, significant DTI metrics alterations were widely observed across 37 segments in 10 fiber tracts (all p < 0.05), mainly characterized by decreased FA and increased radial diffusivity (RD). Among them, 14 DTI metrics in seven fiber tracts were selected as important features to establish ML models, and satisfactory discrimination of MOGAD was obtained in all models (all AUC > 0.85), with the best performance in the logistic regression model (AUC = 0.952). For those features, the FA of left cingulum cingulate and the RD of right inferior frontal-occipital fasciculus were negatively and positively correlated with the expanded disability status scale (r = -0.54, p = 0.014; r = 0.43, p = 0.03), respectively. CONCLUSION: Pediatric MOGAD exhibits extensive WM fiber tract aberration detected by AFQ. Certain fiber tracts exhibit specific patterns of DTI metrics that hold promising potential as biomarkers.

Radiomics derived from T2-FLAIR: the value of 2- and 3-classification tasks for different lesions in multiple sclerosis.

Background: White matter (WM) lesions can be classified into contrast enhancement lesions (CELs), iron rim lesions (IRLs), and non-iron rim lesions (NIRLs) based on different pathological mechanism in relapsing-remitting multiple sclerosis (RRMS). The application of radiomics established by T2-FLAIR to classify WM lesions in RRMS is limited, especially for 3-class classification among CELs, IRLs, and NIRLs. Methods: A total of 875 WM lesions (92 CELs, 367 IRLs, 416 NIRLs) were included in this study. The 2-class classification was only performed between IRLs and NIRLs. For the 2- and 3-class classification tasks, all the lesions were randomly divided into training and testing sets with a ratio of 8:2. We used least absolute shrinkage and selection operator (LASSO), reliefF algorithm, and mutual information (MI) for feature selection, then eXtreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM) were used to establish discrimination models. Finally, the area under the curve (AUC), accuracy, sensitivity, specificity, and precision were used to evaluate the performance of the models. Results: For the 2-class classification model, LASSO classifier with RF model showed the best discrimination performance with the AUC of 0.893 (95% CI: 0.838-0.942), accuracy of 0.813, sensitivity of 0.833, specificity of 0.781, and precision of 0.851. However, the 3-class classification model of LASSO with XGBoost displayed the highest performance with the AUC of 0.920 (95% CI: 0.887-0.950), accuracy of 0.796, sensitivity of 0.839, specificity of 0.881, and precision of 0.846. Conclusions: Radiomics models based on T2-FLAIR images have the potential for discriminating among CELs, IRLs, and NIRLs in RRMS.

Construction of a corresponding empirical model to bridge thermal properties and synthesis of thermoresponsive poloxamines.

The thermoresponsive properties of poloxamine (tetra-branch PEO-PPO block copolymer) hydrogels are related to several variables. Of particular interest to this study were the molecular weight of the polymer, the molar ratio between PEO and PPO blocks, and the concentration of the aqueous solution. Accurately controlling the thermoresponsive behaviors of the polymer is critical to the application of such materials; therefore, the structure-property relationship of tetra-branch PEO-PPO block copolymer was studied by synthesis via anionic ring-opening polymerization (AROP). The structure-property relationships were studied by measuring the thermoresponsive behavior via differential scanning calorimetry (DSC) and developing an empirical model which statistically fit the collected data. This empirical model was then used for designing poloxamines that have critical micellization temperatures (CMT) between room temperature and physiological temperature. The model was validated with three polymers that targeted a CMT of 308 K (35°C). The empirical model showed great success in guiding the synthesis of poloxamines showing a temperature difference of less than 3 K between the predicted and the observed CMTs. This study showed a great potential of using an empirical model to set synthesis parameters to control the properties of the polymer products.

Exploring subtypes of multiple sclerosis through unsupervised machine learning of automated fiber quantification.

PURPOSE: This study aimed to subtype multiple sclerosis (MS) patients using unsupervised machine learning on white matter (WM) fiber tracts and investigate the implications for cognitive function and disability outcomes. MATERIALS AND METHODS: We utilized the automated fiber quantification (AFQ) method to extract 18 WM fiber tracts from the imaging data of 103 MS patients in total. Unsupervised machine learning techniques were applied to conduct cluster analysis and identify distinct subtypes. Clinical and diffusion tensor imaging (DTI) metrics were compared among the subtypes, and survival analysis was conducted to examine disability progression and cognitive impairment. RESULTS: The clustering analysis revealed three distinct subtypes with variations in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Significant differences were observed in clinical and DTI metrics among the subtypes. Subtype 3 showed the fastest disability progression and cognitive decline, while Subtype 2 exhibited a slower rate, and Subtype 1 fell in between. CONCLUSIONS: Subtyping MS based on WM fiber tracts using unsupervised machine learning identified distinct subtypes with significant cognitive and disability differences. WM abnormalities may serve as biomarkers for predicting disease outcomes, enabling personalized treatment strategies and prognostic predictions for MS patients.

Effect and mechanism of iron-carbon micro-electrolysis pretreatment of organic peroxide production wastewater.

The wastewater from organic peroxide production has high chemical oxygen demand (COD) concentration and poor biodegradability, so it is necessary to find a cost-effective treatment method. The iron-carbon microelectrolysis (IC-ME) technology was used to pretreat the organic peroxide production wastewater, and the influence of reaction conditions on the removal effect of pollutants and the degradation mechanism were studied. The effects of initial pH, iron filings, iron-carbon ratio, and reaction time on the wastewater treatment were investigated by single-factor and response surface optimization experiments, and the degradation mechanism was analyzed by three-dimensional fluorescence spectroscopy, UV-Vis, and gas chromatography mass spectrometry (GC-MS). The experimental results showed that the COD removal efficiency was 35.67% and the biodegradability of wastewater was increased from 0.113 to 0.173 under the conditions of initial pH of 3.1, the dosage of iron filings of 30.5 g/L, the ratio of iron-carbon of 1.01, and the reaction time of 122.8 min, and the process of IC-ME for degrading COD of wastewater from the production of organic peroxide was consistent with the secondary reaction. The IC-ME process could decompose macromolecular organic compounds such as tyrosine proteins and aromatic proteins, and improve the biodegradability of wastewater. It provides a theoretical reference for the practical application of IC-ME to treat this type of wastewater.

Radiomics models based on cortical damages for identification of multiple sclerosis with cognitive impairment.

BACKGROUND: Cognitive impairment (CI) is a common symptom in multiple sclerosis (MS) patients. Cortical damages can be closely associated with cognitive network dysfunction and clinically significant CI in MS. So, in this study, We aimed to develop a radiomics model to efficiently identify the MS patients with CI based on clinical data and cortical damages. METHODS: One hundred and eighteen patients with MS were divided into CI and normal cognitive (NC) cohorts (62/56) as defined by the Montreal Cognitive Assessment (MoCA). All participants were randomly divided into train and test sets with a ratio of 7:3. The radiomic features were selected by using the least absolute shrinkage and selection operator (LASSO) method. The discrimination models were built with the support vector machines (SVM) by the clinical data, radiomic features, and merge data, respectively. And the patients were further divided according to each cognitive domain including memory, visuospatial, language, attention and executive, and each domain model was applied by the most suitable classifier. RESULTS: A total of 2298 features were extracted, of which 36 were finally selected. The merge model showed the greatest performance with the area under the curve (AUC) of 0.86 (95 % confidence interval: 0.81-0.91), accuracy (ACC) of 0.78, sensitivity of 0.79 and specificity of 0.77 in test cohort. However, although the visuospatial domain model showed the highest AUC of 0.71 (95 % confidence interval: 0.61-0.81) among five domain models, other domain models did not meet satisfactory results with a relatively low AUC, ACC, sensitivity and specificity. CONCLUSIONS: The radiomics model based on clinical data and cortical damages had a great potential to identify the MS patients with CI for clinical cognitive assessment.

Classification of Myelin Oligodendrocyte Glycoprotein Antibody-Related Disease and Its Mimicking Acute Demyelinating Syndromes in Children Using MRI-Based Radiomics: From Lesion to Subject.

RATIONALE AND OBJECTIVES: To develop MRI-based radiomics models from the lesion level to the subject level and assess their value for differentiating myelin oligodendrocyte glycoprotein antibody-related disease (MOGAD) from non-MOGAD acute demyelinating syndromes in pediatrics. MATERIALS AND METHODS: 66 MOGAD and 66 non-MOGAD children were assigned to the training set (36/35), internal test set (14/16), and external test set (16/15), respectively. At the lesion level, five single-sequence models were developed alongside a fusion model (combining these five sequences). The radiomics features of each lesion were quantified as the lesion-level radscore (LRS) using the best-performing model. Subsequently, a lesion-typing function was employed to classify lesions into two types (MOGAD-like or non-MOGAD-like), and the average LRS of the predominant type lesions in each subject was considered as the subject-level radscore (SRS). Based on SRS, a subject-level model was established and compared to both clinical models and radiologists' assessments. RESULTS: At the lesion level, the fusion model outperformed the five single-sequence models in distinguishing MOGAD and non-MOGAD lesions (0.867 and 0.810 of area under the curve [AUC] in internal and external testing, respectively). At the subject level, the SRS model showed superior performance (0.844 and 0.846 of AUC in internal and external testing, respectively) compared to clinical models and radiologists' assessments for distinguishing MOGAD and non-MOGAD. CONCLUSION: MRI-based radiomics models have potential clinical value for identifying MOGAD from non-MOGAD. The fusion model and SRS model can distinguish between MOGAD and non-MOGAD at the lesion level and subject level, respectively, providing a differential diagnosis method for these two diseases.

Multimodal Investigation of Deep Gray Matter Nucleus in Patients with Multiple Sclerosis and Their Clinical Correlations: A Multivariate Pattern Analysis Study.

Deep gray matter (DGM) nucleus are involved in patients with multiple sclerosis (MS) and are strongly associated with clinical symptoms. We used machine learning approach to further explore microstructural alterations in DGM of MS patients. One hundred and fifteen MS patients and seventy-one healthy controls (HC) underwent brain MRI. The fractional anisotropy (FA), mean diffusivity (MD), quantitative susceptibility value (QSV) and volumes of the caudate nucleus (CN), putamen (PT), globus pallidus (GP), and thalamus (TH) were measured. Multivariate pattern analysis, based on a machine-learning algorithm, was applied to investigate the most damaged regions. Partial correlation analysis was used to investigate the correlation between MRI quantitative metrics and clinical neurological scores. The area under the curve of FA-based classification model was 0.83, while they were 0.93 for MD and 0.81 for QSV. The Montreal cognitive assessment scores were correlated with the volume of the DGM and the expanded disability status scale scores were correlated with the MD of the GP and PT. The study results indicated that MS patients had involvement of DGM with the CN being the most affected. The atrophy of DGM in MS patients mainly affected cognitive function and the microstructural damage of DGM was mainly correlated with clinical disability.

Increased cortical lesion load contributed to pathological changes beyond focal lesion in cortical gray matter of multiple sclerosis: a diffusion kurtosis imaging analysis.

Biomarkers specific to cortical gray matter (cGM) pathological changes of multiple sclerosis (MS) are desperately needed to better understand the disease progression. The cGM damage occurs in cortical lesion (CL) and normal-appearing cGM (NAcGM) areas. While the association between CL load and cGM damage has been reported, little is known about how different CL types, i.e. intracortical lesion (ICL) and leukocortical lesion (LCL) would be associated with cGM damage. In our study, relapsing-remitting MS patients and healthy controls were divided into 4 groups according to CL load level. NAcGM diffusion kurtosis imaging (DKI)/diffusion tensor imaging (DTI) values and cGM volume (cGMV) were used to characterize the pathological changes in cGM. Univariate general linear model was used for group comparisons and stepwise regression analysis was used to assess the effects of ICL volume and LCL volume on NAcGM damage. We found peak values in DKI/DTI values, cGMV and neuropsychological scores in high CL load group. Kurtosis fractional anisotropy (KFA) was the most sensitive in characterizing NAcGM damage, and LCL volume related more to NAcGM damage. Our findings suggested KFA could become a surrogate biomarker to cGM damage, and LCL might be the main factor in whole brain NAcGM damage.

Enlarged choroid plexus related to iron rim lesions and deep gray matter atrophy in relapsing-remitting multiple sclerosis.

BACKGROUND: Choroid plexus (CP) is considered to be linked to inflammation of multiple sclerosis (MS), but its connection with markers of inflammation in vivo in MS is unclear, the markers such as lesions load and brain atrophy, particularly the white matter lesions (WMLs) edge surrounded by an iron rim, termed as iron rim lesions (IRLs). PURPOSE: To investigate the association between CP volume and brain lesions load, especially IRLs load and atrophy in MS, and its relationship with clinical characteristics. METHODS: 3.0 T brain MRI images were acquired from 99 relapsing-remitting MS (RRMS) and 60 healthy controls (HCs) to obtain the volumes of CP, whole brain and lesions. Volumes were expressed as a ratio of intracranial volume. Expanded Disability Status Scale (EDSS), Montreal Cognitive Assessment (MoCA) and Symbol Digit Modalities Test (SDMT) were used to assess the severity of disability and cognitive function. Student's t-test and Multivariable regression analyses were performed to evaluate the difference of CP volumes between RRMS and HC and the association between CP volume and lesions load, brain volumes and clinical scale scores in RRMS. RESULTS: CP volume was 30% larger in patients with RRMS than HCs (p < 0.001) and was 20% larger in patients with IRLs than those without IRLs (p = 0.007). Moreover, the larger CP volume was related to greater WMLs volume in the whole RRMS (r = 0.46, p < 0.001). Further analysis in patients with IRLs showed a positive correlation between CP volume and WMLs volume (r = 0.45, p = 0.003), and IRLs volume (r = 0.51, p < 0.001). Meanwhile, enlarged CP was related to lower volumes in the whole brain (r = -0.30, p = 0.006), deep gray matter (r = -0.51, p < 0.001) and most regional deep gray matter nuclei (except amygdala), but no correlation with cortical lesions or cortex volume (both p > 0.05). In addition, CP volume was significantly higher in patients with cognitive impairment than those with cognitive preservation by MoCA scores (p = 0.011); the larger CP volume was associated with higher EDSS scores (r = 0.25, p = 0.014) and lower SDMT Z scores in RRMS (r = -0.26, p = 0.014). CONCLUSION: The enlargement of CP in RRMS had close correlations with inflammatory lesions, especially IRLs and deep gray matter atrophy, but not the cortex. Meanwhile, the larger CP volume was associated with higher disability and lower cognitive scores. CP volume may be a surrogate imaging marker for MS disease activity.

Rhodium-Catalyzed N-Arylation of 2-Pyridones Enabled by 1,6-Acyl Migratory Rearrangement of 2-Oxypyridines.

An efficient rhodium-catalyzed dearomative rearrangement of 2-oxypyridines with quinone diazides has been developed for the direct synthesis of N-arylated pyridones, in which a novel 1,6-O-to-O rather than 1,4-O-to-C acyl rearrangement has been achieved under mild reaction conditions.

Microstructural alterations in different types of lesions and their perilesional white matter in relapsing-remitting multiple sclerosis based on diffusion kurtosis imaging.

BACKGROUND AND OBJECTIVES: In multiple sclerosis (MS), contrast enhancement lesions and chronic active lesions have been demonstrated to have different degrees of inflammation. Accordingly, they exist different degrees of tissue damage, one is short and acute, and another is slow and longstanding. This study aimed to explore whether diffusion parameters can differentiate different types of lesions, and investigate the microstructural damage between different types of MS lesions by using diffusion magnetic resonance imaging (dMRI) and its correlation with clinical biomarkers of disability and cognitive states. METHODS: We retrospectively identified 77 contrast enhancement lesions (CELs), 384 iron rim lesions (IRLs), 393 non-iron rim lesions (NIRLs), their corresponding perilesional white matter (PLWM), and 68 normal-appearing white matter (NAWM) from 68 relapsing-remitting MS (RRMS). Additionally, 44 white matter in healthy controls (WM in HCs) were also enrolled in this study. The DTI and DKI parameters were measured in the above white matter, including kurtosis fractional anisotropy (KFA), fractional anisotropy (FA), mean kurtosis (MK), and mean diffusivity (MD). All the patients were assessed with the Digital Span Test (DST), the Symbol Digit Modalities Test (SDMT), the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and the Expanded Disability Status Scale (EDSS). RESULTS: The lowest KFA, FA, MK values and the highest MD values were found in CELs, followed by IRLs, NIRLs, NAWM, and WM in HCs. In KFA and FA values, there were significant differences between each type of lesion, as well as each type of PLWM (P < 0.05). The MK values of CELs and IRLs were significantly lower than NIRLs, but inversely for MD (P < 0.05). There were no differences between CELs and IRLs for MK (P = 1) and MD (P = 0.261). The results of MK and MD values in CELs-PLWM and IRLs-PLWM were similar to the CELs and IRLs. There were no significant differences between NAWM and WM in HCs in all the enrolled diffusion parameters (P >0.05) and the FA values between NIRLs-PLWM and NAWM or between NIRLs-PLWM and WM in HCs were no significant differences (P >0.05). The KFA and MD values in IRLs-PLWM (r =0.443, P =0.021; r =-0.518, P =0.006) were correlated with the DST scores and the KFA of CELs-PLWM (r =0.396, P =0.041) was correlated with SDMT scores. CONCLUSION: Our findings demonstrate that the KFA values have the potential to distinguish different types of MS white matter tissues. Furthermore, the diffusion parameters can reflect the microstructure abnormalities in different MS lesions and might help us better understand the pathological mechanism and lesion evolution.

Efficiencies and mechanism of enhanced coagulation pre-treatment on domestic sewage with PAC-HCA compound.

Poly dimethyl diallyl propyl ammonium chloride (HCA) and poly aluminum chloride (PAC) were used to prepare complex coagulants for the enhanced coagulation (EC) pretreatment of domestic sewage. The influences of hydraulic conditions, the dosage ratio of PAC-HCA complex coagulants, initial pH value, and dosage on the removal efficiency of SS, COD, and TP in domestic sewage were investigated. The fractal dimension and Zeta potential were used to verify and characterize the experimental results. The results showed that the optimum coagulant conditions were as follows: G1 = 200.0-265.0 s-1, T1 = 1.5 min, G2 = 40.0 s-1, T2 = 5 min, PAC: HCA = 25:1, dosage = 15 mL/L, pH = 8. At the mentioned point, the removal rates of SS, COD, and TP are 98.74%, 44.63%, and 89.85%, respectively. In addition, through comparative tests, PAC-HCA compound coagulants show better treatment efficiency than PAC and HCA used alone. When the HCA dosage was 15 mg/L, Zeta potential and flocs fractal dimension was 2.29 mv and 0.9844, respectively. This indicates that PAC-HCA has a good treatment effect on domestic sewage, and the mechanism of enhanced coagulation to remove nutrients is mainly electrical neutralization.

Effect of manganese substitution of ferrite nanoparticles on particle grain structure.

To investigate the influence of manganese substitution on the saturation magnetization of manganese ferrite nanoparticles, samples with various compositions (Mn x Fe3-x O4, x = 0, 0.25, 0.5, 0.75, and 1) were synthesized and characterized. The saturation magnetization of such materials was both calculated using density functional theory and measured via vibrating sample magnetometry. A discrepancy was found; the computational data demonstrated a positive correlation between manganese content and saturation magnetization, while the experimental data exhibited an inverse correlation. X-ray diffraction (XRD) and magnetometry results indicated that the crystallite diameter and the magnetic diameter decrease when adding more manganese, which could explain the loss of magnetization of the particles. For 20 nm nanoparticles, with increasing manganese substitution level, the crystallite size decreases from 10.9 nm to 6.3 nm and the magnetic diameter decreases from 15.1 nm to 3.5 nm. Further high resolution transmission electron microscopy (HRTEM) analysis confirmed the manganese substitution induced defects in the crystal lattice, which encourages us to find ways of eliminating crystalline defects to make more reliable ferrite nanoparticles.

The heterogeneity of tissue destruction between iron rim lesions and non-iron rim lesions in multiple sclerosis: A diffusion MRI study.

OBJECTIVES: This study aimed to explore the microstructural heterogeneity of different white matter (WM) tissues in relapsing-remitting multiple sclerosis (RRMS) patients by diffusion magnetic resonance imaging (dMRI) and its correlation with disability and cognitive status. MATERIALS AND METHODS: A total of 337 iron rim lesions (IRLs), 337 perilesional white matters of IRLs (IRLs-PLWMs), 330 non-iron rim lesions (non-IRLs), 330 non-IRLs-PLWMs, 42 normal-appearing white matters (NAWMs) in 42 RRMS patients, and 30 white matters in healthy controls (WMs in HCs) were enrolled in the lesion-wise analysis. Diffusion kurtosis imaging (DKI) parameters including kurtosis fractional anisotropy (KFA) and mean kurtosis (MK), and diffusion tensor imaging (DTI) parameters including fractional anisotropy (FA) and mean diffusivity (MD) were measured in the six types of tissues. Subgroup analysis was performed between non-IRLs with QSM hyperintense (non-IRLs-H) and non-IRLs with QSM isointense or hypointense (non-IRLs-I), as well as between non-IRLs-H-PLWMs and non-IRLs-I-PLWMs. Thirty-four out of forty-two patients were enrolled in patient-wise analysis. The relationships between these diffusion metrics of patients and their Kurtzke Expanded Disability Status Scale (EDSS) score and Symbol Digit Modalities Test (SDMT) score were analyzed separately by partial correlation analysis with age and disease duration (DD) as covariates. RESULTS: The KFA, FA, MK, and MD values were significantly different among the six types of tissues. The lowest KFA, FA, and MK values and the highest MD values were revealed in IRLs. There were significant differences in all the enrolled diffusion metrics between IRLs and non-IRLs, as well as between IRLs-PLWMs and non-IRLs-PLWMs (p < 0.05). There were no significant differences between NAWMs and WMs in HCs (p = 1.000 for all enrolled diffusion metrics). For all the enrolled diffusion metrics, no significant differences were found in the subgroup analysis. The FA, MK, and MD values of total lesions (including IRLs and non-IRLs) (r = -0.420, p = 0.017; r = -0.472, p = 0.006; r = -0.475, p = 0.006) and the MK values of IRLs (r = -0.438, p = 0.012) were correlated with the EDSS scores. There was no significant correlation between the diffusion parameter values and the SDMT scores. CONCLUSION: Our findings demonstrate that IRLs are more destructive than non-IRLs. Similarly, IRLs-PLWMs are more destructive than non-IRLs-PLWMs. Additionally, diffusion parameter values of MS lesions can reflect the disability degree. These findings contribute to a better understanding of the different evolution of MS lesions and the relationship between the disability level of patients and focal lesion damage degree.

Alterations in White Matter Fiber Tracts Characterized by Automated Fiber-Tract Quantification and Their Correlations With Cognitive Impairment in Neuromyelitis Optica Spectrum Disorder Patients.

Objectives: To investigate whether patients with neuromyelitis optica spectrum disorder (NMOSD) have tract-specific alterations in the white matter (WM) and the correlations between the alterations and cognitive impairment. Materials and Methods: In total, 40 patients with NMOSD and 20 healthy controls (HCs) who underwent diffusion tensor imaging (DTI) scan and neuropsychological scale assessments were enrolled. Automated fiber-tract quantification (AFQ) was applied to identify and quantify 100 equally spaced nodes of 18 specific WM fiber tracts for each participant. Then the group comparisons in DTI metrics and correlations between different DTI metrics and neuropsychological scales were performed. Results: Regardless of the entire or pointwise level in WM fiber tracts, patients with NMOSD exhibited a decreased fractional anisotropy (FA) in the left inferior fronto-occipital fasciculus (L_IFOF) and widespread increased mean diffusion (MD), axial diffusivity (AD), and radial diffusivity (RD), especially for the thalamic radiation (TR), corticospinal tract (CST), IFOF, inferior longitudinal fasciculus (ILF), superior longitudinal fasciculus (SLF) [p < 0.05, false discovery rate (FDR) correction], and the pointwise analyses performed more sensitive. Furthermore, the negative correlations among MD, AD, RD, and symbol digit modalities test (SDMT) scores in the left TR (L_TR) were found in NMOSD. Conclusion: Patients with NMOSD exhibited the specific nodes of WM fiber tract damage, which can enhance our understanding of WM microstructural abnormalities in NMOSD. In addition, the altered DTI metrics were correlated with cognitive impairment, which can be used as imaging markers for the early identification of NMOSD cognitive impairment.

The Application of Diffusion Kurtosis Imaging on the Heterogeneous White Matter in Relapsing-Remitting Multiple Sclerosis.

Objectives: To evaluate the microstructural damage in the heterogeneity of different white matter areas in relapsing-remitting multiple sclerosis (RRMS) patients by using diffusion kurtosis imaging (DKI) and its correlation with clinical and cognitive status. Materials and Methods: Kurtosis fractional anisotropy (KFA), fractional anisotropy (FA), mean kurtosis (MK), and mean diffusivity (MD) in T1-hypointense lesions (T1Ls), pure T2-hyperintense lesions (pure-T2Ls), normal-appearing white matter (NAWM), and white matter in healthy controls (WM in HCs) were measured in 48 RRMS patients and 26 sex- and age-matched HCs. All the participants were assessed with the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and the Symbol Digit Modalities Test (SDMT) scores as the cognitive status. The Kurtzke Expanded Disability Status Scale (EDSS) scores were used to evaluate the clinical status in RRMS patients. Results: The lowest KFA, FA, and MK values and the highest MD values were found in T1Ls, followed by pure-T2Ls, NAWM, and WM in HCs. The T1Ls and pure-T2Ls were significantly different in FA (p = 0.002) and MK (p = 0.013), while the NAWM and WM in HCs were significantly different in KFA, FA, and MK (p < 0.001; p < 0.001; p = 0.001). The KFA, FA, MK, and MD values in NAWM (r = 0.360, p = 0.014; r = 0.415, p = 0.004; r = 0.369, p = 0.012; r = -0.531, p < 0.001) were correlated with the MMSE scores and the FA, MK, and MD values in NAWM (r = 0.423, p = 0.003; r = 0.427, p = 0.003; r = -0.359, p = 0.014) were correlated with the SDMT scores. Conclusion: Applying DKI to the imaging-based white matter classification has the potential to reflect the white matter damage and is correlated with cognitive impairment.