Deep Medullary Vein and MRI Markers Were Related to Cerebral Hemorrhage Subtypes.

BACKGROUND: To explore the performance of deep medullary vein (DMV) and magnetic resonance imaging (MRI) markers in different intracerebral hemorrhage (ICH) subtypes in patients with cerebral small vessel disease (CSVD). METHODS: In total, 232 cases of CSVD with ICH were included in this study. The clinical and image data were retrospectively analyzed. Patients were divided into hypertensive arteriopathy (HTNA)-related ICH, cerebral amyloid angiopathy (CAA)-related ICH, and mixed ICH groups. The DMV score was determined in the cerebral hemisphere contralateral to the ICH. RESULTS: The DMV score was different between the HTNA-related and mixed ICH groups (p < 0.01). The MRI markers and CSVD burden score were significant among the ICH groups (p < 0.05). Compared to mixed ICH, HTNA-related ICH diagnosis was associated with higher deep white matter hyperintensity (DWMH) (OR: 0.452, 95% CI: 0.253-0.809, p < 0.05) and high-degree perivascular space (PVS) (OR: 0.633, 95% CI: 0.416-0.963, p < 0.05), and CAA-related ICH diagnosis was associated with increased age (OR: 1.074; 95% CI: 1.028-1.122, p = 0.001). The DMV score correlated with cerebral microbleed (CMB), PVS, DWMH, periventricular white matter hyperintensity (PWMH), and CSVD burden score (p < 0.05) but not with lacuna (p > 0.05). Age was an independent risk factor for the severity of DMV score (OR: 1.052; 95% CI: 0.026-0.076, p < 0.001). CONCLUSION: DMV scores, CSVD markers, and CSVD burden scores were associated with different subtypes of ICH. In addition, DMV scores were associated with the severity of CSVD and CSVD markers.

Detoxification of fluoroglucocorticoid by Acinetobacter pittii C3 via a novel defluorination pathway with hydrolysis, oxidation and reduction: Performance, genomic characteristics, and mechanism.

Biological dehalogenation degradation was an important detoxification method for the ecotoxicity and teratogenic toxicity of fluorocorticosteroids (FGCs). The functional strain Acinetobacter pittii C3 can effectively biodegrade and defluorinate to 1 mg/L Triamcinolone acetonide (TA), a representative FGCs, with 86 % and 79 % removal proportion in 168 h with the biodegradation and detoxification kinetic constant of 0.031/h and 0.016/h. The dehalogenation and degradation ability of strain C3 was related to its dehalogenation genomic characteristics, which manifested in the functional gene expression of dehalogenation, degradation, and toxicity tolerance. Three detoxification mechanisms were positively correlated with defluorination pathways through hydrolysis, oxidation, and reduction, which were regulated by the expression of the haloacid dehalogenase (HAD) gene (mupP, yrfG, and gph), oxygenase gene (dmpA and catA), and reductase gene (nrdAB and TgnAB). Hydrolysis defluorination was the most critical way for TA detoxification metabolism, which could rapidly generate low-toxicity metabolites and reduce toxic bioaccumulation due to hydrolytic dehalogenase-induced defluorination. The mechanism of hydrolytic defluorination was that the active pocket of hydrolytic dehalogenase was matched well with the spatial structure of TA under the adjustment of the hydrogen bond, and thus induced molecular recognition to promote the catalytic hydrolytic degradation of various amino acid residues. This work provided an effective bioremediation method and mechanism for improving defluorination and detoxification performance.

Diffusion kurtosis imaging and diffusion tensor imaging parameters applied to white matter and gray matter of patients with anti-N-methyl-D-aspartate receptor encephalitis.

Objectives: To compare parameters of diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) to evaluate which can better describe the microstructural changes of anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis patients and to characterize the non-Gaussian diffusion patterns of the whole brain and their correlation with neuropsychological impairments in these patients. Materials and methods: DTI and DKI parameters were measured in 57 patients with anti-NMDAR encephalitis and 42 healthy controls. Voxel-based analysis was used to evaluate group differences between white matter and gray matter separately. The modified Rankin Scale (mRS) was used to evaluate the severity of the neurofunctional recovery of patients, the Montreal Cognitive Assessment (MoCA) was used to assess global cognitive performance, and the Hamilton Depression Scale (HAMD) and fatigue severity scale (FSS) were used to evaluate depressive and fatigue states. Results: Patients with anti-NMDAR encephalitis showed significantly decreased radial kurtosis (RK) in the right extranucleus in white matter (P < 0.001) and notably decreased kurtosis fractional anisotropy (KFA) in the right precuneus, the right superior parietal gyrus (SPG), the left precuneus, left middle occipital gyrus, and left superior occipital gyrus in gray matter (P < 0.001). Gray matter regions with decreased KFA overlapped with those with decreased RK in the left middle temporal gyrus, superior temporal gyrus (STG), supramarginal gyrus (SMG), postcentral gyrus (POCG), inferior parietal but supramarginal gyrus, angular gyrus (IPL) and angular gyrus (ANG) (P < 0.001). The KFA and RK in the left ANG, IPL and POCG correlated positively with MoCA scores. KFA and RK in the left ANG, IPL, POCG and SMG correlated negatively with mRS scores. KFA in the left precuneus and right SPG as well as RK in the left STG correlated negatively with mRS scores. No significant correlation between KFA and RK in the abnormal brain regions and HAMD and FSS scores was found. Conclusion: The microstructural changes in gray matter were much more extensive than those in white matter in patients with anti-NMDAR encephalitis. The brain damage reflected by DKI parameters, which have higher sensitivity than parameters of DTI, correlated with cognitive impairment and the severity of the neurofunctional recovery.

Enhancing fluoroglucocorticoid defluorination using defluorinated functional strain Acinetobacter. pittii C3 via humic acid-mediated biotransformation.

Defluorination is a key factor in reducing biologically accumulated carcinogenic and teratogenic toxicity of fluoroglucocorticoids (FGCs). To enhance defluorination efficiency, a highly efficient defluorination-degrading strain Acinetobacter. pittii C3 was isolated, and the promotion mechanism through humic acid (HA)-mediated biotransformation was investigated. Optimal biodegradation conditions for Acinetobacter sp. pittii C3 were pH of 7.0, temperature of 25 â„ƒ, and HA content of 5.5 mg/L, according to response surface methodology analysis. The attenuation rate constant and maximum defluorination percentage of triamcinolone acetonide (TA) in HA-mediated biotransformation system (HA-C3) were 3.99 × 10-2 and 96%, respectively, which were 2.22 and 1.24 times higher than those in the unitary C3 biodegradation system (U-C3), respectively. The major defluorination pathways included elimination, hydrolysis, and hydrogenation, with contributions of 24.5%, 32.4%, and 43.1%, respectively. The bio-reductive hydrodefluorination rate was enhanced by 1.89 times that of HA-mediated, while the other two defluorination pathways exhibited insignificant changes. HA, as the congeries of negatively charged microbes and hydrophobic TA, accelerates the electron transfer rate between Acinetobacter. pittii C3 and TA through the quinone groups. Furthermore, the mutual conversion between the functional groups of hydroxyl oxidation and ketone reduction of HA provided electron donors for TA reductive defluorination and hydrogenation and electron acceptors for TA oxidation. This study provides an effective strategy for FGC-enhanced detoxification using natural HA.

Clinical Variables, Deep Learning and Radiomics Features Help Predict the Prognosis of Adult Anti-N-methyl-D-aspartate Receptor Encephalitis Early: A Two-Center Study in Southwest China.

Objective: To develop a fusion model combining clinical variables, deep learning (DL), and radiomics features to predict the functional outcomes early in patients with adult anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis in Southwest China. Methods: From January 2012, a two-center study of anti-NMDAR encephalitis was initiated to collect clinical and MRI data from acute patients in Southwest China. Two experienced neurologists independently assessed the patients' prognosis at 24 moths based on the modified Rankin Scale (mRS) (good outcome defined as mRS 0-2; bad outcome defined as mRS 3-6). Risk factors influencing the prognosis of patients with acute anti-NMDAR encephalitis were investigated using clinical data. Five DL and radiomics models trained with four single or combined four MRI sequences (T1-weighted imaging, T2-weighted imaging, fluid-attenuated inversion recovery imaging and diffusion weighted imaging) and a clinical model were developed to predict the prognosis of anti-NMDAR encephalitis. A fusion model combing a clinical model and two machine learning-based models was built. The performances of the fusion model, clinical model, DL-based models and radiomics-based models were compared using the area under the receiver operating characteristic curve (AUC) and accuracy and then assessed by paired t-tests (P < 0.05 was considered significant). Results: The fusion model achieved the significantly greatest predictive performance in the internal test dataset with an AUC of 0.963 [95% CI: (0.874-0.999)], and also significantly exhibited an equally good performance in the external validation dataset, with an AUC of 0.927 [95% CI: (0.688-0.975)]. The radiomics_combined model (AUC: 0.889; accuracy: 0.857) provided significantly superior predictive performance than the DL_combined (AUC: 0.845; accuracy: 0.857) and clinical models (AUC: 0.840; accuracy: 0.905), whereas the clinical model showed significantly higher accuracy. Compared with all single-sequence models, the DL_combined model and the radiomics_combined model had significantly greater AUCs and accuracies. Conclusions: The fusion model combining clinical variables and machine learning-based models may have early predictive value for poor outcomes associated with anti-NMDAR encephalitis.

Enhanced bio-reductive degradation of fluoroglucocorticoids in the groundwater fluctuation zone by external electron donors: Performance, microbial community, and functional genes.

This study evaluated the effectiveness of external electron donors on the bio-reductive degradation enhancement of fluoroglucocorticoids (FGCs) in the groundwater fluctuation zone during the wet season when reverse upward fluctuation of the groundwater table occurs and the dry season after the groundwater table declines. The results showed that the external electron donors, provided by the addition of nano zero-valent iron-modified biochar (nZVI@BC), inhibited the migration and enhanced the reductive defluorination of triamcinolone acetonide (TA), a representative FGC. The accumulation rate constant with temporal fluctuation depth and the attenuation rate constant with vertical fluctuation depth were -2.55 × 10-3 and 4.20 × 10-2, respectively, in the groundwater of the natural groundwater fluctuation zone (N-FZ). In contrast, the accumulation and attenuation rate constants were, respectively, 35.6% and 2.64 times higher in the groundwater fluctuation zone amended with nZVI@BC (nZVI@BC-FZ) as compared with those observed in the N-FZ. Furthermore, the decay rate constant of the TA residue in the dry season was 0.843 × 10-2 µg/d in N-FZ and was 2.19 times higher in nZVI@BC-FZ. This enhancement effect, caused by the addition of external electrons, was positively correlated with the evolution of the microbial community and the expression of functional genes. The microbes evolved into functional genera with reductive dehalogenation (Xylophilus and Hydrogenophaga) and iron-oxidizing (Lysobacter, Pseudoxanthomonas, and Sphingomonas) abilities in the nZVI@BC-FZ system, which increased dehalogenation and iron oxide genes by a 4-5 order of magnitude. The utilization proportion of external electrons for TA metabolism was 50.04%, of which 30.82%, 10.26%, and 8.96% were utilized for defluorination, hydrogenation, and ring-opening, respectively. This study provides an effective method to reduce pollutant diffusion and enhance the bio-reductive degradation caused by groundwater table fluctuation.

Deep Learning-Enabled Identification of Autoimmune Encephalitis on 3D Multi-Sequence MRI.

BACKGROUND: Autoimmune encephalitis (AE) is a noninfectious emergency with severe clinical attacks. It is difficult for the earlier diagnosis of acute AE due to the lack of antibody detection resources. PURPOSE: To construct a deep learning (DL) algorithm using multi-sequence magnetic resonance imaging (MRI) for the identification of acute AE. STUDY TYPE: Retrospective. POPULATION: One hundred and sixty AE patients (90 women; median age 36), 177 herpes simplex virus encephalitis (HSVE) (89 women; median age 39), and 184 healthy controls (HC) (95 women; median age 39) were included. Fifty-two patients from another site were enrolled for external validation. FIELD STRENGTH/SEQUENCE: 3.0 T; fast spin-echo (T1 WI, T2 WI, fluid attenuated inversion recovery imaging) and spin-echo echo-planar diffusion weighted imaging. ASSESSMENT: Five DL models based on individual or combined four MRI sequences to classify the datasets as AE, HSVE, or HC. Reader experiment was further carried out by radiologists. STATISTICAL TESTS: The discriminative performance of different models was assessed using the area under the receiver operating characteristic curve (AUC). The optimal threshold cut-off was identified when sensitivity and specificity were maximized (sensitivity + specificity - 1) in the validation set. Classification performance using confusion matrices was reported to evaluate the diagnostic value of the models and the radiologists' assessments before being assessed by the paired t-test (P < 0.05 was considered significant). RESULTS: In the internal test set, the fusion model achieved the significantly greatest diagnostic performance than single-sequence DL models with AUCs of 0.828, 0.884, and 0.899 for AE, HSVE, and HC, respectively. The model demonstrated a consistently high performance in the external validation set with AUCs of 0.831 (AE), 0.882 (HSVE), and 0.892 (HC). The fusion model also demonstrated significantly higher performance than all radiologists in identifying AE (accuracy between the fuse model vs. average radiologist: 83% vs. 72%). DATA CONCLUSION: The proposed DL algorithm derived from multi-sequence MRI provided desirable identification and classification of acute AE. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Prediction of unenhanced lesion evolution in multiple sclerosis using radiomics-based models: a machine learning approach.

BACKGROUND: The volume change of multiple sclerosis (MS) lesion is related to its activity and can be used to assess disease progression. Therefore, the purpose of this study was to develop radiomics models for predicting the evolution of unenhanced MS lesions by using different kinds of machine learning algorithms and explore the optimal model. METHODS: In this prospective observation, 45 follow-up MR images obtained in 36 patients with MS (mean age 32.53±10.91; 23 women, 13 men) were evaluated. The lesions will be defined as interval activity and interval inactivity, respectively, based on the percentage of enlargement or reduction of the lesion >20% in the follow-up MR images. We extracted radiomic features of lesions on FLAIR images, and used recursive feature elimination (RFE), ReliefF algorithm and least absolute shrinkage and selection operator (LASSO) for feature selection, then three classification models including logistic regression, random forest and support vector machine (SVM) were used to build predictive models. The performance of the models were evaluated based on the sensitivity, specificity, precision, negative predictive value (NPV) and receiver operating characteristic curve (ROC) curves analyses. RESULTS: 135 interval inactivity lesions and 110 interval activity lesions were registered in our study. A total of 972 radiomics features were extracted, of which 265 were robust. The consistency and effectiveness of model performance were compared and verified by different combinations of feature selection and machine learning methods in different K-fold cross-validation strategies where K ranges from 5 to 10, thus demonstrating the stability and robustness. SVM classifier with ReliefF algorithm had the best prediction performance with an average accuracy of 0.827, sensitivity of 0.809, specificity of 0.841, precision of 0.921, NPV of 0.948 and the areas under the ROC curves (AUC) of 0.857 (95% CI: 0.812-0.902) in the cohorts. CONCLUSION: The results demonstrated that the radiomics-based machine learning model has potential in predicting the evolution of MS lesions.

Enhanced reductive defluorination and inhibited infiltration of fluoroglucocorticoids in a river receiving reclaimed water amended by nano zero-valent iron-modified biochar: Performance and mechanisms.

The main aim of this study was to investigate the effect of a nano zero-valent iron-modified biochar-amended composite riverbed (nZVI@BC-R) on inhibited infiltration and enhanced biodegradation of fluoroglucocorticoids (FGCs) in a river receiving reclaimed water. The results demonstrated that the removal efficiency of triamcinolone acetonide (TA), a representative FGC, increased from 38.40% and 77.91% to 91.60% in the nZVI@BC-R compared with that of a natural soil riverbed (S-R) and biochar-amended soil riverbed (BC-R). The main removal mechanismwas attributedto adsorption and biodegradation, of which the contribution rates were 32.2% and 59.4% in nZVI@BC-R, 18.9% and 19.5% in S-R, and 24.4% and 53.5% in BC-R, respectively. The removal process could be described by a two-compartment, first-order dynamic model with decay rate constants for adsorption and biodegradation of 4.02700, 22.44400, and 29.07300 d-1 and 0.00286, 0.01562, and 0.03484 d-1 in the S-R, BC-R and nZVI@BC-R, respectively. The mechanism of defluorination accounted for 42.2% of biodegradation in the nZVI@BC-R, which was accompanied by side-chain rupture, oxidation, and ringopening. Functional microbes with iron oxidizing ability and reductive dehalogenating genera, namely Pseudoxanthomonas, Pedobacter, and Bosea, contributed to the high removal rate of TA, particularly in the nZVI@BC-R. Overall, the nZVI@BC-R provided an effective method to inhibit glucocorticoids infiltration into groundwater.

Spatiotemporal evolvement and factors influencing natural and synthetic EDCs and the microbial community at different groundwater depths in the Chaobai watershed: A long-term field study on a river receiving reclaimed water.

In this long-term field study, to restore a dried river ecosystem, reclaimed water was used as a supplementary water source. The main aim of this study was to investigate the accumulation and migration potential of EDCs in groundwater during long-term utilization of reclaimed water and the changes in microbial community during the removal of EDCs. A long-term field study was conducted in order to ascertain the temporal and spatial distribution of four selected endocrine-disrupting chemicals (EDCs) in an underground aquifer in the Chaobai watershed, where reclaimed water is the primary water source. Anew, the microbial community structure at different groundwater depths, along with related environmental factors were also determined. Based on the results obtained from this long-term study, it was found that the EDCs in the surface water of the Chaobai river have entered a depth of 80 m in the groundwater aquifers, within a distance of 360 m from the river. The vertical profiles of the concentrations of bisphenol A (BPA), 4-nonylphenol (NP), estrone (E1), and estriol (E3) decreased significantly from the surface to different groundwater depths with first-order attenuation rates of 0.0416, 0.0343, 0.0498, and 0.0173 m-1. The aquifer depth, water temperature, conductivity, and coexisting anions correlated well with the distribution of EDCs in groundwater.

Optic radiations are thinner and show signs of iron deposition in patients with long-standing remitting-relapsing multiple sclerosis: an enhanced T2*-weighted angiography imaging study.

OBJECTIVE: This study aimed to investigate iron deposition and thickness and signal changes in optic radiation (OR) by enhanced T2*-weighted angiography imaging (ESWAN) in patients with relapsing-remitting multiple sclerosis (RRMS) with unilateral and bilateral lesions or no lesions. METHODS: Fifty-one RRMS patients (42 patients with a disease duration [DD] ≥ 2 years [group Mor], nine patients with a DD < 2 years [group Les]) and 51 healthy controls (group Con) underwent conventional magnetic resonance imaging (MRI) and ESWAN at 3.0 T. The mean phase value (MPV) of the OR was measured on the phase image, and thickness and signal changes of the OR were observed on the magnitude image. RESULTS: The average MPVs for the OR were 1,981.55 ± 7.75 in group Mor, 1,998.45 ± 2.01 in group Les, and 2,000.48 ± 5.53 in group Con. In group Mor, 28 patients with bilateral OR lesions showed bilateral OR thinning with a heterogeneous signal, and 14 patients with unilateral OR lesions showed ipsilateral OR thinning with a heterogeneous signal. In the remaining nine patients without OR lesions and in group Con, the bilateral OR had a normal appearance. In the patients, a negative correlation was found between DD and OR thickness and a positive correlation was found between MPV and OR thickness. CONCLUSIONS: We confirmed iron deposition in the OR in the RRMS patients, and the OR thickness was lower in the patients than in the controls. KEY POINTS: • Enhanced T 2* -weighted magnetic resonance angiography (ESWAN) provides new insights into multiple sclerosis (MS). • Focal destruction of the optic radiation (OR) is detectable by ESWAN. • Iron deposition in OR can be measured on ESWAN phase image in MS patients. • OR thickness was lower in the patients than in the controls. • Iron deposition and thickness changes of the OR are associated with disease duration.