Identification of neurotransmitter imbalances in the cingulate cortex of NMOSD patients using magnetic resonance spectroscopy.

Cognitive impairment affects 29-67% of patients with neuromyelitis optica spectrum disorder. Previous studies have reported glutamate homeostasis disruptions in astrocytes, leading to imbalances in gamma-aminobutyric acid levels. However, the association between these neurotransmitter changes and cognitive deficits remains inadequately elucidated. Point RESolved Spectroscopy and Hadamard Encoding and Reconstruction of MEGA-Edited Spectroscopy techniques were utilized to evaluate gamma-aminobutyric acid, glutamate, glutathione levels, and excitation/inhibition balance in the anterior cingulate cortex, posterior cingulate cortex, and occipital cortex of 39 neuromyelitis optica spectrum disorder patients and 41 healthy controls. Cognitive function was assessed using neurocognitive scales. Results showed decreased gamma-aminobutyric acid levels alongside increased glutamate, glutathione, and excitation/inhibition ratio in the anterior cingulate cortex and posterior cingulate cortex of neuromyelitis optica spectrum disorder patients. Specifically, within the posterior cingulate cortex of neuromyelitis optica spectrum disorder patients, decreased gamma-aminobutyric acid levels and increased excitation/inhibition ratio correlated significantly with anxiety scores, whereas glutathione levels predicted diminished executive function. The results suggest that neuromyelitis optica spectrum disorder patients exhibit dysregulation in the GABAergic and glutamatergic systems in their brains, where the excitation/inhibition imbalance potentially acts as a neuronal metabolic factor contributing to emotional disorders. Additionally, glutathione levels in the posterior cingulate cortex region may serve as predictors of cognitive decline, highlighting the potential benefits of reducing oxidative stress to safeguard cognitive function in neuromyelitis optica spectrum disorder patients.

Migraine versus tension-type headache in automatic emotional processing: A visual mismatch negativity study.

OBJECTIVE: It is important to discriminate different headaches in clinical practice, and neurocognitive biomarkers may serve as objective tools. Several reports have suggested potential cognitive impairment for primary headaches, whereas cognitions within specific domains remain elusive, e.g., emotional processing. In this study, we aimed to characterize processing of facial expressions in migraine and tension-type headache (TTH) by analyzing expression-related visual mismatch negativity (EMMN) and explored whether their processing patterns were distinct. METHODS: Altogether, 73 headache patients (20 migraine with aura (MA), 28 migraine without aura (MwoA), 25 TTH) and 27 age-matched healthy controls were recruited. After a battery of mood/neuropsychological evaluations, an expression-related oddball paradigm containing multiple models of neutral, happy and sad faces was used to investigate automatic emotional processing. RESULTS: We observed cognitive impairment in all headache patients, especially in attention/execution subdomains, but no discrepancy existed among different headaches. Although analyses of P1/N170 did not reach significant levels, amplitude of early and late EMMN was markedly diminished in MA and MwoA compared with controls and TTH, regardless of happy or sad expression. Moreover, sad EMMN was larger (more negative) than happy EMMN only in controls, while not in all headache groups. CONCLUSIONS: Our findings implied that migraine, rather than TTH, might lead to more severe impairment of automatic emotional processing, which was manifested as no observable EMMN elicitation and disappearance of negative bias effect. The EMMN component could assist in discrimination of migraine from TTH and diagnosis of undefined headaches, and its availability needed further validations.

Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme.

BACKGROUND: Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor in adults. This study aimed to construct immune-related long non-coding RNAs (lncRNAs) signature and radiomics signature to probe the prognosis and immune infiltration of GBM patients. METHODS: We downloaded GBM RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) project database, and MRI data were obtained from The Cancer Imaging Archive (TCIA). Then, we conducted a cox regression analysis to establish the immune-related lncRNAs signature and radiomics signature. Afterward, we employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways. Besides, we used CIBERSORT to estimate the abundance of tumor-infiltrating immune cells (TIICs). Furthermore, we investigated the relationship between the immune-related lncRNAs signature, radiomics signature and immune checkpoint genes. Finally, we constructed a multifactors prognostic model and compared it with the clinical prognostic model. RESULTS: We identified four immune-related lncRNAs and two radiomics features, which show the ability to stratify patients into high-risk and low-risk groups with significantly different survival rates. The risk score curves and Kaplan-Meier curves confirmed that the immune-related lncRNAs signature and radiomics signature were a novel independent prognostic factor in GBM patients. The GSEA suggested that the immune-related lncRNAs signature were involved in L1 cell adhesion molecular (L1CAM) interactions and the radiomics signature were involved signaling by Robo receptors. Besides, the two signatures was associated with the infiltration of immune cells. Furthermore, they were linked with the expression of critical immune genes and could predict immunotherapy's clinical response. Finally, the area under the curve (AUC) (0.890,0.887) and C-index (0.737,0.817) of the multifactors prognostic model were greater than those of the clinical prognostic model in both the training and validation sets, indicated significantly improved discrimination. CONCLUSIONS: We identified the immune-related lncRNAs signature and tradiomics signature that can predict the outcomes, immune cell infiltration, and immunotherapy response in patients with GBM.

High-density lipoprotein cholesterol and brain aging amongst rural-dwelling older adults: a population-based magnetic resonance imaging study.

BACKGROUND AND PURPOSE: Current evidence supports the involvement of lipids in brain aging. A range of serum lipids is explored in association with brain structure and cognitive function amongst rural-dwelling older adults. METHODS: This population-based cross-sectional study included 184 rural-dwelling adults (age ≥ 65 years, 39.1% women) in Shandong, China. In 2014-2016, data on demographics, lifestyle, health conditions and serum lipids were collected. Volumes of gray matter, white matter, ventricles, hippocampus and white matter hyperintensity were automatically estimated on brain magnetic resonance imaging. Global cognitive function was assessed with the Mini-Mental State Examination (MMSE), and mild cognitive impairment (MCI) was defined according to Petersen's criteria. Data were analyzed using the general linear regression, logistic regression and mediation models. RESULTS: Of the 184 participants, 47 were defined with MCI. Low high-density lipoprotein cholesterol (HDL-C; <1.55 vs. ≥1.55 mmol/l) was significantly associated with reduced volumes of total white matter (multi-adjusted ß = -9.77, 95% confidence interval -19.48-0.06) and hippocampus (-0.23, -0.46-0.01), a lower MMSE score (-1.49, -2.67-0.31) and a higher likelihood of MCI (multi-adjusted odds ratio 3.21, 95% confidence interval 1.42-7.29). The mediation effects of structural brain measures on the associations between a low level of HDL-C and MMSE score or MCI were not statistically significant (p > 0.05). CONCLUSIONS: This study suggests that low HDL-C may be involved in structural brain aging and cognitive dysfunction amongst rural-dwelling older adults in China, but the association of low HDL-C with cognitive aging phenotypes appears not to be mediated by brain structure.

Three-Dimensional Time-of-Flight Magnetic Resonance Angiography Detection of Duplication of the Vertebral Artery in a Large Chinese Population.

BACKGROUND The aim of this study was to investigate duplication of the vertebral artery (VA) using three-dimensional time-of-flight (3D TOF) magnetic resonance angiography (MRA) in a large study population to further our understanding of vascular variations. MATERIAL AND METHODS A retrospective analysis of 3D TOF-MRA data in 12 826 cases was performed. The occurrence rate of VA duplication was calculated and accompanied vascular anomalies were recoded. RESULTS Twenty-one VA duplication patients were found, with an occurrence rate of 0.164%; 12 of them had left VA duplication with 2 branches initially arising from the aortic arch and left subclavian artery; 9 of them were right VA duplication with the branches originating from the right subclavian artery. In the 21 cases, 11 had other vascular abnormalities. CONCLUSIONS VA duplication is very rare and often associated with other vascular abnormalities. 3D TOF-MRA can accurately display the duplication variation. Better understanding of the variation is instrumental for disease diagnosis, interventional therapy, and surgical operation.

[Correlation between diffusion tensor imaging and cognitive dysfunction in patients with delayed encephalopathy after acute carbon monoxide poisoning].

OBJECTIVE: To explore the diagnostic value of magnetic resonance (MR) diffusion tensor imaging (DTI) in detecting brain white matter (WM) damage of patients with delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) and evaluating their cognitive dysfunction. METHODS: Thirteen patients with DEACMP and thirteen age- and sex-matched volunteers underwent DTI using 1.5T MR scanner. FA and ADC values of 16 WM regions of interests (ROIs) were measured on DTI by two experienced radiologists independently with double blind methods, cognitive functions were evaluated by another experienced neurologist blinded to patient's medical history using the Montreal cognitive assessment (MoCA). ADC and FA values in DEACMP patients, and their correlations with cognitive dysfunction were analyzed. RESULTS: ADC values of DEACMP patients increased significantly in all ROIs (P < 0.05) in comparison with the corresponding ROIs of healthy controls, whereas FA values were significantly decreased in all ROIs (P < 0.05) in comparison with that in controls except the bilateral optic radiations, anterior and posterior internal capsules. MoCA scores were positively correlated with FA values of bilateral lower frontal (r(L) = 0.736, P = 0.011; r(R) = 0.762, P = 0.003) lobe, temporal lobe (r(L) = 0.605, P = 0.016; r(R) = 0.559, P = 0.021) and total average WM (r(A) = 0.688, P = 0.001), however it inversely correlated with ADC values of bilateral lower frontal WM (r(L) = -0.674, P = 0.007; r(R) = -0.681, P = 0.019). CONCLUSION: DTI can quantitatively reveal WM microstructure damage of DEACMP patients, indicate the severity of cognitive dysfunctions, and provide important information for pathogenesis and pathological study for DEACMP.

A case of primary osteosarcoma in the occipital bone: A relatively common tumor in an uncommon location.

Osteosarcomas predominantly manifest in the long bones of the extremities, with rare occurrences in the skull. A case involving of a 53-year-old female who presented to the authors' hospital for examination due to dizziness was incidentally found to have an occipital bone mass, which was initially diagnosed as a benign tumor and did not receive sufficient attention. Two years later, owing to tumor enlargement, the patient underwent further evaluation at the same institution, which revealed evidence of occipital bone destruction. Pathological analysis confirmed the diagnosis of osteosarcoma. The patient underwent surgical resection followed by radiotherapy. Despite its infrequency and uncharacteristic initial presentation, skull osteosarcomas should not be overlooked.

Development and validation of a clinical prediction model for glioma grade using machine learning.

BACKGROUND: Histopathological evaluation is currently the gold standard for grading gliomas; however, this technique is invasive. OBJECTIVE: This study aimed to develop and validate a diagnostic prediction model for glioma by employing multiple machine learning algorithms to identify risk factors associated with high-grade glioma, facilitating the prediction of glioma grading. METHODS: Data from 1114 eligible glioma patients were obtained from The Cancer Genome Atlas (TCGA) database, which was divided into a training set (n= 781) and a test set (n= 333). Fifty machine learning algorithms were employed, and the optimal algorithm was selected to construct a prediction model. The performance of the machine learning prediction model was compared to the clinical prediction model in terms of discrimination, calibration, and clinical validity to assess the performance of the prediction model. RESULTS: The area under the curve (AUC) values of the machine learning prediction models (training set: 0.870 vs. 0.740, test set: 0.863 vs. 0.718) were significantly improved from the clinical prediction models. Furthermore, significant improvement in discrimination was observed for the Integrated Discrimination Improvement (IDI) (training set: 0.230, test set: 0.270) and Net Reclassification Index (NRI) (training set: 0.170, test set: 0.170) from the clinical prognostic model. Both models showed a high goodness of fit and an increased net benefit. CONCLUSION: A strong prediction accuracy model can be developed using machine learning algorithms to screen for high-grade glioma risk predictors, which can serve as a non-invasive prediction tool for preoperative diagnostic grading of glioma.

Exploring the prognostic value and biological pathways of transcriptomics and radiomics patterns in glioblastoma multiforme.

Objectives: To develop a multi-omics prognostic model integrating transcriptomics and radiomics for predicting overall survival in patients with glioblastoma multiforme (GBM), and investigate the biological pathways of radiomics patterns. Materials and methods: Transcription profiles of GBM patients and normal controls were used to obtain differentially expressed mRNAs and long non-coding RNAs (lncRNAs). Radiomics features were extracted from magnetic resonance imaging (MRI). Least absolute shrinkage and selection operator (LASSO) Cox regression was employed to select survival-associated features for the construction of transcriptomics and radiomics signatures. Genes associated with GBM prognosis were identified through the analysis of lncRNA-mRNA co-expression networks and Weighted Gene Co-expression Network Analysis (WGCNA), and their biological pathways were investigated using Genomes enrichment analysis. Transcriptomics, radiomics, and clinical data were integrated to evaluate the multi-omics prognostic model's performance. Results: LASSO Cox regression yielded 21 survival-related features, including 19 transcriptomics features and 2 radiomics features. Based on transcriptomics and radiomics signature, GBM patients were classified as high-risk or low-risk. The genes obtained from the co-expression network screen were associated with microtubule binding, while those from the WGCNA screen were associated with growth factor receptor binding. In the training set, the AUC values for the multi-omics model and clinical model were 0.964 and 0.830, respectively, while in the validation set, they were 0.907 and 0.787. The multi-omics prognostic model outperformed the clinical prognostic model. Conclusions: The co-expression network and WGCNA methods revealed genes associated with multiple biological pathways in GBM. The multi-omics prognostic model demonstrated excellent performance and indicated significant potential for clinical application.

Deep learning-based platform performs high detection sensitivity of intracranial aneurysms in 3D brain TOF-MRA: An external clinical validation study.

PURPOSE: To evaluate the diagnostic efficacy of a developed artificial intelligence (AI) platform incorporating deep learning algorithms for the automated detection of intracranial aneurysms in time-of-flight (TOF) magnetic resonance angiography (MRA). METHOD: This retrospective study encompassed 3D TOF MRA images acquired between January 2023 and June 2023, aiming to validate the presence of intracranial aneurysms via our developed AI platform. The manual segmentation results by experienced neuroradiologists served as the "gold standard". Following annotation of MRA images by neuroradiologists using InferScholar software, the AI platform conducted automatic segmentation of intracranial aneurysms. Various metrics including accuracy (ACC), balanced ACC, area under the curve (AUC), sensitivity (SE), specificity (SP), F1 score, Brier Score, and Net Benefit were utilized to evaluate the generalization of AI platform. Comparison of intracranial aneurysm identification performance was conducted between the AI platform and six radiologists with experience ranging from 3 to 12 years in interpreting MR images. Additionally, a comparative analysis was carried out between radiologists' detection performance based on independent visual diagnosis and AI-assisted diagnosis. Subgroup analyses were also performed based on the size and location of the aneurysms to explore factors impacting aneurysm detectability. RESULTS: 510 patients were enrolled including 215 patients (42.16 %) with intracranial aneurysms and 295 patients (57.84 %) without aneurysms. Compared with six radiologists, the AI platform showed competitive discrimination power (AUC, 0.96), acceptable calibration (Brier Score loss, 0.08), and clinical utility (Net Benefit, 86.96 %). The AI platform demonstrated superior performance in detecting aneurysms with an overall SE, SP, ACC, balanced ACC, and F1 score of 91.63 %, 92.20 %, 91.96 %, 91.92 %, and 90.57 % respectively, outperforming the detectability of the two resident radiologists. For subgroup analysis based on aneurysm size and location, we observed that the SE of the AI platform for identifying tiny (diameter＜3mm), small (3 mm ≤ diameter＜5mm), medium (5 mm ≤ diameter＜7mm) and large aneurysms (diameter ≥ 7 mm) was 87.80 %, 93.14 %, 95.45 %, and 100 %, respectively. Furthermore, the SE for detecting aneurysms in the anterior circulation was higher than that in the posterior circulation. Utilizing the AI assistance, six radiologists (i.e., two residents, two attendings and two professors) achieved statistically significant improvements in mean SE (residents: 71.40 % vs. 88.37 %; attendings: 82.79 % vs. 93.26 %; professors: 90.07 % vs. 97.44 %; P < 0.05) and ACC (residents: 85.29 % vs. 94.12 %; attendings: 91.76 % vs. 97.06 %; professors: 95.29 % vs. 98.82 %; P < 0.05) while no statistically significant change was observed in SP. Overall, radiologists' mean SE increased by 11.40 %, mean SP increased by 1.86 %, and mean ACC increased by 5.88 %, mean balanced ACC promoted by 6.63 %, mean F1 score grew by 7.89 %, and Net Benefit rose by 12.52 %, with a concurrent decrease in mean Brier score declined by 0.06. CONCLUSIONS: The deep learning algorithms implemented in the AI platform effectively detected intracranial aneurysms on TOF-MRA and notably enhanced the diagnostic capabilities of radiologists. This indicates that the AI-based auxiliary diagnosis model can provide dependable and precise prediction to improve the diagnostic capacity of radiologists.

Tracking of Stem Cells from Human Exfoliated Deciduous Teeth Labeled with Molday ION Rhodamine-B during Periodontal Bone Regeneration in Rats.

Background and Objectives: Chronic periodontitis can lead to alveolar bone resorption and eventually tooth loss. Stem cells from exfoliated deciduous teeth (SHED) are appropriate bone regeneration seed cells. To track the survival, migration, and differentiation of the transplanted SHED, we used super paramagnetic iron oxide particles (SPIO) Molday ION Rhodamine-B (MIRB) to label and monitor the transplanted cells while repairing periodontal bone defects. Methods and Results: We determined an appropriate dose of MIRB for labeling SHED by examining the growth and osteogenic differentiation of labeled SHED. Finally, SHED was labeled with 25 µg Fe/ml MIRB before being transplanted into rats. Magnetic resonance imaging was used to track SHED survival and migration in vivo due to a low-intensity signal artifact caused by MIRB. HE and immunohistochemical analyses revealed that both MIRB-labeled and unlabeled SHED could promote periodontal bone regeneration. The colocalization of hNUC and MIRB demonstrated that SHED transplanted into rats could survive in vivo. Furthermore, some MIRB-positive cells expressed the osteoblast and osteocyte markers OCN and DMP1, respectively. Enzyme-linked immunosorbent assay revealed that SHED could secrete protein factors, such as IGF-1, OCN, ALP, IL-4, VEGF, and bFGF, which promote bone regeneration. Immunofluorescence staining revealed that the transplanted SHED was surrounded by a large number of host-derived Runx2- and Col II-positive cells that played important roles in the bone healing process. Conclusions: SHED could promote periodontal bone regeneration in rats, and the survival of SHED could be tracked in vivo by labeling them with MIRB. SHED are likely to promote bone healing through both direct differentiation and paracrine mechanisms.

Microstructural Alteration of Trigeminal Nerve in Patients with Classic Trigeminal Neuralgia Shown by Diffusion Tensor Imaging and Its Correlation with Vascular Compression and Pain.

OBJECTIVE: To investigate the value of magnetic resonance diffusion tensor imaging in evaluating the microstructural alteration of trigeminal nerve in patients with classic trigeminal neuralgia (CTN) and its correlation with the degree of vascular compression and patient pain. METHODS: A total of 108 patients with CTN were enrolled in this study. Patients were divided into 2 groups according to whether the asymptomatic side trigeminal nerve had neurovascular compression (NVC) or not: group A (32 cases) with NVC and group B (76 cases) without NVC. The anisotropy fraction (FA) and apparent diffusion coefficient of bilateral trigeminal nerves were measured. A visual analog scale (VAS) was used to evaluate the pain degree of the patients. The severity of NVC on the symptomatic side was classified as grade I, II, or III by neurosurgeons according to the findings during microvascular decompression. RESULTS: The FA values of the trigeminal nerve on the symptomatic side were significantly lower than those on the asymptomatic side in group A (P < 0.001) and group B (P < 0.001). Thirty-six patients were treated with microvascular decompression. The FA values of the trigeminal nerve were grade I 0.309 ± 0.011, grade II 0.295 ± 0.015, and grade III 0.286 ± 0.022. The difference was statistically significant (P = 0.011). The FA of the trigeminal nerve on the symptomatic side was negatively correlated with the degree of NVC and pain (P < 0.05). CONCLUSIONS: Patients with NVC had significant decreases in FA and it negatively correlated with NVC and VAS scores.

Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme.

Objective: To compare the performance of radiomics-based machine learning survival models in predicting the prognosis of glioblastoma multiforme (GBM) patients. Methods: 131 GBM patients were included in our study. The traditional Cox proportional-hazards (CoxPH) model and four machine learning models (SurvivalTree, Random survival forest (RSF), DeepSurv, DeepHit) were constructed, and the performance of the five models was evaluated using the C-index. Results: After the screening, 1792 radiomics features were obtained. Seven radiomics features with the strongest relationship with prognosis were obtained following the application of the least absolute shrinkage and selection operator (LASSO) regression. The CoxPH model demonstrated that age (HR = 1.576, p = 0.037), Karnofsky performance status (KPS) score (HR = 1.890, p = 0.006), radiomics risk score (HR = 3.497, p = 0.001), and radiomics risk level (HR = 1.572, p = 0.043) were associated with poorer prognosis. The DeepSurv model performed the best among the five models, obtaining C-index of 0.882 and 0.732 for the training and test set, respectively. The performances of the other four models were lower: CoxPH (0.663 training set / 0.635 test set), SurvivalTree (0.702/0.655), RSF (0.735/0.667), DeepHit (0.608/0.560). Conclusion: This study confirmed the superior performance of deep learning algorithms based on radiomics relative to the traditional method in predicting the overall survival of GBM patients; specifically, the DeepSurv model showed the best predictive ability.

Improved image quality in dual-energy abdominal CT: comparison of iterative reconstruction in image space and filtered back projection reconstruction.

OBJECTIVE: The purpose of this study is to investigate whether an iterative reconstruction in image space (IRIS) algorithm improves the image quality of dual-energy CT abdominal examinations performed during the hepatic arterial phase. MATERIALS AND METHODS: Seventy patients with suspected liver masses underwent contrast-enhanced multiphase abdominal examination and were enrolled in the study. A dual-energy CT protocol was performed in the hepatic arterial phase (parameters: tube A, 140 kV and 90 mA; tube B, 80 kV and 382 mA; automatic tube current modulation on; and collimation, 14 × 1.2 mm). The reconstructions were performed with filtered back projection (FBP) and IRIS algorithms at a slice thickness of 3 mm and kernels of B30 and I30. The image noise was measured on the liver, aorta, and subcutaneous fat on the FBP and IRIS fusion images (m = 0.3) at the same position. The image noise and diagnostic acceptability of all images were scored by two radiologists. RESULTS: The image noise using the IRIS algorithm was lower than that using the standard FBP algorithm on the liver, aorta, and subcutaneous fat, respectively. The signal-to-noise ratio and contrast-to-noise ratio of images reconstructed with the IRIS algorithm also were significantly higher than for those reconstructed with the FBP algorithm. The diagnostic acceptability score using the IRIS algorithm was higher than that using the FBP algorithm at the same dose level (1.20 ± 0.40 vs 1.37 ± 0.57; p < 0.05). CONCLUSION: Compared with standard FBP reconstruction, an IRIS algorithm enables significant reduction of image noise and improvement of image quality and has the potential to decrease radiation exposure during contrast-enhanced dual-energy CT abdominal examination.

Bilateral type IIpersistent proatlantal intersegmental artery: a rare variant of persistent carotid-vertebrobasilar anastomoses.

The persistent proatlantal intersegmental artery is a rare variant of persistent carotid-vertebrobasilar anastomoses, especially their bilateral presence is rarer. We report a case of bilateral typeII persistent proatlantal intersegmental artery. The absence of bilateral vertebral arteries was incidentally noted on neck ultrasound examination. Subsequent time-of-flight MR angiography confirmed this. The bilateral typeIIpersistent proatlantal intersegmental artery arose from the cervical external carotid artery, penetrated the C1 transverse foramen, entered the skull via the foramen magnum, and joined the lower portion of the basilar artery.

Characterizing Global and Regional Brain Structures in Amnestic Mild Cognitive Impairment Among Rural Residents: A Population-Based Study.

BACKGROUND: Structural brain magnetic resonance imaging (MRI) scans may provide reliable neuroimaging markers for defining amnestic mild cognitive impairment (aMCI). OBJECTIVE: We sought to characterize global and regional brain structures of aMCI among rural-dwelling older adults with limited education in China. METHODS: This population-based study included 180 participants (aged≥65 years, 42 with aMCI and 138 normal controls) in the Shandong Yanggu Study of Aging and Dementia during 2014-2016. We defined aMCI following the Petersen's criteria. Global and regional brain volumes were automatically segmented on MRI scans and compared using a region-of-interest approach. Data were analyzed using general linear regression models. RESULTS: Multi-adjusted ß-coefficient (95% confidence interval) of brain volumes (cm3) associated with aMCI was -12.07 (-21.49, -2.64) for global grey matter (GM), -18.31 (-28.45, -8.17) for global white matter (WM), 28.17 (12.83, 44.07) for cerebrospinal fluid (CSF), and 2.20 (0.24, 4.16) for white matter hyperintensities (WMH). Furthermore, aMCI was significantly associated with lower GM volumes in bilateral superior temporal gyri, thalamus and right cuneus, and lower WM volumes in lateral areas extending from the frontal to the parietal, temporal, and occipital lobes, as well as right hippocampus (p < 0.05). CONCLUSION: Brain structure of older adults with aMCI is characterized by reduced global GM and WM volumes, enlarged CSF volume, increased WMH burden, reduced GM volumes in bilateral superior temporal gyri, thalamus, and right cuneus, and widespread reductions of lateral WM volumes.

The diagnostic value of quantitative analysis of ASL, DSC-MRI and DKI in the grading of cerebral gliomas: a meta-analysis.

OBJECTIVE: To perform quantitative analysis on the efficacy of using relative cerebral blood flow (rCBF) in arterial spin labeling (ASL), relative cerebral blood volume (rCBV) in dynamic magnetic sensitivity contrast-enhanced magnetic resonance imaging (DSC-MRI), and mean kurtosis (MK) in diffusion kurtosis imaging (DKI) to grade cerebral gliomas. METHODS: Literature regarding ASL, DSC-MRI, or DKI in cerebral gliomas grading in both English and Chinese were searched from PubMed, Embase, Web of Science, CBM, China National Knowledge Infrastructure (CNKI), and Wanfang Database as of 2019. A meta-analysis was performed to evaluate the efficacy of ASL, DSC-MRI, and DKI in the grading of cerebral gliomas. RESULT: A total of 54 articles (11 in Chinese and 43 in English) were included. Three quantitative parameters in the grading of cerebral gliomas, rCBF in ASL, rCBV in DSC-MRI, and MK in DKI had the pooled sensitivity of 0.88 [95% CI (0.83,0.92)], 0.92 [95% CI (0.83,0.96)], 0.88 [95% CI (0.82,0.92)], and the pooled specificity of 0.91 [95% CI (0.84,0.94)], 0.81 [95% CI (0.73,0.88)], 0.86 [95% CI (0.78,0.91)] respectively. The pooled area under the curve (AUC) were 0.95 [95% CI (0.93,0.97)], 0.91 [95% CI (0.89,0.94)], 0.93 [95% CI (0.91,0.95)] respectively. CONCLUSION: Quantitative parameters rCBF, rCBV and MK have high diagnostic accuracy for preoperative grading of cerebral gliomas.

GAA compound heterozygous mutations associated with autophagic impairment cause cerebral infarction in Pompe disease.

Clinical manifestations of the late-onset adult Pompe disease (glycogen storage disease type II) are heterogeneous. To identify genetic defects of a special patient population with cerebrovascular involvement as the main symptom, we performed whole-genome sequencing (WGS) analysis on a consanguineous Chinese family of total eight members including two Pompe siblings both had cerebral infarction. Two novel compound heterozygous variants were found in GAA gene: c.2238G>C in exon 16 and c.1388_1406del19 in exon 9 in the two patients. We verified the function of the two mutations in leading to defects in GAA protein expression and enzyme activity that are associated with autophagic impairment. We further performed a gut microbiome metagenomics analysis, found that the child's gut microbiome metagenome is very similar to his mother. Our finding enriches the gene mutation spectrum of Pompe disease, and identified the association of the two new mutations with autophagy impairment. Our data also indicates that gut microbiome could be shared within Pompe patient and cohabiting family members, and the abnormal microbiome may affect the blood biochemical index. Our study also highlights the importance of deep DNA sequencing in potential clinical applications.