A multi-cohort study of the hippocampal radiomics model and its associated biological changes in Alzheimer's Disease.

There have been no previous reports of hippocampal radiomics features associated with biological functions in Alzheimer's Disease (AD). This study aims to develop and validate a hippocampal radiomics model from structural magnetic resonance imaging (MRI) data for identifying patients with AD, and to explore the mechanism underlying the developed radiomics model using peripheral blood gene expression. In this retrospective multi-study, a radiomics model was developed based on the radiomics discovery group (n = 420) and validated in other cohorts. The biological functions underlying the model were identified in the radiogenomic analysis group using paired MRI and peripheral blood transcriptome analyses (n = 266). Mediation analysis and external validation were applied to further validate the key module and hub genes. A 12 radiomics features-based prediction model was constructed and this model showed highly robust predictive power for identifying AD patients in the validation and other three cohorts. Using radiogenomics mapping, myeloid leukocyte and neutrophil activation were enriched, and six hub genes were identified from the key module, which showed the highest correlation with the radiomics model. The correlation between hub genes and cognitive ability was confirmed using the external validation set of the AddneuroMed dataset. Mediation analysis revealed that the hippocampal radiomics model mediated the association between blood gene expression and cognitive ability. The hippocampal radiomics model can accurately identify patients with AD, while the predictive radiomics model may be driven by neutrophil-related biological pathways.

Altered functional connectivity in patients with post-stroke fatigue: A resting-state fMRI study.

BACKGROUND: Post-stroke fatigue (PSF) was a common complication after stroke. This study aimed to explore the neuroimaging mechanism of PSF, which was rarely studied. METHODS: Patients with the first episode of ischemic stroke were recruited from the First Affiliated Hospital of Wenzhou Medical University between March 2021 and December 2022. The fatigue severity scale (FSS) was used to assess fatigue symptoms. PSF was diagnosed by a neurologist based on the FSS score and PSF diagnostic criteria. All the patients were scanned by resting-state functional MRI (rs-fMRI). Precuneus, the posterior node of default-mode network (pDMN), was related to fatigue. Therefore, imaging data were further analyzed by the seed-based resting-state functional connectivity (FC) approach, with the left (PCUN.L) and right precuneus (PCUN.R) being the seeds. RESULTS: A total of 70 patients with acute ischemic stroke were finally recruited, comprising 40 patients with PSF and 30 patients without PSF. Both the PCUN.L and PCUN.R seeds (pDMN) exhibited decreased FC with the prefrontal lobes located at the anterior part of DMN (aDMN), and the FC values were negatively correlated with FSS scores (both p < 0.001). These two seeds also exhibited increased FC with the right insula, and the FC values were positively correlated with FSS scores (both p < 0.05). CONCLUSION: The abnormal FC between the aDMN and pDMN was associated with PSF. Besides, the insula, related to interoception, might also play an important role in PSF.

Deep Learning-Based Multiparametric MRI Model for Preoperative T-Stage in Rectal Cancer.

BACKGROUND: Conventional MRI staging can be challenging in the preoperative assessment of rectal cancer. Deep learning methods based on MRI have shown promise in cancer diagnosis and prognostication. However, the value of deep learning in rectal cancer T-staging is unclear. PURPOSE: To develop a deep learning model based on preoperative multiparametric MRI for evaluation of rectal cancer and to investigate its potential to improve T-staging accuracy. STUDY TYPE: Retrospective. POPULATION: After cross-validation, 260 patients (123 with T-stage T1-2 and 134 with T-stage T3-4) with histopathologically confirmed rectal cancer were randomly divided to the training (N = 208) and test sets (N = 52). FIELD STRENGTH/SEQUENCE: 3.0 T/Dynamic contrast enhanced (DCE), T2-weighted imaging (T2W), and diffusion-weighted imaging (DWI). ASSESSMENT: The deep learning (DL) model of multiparametric (DCE, T2W, and DWI) convolutional neural network were constructed for evaluating preoperative diagnosis. The pathological findings served as the reference standard for T-stage. For comparison, the single parameter DL-model, a logistic regression model composed of clinical features and subjective assessment of radiologists were used. STATISTICAL TESTS: The receiver operating characteristic curve (ROC) was used to evaluate the models, the Fleiss' kappa for the intercorrelation coefficients, and DeLong test for compare the diagnostic performance of ROCs. P-values less than 0.05 were considered statistically significant. RESULTS: The Area Under Curve (AUC) of the multiparametric DL-model was 0.854, which was significantly higher than the radiologist's assessment (AUC = 0.678), clinical model (AUC = 0.747), and the single parameter DL-models including T2W-model (AUC = 0.735), DWI-model (AUC = 0.759), and DCE-model (AUC = 0.789). DATA CONCLUSION: In the evaluation of rectal cancer patients, the proposed multiparametric DL-model outperformed the radiologist's assessment, the clinical model as well as the single parameter models. The multiparametric DL-model has the potential to assist clinicians by providing more reliable and precise preoperative T staging diagnosis. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Uncertainty-guided transformer for brain tumor segmentation.

Multi-model data can enhance brain tumor segmentation for the rich information it provides. However, it also introduces some redundant information that interferes with the segmentation estimation, as some modalities may catch features irrelevant to the tissue of interest. Besides, the ambiguous boundaries and irregulate shapes of different grade tumors lead to a non-confidence estimate of segmentation quality. Given these concerns, we exploit an uncertainty-guided U-shaped transformer with multiple heads to construct drop-out format masks for robust training. Specifically, our drop-out masks are composed of boundary mask, prior probability mask, and conditional probability mask, which can help our approach focus more on uncertainty regions. Extensive experimental results show that our method achieves comparable or higher results than previous state-of-the-art brain tumor segmentation methods, achieving average dice coefficients of [Formula: see text] and Hausdorff distance of 4.91 on the BraTS2021 dataset. Our code is freely available at https://github.com/chaineypung/BTS-UGT.

The interaction of global small vessel disease burden and Alzheimer's disease pathologies do not change the independent association of amyloid-beta with hippocampal volume: A longitudinal study on mild cognitive impairment subjects.

The purpose of this study was to investigate whether the co-existence of global small vessel disease (SVD) burdens and Alzheimer's disease (AD) pathologies change hippocampal volume (HV) and cognitive function of mild cognitive impairment (MCI) subjects. We obtained MRI images, cerebrospinal fluid biomarkers (Aß1-42 and p-tau), and neuropsychological tests of 310 MCI subjects from ADNI. The global SVD score was assessed. We used linear regression and linear mixing effect to analyze the effects of global SVD burdens, AD pathologies, and their interactions (SVD*AD) on baseline and longitudinal HV and cognition respectively. We used simple mediation effect to analyze the influencing pathways. After adjusting for global SVD and SVD*AD, Aß remained independently correlated with baseline and longitudinal HV (std ß = 0.294, p = .007; std ß = 0.292, p < .001), indicating that global SVD did not affect the correlation between Aß and HV. Global SVD score was correlated with longitudinal but not baseline HV (std ß = 0.470, p = .050), suggesting that global SVD may be more representative of long-term permanent impairment. Global SVD, AD pathologies, and SVD*AD were independently correlated with baseline and longitudinal cognitions, in which the association of Aß (B = 0.005, 95% CI: 0.005; 0.024) and p-tau (B = -0.002, 95% CI: -0.004; -0.000) with cognition were mediated by HV, suggesting that HV is more likely to explain the progression caused by AD pathology than SVD. The co-existence of global SVD and AD pathologies did not affect the individual association of Aß on HV; HV played a more important role in the influence of AD pathology on cognition than in SVD.

Lactate Dehydrogenase Predicts Hemorrhagic Transformation in Patients with Acute Ischemic Stroke.

INTRODUCTION: Hemorrhagic transformation (HT) is a severe but frequent complication of acute ischemic stroke (AIS). This study aimed to evaluate the relationship between serum lactate dehydrogenase (LDH) levels and HT. METHODS: We retrospectively included 542 AIS patients with HT and 1,091 age- and gender-matched patients without HT. Demographic and clinical data were obtained from medical records, and blood samples were obtained within 24 h after admission. The characteristics of the groups were compared. With the receiver operating characteristic (ROC) curve analysis, we assessed the discriminating capacity of LDH levels in predicting HT in patients with AIS. The logistic regression model was used to determine the connection between LDH and HT. RESULTS: The HT group had considerably higher LDH levels than the non-HT group (263.0 [216.0-323.3] U/L versus 178.0 [162.0-195.0] U/L, p < 0.001). We also observed that the levels of LDH in the parenchymal hemorrhage subgroup were significantly higher than those in the hemorrhagic infarction subgroup (281.0 [230.0-340.0] U/L versus 258.0 [209.0-311.0] U/L, p < 0.001). The area under the ROC curve of LDH was 0.890 (95% confidence level [CI] 0.874-0.905, p < 0.001). Besides, logistic regression revealed that high LDH levels (LDH >215 U/L) showed a higher risk of HT (odds ratio = 10.958, 95% CI 7.964-15.078, p < 0.001). CONCLUSION: High LDH levels were linked with an increased risk of HT in AIS patients. Practical measures should be considered in patients with increased LDH levels (LDH >215 U/L).

Semi-supervised region-connectivity-based cerebrovascular segmentation for time-of-flight magnetic resonance angiography image.

Deep-learning-based methods have achieved state-of-the-art results in cerebrovascular segmentation. However, it is costly and time-consuming to acquire labeled data because of the complex structure of cerebral vessels. In this paper, we propose a novel semi-supervised cerebrovascular segmentation with a region-connectivity-based mean teacher model (RC-MT) from time-of-flight magnetic resonance angiography (TOF-MRA), where unlabeled data is introduced into the training. Concretely, the RC-MT framework consists of a mean teachers (MT) model and a region-connectivity-based model. The region-connectivity-based model dynamically controls the balance between the supervised loss and unsupervised consistency loss by taking into account that the predicted vessel voxels should be continuous in the underlying anatomy of the brain. Meanwhile, we design a novel multi-scale channel attention fusion Unet (MSCAF-Unet) as a backbone for the student model and the teacher model. The MSCAF-Unet is a multi-scale channel attention fusion layer used to construct an image pyramid input and achieve multi-level receptive field fusion. The proposed method is evaluated on diverse TOF-MRA datasets (three clinical datasets and a public dataset). Experimental results show that the proposed method achieves high-performance gains by incorporating the unlabeled data and outperforms competing semi-supervised-based methods. The code will be openly available at https://github.com/IPIS-XieLei/RC-MT.

Observation of Curative Effect of Lung Recruitment in Patients with Acute Respiratory Distress Syndrome after Cardiopulmonary Bypass Surgery.

Recruitment maneuver (RM) has become a routine supplementary maneuver for clinical rescue of severe ARDS with low tidal volume/pressure-limited mechanical ventilation. Recruitment of patients with ARDS mechanical ventilation can improve the lung compliance, promote the opening of collapsed alveoli, improve the ratio of ventilation to blood flow, reduce dead space, reduce shunt flow, and improve oxygenation function. In this paper, the patients were divided into lung recruitment group and conventional treatment group by the random number permutation table method. When the patient's percutaneous oxygen saturation is less than or equal to 88%, the partial pressure of oxygen in the arterial blood gas is less than or equal to 55 mmHg, or the ventilator tube is disconnected during sputum suction or other accidents, a CPAP × 60 - second lung recruitment maneuver is required. Then adjust the ventilator parameters in the same way. In the process of lung recruitment, the changes in invasive continuous arterial blood pressure will also be observed. If the blood pressure dropped to ≤90/60 mmHg, one recruitment maneuver was terminated in advance. And both groups of patients used the Dräger- or PB840-imported multifunctional ventilator. The treatment of primary disease and predisposing factors, fluid management strategies, antibiotics and glucocorticoids, nutrition, and metabolic support in the two groups of patients in the study were the same. The PaO2/FiO2 value improved by 51% 10 minutes after recruitment, and the median increased from 111 (IQR, 73-265) before recruitment to 170 (IQR, 102-340) (P < 0.01), the improvement of PaO2/FiO2 at 4 hours after recruitment and 12 hours after recruitment was 78% (P < 0.05) and 39% (P < 0.01), respectively, and the median PaO2/FiO2 at 4 hours after recruitment was 198 (IQR, 116-256). The median PaO2/FiO2 became 155 (IQR, 127-235) 12 hours after recruitment. Recruitment can reduce the accumulation of neutrophils in lung tissue, reduce the release of inflammatory factors, reduce pulmonary edema, and reduce pathological damage.

Automated quantification of brain connectivity in Alzheimer's disease using ClusterMetric.

Diffusion magnetic resonance imaging tractography allows investigating brain structural connections in a noninvasive way and has been widely used for understanding neurological disease. Quantification of brain connectivity along with its length by dividing a fiber bundle into multiple segments (node) is a powerful approach to assess biological properties, which is termed as tractometry. However, current tractometry methods face challenges in node identification along with the length of complex bundles whose morphology is difficult to summarize. In addition, the anatomic measure reflecting the macroscopic fiber cross-section has not been followed in previous tractometry. In this paper, we propose an automated fiber bundle quantification, which we refer to as ClusterMetric. The ClusterMetric uses a data-driven approach to identify fiber clusters corresponding to subdivisions of the white matter anatomy and identify consistent space nodes along the length of clusters across individuals. The proposed method is demonstrated by applicating to our collected dataset including 23 Alzheimer's disease (AD) patients and 22 healthy controls (HCs) and a public dataset of ADNI including 53 AD patients and 85 HCs. The altered white matter tracts in AD group are observed using both datasets, which involve several major fiber tracts including the corpus callosum, corona-radiata-frontal, arcuate fasciculus, inferior occipito-frontal fasciculus, uncinate fasciculus, thalamo-frontal, superior longitudinal fasciculus, inferior cerebellar peduncle, cingulum bundle, and extreme capsule. These fiber clusters represent the white matter connections that could be most affected in AD, suggesting the ability of our method in identifying potential abnormalities specific to local regions within a fiber cluster.

Brain network connectivity feature extraction using deep learning for Alzheimer's disease classification.

Early diagnosis and therapeutic intervention for Alzheimer's disease (AD) is currently the only viable option for improving clinical outcomes. Combining structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI) to diagnose AD has yielded promising results. Most studies assume fixed time lags when constructing functional networks. Since the propagation delays between brain signals are constantly changing, these methods cannot reflect more detailed relationships between brain regions. In this work, we use a deep learning-based Granger causality estimator for brain connectivity construction. It exploits the strength of long short-term memory in ever-changing time series processing. This research involves data analysis from sMRI and rs-fMRI. We use sMRI to analyze the cerebral cortex properties and use rs-fMRI to analyze the graph metrics of functional networks. We extract a small subset of optimal features from both types of data. A support vector machine (SVM) is trained and tested to classify AD (n = 27) from healthy controls (n = 20) using rs-fMRI and sMRI features. Using a subset of optimal features in SVM, we achieve a classification accuracy of 87.23% for sMRI, 78.72% for rs-fMRI, and 91.49% for combined sMRI with rs-fMRI. The results show the potential to identify AD from healthy controls by integrating rs-fMRI and sMRI. The integration of sMRI and rs-fMRI modalities can provide supplemental information to improve the diagnosis of AD relative to either the sMRI or fMRI modalities alone.

Integrative Analysis of 5-Hydroxymethylcytosine and Transcriptional Profiling Identified 5hmC-Modified lncRNA Panel as Non-Invasive Biomarkers for Diagnosis and Prognosis of Pancreatic Cancer.

Pancreatic adenocarcinoma (PAAD) is the fourth leading cause of cancer-related deaths worldwide. 5-Hydroxymethylcytosine (5hmC)-mediated epigenetic regulation has been reported to be involved in cancer pathobiology and has emerged to be promising biomarkers for cancer diagnosis and prognosis. However, 5hmC alterations at long non-coding RNA (lncRNA) genes and their clinical significance remained unknown. In this study, we performed the genome-wide investigation of lncRNA-associated plasma cfDNA 5hmC changes in PAAD by plotting 5hmC reads against lncRNA genes, and identified six PAAD-specific lncRNAs with abnormal 5hmC modifications compared with healthy individuals. Then we applied machine-learning and Cox regression approaches to develop predictive diagnostic (5hLRS) and prognostic (5hLPS) models using the 5hmC-modified lncRNAs. The 5hLRS demonstrated excellent performance in discriminating PAAD from healthy controls with an area under the curve (AUC) of 0.833 in the training cohort and 0.719 in the independent testing cohort. The 5hLPS also effectively divides PAAD patients into high-risk and low-risk groups with significantly different clinical outcomes in the training cohort (log-rank test p = 0.04) and independent testing cohort (log-rank test p = 0.0035). Functional analysis based on competitive endogenous RNA (ceRNA) and enrichment analysis suggested that these differentially regulated 5hmC modified lncRNAs were associated with angiogenesis, circulatory system process, leukocyte differentiation and metal ion homeostasis that are known important events in the development and progression of PAAD. In conclusion, our study indicated the potential clinical utility of 5hmC profiles at lncRNA loci as valuable biomarkers for non-invasive diagnosis and prognostication of cancers.

White matter alterations in heart-kidney imbalance insomnia and Jiao-Tai-Wan treatment: A diffusion-tensor imaging study.

Previous studies have reported changes in white matter microstructures in patients with insomnia. However, few neuroimaging studies have focused specifically on white matter tracts in insomnia patients after having received treatment. In this prospective study, diffusion-tensor imaging was used in two samples of heart-kidney imbalance insomnia patients who were treated with placebo or Jiao-Tai-Wan, a traditional Chinese medicine commonly used to treat heart-kidney imbalance insomnia, to assess the changes in white matter tracts. Tract-based spatial statistical analyses were first applied to compare the changes in mean diffusivity and fractional anisotropy of white matter between 75 heart-kidney imbalance insomnia patients and 41 healthy control participants. In subsequent randomized, double-blind, placebo-controlled trials, comparisons of mean diffusivity and fractional anisotropy were also performed in 24 heart-kidney imbalance insomnia patients (8 males; 16 females; 42.5 ± 10.4 years) with Jiao-Tai-Wan and 26 heart-kidney imbalance insomnia patients (11 males; 15 females; 39.7 ± 9.4 years) with a placebo, with age and sex as covariates. Fractional anisotropy values in left corticospinal tract were increased in heart-kidney imbalance insomnia patients. Heart-kidney imbalance insomnia patients showed lower mean diffusivity and fractional anisotropy values of several white matter tracts than healthy control participants, such as the bilateral anterior limb of internal capsule, bilateral superior longitudinal fasciculus and bilateral posterior corona radiata. After being treated with Jiao-Tai-Wan, heart-kidney imbalance insomnia patients showed a trend towards reduced fractional anisotropy values in the left corticospinal tract. Jiao-Tai-Wan may improve the sleep quality by reversing the structural changes of the left corticospinal tract caused by heart-kidney imbalance insomnia.

Intravoxel incoherent motion diffusion-weighted imaging in the characterization of Alzheimer's disease.

OBJECTIVES: Alzheimer's disease (AD) is the most common type of dementia, and characterizing brain changes in AD is important for clinical diagnosis and prognosis. This study was designed to evaluate the classification performance of intravoxel incoherent motion (IVIM) diffusion-weighted imaging in differentiating between AD patients and normal control (NC) subjects and to explore its potential effectiveness as a neuroimaging biomarker. METHODS: Thirty-one patients with probable AD and twenty NC subjects were included in the prospective study. IVIM data were subjected to postprocessing, and parameters including the apparent diffusion coefficient (ADC), slow diffusion coefficient (Ds), fast diffusion coefficient (Df), perfusion fraction (fp) and Df*fp were calculated. The classification model was developed and confirmed with cross-validation (group A/B) using Support Vector Machine (SVM). Correlations between IVIM parameters and Mini-Mental State Examination (MMSE) scores in AD patients were investigated using partial correlation analysis. RESULTS: Diffusion MRI revealed significant region-specific differences that aided in differentiating AD patients from controls. Among the analyzed regions and parameters, the Df of the right precuneus (PreR) (ρ = 0.515; P = 0.006) and the left cerebellum (CL) (ρ = 0.429; P = 0.026) demonstrated significant associations with the cognitive function of AD patients. An area under the receiver operating characteristics curve (AUC) of 0.84 (95% CI: 0.66, 0.99) was calculated for the validation in dataset B after the prediction model was trained on dataset A. When the datasets were reversed, an AUC of 0.90 (95% CI: 0.75, 1.00) was calculated for the validation in dataset A, after the prediction model trained in dataset B. CONCLUSION: IVIM imaging is a promising method for the classification of AD and NC subjects, and IVIM parameters of precuneus and cerebellum might be effective biomarker for the diagnosis of AD.

Mean apparent diffusion coefficient in a single slice may predict tumor response to whole-brain radiation therapy in non-small-cell lung cancer patients with brain metastases.

OBJECTIVES: This study aimed to access the performance of apparent diffusion coefficient (ADC) as a predictor for treatment response to whole-brain radiotherapy (WBRT) in patients with brain metastases (BMs) from non-small-cell lung cancer (NSCLC). METHODS: A retrospective analysis was conducted of 102 NSCLC patients with BMs who underwent WBRT between 2012 and 2016. Diffusion-weighted MRI were performed pre-WBRT and within 12 weeks after WBRT started. Mean single-plane ADC value of ROIs was evaluated by two radiologists blinded to results of each other. The treatment response rate, intracranial progression-free survival (PFS), and overall survival (OS) were analyzed based on the ADC value and ΔADC respectively. At last, we used COX and logistic regression to do the multivariate analysis. RESULTS: There was good inter-observer agreement of mean ADC value pre-WBRT, post-WBRT, and ΔADC between the 2 radiologists (Pearson correlation 0.915 [pre-WBRT], 0.950 [post-WBRT], 0.937 [ΔADC], p < 0.001, for each one). High mean ADC value were related with better response rate (72.2% vs 37.5%, p = 0.001) and iPFS (7.6 vs 6.4 months, p = 0.031). High ΔADC were related with better response rate (73.6% vs 36.7%, p < 0.001). Multivariate analysis shows that histopathology, BMs number, high ADC value pre-WBRT, and high ΔADC post-WBRT were related to better treatment response of WBRT, and KPS, BMs number, and low ADC value pre-WBRT increased the risk of developing intracranial relapse. CONCLUSIONS: The mean single-plane ADC value pre-WBRT and ΔADC post-WBRT were potential predictor for intracranial tumor response to WBRT in NSCLC patients with brain metastases. KEY POINTS: • ADC value is a potential predictor of intracranial treatment response to WBRT in NSCLC patients with brain metastases. • Higher mean ADC value pre-WBRT and ΔADC post-WBRT of brain metastases were related to better intracranial tumor response. • Prediction of response before WBRT using ADC value can help oncologists to make better therapy plans and avoid missing opportunities for rescue therapy.

MRI compared with low-dose CT scanning in the diagnosis of axial spondyloarthritis.

OBJECTIVES: To compare the performance of conventional radiography, ldCT, and MRI in the diagnosis of sacroiliitis in suspected axial spondyloarthritis (axSpA). METHODS: Patients presenting with > 3 months chronic back pain were assessed by axSpA-experienced rheumatologists and diagnosed as axSpA or not; axSpA patients were then considered nr-axSpA or AS using plain radiography. Non-axSpA patients were recruited as controls, and divided into non-inflammatory and inflammatory groups on the basis of inflammatory back pain and/or CRP/ESR elevation. Clinical variables, pelvic radiography, sacroiliac joint (SIJ) ldCT, and SIJ MRI were obtained. RESULTS: A total of 121 patients were included and had SIJ radiography and ldCT, of whom 71 additionally had an SIJ MRI. These included 23 non-inflammatory controls, 21 inflammatory controls, 32 nr-axSpA cases, and 45 AS cases. Fourteen of 32 (44%) nr-axSpA patients had positive ldCT scans, 21/24 (88%) had MRI-BMO, and 11/24 (46%) had MRI-structural lesions. ldCT had high specificity with only 1/23 (4%) non-inflammatory controls being positive. MRI-BMO had the highest sensitivity for nr-axSpA, but compared with ldCT lower specificity, with 5/15 (33%) of non-inflammatory controls being positive, and similar sensitivity for AS (20/22 (91%) vs 44/44 for ldCT). CONCLUSIONS: ldCT identifies evidence of radiographic change in a significant proportion of nr-axSpA cases and is highly specific for axSpA. MRI-BMO lesions are more sensitive than either conventional radiography or MRI-structural assessment for axSpA. The relative position of these imaging modalities in screening for axSpA needs to be reconsidered, also taking into account the costs involved.Key Points• ldCT is more sensitive for erosions or sclerosis in axSpA than plain radiography, with 44% of patients with nr-axSpA having evidence of AS-related sacroiliac joint changes on ldCT.• MRI-structural lesions are no more sensitive but are less specific for AS than ldCT.• MRI-BMO is the most sensitive test for nr-axSpA of the modalities tested but is less specific for axSpA than for ldCT.

Characterization of Alzheimer's Disease Using Ultra-high b-values Apparent Diffusion Coefficient and Diffusion Kurtosis Imaging.

The aim of the study is to investigate the diffusion characteristics of Alzheimer's disease (AD) patients using an ultra-high b-values apparent diffusion coefficient (ADC_uh) and diffusion kurtosis imaging (DKI). A total of 31 AD patients and 20 healthy controls (HC) who underwent both MRI examination and clinical assessment were included in this study. Diffusion weighted imaging (DWI) was acquired with 14 b-values in the range of 0 and 5000 s/mm2. Diffusivity was analyzed in selected regions, including the amygdala (AMY), hippocampus (HIP), thalamus (THA), caudate (CAU), globus pallidus (GPA), lateral ventricles (LVe), white matter (WM) of the frontal lobe (FL), WM of the temporal lobe (TL), WM of the parietal lobe (PL) and centrum semiovale (CS). The mean, median, skewness and kurtosis of the conventional apparent diffusion coefficient (ADC), DKI (including two variables, Dapp and Kapp) and ADC_uh values were calculated for these selected regions. Compared to the HC group, the ADC values of AD group were significantly higher in the right HIP and right PL (WM), while the ADC_uh values of the AD group increased significantly in the WM of the bilateral TL and right CS. In the AD group, the Kapp values in the bilateral LVe, bilateral PL/left TL (WM) and right CS were lower than those in the HC group, while the Dapp value of the right PL (WM) increased. The ADC_uh value of the right TL was negatively correlated with MMSE (mean, r=-0.420, p=0.019). The ADC value and Dapp value have the same regions correlated with MMSE. Compared with the ADC_uh, combining ADC_uh and ADC parameters will result in a higher AUC (0.894, 95%CI=0.803-0.984, p=0.022). Comparing to ADC or DKI, ADC_uh has no significant difference in the detectability of AD, but ADC_uh can better reflect characteristic alternation in unconventional brain regions of AD patients.

Molecular Mechanism Exploration of Pancreatitis Based on miRNA Expression Profile.

BACKGROUND: Pancreatitis is a popular disease around the world, and can also lead to pancreatic cancer. Pancreatitis can be distinguished into two types, acute pancreatitis (AP) and chronic pancreatitis (CP). Every year, AP leads to approximately 275,000 new cases and is the most frequent gastrointestinal disease in American. METHODS: The miRNA expression profile of pancreatic cancer and pancreatitis was downloaded from GEO with accession id GSE24279. First, the differentially expressed miRNAs with |fold change| ≥ 2 and p-value ≤ 0.05 and then the target genes of significantly differentially expressed miRNAs in pancreatitis were identified and the interaction network was constructed. Also the biological functions of the target genes were explored based on GO and KEGG enrichment. Finally, the expression values of hsa-miR-373-5p and hsa-miR-374a-5p were validated using RT-PCR. RESULTS: A total of 40 and 13 differentially expressed miRNAs were screened out for pancreatic and pancreatitis, respectively. Two miRNAs, hsa-miR-373-5p and hsa-miR-374a-5p, had significantly down-regulated expression in pancreatitis. Target gene analysis showed that hsa-miR-373-5p probably participates in the development of pancreatitis by regulating MBL2, MAT2B, and BCL10. In addition, has-miR-374a-5p can regulate the expression of NCK1, MMP14. Those genes are involved in nuclear factor kappa B and p38 signaling in the early stage of pancreatitis. Also, NCK1 can regulate pancreatic ß-cell proinsulin content and participate in the progression of pancreatic cancer development. CONCLUSIONS: In summary, the findings in this study deciphered the potential miRNA regulation mechanism in pancreatitis, and identified valuable biomarkers for the diagnosis of pancreatitis.

An Intelligent Parkinson's Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach.

Parkinson's disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artificial intelligence methods have been used for the diagnosis of PD. In this study, an enhanced fuzzy k-nearest neighbor (FKNN) method for the early detection of PD based upon vocal measurements was developed. The proposed method, an evolutionary instance-based learning approach termed CBFO-FKNN, was developed by coupling the chaotic bacterial foraging optimization with Gauss mutation (CBFO) approach with FKNN. The integration of the CBFO technique efficiently resolved the parameter tuning issues of the FKNN. The effectiveness of the proposed CBFO-FKNN was rigorously compared to those of the PD datasets in terms of classification accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic curve). The simulation results indicated the proposed approach outperformed the other five FKNN models based on BFO, particle swarm optimization, Genetic algorithms, fruit fly optimization, and firefly algorithm, as well as three advanced machine learning methods including support vector machine (SVM), SVM with local learning-based feature selection, and kernel extreme learning machine in a 10-fold cross-validation scheme. The method presented in this paper has a very good prospect, which will bring great convenience to the clinicians to make a better decision in the clinical diagnosis.

Visualization of Anatomic Variation of the Anterior Septal Vein on Susceptibility-Weighted Imaging.

BACKGROUND AND PURPOSE: Understanding the anatomy of the anterior septal vein (ASV) is critical for minimally invasive procedures to the third ventricle and for assessing lesion size and venous drainage in the anterior cranial fossa. Accordingly, this study evaluated topographic anatomy and anatomic variation of the ASV using susceptibility-weighted imaging (SWI). METHODS: Sixty volunteers were examined using a 3.0T MR system. The diameter of the ASV and distance between bilateral septal points were measured. ASVs were divided into types 1 (only drains frontal lobe) and 2 (drains both frontal lobe and head of the caudate nucleus). We evaluated the ASV-internal cerebral vein (ICV) junction based on its positional relationship with the appearance of a venous angle or a false venous angle and the foramen of Monro. Fused SW and T1-weighted images were used to observe positional relationships between the course of the ASV and the surrounding brain structures. RESULTS: The ASV and its small tributaries were clearly visualized in 120 hemispheres (100%). The average diameter of ASVs was 1.05±0.17 mm (range 0.9-1.6 mm). The average distance between bilateral septal points was 2.23±1.03 mm (range 1.3-6.6 mm). The ASV types 1 and 2 were in 77 (64.2%) and 43 (35.8%) hemispheres, respectively. In 83 (69.2%) hemispheres, the ASV-ICV junction was situated at the venous angle and the posterior margin of the foramen of Monro. In 37 (30.8%) hemispheres, the ASV-ICV junction was situated beyond the posterior margin of the foramen of Monro. The average distance between the posteriorly located ASV-ICV junction and the posterior margin of the foramen of Monro was 6.41±3.95 mm (range 2.4-15.9 mm). CONCLUSION: Using SWI, the topographic anatomy and anatomic variation of the ASV were clearly demonstrated. Preoperative assessment of anatomic variation of the ASV may be advantageous for minimally invasive neurosurgical procedures.

Lipid storage changes in human skeletal muscle during detraining.

UNLABELLED: Exercise training is known to increase intramuscular triglyceride content in both trained and untrained legs. The purpose of the study was to determine the changes of intramyocellular lipids (IMCL) and extramyocellular lipids (EMCL) of both trained and untrained legs during detraining. We measured both IMCL and EMCL levels in previously trained vs. untrained legs during 4-weeks of detraining after 6-weeks of strength training. Eight young men (aged 21.4 ± 1.4 years) trained their vastus lateralis muscle in one leg using a dynamometer, whereas the contralateral leg served as untrained control. Muscle cross-sectional area (CSA), IMCL, EMCL, total creatine (creatine + phophocreatine) of extensor (vastus lateralis) muscles were assessed using magnetic resonance imaging (MRI) and proton magnetic resonance spectra ((1)H-MRS) before training, 3 days after and 28 days after the last bout of training. CSA was increased in both legs by Day 3 after training, and was still high at Day 28 post-training; IMCL increased in both legs by Day 3 after training, then decreased at Day 28 post-training only in the untrained leg; EMCL shows no significant change by Day 3 after training, but at Day 28 post-training has increased in the trained leg and decreased in the untrained leg; total creatine did not change significantly. CONCLUSION: Decreases of IMCL and EMCL storages in previously untrained leg during detraining indicates an ectopic influence on tissue lipid storage by different metabolic demand among tissues in the same human body.