Comparison of 18F-FDG PET and arterial spin labeling MRI in evaluating Alzheimer's disease and amnestic mild cognitive impairment using integrated PET/MR.

BACKGROUND: Developing biomarkers for early stage AD patients is crucial. Glucose metabolism measured by 18F-FDG PET is the most common biomarker for evaluating cellular energy metabolism to diagnose AD. Arterial spin labeling (ASL) MRI can potentially provide comparable diagnostic information to 18F-FDG PET in patients with neurodegenerative disorders. However, the conclusions about the diagnostic performance of AD are still controversial between 18F-FDG PET and ASL. This study aims to compare quantitative cerebral blood flow (CBF) and glucose metabolism measured by 18F-FDG PET diagnostic values in patients with Alzheimer's disease (AD) and amnestic mild cognitive impairment (aMCI) using integrated PET/MR. RESULTS: Analyses revealed overlapping between decreased regional rCBF and 18F-FDG PET SUVR in patients with AD compared with NC participants in the bilateral parietotemporal regions, frontal cortex, and cingulate cortex. Compared with NC participants, patients with aMCI exclusively demonstrated lower 18F-FDG PET SUVR in the bilateral temporal cortex, insula cortex, and inferior frontal cortex. Comparison of the rCBF in patients with aMCI and NC participants revealed no significant difference (P > 0.05). The ROC analysis of rCBF in the meta-ROI could diagnose patients with AD (AUC, 0.87) but not aMCI (AUC, 0.61). The specificity of diagnosing aMCI has been improved to 75.56% when combining rCBF and 18F-FDG PET SUVR. CONCLUSION: ASL could detect similar aberrant patterns of abnormalities compared to 18F-FDG PET in patients with AD compared with NC participants but not in aMCI. The diagnostic efficiency of 18F-FDG-PET for AD and aMCI patients remained higher to ASL. Our findings support that applying 18F-FDG PET may be preferable for diagnosing AD and aMCI.

Multiparametric hippocampal signatures for early diagnosis of Alzheimer's disease using 18F-FDG PET/MRI Radiomics.

PURPOSE: This study aimed to explore the utility of hippocampal radiomics using multiparametric simultaneous positron emission tomography (PET)/magnetic resonance imaging (MRI) for early diagnosis of Alzheimer's disease (AD). METHODS: A total of 53 healthy control (HC) participants, 55 patients with amnestic mild cognitive impairment (aMCI), and 51 patients with AD were included in this study. All participants accepted simultaneous PET/MRI scans, including 18F-fluorodeoxyglucose (18F-FDG) PET, 3D arterial spin labeling (ASL), and high-resolution T1-weighted imaging (3D T1WI). Radiomics features were extracted from the hippocampus region on those three modal images. Logistic regression models were trained to classify AD and HC, AD and aMCI, aMCI and HC respectively. The diagnostic performance and radiomics score (Rad-Score) of logistic regression models were evaluated from 5-fold cross-validation. RESULTS: The hippocampal radiomics features demonstrated favorable diagnostic performance, with the multimodal classifier outperforming the single-modal classifier in the binary classification of HC, aMCI, and AD. Using the multimodal classifier, we achieved an area under the receiver operating characteristic curve (AUC) of 0.98 and accuracy of 96.7% for classifying AD from HC, and an AUC of 0.86 and accuracy of 80.6% for classifying aMCI from HC. The value of Rad-Score differed significantly between the AD and HC (p < 0.001), aMCI and HC (p < 0.001) groups. Decision curve analysis showed superior clinical benefits of multimodal classifiers compared to neuropsychological tests. CONCLUSION: Multiparametric hippocampal radiomics using PET/MRI aids in the identification of early AD, and may provide a potential biomarker for clinical applications.

Machine learning for predicting breast-conserving surgery candidates after neoadjuvant chemotherapy based on DCE-MRI.

Purpose: This study aimed to investigate a machine learning method for predicting breast-conserving surgery (BCS) candidates, from patients who received neoadjuvant chemotherapy (NAC) by using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) obtained before and after NAC. Materials and methods: This retrospective study included 75 patients who underwent NAC and breast surgery. First, 3,390 features were comprehensively extracted from pre- and post-NAC DCE-MRIs. Then patients were then divided into two groups: type 1, patients with pathologic complete response (pCR) and single lesion shrinkage; type 2, major residual lesion with satellite foci, multifocal residual, stable disease (SD), and progressive disease (PD). The logistic regression (LR) was used to build prediction models to identify the two groups. Prediction performance was assessed using the area under the curve (AUC), accuracy, sensitivity, and specificity. Results: Radiomics features were significantly related to breast cancer shrinkage after NAC. The combination model achieved an AUC of 0.82, and the pre-NAC model was 0.64, the post-NAC model was 0.70, and the pre-post-NAC model was 0.80. In the combination model, 15 features, including nine wavelet-based features, four Laplacian-of-Gauss (LoG) features, and two original features, were filtered. Among these selected were four features from pre-NAC DCE-MRI, six were from post-NAC DCE-MRI, and five were from pre-post-NAC features. Conclusion: The model combined with pre- and post-NAC DCE-MRI can effectively predict candidates to undergo BCS and provide AI-based decision support for clinicians with ensured safety. High-order (LoG- and wavelet-based) features play an important role in our machine learning model. The features from pre-post-NAC DCE-MRI had better predictive performance.

Machine Learning-Based Perihematomal Tissue Features to Predict Clinical Outcome after Spontaneous Intracerebral Hemorrhage.

OBJECTIVES: To explore whether radiomic features of perihematomal tissue can improve the forecasting accuracy for the prognosis of patients with an intracerebral hemorrhage (ICH). MATERIALS AND METHODS: In total, 118 ICH patients were retrospectively studied that had a clinical and radiological diagnosis of spontaneous ICH. The functional outcome 3 months after ictus was measured using the modified Rankin Scale (mRS), which was divided into good (mRS ≤ 2) and poor outcomes (mRS > 2). A total of 2260 radiomics features were obtained from non-contrast computer tomography (NCCT) images, with 1130 features extracted from the hematoma and the hematoma plus perihematoma. The high-dimensional data was modeled by a logistic regression algorithm and the accuracy of the model was verified by five-fold cross-validation. The predictive performance of radiomics models was assessed by the area under the receiver operating characteristic (ROC) curve. RESULTS: In the test set, the mean ROC area under the curve (AUC) of the hematoma set to predict the prognosis of ICH was 0.83, and the specificity and sensitivity were 78% and 81%, respectively. When the hematoma and perihematomal tissue were combined, the mean AUC increased to 0.88, and the specificity and sensitivity reached 85% and 84%, respectively. The hematoma plus perihematoma model showed a significantly higher AUC and specificity. CONCLUSIONS: Analysis of the hematoma and perihematomal tissue NCCT-based radiomics could potentially identify the progression of a hematoma more accurately and could be a valuable clinical target to enhance the prediction of outcomes in patients with ICH.

Ratiometric and colorimetric sensors for highly sensitive detection of water in organic solvents based on hydroxyl-containing polyimide-fluoride complexes.

Two new hydroxyl-containing polyimides (PIs) with excellent comprehensive performances were designed and synthesized. After PIs react with fluoride ion (F-), the resulting polyimide-fluoride complexes (PI-1·F and PI-2·F) are exploited as ratiometric and colorimetric sensors for detecting trace water in DMSO/DMF with high sensitivity. Both sensors PI-1·F and PI-2·F exhibit a good naked-eye visual detection as well as an excellent linear relationship between the UV-vis absorbance ratio and the low water content in DMSO/DMF, which constitutes a quantitative method for determining water content in DMSO/DMF. The limits of detection (LOD) of sensor PI-1·F for water in DMSO and DMF are as low as 0.0035% and 0.0031% (v/v), respectively. The sensor PI-2·F shows the higher sensitivity for water detection with extremely low detection limits of 0.00084% and 0.0015% in DMSO and DMF, respectively. Furthermore, PI films have been directly used for the visual sensing of water in acetonitrile, acetone and ethanol, which provides an effective way for fabricating high-performance film-based water sensor devices.

Microwave-assisted synthesis of high thermal stability and colourless polyimides containing pyridine.

A novel aromatic diamine containing pyridyl side group, 4-pyridine-4,4-bis(3,5-dimethyl-5-aminophenyl)methane (PyDPM), was successfully synthesized via electrophilic substitution reaction. The polyimides (PIs) containing pyridine were obtained via the microwave-assisted one-step polycondensation of the PyDPM with pyromellitic dianhydride (PMDA), 3,3',4,4'-biphenyltetracarboxylic dianhydride (BPDA), 3,3',4,4'-diphenylether tetracarboxylic dianhydride (ODPA) and 4,4'-(hexafluoroisopropylidene)diphthalic anhydride (6FDA). Contrarily to the reported similar PIs, these PIs exhibit much higher thermal stability or heat resistance, i.e. high glass transition temperatures (T gs) in the range of 358-473°C, and the decomposition temperatures at 5% weight loss over 476°C under nitrogen. They can afford flexible and strong films with tensile strength of 82.1-93.3 MPa, elongation at break of 3.7%-15.2%, and Young's modulus of 3.3-3.8 GPa. Furthermore, The PI films exhibit good optical transparency with the cut-off wavelength at 313-366 nm and transmittance higher than 73% at 450 nm. The excellent thermal and optical transmittance can be attributed to synthesis method and the introduction of pyridine rings and ortho-methyl groups. The inherent viscosities of PIs via one-step method were found to be 0.58-1.12 dl g-1 in DMAc, much higher than those via two-step method. These results indicate these PIs could be potential candidates for optical substrates of organic light emitting diodes (OLEDs).

Epoxy resin composites with commercially available graphene: toward high toughness and rigidity.

Some commercially available graphene (CG) samples, actually graphite nanoplates, are difficult to be applied in polymers due to their large thickness, even though their fabrication is more cost-effective. In this work, a CG sample with a thickness of about 25 nm is pre-treated via a facile acid soaking technique, and then the acid-treated CG is introduced into epoxy resin (EP) to improve the toughness while maintaining the high rigidity of the EP. The thickness of the acid-treated CG is dramatically decreased to about 1 nm, well-dispersing CG in the EP matrix. The tensile strength and the fracture energy of EP composite with 0.2 wt% acid-treated CG are increased to 85.2 MPa and 3.6 kJ m-2 from 74.3 MPa and 2.3 kJ m-2 of pure EP, respectively. In addition, the tensile-fractured surfaces of EP composites reveal that the toughening mechanism is mainly attributed to the crack deflection and crack pinning caused by the firmly-embedded CG nanosheets in the EP matrix. Besides, the glass transition temperature of EP composites is increased to 170.5 °C from 163.9 °C of pure EP. The present study provides a valuable approach for making use of the cost-effective graphite nanoplates to achieve a similar performance as graphene in EP.

Structure of the Complex between a Heparan Sulfate Octasaccharide and Mycobacterial Heparin-Binding Hemagglutinin.

Heparin-binding hemagglutinin (HBHA) is a 199 amino acid virulence factor at the envelope of Mycobacterium tuberculosis that contributes to latent tuberculosis. The binding of HBHA to respiratory epithelial cells, which leads to extrapulmonary dissemination of the pathogen, is mediated by cell-surface heparan sulfate (HS). We report the structural characterization of the HBHA/HS complex by NMR spectroscopy. To develop a model for the molecular recognition, the first chemically synthesized uniformly 13 C- and 15 N-labeled HS octasaccharide and a uniformly 13 C- and 15 N-labeled form of HBHA were prepared. Residues 180-195 at the C-terminal region of HBHA show large chemical shift perturbation upon association with the octasaccharide. Molecular dynamics simulations conforming to the multidimensional NMR data revealed key electrostatic and even hydrophobic interactions between the binding partners that may aid in the development of agents targeting the binding event.

Interactions that influence the binding of synthetic heparan sulfate based disaccharides to fibroblast growth factor-2.

Heparan sulfate (HS) is a linear sulfated polysaccharide that mediates protein activities at the cell-extracellular interface. Its interactions with proteins depend on the complex patterns of sulfonations and sugar residues. Previously, we synthesized all 48 potential disaccharides found in HS and used them for affinity screening and X-ray structural analysis with fibroblast growth factor-1 (FGF1). Herein, we evaluated the affinities of the same sugars against FGF2 and determined the crystal structures of FGF2 in complex with three disaccharides carrying N-sulfonated glucosamine and 2-O-sulfonated iduronic acid as basic backbones. The crystal structures show that water molecules mediate different interactions between the 3-O-sulfonate group and Lys125. Moreover, the 6-O-sulfonate group forms intermolecular interactions with another FGF2 unit apart from the main binding site. These findings suggest that the water-mediated interactions and the intermolecular interactions influence the binding affinity of different disaccharides with FGF2, correlating with their respective dissociation constants in solution.

Divergent synthesis of 48 heparan sulfate-based disaccharides and probing the specific sugar-fibroblast growth factor-1 interaction.

Several biological processes involve glycans, yet understanding their ligand specificities is impeded by their inherent diversity and difficult acquisition. Generating broad synthetic sugar libraries for bioevaluations is a powerful tool in unraveling glycan structural information. In the case of the widely distributed heparan sulfate (HS), however, the 48 theoretical possibilities for its repeating disaccharide call for synthetic approaches that should minimize the effort in an undoubtedly huge undertaking. Here we employed a divergent strategy to afford all 48 HS-based disaccharides from just two orthogonally protected disaccharide precursors. Different combinations and sequence of transformation steps were applied with many downstream intermediates leading up to multiple target products. With the full disaccharide library in hand, affinity screening with fibroblast growth factor-1 (FGF-1) revealed that four of the synthetic sugars bind to FGF-1. The molecular details of the interaction were further clarified through X-ray analysis of the sugar-protein cocrystals. The capability of comprehensive sugar libraries in providing key insights in glycan-ligand interaction is, thus, highlighted.