Noble metal nanomaterials have been widely used as catalysts. Common techniques for the synthesis of noble metal often result in crystalline nanostructures. The synthesis of amorphous noble metal ...nanostructures remains a substantial challenge. We present a general route for preparing dozens of different amorphous noble metal nanosheets with thickness less than 10 nm by directly annealing the mixture of metal acetylacetonate and alkali salts. Tuning atom arrangement of the noble metals enables to optimize their catalytic properties. Amorphous Ir nanosheets exhibit a superior performance for oxygen evolution reaction under acidic media, achieving 2.5-fold, 17.6-fold improvement in mass activity (at 1.53 V vs. reversible hydrogen electrode) over crystalline Ir nanosheets and commercial IrO
catalyst, respectively. In situ X-ray absorption fine structure spectra indicate the valance state of Ir increased to less than + 4 during the oxygen evolution reaction process and recover to its initial state after the reaction.
Purpose
Positron emission tomography (PET) with
18
F-fluorodeoxyglucose (FDG) reveals altered cerebral metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer’s dementia (AD). ...Previous metabolic connectome analyses derive from groups of patients but do not support the prediction of an individual’s risk of conversion from present MCI to AD. We now present an individual metabolic connectome method, namely the Kullback-Leibler Divergence Similarity Estimation (KLSE), to characterize brain-wide metabolic networks that predict an individual’s risk of conversion from MCI to AD.
Methods
FDG-PET data consisting of 50 healthy controls, 332 patients with stable MCI, 178 MCI patients progressing to AD, and 50 AD patients were recruited from ADNI database. Each individual’s metabolic brain network was ascertained using the KLSE method. We compared intra- and intergroup similarity and difference between the KLSE matrix and group-level matrix, and then evaluated the network stability and inter-individual variation of KLSE. The multivariate Cox proportional hazards model and Harrell’s concordance index (C-index) were employed to assess the prediction performance of KLSE and other clinical characteristics.
Results
The KLSE method captures more pathological connectivity in the parietal and temporal lobes relative to the typical group-level method, and yields detailed individual information, while possessing greater stability of network organization (within-group similarity coefficient, 0.789 for sMCI and 0.731 for pMCI). Metabolic connectome expression was a superior predictor of conversion than were other clinical assessments (hazard ratio (HR) = 3.55; 95% CI, 2.77–4.55;
P
< 0.001). The predictive performance improved further upon combining clinical variables in the Cox model, i.e., C-indices 0.728 (clinical), 0.730 (group-level pattern model), 0.750 (imaging connectome), and 0.794 (the combined model).
Conclusion
The KLSE indicator identifies abnormal brain networks predicting an individual’s risk of conversion from MCI to AD, thus potentially constituting a clinically applicable imaging biomarker.
An ordered mesh of palladium with a thickness of about 3 nm was synthesized by a solution‐based oxidative etching. The ultrathin palladium nanomeshes have an interconnected two‐dimensional network of ...densely arrayed, ultrathin quasi‐nanoribbons that form ordered open holes. The unique mesoporous structure and high specific surface area make these ultrathin Pd nanomeshes display superior catalytic performance for ethanol electrooxidation (mass activity of 5.40 Am g−1 and specific activity of 7.09 mA cm−2 at 0.8 V vs. RHE). Furthermore, the regular mesh structure can be applied to support other noble metals, such as platinum, which exhibits extremely high hydrogen evolution reaction (HER) activity and durability.
Meshing around: A novel ultrathin Pd nanomesh with a densely arrayed, ultrathin quasi‐nanoribbons and regular open holes was transformed from a Pd nanosheet by a solution‐based free corrosion, which exhibits exceptional electrocatalytic performance.
Understanding and mastering the structural evolution of water oxidation electrocatalysts lays the foundation to finetune their catalytic activity. Herein, we demonstrate that surface reconstruction ...of spinel oxides originates from the metal-oxygen covalency polarity in the M
-O-M
backbone. A stronger M
-O covalency relative to M
-O covalency is found beneficial for a more thorough reconstruction towards oxyhydroxides. The structure-reconstruction relationship allows precise prediction of the reconstruction ability of spinel pre-catalysts, based on which the reconstruction degree towards the in situ generated oxyhydroxides can be controlled. The investigations of oxyhydroxides generated from spinel pre-catalysts with the same reconstruction ability provide guidelines to navigate the cation selection in spinel pre-catalysts design. This work reveals the fundamentals for manipulating the surface reconstruction of spinel pre-catalysts for water oxidation.
Rapid eye movement sleep behaviour disorder has been evaluated using Parkinson's disease-related metabolic network. It is unknown whether this disorder is itself associated with a unique metabolic ...network. 18F-fluorodeoxyglucose positron emission tomography was performed in 21 patients (age 65.0±5.6 years) with idiopathic rapid eye movement sleep behaviour disorder and 21 age/gender-matched healthy control subjects (age 62.5±7.5 years) to identify a disease-related pattern and examine its evolution in 21 hemi-parkinsonian patients (age 62.6±5.0 years) and 16 moderate parkinsonian patients (age 56.9±12.2 years). We identified a rapid eye movement sleep behaviour disorder-related metabolic network characterized by increased activity in pons, thalamus, medial frontal and sensorimotor areas, hippocampus, supramarginal and inferior temporal gyri, and posterior cerebellum, with decreased activity in occipital and superior temporal regions. Compared to the healthy control subjects, network expressions were elevated (P<0.0001) in the patients with this disorder and in the parkinsonian cohorts but decreased with disease progression. Parkinson's disease-related network activity was also elevated (P<0.0001) in the patients with rapid eye movement sleep behaviour disorder but lower than in the hemi-parkinsonian cohort. Abnormal metabolic networks may provide markers of idiopathic rapid eye movement sleep behaviour disorder to identify those at higher risk to develop neurodegenerative parkinsonism.
Accurate evaluation of level of disorder of consciousness (DOC) is clinically challenging.
This study aimed to establish a distinctive DOC-related pattern (DOCRP) for assessing disease severity and ...distinguishing unresponsive wakefulness syndrome (UWS) from minimally conscious state (MCS).
Fifteen patients with DOC and eighteen health subjects with F-18-fluorodeoxyglucose (F-18-FDG) positron emission tomography (PET) were enrolled in this study. All patients were assessed by Coma Recovery Scale-Revised (CRS-R) and all individuals were randomly divided into two cohorts (Cohort A and B). DOCRP was identified in Cohort A and subsequently validated in Cohort B and A+B. We also assessed the discriminatory power of DOCRP between MCS and UWS.
The DOCRP was characterized bilaterally by relatively decreased metabolism in the medial and lateral frontal lobes, parieto-temporal lobes, cingulate gyrus and caudate, associated with relatively increased metabolism in the cerebellum and brainstem. DOCRP expression exhibited high accuracy in differentiating DOC patients from controls (P<0.0001, AUC=1.000), and furthermore could effectively distinguish MCS from UWS (P=0.037, AUC=0.821, sensitivity: 85.7 %, specificity: 75.0 %). Particularly in the subgroup of DOC patients survived global hypoxic-ischemic brain injury, DOCRP expression exhibited even better discriminatory power between MCS and UWS (P=0.046, AUC=1.000).
DOCRP might serve as an objective biomarker in distinguishing between UWS and MCS, especially in patients survived global hypoxic-ischemic brain injury.
ChiCTR2300073717 (Chinese clinical trial registry site, http://www.chictr.org)
•In this study, we innovatively defined a disease-specific brain metabolic network in patients with disorder of consciousness (DOC).•The metabolic network activity demonstrated high comparability and reproducibility in DOC patients and exhibited high accuracy in separating unresponsive wakefulness syndrome (UWS) from minimally conscious state (MCS).•The discriminatory power of the metabolic network acitivty was particularly well in the subgroup of patients survived global hypoxic-ischemic brain injury.•The results suggested that metabolic network activity could serve as an objective marker for evaluating the consciousness level in clinical practice.
Perovskite oxides
based on earth-abundant transition metals have
been extensively explored as promising oxygen evolution reaction (OER)
catalysts in alkaline media. The (electro)chemically induced ...transformation
of their initially crystalline surface into an amorphous state has
been reported for a few highly active perovskite catalysts. However,
little knowledge is available to distinguish the contribution of the
amorphized surface from that of the remaining bulk toward the OER.
In this work, we utilize the promoting effects of two types of Fe
modification, i.e., bulk Fe dopant and Fe ions absorbed from the electrolyte,
on the OER activity of SrCoO
3−δ
model perovskite
to identify the active phase. Transmission electron microscopy and
X-ray photoelectron spectroscopy confirmed the surface amorphization
of SrCoO
3−δ
as well as SrCo
0.8
Fe
0.2
O
3−δ
after potential cycling in
Fe-free KOH solution. By further cycling in Fe-spiked electrolyte,
Fe was incorporated into the amorphized surface of SrCoO
3−δ
(SrCoO
3−δ
+ Fe
3+
), yielding approximately
sixfold increase in activity. Despite the difference in remaining
perovskites, SrCoO
3−δ
+ Fe
3+
and
SrCo
0.8
Fe
0.2
O
3−δ
exhibited
remarkably similar activity. These results reflect that the in situ
developed surface species are directly responsible for the measured
OER activity, whereas the remaining bulk phases have little impact.
6-
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Ffluoro-L-DOPA is a radiotracer widely used in the diagnosis of a range of diseases, including neuro-oncology, endocrinology, and Parkinson’s disease. To meet the rapidly growing clinical need ...for this radioactive compound, this study reports an optimized radiosynthesis for this molecule, which proved to be highly reliable and compatible with different types of automated radiosynthesizers. Moreover, with 6-
18
Ffluoro-L-DOPA, the PET/CT imaging of a total of 23 patients has been conducted, further demonstrating this radiotracer as a clinically valuable reagent to diagnose congenital hyperinsulinism (CHI) of infants in a non-invasive manner and, more importantly, localize the exact lesion on pancreas.
Graphical abstract
As a medical imaging technology which can show the metabolism of the brain, 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) is of great value for the diagnosis of Parkinson's Disease ...(PD). With the development of pattern recognition technology, analysis of brain images using deep learning are becoming more and more popular. However, existing computer-aided-diagnosis technologies often over fit and have poor generalizability. Therefore, we aimed to improve a framework based on Group Lasso Sparse Deep Belief Network (GLS-DBN) for discriminating PD and normal control (NC) subjects based on FDG-PET imaging. In this study, 225 NC and 125 PD cohorts from Huashan and Wuxi 904 hospitals were selected. They were divided into the training & validation dataset and 2 test datasets. First, in the training & validation set, subjects were randomly partitioned 80:20, with multiple training iterations for the deep learning model. Next, Locally Linear Embedding was used as a dimension reduction algorithm. Then, GLS-DBN was used for feature learning and classification. Different sparse DBN models were used to compare datasets to evaluate the effectiveness of our framework. Accuracy, sensitivity, and specificity were examined to validate the results. Output variables of the network were also correlated with longitudinal changes of rating scales about movement disorders (UPDRS, H&Y). As a result, accuracy of prediction (90% in Test 1, 86% in Test 2) for classification of PD and NC patients outperformed conventional approaches. Output scores of the network were strongly correlated with UPDRS and H&Y (
= 0.705,
< 0.001;
= 0.697,
< 0.001 in Test 1;
= 0.592,
= 0.0018,
= 0.528,
= 0.0067 in Test 2). These results show the GLS-DBN is feasible method for early diagnosis of PD.
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