Tailoring atomic chemistry to refine reaction pathway for the most enhancement by magnetization in water oxidation.

Water oxidation on magnetic catalysts has generated significant interest due to the spin-polarization effect. Recent studies have revealed that the disappearance of magnetic domain wall upon magnetization is responsible for the observed oxygen evolution reaction (OER) enhancement. However, an atomic picture of the reaction pathway remains unclear, i.e., which reaction pathway benefits most from spin-polarization, the adsorbent evolution mechanism, the intermolecular mechanism (I2M), the lattice oxygen-mediated one, or more? Here, using three model catalysts with distinguished atomic chemistries of active sites, we are able to reveal the atomic-level mechanism. We found that spin-polarized OER mainly occurs at interconnected active sites, which favors direct coupling of neighboring ligand oxygens (I2M). Furthermore, our study reveals the crucial role of lattice oxygen participation in spin-polarized OER, significantly facilitating the coupling kinetics of neighboring oxygen radicals at active sites.

Incremental value of amyloid PET in a tertiary memory clinic setting in China.

INTRODUCTION: The objective of this study is to investigate the incremental value of amyloid positron emission tomography (Aß-PET) in a tertiary memory clinic setting in China. METHODS: A total of 1073 patients were offered Aß-PET using 18F-florbetapir. The neurologists determined a suspected etiology (Alzheimer's disease [AD] or non-AD) with a percentage estimate of their confidence and medication prescription both before and after receiving the Aß-PET results. RESULTS: After disclosure of the Aß-PET results, etiological diagnoses changed in 19.3% of patients, and diagnostic confidence increased from 69.3% to 85.6%. Amyloid PET results led to a change of treatment plan in 36.5% of patients. Compared to the late-onset group, the early-onset group had a more frequent change in diagnoses and a higher increase in diagnostic confidence. DISCUSSION: Aß-PET has significant impacts on the changes of diagnoses and management in Chinese population. Early-onset cases are more likely to benefit from Aß-PET than late-onset cases. HIGHLIGHTS: Amyloid PET contributes to diagnostic changes and its confidence in Chinese patients. Amyloid PET leads to a change of treatment plans in Chinese patients. Early-onset cases are more likely to benefit from amyloid PET than late-onset cases.

Colocalization of Increased Midbrain Signals in Neuroinflammation and Tau PET Imaging Suggests the Diagnosis of Progressive Supranuclear Palsy.

ABSTRACT: Clinical overlap with multiple other neurological diseases makes the diagnosis of autoimmune encephalitis challenging; consequently, a broad range of neurological diseases are misdiagnosed as autoimmune encephalitis. A 58-year-old man presented with abnormal behavior, irritability for 3 years, oculomotor disturbance, unsteady walking, and dysphagia and was suspected as having anti-dipeptidyl-peptidase-like protein 6 (DPPX) encephalitis as the anti-DPPX antibody was positive in the serum. However, the therapeutic effect of immunotherapy was unsatisfactory. Subsequently, colocalization of increased midbrain signals was observed in neuroinflammation PET using [ 18 F]DPA-714 and in tau PET using [ 18 F]florzolotau, suggesting the diagnosis of progressive supranuclear palsy.

Disorder of consciousness related pattern could distinguish minimally conscious state from unresponsive wakefulness syndrome: A F-18-FDG-PET study.

BACKGROUND: Accurate evaluation of level of disorder of consciousness (DOC) is clinically challenging. OBJECTIVE: 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). METHODS: 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. RESULTS: 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). CONCLUSIONS: DOCRP might serve as an objective biomarker in distinguishing between UWS and MCS, especially in patients survived global hypoxic-ischemic brain injury. TRIAL REGISTRATION NUMBER: ChiCTR2300073717 (Chinese clinical trial registry site, http://www.chictr.org).

Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson's disease: a systematic review and meta-analysis.

Artificial intelligence (AI)-assisted PET imaging is emerging as a promising tool for the diagnosis of Parkinson's disease (PD). We aim to systematically review the diagnostic accuracy of AI-assisted PET in detecting PD. The Ovid MEDLINE, Ovid Embase, Web of Science, and IEEE Xplore databases were systematically searched for related studies that developed an AI algorithm in PET imaging for diagnostic performance from PD and were published by August 17, 2023. Binary diagnostic accuracy data were extracted for meta-analysis to derive outcomes of interest: area under the curve (AUC). 23 eligible studies provided sufficient data to construct contingency tables that allowed the calculation of diagnostic accuracy. Specifically, 11 studies were identified that distinguished PD from normal control, with a pooled AUC of 0.96 (95% CI: 0.94-0.97) for presynaptic dopamine (DA) and 0.90 (95% CI: 0.87-0.93) for glucose metabolism (18F-FDG). 13 studies were identified that distinguished PD from the atypical parkinsonism (AP), with a pooled AUC of 0.93 (95% CI: 0.91 - 0.95) for presynaptic DA, 0.79 (95% CI: 0.75-0.82) for postsynaptic DA, and 0.97 (95% CI: 0.96-0.99) for 18F-FDG. Acceptable diagnostic performance of PD with AI algorithms-assisted PET imaging was highlighted across the subgroups. More rigorous reporting standards that take into account the unique challenges of AI research could improve future studies.

Radiomics-Guided Deep Learning Networks Classify Differential Diagnosis of Parkinsonism.

The differential diagnosis between atypical Parkinsonian syndromes may be challenging and critical. We aimed to proposed a radiomics-guided deep learning (DL) model to discover interpretable DL features and further verify the proposed model through the differential diagnosis of Parkinsonian syndromes. We recruited 1495 subjects for 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scanning, including 220 healthy controls and 1275 patients diagnosed with idiopathic Parkinson's disease (IPD), multiple system atrophy (MSA), or progressive supranuclear palsy (PSP). Baseline radiomics and two DL models were developed and tested for the Parkinsonian diagnosis. The DL latent features were extracted from the last layer and subsequently guided by radiomics. The radiomics-guided DL model outperformed the baseline radiomics approach, suggesting the effectiveness of the DL approach. DenseNet showed the best diagnosis ability (sensitivity: 95.7%, 90.1%, and 91.2% for IPD, MSA, and PSP, respectively) using retained DL features in the test dataset. The retained DL latent features were significantly associated with radiomics features and could be interpreted through biological explanations of handcrafted radiomics features. The radiomics-guided DL model offers interpretable high-level abstract information for differential diagnosis of Parkinsonian disorders and holds considerable promise for personalized disease monitoring.

Recent progress in the applications of presynaptic dopaminergic positron emission tomography imaging in parkinsonism.

Nowadays, presynaptic dopaminergic positron emission tomography, which assesses deficiencies in dopamine synthesis, storage, and transport, is widely utilized for early diagnosis and differential diagnosis of parkinsonism. This review provides a comprehensive summary of the latest developments in the application of presynaptic dopaminergic positron emission tomography imaging in disorders that manifest parkinsonism. We conducted a thorough literature search using reputable databases such as PubMed and Web of Science. Selection criteria involved identifying peer-reviewed articles published within the last 5 years, with emphasis on their relevance to clinical applications. The findings from these studies highlight that presynaptic dopaminergic positron emission tomography has demonstrated potential not only in diagnosing and differentiating various Parkinsonian conditions but also in assessing disease severity and predicting prognosis. Moreover, when employed in conjunction with other imaging modalities and advanced analytical methods, presynaptic dopaminergic positron emission tomography has been validated as a reliable in vivo biomarker. This validation extends to screening and exploring potential neuropathological mechanisms associated with dopaminergic depletion. In summary, the insights gained from interpreting these studies are crucial for enhancing the effectiveness of preclinical investigations and clinical trials, ultimately advancing toward the goals of neuroregeneration in parkinsonian disorders.