Radiosynthesis and whole-body distribution in mice of a 18 F-labeled azepino[4,3-b]indole-1-one derivative with multimodal activity for the treatment of Alzheimer's disease.

Recently, the azepino[4,3-b]indole-1-one derivative 1 showed in vitro nanomolar inhibition against butyrylcholinesterase (BChE), the ChE isoform that plays a role in the progression and pathophysiology of Alzheimer's disease (AD), and protects against N-methyl- d-aspartate-induced neuronal toxicity. Three 9-R-substituted (R = F, Br, OMe) congeners were investigated. The 9-F derivative (2a) was found more potent as BChE inhibitors (half-maximal inhibitory concentration value = 21 nM) than 2b (9-Br) and 2c (9-OMe), achieving a residence time (38 s), assessed by surface plasmon resonance, threefold higher than that of 1. To progress in featuring the in vivo pharmacological characterization of 2a, herein the 18 F-labeled congener 2a was synthesized, by applying the aromatic 18 F-fluorination method, and its whole-body distribution in healthy mice, including brain penetration, was evaluated through positron emission tomography imaging. [18 F]2a exhibited a rapid and high brain uptake (3.35 ± 0.26% ID g-1 at 0.95 ± 0.15 min after injection), followed by a rapid clearance (t1/2 = 6.50 ± 0.93 min), showing good blood-brain barrier crossing. After a transient liver accumulation of [18 F]2a, the intestinal and urinary excretion was quantified. Finally, ex vivo pharmacological experiments in mice showed that the unlabeled 2a affects the transmitters' neurochemistry, which might be favorable to reverse cognition impairment in mild-to-moderate AD-related dementias.

Potential role of ANGPTL4 in cancer progression, metastasis, and metabolism: a brief review.

Angiopoietin-like 4 (ANGPTL4) has been identified as an adipokine involved in several non-metabolic and metabolic diseases, including angiogenesis, glucose homeostasis, and lipid metabolism. To date, the role of ANGPTL4 in cancer growth and progression, and metastasis, has been variable. Accumulating evidence suggests that proteolytic processing and posttranslational modifications of ANGPTL4 can significantly alter its function, and may contribute to the multiple and conflicting roles of ANGPTL4 in a tissue-dependent manner. With the growing interest in ANGPTL4 in cancer diagnosis and therapy, we aim to provide an up-to-date review of the implications of ANGPTL4 as a biomarker/oncogene in cancer metabolism, metastasis, and the tumor microenvironment (TME). In cancer cells, ANGPTL4 plays an important role in regulating metabolism by altering intracellular glucose, lipid, and amino acid metabolism. We also highlight the knowledge gaps and future prospect of ANGPTL4 in lymphatic metastasis and perineural invasion through various signaling pathways, underscoring its importance in cancer progression and prognosis. Through this review, a better understanding of the role of ANGPTL4 in cancer progression within the TME will provide new insights into other aspects of tumorigenesis and the potential therapeutic value of ANGPTL4.

Exploration of neuroanatomical characteristics to differentiate prodromal Alzheimer's disease from cognitively unimpaired amyloid-positive individuals.

Differentiating clinical stages based solely on positive findings from amyloid PET is challenging. We aimed to investigate the neuroanatomical characteristics at the whole-brain level that differentiate prodromal Alzheimer's disease (AD) from cognitively unimpaired amyloid-positive individuals (CU A+) in relation to amyloid deposition and regional atrophy. We included 45 CU A+ participants and 135 participants with amyloid-positive prodromal AD matched 1:3 by age, sex, and education. All participants underwent 18F-florbetaben positron emission tomography and 3D structural T1-weighted magnetic resonance imaging. We compared the standardized uptake value ratios (SUVRs) and volumes in 80 regions of interest (ROIs) between CU A+ and prodromal AD groups using independent t-tests, and employed the least absolute selection and shrinkage operator (LASSO) logistic regression model to identify ROIs associated with prodromal AD in relation to amyloid deposition, regional atrophy, and their interaction. After applying False Discovery Rate correction at < 0.1, there were no differences in global and regional SUVR between CU A+ and prodromal AD groups. Regional volume differences between the two groups were observed in the amygdala, hippocampus, entorhinal cortex, insula, parahippocampal gyrus, and inferior temporal and parietal cortices. LASSO logistic regression model showed significant associations between prodromal AD and atrophy in the entorhinal cortex, inferior parietal cortex, both amygdalae, and left hippocampus. The mean SUVR in the right superior parietal cortex (beta coefficient = 0.0172) and its interaction with the regional volume (0.0672) were also selected in the LASSO model. The mean SUVR in the right superior parietal cortex was associated with an increased likelihood of prodromal AD (Odds ratio [OR] 1.602, p = 0.014), particularly in participants with lower regional volume (OR 3.389, p < 0.001). Only regional volume differences, not amyloid deposition, were observed between CU A+ and prodromal AD. The reduced volume in the superior parietal cortex may play a significant role in the progression to prodromal AD through its interaction with amyloid deposition in that region.

A Case-Control Clinical Trial on a Deep Learning-Based Classification System for Diagnosis of Amyloid-Positive Alzheimer's Disease.

OBJECTIVE: A deep learning-based classification system (DLCS) which uses structural brain magnetic resonance imaging (MRI) to diagnose Alzheimer's disease (AD) was developed in a previous recent study. Here, we evaluate its performance by conducting a single-center, case-control clinical trial. METHODS: We retrospectively collected T1-weighted brain MRI scans of subjects who had an accompanying measure of amyloid-beta (Aß) positivity based on a 18F-florbetaben positron emission tomography scan. The dataset included 188 Aß-positive patients with mild cognitive impairment or dementia due to AD, and 162 Aß-negative controls with normal cognition. We calculated the sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) of the DLCS in the classification of Aß-positive AD patients from Aß-negative controls. RESULTS: The DLCS showed excellent performance, with sensitivity, specificity, positive predictive value, negative predictive value, and AUC of 85.6% (95% confidence interval [CI], 79.8-90.0), 90.1% (95% CI, 84.5-94.2), 91.0% (95% CI, 86.3-94.1), 84.4% (95% CI, 79.2-88.5), and 0.937 (95% CI, 0.911-0.963), respectively. CONCLUSION: The DLCS shows promise in clinical settings where it could be routinely applied to MRI scans regardless of original scan purpose to improve the early detection of AD.