Synthesis and preliminary evaluation of a novel positron emission tomography (PET) ligand for imaging fatty acid amide hydrolase (FAAH).

Fatty acid amide hydrolase (FAAH) exerts its main function in the catabolism of the endogenous chemical messenger anandamide (AEA), thus modulating the endocannabinoid (eCB) pathway. Inhibition of FAAH may serve as an effective strategy to relieve anxiety and possibly other central nervous system (CNS)-related disorders. Positron emission tomography (PET) would facilitate us to better understand the relationship between FAAH in certain disease conditions, and accelerate clinical translation of FAAH inhibitors by providing in vivo quantitative information. So far, most PET tracers show irreversible binding patterns with FAAH, which would result in complicated quantitative processes. Herein, we have identified a new FAAH inhibitor (1-((1-methyl-1H-indol-2-yl)methyl)piperidin-4-yl)(oxazol-2-yl)methanone (8) which inhibits the hydrolysis of AEA in the brain with high potency (IC50 value 11 nM at a substrate concentration of 0.5 µM), and without showing time-dependency. The PET tracer [11C]8 (also called [11C]FAAH-1906) was successfully radiolabeled with [11C]MeI in 17 ± 6% decay-corrected radiochemical yield (n = 7) with >74.0 GBq/µmol (2 Ci/µmol) molar activity and >99% radiochemical purity. Ex vivo biodistribution and blocking studies of [11C]8 in normal mice were also conducted, indicating good brain penetration, high brain target selectivity, and modest to excellent target selectivity in peripheral tissues. Thus, [11C]8 is a potentially useful PET ligand with enzyme inhibitory and target binding properties consistent with a reversible mode of action.

Radiosynthesis and preliminary evaluation of 11C-labeled 4-cyclopropyl-7-(3-methoxyphenoxy)-3,4-dihydro-2H-benzo[e] [1,2,4] thiadiazine 1,1-dioxide for PET imaging AMPA receptors.

The α-amino-3-hydroxyl-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) belong to the family of ionotropic transmembrane receptors for glutamate (iGluRs) that are implicated in the pathology of neurological disorders and neurodegenerative diseases. Inspired by a recently developed positive allosteric modulator of AMPARs, 4-cyclopropyl-7-(3-methoxyphenoxy)-3,4-dihydro-2H-benzo[ e ][1,2,4]thiadiazine 1,1-dioxide (16; EC50 = 2.0 nM), we designed a new synthetic route for N-protected phenolic precursor 13 and efficiently radiolabeled a PET ligand [11C]AMPA-1905 ([11C]16) using a modified one-pot two-step strategy in high radiochemical yield and high molar activity. Preliminary in vivo evaluation was carried out to investigate the suitability of [11C]16 as a potential PET probe for AMPAR imaging.

Age-related changes in lumbar bone mineral density measured using quantitative computed tomography in healthy female cynomolgus monkeys.

Background: Cynomolgus monkeys are widely used in studies related to osteoporosis, and there is no evidence of age-related changes in volumetric bone mineral density (vBMD) measured using quantitative computed tomography (QCT) in nonhuman primates. This study aimed to describe changes in the characteristics of lumbar vBMD with age, to analyze the relationship between lumbar vBMD and body composition, and to investigate the precision of QCT measurements in healthy female cynomolgus monkeys. Methods: Age-related changes in lumbar vBMD were described using cubic regression models, and the accumulated bone loss rates (ABLR) of the lumbar spine were calculated. Spearman rank correlation and ridge regression analysis were used to investigate the relationship of the average lumbar vBMD and body components. Thirty animals were selected to analyze the short-term in vivo precision of the QCT measurements. The precision was expressed as the root-mean-square coefficient of variation (RMS-CV%) or root-mean-square standard deviation (RMS-SD). Results: A total of 72 healthy female cynomolgus monkeys, aged 1-25 years, were included in this study. The average lumbar vBMD of female cynomolgus monkeys increased with age until the age of 10 years, around which it reached peak bone mass, with a relatively marked decline after the age of 13 years. The ABLRs of female cynomolgus monkeys in the premenopausal (13-19 years) and postmenopausal age groups (20-25 years) were -4.9% and -21.2%, respectively. Ridge regression analysis showed that age and subcutaneous adipose tissue (SAT) contributed positively to the average lumbar vBMD in animals aged ≤10 years, whereas in animals aged >10 years, age contributed negatively to lumbar vBMD. The RMS-CV% (RMS-SD) of the lumbar vBMD measured using QCT ranged from 0.47% to 1.60% (1.91-6.31 mg/cm3). Conclusions: Age-related changes in lumbar vBMD measured using QCT in healthy female monkeys showed similar trends to those in humans. Age and SAT may affect the lumbar vBMD in female cynomolgus monkeys. QCT revealed good precision in measuring the lumbar vBMD in female cynomolgus monkeys.

Focalizing regions of biomarker relevance facilitates biomarker prediction on histopathological images.

Image-based AI has thrived as a potentially revolutionary tool for predicting molecular biomarker statuses, which aids in categorizing patients for appropriate medical treatments. However, many methods using hematoxylin and eosin-stained (H&E) whole-slide images (WSIs) have been found to be inefficient because of the presence of numerous uninformative or irrelevant image patches. In this study, we introduced the region of biomarker relevance (ROB) concept to identify the morphological areas most closely associated with biomarkers for accurate status prediction. We actualized this concept within a framework called saliency ROB search (SRS) to enable efficient and effective predictions. By evaluating various lung adenocarcinoma (LUAD) biomarkers, we showcased the superior performance of SRS compared to current state-of-the-art AI approaches. These findings suggest that AI tools, built on the ROB concept, can achieve enhanced molecular biomarker prediction accuracy from pathological images.

The precision study of dual energy X-ray absorptiometry for bone mineral density and body composition measurements in female cynomolgus monkeys.

Background: Dual-energy X-ray absorptiometry (DXA) is a well-accepted tool for monitoring skeletal and body composition changes in biomedical studies. The nonhuman primate model is suitable for studies exploring the pathogenesis of and novel treatments for osteoporosis. Our objectives are to determine the precision of DXA detection in cynomolgus monkeys and to identify the difference in precision in lumbar bone mineral density (BMD) with various segment selections. Methods: Thirty adult female cynomolgus monkeys underwent duplicate total body DXA scans. Total body bone mineral density (BMDTB) and body composition, including bone mineral content (BMCTB), lean mass (LMTB), and fat mass (FMTB), were analyzed by enCORE software, while lumbar BMD was obtained by manual region-of-interest analysis. The precision was represented as the root-mean-square standard deviation (RMS-SD) and coefficient of variation (RMS-CV%), and least significant changes (LSCs) were reported. Results: The RMS-SD (RMS-CV%) of the repeated DXA analyses for BMDTB, BMCTB, LMTB and FMTB were 0.002 g/cm2 (0.50%), 0.90 g (0.42%), 0.015 kg (0.49%), and 0.031 kg (2.71%), respectively. The regional BMD precision (RMS-CV%) of the lumbar spine with various combinations ranged from 0.70% to 1.09%, The LSCs with 80% statistical confidence (LSC80) ranged from 1.27% to 1.97%, and LSC95 ranged from 1.94% to 3.01%. Conclusions: DXA provided excellent reproducibility in the measurements of BMD and body composition in nonhuman primates. We find DXA reliable for total and regional measurement in skeletal research and the evaluation of osteoporosis treatment with monkeys as animal models.

Erratum: Author Correction: Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.

[This corrects the article DOI: 10.1038/s42256-021-00421-z.].

Localizing Epileptic Foci Before Surgery in Patients With MRI-Negative Refractory Epilepsy Using Statistical Parameter Mapping and Three-Dimensional Stereotactic Surface Projection Based on 18F-FDG PET.

Patients with refractory epilepsy are not only free of seizures after resecting epileptic foci, but also experience significantly improved quality of life. Fluorine-18-fluorodeoxyglucose positron-emission tomography (18F-FDG PET) is a promising avenue for detecting epileptic foci in patients with magnetic resonance imaging (MRI)-negative refractory epilepsy. However, the detection of epileptic foci by visual assessment based on 18F-FDG PET is often complicated by a variety of factors in clinical practice. Easy imaging methods based on 18F-FDG PET images, such as statistical parameter mapping (SPM) and three-dimensional stereotactic surface projection (3D-SSP), can objectively detect epileptic foci. In this study, the regions of surgical resection of patients with over 1 year follow-up and no seizures were defined as standard epileptic foci. We retrospectively analyzed the sensitivity of visual assessment, SPM and 3D-SSP based on 18F-FDG PET to detect epileptic foci in MRI-negative refractory epilepsy patients and obtained the sensitivities of visual assessment, SPM and 3D-SSP are 57, 70 and 60% respectively. Visual assessment combined with SPM or 3D-SSP can improve the sensitivity of detecting epileptic foci. The sensitivity was highest when the three methods were combined, but decreased consistency, in localizing epileptic foci. We conclude that SPM and 3D-SSP can be used as objective methods to detect epileptic foci before surgery in patients with MRI-negative refractory epilepsy. Visual assessment is the preferred method for PET image analysis in MRI-negative refractory epilepsy. When the visual assessment is inconsistent with the patient's electroclinical information, SPM or 3D-SSP was further selected to assess the epileptic foci. If the combination of the two methods still fails to accurately locate the epileptic foci, comprehensive evaluation can be performed by combining the three methods.

Electroclinical and Multimodality Neuroimaging Characteristics and Predictors of Post-Surgical Outcome in Focal Cortical Dysplasia Type IIIa.

Focal cortical dysplasia (FCD) type IIIa is an easily ignored cause of intractable temporal lobe epilepsy. This study aimed to analyze the clinical, electrophysiological, and imaging characteristics in FCD type IIIa and to search for predictors associated with postoperative outcome in order to identify potential candidates for epilepsy surgery. We performed a retrospective review including sixty-six patients with FCD type IIIa who underwent resection for drug-resistant epilepsy. We evaluated the clinical, electrophysiological, and neuroimaging features for potential association with seizure outcome. Univariate and multivariate analyses were conducted to explore their predictive role on the seizure outcome. We demonstrated that thirty-nine (59.1%) patients had seizure freedom outcomes (Engel class Ia) with a median postsurgical follow-up lasting 29.5 months. By univariate analysis, duration of epilepsy (less than 12 years) (p = 0.044), absence of contralateral insular lobe hypometabolism on PET/MRI (p Log-rank = 0.025), and complete resection of epileptogenic area (p Log-rank = 0.004) were associated with seizure outcome. The incomplete resection of the epileptogenic area (hazard ratio = 2.977, 95% CI 1.218-7.277, p = 0.017) was the only independent predictor for seizure recurrence after surgery by multivariate analysis. The results of past history, semiology, electrophysiological, and MRI were not associated with seizure outcomes. Carefully included patients with FCD type IIIa through a comprehensive evaluation of their clinical, electrophysiological, and neuroimaging characteristics can be good candidates for resection. Several preoperative factors appear to be predictive of the postoperative outcome and may help in optimizing the selection of ideal candidates to benefit from epilepsy surgery.

Positron Emission Tomography Imaging of the Endocannabinoid System: Opportunities and Challenges in Radiotracer Development.

The endocannabinoid system (ECS) is involved in a wide range of biological functions and comprises cannabinoid receptors and enzymes responsible for endocannabinoid synthesis and degradation. Over the past 2 decades, significant advances toward developing drugs and positron emission tomography (PET) tracers targeting different components of the ECS have been made. Herein, we summarized the recent development of PET tracers for imaging cannabinoid receptors 1 (CB1R) and 2 (CB2R) as well as the key enzymes monoacylglycerol lipase (MAGL) and fatty acid amide hydrolase (FAAH), particularly focusing on PET neuroimaging applications. State-of-the-art PET tracers for the ECS will be reviewed including their chemical design, pharmacological properties, radiolabeling, as well as preclinical and human PET imaging. In addition, this review addresses the current challenges for ECS PET biomarker development and highlights the important role of PET ligands to study disease pathophysiology as well as to facilitate drug discovery.

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence.

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence.

Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses; however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals without the federated learning framework) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans from 3,336 patients collected from 23 hospitals located in China and the United Kingdom. Collectively, our work advanced the prospects of utilizing federated learning for privacy-preserving AI in digital health.

A collaborative online AI engine for CT-based COVID-19 diagnosis.

Artificial intelligence can potentially provide a substantial role in streamlining chest computed tomography (CT) diagnosis of COVID-19 patients. However, several critical hurdles have impeded the development of robust AI model, which include deficiency, isolation, and heterogeneity of CT data generated from diverse institutions. These bring about lack of generalization of AI model and therefore prevent it from applications in clinical practices. To overcome this, we proposed a federated learning-based Unified CT-COVID AI Diagnostic Initiative (UCADI, http://www.ai-ct-covid.team/), a decentralized architecture where the AI model is distributed to and executed at each host institution with the data sources or client ends for training and inferencing without sharing individual patient data. Specifically, we firstly developed an initial AI CT model based on data collected from three Tongji hospitals in Wuhan. After model evaluation, we found that the initial model can identify COVID from Tongji CT test data at near radiologist-level (97.5% sensitivity) but performed worse when it was tested on COVID cases from Wuhan Union Hospital (72% sensitivity), indicating a lack of model generalization. Next, we used the publicly available UCADI framework to build a federated model which integrated COVID CT cases from the Tongji hospitals and Wuhan Union hospital (WU) without transferring the WU data. The federated model not only performed similarly on Tongji test data but improved the detection sensitivity (98%) on WU test cases. The UCADI framework will allow participants worldwide to use and contribute to the model, to deliver a real-world, globally built and validated clinic CT-COVID AI tool. This effort directly supports the United Nations Sustainable Development Goals' number 3, Good Health and Well-Being, and allows sharing and transferring of knowledge to fight this devastating disease around the world.