Neurodegenerative diseases (NDs) stand for a group of disorders characterized by the progressive loss of neurons in the brain and peripheral organs, resulting in motor and cognitive dysfunction. The global prevalence of NDs, including Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and amyotrophic lateral sclerosis, is on the rise globally, primarily due to an aging population, positioning NDs as an increasing significant public health concern. Despite intensive research, few effective therapies that prevent or delay ND progression have been developed. Mounting evidence indicates that one of the well-defined risk factors for NDs is type 2 diabetes mellitus, and insulin resistance has also been proven to be related to cognitive decline. Certain antidiabetic drugs, such as glucagon-like peptide-1 receptor agonists, peroxisome proliferator-activated receptor gamma agonists, and metformin, have shown promise in offering neuroprotective benefits and alleviating ND symptoms beyond their glucose-lowering effects. Although the exact mechanisms remain elusive, these drugs offer a promising novel strategy for managing cognitive disorders. In this review, we first highlight the benefits and specific neuroprotective effects of multiple antidiabetic drugs and discuss the main mechanisms of action of antidiabetic drugs in treating NDs. These mechanisms include reducing protein aggregation and improving apoptosis, mitochondrial dysfunction, oxidative stress, and neuroinflammation. Finally, we summarize clinical trials evaluating these drugs for treating NDs.

This study aimed to evaluate [¹⁸F]GSK1482160 Positron emission tomography imaging for targeting P2X7R, a biomarker for neuroinflammation. Studies of acute neuroinflammation in rodents and transgenic mice with Alzheimer’s disease (AD), as well as wild-type (WT) controls, were conducted via PET-CT-MRI scans after tail vein injection of [¹⁸F]GSK1482160. Imaging was quantified based on the timeactivity curve, the standardized uptake value ratio, and the binding kinetics distribution volume ratio (DVR) to assess the expression of P2X7R. Tissues were collected post-PET for immunofluorescence staining. Correlation analysis was performed between DVR and Morris water maze test results. Finally, dynamic Positron Emission Tomography-Magnetic Resonance Imaging (PET-MRI) scans were performed in healthy non-human primates (NHPs). Our study demonstrated that AD mice had a significantly higher DVR than WT mice in the hippocampus (0.92 ± 0.06 vs. 0.79 ± 0.02, p < 0.05), cortex (1.09 ± 0.03 vs. 0.88 ± 0.04, p < 0.05), and striatum (1.02 ± 0.10 vs. 0.83 ± 0.1, p < 0.05). Immunofluorescence staining showed increased expression of P2X7R in the AD, along with its colocalization with activated microglia and astrocytes. Correlation analysis indicated that brain regions with higher binding of [¹⁸F]GSK1482160 (i.e., the cortex, striatum, and hippocampus) were more vulnerable to cognitive impairment. PET-MRI scans of healthy NHPs demonstrated the feasibility of brain penetration and P2X7R target engagement for the translation of [¹⁸F]GSK1482160 in human studies.

Hybrid neurophysiological signals, such as the combination of electroencephalography (EEG) and electromyography (EMG), can be used to reduce road traffic accidents by obtaining the driver’s intentions in advance and accordingly applying appropriate auxiliary controls. However, whether they can be used in combination and can achieve better results in situations of detecting emergency braking from normal driving and soft braking has not been explored. This study used one featurelevel (hybrid BCI-FL) and three classifier-level (hybrid BCIs-CLs) hybrid strategies, the spectral band, and spectral point features to construct recognition models. Offline and pseudo-online experiments were conducted. The recognition performance with the spectral point features showed a better result than that with spectral band features. In all experiments, the two proposed hybrid BCI strategies could achieve a detection accuracy close to or above 95%, while the detection advanced time is less than 300 ms. In particular, for the developed hybrid BCI recognition models, the hybrid BCI-FL and hybrid BCI-CL2 recognition models with spectral point features achieved 4.25% (p < 0.015) and 4.69% (p < 0.006) higher system accuracies, respectively, than that of the current better single EMG-based recognition model. This research promotes the application of hybrid EEG and EMG signals in intelligent driving assistance systems.