GV1001 modulates neuroinflammation and improves memory and behavior through the activation of gonadotropin-releasing hormone receptors in a triple transgenic Alzheimer's disease mouse model.

GV1001 protects neural cells from amyloid-ß (Aß) toxicity and other stressors in in vitro studies and demonstrates clinically beneficial effects in patients with moderate to severe Alzheimer's disease (AD). Here, we investigated the protective effects and mechanism of action of GV1001 in triple transgenic AD (3xTg-AD) mice. We found that GV1001 improved memory and cognition in middle- and old-aged 3xTg-AD mice. Additionally, it reduced Aß oligomer and phospho-tau (Ser202 and Thr205) levels in the brain, and mitigated neuroinflammation by promoting a neuroprotective microglial and astrocyte phenotype while diminishing the neurotoxic ones. In vitro, GV1001 bound to gonadotropin releasing hormone receptors (GnRHRs) with high affinity. Levels of cyclic adenosine monophosphate, a direct downstream effector of activated GnRHRs, increased after GV1001 treatment. Furthermore, inhibition of GnRHRs blocked GV1001-induced effects. Thus, GV1001 might improve cognitive and memory functions of 3xTg-AD mice by suppressing neuroinflammation and reducing Aß oligomers levels and phospho-tau by activating GnRHRs and their downstream signaling pathways.

Standoff Detection and Identification of Liquid Chemicals on a Reflective Substrate Using a Wavelength-Tunable Quantum Cascade Laser.

Standoff chemical detection and identification techniques are necessary for ensuring safe exposure to dangerous substances. Molecular fingerprints of unknown chemicals can be measured using wavelength-tunable quantum cascade lasers operating in long-wavelength infrared. In this work, we present a method that can identify liquid chemicals on a reflective substrate via diffuse reflection spectra measurement from 50 cm away and multiple nonlinear regression analysis. Experimental measurements and numerical analyses were conducted for different chemical surface densities and angles of light incidence using diethyl phthalate (DEP) and dimethyl methylphosphonate (DMMP). Candidate substances can be classified using a deep learning model to reduce analysis time.

Development of peptide aptamers as alternatives for antibody in the detection of amyloid-beta 42 aggregates.

Alzheimer's disease (AD) causes cognitive impairment and serious social isolation. However, there are no effective treatments and even no established confirmatory diagnostic tools for the disease. Amyloid beta (Aß) aggregation in the brain is the best-known pathognomonic mechanism of AD, so various methods for Aß detection have been developed for the diagnosis of this disease. We synthesized two novel, ultra-sensitive peptide probes specialized in detecting Aß aggregates, and examined their potential for future diagnostic application. The peptides are produced through phage high-throughput screening (HTS) and amplified through a serial process called biopanning, which is a repeating method of elution and amplification of probes. We picked phages specific for amyloid from two kinds of phage display. The synthesized peptides were confirmed to have excellent binding affinity to Aß aggregates, by immunohistochemical staining and western blotting using the brains of 3X transgenic (Tg) AD mice at different stages (5-7, 12-17 months old) of AD severity. In the present study, it was confirmed that newly developed amyloid-binding peptides could be used as novel probes for the detection of Aß aggregates, which can be used for clinical diagnosis of AD in the future.

Improving Sensitivity on Identification and Delineation of Intracranial Hemorrhage Lesion Using Cascaded Deep Learning Models.

Highly accurate detection of the intracranial hemorrhage without delay is a critical clinical issue for the diagnostic decision and treatment in an emergency room. In the context of a study on diagnostic accuracy, there is a tradeoff between sensitivity and specificity. In order to improve sensitivity while preserving specificity, we propose a cascade deep learning model constructed using two convolutional neural networks (CNNs) and dual fully convolutional networks (FCNs). The cascade CNN model is built for identifying bleeding; hereafter the dual FCN is to detect five different subtypes of intracranial hemorrhage and to delineate their lesions. Using a total of 135,974 CT images including 33,391 images labeled as bleeding, each of CNN/FCN models was trained separately on image data preprocessed by two different settings of window level/width. One is a default window (50/100[level/width]) and the other is a stroke window setting (40/40). By combining them, we obtained a better outcome on both binary classification and segmentation of hemorrhagic lesions compared to a single CNN and FCN model. In determining whether it is bleeding or not, there was around 1% improvement in sensitivity (97.91% [± 0.47]) while retaining specificity (98.76% [± 0.10]). For delineation of bleeding lesions, we obtained overall segmentation performance at 80.19% in precision and 82.15% in recall which is 3.44% improvement compared to using a single FCN model.

Activation of the phosphatidylinositol 3-kinase pathway plays important roles in reduction of cerebral infarction by cilnidipine.

Cerebral infarction causes permanent neuronal loss inducing severe morbidity and mortality. Because hypertension is the main risk factor for cerebral infarction and most patients with hypertension take antihypertensive drugs daily, the neuroprotective effects and mechanisms of anti-hypertensive drugs need to be investigated. Cilnidipine, a long-acting, new generation 1,4-dihydropyridine inhibitor of both L- and N-type calcium channels, was reported to reduce oxidative stress. In this study, we investigated whether cilnidipine has therapeutic effects in an animal model of cerebral infarction. After determination of the most effective dose of cilnidipine, a total of 128 rats were subjected to middle cerebral artery occlusion. Neurobehavioral function test and brain MRI were performed, and rats with similar sized infarcts were randomized to either the cilnidipine group or the control group. Cilnidipine treatment was performed with reperfusion after 2-h occlusion. Western blots and immunohistochemistry were also performed after 24-h occlusion. Initial infarct volume on diffusion-weighted MRI was not different between the cilnidipine group and the control group; however, fluid-attenuated inversion recovery MRI at 24 h showed significantly reduced infarct volume in the cilnidipine group compared with the control group. Cilnidipine treatment significantly decreased the number of triphosphate nick end labeling-positive cells compared to the control group. Western blot and immunohistochemistry showed increased expression of phosphorylated Akt (Ser473), phosphorylated glycogen synthase kinase-3ß, and Bcl-2 and decreased expression of Bax and cleaved caspase-3. These results suggest that cilnidipine, which is used for the treatment of hypertension, has neuroprotective effects in the ischemic brain through activation of the PI3K pathway. We investigated whether cilnidipine has neuroprotective effects on ischemic stroke in an animal model. We have demonstrated that the neuroprotective effect of cilnidipine is associated with the activation of the PI3K pathway. Considering the daily use of antihypertensive drugs for patients with hypertension, cilnidipine could be beneficial for patients with ischemic stroke.

GV1001 reduces neurodegeneration and prolongs lifespan in 3xTg-AD mouse model through anti-aging effects.

GV1001, which mimics the activity of human telomerase reverse transcriptase, protects neural cells from amyloid beta (Aß) toxicity and other stressors through extra-telomeric function, as noted in our prior in vitro studies. As per a recent phase II clinical trial, it improves cognitive function in patients with moderate to severe dementia. However, the underlying protective mechanisms remain unclear. This study aimed to investigate the effects of GV1001 on neurodegeneration, senescence, and survival in triple transgenic Alzheimer's disease (3xTg-AD) mice. GV1001 (1 mg/kg) was subcutaneously injected into old 3xTg-AD mice thrice a week until the endpoint for sacrifice, and survival was analysed. Magnetic resonance imaging (MRI) and Prussian blue staining (PBS) were performed to evaluate entry of GV1001 entrance into the brain. Diverse molecular studies were performed to investigate the effect of GV1001 on neurodegeneration and cellular senescence in AD model mice, with a particular focus on BACE, amyloid beta1-42 (Aß1-42), phosphorylated tau, volume of dentate gyrus, ß-galactosidase positive cells, telomere length, telomerase activity, and ageing-associated proteins. GV1001 crossed the blood-brain barrier, as confirmed by assessing the status of ferrocenecarboxylic acid-conjugated GV1001 using magnetic resonance imaging and PBS. GV1001 increased the survival of 3xTg-AD mice. It decreased BACE and Aß1-42 levels, neurodegeneration (i.e., reduced CA1, CA3 and dentate gyrus volume, decreased levels of senescence-associated ß-galactosidase positive cells, and increased telomere length and telomerase activity), and levels of ageing-associated proteins. We suggest that GV1001 exerts anti-ageing effects in 3xTg-AD mice by reducing neurodegeneration and senescence, which contributes to improved survival.

Neuroprotective Effects of GV1001 in Animal Stroke Model and Neural Cells Subject to Oxygen-Glucose Deprivation/Reperfusion Injury.

BACKGROUND AND PURPOSE: Previous studies have revealed the diverse neuroprotective effects of GV1001. In this study, we investigated the effects of GV1001 on focal cerebral ischemia-reperfusion injury (IRI) in rats and oxygen-glucose deprivation/reoxygenation (OGD/R)-induced injury in neural stem cells (NSCs) and cortical neurons. METHODS: Focal cerebral IRI was induced by transient middle cerebral artery occlusion (MCAO). Brain diffusion-weighted imaging (DWI) was performed 2 hours after occlusion, and a total of 37 rats were treated by reperfusion with GV1001 or saline 2 hours after occlusion. Fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging, immunohistochemistry, and neurobehavioral function analyses were performed. Additionally, OGD/R-injured NSCs and cortical neurons were treated with different GV1001 concentrations. Cell viability, proliferation, migration, and oxidative stress were determined by diverse molecular analyses. RESULTS: In the stroke model, GV1001 protected neural cells against IRI. The most effective dose of GV1001 was 60 µM/kg. The infarct volume on FLAIR 48 hours after MCAO compared to lesion volume on DWI showed a significantly smaller ratio in the GV1001-treated group. GV1001-treated rats exhibited better behavioral functions than the saline-treated rats. Treatment with GV1001 increased the viability, proliferation, and migration of the OGD/R-injured NSCs. Free radicals were significantly restored by treatment with GV1001. These neuroprotective effects of GV1001 have also been demonstrated in OGD/R-injured cortical neurons. CONCLUSIONS: The results suggest that GV1001 has neuroprotective effects against IRI in NSCs, cortical neurons, and the rat brain. These effects are mediated through the induction of cellular proliferation, mitochondrial stabilization, and anti-apoptotic, anti-aging, and antioxidant effects.

Clinical usefulness of deep learning-based automated segmentation in intracranial hemorrhage.

BACKGROUND: Doctors with various specializations and experience order brain computed tomography (CT) to rule out intracranial hemorrhage (ICH). Advanced artificial intelligence (AI) can discriminate subtypes of ICH with high accuracy. OBJECTIVE: The purpose of this study was to investigate the clinical usefulness of AI in ICH detection for doctors across a variety of specialties and backgrounds. METHODS: A total of 5702 patients' brain CTs were used to develop a cascaded deep-learning-based automated segmentation algorithm (CDLA). A total of 38 doctors were recruited for testing and categorized into nine groups. Diagnostic time and accuracy were evaluated for doctors with and without assistance from the CDLA. RESULTS: The CDLA in the validation set for differential diagnoses among a negative finding and five subtypes of ICH revealed an AUC of 0.966 (95% CI, 0.955-0.977). Specific doctor groups, such as interns, internal medicine, pediatrics, and emergency junior residents, showed significant improvement with assistance from the CDLA (p= 0.029). However, the CDLA did not show a reduction in the mean diagnostic time. CONCLUSIONS: Even though the CDLA may not reduce diagnostic time for ICH detection, unlike our expectation, it can play a role in improving diagnostic accuracy in specific doctor groups.

CT window trainable neural network for improving intracranial hemorrhage detection by combining multiple settings.

Window settings to rescale and contrast stretch raw data from radiographic images such as Computed Tomography (CT), X-ray and Magnetic Resonance images is a crucial step as data pre-processing to examine abnormalities and diagnose diseases. We propose a distant-supervised method for determining automatically the best window settings by attaching a window estimator module (WEM) to a deep convolutional neural network (DCNN)-based lesion classifier and training them in conjunction. Aside from predicting a flexible window setting for each raw image, we statistically identify the top four window settings by calculating the mean and standard deviations for the entire dataset. Images are scaled on each of the top settings estimated by WEM and following lesion classifiers are subsequently trained. We study the effects of only using the flexible window, the single fixed window as either a known default window used by radiologists or an estimated mean value, and two different approaches to combine results from the top window settings to improve the detection of intracranial hemorrhage (ICH) from brain CT images. Experimental results showed that using the top four window settings identified from the window estimator module and combining the results had the best performance.

Glia-Like Cells from Late-Passage Human MSCs Protect Against Ischemic Stroke Through IGFBP-4.

Stem cell therapy is considered to be a promising future treatment for intractable neurological diseases, although all the clinical trials using stem cells have not yet shown any good results. Early passage mesenchymal stem cells (MSCs) have been used in most clinical trials because of the issues on safety and efficacy. However, it is not easy to get plenty of cells enough for the treatment and it costs too much. Lots of late passage MSCs can be obtained at lower cost but their efficacy would be a big hurdle for clinical trials. If late passage MSCs with better efficacy could be used in clinical trials, it could be a new and revolutionary solution to reduce cost and enhance easier clinical trials. In the present study, it was investigated whether late passage MSCs could be induced into glia-like cells (ghMSCs); ghMSCs had better efficacy and they protected neurons and the brain from ischemia, and insulin-like growth factor binding protein-4 (IGFBP-4) played a critical role in beneficial effect of ghMSCs. ghMSCs were induced from MSCs and treated in in vitro and in vivo models of ischemia. They effectively protected neurons from ischemia and restored the brain damaged by cerebral infarction. These beneficial effects were significantly blocked by IGFBP-4 antibody. The current study demontsrated that late passage hMSCs can be efficiently induced into ghMSCs with better neuroprotective effect on ischemic stroke. Moreover, the results indicate that IGFBP-4 released from ghMSCs may serve as one of the key neuronal survival factors secreted from ghMSCs.

Tracking and protection of transplanted stem cells using a ferrocenecarboxylic acid-conjugated peptide that mimics hTERT.

In vivo tracking of transplanted stem cells has been a central aim of stem cell therapy. Although many tracking systems have been introduced, no method has yet been validated for clinical applications. We developed a novel sophisticated peptide (GV1001) that mimics hTERT (human telomerase reverse transcriptase) and analysed its ability to track and protect stem cells after transplantation. Ferrocenecarboxylic acid-conjugated GV1001 (Fe-GV1001) efficiently penetrated stem cells with no adverse effects. Moreover, Fe-GV1001 improved the viability, proliferation, and migration of stem cells under hypoxia. After Fe-GV1001-labelled stem cells were transplanted into the brains of rats after stroke, the labelled cells were easily tracked by MRI. Our findings indicate that Fe-GV1001 can be used for the in vivo tracking of stem cells after transplantation into the brain and can improve the efficacy of stem cell therapy by sustaining and enhancing stem cell characteristics under disease conditions.

Surface-Based Parameters of Brain Imaging in Male Patients with Alcohol Use Disorder.

OBJECTIVE: The structural alteration of brain shown in patients with alcohol use disorder (AUD) can originate from both alcohol effects and genetic or developmental processes. We compared surface-based parameters of patients with AUD with healthy controls to prove the applicability of surface-based morphometry with head size correction and to determine the areas that were sensitive to brain alteration related to AUD. METHODS: Twenty-six abstinent male patients with AUD (alcohol group, mean abstinence=13.2 months) and twenty-eight age-matched healthy participants (control group) were recruited from an inpatient mental hospital and community. All participants underwent a 3T MRI scan. Surface-based parameters were determined by using FreeSurfer. RESULTS: Every surface-based parameter of the alcohol group was lower than the corresponding control group parameter. There were large group differences in the whole brain, grey and white matter volume, and the differences were more prominent after head size correction. Significant group differences were shown in cortical thicknesses in entire brain regions, especially in parietal, temporal and frontal areas. There were no significant group differences in surface areas, but group difference trends in surface areas of the frontal and parietal cortices were shown after head size correction. CONCLUSION: Most of the surface-based parameters in alcohol group were altered because of incomplete recovery from chronic alcohol exposure and possibly genetic or developmental factors underlying the risk of AUD. Surface-based morphometry with controlling for head size is useful in comparing the volumetric parameters and the surface area to a lesser extent in alcohol-related brain alteration.

Learning word sense disambiguation in biomedical text with difference between training and test distributions.

Word Sense Disambiguation methods based on machine learning techniques with lexical features suffer from the discordance between distributions of the training and test documents, due to the diversity of lexical space. To tackle this problem, this paper proposes Support Vector Machines with Example-wise Weights. In this method, the training distribution is matched with the test distribution by weighting training examples according to their similarity to all test data. The experimental results show the distribution change between the training and test data is actually recognised and the proposed method which considers this change in its training phase outperforms ordinary SVMs.