The Role of Zinc in the Development of Vascular Dementia and Parkinson's Disease and the Potential of Carnosine as Their Therapeutic Agent.

Synaptic zinc ions (Zn2+) play an important role in the development of vascular dementia (VD) and Parkinson's disease (PD). In this article, we reviewed the current comprehension of the Zn2+-induced neurotoxicity that leads to the pathogenesis of these neuronal diseases. Zn2+-induced neurotoxicity was investigated by using immortalised hypothalamic neurons (GT1-7 cells). This cell line is useful for the development of a rapid and convenient screening system for investigating Zn2+-induced neurotoxicity. GT1-7 cells were also used to search for substances that prevent Zn2+-induced neurotoxicity. Among the tested substances was a protective substance in the extract of Japanese eel (Anguilla japonica), and we determined its structure to be like carnosine (ß-alanylhistidine). Carnosine may be a therapeutic drug for VD and PD. Furthermore, we reviewed the molecular mechanisms that involve the role of carnosine as an endogenous protector and its protective effect against Zn2+-induced cytotoxicity and discussed the prospects for the future therapeutic applications of this dipeptide for neurodegenerative diseases and dementia.

Toxic Effects of Two Redox States of Thallium on Immortalised Hypothalamic GT1-7 Neuronal Cells.

Thallium (Tl), is a highly toxic heavy metal that exists in monovalent (Tl(I)) and trivalent (Tl(III)) ionic states. This study aimed to compare the toxicities of Tl(I) and Tl(III) in a mouse hypothalamic GT1-7 neuronal cell line. Decreased viability and increased cytotoxicity were observed in the GT1-7 cells 16 h after Tl(I) or Tl(III) treatment. Tl(III) was more cytotoxic, than Tl(I), as indicated by extracellular lactate dehydrogenase levels. Both treatments induced caspase 3 activity, DNA fragmentation, malondialdehyde (MDA) production, and superoxide dismutase activity in the cells. MDA production was higher after Tl(III) than after Tl(I) treatment. Moreover, co-treatment with antioxidants, such as mannitol, ascorbic acid, or tocopherol, significantly attenuated the Tl-induced decrease in GT1-7 cell numbers. Therefore, both treatments induced oxidative stress-related apoptosis. Furthermore, Tl(III) reduced the cell viability more subtly than Tl(I) after 1 and 3 h of treatment. This effect was enhanced by co-treatment with maltol or citric acid, which promoted the influx of metallic elements into the cells. Thus, Tl(III) entered GT1-7 cells later than Tl(I) and had a delayed onset of toxicity. However, Tl(III) likely produces more extracellular lipid peroxides, which may explain its stronger cytotoxicity.

Diagnosing Drowning in Postmortem CT Images Using Artificial Intelligence.

Imaging features of the lung in postmortem computed tomography (CT) scans have been reported in drowning cases. However, it is difficult for forensic pathologists with limited experience to distinguish subtle differences in CT images. In this study, artificial intelligence (AI) with deep learning capability was used to diagnose drowning in postmortem CT images, and its performance was evaluated. The samples consisted of high-resolution CT images of the chest of 153 drowned and 160 non-drowned bodies captured by an 8- or 64-row multislice CT system. The images were captured with an image slice thickness of 1.0 mm and spacing of 30 mm, and 28 images were typically captured. A modified AlexNet was used as the AI architecture. The output result was the drowning probability for each component image. To evaluate the performance of the proposed model, the area under the receiver operating characteristic curve (AUC) was analyzed, and the AUC value of 0.95 was obtained. This indicates that the proposed AI architecture is a useful and powerful complementary testing approach for diagnosing drowning in postmortem CT images. Notably, the accuracy was 81% (62/77) for cases in which resuscitation was performed, and 92% (216/236) for cases in which resuscitation was not attempted. Therefore, the proposed AI method should not be used to diagnose the cause of death when aggressive cardiopulmonary resuscitation was performed. Additionally, because honeycomb lungs are likely to exhibit different morphologies, emphysema cases should also be treated with caution when the proposed AI method is used to diagnose drowning.