Semi-Automated Determination of Heavy Metals in Autopsy Tissue Using Robot-Assisted Sample Preparation and ICP-MS

High Selectivity and Sensitivity in Chemiresistive Sensing of Co(II) Ions with Liquid-Phase Exfoliated Functionalized MoS2 : A Supramolecular Approach

Chemical sensing of water contamination by heavy metal ions is key as it represents a most severe environmental problem. Liquid-phase exfoliated two-dimensional (2D) transition metal dichalcogenides (TMDs) are suitable candidates for chemical sensing thanks to their high surface-to-volume ratio, sensitivity, unique electrical characteristics, and scalability. However, TMDs lack selectivity due to nonspecific analyte-nanosheet interactions. To overcome this drawback, defect engineering enables controlled functionalization of 2D TMDs. Here, ultrasensitive and selective sensors of cobalt(II) ions via the covalent functionalization of defect-rich MoS2 flakes with a specific receptor, 2,2':6',2″-terpyridine-4'-thiol is developed. A continuous network is assembled by healing of MoS2 sulfur vacancies in a tailored microfluidic approach, enabling high control over the assembly of thin and large hybrid films. The Co2+ cations complexation represents a powerful gauge for low concentrations of cationic species which can be best monitored in a chemiresisitive ion sensor, featuring a 1 pm limit of detection, sensing in a broad concentration range (1 pm - 1 µm) and sensitivity as high as 0.308 ± 0.010 lg([Co2+ ])-1 combined with a high selectivity towards Co2+ over K+ , Ca2+ , Mn2+ , Cu2+ , Cr3+ , and Fe3+ cations. This supramolecular approach based on highly specific recognition can be adapted for sensing other analytes through specific ad-hoc receptors.

Artificial intelligence in forensic medicine and forensic dentistry

This review article aims to highlight the current possibilities for applying Artificial Intelligence in modern forensic medicine and forensic dentistry and present the advantages and disadvantages of its use. For this purpose, the relevant academic literature was searched using PubMed, Web of Science and Scopus. The application of Artificial Intelligence in forensic medicine and forensic dentistry is still in its early stages. However, the possibilities are great, and the future will show what is applicable in daily practice. Artificial Intelligence will improve the accuracy and efficiency of work in forensic medicine and forensic dentistry; it can automate some tasks; and enhance the quality of evidence. Disadvantages of the application of Artificial Intelligence may be related to discrimination, transparency, accountability, privacy, security, ethics and others. Artificial Intelligence systems should be used as a support tool, not as a replacement for forensic experts.

Linking Metallic Micronutrients and Toxic Xenobiotics to Atherosclerosis and Fatty Liver Disease-Postmortem ICP-MS Analysis of Selected Human Tissues

Dyslipidaemia is a disorder of the lipid metabolism, caused mainly by poor eating habits. The most severe consequence of an inappropriate diet is the development of atherosclerosis and hepatic steatosis. It is generally believed that a change in nutrition, and increased physical activity can eliminate these health problems. The contemporary research and therapies used to treat dyslipidemia mainly focus on lowering the triglyceride and cholesterol levels. However, disturbances in trace element homeostasis or the accumulation of toxic elements can also affect physiological processes, and be involved in the development of metabolically mediated diseases. The present study aimed to determine the mineral profiles of liver and brain tissues collected at autopsy (n = 39) in groups of people with hepatic steatosis (n = 5), atherosclerosis (n = 9), hepatic steatosis, and atherosclerosis (n = 16), and others without the selected disorders (n = 9). Concentrations of 51 elements were analysed via inductively coupled plasma mass spectrometry (ICP-MS) after the initial wet mineralisation of the samples with nitric acid. The results obtained allow us to conclude that the hepatic steatosis group suffers from a deficiency of important trace elements, such as copper, zinc, and molybdenum (p < 0.05), whereas the group with atherosclerosis is characterised by elevated levels of cadmium in the liver tissue (p = 0.01). Analysing the mean values of the element concentrations measured in 11 brain areas, statistically significant higher levels of calcium and copper (p < 0.001) were found in the atherosclerosis group, compared to the hepatic steatosis group, confirming the involvement of these elements in the pathogenesis of atherosclerosis. In addition, an accumulation of cadmium, lead, titanium, and strontium in the brain tissue was observed in the atherosclerosis group. While the accumulation of individual elements differs in different parts of the brain, the differences in the cadmium content (p < 0.05) between the study groups apply to the whole brain, except for the nucleus accumbens septi area, where a statistically significant titanium accumulation occurs in the atherosclerosis and steatosis groups, compared to the others (p < 0.05). In addition, the disruption of elemental homeostasis in the brain of a single case with bipolar disorder, and a case with hip replacement was observed. Our results confirm the involvement of chemical elements in the pathogenesis of selected metabolic diseases, and the need for further studies in larger populations.

Insufficient evidence to link human exposure to heavy metals with biomarkers of glioma

Information on molecular mechanisms has implicated potential association between the concentrations of heavy metals and incidences of glioma, but experimental data on human brain tissue remain sparse. To address this data gap, 13 heavy metals were measured in 137 glioma and 35 non-glioma samples collected from 161 alive patients in Guangdong Province, China in 2019 - 2020. All target heavy metals were detected, suggesting they could cross the blood-brain barrier. Concentrations of Mn, Cu, and Zn were higher in glioma than in non-glioma samples, while those of Ni and Se were higher in non-glioma samples, probably suggesting that these five heavy metals are more prone to be altered by changing pathological conditions. In addition, Cu/Zn, Cr/Mn, Cr/Se, Ni/Se, Pb/Mn, and Pb/Se were statistically different between glioma and non-glioma samples by a difference test and a multiple logistic regression model. These concentration ratios may serve as chemical markers to assist pathological analysis for differentiating between tumor and healthy tissues. However, no direct link between heavy metal concentrations or concentration ratios and biomarkers of glioma (i.e., tumor grade, P53, and Ki-67) was observed. No sufficient evidence was obtained to implicate the role of heavy metals in inducing glioma, largely caused by the limited number of samples. Different concentrations and concentration ratios of heavy metals may be the consequence rather than the cause of pathological changes in brain tumors.

System Concepts for Robots in Life Science Applications

For a long time, robot-based automation solutions have found their way into industrial production and manufacturing [...]