Utility of artificial intelligence to identify antihyperglycemic agents poisoning in the USA: introducing a practical web application using National Poison Data System (NPDS).

Clinical effects of antihyperglycemic agents poisoning may overlap each other. So, distinguishing exposure to these pharmaceutical drugs may take work. This study examined the application of machine learning techniques in identifying antihyperglycemic agent exposure using the national poisoning database in the USA. In this study, the data of single exposure due to Biguanides and Sulfonylureas (n=6183) was requested from the National Poison Data System (NPDS) for 2014-2018. We have tried five machine learning models (random forest classifier, k-nearest neighbors, Xgboost classifier, logistic regression, neural network Keras). For the multiclass classification modeling, we have divided the dataset into two parts: train (75%) and test (25%). The performance metrics used were accuracy, specificity, precision, recall, and F1-score. The algorithms used to get the classification results of different models to diagnose antihyperglycemic agents were very accurate. The accuracy of our model in determining these two antihyperglycemic agents was 91-93%. The precision-recall curve showed average precision of 0.91, 0.97, 0.97, and 0.98 for k-nearest neighbors, logistic regression, random forest, and XGB, respectively. The logistic regression, random forest, and XGB had the highest AUC (AUC=0.97) among both biguanides and sulfonylureas groups. The negative predictive values (NPV) for all the models were between 89 and 93%. We introduced a practical web application to help physicians distinguish between these agents. Despite variations in accuracy among the different types of algorithms used, all of them could accurately determine the specific exposure to biguanides and sulfonylureas retrospectively. Machine learning can distinguish antihyperglycemic agents, which may be useful for physicians without any background in medical toxicology. Besides, Our suggested ML-based Web application might help physicians in their diagnosis.

Large-scale analysis of MicroRNA expression in motor neuron-like cells derived from human umbilical cord blood mesenchymal stem cells.

Motor neuron diseases such as spinal cord injuries and amyotrophic lateral sclerosis are known as the most common disorders worldwide. Using stem cells (e.g., human umbilical cord blood mesenchymal stem cells) is currently a potent medical approach for modulating the impact of neural damages and regeneration of spinal cord injuries. MicroRNAs (miRNA) are taken into account as principal regulators during differentiation. The miRNAs play a significant role in stem cell self-renewal and fate determination. There are few studies on how miRNAs regulate neural differentiation in stem cells. The purpose of this study is to explore miRNA profiles of CB-MSCs during differentiation into motor neuron-like cells. Human CB-MSCs were isolated and characterized using flow cytometry. Cell differentiation has been induced by combining retinoic acid (RA) and sonic hedgehog (Shh) in a two-step protocol for 14 days. Then, cell differentiation was confirmed by immunocytochemistry and flow cytometry. The miRNA was analyzed using Illumina/Solexa sequencing platform. In this regard, three libraries were prepared to investigate the effect of these two biological morphogens on the miRNA profile of the differentiating cells. These libraries were Control (non-treated CB-MSCs), Test 1 (RA + /Shh +), and Test 2 (RA-/Shh-). Quantitative RT-PCR was employed to verify miRNA expression. CB-MSCs were spindle-shaped in morphology, and they did not express hematopoietic markers. After differentiation, the cells expressed motor neuron markers (i.e., Islet-1, SMI-32, and ChAT) at the protein level after 14 days. The analysis of miRNA sequencing demonstrated a significant up-regulation of miR-9-5p and miR-324-5p in Test 1 (RA + /Shh +). Also, there is a considerable down-regulation of mir-137 and let-7b in Test 2 (RA-/Shh-). These results have been obtained by comparing them with the Control library. Indeed, they were responsible for neuron and motor neuron differentiation and suppression of proliferation in neural progenitor cells. Furthermore, significant up-regulation was detected in some novel microRNAs involved in cholinergic, JAK-STAT, and Hedgehog and MAPK signaling pathways. CB-MSCs are potent to express motor neuron markers. This procedure has been performed by developing a two-week protocol and employing Shh and RA. The miRNA profile analysis showed a significant up-regulation in the expression of some miRs involved in neuron differentiation and motor neuron maturation. MiR-9-5p and miR-324-5p were up-regulated at the early stage of differentiation. Also, miR-137 and miR-let-7b were downregulated in the absence of RA and Shh. Furthermore, several novel miRNAs involved in cholinergic, Hedgehog, MAPK, and JAK-STAT signaling pathways have been detected. However, further studies are still necessary to validate their functions during motor neuron generation and maturation.

Abnormal Dielectric Constant of Nanoconfined Water between Graphene Layers in the Presence of Salt.

Ultra-low dielectric constant of nanoconfined water between two flat slabs is a subject of recent experimental and theoretical research. The impact of dissolution of sodium chloride (NaCl) with various concentrations on the dielectric properties of nanoconfined water between graphene layers are investigated using molecular dynamics simulations. We found that, with increasing salt concentration, (i) the out-of-plane dielectric constant increases and (ii) the in-plane dielectric constant decreases non-linearly. Surprisingly, for channels with heights 6.8Å < h < 8 Å, we found an abnormal increase in the in-plane dielectric constant versus salt concentration, which can be linked to the formation of 2D-ice-like structure. This study sheds light on the variation of dielectric properties of nanoconfined water between graphene layers in the presence of salt, which is of importance in ion transport and electrochemical energy storage.

Differentiation of Mesenchymal Stem Cells Derived From Human Adipose Tissue Into Cholinergic-like Cells: An in Vitro Study.

INTRODUCTION: Cholinergic-associated diseases currently constitute a significant cause of neurological and neurodegenerative disabilities. As the drugs are not efficient in improving the suffered tissues, stem cell treatment is considered an effective strategy for substituting the lost cells. METHODS: In the current study, we set out to investigate the differentiation properties of human Adipose-Derived Mesenchymal Stem Cells (AD-MSCs) into cholinergic-like cells by two morphogens of Retinoic Acid (RA) and Sonic Hedgehog (Shh) using a three-step in vitro procedure. The results were evaluated using real-time PCR, flow cytometry, and immunocytochemistry for two weeks. RESULTS: Our data showed that the cells could express cholinergic specific markers, including Islet-1, Acetylcholinesterase (AChE), SMI-32, and Nestin, at mRNA and protein levels. We could also quantitatively evaluate the expression of Islet-1, AChE, and Nestin at 14 days post-induction using flow cytometry. CONCLUSION: Human AD-MSCs are potent cells to differentiate into cholinergic-like cells in the presence of RA and Shh through a three-step protocol. Thus, they could be a suitable cell candidate for the regeneration of cholinergic-associated diseases. However, more functional and electrophysiological analyses are needed in this regard.

Temperature distribution in graphene doped with nitrogen and graphene with grain boundary.

Graphene doped with nitrogen exhibits unique properties different than perfect graphene. The temperature distribution in nitrogen-doped graphene (N-graphene) and in the graphene with grain boundary is investigated using molecular dynamics simulations. The temperature distribution in nitrogen-doped graphene nanoribbon, containing two types of grain boundaries, was found to be sensitive to the number of dopants and grain boundary. We also found that there is a remarkable temperature gap in the temperature profile of N-graphene nanoribbon-containing a grain boundary. For any doping ratio N/C we found that the nitrogen atoms enhance roughness of N-graphene and decrease thermal conductivity.

Molecular dynamics simulation of temperature profile in partially hydrogenated graphene and graphene with grain boundary.

Temperature profile in graphene, graphene with grain boundary and vacancy defects and hydrogenated graphene with different percentage of H-atoms are determined using molecular dynamics simulation. We also obtained the temperature profile in a graphene nanoribbon containing two types of grain boundaries with different misorientation angles, θ=21.8° and θ=32.2°. We found that a temperature gap appears in the temperature profile of a graphene nanoribbon with a grain boundary at the middle. Moreover, we found that the temperature profile in the partially hydrogenated graphene varies with the percentage of hydrogens, i.e. the C:H ratio. Our results show that a grain boundary line in the graphene sheet can change the thermal transport through the system which might be useful for controlling thermal flow in nanostructured graphene.