An Fe3O4 supported O-phenylenediamine based tetraaza Schiff base-Cu(ii) complex as a novel nanomagnetic catalytic system for synthesis of pyrano[2,3-c]pyrazoles.

In this research, we present a post-synthetic method for synthesizing a novel nanomagnetic Cu(II) Schiff base complex and investigate its efficiency in catalytic organic conversion reactions. Various spectroscopic analyses were employed to characterize the physiochemical characteristics of the resulting nanocomposite. The experimental results successfully demonstrate the catalytic application of the prepared Cu-complex in the preparation of pyrano[2,3-c]pyrazole heterocycles. This synthesis involved a one-pot three-component condensation reaction, wherein hydrazine hydrate, ethyl acetoacetate, malononitrile, and aromatic aldehydes were combined under reflux conditions using water as the solvent. Notably, the heterogenized complex exhibited exceptional catalytic performance, achieving remarkable conversion rates and selectivity, all accomplished using only 12 mg of the catalyst. Furthermore, thorough stability assessments of this catalyst were conducted through reusability and hot filtration tests, which confirmed its non-leaching properties and demonstrated excellent results over the course of five consecutive runs.

Mechanical quantum analysis on the role of transition metals on the delivery of metformin anticancer drug by the boron phosphide nanotube.

We scrutinized the impact of doping of X atoms (X = Fe, Co, Ni, Cu, and Zn) on the metformin (MF) drug delivery performance of a BP nanotube (BPNT) using density functional B3LYP calculations. The pristine BPNT was not ideal for the drug delivery of MF because of a weak interaction between the drug and nanotube. Doping of the Zn, Cu, Ni, Co, and Fe into the BPNT surface raised the adsorption energy of MF from -5.3 to -29.1, -28.7, -29.8, -32.1, and -26.9 kcal/mol, respectively, demonstrating that the sensitiveness of the metal-doped BPNT increased after increasing the radius atomic of metals. Ultimately, there was an increase in the adhesion performance and capacity of the MF after X (especially Co atom) doping, making the nanotube suitable for MF drug delivery. The mechanism of MF reaction with the BPNT changed from covalent bonding in the natural environment to hydrogen bonding in the cancerous cells with high acidity.

A DFT study on the probability of using the heteroatom decorated graphitic carbonitride (g-C3N4) species for delivering of three novel Multiple sclerosis drugs.

In this project, the possibility of drug delivery application of three anti-Multiple sclerosis (MS) agents (containing diroximel fumarate (DXF), dimethyl fumarate (DMF), and mono methyl fumarate (MMF)) by using some heteroatom decorated graphitic carbonitride (g-C3N4) (as nano-sized carriers) have been systematically investigated. The results of the study have indicated that As-g-C3N4 QD is not a suitable candidate for drug delivery (at least in the cases of DMF, and DXF drugs); while, it would be an accurate semiconductor sensor for selective detection of each mentioned agents. Also, the use of the P-doped as well as pristine g-C3N4 QD could lead to weak electronic signals with relatively same values (in electronvolts). It means that P-g-C3N4, and g-C3N4 QDs are not good sensors for detection of each of the three considered drugs. However, those two sorbents would be suitable carriers for delivering of all three mentioned pharmaceuticals.

The pathological and therapeutically role of mesenchymal stem cell (MSC)-derived exosome in degenerative diseases; Particular focus on LncRNA and microRNA.

By releasing exosomes, which create the ideal milieu for the resolution of inflammation, mesenchymal stem cells (MSCs) enhance tissue healing and have strong immunomodulatory capabilities. MSCs-derived exosome also can affect tumor progress by a myriad of mechanisms. Exosomes function as a cell-cell communication tool to affect cellular activity in recipient cells and include an array of efficient bioactive chemicals. Understanding the fundamental biology of inflammation ablation, tissue homeostasis, and the creation of therapeutic strategies is particularly interested in the horizontal transfer of exosomal long non-coding RNAs (lncRNA) and microRNAs (miRNAs) to recipient cells, where they affect target gene expression. Herein, we propose an exosomal lncRNA and microRNA profile in neurological, renal, cardiac, lung, and liver diseases as well as skin wounds and arthritis.

Fe3O4 supported [Cu(ii)(met)(pro-H)2] complex as a novel nanomagnetic catalytic system for room temperature C-O coupling reactions.

In this study, a newly-designed copper(ii) complex of metformin and l-proline which was immobilized on Fe3O4 MNPs was developed. The structure of the catalyst platform was fully characterized using spectroscopic analyses. Moreover, the catalytic activity of [Fe3O4@Cu(ii)(Met)(Pro-H)2] was investigated in a one-pot synthesis of a variety of functionalized ethers in reasonable to excellent yields through Ullman reaction in an aqueous environment using various aryl halides, phenol, and Cs2CO3 and without using any external Cu-reducing agents. Notably, gentle catalytic conditions, quick reaction times, applicability, low cost, and preventing dangerous chemicals and solvents during synthesis and catalytic application are some of the superior properties of the [Fe3O4@Cu(ii)(Met)(Pro-H)2] complex. Furthermore, the catalyst can be reused for several runs (at least eight times) without remarkable loss in efficiency.

Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters.

This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps.