Physico-chemical analysis, systematic benchmarking, and toxicological aspects of the JWH aminoalkylindole class-derived synthetic JWH cannabinoids

We are presenting the results obtained by computing different toxicity indices for some of the newest JWH synthetic cannabinoids, by using (Q)SAR models, ADME(T) predictions, simulations of NMR spectral techniques and other different computational dedicated software packages and forensic analytical tools. We have examined the main physical and chemical properties and evaluated the behavioral neurotoxicity and pharmacokinetic profile of 16 aminoalkylindole class-derived synthetic cannabinoids JWH as compared to the Delta-9-tetrahydrocannabinol (Δ9-THC), which was chosen as a standard compound. For this purpose, the geometries of the molecules have been optimized by using the AM1 semi-empirical quantum method. The conclusions of a comparative analysis of the toxicities of synthetic and natural cannabinoids are presented.

Molecular descriptors – an useful tool for assessing the physico-chemical properties of hallucinogenic drugs of abuse

Molecular descriptors play a fundamental role in chemistry and pharmaceutical sciences, being defined as a way to transform molecules into a set of numbers, allowing the mathematical assessment of the chemical information characterizing a molecule. This study presents the usefulness of molecular descriptors for the determination of physico-chemical properties of a series of new hallucinogenic amphetamines. Molecular descriptors of these compounds was performed with the Dragon 5.5 software. The chemometric processing of the information allowed the determination of structural correlations (similarities) between this class of designer drugs and an unknown compound.

Study on the chemical potential of apigenin, luteolin, quercetin, and myricetin using the molecular modeling

In the present paper, the chemical potential of four flavonoids i.e. apigenin, luteolin, quercetin, and myricetin, of interest in the pharmaceutical industry was investigated using molecular modelling. The equilibrium geometry of molecular structures was calculated in the gas phase and ground state by using B3LYP hybrid functional in conjunction with a 6-311G(d,p) basis set. In order to assess the chemical potential of investigated flavonoids, the main quantum molecular descriptors, such as the dipole moment, the energy of the highest/lowest occupied/unoccupied molecular orbital, the gap energy, the electronegativity, the chemical hardness/softness, and the electrophilicity index have been computed. Also, the influence of the hydroxylation degree of chemical compounds on the chemical potential is discussed.

3D volume reconstruction of brain tissues using nonlinear filters, k-means clustering, and Bland-Altman analysis

This paper aims to provide a sound estimation of the true value and proportion of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) of the brain DTI images for a proper 3D volume reconstruction. During the pre-processing stage, two nonlinear filters are operated, i.e. bilateral and anisotropic diffusion. The segmentation of each brain tissue is performed using the k-means clustering algorithm. To minimize filters bias and for obtaining the best reproducible results, a statistical analysis has been performed. Thus, the skewness and kurtosis statistics features were computed for each segmented brain tissue and filter. The fuzzy k-means method allows for clustering analysis and the Bland-Altman analysis investigates the agreement between two filtering techniques of the same statistics feature and brain tissue. Then the 3D reconstruction method is presented using ImageJ and the image stacks for raw and processed data. We conclude that anisotropic diffusion filter offers the best results and 3D reconstruction of brain tissues is feasible.

Management of nuclear materials containing natural uranium and thorium salts

This paper highlights an experimental model proposed for the management of nuclear materials containing natural uranium and thorium salts, based on technical and legislative methods. The investigated nuclear materials originate from laboratory chemicals with expired validity, having as manufacturers companies specialized in the manufacture of laboratory substances such as: Merck, Chemapol, Sigma Aldrich, Bucharest Reagent. The experimental program refers to several issues of great importance in the waste and environmental management, such as: a) the processing of radioactive substances containing nuclear materials and radioactive waste represented by solid objects contaminated with radionuclides from the radioactive series of U-238 and Th-232; b) gamma dose rate measurement during handling and processing of open sources of ionized radiation; c) measurement of suspicious contamination of the operating personnel which handles the equipment, including the materials used in the processing of open sources of ionizing radiation; and d) the inventory of nuclear materials according to the chemical formula, the mass of chemical substance, the mass of the nuclear element in each container and type of packaging. For the good development of processing these open sources of ionizing radiation containing nuclear materials, the ALARA principle (As Low As Reasonably Achievable) was applied, which is fundamental to the principles of radiation protection. All the measurements for determining the gamma dose rate and suspicious contamination were performed with the aid of a CoMo 170 radiometric device produced by Nuvia Instruments Gmbh Germany, equipped with a 170x100 mm2 PL detector with zinc sulfide calibrated with the aid of C-14, Co-60, Cs-137, U-238 and Am-241 radioactive isotopes and an external probe containing a scintillating crystal with sodium iodide enriched with thallium calibrated with Cs-137.

Study of the biosorption efficiency of seaweed species collected from the Black Sea for heavy metals removal in industrial wastewaters

In this article, it will be presented the results obtained from research conducted in order to reduce the concentrations of metals in industrial wastewater resulted from heavy metal polluting industries, especially the metallurgical industry. Most of the world's water sources are profoundly negatively affected by human activities, and the population faces critical water supply and drinking water quality problems. Millions of people develop various diseases from drinking water from unsafe or poor quality sources, creating a global public health problem. Due to massive industrialisation, current water treatment methods are outdated, which is why water treatment and purification laws, regulations, and controls need to be updated to minimise and stop contamination of the food chain. It is the responsibility of the researchers to make the public aware of the dangers to which they are exposed due to their own negligence and to offer possible solutions to these problems. New, reliable, viable, cheap, and sustainable technologies must be developed to improve drinking water quality. One such technology that can be developed and implemented is using the biological method of biosorption. Stranded seaweed on the Romanian Black Sea coast is currently treated as waste, but it could be exploited as biomass in the biosorption process. The research aimed to investigate the possibility of valorification of macrophyte seaweed species in this direction. Five different species of stranded macrophyte seaweed were collected and used to remove selected metallic ions (Cr, Fe, Zn, Cu) from a source of wastewater resulted from the metallurgical industry processes. The wastewater samples were analyzed before and after the application of the biosorption technique using the spectrophotometric method.

Color space influence on ANN skin lesion classification using statistics texture feature

This study aims to investigate the ability of an artificial neural network to differentiate between malign and benign skin lesions based on two statistics terms and for RGB (R red, G green, B blue) and YIQ (Y luminance, and I and Q chromatic differences) color spaces. The targeted statistics texture features are skewness (S) and kurtosis (K) which are extracted from the histograms of each color channel corresponding to the color spaces and for the two classes of lesions: nevi and melanomas. The extracted data is used to train the Feed-Forward Back Propagation Networks (FFBPNs). The number of neurons in the hidden layer varies: it can be 8, 16, 24, or 32. The results indicate skewness features computed for the red channel in the RGB color space as the best choice to reach the goal of our study. The reported result shows the advantages of monochrome channels representation for skin lesions diagnosis.

2C-x and DOx hallucinogens: a systematic review

The drug market has seen a significant global expansion in the last decades. The synthetic designer drugs belonging to the class of amphetamines and derived phenylethylamines have experienced the greatest spread in the drug market for abuse. As phenylethylamines are organic compounds that stimulate the central nervous system of humans, many are abused as recreational drugs. A large array of substituted amphetamines can be easily synthetized by replacing one or more hydrogen atoms in the structure of phenylethylamine. Among these, the most dangerous for human health are those analogues or homologues that have hallucinogenic effects (besides the stimulant pharmacological activity), such as those belonging to the 2C-x and DOx classes of amphetamines. This review describes the physico-chemical and spectral properties of the most representative compounds of the two classes.

Dynamics of microelements (B, Al) in the water samples from the Prut river during the 2020 year

The purpose of this research was to study the dynamics of microelement content, the correlation between the main forms of migration of microelements (B, Al) - dissolved and suspended forms, except colloidal, and to study their seasonal variation. The main form of migration of the metalloid B in the waters of the Prut river is the dissolved one, while the migration form of Al is the suspensions. In this research, the content of the mentioned microelements was investigated in the water samples collected from the Prut River in 2020 (winter, spring, summer, and autumn) from 7 collection points. The microelements were determined using atomic absorption spectrophotometry (Perkin-Elmer AAnalist 400 atomic absorption spectrophotometer). The importance of determining trace elements in natural waters is determined by a number of issues, including the need to monitor the environment and assess environmental risks and assess their influence on living systems. The results of studying these forms of migration (dissolved and suspended) allow us a fuller understanding of their role in the migration of microelements in aquatic ecosystems in the system "water-suspensions-underwater deposits-hydrobionts".

Feed-Forward Back Propagation Network for the prediction of diabetic retinopathy disorder

Some retina disorders mainly involve some blocked blood clots so that, the retinal vessels change their structure, being unable to completely nourish the retina. For an accurate investigation of retina disorders, the extraction of the retinal vessel anatomical structures or lesions is the main task. This paper reports a combination of various features extracted from retinal images, that are further used to train a Feed-Forward Back Propagation Network (FFBPN) as a decision system. The main goal is determining the combination of the appropriate features for more accurate classification of healthy and diseased patients. To achieve this goal, 120 binary images covering both categories of patients that belong to the STARE (Structured Analysis of the Retina) database were analyzed. The input data are the number of ridges, bifurcation, and bridges for retinal vessel pattern recognition. The FFBPNs with 4, 8, 12, 16, and 20 neurons in the hidden layer are trained. The FFBNP architecture with 12 neurons in the hidden layer, using the tansig transfer function in the hidden layer and linear transfer function in the output layer provides the most appropriate model for retinopathy disease classification. The correlation between the number of ridges and bridges computed for healthy patients (as actual values) and the number of ridges and bridges for diabetic patients (as predicted values) provides the best result, a regression coefficient (R) of 0. 8575 and a mean-square error (MSE) of 0.00163.