Search

BackgroundPredicting the popularity of and harm caused by psychoactive agents is a serious problem that would be difficult to do by a single simple method. However, because of the growing number of drugs it is very important to provide a simple and fast tool for predicting some characteristics of these substances. We were inspired by the Google Flu Trends study on the activity of the influenza virus, which showed that influenza virus activity worldwide can be monitored based on queries entered into the Google search engine. ObjectiveOur aim was to propose a fast method for ranking the most popular and most harmful drugs based on easily available data gathered from the Internet. MethodsWe used the Google search engine to acquire data for the ranking lists. Subsequently, using the resulting list and the frequency of hits for the respective psychoactive drugs combined with the word “harm” or “harmful”, we estimated quickly how much harm is associated with each drug. ResultsWe ranked the most popular and harmful psychoactive drugs. As we conducted the research over a period of several months, we noted that the relative popularity indexes tended to change depending on when we obtained them. This suggests that the data may be useful in monitoring changes over time in the use of each of these psychoactive agents. ConclusionsOur data correlate well with the results from a multicriteria decision analysis of drug harms in the United Kingdom. We showed that Google search data can be a valuable source of information to assess the popularity of and harm caused by psychoactive agents and may help in monitoring drug use trends.

We present a methodology for the synthesis of inorganic-organic Janus-type molecules based on mono-T8 and difunctionalized double-decker silsesquioxanes (DDSQs) via hydrosilylation reactions, achieving exceptionally high yields and selectivities. The synthesized compounds were extensively characterized using various spectroscopic techniques, and their sizes and spatial arrangements were predicted through molecular modelling and density functional theory (DFT) calculations. Quantum chemical calculations were employed to examine the interactions among four molecules of the synthesized compounds. These computational results allowed us to determine the propensity for molecular aggregation, identify the functional groups involved in these interactions, and understand the changes in interatomic distances during aggregation. Understanding the aggregation behaviour of silsesquioxane molecules is crucial for tailoring their properties for specific applications, such as nanocomposites, surface coatings, drug delivery systems, and catalysts. Through a combination of experimental and computational approaches, this study provides valuable insights into the design and optimization of silsesquioxane-based Janus-type molecules for enhanced performance across various fields. [ABSTRACT FROM AUTHOR]

In this study, we used ultraviolet-visible (UV-Vis), fluorescence, and circular dichroism (CD) techniques, as well as molecular modeling, to probe the interactions between carbazole derivatives and the G-quadruplex structure formed in the promoter region of gene Bcl-2. This gene is a rational target for anticancer therapy due to its high expression in a variety of tumors as well as resistance to chemotherapy-induced apoptosis. We employed a sequence with a specific dual G-to-T mutation that may form a mixed-type hybrid G-quadruplex structure in the Bcl-2 P1 promoter region. The three tested carbazole compounds differing in substitution on the nitrogen atom of carbazole interact with the Bcl-2 G-quadruplex by the same binding mode with the very comparable binding affinities in the order of 105 M−1. During absorption and fluorescence measurements, large changes in the ligand spectra were observed at higher G4 concentrations. The spectrophotometric titration results showed a two-step complex formation between the ligands and the G-quadruplex in the form of initial hypochromicity followed by hyperchromicity with a bathochromic shift. The strong fluorescence enhancement of ligands was observed after binding to the DNA. All of the used analytical techniques, as well as molecular modeling, suggested the π–π interaction between carbazole ligands and a guanine tetrad of the Bcl-2 G-quadruplex. Molecular modeling has shown differences in the interaction between each of the ligands and the tested G-quadruplex, which potentially had an impact on the binding strength. [ABSTRACT FROM AUTHOR]

Future 6G networks are expected to utilize Massive Multiple-Input Multiple-Output (M-MIMO) and follow a user-centric cell-free (UCCF) architecture. In a UCCF M-MIMO network, the user can be potentially served by multiple surrounding Radio Units (RUs) and Distributed Units (DUs) controlled and coordinated by a single virtualized Centralized Unit (CU). Moreover, in an M-MIMO network, each transmit frontend is equipped with a Power Amplifier (PA), typically with nonlinear characteristics, that can have a significant impact on the throughput achieved by network users. This work evaluates a UCCF M-MIMO network within an advanced system-level simulator considering multicarrier transmission, using Orthogonal Frequency-Division Multiplexing (OFDM), realistic signal-processing steps, e.g., per resource block scheduling, and a nonlinear radio frontend. Moreover, both idealistic independent and identically distributed (i.i.d.) Rayleigh and 3D ray-tracing-based radio channels are evaluated. The results show that under the realistic radio channel, the novel user-centric network architecture can lead to an almost uniform distribution of user throughput and improve the rate of the users characterized by the worst radio conditions by over 3 times in comparison to a classical, network-centric design. At the same time, the nonlinear characteristics of the PA can cause significant degradation of the UCCF M-MIMO network's performance when operating close to its saturation power. [ABSTRACT FROM AUTHOR]

In the contemporary era, the exploration of machine learning (ML) has gained widespread attention and is being leveraged to augment traditional methodologies in quantitative structure–activity relationship (QSAR) investigations. The principal objective of this research was to assess the anticancer potential of colchicine-based compounds across five distinct cell lines. This research endeavor ultimately sought to construct ML models proficient in forecasting anticancer activity as quantified by the I C 50 value, while concurrently generating innovative colchicine-derived compounds. The resistance index (RI) is computed to evaluate the drug resistance exhibited by LoVo/DX cells relative to LoVo cancer cell lines. Meanwhile, the selectivity index (SI) is computed to determine the potential of a compound to demonstrate superior efficacy against tumor cells compared to its toxicity against normal cells, such as BALB/3T3. We introduce a novel ML system adept at recommending novel chemical structures predicated on known anticancer activity. Our investigation entailed the assessment of inhibitory capabilities across five cell lines, employing predictive models utilizing various algorithms, including random forest, decision tree, support vector machines, k-nearest neighbors, and multiple linear regression. The most proficient model, as determined by quality metrics, was employed to predict the anticancer activity of novel colchicine-based compounds. This methodological approach yielded the establishment of a library encompassing new colchicine-based compounds, each assigned an I C 50 value. Additionally, this study resulted in the development of a validated predictive model, capable of reasonably estimating I C 50 values based on molecular structure input. [ABSTRACT FROM AUTHOR]

Rationale: Cytosine and its conjugates are prone to form protonated, triply‐bonded dimers. Therefore, the nucleic‐acid cytosine‐rich sequence forms the four‐stranded noncanonical secondary structure known as the intercalated motif (i‐motif). This process has resulted in studies on cytosine protonated dimers. This communication focuses on the protonated dimers of cytosine and its nucleoside using the survival yield (SY) method and quantum mechanics calculations. Methods: To obtain the precursor ion fragmentation curve, the plot of SY against Ecomδ, the product ion spectra of the protonated dimers were obtained using a Waters/Micromass Q‐TOF Premier mass spectrometer. Quantum mechanics calculations were performed using GAUSSIAN 16, and full geometry optimizations and energy calculations were performed within the density functional theory framework at B3LYP/6‐31G(d,p). Results: The precursor ion fragmentation curve allowed the rating of the gas‐phase stabilities of the analyzed protonated dimers. Substitution of sugar moiety at N1 cytosine atom decreased the gas‐phase stabilities of the protonated dimers. The deoxycytidine dimer was found to be more stable than the cytidine dimer and cytidine–deoxycytidine dimer. Quantum chemical calculations indicated that cytosine aminohydroxy tautomer may be involved in the formation of protonated cytosine–cytosine nucleoside dimers but not in the formation of cytosine dimers. Conclusions: The results obtained for nucleoside dimers indicated that the SY method may reflect the i‐motif stabilities observed under physiological conditions. Therefore, the analysis of other protonated dimers of variously substituted cytosine–cytosine nucleoside using the SY method may be important to study the effect of cytosine substitution on the i‐motif stabilities. Cytosine tautomer containing C2‐OH... N(2H)‐C4 moiety may be involved in the formation of protonated cytosine–cytosine nucleoside dimers. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is widely explored nowadays, and it gives opportunities to enhance classical approaches in QSAR studies. The aim of this study was to investigate the cytoprotective activity parameter under oxidative stress conditions for indole-based structures, with the ultimate goal of developing AI models capable of predicting cytoprotective activity and generating novel indole-based compounds. We propose a new AI system capable of suggesting new chemical structures based on some known cytoprotective activity. Cytoprotective activity prediction models, employing algorithms such as random forest, decision tree, support vector machines, K-nearest neighbors, and multiple linear regression, were built, and the best (based on quality measurements) was used to make predictions. Finally, the experimental evaluation of the computational results was undertaken in vitro. The proposed methodology resulted in the creation of a library of new indole-based compounds with assigned cytoprotective activity. The other outcome of this study was the development of a validated predictive model capable of estimating cytoprotective activity to a certain extent using molecular structure as input, supported by experimental confirmation. [ABSTRACT FROM AUTHOR]