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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 the field of anticancer therapy study it is of great interest to find effective G‐quadruplex ligands which may be of potential use in medical treatment or cancer prevention. Since among the compounds of natural origin, flavonoids have attracted notable attention because of their unique properties and promising therapeutic applications, an interesting question was to identify the flavonoid structural features that could provide effective binding properties toward G‐quadruplex. By using electrospray ionization mass spectrometry, followed by the survival yield method, it has been shown that the flavonoid molecules which contain an available C4=O carbonyl group form more stable adducts with G‐tetrads than the other ones. Molecular docking has shown that C4=O carbonyl group can be a source of hydrogen bonds and/or π‐stacking interactions. Therefore, the flavonoid molecules which contain an available C4=O carbonyl group can be regarded as good binders of G‐quadruplexes. [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]

This paper explores the new potential strategy of using fine recycled aggregates (fRA) derived from waste 3D printed concrete (3DPC) as a substitute for cement in additive manufacturing. This study hypothesizes that fRA can optimize mixture design, reduce cement content, and contribute to sustainable construction practices. Experimental programs were conducted to evaluate the fresh and hardened properties, printability window, and buildability of 3DPC mixes containing fRA. Mixes with replacement rates of cement with fRA by 10 vol%, 20 vol%, 30 vol%, 40 vol%, and 50 vol% were produced. A comprehensive experimental protocol consisting of rheological studies (static and dynamic yield stress), dynamic elastic modulus determination (first 24 h of hydration), flexural and compressive strengths (2 d and 28 d), and an open porosity test was performed. The obtained results were verified by printing tests. In addition, an economic and environmental life cycle assessment (LCA) of the mixes was performed. The results indicate that up to 50 vol% cement replacement with fRA is feasible, albeit with some technical drawbacks. While fRA incorporation enhances sustainability by reducing CO2 emissions and material costs, it adversely affects the printability window, green strength, setting time, and mechanical properties, particularly in the initial curing stages. Therefore, with higher replacement rates (above 20 vol%), potential optimization efforts are needed to mitigate drawbacks such as reduced green strength and buildability. Notably, replacement rates of up to 20 vol% can be successfully used without compromising the overall material properties or altering the mixture design. The LCA analysis shows that reducing the cement content and increasing the fRA addition results in a significant reduction in mix cost (up to 24%) and a substantial decrease in equivalent CO2 emissions (up to 48%). In conclusion, this study underscores the potential of fRA as a sustainable alternative to cement in 3D printed concrete. [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]

Each subsequent generation of wireless standards imposes stricter spectral and energy efficiency demands. So far, layered wireless transceiver architectures have been used, allowing for instance to separate channel decoding algorithms from the front-end design. Such an approach may need to be reconsidered in the upcoming 6G era. Especially hardware-originated distortions have to be taken into account while designing other layer algorithms, as high throughput and energy efficiency requirements will push these devices to their limits, revealing their non-linear characteristics. In such a context, this paper will shed some light on the new degrees of freedom enjoyed while cross-layer designing as well as controlling multicarrier and multiantenna transceivers in 6G systems. [ABSTRACT FROM AUTHOR]

The reactions of vinyl arenes with hydrodisiloxanes in the presence of sodium triethylborohydride were studied using experimental and computational methods. The expected hydrosilylation products were not detected because triethylborohydrides did not exhibit the catalytic activity observed in previous studies; instead, the product of formal silylation with dimethylsilane was identified, and triethylborohydride was consumed in stoichiometric amounts. In this article, the mechanism of the reaction is described in detail, with due consideration given to the conformational freedom of important intermediates and the two-dimensional curvature of the potential energy hypersurface cross sections. A simple way to reestablish the catalytic character of the transformation was identified and explained with reference to its mechanism. The reaction presented here is an example of the application of a simple transition-metal-free catalyst in the synthesis of silylation products, with flammable gaseous reagents replaced by a more convenient silane surrogate. [ABSTRACT FROM AUTHOR]

Drug design with machine learning support can speed up new drug discoveries. While current databases of known compounds are smaller in magnitude (approximately 10 8 ), the number of small drug-like molecules is estimated to be between 10 23 and 10 60 . The use of molecular docking algorithms can help in new drug development by sieving out the worst drug-receptor complexes. New chemical spaces can be efficiently searched with the application of artificial intelligence. From that, new structures can be proposed. The research proposed aims to create new chemical structures supported by a deep neural network that will possess an affinity to the selected protein domains. Transferring chemical structures into SELFIES codes helped us pass chemical information to a neural network. On the basis of vectorized SELFIES, new chemical structures can be created. With the use of the created neural network, novel compounds that are chemically sensible can be generated. Newly created chemical structures are sieved by the quantitative estimation of the drug-likeness descriptor, Lipinski's rule of 5, and the synthetic Bayesian accessibility classifier score. The affinity to selected protein domains was verified with the use of the AutoDock tool. As per the results, we obtained the structures that possess an affinity to the selected protein domains, namely PDB IDs 7NPC, 7NP5, and 7KXD. [ABSTRACT FROM AUTHOR]

Sodium trialkylborohydrides were found to be initiators of selective hydrogermylation of aromatic alkenes. Addition of phenylgermane and diphenylgermane in the presence of 10 mol% of NaHB(sec-Bu)3 proceeded in a highly selective manner to give – in contrast to the analogous hydrosilylation process – β-germylated products. The nature of this process was explained with the aid of DFT calculations and it was proposed that the mechanism proceeds via a trisubstituted germanide anion whose attack on the terminal vinyl carbon is the source of selectivity. [ABSTRACT FROM AUTHOR]

Herein, we present the application of fluorinated carbohydrate-derived building blocks for α-hydroxy β-fluoro/β-trifluoromethyl and unsaturated phosphonates synthesis. Pudovik and Horner–Wadsworth–Emmons reactions were applied to achieve this goal. The proposed pathway of the key reactions is supported by the experimental results, as well as quantum chemical calculations. The structure of the products was established by spectroscopic (1D, 2D NMR) and spectrometric (MS) techniques. Based on our data received, we claim that the progress of the Pudovik and HWE reactions is significantly influenced by the acidic protons present in the molecules as assessed by pKa values of the reagent. [ABSTRACT FROM AUTHOR]