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Abstract The study aimed to evaluate the antimicrobial activity, antioxidant, toxicity and phytochemical screening of the Red Propolis Alagoas. Antimicrobial activity was evaluated by disk diffusion method. Determination of antioxidant activity was performed using the DPPH assay (1.1-diphenyl-2-picrylhydrazyl), FTC (ferric thiocyanate) and determination of phenolic compounds by Follin method. Toxicity was performed by the method of Artemia salina and cytotoxicity by MTT method. The phytochemical screening for the detection of allelochemicals was performed. The ethanol extract of propolis of Alagoas showed significant results for antimicrobial activity, and inhibitory activity for Staphylococcus aureus and Candida krusei. The antioxidant activity of the FTC method was 80% to 108.3% hydrogen peroxide kidnapping, the DPPH method showed an EC50 3.97 mg/mL, the content of total phenolic compounds was determined by calibration curve gallic acid, resulting from 0.0005 mg/100 g of gallic acid equivalent. The extract was non-toxic by A. salina method. The propolis extract showed high activity with a higher percentage than 75% inhibition of tumor cells OVCAR-8, SF-295 and HCT116. Chemical constituents were observed as flavonones, xanthones, flavonols, and Chalcones Auronas, Catechins and leucoanthocyanidins. It is concluded that the extract can be tested is considered a potential source of bioactive metabolites. Resumo O trabalho teve como objetivo avaliar a atividade antimicrobiana, antioxidante, a toxicidade e a prospecção fitoquímica da Própolis Vermelha de Alagoas. A atividade antimicrobiana foi avaliada pelo método de difusão em disco. A determinação do potencial antioxidante foi realizada utilizando o método de DPPH (1,1-difenil-2-picrilhidrazil), FTC (Tiocianato Férrico) e determinação de compostos fenólicos pelo método de Follin. A toxicidade foi realizada pelo método de Artemia salina e a citotoxicidade pelo método do MTT. Foi realizada a prospecção fitoquímica para a pesquisa de aleloquímicos. O extrato etanólico da própolis vermelha de Alagoas apresentou resultados significantes para atividade antimicrobiana, tendo a atividade inibitória para Staphylococcus aureus e Candida krusei. Quanto a atividade antioxidante o método de FTC teve 80% a 108,3% de sequestro de peróxido de hidrogênio, o método de DPPH apresentou um CE50 de 3,97 μg/mL, o teor de compostos fenólicos totais foi determinado mediante curva de calibração do ácido gálico, tendo resultado de 0,0005 mg/100 g equivalente de ácido gálico. O extrato foi atóxico pelo método de A. salina. O extrato da própolis mostrou elevada atividade com percentual de inibição maior que 75% sobre células tumorais OVCAR-8, SF-295 e HCT116. Foram observados constituintes químicos como flavononas, xantonas, flavonóis, Chalconas e Auronas, Catequinas e Leucoantocianidinas. Conclui-se que o extrato testado pode ser considerado é uma fonte potencial de metabólitos bioativos.

Fatigue and cognitive deficits are common and disabling symptoms in patients experiencing post-COVID condition. This randomized parallel study aimed to evaluate the effects of transcranial direct current stimulation (tDCS) over the primary motor cortex combined with cognitive training (M1 + CT), compared to tDCS over the dorsolateral prefrontal cortex with cognitive training (DLPFC + CT), on fatigue, cognition, and other clinical symptoms in post-COVID. Sixty-three patients completed the treatment (n = 32 in the M1 + CT group and n = 31 in the DLPFC + CT group) with a mean age of 47 years and an average symptom duration of 32 months. Both groups underwent comprehensive neuropsychological and clinical evaluations, including ecological momentary assessments of fatigue, at baseline, post-treatment, and one-month follow-up. The Fatigue Severity Scale (FSS) was used as the primary endpoint. Patients were randomly assigned to the M1 + CT or DLPFC + CT groups and received 15 sessions of tDCS administered concurrently with adaptive CT. The M1 + CT group showed a slightly higher efficacy in reducing fatigue and improving sleep quality than the DLPFC + CT group. Both groups demonstrated significant improvements in cognition, anxiety, depression, pain, and sleep quality. These improvements were sustained over time. These findings indicate that tDCS combined with cognitive training is a feasible, safe, and effective approach for reducing fatigue and enhancing cognition in post-COVID patients. The results highlight the potential of brain stimulation and cognitive training to alleviate fatigue and cognitive impairment in post-COVID, warranting further confirmation through additional randomized controlled trials. Trial registration: ClinicalTrials.gov NCT05753202.

In this review, the roles of room temperature ionic liquids (RTILs) and RTIL based solvent systems as proposed alternatives for conventional organic electrolyte solutions are described. Ionic liquids are introduced as well as the relevant properties for their use in electrochemistry (reduction of ohmic losses), such as diffusive molecular motion and ionic conductivity. We have restricted ourselves to provide a survey on the latest, most representative developments and progress made in the use of ionic liquids as electrolytes, in particular achieved by the cyclic voltammetry technique. Thus, the present review comprises literature from 2015 onward covering the different aspects of RTILs, from the knowledge of these media to the use of their properties for electrochemical processes. Out of the scope of this review are heat transfer applications, medical or biological applications, and multiphasic reactions.

Structure-based drug design has often been restricted by the rather static picture of protein–ligand complexes presented by crystal structures, despite the widely accepted importance of protein flexibility in biomolecular recognition. Here we report a detailed experimental and computational study of the drug target, human heat shock protein 90, to explore the contribution of protein dynamics to the binding thermodynamics and kinetics of drug-like compounds. We observe that their binding properties depend on whether the protein has a loop or a helical conformation in the binding site of the ligand-bound state. Compounds bound to the helical conformation display slow association and dissociation rates, high-affinity and high cellular efficacy, and predominantly entropically driven binding. An important entropic contribution comes from the greater flexibility of the helical relative to the loop conformation in the ligand-bound state. This unusual mechanism suggests increasing target flexibility in the bound state by ligand design as a new strategy for drug discovery. An understanding of the dynamics of drug binding and unbinding processes is important for drug discovery. Here, the authors give insights into the binding mechanism of small drug-like molecules to human Hsp90 by combining thermodynamics and kinetics studies as well as molecular dynamics simulations.

A wide range of applications such as health, human comfort, agriculture, food processing and storage, and electronic manufacturing, among others, require fast and accurate measurement of humidity. Sensors based on optical fibers present several advantages over electronic sensors and great research efforts have been made in recent years in this field. The present paper reports the current trends of optical fiber humidity sensors. The evolution of optical structures developed towards humidity sensing, as well as the novel materials used for this purpose, will be analyzed. Well-known optical structures, such as long-period fiber gratings or fiber Bragg gratings, are still being studied towards an enhancement of their sensitivity. Sensors based on lossy mode resonances constitute a platform that combines high sensitivity with low complexity, both in terms of their fabrication process and the equipment required. Novel structures, such as resonators, are being studied in order to improve the resolution of humidity sensors. Moreover, recent research on polymer optical fibers suggests that the sensitivity of this kind of sensor has not yet reached its limit. Therefore, there is still room for improvement in terms of sensitivity and resolution.

Microbial organisms inhabit virtually all environments and encompass a vast biological diversity. The pangenome concept aims to facilitate an understanding of diversity within defined phylogenetic groups. Hence, pangenomes are increasingly used to characterize the strain diversity of prokaryotic species. To understand the interdependence of pangenome features (such as the number of core and accessory genes) and to study the impact of environmental and phylogenetic constraints on the evolution of conspecific strains, we computed pangenomes for 155 phylogenetically diverse species (from ten phyla) using 7,000 high-quality genomes to each of which the respective habitats were assigned. Species habitat ubiquity was associated with several pangenome features. In particular, core-genome size was more important for ubiquity than accessory genome size. In general, environmental preferences had a stronger impact on pangenome evolution than phylogenetic inertia. Environmental preferences explained up to 49% of the variance for pangenome features, compared with 18% by phylogenetic inertia. This observation was robust when the dataset was extended to 10,100 species (59 phyla). The importance of environmental preferences was further accentuated by convergent evolution of pangenome features in a given habitat type across different phylogenetic clades. For example, the soil environment promotes expansion of pangenome size, while host-associated habitats lead to its reduction. Taken together, we explored the global principles of pangenome evolution, quantified the influence of habitat, and phylogenetic inertia on the evolution of pangenomes and identified criteria governing species ubiquity and habitat specificity.

In a scenario of increasing globalization, sustainable development has emerged as an attractive and strategic issue for both countries and individual organizations and their supply chains. Companies have faced different challenges in seeking to combine the best economic performance with increased social and environmental responsibility. Monitoring sustainability is essential for decision-making and management of activities that comprise an organization’s system processes. Evaluation can be performed using indices or a set of indicators. In addition to increasing organizational effectiveness and improving competitiveness, customer service and profitability, it is also a crucial influence on the development of business sustainability. This paper proposes a sustainability index that provides companies with information about their level of economic, social and environmental sustainability, showing their performance at both individual and supply chain level. The importance of the indicators is assessed by using the Analytic Hierarchy Process (AHP) methodology applied to a case study of a supply chain in the automotive industry. The various stages experienced during the construction of the index are also shown. The final results achieved are then presented and discussed in light of the objectives.

Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a ‘low-quality’ cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA (mtDNA) encoded genes and (ii) if a small number of genes are detected. Current best practices use these QC metrics independently with either arbitrary, uniform thresholds (e.g. 5%) or biological context-dependent (e.g. species) thresholds, and fail to jointly model these metrics in a data-driven manner. Current practices are often overly stringent and especially untenable on certain types of tissues, such as archived tumor tissues, or tissues associated with mitochondrial function, such as kidney tissue [1]. We propose a data-driven QC metric (miQC) that jointly models both the proportion of reads mapping to mtDNA genes and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. We demonstrate how our QC metric easily adapts to different types of single-cell datasets to remove low-quality cells while preserving high-quality cells that can be used for downstream analyses. Our software package is available at https://bioconductor.org/packages/miQC .

Toxic heavy metals are priority pollutants in wastewater, commonly present in dangerous concentrations in many places across the globe. Although in trace quantities copper is a heavy metal essential to human life, in excess it causes various diseases, whereby its removal from wastewater is a necessity. Among several reported materials, chitosan is a highly abundant, non-toxic, low-cost, biodegradable polymer, comprising free hydroxyl and amino groups, that has been directly applied as an adsorbent or chemically modified to increase its performance. Taking this into account, reduced chitosan derivatives (RCDs 1–4) were synthesised by chitosan modification with salicylaldehyde, followed by imine reduction, characterised by RMN, FTIR-ATR, TGA and SEM, and used to adsorb Cu(II) from water. A reduced chitosan (RCD3), with a moderate modification percentage (43%) and a high imine reduction percentage (98%), proved to be more efficient than the remainder RCDs and even chitosan, especially at low concentrations under the best adsorption conditions (pH 4, RS/L = 2.5 mg mL−1). RCD3 adsorption data were better described by the Langmuir–Freundlich isotherm and the pseudo-second-order kinetic models. The interaction mechanism was assessed by molecular dynamics simulations, showing that RCDs favour Cu(II) capture from water compared to chitosan, due to a greater Cu(II) interaction with the oxygen of the glucosamine ring and the neighbouring hydroxyl groups.

The last two decades have witnessed the emergence of optogenetics; a field that has given researchers the ability to use light to control biological processes at high spatiotemporal and quantitative resolutions, in a reversible manner with minimal side-effects. Optogenetics has revolutionized the neurosciences, increased our understanding of cellular signalling and metabolic networks and resulted in variety of applications in biotechnology and biomedicine. However, implementing optogenetics in plants has been less straightforward, given their dependency on light for their life cycle. Here, we highlight some of the widely used technologies in microorganisms and animal systems derived from plant photoreceptor proteins and discuss strategies recently implemented to overcome the challenges for using optogenetics in plants.