Augmentation of antifungal activity of fluconazole using a clove oil nanoemulgel formulation optimized by factorial randomized D-optimal design

In the present study, fluconazole (FLU) showed the highest solubility in clove oil and was selected as the oil phase for the FLU-loaded nanoemulsion (FLU-NE). Among the studied cosurfactants, Labrafac was better than ethanol at providing globules with acceptable sizes and a lower polydispersity index (PDI) when Tween 80 was the surfactant. This optimized FLU-NE was thermodynamically stable. Furthermore, FLU-NE stored at 40 ± 2 °C and 75 ± 5% relative humidity for 6 months demonstrated good stability. The FLU-NE was converted to a FLU-loaded nanoemulsion gel (FLU-NEG) using 2% w/v sodium carboxymethyl cellulose. The FLU-NEG was acceptable in terms of visual appearance and spreadability. Rheological studies revealed pseudoplastic behavior for FLU-NEG. The viscosity of FLU-NEG decreased when the applied rpm was increased. FLU-NEG showed greater drug release than that from a FLU-GEL formulation. Furthermore, the FLU release from FLU-NEG followed the Higuchi model. The results from the in vitro antifungal evaluation of FLU-NEG on Candida albicans ATCC 76615 strain confirmed the increase in the antifungal activity of FLU by clove oil. Significant differences were observed in the zones of inhibition produced by FLU-NEG compared to those produced by the blank nanoemulsion gel (B-NEG), fluconazole suspension (FLU-SUS), and nystatin samples. Thus, the increase in the antifungal activity of FLU using clove oil as the oil phase in its nanoemulsion formulation was quite evident from our results. Therefore, the developed FLU-NEG could be considered a potential candidate for further preclinical and clinical studies.

Implementation of multiobjective decision-making algorithms and image analysis in HPTLC-guided extraction optimization of natural products

A new, efficient, and low-cost approach for monitoring extraction optimization was proposed based on high-performance thin-layer chromatography (HPTLC) coupled with digital image analysis. Since HPTLC produces rich chromatographic signals corresponding to various analytes which may be differently affected by extraction conditions, four multicriteria decision-making (MCDM) techniques were compared for their ability to aggregate multiple chromatographic responses: Derringer's desirability approach, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE-2), and the Sum of ranking differences (SRD). Ultrasound-assisted extraction (UAE) of green tea leaves with ethanol-water mixtures was used as a model system. The amount of ethanol and extraction time were varied according to the central composite design. Ranking eleven extracts by Derringer's desirability approach, TOPSIS, and PROMETHEE-2 showed the same results. SRD analysis yielded slightly different results from previous methods. Response surface models (RSM) based on the previous three MCDM approaches demonstrated that extraction conditions with moderate amounts of ethanol (73%) and extraction times (46 min) lead to optimal chromatographic profiles. RSM optimization performed on individual peaks, tentatively corresponding to rutin, chlorophyll, and gallic acid, led to different results, which justified the use of MCDM algorithms for aggregation of multiple responses. Aside from natural products, the proposed approach has the potential to be implemented in various extraction optimizations.

Data Processing for Predicting DNA Damaging Properties of Complex UV Sources

A growing number of experimental evidence emphasizes that photobiological phenomena are not always the sum of the effect of individual wavelengths present in the emission spectrum of light sources. Unfortunately, tools are missing to identify such non-additive effects and predict effects of various exposure conditions. In the present work, we addressed these points for the formation of pyrimidine dimers in DNA upon co-exposure to UVC, UVB and UVA radiation. We first applied a combination index approach to determine whether mixtures of theses UV ranges exhibited additive, inhibitory or synergistic effects on the formation of cyclobutane pyrimidine dimers, (6-4) photoproducts and Dewar valence isomers. A predictive approach based on an experimental design strategy was then used to quantify the contribution of each wavelength range to the formation of DNA photoproducts. The obtained models allowed us to accurately predict the level of pyrimidine dimers in DNA irradiated under different conditions. The data were found to be more accurate than those obtained with the simple additive approach underlying the use of action spectra. Experimental design thus appears as an attractive concept that could be widely applied in photobiology even for cellular experiments.

Xylitol production from passion fruit peel hydrolysate: Optimization of hydrolysis and fermentation processes

The passion fruit peel (PFP) has a high cellulose and hemicellulose content, which can be used to produce fermentable sugars. In this context, this study aims to optimize the release of xylose and the production of xylitol from PFP. The optimized conditions were 0.71 M dilute sulfuric acid and a 21.84-minute treatment, yielding 19.03 g/L of xylose (PFP-1). Different PFP hydrolysates were evaluated to improve xylitol production by the yeast Kluyveromyces marxianus ATCC 36907: PFP-2 (PFP1 treated with Ca(OH)2), PFP-3 (PFP-1 treated with Ca(OH)2 and activated carbon), PFP-4 (PFP-3 with biological elimination of glucose with S. cerevisiae, and concentrated at different xylose concentrations). The applied methods resulted in higher xylitol production (14.97 g/L), when PFP hydrolysate was detoxified with Ca(OH)2, treated with activated charcoal for 1 h, biotreated for glucose removal, and concentrated to 40 g/L of xylose.

Measuring empathic stress - A systematic review of methodology and practical considerations for future research

Aside from stressors that each of us experience directly, we also share the stress of the people around us. Such empathic stress exists on psychological and physiological levels, including subjective, sympathetic, parasympathetic and endocrine activation. The objective of this review is to offer an overview of methodology over the past fifteen years of empathic stress research and derive practical considerations for future research endeavors in the field. We used a keyword search strategy in the databases Web of Science, PsycInfo and PubMed to find empathic stress studies published until December 2023, and included 17 studies into our review. The reviewed laboratory studies provide initial yet consistent evidence for the existence of empathic stress across different populations, in intimate and stranger dyads, with direct and virtual contact, across multiple levels of the stress system, and based on diverse statistical analysis methods. We discuss all findings and derive practical considerations for future empathic stress research. The diversity of methods established provides a solid foundation upon which future studies can expand.

Experimental design and multiple response optimization of batch leaching tests of volcanic ashes

Volcanic eruptions can release large amounts of tephra, lava, and gases, drawing attention due to their magnitude, energy, and impact on life and the environment. Among the most documented and sometimes dramatic effects of volcanic ashes are those linked to the input of diverse elements in the environment, which are released as a consequence of ash weathering. Laboratory studies have been conducted to investigate and predict the environmental input of chemical elements from volcanic ashes. This research paper describes the optimization of batch leaching tests used to investigate the release of ions from ashes collected in the Andes Cordillera after the eruption of the Puyehue volcano in 2011. Chemometric multivariate strategies were employed to evaluate the influence of variables affecting the leaching of volcanic ash. The effects of the main variables, namely contact time, the acidity of the leaching agent, the solid/liquid ratio, the particle size, and the stirring speed, were studied in leaching tests. To determine the optimal conditions for selected metal determinations, we employ Darringer's desirability function, which allows for the simultaneous optimization of the selected responses (element concentrations during the leaching process). Multielemental analysis (Na, Mg, Al, Si, P, Cl, K, Ca, V, Cr, Mn, Fe, Ni, Cu, Zn, Sr, Cd, Hg, Tl, and Pb) was quantified by ICP-MS (inductively coupled plasma-mass spectrometry) following adequate dilution of test leaching. These results established the optimal experimental conditions for leaching volcanic ash. The most significant variables were the solid/liquid ratio and the stirring speed, resulting in two groups of elements with an adequate global desirability function (D) value.

Half the price, twice the gain: How to simultaneously decrease animal numbers and increase precision with good experimental design

Animal research often involves experiments in which the effect of several factors on a particular outcome is of scientific interest. Many researchers approach such experiments by varying just one factor at a time. As a consequence, they design and analyze the experiments based on a pairwise comparison between two groups. However, this approach uses unreasonably large numbers of animals and leads to severe limitations in terms of the research questions that can be answered. Factorial designs and analyses offer a more efficient way to perform and assess experiments with multiple factors of interest. We will illustrate the basic principles behind these designs, discussing a simple example with only two factors before suggesting how to design and analyze more complex experiments involving larger numbers of factors based on multiway analysis of variance.

Optimized high-throughput protocols for comprehensive metabolomic and lipidomic profiling of brain sample

Establishing direct causal and functional links between genotype and phenotype requires thoroughly analyzing metabolites and lipids in systems biology. Tissue samples, which provide localized and direct information and contain unique compounds, play a significant role in objectively classifying diseases, predicting prognosis, and deciding personalized therapeutic strategies. Comprehensive metabolomic and lipidomic analyses in tissue samples need efficient sample preparation steps, optimized analysis conditions, and the integration of orthogonal analytical platforms because of the physicochemical diversities of biomolecules. Here, we propose simple, rapid, and robust high-throughput analytical protocols based on the design of experiment (DoE) strategies, with the various parameters systematically tested for comprehensively analyzing the heterogeneous brain samples. The suggested protocols present a systematically DoE-based strategy for performing the most comprehensive analysis for integrated GC-MS and LC-qTOF-MS from brain samples. The five different DoE models, including D-optimal, full factorial, fractional, and Box-Behnken, were applied to increase extraction efficiency for metabolites and lipids and optimize instrumental parameters, including sample preparation and chromatographic parameters. The superior simultaneous extraction of metabolites and lipids from brain samples was achieved by the methanol-water-dichloromethane (2:1:3, v/v/v) mixture. For GC-MS based metabolomics analysis, incubation time, temperature, and methoxyamine concentration (10 mg/mL) affected metabolite coverage significantly. For LC-qTOF-MS based metabolomics analysis, the extraction solvent (methanol-water; 2:1, v/v) and the reconstitution solvent (%0.1 FA in acetonitrile) were superior on the metabolite coverage. On the other hand, the ionic strength and column temperature were critical and significant parameters for high throughput metabolomics and lipidomics studies using LC-qTOF-MS. In conclusion, DoE-based optimization strategies for a three-in-one single-step extraction enabled rapid, comprehensive, high-throughput, and simultaneous analysis of metabolites, lipids, and even proteins from a 10 mg brain sample. Under optimized conditions, 475 lipids and 158 metabolites were identified in brain samples.

Natural deep eutectic solvent-based dispersive liquid-liquid microextraction of pesticides in drinking waters combined with GC-MS/MS detection

The current research aims to develop a new analytical method applying a dispersive liquid-liquid microextraction (DLLME) assisted by vortex and using an environmentally friendly extractant for the preconcentration of organochlorine and organophosphorus pesticides followed by gas chromatography-tandem mass spectrometry (GC-MS/MS) analysis. The extractant (i.e., natural deep eutectic solvent (NADES)) is safe, cheap, biodegradable and can be prepared by simply mixing DL-menthol and decanoic acid (molar ratio 2:1). The main experimental factors affecting the extraction of all analytes evaluated (19 organochlorine and organophosphorus pesticides) have been optimised using a multivariate analysis consisting in two steps: a Plackett-Burman design followed by a central composite design (CCD). Seven experimental factors have been evaluated: (i) sample volume; (ii) NADES volume; (iii) sample pH; (iv) extraction time; (v) centrifugation time; (vi) centrifugation speed; and (vii) ionic strength (NaCl %, w v-1). For the significant variables, the optimum values were 10 mL sample and 45 μL NADES. No pH adjustment as well as addition of NaCl were needed. The other variables were set at 3 min extraction time, 5 min centrifugation time and 900×g centrifugation speed, respectively. Under the optimised extraction conditions, the limit of quantification (LOQ) values ranged between 0.2 and 78 ng L-1 for all analysed pesticides. Furthermore, the proposed analytical method has been successfully applied to drinking water (bottled spring water). The recovery study (n = 3) has been evaluated at 0.1, 1.0 and 5.0 μg L-1 spiking levels, obtaining relative recovery values within the range of 70 % and 117 % and RSD values between 1 % and 20 % for all the analytes studied, except for p,p-DDT (56-77 % in high conductivity water samples).

Analytical quality by design-based development of a capillary electrophoresis method for Omeprazole impurity profiling

Omeprazole (OME) is a proton pump inhibitor used to treat gastroesophageal reflux disease associated conditions. The current study presents an Analytical Quality by Design-based approach for the development of a CE method for OME impurity profiling. The scouting experiments suggested the selection of solvent modified Micellar ElectroKinetic Chromatography operative mode using a pseudostationary phase composed of sodium dodecyl sulfate (SDS) micelles and n-butanol as organic modifier in borate buffer. A symmetric three-level screening matrix 37//16 was used to evaluate the effect of Critical Method Parameters, including Background Electrolyte composition and instrumental settings, on Critical Method Attributes (critical resolution values, OME peak width and analysis time). The analytical procedure was optimized using Response Surface Methodology through a Central Composite Orthogonal Design. Risk of failure maps made it possible to define the Method Operable Design Region, within which the following optimized conditions were selected: 72 mM borate buffer pH 10.0, 96 mM SDS, 1.45 %v/v n-butanol, capillary temperature 21 °C, applied voltage 25 kV. The method was validated according to ICH guidelines and robustness was evaluated using a Plackett-Burman design. The developed procedure enables the simultaneous determination of OME and seven related impurities, and has been successfully applied to the analysis of pharmaceutical formulations.

Birthing a new maternal cognition literature: 10 recommendations for future research

Whilst the field of maternal cognition is gaining interest, with a recent increase in publications, there are still only a handful of existing studies. This presents a unique opportunity for reflection and growth, advancing scientific rigor to ensure that future interpretations of maternal cognitive functioning are based on robust, generalizable data. With this in mind, we offer ten recommendations for future cognitive research in motherhood, with a focus on intentional study design. A study's design dictates the questions that can be asked, and the answers that can be gleaned from collected data, making study design a cornerstone of robust and reproducible science. These recommendations are intended as a resource for study conceptualization and design, participant recruitment, result interpretation, and peer review.

An examination of the methods and variables used in experimental design that impact the toxicological outcomes of e-cigarettes

A clear answer on whether vaping is safe and, if not, to what degree it threatens human health and well-being, still needs to be communicated. Such an answer requires collecting, analyzing, and interpreting sometimes conflicting and indeterminate results. This paper reviews the most recently published research articles that examine vaping toxicities. It highlights the differences in the techniques employed from one paper to another. While e-cigarettes do not appear to cause the same degree of harm as cigarettes, they pose a real biological threat regarding inflammation, oxidative stress, mucociliary interference, and membrane damage. The concentration of nicotine present is directly related to these endpoints and is often higher in fourth-generation devices. However, third-generation devices can do more harm than their successors, possibly due to their high voltage and low resistance capabilities. In addition to nicotine, the flavorants used in e-cigarettes have also been shown to relate to biological stress, and the adverse health effects increase in vape formulations with higher concentrations and numbers of flavor types. Different biological models also yield different health effects, especially when comparing bronchial and alveolar cells or tissues. To universalize the results of vape experiments, researchers should seek greater consistency within the experimental design. Key methodological variables must be recognized and disclosed in future research, including puff duration and number, types of e-cigarettes and e-liquids being tested, device settings during aerosolization, and any details of the employed exposure method that may affect dosimetry.

Strategies for producing probiotic biomass and postbiotics from Akkermansia muciniphila in submerged cultivations incorporating prebiotic sources

This research propounds an innovative technology focused on sustainability to increase the biomass yield of Akkermansia muciniphila, the next-generation probiotic, using prebiotic sources to replace or reduce animal mucin levels. A series of experimental design approaches were developed aiming to optimize the growth of Akkermansiamuciniphila by incorporating extracts of green leafy vegetables and edible mushroom into the cultivation media. Experiments using kale extract (KE), Brassica oleracea L., associated with lyophilized mushroom extract (LME) of Pleurotus ostreatus were the most promising, highlighting the assays with 0.376% KE and 0.423% LME or 1.05% KE and 0.5% LME, in which 3.5 × 1010 CFU (Colony Forming Units) mL- 1 was achieved - higher than in experiments in optimized synthetic media. Such results enhance the potential of using KE and LME not only as mucin substitutes, but also as a source to increase Akkermansia muciniphila biomass yields and release short-chain fatty acids. The work is relevant to the food and pharmaceutical industries in the preparation of the probiotic ingredient.

Disentangling Selection into Mode from Mode Effects

OBJECTIVES

We investigate the impact of data collection mode on responses to variables in NSHAP Round 4 and discuss how potential mode differences should (and should not) be addressed in substantive analyses.

METHODS

Among the set of respondents who were eligible to be contacted remotely in Round 4, we randomly selected 398 to be contacted instead for an in-person interview. We compare response rates and the distributions of selected key outcomes among those 398 respondents to those among the control group who were initially approached remotely. As a contrast, we compare all R4 respondents according to the mode in which they completed the interview, including those not part of the randomized experiment.

RESULTS

Among those included in the experiment, there was no evidence of systematic differences in responses to physical and mental health questions between remote and in-person modes, nor in responses to number recall measures. In-person respondents scored moderately lower on cognitive function measures requiring careful attention to a figure and/or task, though this difference became less with each similar item. Remote respondents named fewer social network members. Comparing all respondents according to their final mode yielded substantially different results in all cases.

DISCUSSION

Mode did not appear to affect reports of physical and mental health based on a randomized comparison, though it did moderately affect other items in predictable ways. Naïve estimates of mode effects based on comparing all respondents according to mode yielded misleading results, and should not be used to adjust for mode differences in analyses.

Optimizing Cr(VI) reduction to Cr(III) using Pd-CNTs nanocatalyst: kinetic Monte Carlo simulation and experimental design insights

In this investigation, we explored the kinetics of Cr(VI) reduction to Cr(III) on carbon nanotube decorated with palladium (Pd-CNTs) nanocatalyst, using formic acid as the reducing agent. This study has been bone utilizing kinetic Monte Carlo simulation and experimental design methods. The mechanism and kinetic parameters of this reaction are provided. The effect various factors such as reaction time, pH level, dichromate (Cr2O72-) concentration, and formic acid concentration on Cr(VI) reduction was studied. Concentrations of HCOOH and Cr2O72- were identified as the crucial variables, while the HCOOH concentration has the most significant impact. Positive influences on Cr(VI) reduction were observed with increasing pH level and HCOOH concentration. Reaction time positively affects on Cr(VI) reduction efficiency. However, the concentration of Cr2O72- showed an increasing effect up to a threshold, negatively impacting the efficiency. The optimal conditions (Reaction time = 60 min, pH = 4.5, [Cr2O72-] = 5.05 × 10-3 M, and [HCOOH] = 0.95 M) for Cr(VI) reduction. At optimal conditions, the Cr(VI) reduction efficiency was obtained to be 100%.

Considering ultraviolet radiation in experimental biology: a neglected pervasive stressor

Ultraviolet radiation (UVR) is a pervasive factor that has shaped the evolution of life on Earth. Ambient levels of UVR mediate key biological functions but can also cause severe lethal and sublethal effects in a wide range of organisms. Furthermore, UVR is a powerful modulator of the effects of other environmental factors on organismal physiology, such as temperature, disease, toxicology and pH, among others. This is critically important in the context of global change, where understanding the effects of multiple stressors is a key challenge for experimental biologists. Ecological physiologists rarely afford UVR discussion or include UVR in experimental design, even when it is directly relevant to their study system. In this Commentary, we provide a guide for experimental biologists to better understand if, when, and how UVR can be integrated into experimental designs to improve the ecological realism of their experiments.

Dataset: Spatial visualization ability assessment among undergraduate students at Jiaying University

This dataset, collected through a comprehensive online survey and testing process, evaluates spatial visualization ability among undergraduate students at Jiaying University. Utilizing the Revised Purdue Spatial Visualization Test: Rotations (Revised PSVT: R), the dataset encompasses demographic information and responses to Likert-scale questions. With applications in experimental and cognitive psychology, the dataset offers valuable insights into spatial cognition and its implications for educational contexts. Researchers can utilize this dataset as a benchmark for comparative studies, explore correlations with demographic factors, and develop educational interventions to enhance spatial ability. The dataset, accessible on the repository, can be retrieved through the following citation [1].

Development and characterization of curcumin nanosuspension-embedded genipin-crosslinked chitosan/polyvinylpyrrolidone hydrogel patch for effective wound healing

This study investigated the development of a genipin-crosslinked chitosan (CS)-based polyvinylpyrrolidone (PVP) hydrogel containing curcumin nanosuspensions (Cur-NSs) to promote wound healing in an excisional wound model. Cur-NSs were prepared, and a simplex centroid mixture design was employed to optimize hydrogel properties for high water absorption, degree of crosslinking, and sufficient toughness. The in vivo wound healing effect was tested in Wistar rats. The optimized hydrogel consisted of a 70:30 ratio of CS:PVP, crosslinked with a 2 % w/w genipin solution. It exhibited high swelling capability (486 %) while maintaining solidity, robustness, and durability. Incorporating 5 % w/w Cur-NSs resulted in a more compact structure, although with a reduction in swelling properties. The release kinetics of Cur from the hydrogel followed the Korsmeyer-Peppas Fickian diffusion model. In vitro biocompatibility studies demonstrated that the hydrogel was non-toxic to skin fibroblast cells. The in vivo experiment revealed a desirable wound healing rate with over 80 % recovery by day 7. Cur-NSs likely aided wound healing by reducing the inflammatory response and stimulating fibroblast proliferation. Additionally, the CS-based hydrogel provided a moist wound environment with hydration and gas transfer, further accelerating wound closure. These findings suggest that the Cur-NS-embedded hydrogel shows promise as a wound dressing material.

Insights on the role of blocking agent on the properties of the lipase from Thermomyces lanuginosus immobilized on heterofunctional support for hydroesterification reactions

Here, we report a study of the effect of the blocking agent on the properties of the lipase from Thermomyces lanuginosus (TLL) immobilized on a heterofunctional support (Purolite C18-ethylnediamina (EDA)- vinyl sulfone (VS)-TLL-blocking agent) in different reactions. The performance of the biocatalysts was compared to those immobilized on standard hydrophobic support (Purolite C18-TLL) and the commercial one (TLL-IM). The nature of the blocking agent (Cys, Gly and Asp) altered the enzyme features. TLL-IM always gave a comparatively worse performance, with its specificity for the oil being very different to the Purolite biocatalysts. Under optimized conditions, Purolite C18-TLL yielded 97 % of hydrolysis conversion after 4 h using a water/waste cooking soybean oil (WCSO) mass ratio of 4.3, biocatalyst load of 6.5 wt% and a temperature of 44.2 °C (without buffer or emulsification agent). In esterification reactions of the purified free fatty acids (FFAs) obtained from WCSO, the best TLL biocatalysts depended on the utilized alcohol: linear amyl alcohol was preferred by Purolite C18-TLL and Purolite C18-EDA-VS-TLL-Gly, while higher activity was achieved utilizing isoamyl alcohol as nucleophile by Purolite C18-EDA-VS-TLL-Cys, Purolite C18-EDA-VS-TLL-Asp and IM-TLL as catalysts. All the results indicate the influence of the blocking step on the final biocatalyst features.

Chemometrically-aided general approach to novel adsorbents studies: Case study on the adsorption of pharmaceuticals by the carbonized Ailanthus altissima leaves

A chemometrically based approach was applied to select the most efficient drug adsorbent among the biochars obtained from the novel feedstock, the leaves of the invasive plant (Ailanthus altissima). The representative target adsorbates (atenolol, paracetamol, ketorolac and tetracycline) were selected on the basis of their physicochemical properties to cover a wide chemical space, which is the usual analytical challenge. Their adsorption was investigated using design of experiments as a comprehensive approach to optimise the performance of the adsorption system, rationalise the procedure and overcome common drawbacks. Among the response surface designs, the central composite design was selected as it allows the identification of important experimental factors (solid-to-liquid ratio, pH, ionic strength) and their interactions, and allows the selection of optimal experimental conditions to maximise adsorption performance. The biochars were prepared by pyrolysis at 500 °C and 800 °C (BC-500 and BC-800) and the ZnCl2-activated biochars were prepared at 650 °C and 800 °C (AcBC-650 and AcBC-800). The FTIR spectra revealed that increasing the pyrolysis temperature without activator decreases the intensity of all bands, while activation preserves functional groups, as evidenced by the spectra of AcBC-650 and AcBC-800. High temperatures during activation promoted the development of an efficient surface area, with the maximum observed for AcBC-800 reaching 347 m2 g-1. AcBC-800 was found to be the most efficient adsorbent with removal efficiencies of 34.1, 51.3, 55.9 and 38.2 % for atenolol, paracetamol, ketorolac and tetracycline, respectively. The models describing the relationship between the removal efficiency of AcBC-800 and the experimental factors studied, showed satisfactory predictive ability (predicted R2 > 0.8) and no significant lack-of-fit was observed. The results obtained, including the mathematical models, the properties of the adsorbates and the adsorbents, clearly indicate that the adsorption mechanisms of activated biochars are mainly based on hydrophobic interactions, pore filling and hydrogen bonding.

Green method for 17-hydroxyprogesterone extraction and determination using PDMS stir bar sorptive extraction coupled with HPLC: optimization by response surface methodology

Quantifying small amounts of the 17-hydroxyprogesterone in various matrix is crucial for different purposes. In this study, a commercial polydimethylsiloxane stir bar was used to extract hormone from water and urine samples. Analysis was performed by high-performance liquid chromatography using a UV detector. The response surface methodology was used to optimize the desorption and extraction steps, with predicted optimal point relative errors of 1.25% and 6.40%, respectively. The optimized method was validated with a linear range of 1.21-1000.00 for aqueous and 2.43-2000.00 ng mL-1 for urine samples. The coefficient of determination was 0.9998 and 0.9967, and the detection limit of the proposed method was obtained to be 0.40 and 0.80 ng mL-1 for aqueous and urine samples, respectively. The recovery percentage and relative standard deviation within a day and between three days after the addition of three different concentration levels of the standard to the control sample were 87-103% and 0.4-3.6% for aqueous and 87.5-101% and 0.1-5.2% for urine samples, respectively. The results show that the proposed method can be appropriate and cost-effective for extracting and analyzing this hormone. In addition, using three different tools, the greenness of the proposed method was proven.

Artificial intelligence for neuro MRI acquisition: a review

OBJECT

To review recent advances of artificial intelligence (AI) in enhancing the efficiency and throughput of the MRI acquisition workflow in neuroimaging, including planning, sequence design, and correction of acquisition artifacts.

MATERIALS AND METHODS

A comprehensive analysis was conducted on recent AI-based methods in neuro MRI acquisition. The study focused on key technological advances, their impact on clinical practice, and potential risks associated with these methods.

RESULTS

The findings indicate that AI-based algorithms have a substantial positive impact on the MRI acquisition process, improving both efficiency and throughput. Specific algorithms were identified as particularly effective in optimizing acquisition steps, with reported improvements in workflow efficiency.

DISCUSSION

The review highlights the transformative potential of AI in neuro MRI acquisition, emphasizing the technological advances and clinical benefits. However, it also discusses potential risks and challenges, suggesting areas for future research to mitigate these concerns and further enhance AI integration in MRI acquisition.

Hope messages influence health behavior intentions more than fear messages: An experimental study during COVID-19

BACKGROUND

Fear tactics were used in the pandemic of Coronavirus Disease 2019.

PURPOSE

We tested how messaging style, fear vs. hope, and differences in age and political affiliation related to intentions to engage in preventive behaviors during Coronavirus Disease 2019.

METHODS

Participants (N = 606) aged 18 to 94 were randomly assigned to receive health messages that emphasized the dangers of the virus (fear messages) or the ability of health behaviors to mitigate the impact of the virus (hope messages). The primary outcome was health behavior intentions.

DISCUSSION

Hope messaging rather than fear messaging promoted health behavior intentions with no moderation by age or political affiliation. Older and Democratic-identified adults had higher health behavior intentions. Health behaviors were mediated by death anxiety and perceived credibility of the messages.

CONCLUSION

Tailored hope messaging may improve health behaviors by increasing the credibility of messages.

Multilevel Intervention Stepped Wedge Designs (MLI-SWDs)

Multilevel interventions (MLIs) hold promise for reducing health inequities by intervening at multiple types of social determinants of health consistent with the socioecological model of health. In spite of their potential, methodological challenges related to study design compounded by a lack of tools for sample size calculation inhibit their development. We help address this gap by proposing the Multilevel Intervention Stepped Wedge Design (MLI-SWD), a hybrid experimental design which combines cluster-level (CL) randomization using a Stepped Wedge design (SWD) with independent individual-level (IL) randomization. The MLI-SWD is suitable for MLIs where the IL intervention has a low risk of interference between individuals in the same cluster, and it enables estimation of the component IL and CL treatment effects, their interaction, and the combined intervention effect. The MLI-SWD accommodates cross-sectional and cohort designs as well as both incomplete (clusters are not observed in every study period) and complete observation patterns. We adapt recent work using generalized estimating equations for SWD sample size calculation to the multilevel setting and provide an R package for power and sample size calculation. Furthermore, motivated by our experiences with the ongoing NC Works 4 Health study, we consider how to apply the MLI-SWD when individuals join clusters over the course of the study. This situation arises when unemployment MLIs include IL interventions that are delivered while the individual is unemployed. This extension requires carefully considering whether the study interventions will satisfy additional causal assumptions but could permit randomization in new settings.

Optimization of lignin precipitation from black liquor using organic acids and its valorization by preparing lignin nanoparticles

This work focuses on the precipitation of lignin from kraft black liquor (BL) along with its valorization into lignin nanoparticles (LNP). Two organic acids namely, acetic acid, and lactic acid were used for the precipitation of lignin as an alternative to sulfuric acid. An optimization study was carried out to determine the effect of three key variables, namely acid type, temperature, and pH, on the isolation yield and purity of lignin. The study showed that all factors primarily influenced the lignin yield, while the purity of precipitated lignin varied only around 1 % between minimum to maximum purity. Further, the acid precipitation method was selected for the preparation of LNP. The study aimed to observe the effect of pH, lignin concentration, and surfactant concentration over the properties of the prepared nanoparticles. The results showed that a smaller nanoparticle size and maximization of phenolic content was achieved with a lignin concentration of 35 mg/mL, a surfactant concentration of 10 % (w/w lignin), and a pH of 5. Additionally, the antibacterial activity of LNPs against Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa bacteria was evaluated. The results showed only minor activity against Staphylococcus aureus. Overall, the study demonstrates the potential method for precipitation and valorization of lignin through the production of LNP with desirable properties.