Desmin expression profile in reactive astrocytes in the 3-nitropropionic acid-lesioned striatum of rat: Characterization and comparison with glial fibrillary acidic protein and nestin

Desmin, a muscle-specific, type-III intermediate-filament protein, is reportedly expressed in astrocytes in the central nervous system. These cells become reactive astrocytes in response to brain injuries. To elucidate whether desmin is involved in this process, we examined the spatiotemporal expression profiles of desmin and their relationship with two astroglial intermediate filaments, glial fibrillary acidic protein (GFAP) and nestin, in the striatum of rats treated with the mitochondrial toxin 3-nitropropionic acid (3-NP). Weak, constitutive immunoreactivity for desmin was observed in astrocytes generally, and in reactive astrocytes in the peri-lesional area, its expression increased in parallel with that of GFAP over 3 d post-lesion and was maintained until at least day 28. Desmin, GFAP, and nestin showed characteristic time-dependent expression patterns in reactive astrocytes forming the astroglial scar; delayed and long-lasting induction of desmin and GFAP, and rapid but transient induction of nestin. In the lesion core, desmin was expressed in two categories of perivascular cells: nestin-negative and nestin-positive. These findings show that desmin, together with GFAP and nestin, is a dynamic component of intermediate filaments in activated astroglia, which may account for the dynamic structural changes seen in these cells in response to brain injuries.

And [They] Even Followed Her Into the Hospital: Primary Care Providers' Attitudes Toward Referral for Traditional Healing Practices and Integrating Care for Indigenous Patients

Introduction/Importance: Given the promise of integrating traditional healing practices into primary care, we sought to examine the influence of primary care providers' racial concordance and Indigenous patients' ethnic salience on traditional healing treatment decisions.

METHOD

Using a descriptive comparative design with an online clinical case vignette, we measured provider decision making via a 5-point Provider Acceptance of Traditional Healing-Referral and Consult questionnaire. Aggregated results of the main effects and interactional effects were analyzed using a 2 × 2 analysis of variance between-subjects design.

RESULTS

The main effect for patient racial concordance on the dependent variable was significant, F(1, 89) = 5.71, p = .02.

CONCLUSIONS

Provider-patient racial concordance does increase the providers' likelihood of consulting with and referring patients to traditional healing practices, regardless of the patient's ethnic salience.

IMPLICATIONS FOR PRACTICE

All health care providers require training in traditional healing practices for Indigenous persons as guided by the cultural safety framework.

Metabolomics reveals variation and correlation among different tissues of olive (Olea europaea L.)

Metabolites in olives are associated with nutritional value and physiological properties. However, comprehensive information regarding the olive metabolome is limited. In this study, we identified 226 metabolites from three different tissues of olive using a non-targeted metabolomic profiling approach, of which 76 named metabolites were confirmed. Further statistical analysis revealed that these 76 metabolites covered different types of primary metabolism and some of the secondary metabolism pathways. One-way analysis of variance (ANOVA) statistical assay was performed to calculate the variations within the detected metabolites, and levels of 65 metabolites were differentially expressed in different samples. Hierarchical cluster analysis (HCA) dendrograms showed variations among different tissues that were similar to the metabolite profiles observed in new leaves and fruit. Additionally, 5776 metabolite-metabolite correlations were detected by a Pearson correlation coefficient approach. Screening of the calculated correlations revealed 3136, 3025, and 5184 were determined to metabolites and had significant correlations in three different combinations, respectively. This work provides the first comprehensive metabolomic of olive, which will provide new insights into understanding the olive metabolism, and potentially help advance studies in olive metabolic engineering.

Summary: This work provides the first comprehensive metabolomic of olive, which will provide new insights into understanding the olive metabolism and potentially help advance studies in olive metabolic engineering.

Array Analysis Manager-An automated DNA microarray analysis tool simplifying microarray data filtering, bias recognition, normalization, and expression analysis

Desoxyribonucleic acid (DNA) microarray experiments generate big datasets. To successfully harness the potential information within, multiple filtering, normalization, and analysis methods need to be applied. An in-depth knowledge of underlying physical, chemical, and statistical processes is crucial to the success of this analysis. However, due to the interdisciplinarity of DNA microarray applications and experimenter backgrounds, the published analyses differ greatly, for example, in methodology. This severely limits the comprehensibility and comparability among studies and research fields. In this work, we present a novel end-user software, developed to automatically filter, normalize, and analyze two-channel microarray experiment data. It enables the user to analyze single chip, dye-swap, and loop experiments with an extended dynamic intensity range using a multiscan approach. Furthermore, to our knowledge, this is the first analysis software solution, that can account for photobleaching, automatically detected by an artificial neural network. The user gets feedback on the effectiveness of each applied normalization regarding bias minimization. Standardized methods for expression analysis are included as well as the possibility to export the results in the Gene Expression Omnibus (GEO) format. This software was designed to simplify the microarray analysis process and help the experimenter to make educated decisions about the analysis process to contribute to reproducibility and comparability.

Finding the SNARC Instead of Hunting It: A 20∗20 Monte Carlo Investigation

The Spatial Numerical Association of Response Codes (SNARC) effect describes a stimulus-response association of left with small magnitude and right with large magnitude. Usually, it is estimated by means of regression slopes, where the independent variable only has a limited number of levels. Inspection of the literature reveals that it is not difficult to detect a SNARC effect within a group, but it has been quite unusual to find group differences. Is the SNARC effect as it is usually estimated using regression slopes largely insensitive to group differences, and are there design parameters necessary to increase sensitivity in group comparison analyses? Using numerical simulations, we provide evidence that both sample size and the number of stimulus repetitions, as well as intra-individual variability, contribute in a substantial way to the probability of detecting an existing SNARC effect. Our results show that the adequate choice of either sample size or number of repetitions per experimental cell does not fully compensate for a poor choice of the other parameter. Moreover, repeated failures to find significant group differences in the SNARC effect can be explained by insufficient power. Fortunately, increasing the number of repetitions to about 20 and testing at least 20 participants provides in most cases sufficient sensitivity to reliably detect the SNARC effect as well as group differences. Power plots are provided, which may help to improve both the economy and sensitivity of experimental design in future SNARC experiments, or, more generally when regression slopes are estimated intra-individually.

Statistical Analysis of the Main Configuration Parameters of the Network Dynamic and Adaptive Radio Protocol (DARP)

The present work analyses the wireless sensor network protocol (DARP) and the impact of different configuration parameter sets on its performance. Different scenarios have been considered, in order to gain a better understanding of the influence of the configuration on network protocols. The developed statistical analysis is based on the method known as Analysis of Variance (ANOVA), which focuses on the effect of the configuration on the performance of DARP. Three main dependent variables were considered: number of control messages sent during the set-up time, energy consumption and convergence time. A total of 20,413 simulations were carried out to ensure greater robustness in the statistical conclusions. The main goal of this work is to discover the most critical configuration parameters for the protocol, with a view to potential applications in Smart City type scenarios.

Exploration of solar radiation data from three geo-political zones in Nigeria

In this paper, readings of solar radiation received at three meteorological sites in Nigeria were analysed. Analysis of Variance (ANOVA) statistical test was carried out on the data set to observe the significant differences on radiations for each quarter of the specified years. The data were obtained in raw form from Nigerian Meteorological Agency (NIMET), Oshodi, Lagos. In order to get a clear description and visualization of the fluctuations of the radiation data, each year were considered independently, where it was discovered that for the 3rd quarter of each year, there is a great fall in the intensity of the solar radiation to as low as 73.27 (W/m²), 101.66 (W/m²), 158.51 (W/m²) for Ibadan, Port-Harcourt and Sokoto respectively. A detailed data description is available for the averages across months for each quarter. The data can provide insights on the health implications of exposure to solar radiation and the effect of solar radiation on climate change, food production, rainfall and flood patterns.

Influence of Sampling Component on Determination of Soluble Solids Content of Fuji Apple Using Near-Infrared Spectroscopy

Fuji apples from two production areas were separated into six batches by different experimenters. After applying light (500-1010 nm) on the surface of intact ones for their visible and near-infrared (NIR) spectra, destructive samples of three apple components were taken to determine the soluble solids content (SSC). Correlation and regression coefficients between the second Savitzky-Golay derivative of the spectra and SSC were analyzed to reveal that SSC values derived from the different apple components showed significantly different responses in the visible region. However, similar responses, particularly in the NIR section (730-932 nm), remained, including two sugar bands at 890 and 906 nm. On the basis of applying above characteristic bands to remove the interference signals, partial least square (PLS) and multiple linear regression (MLR) showed similar effective performances. According to the analysis of variance (ANOVA) method, sampling methods had significant effect on quantitative accuracy, and the model, using SSC values detected from the outer flesh cuboid (2.5 × 2.5 × 1.5 cm³), provided the best performance with lower root mean square error of prediction and higher correlation coefficient.

Multi-modal, Multi-measure, and Multi-class Discrimination of ADHD with Hierarchical Feature Extraction and Extreme Learning Machine Using Structural and Functional Brain MRI

Structural and functional MRI unveil many hidden properties of the human brain. We performed this multi-class classification study on selected subjects from the publically available attention deficit hyperactivity disorder ADHD-200 dataset of patients and healthy children. The dataset has three groups, namely, ADHD inattentive, ADHD combined, and typically developing. We calculated the global averaged functional connectivity maps across the whole cortex to extract anatomical atlas parcellation based features from the resting-state fMRI (rs-fMRI) data and cortical parcellation based features from the structural MRI (sMRI) data. In addition, the preprocessed image volumes from both of these modalities followed an ANOVA analysis separately using all the voxels. This study utilized the average measure from the most significant regions acquired from ANOVA as features for classification in addition to the multi-modal and multi-measure features of structural and functional MRI data. We extracted most discriminative features by hierarchical sparse feature elimination and selection algorithm. These features include cortical thickness, image intensity, volume, cortical thickness standard deviation, surface area, and ANOVA based features respectively. An extreme learning machine performed both the binary and multi-class classifications in comparison with support vector machines. This article reports prediction accuracy of both unimodal and multi-modal features from test data. We achieved 76.190% (p < 0.0001) classification accuracy in multi-class settings as well as 92.857% (p < 0.0001) classification accuracy in binary settings. In addition, we found ANOVA-based significant regions of the brain that also play a vital role in the classification of ADHD. Thus, from a clinical perspective, this multi-modal group analysis approach with multi-measure features may improve the accuracy of the ADHD differential diagnosis.

Habitat-related variation in composition of the essential oil of Seseli rigidum Waldst. & Kit. (Apiaceae)

Plant specialised metabolites like essential oils are highly variable depending on genetic and various ecological factors. The aim of the present work was to characterise essential oils of the species Seseli rigidum Waldst. & Kit. (Apiaceae) in various organs on the individual and populational levels. Geographical variability and the impact of climate and soil type on essential oil composition were also investigated. Individually sampled essential oils of roots, aerial parts and fruits of plants from seven populations were analysed by GC-FID and GC-MS. The investigated populations showed high interpopulational and especially intrapopulational variability of essential oil composition. In regard to the variability of essential oils, different chemotypes were defined. The essential oils of S. rigidum roots represented a falcarinol chemotype, oils of aerial parts constituted an α-pinene or α-pinene/sabinene chemotype and fruit essential oils can be characterised as belonging to a complex sabinene/α-pinene/β-phellandrene/falcarinol/germacrene B chemotype. At the species level, analysis of variance (ANOVA), principal component analysis (PCA) and canonical discriminant analysis (CDA) showed that the plant part exerted the strongest influence on the composition of essential oils. Climate had a high impact on composition of the essential oils of roots, aerial parts and fruits, while influence of the substrate was less pronounced. The variations in main compounds of essential oils based on climate or substrate were complex and specific to the plant part.

Subcritical water oxidation of 6-aminopenicillanic acid and cloxacillin using H2O2, K2S2O8, and O2

This study was undertaken to investigate the degradation of 6-aminopenicillanic acid (6-APA) and cloxacillin in aqueous solution by the combined effect of subcritical water and the oxidising agents O₂, H₂O₂, and K₂S₂O₈. Nano ZnO was used as a solid catalyst. Response surface methodology was used to determine the optimum experimental parameters (temperature, treatment time, and concentration of oxidising agent). For 6-APA, the maximum organic carbon (TOC) removal rates of 83.54%, 81.11% and 42.42% were obtained using H₂O₂, K₂S₂O₈, and O₂, respectively. For cloxacillin, the maximum TOC removal rates of 67.69%, 76.02% and 14.45% were obtained using H₂O₂, K₂S₂O₈, and O₂, respectively. Additionally, the impact of nano and commercial ZnO on TOC removal rates was determined. Secondary ions produced during the degradation process-such as nitrite, nitrate, sulphate and chloride-were determined using ion chromatography.

Descriptive and multivariate analyses of four Tunisian wastewater treatment plants: A comparison between different treatment processes and their efficiency improvement

This study was undertaken to evaluate the performance of four wastewater treatment plants/processes over a 4 year period. The wastewater flow evolution, energy consumption, and quality indicator parameters (BOD₅, COD and TSS) at the inlet and outlet sites of the plants were determined. In comparing three domestic WWTPs with different wastewater treatment processes, the multivariate analyses (RDA and ANOVA) showed that although the Agareb plant received the highest pollution load, it displayed a high level of removal efficiency especially for COD, BOD, TSS, TKN and NH₄⁺. It also revealed that the fluctuations in the wastewater composition and its contamination by varied industrial discharge could lead to the decrease in performance of the WWTP with activated sludge process as observed for the Southern Sfax plant. However, the electrolysis of the outlet water of Southern Sfax plant showed a significant improvement in COD removal.

Seasonal variations and sources study by way of back trajectories and ANOVA for ambient air pollutants (particulates and metallic elements) within a mixed area at Longjing, central Taiwan: 1-year observation

This study measured the concentrations of particulates and metallic elements in ambient air by using PS-1 sampler (TSP) at Longjing area. And this study focuses on the collection of ambient air particulates, metallic elements, particulate-bound mercury Hg(p), concentrations. In addition, the sources of ambient pollutants by way of back trajectory analysis are found. Moreover, test mean concentration variance differences for metallic elements (PM, Hg(p), Mn, Fe, Zn, Cr, Cu, and Pb) among the four seasons (spring, summer, autumn and winter) through ANOVA are calculated. The result indicates that the average highest particulate concentration occurred in winter season, and the order was winter > spring > autumn > summer, and the mostly highest average metallic element (Mn, Fe, Zn, Cr, Cu, Pb) concentrations occurred in autumn. Moreover, the mostly average lowest metallic element concentrations occurred in summer. In addition, the above results of backward trajectories that the major particulate pollutants parcel mainly come from northeastern Taiwan. Moreover, when comparing the results of the first half year to that of the second half year, the they indicated that all metallic elements displayed significant differences in concentrations except those of Hg(p), Mn, Fe, Zn. Finally, metallic element Hg(p) is the only one which showed no significant concentration difference from either seasonal variations or half-year observations.

Roasting Enhances the Anti-Cataract Effect of Coffee Beans: Ameliorating Selenite-Induced Cataracts in Rats

PURPOSE

Coffee is a widely consumed beverage. While recent studies have linked its intake to a reduced risk of cataracts, caffeine is believed to be the key factor for its effect. To know how roasting beans affects the effect of coffee on cataract formation, we investigated the impact roasting using a selenite-induced cataract rat model.

MATERIALS AND METHODS

Sprague Dawley rats were given a single injection of sodium selenite, which induced formation of nuclear cataracts by day 6, with or without coffee intake (100% coffee, 0.2 mL/day) for following 3 days.

RESULTS

The concentrations of glutathione (GSH) and ascorbic acid (AsA) in selenite-induced cataract lenses declined to half that of controls. However, 3 days of coffee intake ameliorated the reduction of GSH and AsA so that concentrations remained at 70-80% that of controls. Roasting enhanced the preventive effect of coffee by further reducing cataract formation and ameliorating selenite-induced reduction of antioxidants. High-performance liquid chromatography analysis revealed degradation of chlorogenic acid and generation of pyrocatechol during the coffee roasting process. We discovered that pyrocatechol, at doses equivalent to that found in dark-roasted coffee, was equally effective as caffeine at reducing cataract formation and ameliorating the reduction of antioxidants.

CONCLUSION

Our results indicate that pyrocatechol, generated during the roasting process, acts as an antioxidant together with caffeine to prevent cataract formation.

Single Marker Association Analysis for Unrelated Samples

Methods for single marker association analysis are presented for binary and quantitative traits. For a binary trait, we focus on the analysis of retrospective case-control data using Pearson's chi-squared test, the trend test and a robust test. For a continuous trait, typical methods are based on a linear regression model or the analysis of variance. We illustrate how these tests can be applied using a publicly available R package "Rassoc" and some existing R functions. Guidelines for single-marker analysis are provided.

Synthesis of polyaniline nanoparticles and their application for the removal of Crystal Violet dye by ultrasonicated adsorption process based on Response Surface Methodology

The present study focuses the synthesis of polyaniline nanoparticles (PANP) by rapid mixing polymerization method. They were recognized by FTIR and SEM techniques. Moreover they were utilized for the removal of Crystal Violet (CV) dye by ultrasonicated adsorption process. It ensures a quick alternative method compared to other conventional processes, which led to enhancement of mass transfer by ultrasound waves. The effectiveness of the process was confirmed through the effect of certain conditions like sonication time, temperature, adsorbent dosage and CV concentrations. The validity of the process was estimated by various adsorption isotherms. Kinetics and thermodynamic studies was also conducted to authenticate the process. The optimum operating parameters (OOP) were evaluated by Response Surface Methodology (RSM) based on central composite design (CCD) for the removal of CV dye. Moreover analysis of variances (ANOVA) was employed to estimate the significance of experimental variables. The predicated removal efficiency was found to be 94.29% which prove to be effectiveness of the process.

Computing the noncentral-F distribution and the power of the F-test with guaranteed accuracy

The computations involving the noncentral-F distribution are notoriously difficult to implement properly in floating-point arithmetic: Catastrophic loss of precision, floating-point underflow and overflow, drastically increasing computation time and program hang-ups, and instability due to numerical cancellation have all been reported. It is therefore recommended that existing statistical packages are cross-checked, and the present paper proposes a numerical algorithm precisely for this purpose. To the best of our knowledge, the proposed method is the first method that can compute the noncentrality parameter of the noncentral-F distribution with guaranteed accuracy over a wide parameter range that spans the range relevant for practical applications. Although the proposed method is limited to cases where the the degree of freedom of the denominator of the F test statistic is even, it does not affect its usefulness significantly: All of those algorithmic failures and inaccuracies that we can still reproduce today could have been prevented by simply cross-checking against the proposed method. Two numerical examples are presented where the intermediate computations went wrong silently, but the final result of the computations seemed nevertheless plausible, and eventually erroneous results were published. Cross-checking against the proposed method would have caught the numerical errors in both cases. The source code of the algorithm is available on GitHub, together with self-contained command-line executables. These executables can read the data to be cross-checked from plain text files, making it easy to cross-check any statistical software in an automated fashion.

Application of pseudo-emulsion-based hollow fiber strip dispersion for the extraction of p-nitrophenol from aqueous solutions

The extraction of p-nitrophenol (PNP) from aqueous solutions through a pseudo-emulsion hollow fiber strip dispersion (PEHFSD) system was conducted in a microporous hydrophobic polypropylene hollow fiber membrane contactor. For the optimization of the process variables, face-centered central composite design (FCCD) has been used. It was observed that initial feed concentration, carrier composition and stripping phase concentration were the three FCCD factors, which influenced the nitrophenol extraction. Using the optimized process conditions for the separation of PNP, experiments were also performed for the separation of other nitrophenols through PEHFSD system. By the FCCD design and analysis, almost 99% extraction of all three nitrophenols was achieved at optimum conditions. A mass transfer model was also developed and aqueous and membrane resistances were evaluated as 196.46 s cm(-1) and 50.14 s cm(-1), respectively.

Random Effects: Variance Is the Spice of Life

Covariates in regression analyses allow us to understand how independent variables of interest impact our dependent outcome variable. Often, we consider fixed effects covariates (e.g., gender or diabetes status) for which we examine subjects at each value of the covariate. We examine both men and women and, within each gender, examine both diabetic and nondiabetic patients. Occasionally, however, we consider random effects covariates for which we do not examine subjects at every value. For example, we examine patients from only a sample of hospitals and, within each hospital, examine both diabetic and nondiabetic patients. The random sampling of hospitals is in contrast to the complete coverage of all genders. In this column I explore the differences in meaning and analysis when thinking about fixed and random effects variables.

Objective Bayesian Comparison of Constrained Analysis of Variance Models

In the social sciences we are often interested in comparing models specified by parametric equality or inequality constraints. For instance, when examining three group means [Formula: see text] through an analysis of variance (ANOVA), a model may specify that [Formula: see text], while another one may state that [Formula: see text], and finally a third model may instead suggest that all means are unrestricted. This is a challenging problem, because it involves a combination of nonnested models, as well as nested models having the same dimension. We adopt an objective Bayesian approach, requiring no prior specification from the user, and derive the posterior probability of each model under consideration. Our method is based on the intrinsic prior methodology, suitably modified to accommodate equality and inequality constraints. Focussing on normal ANOVA models, a comparative assessment is carried out through simulation studies. We also present an application to real data collected in a psychological experiment.

Empirical Likelihood-Based ANOVA for Trimmed Means

In this paper, we introduce an alternative to Yuen's test for the comparison of several population trimmed means. This nonparametric ANOVA type test is based on the empirical likelihood (EL) approach and extends the results for one population trimmed mean from Qin and Tsao (2002). The results of our simulation study indicate that for skewed distributions, with and without variance heterogeneity, Yuen's test performs better than the new EL ANOVA test for trimmed means with respect to control over the probability of a type I error. This finding is in contrast with our simulation results for the comparison of means, where the EL ANOVA test for means performs better than Welch's heteroscedastic F test. The analysis of a real data example illustrates the use of Yuen's test and the new EL ANOVA test for trimmed means for different trimming levels. Based on the results of our study, we recommend the use of Yuen's test for situations involving the comparison of population trimmed means between groups of interest.

Patterns of Substitution Rate Variation at Many Nuclear Loci in Two Species Trios in the Brassicaceae Partitioned with ANOVA

There are marked variations among loci and among lineages in rates of nucleotide substitution. The generation time hypothesis (GTH) is a neutral explanation for substitution rate heterogeneity that has genomewide application, predicting that species with shorter generation times accumulate DNA sequence substitutions faster than species with longer generation times do since faster genome replication provides more opportunities for mutations to occur and reach fixation by genetic drift. Relatively few studies have rigorously evaluated the GTH in plants, and there are numerous alternative hypotheses for plant substitution rate variation. One major challenge has been finding pairs of closely related plant species with contrasting generation times and appropriate outgroup taxa that all also have DNA sequence data for numerous loci. To test for causes of rate variation, we obtained sequence data for 256 genes for Arabidopsis thaliana, normally reproducing every year, and the biennial Arabidopsis lyrata with three closely related outgroup taxa (Brassica rapa, Capsella grandiflora, and Neslia paniculata) as well as the biennial Brassica oleracea and the annual B. rapa lineage with the outgroup N. paniculata. A sign test indicated that more loci than expected by chance have faster rates of substitution on the branch leading to the annual than to the perennial for one three-species trio but not another. Tajima's 1D and 2D tests, and a likelihood ratio test that incorporated saturation correction, rejected rate homogeneity for up to 26 genes (up to 14 genes when correcting for multiple tests), consistently showing faster rates for the annual lineage in the Arabidopsis species trio. ANOVA showed significant rate heterogeneity between the Arabidopsis and Brassica species trios (about 6 % of rate variation) and among loci (about 26-32 % of rate variation). The lineage-by-locus interaction which would be caused by locus- and lineage-specific natural selection explained about 13 % of substitution rate variation in one ANOVA model using substitution rates from genes partitioned into odd and even codons but was not a significant effect without partitioned genes. Annual/perennial lineage and species trio by annual/perennial lineage each explained about 1 % of substitution rate variation.

Researchers' choice of the number and range of levels in experiments affects the resultant variance-accounted-for effect size

In psychology, the reporting of variance-accounted-for effect size indices has been recommended and widely accepted through the movement away from null hypothesis significance testing. However, most researchers have paid insufficient attention to the fact that effect sizes depend on the choice of the number of levels and their ranges in experiments. Moreover, the functional form of how and how much this choice affects the resultant effect size has not thus far been studied. We show that the relationship between the population effect size and number and range of levels is given as an explicit function under reasonable assumptions. Counterintuitively, it is found that researchers may affect the resultant effect size to be either double or half simply by suitably choosing the number of levels and their ranges. Through a simulation study, we confirm that this relation also applies to sample effect size indices in much the same way. Therefore, the variance-accounted-for effect size would be substantially affected by the basic research design such as the number of levels. Simple cross-study comparisons and a meta-analysis of variance-accounted-for effect sizes would generally be irrational unless differences in research designs are explicitly considered.

Repeated ketamine treatment induces sex-specific behavioral and neurochemical effects in mice

One of the most striking discoveries in the treatment of major depression was the finding that infusion of a single sub-anesthetic dose of ketamine induces rapid and sustained antidepressant effects in treatment-resistant depressed patients. However, ketamine's antidepressant-like actions are transient and can only be sustained by repeated drug treatment. Despite the fact that women experience major depression at roughly twice the rate of men, research regarding the neurobiological antidepressant-relevant effects of ketamine has focused almost exclusively on the male sex. Importantly, knowledge regarding the sex-differentiated effects, the frequency and the dose on which repeated ketamine administration stops being beneficial, is limited. In the current study, we investigated the behavioral, neurochemical and synaptic molecular effects of repeated ketamine treatment (10mg/kg; 21days) in male and female C57BL/6J mice. We report that ketamine induced beneficial antidepressant-like effects in male mice, but induced both anxiety-like (i.e., decreased time spent in the center of the open field arena) and depressive-like effects (i.e., enhanced immobility duration in the forced swim test; FST) in their female counterparts. Moreover, repeated ketamine treatment induced sustained sex-differentiated neurochemical and molecular effects, as it enhanced hippocampal synapsin protein levels and serotonin turnover in males, but attenuated glutamate and aspartate levels in female mice. Taken together, our findings indicate that repeated ketamine treatment induces opposite behavioral effects in male and female mice, and thus, present data have far-reaching implications for the sex-oriented use of ketamine in both experimental and clinical research settings.