Computer-aided design and implementation of efficient biosynthetic pathways to produce high added-value products derived from tyrosine in Escherichia coli.

Developing efficient bioprocesses requires selecting the best biosynthetic pathways, which can be challenging and time-consuming due to the vast amount of data available in databases and literature. The extension of the shikimate pathway for the biosynthesis of commercially attractive molecules often involves promiscuous enzymes or lacks well-established routes. To address these challenges, we developed a computational workflow integrating enumeration/retrosynthesis algorithms, a toolbox for pathway analysis, enzyme selection tools, and a gene discovery pipeline, supported by manual curation and literature review. Our focus has been on implementing biosynthetic pathways for tyrosine-derived compounds, specifically L-3,4-dihydroxyphenylalanine (L-DOPA) and dopamine, with significant applications in health and nutrition. We selected one pathway to produce L-DOPA and two different pathways for dopamine-one already described in the literature and a novel pathway. Our goal was either to identify the most suitable gene candidates for expression in Escherichia coli for the known pathways or to discover innovative pathways. Although not all implemented pathways resulted in the accumulation of target compounds, in our shake-flask experiments we achieved a maximum L-DOPA titer of 0.71 g/L and dopamine titers of 0.29 and 0.21 g/L for known and novel pathways, respectively. In the case of L-DOPA, we utilized, for the first time, a mutant version of tyrosinase from Ralstonia solanacearum. Production of dopamine via the known biosynthesis route was accomplished by coupling the L-DOPA pathway with the expression of DOPA decarboxylase from Pseudomonas putida, resulting in a unique biosynthetic pathway never reported in literature before. In the context of the novel pathway, dopamine was produced using tyramine as the intermediate compound. To achieve this, tyrosine was initially converted into tyramine by expressing TDC from Levilactobacillus brevis, which, in turn, was converted into dopamine through the action of the enzyme encoded by ppoMP from Mucuna pruriens. This marks the first time that an alternative biosynthetic pathway for dopamine has been validated in microbes. These findings underscore the effectiveness of our computational workflow in facilitating pathway enumeration and selection, offering the potential to uncover novel biosynthetic routes, thus paving the way for other target compounds of biotechnological interest.

Integrative Genetic Variation, DNA Methylation, and Gene Expression Analysis of Escitalopram and Aripiprazole Treatment Outcomes in Depression: A CAN-BIND-1 Study.

INTRODUCTION: Little is known about the interplay between genetics and epigenetics on antidepressant treatment (1) response and remission, (2) side effects, and (3) serum levels. This study explored the relationship among single nucleotide polymorphisms (SNPs), DNA methylation (DNAm), and mRNA levels of four pharmacokinetic genes, CYP2C19, CYP2D6, CYP3A4, and ABCB1, and its effect on these outcomes. METHODS: The Canadian Biomarker Integration Network for Depression-1 dataset consisted of 177 individuals with major depressive disorder treated for 8 weeks with escitalopram (ESC) followed by 8 weeks with ESC monotherapy or augmentation with aripiprazole. DNAm quantitative trait loci (mQTL), identified by SNP-CpG associations between 20 SNPs and 60 CpG sites in whole blood, were tested for associations with our outcomes, followed by causal inference tests (CITs) to identify methylation-mediated genetic effects. RESULTS: Eleven cis-SNP-CpG pairs (q<0.05) constituting four unique SNPs were identified. Although no significant associations were observed between mQTLs and response/remission, CYP2C19 rs4244285 was associated with treatment-related weight gain (q=0.027) and serum concentrations of ESCadj (q<0.001). Between weeks 2-4, 6.7% and 14.9% of those with *1/*1 (normal metabolizers) and *1/*2 (intermediate metabolizers) genotypes, respectively, reported ≥2 lbs of weight gain. In contrast, the *2/*2 genotype (poor metabolizers) did not report weight gain during this period and demonstrated the highest ESCadj concentrations. CITs did not indicate that these effects were epigenetically mediated. DISCUSSION: These results elucidate functional mechanisms underlying the established associations between CYP2C19 rs4244285 and ESC pharmacokinetics. This mQTL SNP as a marker for antidepressant-related weight gain needs to be further explored.

Large Individual Differences in Functional Connectivity in the Context of Major Depression and Antidepressant Pharmacotherapy.

Clinical studies of major depression (MD) generally focus on group effects, yet interindividual differences in brain function are increasingly recognized as important and may even impact effect sizes related to group effects. Here, we examine the magnitude of individual differences in relation to group differences that are commonly investigated (e.g., related to MD diagnosis and treatment response). Functional MRI data from 107 participants (63 female, 44 male) were collected at baseline, 2, and 8 weeks during which patients received pharmacotherapy (escitalopram, N = 68) and controls (N = 39) received no intervention. The unique contributions of different sources of variation were examined by calculating how much variance in functional connectivity was shared across all participants and sessions, within/across groups (patients vs controls, responders vs nonresponders, female vs male participants), recording sessions, and individuals. Individual differences and common connectivity across groups, sessions, and participants contributed most to the explained variance (>95% across analyses). Group differences related to MD diagnosis, treatment response, and biological sex made significant but small contributions (0.3-1.2%). High individual variation was present in cognitive control and attention areas, while low individual variation characterized primary sensorimotor regions. Group differences were much smaller than individual differences in the context of MD and its treatment. These results could be linked to the variable findings and difficulty translating research on MD to clinical practice. Future research should examine brain features with low and high individual variation in relation to psychiatric symptoms and treatment trajectories to explore the clinical relevance of the individual differences identified here.

Depression-Anxiety Coupling Strength as a predictor of relapse in major depressive disorder: A CAN-BIND wellness monitoring study report.

BACKGROUND: A critical challenge in the study and management of major depressive disorder (MDD) is predicting relapse. We examined the temporal correlation/coupling between depression and anxiety (called Depression-Anxiety Coupling Strength, DACS) as a predictor of relapse in patients with MDD. METHODS: We followed 97 patients with remitted MDD for an average of 394 days. Patients completed weekly self-ratings of depression and anxiety symptoms using the Quick Inventory of Depressive Symptoms (QIDS-SR) and the Generalized Anxiety Disorder 7-item scale (GAD-7). Using these longitudinal ratings we computed DACS as random slopes in a linear mixed effects model reflecting individual-specific degree of correlation between depression and anxiety across time points. We then tested DACS as an independent variable in a Cox proportional hazards model to predict relapse. RESULTS: A total of 28 patients (29 %) relapsed during the follow-up period. DACS significantly predicted confirmed relapse (hazard ratio [HR] 1.5, 95 % CI [1.01, 2.22], p = 0.043; Concordance 0.79 [SE 0.04]). This effect was independent of baseline depressive or anxiety symptoms or their average levels over the follow-up period, and was identifiable more than one month before relapse onset. LIMITATIONS: Small sample size, in a single study. Narrow phenotype and comorbidity profiles. CONCLUSIONS: DACS may offer opportunities for developing novel strategies for personalized monitoring, early detection, and intervention. Future studies should replicate our findings in larger, diverse patient populations, develop individual patient prediction models, and explore the underlying mechanisms that govern the relationship of DACS and relapse.

Rhizophagus intraradices and Azospirillum brasilense improve growth of herbaceous plants and soil biological activity in revegetation of a recovering coal-mining area.

We assessed, in a field experiment, the effects of arbuscular mycorrhizal fungi (Rhizophagus intraradices) and plant growth-promoting bacteria (Azospirillum brasilense) on the soil biological activity and the growth of key pioneer species used in the revegetation of coal-mining areas undergoing recovery. We applied four inoculation treatments to the pioneer plant species (Lablab purpureus, Paspalum notatum, Crotalaria juncea, Neonotonia wightii, Stylosanthes guianensis, Andropogon gayanus and Trifolium repens) used in the recovery process: NI (Control - Non-inoculated), AZO (A. brasilense), AMF (R. intraradices), and co-inoculation of AZO and AMF. On the 75th and 180th days, we measured plant dry mass, mycorrhizal colonization, N and P concentration, and accumulation in plant tissue. We collected soil to quantify glomalin content and soil enzyme activity. After 180 days, we did a phytosociological characterization of the remaining spontaneous plants.The both microorganisms, singly or co-inoculated, promoted increases in different fractions of soil glomalin, acid phosphatase activity, and fluorescein diacetate activity at 75 and 180 days. The inoculation was linked to higher plant biomass production (62-89%) and increased plant P and N accumulation by 34-75% and 70-85% at 180 days, compared with the non-inoculated treatment. Among the pioneer species sown Crotalaria juncea produced the highest biomass at the 75th and 180th days (67% and 76% of all biomass), followed by Lablab purpureus (3% and 0.5%), while the other species failed to establish. At 180 days, we observed twenty spontaneous plant species growing in the area, primarily from the Poaceae family (74%). That suggests that the pioneer species present in the area do not hinder the ecological succession process. Inoculation of R. intraradices and A. brasilense, isolated or combined, increases soil biological activity, growth, and nutrient accumulation in key pioneer plant species, indicating the potential of that technique for the recovery of lands degraded by coal mining.

Canadian Network for Mood and Anxiety Treatments (CANMAT) 2023 Update on Clinical Guidelines for Management of Major Depressive Disorder in Adults: Réseau canadien pour les traitements de l'humeur et de l'anxiété (CANMAT) 2023 : Mise à jour des lignes directrices cliniques pour la prise en charge du trouble dépressif majeur chez les adultes.

BACKGROUND: The Canadian Network for Mood and Anxiety Treatments (CANMAT) last published clinical guidelines for the management of major depressive disorder (MDD) in 2016. Owing to advances in the field, an update was needed to incorporate new evidence and provide new and revised recommendations for the assessment and management of MDD in adults. METHODS: CANMAT convened a guidelines editorial group comprised of academic clinicians and patient partners. A systematic literature review was conducted, focusing on systematic reviews and meta-analyses published since the 2016 guidelines. Recommendations were organized by lines of treatment, which were informed by CANMAT-defined levels of evidence and supplemented by clinical support (consisting of expert consensus on safety, tolerability, and feasibility). Drafts were revised based on review by patient partners, expert peer review, and a defined expert consensus process. RESULTS: The updated guidelines comprise eight primary topics, in a question-and-answer format, that map a patient care journey from assessment to selection of evidence-based treatments, prevention of recurrence, and strategies for inadequate response. The guidelines adopt a personalized care approach that emphasizes shared decision-making that reflects the values, preferences, and treatment history of the patient with MDD. Tables provide new and updated recommendations for psychological, pharmacological, lifestyle, complementary and alternative medicine, digital health, and neuromodulation treatments. Caveats and limitations of the evidence are highlighted. CONCLUSIONS: The CANMAT 2023 updated guidelines provide evidence-informed recommendations for the management of MDD, in a clinician-friendly format. These updated guidelines emphasize a collaborative, personalized, and systematic management approach that will help optimize outcomes for adults with MDD.

Influence of CYP2C19, CYP2D6, and ABCB1 Gene Variants and Serum Levels of Escitalopram and Aripiprazole on Treatment-Emergent Sexual Dysfunction: A Canadian Biomarker Integration Network in Depression 1 (CAN-BIND 1) Study.

OBJECTIVES: Treatment-emergent sexual dysfunction is frequently reported by individuals with major depressive disorder (MDD) on antidepressants, which negatively impacts treatment adherence and efficacy. We investigated the association of polymorphisms in pharmacokinetic genes encoding cytochrome-P450 drug-metabolizing enzymes, CYP2C19 and CYP2D6, and the transmembrane efflux pump, P-glycoprotein (i.e., ABCB1), on treatment-emergent changes in sexual function (SF) and sexual satisfaction (SS) in the Canadian Biomarker Integration Network in Depression 1 (CAN-BIND-1) sample. METHODS: A total of 178 adults with MDD received treatment with escitalopram (ESC) from weeks 0-8 (Phase I). At week 8, nonresponders were augmented with aripiprazole (ARI) (i.e., ESC + ARI, n = 91), while responders continued ESC (i.e., ESC-Only, n = 80) from weeks 8-16 (Phase II). SF and SS were evaluated using the sex effects (SexFX) scale at weeks 0, 8, and 16. We assessed the primary outcomes, SF and SS change for weeks 0-8 and 8-16, using repeated measures mixed-effects models. RESULTS: In ESC-Only, CYP2C19 intermediate metabolizer (IM) + poor metabolizers (PMs) showed treatment-related improvements in sexual arousal, a subdomain of SF, from weeks 8-16, relative to CYP2C19 normal metabolizers (NMs) who showed a decline, F(2,54) = 8.00, p < 0.001, q = 0.048. Specifically, CYP2C19 IM + PMs reported less difficulty with having and sustaining vaginal lubrication in females and erection in males, compared to NMs. Furthermore, ESC-Only females with higher concentrations of ESC metabolite, S-desmethylcitalopram (S-DCT), and S-DCT/ESC ratio in serum demonstrated more decline in SF (r = -0.42, p = 0.004, q = 0.034) and SS (r = -0.43, p = 0.003, q = 0.034), respectively, which was not observed in males. ESC-Only females also demonstrated a trend for a correlation between S-DCT and sexual arousal change in the same direction (r = -0.39, p = 0.009, q = 0.052). CONCLUSIONS: CYP2C19 metabolizer phenotypes may be influencing changes in sexual arousal related to ESC monotherapy. Thus, preemptive genotyping of CYP2C19 may help to guide selection of treatment that circumvents selective serotonin reuptake inhibitor-related sexual dysfunction thereby improving outcomes for patients. Additionally, further research is warranted to clarify the role of S-DCT in the mechanisms underlying ESC-related changes in SF and SS. This CAN-BIND-1 study was registered on clinicaltrials.gov (Identifier: NCT01655706) on 27 July 2012.

Machine Learning Prediction of Quality of Life Improvement During Antidepressant Treatment of Patients With Major Depressive Disorder: A STAR*D and CAN-BIND-1 Report.

Background: Quality of life (QoL) is an important patient-centric outcome to evaluate in treatment of major depressive disorder (MDD). This work sought to investigate the performance of several machine learning methods to predict a return to normative QoL in patients with MDD after antidepressant treatment.Methods: Several binary classification algorithms were trained on data from the first 2 weeks of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study (n = 651, conducted from 2001 to 2006) to predict week 9 normative QoL (score ≥ 67, based on a community normative sample, on the Quality of Life Enjoyment and Satisfaction Questionnaire-Short Form [Q-LES-Q-SF]) after treatment with citalopram. Internal validation was performed using a STAR*D holdout dataset, and external validation was performed using the Canadian Biomarker Integration Network in Depression-1 (CAN-BIND-1) dataset (n = 175, study conducted from 2012 to 2017) after treatment with escitalopram. Feature importance was calculated using SHapley Additive exPlanations (SHAP).Results: Random Forest performed most consistently on internal and external validation, with balanced accuracy (area under the receiver operator curve) of 71% (0.81) on the STAR*D dataset and 69% (0.75) on the CAN-BIND-1 dataset. Random Forest Classifiers trained on Q-LES-Q-SF and Quick Inventory of Depressive Symptomatology-Self-Rated variables had similar performance on both internal and external validation. Important predictive variables came from psychological, physical, and socioeconomic domains.Conclusions: Machine learning can predict normative QoL after antidepressant treatment with similar performance to that of prior work predicting depressive symptom response and remission. These results suggest that QoL outcomes in MDD patients can be predicted with simple patient-rated measures and provide a foundation to further improve performance and demonstrate clinical utility.Trial Registration: ClinicalTrials.gov identifiers NCT00021528 and NCT01655706.

Applications and Market of Micro-Organism-Based and Plant-Based Inputs in Brazilian Agriculture.

The use of plant-based and micro-organism-based biological inputs is a sustainable agricultural practice. It promotes a suitable and better utilization of non-renewable resources in the environment. The benefits of using micro-organisms are associated with direct and indirect mechanisms, mainly related to improvements in the absorption and availability of nutrients, resulting in a consequent impact on plant growth. The main benefits of using biochemical pesticides are the promotion of sustainability and the management of resistance to pests and diseases. Although the use of micro-organisms and botanical metabolites is a promising agricultural alternative, they are still primarily concentrated in grain crops. There is a huge opportunity to expand the plant-based and micro-organism-based biological inputs used in agriculture due to the wide range of mechanisms of action of those products. At a global level, several terminologies have been adopted to characterize biological inputs, but many terms used conflict with Brazilian legislation. This review will clarify the classes of biological inputs existing in Brazil as well as present the application and evolution of the market for microbiological and plant-based inputs.

Microbial production of the plant flavanone hesperetin from caffeic acid.

OBJECTIVE: Hesperetin is an important O-methylated flavonoid produced by citrus fruits and of potential pharmaceutical relevance. The microbial biosynthesis of hesperetin could be a viable alternative to plant extraction, as plant extracts often yield complex mixtures of different flavonoids making it challenging to isolate pure compounds. In this study, hesperetin was produced from caffeic acid in the microbial host Escherichia coli. We combined a previously optimised pathway for the biosynthesis of the intermediate flavanone eriodictyol with a combinatorial library of plasmids expressing three candidate flavonoid O-methyltransferases. Moreover, we endeavoured to improve the position specificity of CCoAOMT7, a flavonoid O-methyltransferase from Arabidopsis thaliana that has been demonstrated to O-methylate eriodictyol in both the para- and meta-position, thus leading to a mixture of hesperetin and homoeriodictyol. RESULTS: The best performing flavonoid O-methyltransferase in our screen was found to be CCoAOMT7, which could produce up to 14.6 mg/L hesperetin and 3.8 mg/L homoeriodictyol from 3 mM caffeic acid in E. coli 5-alpha. Using a platform for enzyme engineering that scans the mutational space of selected key positions, predicting their structures using homology modelling and inferring their potential catalytic improvement using docking simulations, we were able to identify a CCoAOMT7 mutant with a two-fold higher position specificity for hesperetin. The mutant's catalytic activity, however, was considerably diminished. Our findings suggest that hesperetin can be created from central carbon metabolism in E. coli following the introduction of a caffeic acid biosynthesis pathway.

Predicting recurrence of major depressive episodes using the Depression Implicit Association Test: A Canadian biomarker integration network in depression (CAN-BIND) report.

Identifying clinically relevant predictors of depressive recurrence following treatment for Major Depressive Disorder (MDD) is critical for relapse prevention. Implicit self-depressed associations (SDAs), defined as implicit cognitive associations between elements of depression (e.g., sad, miserable) and oneself, often persist following depressive episodes and may represent a cognitive biomarker for future recurrences. Thus, we examined whether SDAs, and changes in SDAs over time, prospectively predict depressive recurrence among treatment responders in the CAN-BIND Wellness Monitoring for MDD Study, a prospective cohort study conducted across 5 clinical centres. A total of 96 patients with MDD responding to various treatments were followed an average of 1.01 years. Participants completed the Depression Implicit Association Test (DIAT) - a computer-based measure of SDAs - every 8 weeks on a tablet device. Survival analyses indicated that greater SDAs at baseline and increases in SDAs over time predicted shorter time to MDD recurrence, even after accounting for depressive symptom severity. The findings show that SDAs are a robust prognostic indicator of risk for MDD recurrence, and that the DIAT may be a feasible and low-cost clinical screening tool. SDAs also represent a potential mechanism underlying the course of recurrent depression and are a promising target for relapse prevention interventions.

Personalized relapse prediction in patients with major depressive disorder using digital biomarkers.

Major depressive disorder (MDD) is a chronic illness wherein relapses contribute to significant patient morbidity and mortality. Near-term prediction of relapses in MDD patients has the potential to improve outcomes by helping implement a 'predict and preempt' paradigm in clinical care. In this study, we developed a novel personalized (N-of-1) encoder-decoder anomaly detection-based framework of combining anomalies in multivariate actigraphy features (passive) as triggers to utilize an active concurrent self-reported symptomatology questionnaire (core symptoms of depression and anxiety) to predict near-term relapse in MDD. The framework was evaluated on two independent longitudinal observational trials, characterized by regular bimonthly (every other month) in-person clinical assessments, weekly self-reported symptom assessments, and continuous activity monitoring data with two different wearable sensors for ≥ 1 year or until the first relapse episode. This combined passive-active relapse prediction framework achieved a balanced accuracy of ≥ 71%, false alarm rate of ≤ 2.3 alarm/patient/year with a median relapse detection time of 2-3 weeks in advance of clinical onset in both studies. The study results suggest that the proposed personalized N-of-1 prediction framework is generalizable and can help predict a majority of MDD relapses in an actionable time frame with relatively low patient and provider burden.

A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study.

Monitoring sleep and activity through wearable devices such as wrist-worn actigraphs has the potential for long-term measurement in the individual's own environment. Long periods of data collection require a complex approach, including standardized pre-processing and data trimming, and robust algorithms to address non-wear and missing data. In this study, we used a data-driven approach to quality control, pre-processing and analysis of longitudinal actigraphy data collected over the course of 1 year in a sample of 95 participants. We implemented a data processing pipeline using open-source packages for longitudinal data thereby providing a framework for treating missing data patterns, non-wear scoring, sleep/wake scoring, and conducted a sensitivity analysis to demonstrate the impact of non-wear and missing data on the relationship between sleep variables and depressive symptoms. Compliance with actigraph wear decreased over time, with missing data proportion increasing from a mean of 4.8% in the first week to 23.6% at the end of the 12 months of data collection. Sensitivity analyses demonstrated the importance of defining a pre-processing threshold, as it substantially impacts the predictive value of variables on sleep-related outcomes. We developed a novel non-wear algorithm which outperformed several other algorithms and a capacitive wear sensor in quality control. These findings provide essential insight informing study design in digital health research.

Developing an Electroencephalography-Based Model for Predicting Response to Antidepressant Medication.

Importance: Untreated depression is a growing public health concern, with patients often facing a prolonged trial-and-error process in search of effective treatment. Developing a predictive model for treatment response in clinical practice remains challenging. Objective: To establish a model based on electroencephalography (EEG) to predict response to 2 distinct selective serotonin reuptake inhibitor (SSRI) medications. Design, Setting, and Participants: This prognostic study developed a predictive model using EEG data collected between 2011 and 2017 from 2 independent cohorts of participants with depression: 1 from the first Canadian Biomarker Integration Network in Depression (CAN-BIND) group and the other from the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) consortium. Eligible participants included those aged 18 to 65 years who had a diagnosis of major depressive disorder. Data were analyzed from January to December 2022. Exposures: In an open-label trial, CAN-BIND participants received an 8-week treatment regimen of escitalopram treatment (10-20 mg), and EMBARC participants were randomized in a double-blind trial to receive an 8-week sertraline (50-200 mg) treatment or placebo treatment. Main Outcomes and Measures: The model's performance was estimated using balanced accuracy, specificity, and sensitivity metrics. The model used data from the CAN-BIND cohort for internal validation, and data from the treatment group of the EMBARC cohort for external validation. At week 8, response to treatment was defined as a 50% or greater reduction in the primary, clinician-rated scale of depression severity. Results: The CAN-BIND cohort included 125 participants (mean [SD] age, 36.4 [13.0] years; 78 [62.4%] women), and the EMBARC sertraline treatment group included 105 participants (mean [SD] age, 38.4 [13.8] years; 72 [68.6%] women). The model achieved a balanced accuracy of 64.2% (95% CI, 55.8%-72.6%), sensitivity of 66.1% (95% CI, 53.7%-78.5%), and specificity of 62.3% (95% CI, 50.1%-73.8%) during internal validation with CAN-BIND. During external validation with EMBARC, the model achieved a balanced accuracy of 63.7% (95% CI, 54.5%-72.8%), sensitivity of 58.8% (95% CI, 45.3%-72.3%), and specificity of 68.5% (95% CI, 56.1%-80.9%). Additionally, the balanced accuracy for the EMBARC placebo group (118 participants) was 48.7% (95% CI, 39.3%-58.0%), the sensitivity was 50.0% (95% CI, 35.2%-64.8%), and the specificity was 47.3% (95% CI, 35.9%-58.7%), suggesting the model's specificity in predicting SSRIs treatment response. Conclusions and Relevance: In this prognostic study, an EEG-based model was developed and validated in 2 independent cohorts. The model showed promising accuracy in predicting treatment response to 2 distinct SSRIs, suggesting potential applications for personalized depression treatment.

Fracture in Irradiated Rat Femur: A Description of an Experimental Model and Evaluation of its Effectiveness.

Objective We aim to describe an experimental model for studying femoral fractures in rats after exposure to ionizing radiation, demonstrating a way to apply a substance for analysis, the method for patterning fracture and irradiation, and how to evaluate its effectiveness based on radiographic studies. Methods We used 24 rats divided into 2 groups of 12 animals each. The STUDY group was exposed to ionizing radiation and treated with saline solution, and the CONTROL group was not exposed to radiation and was treated with saline solution. All animals were subjected to standardized fracture of the right femur that was fixed with intramedullary wire. The efficiency of the bone union was assessed by radiographic exam. Results Fracture healing was more efficient in bones not exposed to ionizing radiation ( p = 0.012). All fractures met the criteria of being simple, diaphyseal, transverse or short oblique. Conclusion The experimental model presented is an efficient alternative for the study of fractures in irradiated bones in rats.

Fracture in Irradiated Rat Femur: A Description of an Experimental Model and Evaluation of its Effectiveness / Fratura em fêmur irradiado de rato: Descrição de um modelo experimental e avaliação de sua eficácia

Abstract Objective We aim to describe an experimental model for studying femoral fractures in rats after exposure to ionizing radiation, demonstrating a way to apply a substance for analysis, the method for patterning fracture and irradiation, and how to evaluate its effectiveness based on radiographic studies. Methods We used 24 rats divided into 2 groups of 12 animals each. The STUDY group was exposed to ionizing radiation and treated with saline solution, and the CONTROL group was not exposed to radiation and was treated with saline solution. All animals were subjected to standardized fracture of the right femur that was fixed with intramedullary wire. The efficiency of the bone union was assessed by radiographic exam. Results Fracture healing was more efficient in bones not exposed to ionizing radiation (p = 0.012). All fractures met the criteria of being simple, diaphyseal, transverse or short oblique. Conclusion The experimental model presented is an efficient alternative for the study of fractures in irradiated bones in rats.

Resumo Objetivo Nosso objetivo é descrever um modelo experimental para estudo de fraturas de fêmur em ratos após exposição a radiação ionizante, demonstrando uma forma de aplicação de uma substância para análise, o método de padronização de fratura e irradiação e a forma de avaliação de sua eficácia com base em estudos radiográficos. Métodos Utilizamos 24 ratos divididos em dois grupos de 12 animais cada. O grupo ESTUDO foi exposto à radiação ionizante e tratado com soro fisiológico, enquanto o grupo CONTROLE não foi exposto à radiação e foi tratado com soro fisiológico. Todos os animais foram submetidos à fratura padronizada do fêmur direito e sua fixação com fio intramedular. A eficácia da consolidação óssea foi determinada por exame radiográfico. Resultados A cicatrização de fraturas foi mais eficiente em ossos não expostos à radiação ionizante (p = 0,012). Todas as fraturas atenderam aos critérios de serem simples, diafisárias, transversas ou oblíquas curtas. Conclusão O modelo experimental apresentado é uma boa alternativa para o estudo de fraturas em ossos irradiados em ratos.

SNORD90 induces glutamatergic signaling following treatment with monoaminergic antidepressants.

Pharmacotherapies for the treatment of major depressive disorder were serendipitously discovered almost seven decades ago. From this discovery, scientists pinpointed the monoaminergic system as the primary target associated with symptom alleviation. As a result, most antidepressants have been engineered to act on the monoaminergic system more selectively, primarily on serotonin, in an effort to increase treatment response and reduce unfavorable side effects. However, slow and inconsistent clinical responses continue to be observed with these available treatments. Recent findings point to the glutamatergic system as a target for rapid acting antidepressants. Investigating different cohorts of depressed individuals treated with serotonergic and other monoaminergic antidepressants, we found that the expression of a small nucleolar RNA, SNORD90, was elevated following treatment response. When we increased Snord90 levels in the mouse anterior cingulate cortex (ACC), a brain region regulating mood responses, we observed antidepressive-like behaviors. We identified neuregulin 3 (NRG3) as one of the targets of SNORD90, which we show is regulated through the accumulation of N6-methyladenosine modifications leading to YTHDF2-mediated RNA decay. We further demonstrate that a decrease in NRG3 expression resulted in increased glutamatergic release in the mouse ACC. These findings support a molecular link between monoaminergic antidepressant treatment and glutamatergic neurotransmission.

Response trajectories during escitalopram treatment of patients with major depressive disorder.

Depression is a leading global cause of disability, yet about half of patients do not respond to initial antidepressant treatment. This treatment difficulty may be in part due to the heterogeneity of depression and corresponding response to treatment. Unsupervised machine learning allows underlying patterns to be uncovered, and can be used to understand this heterogeneity by finding groups of patients with similar response trajectories. Prior studies attempting this have clustered patients using a narrow range of data primarily from depression scales. In this work, we used unsupervised machine learning to cluster patients receiving escitalopram therapy using a wide variety of subjective and objective clinical features from the first eight weeks of the Canadian Biomarker Integration Network in Depression-1 trial. We investigated how these clusters responded to treatment by comparing changes in symptoms and symptom categories, and by using Principal Component Analysis (PCA). Our algorithm found three clusters, which broadly represented non-responders, responders, and remitters. Most categories of features followed this response pattern except for objective cognitive features. Using PCA with our clusters, we found that subjective mood state/anhedonia is the core feature of response with escitalopram, but there exists other distinct patterns of response around neurovegetative symptoms, activation, and cognition.

Menopause and Mood: The Role of Estrogen in Midlife Depression and Beyond.

Depression is a disabling condition that often leads to significant burden. Women are more vulnerable to depression during reproductive-related "windows of vulnerability" such as the menopause transition and early postmenopausal years. This heightened vulnerability can be attributed, at least in part, to the neuromodulatory effects of estrogen on mood and cognition and the exposure to rapid fluctuations of estradiol levels during midlife years. The management of midlife depression can be challenging due to the presence and severity of other complaints such as vasomotor symptoms and sleep disturbances. Psychopharmacologic, behavioral, and hormonal interventions should be part of the treatment armamentarium.

Resting-state EEG delta and alpha power predict response to cognitive behavioral therapy in depression: a Canadian biomarker integration network for depression study.

Cognitive behavioral therapy (CBT) is often recommended as a first-line treatment in depression. However, access to CBT remains limited, and up to 50% of patients do not benefit from this therapy. Identifying biomarkers that can predict which patients will respond to CBT may assist in designing optimal treatment allocation strategies. In a Canadian Biomarker Integration Network for Depression (CAN-BIND) study, forty-one adults with depression were recruited to undergo a 16-week course of CBT with thirty having resting-state electroencephalography (EEG) recorded at baseline and week 2 of therapy. Successful clinical response to CBT was defined as a 50% or greater reduction in Montgomery-Åsberg Depression Rating Scale (MADRS) score from baseline to post-treatment completion. EEG relative power spectral measures were analyzed at baseline, week 2, and as early changes from baseline to week 2. At baseline, lower relative delta (0.5-4 Hz) power was observed in responders. This difference was predictive of successful clinical response to CBT. Furthermore, responders exhibited an early increase in relative delta power and a decrease in relative alpha (8-12 Hz) power compared to non-responders. These changes were also found to be good predictors of response to the therapy. These findings showed the potential utility of resting-state EEG in predicting CBT outcomes. They also further reinforce the promise of an EEG-based clinical decision-making tool to support treatment decisions for each patient.