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Abstract Current pharmacological agents for depression have limited efficacy in achieving remission. Developing and validating new medications is challenging due to limited biological targets. This study aimed to link electrophysiological data and symptom improvement to better understand mechanisms underlying treatment response. Longitudinal changes in neural oscillations were assessed using resting-state electroencephalography (EEG) data from two Canadian Biomarker Integration Network in Depression studies, involving pharmacological and cognitive behavioral therapy (CBT) trials. Patients in the pharmacological trial received eight weeks of escitalopram, with treatment response defined as ≥ 50% decrease in Montgomery–Åsberg Depression Rating Scale (MADRS). Early (baseline to week 2) and late (baseline to week 8) changes in neural oscillation were investigated using relative power spectral measures. An association was found between an initial increase in theta and symptom improvement after 2 weeks. Additionally, late increases in delta and theta, along with a decrease in alpha, were linked to a reduction in MADRS after 8 weeks. These late changes were specifically observed in responders. To assess specificity, we extended our analysis to the independent CBT cohort. Responders exhibited an increase in delta and a decrease in alpha after 2 weeks. Furthermore, a late (baseline to week 16) decrease in alpha was associated with symptom improvement following CBT. Results suggest a common late decrease in alpha across both treatments, while modulatory effects in theta may be specific to escitalopram treatment. This study offers insights into electrophysiological markers indicating a favorable response to antidepressants, enhancing our comprehension of treatment response mechanisms in depression.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract Cytochrome P450 drug-metabolizing enzymes may contribute to interindividual differences in antidepressant outcomes. We investigated the effects of CYP2C19 and CYP2D6 gene variants on response, tolerability, and serum concentrations. Patients (N = 178) were treated with escitalopram (ESC) from weeks 0–8 (Phase I), and at week 8, either continued ESC if they were responders or were augmented with aripiprazole (ARI) if they were non-responders (

Introducción: el servicio de Kinesiología del Hospital Italiano de Buenos Aires adoptó la virtualidad para la atención de pacientes durante la pandemia de COVID-19. Se decidió realizar una adaptación transcultural del cuestionario de 17 ítems validado al español de España Telemedicine Satisfaction and Usefulness Questionnaire (TSUQ) para conocer la satisfacción de los pacientes. Métodos: dos investigadores nativos realizaron una adaptación del cuestionario TSUQ al español rioplatense. Participaron pacientes atendidos entre mayo de 2021 y marzo de 2022 que habían realizado al menos cuatro sesiones de TR. Fue evaluada la correlación de la puntuación del instrumento resultante con la de un ítem agregado a modo de criterio externo concurrente. La validación del constructo fue llevada a cabo mediante sendos análisis factoriales exploratorios y confirmatorios. Resultados: obtuvimos 293 cuestionarios (media de edad 57 años, 64 % sexo femenino). Luego de los resultados del AFE (Análisis factorial Exploratorio) (n=101), consensuamos eliminar 5 ítems. El cuestionario resultante (12 ítems) fue luego validado en una nueva muestra (n=192) a través de un AFC (Análisis factorial Confirmatorio). La fiabilidad compuesta, la varianza media extractada y la validez convergente fueron adecuadas, mientras que la validez discriminante fue escasa. Documentamos una moderada correlación (Spearman de 0,35, p < 0,0001) entre el puntaje total del cuestionario y el de la pregunta agregada como criterio externo concurrente de validación y una excelente correlación entre versiones. Conclusión: la versión abreviada del cuestionario TSUQ en español tiene propiedades psicométricas adecuadas, lo que lo vuelve un instrumento valioso para evaluar la satisfacción de los pacientes que realizan Tele-Rehabilitación.

Summary: A crucial component in structure-based drug discovery is the availability of high-quality three-dimensional structures of the protein target. Whenever experimental structures were not available, homology modeling has been, so far, the method of choice. Recently, AlphaFold (AF), an artificial-intelligence-based protein structure prediction method, has shown impressive results in terms of model accuracy. This outstanding success prompted us to evaluate how accurate AF models are from the perspective of docking-based drug discovery. We compared the high-throughput docking (HTD) performance of AF models with their corresponding experimental PDB structures using a benchmark set of 22 targets. The AF models showed consistently worse performance using four docking programs and two consensus techniques. Although AlphaFold shows a remarkable ability to predict protein architecture, this might not be enough to guarantee that AF models can be reliably used for HTD, and post-modeling refinement strategies might be key to increase the chances of success.

Abstract The emergence and rapid global spread of SARS-CoV-2 demonstrates the importance of infectious disease surveillance, particularly during the early stages. Viral genomes can provide key insights into transmission chains and pathogenicity. Nasopharyngeal swabs were obtained from thirty-two of the first SARS-CoV-2 positive cases (March 18–30) in Kingston Ontario, Canada. Viral genomes were sequenced using Ion Torrent (n = 24) and MinION (n = 27) sequencing platforms. SARS-CoV-2 genomes carried forty-six polymorphic sites including two missense and three synonymous variants in the spike protein gene. The D614G point mutation was the predominate viral strain in our cohort (92.6%). A heterozygous variant (C9994A) was detected by both sequencing platforms but filtered by the ARTIC network bioinformatic pipeline suggesting that heterozygous variants may be underreported in the SARS-CoV-2 literature. Phylogenetic analysis with 87,738 genomes in the GISAID database identified global origins and transmission events including multiple, international introductions as well as community spread. Reported travel history validated viral introduction and transmission inferred by phylogenetic analysis. Molecular epidemiology and evolutionary phylogenetics may complement contact tracing and help reconstruct transmission chains of emerging diseases. Earlier detection and screening in this way could improve the effectiveness of regional public health interventions to limit future pandemics.

It is not fully understood why some patients respond or do not respond to antidepressant treatment. Here the authors show that in the blood of individuals with depression, GPR56 expression increases in responders to antidepressant treatment, but not in non-responders.

The Ontario Brain Institute's “Brain-CODE” is a large-scale informatics platform designed to support the collection, storage and integration of diverse types of data across several brain disorders as a means to understand underlying causes of brain dysfunction and developing novel approaches to treatment. By providing access to aggregated datasets on participants with and without different brain disorders, Brain-CODE will facilitate analyses both within and across diseases and cover multiple brain disorders and a wide array of data, including clinical, neuroimaging, and molecular. To help achieve these goals, consensus methodology was used to identify a set of core demographic and clinical variables that should be routinely collected across all participating programs. Establishment of Common Data Elements within Brain-CODE is critical to enable a high degree of consistency in data collection across studies and thus optimize the ability of investigators to analyze pooled participant-level data within and across brain disorders. Results are also presented using selected common data elements pooled across three studies to better understand psychiatric comorbidity in neurological disease (Alzheimer's disease/amnesic mild cognitive impairment, amyotrophic lateral sclerosis, cerebrovascular disease, frontotemporal dementia, and Parkinson's disease).

The development of computational models for assessing the transfer of chemicals across the placental membrane would be of the utmost importance in drug discovery campaigns, in order to develop safe therapeutic options. We have developed a low-dimensional machine learning model capable of classifying compounds according to whether they can cross or not the placental barrier. To this aim, we compiled a database of 248 compounds with experimental information about their placental transfer, characterizing each compound with a set of ∼5.4 thousand descriptors, including physicochemical properties and structural features. We evaluated different machine learning classifiers and implemented a genetic algorithm, in a five cross validation scheme, to perform feature selection. The optimization was guided towards models displaying a low number of false positives (molecules that actually cross the placental barrier, but are predicted as not crossing it). A Linear Discriminant Analysis model trained with only four structural features resulted to be robust for this task, exhibiting only one false positive case across all testing folds. This model is expected to be useful in predicting placental drug transfer during pregnancy, and thus could be used as a filter for chemical libraries in virtual screening campaigns.

RESUMEN En este estudio se describe el ensamble de aves del humedal de Trumao y su variación estacional según régimen hidrológico durante primavera 2017 y verano 2018 (no inundado) e inviernos 2017 y 2018 (inundado) y se evalúa el tipo de hábitat. Se registraron 29 especies de aves con una abundancia total de 2,341 individuos. El número total de especies acumuladas observadas para el período inundado y no inundado correspondió a 16 y 22 especies, respectivamente, siendo Chroicocephalus maculipennis (Lichtenstein, 1823) (Gaviota cáhuil) la especie dominante durante el período de estudio. El índice de similitud de Sorensen entre los períodos de inundación y no inundación del humedal fue de 73.2%, mientras que la similitud de Bray-Curtis resultó en un 32.65%. Al compararse el periodo invernal inundado (4 m profundidad) y el periodo estival no inundado (1 m profundidad), el índice de diversidad registró diferencias estadísticamente significativas, en cambio, la abundancia no presentó diferencias significativas. El análisis SIMPER arrojó que entre el periodo invernal inundado y el periodo estival no inundado las especies que mostraron un mayor porcentaje de disimilitud en su abundancia entre estas temporadas fueron la Gaviota cáhuil y Loro choroy [Enicognathus leptorhynchus (King, 1831)]. El uso de hábitat en el humedal de Trumao, por parte de las aves, fue altamente variable, tanto estacional como espacialmente, resultado esperable para este tipo de ecosistemas. Finalmente, nuestros resultados nos indican que las aves ocupan los humedales según fluctuaciones estacionales y según el hábitat que se encuentre disponible, sin embargo, la abundancia no se ve afectada por los cambios hidrológicos. Con estos resultados es fundamental seguir realizando más investigaciones en el humedal para determinar el uso (nidificación, alimentación, reproducción) que realizan las aves en los distintos hábitats para de esta manera generar medidas de protección y conservación para la gestión del humedal de Trumao.

Today high-throughput docking is one of the most commonly used computational tools in drug lead discovery. While there has been an impressive methodological improvement in docking accuracy, docking scoring still remains an open challenge. Most docking programs are rooted in classical molecular mechanics. However, to better characterize protein-ligand interactions, the use of a more accurate quantum mechanical (QM) description would be necessary. In this work, we introduce a QM-based docking scoring function for high-throughput docking and evaluate it on 10 protein systems belonging to diverse protein families, and with different binding site characteristics. Outstanding results were obtained, with our QM scoring function displaying much higher enrichment (screening power) than a traditional docking method. It is acknowledged that developments in quantum mechanics theory, algorithms and computer hardware throughout the upcoming years will allow semi-empirical (or low-cost) quantum mechanical methods to slowly replace force-field calculations. It is thus urgently needed to develop and validate novel quantum mechanical-based scoring functions for high-throughput docking toward more accurate methods for the identification and optimization of modulators of pharmaceutically relevant targets.

Antidepressant drugs are the most common treatment for depressive episodes but only a fraction of patients experience adequate response. Here the authors find dysregulation of miRNAs in peripheral blood samples from depressed patients after antidepressant treatment, and show that the miRNAs are regulators of psychiatrically relevant signalling pathways.

Neuropsychiatric disorders and type 2 diabetes (T2D) are major public health concerns proposed to be intimately connected. T2D is associated with increased risk of dementia, neuropsychiatric and mood disorders. Evidences of the involvement of insulin signaling on brain mechanisms related to depression indicate that insulin resistance, a hallmark of type 2 diabetes, could develop in the brains of depressive patients. In this article, we briefly review possible molecular mechanisms associating defective brain insulin signaling with reward system, neurogenesis, synaptic plasticity and hypothalamic-pituitary-adrenal (HPA) stress axis in depression. We further discuss the involvement of tumor necrosis factor α (TNFα) promoting defective insulin signaling and depressive-like behavior in rodent models. Finally, due to the high resistant rate of anti-depressants, novel insights into the link between insulin resistance and depression may advance the development of alternative treatments for this disease.

Today computational chemistry is a consolidated tool in drug lead discovery endeavors. Due to methodological developments and to the enormous advance in computer hardware, methods based on quantum mechanics (QM) have gained great attention in the last 10 years, and calculations on biomacromolecules are becoming increasingly explored, aiming to provide better accuracy in the description of protein-ligand interactions and the prediction of binding affinities. In principle, the QM formulation includes all contributions to the energy, accounting for terms usually missing in molecular mechanics force-fields, such as electronic polarization effects, metal coordination, and covalent binding; moreover, QM methods are systematically improvable, and provide a greater degree of transferability. In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the drug discovery pipeline.

Bovine viral diarrhea virus (BVDV) is a member of the genus Pestivirus within the family Flaviviridae. BVDV causes both acute and persistent infections in cattle, leading to substantial financial losses to the livestock industry each year. The global prevalence of persistent BVDV infection and the lack of a highly effective antiviral therapy have spurred intensive efforts to discover and develop novel anti-BVDV therapies in the pharmaceutical industry. Antiviral targeting of virus envelope proteins is an effective strategy for therapeutic intervention of viral infections. We performed prospective small-molecule high-throughput docking to identify molecules that likely bind to the region delimited by domains I and II of the envelope protein E2 of BVDV. Several structurally different compounds were purchased or synthesized, and assayed for antiviral activity against BVDV. Five of the selected compounds were active displaying IC50 values in the low- to mid-micromolar range. For these compounds, their possible binding determinants were characterized by molecular dynamics simulations. A common pattern of interactions between active molecules and aminoacid residues in the binding site in E2 was observed. These findings could offer a better understanding of the interaction of BVDV E2 with these inhibitors, as well as benefit the discovery of novel and more potent BVDV antivirals.

A heterose é a principal responsável pelos incrementos na produtividade de milho neste século. No entanto. as variedades exploram de maneira diferencial esse fenômeno. Assim sendo, este trabalho teve por objetivo comparar o desempenho de populações de polinização aberta, seus cruzamentos e híbridos comerciais de milho em dois ambientes. Os resultados demonstraram que o ambiente tem importância fundamental na expressão do potencial genético. No ambiente favorável, os cruzamentos proporcionam maior resposta do que as populações, sendo que os híbridos comerciais apresentaram uma resposta ainda superior. No ambiente desfavorável, todos os genótipos revelaram baixos rendimentos de grãos. Este fato demonstrou que em ambientes de menor potencial para o desenvolvimento das plantas, genótipos geneticamente superiores não conseguem expressar todo o seu potencial. Desta forma, a escolha da variedade comercial a ser adquirida pelos agricultores deve levar em consideração o ambiente de cultivo e o nível de manejo que será adotado na lavoura.

Background: The Parque Estadual do Forno Grande is a fully protected area in the southern Espírito Santo State, Brazil. It belongs to the Atlantic Forest domain, with predominantly dense, ombrophilous, seasonal semi-deciduous forests and herbaceous/shrubby vegetation on rock outcrops. The area is recognised as highly important for conservation, designated as a priority biological area for protecting the Atlantic Forest's biodiversity. Although the importance of Protected Areas in conserving the Atlantic Forest biodiversity is unquestionable, it is crucial to understand the floristic patterns within these regions to develop effective conservation strategies. We utilised national online databases to compile species lists containing relevant information about their biodiversity. The updated list of vascular plants recorded in the Parque Estadual do Forno Grande is available in the "Catálogo de Plantas das Unidades de Conservação do Brasil" and it is presented here with further information on richness, endemism and conservation status. New information: The Parque Estadual do Forno Grande harbours 958 species of vascular plants, of which 79.2% are angiosperms, 18.4% are ferns and 2.4% are lycophytes. Amongst these species, 44% are endemic to the Atlantic Forest. There are 58 threatened species, of which six are Critically Endangered, 39 are Endangered and 13 are Vulnerable. Amongst the threatened species, 51 are endemic to the Atlantic Forest. The number of records and the species richness in this area are notably high for Atlantic Forest standards. Our findings suggest that floristic inventories of Brazilian Protected Areas are a key contribution to the general perception of how much we still do not know about our flora. It also highlights the necessity of supporting floristic surveys in poorly-known areas, especially those remaining as forest remnants. [ABSTRACT FROM AUTHOR]

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Studies of clinical populations that combine MRI data generated at multiple sites are increasingly common. The Canadian Biomarker Integration Network in Depression (CAN-BIND; www.canbind.ca) is a national depression research program that includes multimodal neuroimaging collected at several sites across Canada. The purpose of the current paper is to provide detailed information on the imaging protocols used in a number of CAN-BIND studies. The CAN-BIND program implemented a series of platform-specific MRI protocols, including a suite of prescribed structural and functional MRI sequences supported by real-time monitoring for adherence and quality control. The imaging data are retained in an established informatics and databasing platform. Approximately 1300 participants are being recruited, including almost 1000 with depression. These include participants treated with antidepressant medications, transcranial magnetic stimulation, cognitive behavioural therapy and cognitive remediation therapy. Our ability to analyze the large number of imaging variables available may be limited by the sample size of the substudies. The CAN-BIND program includes a multimodal imaging database supported by extensive clinical, demographic, neuropsychological and biological data from people with major depression. It is a resource for Canadian investigators who are interested in understanding whether aspects of neuroimaging--alone or in combination with other variables--can predict the outcomes of various treatment modalities., Introduction Treatment of major depressive disorder (MDD) is evidence-based, but treatment selection is not personalized to the features of an individual's illness. (1) The discovery of biomarkers --or predictors--of treatment [...]