Prediction of depression treatment outcome from multimodal data: a CAN-BIND-1 report.

BACKGROUND: Prediction of treatment outcomes is a key step in improving the treatment of major depressive disorder (MDD). The Canadian Biomarker Integration Network in Depression (CAN-BIND) aims to predict antidepressant treatment outcomes through analyses of clinical assessment, neuroimaging, and blood biomarkers. METHODS: In the CAN-BIND-1 dataset of 192 adults with MDD and outcomes of treatment with escitalopram, we applied machine learning models in a nested cross-validation framework. Across 210 analyses, we examined combinations of predictive variables from three modalities, measured at baseline and after 2 weeks of treatment, and five machine learning methods with and without feature selection. To optimize the predictors-to-observations ratio, we followed a tiered approach with 134 and 1152 variables in tier 1 and tier 2 respectively. RESULTS: A combination of baseline tier 1 clinical, neuroimaging, and molecular variables predicted response with a mean balanced accuracy of 0.57 (best model mean 0.62) compared to 0.54 (best model mean 0.61) in single modality models. Adding week 2 predictors improved the prediction of response to a mean balanced accuracy of 0.59 (best model mean 0.66). Adding tier 2 features did not improve prediction. CONCLUSIONS: A combination of clinical, neuroimaging, and molecular data improves the prediction of treatment outcomes over single modality measurement. The addition of measurements from the early stages of treatment adds precision. Present results are limited by lack of external validation. To achieve clinically meaningful prediction, the multimodal measurement should be scaled up to larger samples and the robustness of prediction tested in an external validation dataset.

Pro-inflammatory markers are associated with response to sequential pharmacotherapy in major depressive disorder: a CAN-BIND-1 report.

OBJECTIVE: There is limited literature on associations between inflammatory tone and response to sequential pharmacotherapies in major depressive disorder (MDD). METHODS: In a 16-week open-label clinical trial, 211 participants with MDD were treated with escitalopram 10-20 mg daily for 8 weeks. Responders continued escitalopram while non-responders received adjunctive aripiprazole 2-10 mg daily for 8 weeks. Plasma levels of pro-inflammatory markers-C-reactive protein, interleukin (IL)-1ß, IL-6, IL-17, interferon-gamma (IFN)-Γ, tumor necrosis factor (TNF)-α, and Chemokine C-C motif ligand-2 (CCL-2)-measured at baseline, and after 2, 8 and 16 weeks were included in logistic regression analyzes to assess associations between inflammatory markers and treatment response. RESULTS: Pre-treatment IFN-Γ and CCL-2 levels were significantly associated with a lower of odds of response to escitalopram at 8 weeks. Increases in CCL-2 levels from weeks 8 to 16 in escitalopram non-responders were significantly associated with higher odds of non-response to adjunctive aripiprazole at week 16. CONCLUSION: Higher pre-treatment levels of IFN-Γ and CCL-2 were associated with non-response to escitalopram. Increasing levels of these pro-inflammatory markers may be associated with non-response to adjunctive aripiprazole. These findings require validation in independent clinical populations.

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale.

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

"One Degree of Separation": A Mixed-Methods Evaluation of Canadian Mental Health Care User and Provider Experiences With Remote Care During COVID-19.

OBJECTIVES: The COVID-19 pandemic has contributed to a shift from in-person to remote mental health care. While remote care methods have long existed, their widespread use is unprecedented. There is little research about mental health care user and provider experiences with this transition, and no published studies to date have compared satisfaction between these groups. METHODS: Canadian mental health care users (n = 332) and providers (n = 107) completed an online self-report survey from October 2020 to February 2021 hosted by the Canadian Biomarker Integration Network in Depression. Using a mixed-methods approach, participants were asked about their use of remote care, including satisfaction, barriers to use, helpful and unhelpful factors, and suggestions for improvement. RESULTS: Overall, 59% to 63% of health care users and 59% of health care providers were satisfied with remote care. Users reported the greatest satisfaction with the convenience of remote care, while providers were most satisfied with the speed of provision of care; all groups were least satisfied with therapeutic rapport. Health care providers were less satisfied with the user-friendliness of remote care (P < 0.001) than users, while health care users were less satisfied than providers with continuity of care (P < 0.001). The use of a video-based platform was associated with remote care satisfaction among health care users (P < 0.02), and qualitative responses support the importance of visual cues in maintaining therapeutic rapport remotely. The majority of users (55%) and providers (87%) reported a likelihood of using remote care after the pandemic. CONCLUSIONS: Remote mental health care is generally accepted by both users and providers, and the majority would consider using remote care following the pandemic. Suggestions for improvement include greater use of video, increased attention to body language and eye contact, consistency with in-person care, as well as increased provider training and administrative support.

A randomized, crossover comparison of ketamine and electroconvulsive therapy for treatment of major depressive episodes: a Canadian biomarker integration network in depression (CAN-BIND) study protocol.

BACKGROUND: Recent evidence underscores the utility of rapid-acting antidepressant interventions, such as ketamine, in alleviating symptoms of major depressive episodes (MDE). However, to date, there have been limited head-to-head comparisons of intravenous (IV) ketamine infusions with other antidepressant treatment strategies in large randomized trials. This study protocol describes an ongoing multi-centre, prospective, randomized, crossover, non-inferiority trial comparing acute treatment of individuals meeting diagnostic criteria for a major depressive episode (MDE) with ketamine and electroconvulsive therapy (ECT) on efficacy, speed of therapeutic effects, side effects, and health care resource utilization. A secondary aim is to compare a 6-month maintenance strategy for ketamine responders to standard of care ECT maintenance. Finally, through the measurement of clinical, cognitive, neuroimaging, and molecular markers we aim to establish predictors and moderators of treatment response as well as treatment-elicited effects on these outcomes. METHODS: Across four participating Canadian institutions, 240 patients with major depressive disorder or bipolar disorder experiencing a MDE are randomized (1:1) to a course of ECT or racemic IV ketamine (0.5 mg/kg) administered 3 times/week for 3 or 4 weeks. Non-responders (< 50% improvement in Montgomery-Åsberg Depression Rating Scale [MADRS] scores) crossover to receive the alternate treatment. Responders during the randomization or crossover phases then enter the 6-month maintenance phase during which time they receive clinical assessments at identical intervals regardless of treatment arm. ECT maintenance follows standard of care while ketamine maintenance involves: weekly infusions for 1 month, then bi-weekly infusions for 2 months, and finally monthly infusions for 3 months (returning to bi-weekly in case of relapse). The primary outcome measure is change in MADRS scores after randomized treatment as assessed by raters blind to treatment modality. DISCUSSION: This multi-centre study will help identify molecular, imaging, and clinical characteristics of patients with treatment-resistant and/or severe MDEs who would benefit most from either type of therapeutic strategy. In addition to informing clinical practice and influencing health care delivery, this trial will add to the robust platform and database of CAN-BIND studies for future research and biomarker discovery. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT03674671. Registered September 17, 2018.

Experimental Malaria in Pregnancy Induces Neurocognitive Injury in Uninfected Offspring via a C5a-C5a Receptor Dependent Pathway.

The in utero environment profoundly impacts childhood neurodevelopment and behaviour. A substantial proportion of pregnancies in Africa are at risk of malaria in pregnancy (MIP) however the impact of in utero exposure to MIP on fetal neurodevelopment is unknown. Complement activation, in particular C5a, may contribute to neuropathology and adverse outcomes during MIP. We used an experimental model of MIP and standardized neurocognitive testing, MRI, micro-CT and HPLC analysis of neurotransmitter levels, to test the hypothesis that in utero exposure to malaria alters neurodevelopment through a C5a-C5aR dependent pathway. We show that malaria-exposed offspring have persistent neurocognitive deficits in memory and affective-like behaviour compared to unexposed controls. These deficits were associated with reduced regional brain levels of major biogenic amines and BDNF that were rescued by disruption of C5a-C5aR signaling using genetic and functional approaches. Our results demonstrate that experimental MIP induces neurocognitive deficits in offspring and suggest novel targets for intervention.

Oral Ketamine in Treatment-Resistant Depression: A Clinical Effectiveness Case Series.

PURPOSE: The aim of the study was to assess the effectiveness, tolerability, and safety of oral ketamine as an antidepressant treatment in adults with treatment-resistant depression. METHODS: We reviewed retrospective data on 22 patients with treatment-resistant depression, who failed at least 3 adequate antidepressant treatment trials and 1 adequate trial of repetitive transcranial magnetic stimulation; subsequently, they received open-label treatment with oral ketamine, commenced at a dose of 50 mg every 3 days, titrated up by 25 mg every 3 days, according to response and tolerability. The primary outcome measure was the Beck Depression Inventory II, which was used to rate subjective mood improvement at baseline and then at each follow-up visit. Data about adverse effects related to ketamine and a self-harm risk assessment were also obtained. FINDINGS: Over the course of treatment, 18% of the patients showed greater than 50% reduction in the Beck Depression Inventory II scores, 14% reported partial improvement in mood symptoms, while 45% had no response to ketamine and 23% showed a mild worsening in their depressive symptoms. The most frequent adverse effects were acute dissociation, dizziness, blurred vision, numbness and sedation. Neither serious adverse effects, nor any cases of abuse or dependence were observed. CONCLUSIONS: Although this case series found oral ketamine to be safe and well tolerated, the findings also showed rather modest effectiveness of oral ketamine in treatment-resistant depression, with only approximately 30% reporting some benefit and approximately 70% reporting no change or worsening of mood. However, bearing in mind the limitations of this small, open-label case series, further exploration of the effectiveness of oral ketamine is warranted.

PPARγ agonists improve survival and neurocognitive outcomes in experimental cerebral malaria and induce neuroprotective pathways in human malaria.

Cerebral malaria (CM) is associated with a high mortality rate, and long-term neurocognitive impairment in approximately one third of survivors. Adjunctive therapies that modify the pathophysiological processes involved in CM may improve outcome over anti-malarial therapy alone. PPARγ agonists have been reported to have immunomodulatory effects in a variety of disease models. Here we report that adjunctive therapy with PPARγ agonists improved survival and long-term neurocognitive outcomes in the Plasmodium berghei ANKA experimental model of CM. Compared to anti-malarial therapy alone, PPARγ adjunctive therapy administered to mice at the onset of CM signs, was associated with reduced endothelial activation, and enhanced expression of the anti-oxidant enzymes SOD-1 and catalase and the neurotrophic factors brain derived neurotrophic factor (BDNF) and nerve growth factor (NGF) in the brains of infected mice. Two months following infection, mice that were treated with anti-malarials alone demonstrated cognitive dysfunction, while mice that received PPARγ adjunctive therapy were completely protected from neurocognitive impairment and from PbA-infection induced brain atrophy. In humans with P. falciparum malaria, PPARγ therapy was associated with reduced endothelial activation and with induction of neuroprotective pathways, such as BDNF. These findings provide insight into mechanisms conferring improved survival and preventing neurocognitive injury in CM, and support the evaluation of PPARγ agonists in human CM.

Combined Chemical Activation and Fenton Degradation to Convert Waste Polyethylene into High-Value Fine Chemicals.

Plastic waste is a valuable organic resource. However, proper technologies to recover usable materials from plastic are still very rare. Although the conversion/cracking/degradation of certain plastics into chemicals has drawn much attention, effective and selective cracking of the major waste plastic polyethylene is extremely difficult, with degradation of C-C/C-H bonds identified as the bottleneck. Pyrolysis, for example, is a nonselective degradation method used to crack plastics, but it requires a very high energy input. To solve the current plastic pollution crisis, more effective technologies are needed for converting plastic waste into useful substances that can be fed into the energy cycle or used to produce fine chemicals for industry. In this study, we demonstrate a new and effective chemical approach by using the Fenton reaction to convert polyethylene plastic waste into carboxylic acids under ambient conditions. Understanding the fundamentals of this new chemical process provides a possible protocol to solve global plastic-waste problems.

Synthesis of a New Bimetallic Re(I)-NCS-Pt(II) Complex as Chemodosimetric Ensemble for the Selective Detection of Mercapto-Containing Pesticides.

Detection of mercapto-containing pesticides plays a crucial role in food and water safety. A new Re(I)-NCS-Pt(II) complex, [Re(4,4'-di-tert-butyl-2,2'-bipyridine)(CO)3(NCS)]-[Pt(DMSO)(Cl)2] (1), was synthesized and characterized. The synthetic procedure, characterization results, and photophysical data for 1 are reported in this paper. Solvated complex 1 demonstrated luminescent chemodosimetric selectivity for phorate, demeton, and aldicarb (three common mercapto-containing pesticides) with method detection limits (MDLs) of 1.00, 2.87, and 2.08 ppm, respectively. The binding constants (log K) of 1 toward them were in the 3.24-3.44 range. The analyte selectivity of the complex was found to be dependent on the bridging linkage (C≡N and N═C═S) between the Re(I) and Pt(II) centers. The solid-supported dosimetric device 1 was fabricated by blending complex 1 with Al2O3 and poly(vinyl chloride) (PVC) powder. The MDLs of the device toward the mercapto-containing pesticides were 0.48-0.60 ppm. The device was applicable to pesticides in real water bodies such as taps, rivers, lakes, and underground water bodies with excellent recoveries and relative standard deviations of 76.2-108.0% and 2.9-6.7%, respectively. Its spectrofluorimetric changes could be analyzed by naked eye within 20 min with a linear luminometric response toward increases in the phorate concentration (0-8.0 ppm) with R = 0.999.

Interactome analyses of mature γ-secretase complexes reveal distinct molecular environments of presenilin (PS) paralogs and preferential binding of signal peptide peptidase to PS2.

γ-Secretase plays a pivotal role in the production of neurotoxic amyloid ß-peptides (Aß) in Alzheimer disease (AD) and consists of a heterotetrameric core complex that includes the aspartyl intramembrane protease presenilin (PS). The human genome codes for two presenilin paralogs. To understand the causes for distinct phenotypes of PS paralog-deficient mice and elucidate whether PS mutations associated with early-onset AD affect the molecular environment of mature γ-secretase complexes, quantitative interactome comparisons were undertaken. Brains of mice engineered to express wild-type or mutant PS1, or HEK293 cells stably expressing PS paralogs with N-terminal tandem-affinity purification tags served as biological source materials. The analyses revealed novel interactions of the γ-secretase core complex with a molecular machinery that targets and fuses synaptic vesicles to cellular membranes and with the H(+)-transporting lysosomal ATPase macrocomplex but uncovered no differences in the interactomes of wild-type and mutant PS1. The catenin/cadherin network was almost exclusively found associated with PS1. Another intramembrane protease, signal peptide peptidase, predominantly co-purified with PS2-containing γ-secretase complexes and was observed to influence Aß production.

Functional neuroimaging biomarkers of anhedonia response to escitalopram plus adjunct aripiprazole treatment for major depressive disorder.

BACKGROUND: Identifying neuroimaging biomarkers of antidepressant response may help guide treatment decisions and advance precision medicine. AIMS: To examine the relationship between anhedonia and functional neurocircuitry in key reward processing brain regions in people with major depressive disorder receiving aripiprazole adjunct therapy with escitalopram. METHOD: Data were collected as part of the CAN-BIND-1 study. Participants experiencing a current major depressive episode received escitalopram for 8 weeks; escitalopram non-responders received adjunct aripiprazole for an additional 8 weeks. Functional magnetic resonance imaging (on weeks 0 and 8) and clinical assessment of anhedonia (on weeks 0, 8 and 16) were completed. Seed-based correlational analysis was employed to examine the relationship between baseline resting-state functional connectivity (rsFC), using the nucleus accumbens (NAc) and anterior cingulate cortex (ACC) as key regions of interest, and change in anhedonia severity after adjunct aripiprazole. RESULTS: Anhedonia severity significantly improved after treatment with adjunct aripiprazole.There was a positive correlation between anhedonia improvement and rsFC between the ACC and posterior cingulate cortex, ACC and posterior praecuneus, and NAc and posterior praecuneus. There was a negative correlation between anhedonia improvement and rsFC between the ACC and anterior praecuneus and NAc and anterior praecuneus. CONCLUSIONS: Eight weeks of aripiprazole, adjunct to escitalopram, was associated with improved anhedonia symptoms. Changes in functional connectivity between key reward regions were associated with anhedonia improvement, suggesting aripiprazole may be an effective treatment for individuals experiencing reward-related deficits. Future studies are required to replicate our findings and explore their generalisability, using other agents with partial dopamine (D2) agonism and/or serotonin (5-HT2A) antagonism.

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.

An empirical analysis of structural neuroimaging profiles in a staging model of depression.

We examine structural brain characteristics across three diagnostic categories: at risk for serious mental illness; first-presenting episode and recurrent major depressive disorder (MDD). We investigate whether the three diagnostic groups display a stepwise pattern of brain changes in the cortico-limbic regions. Integrated clinical and neuroimaging data from three large Canadian studies were pooled (total n = 622 participants, aged 12-66 years). Four clinical profiles were used in the classification of a clinical staging model: healthy comparison individuals with no history of depression (HC, n = 240), individuals at high risk for serious mental illness due to the presence of subclinical symptoms (SC, n = 80), first-episode depression (FD, n = 82), and participants with recurrent MDD in a current major depressive episode (RD, n = 220). Whole-brain volumetric measurements were extracted with FreeSurfer 7.1 and examined using three different types of analyses. Hippocampal volume decrease and cortico-limbic thinning were the most informative features for the RD vs HC comparisons. FD vs HC revealed that FD participants were characterized by a focal decrease in cortical thickness and global enlargement in amygdala volumes. Greater total amygdala volumes were significantly associated with earlier onset of illness in the FD but not the RD group. We did not confirm the construct validity of a tested clinical staging model, as a differential pattern of brain alterations was identified across the three diagnostic groups that did not parallel a stepwise clinical staging approach. The pathological processes during early stages of the illness may fundamentally differ from those that occur at later stages with clinical progression.

Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo.

Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Our objective was to identify the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants or placebo. In the COORDINATE-MDD consortium, raw MRI data were shared from international samples (N = 1,384) of medication-free individuals with first-episode and recurrent MDD (N = 685) in a current depressive episode of at least moderate severity, but not treatment-resistant depression, as well as healthy controls (N = 699). Prospective longitudinal data on treatment response were available for a subset of MDD individuals (N = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional brain volumes that are associated with disease. MDD was optimally characterized by two neuroanatomical dimensions that exhibited distinct treatment responses to placebo and SSRI antidepressant medications. Dimension 1 was characterized by preserved gray and white matter (N = 290 MDD), whereas Dimension 2 was characterized by widespread subtle reductions in gray and white matter (N = 395 MDD) relative to healthy controls. Although there were no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, there was a significant interaction effect between dimensions and treatment response. Dimension 1 showed a significant improvement in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%). By contrast, Dimension 2 showed comparable improvements to either SSRI (46.9%) or placebo (42.2%) (ß = -18.3, 95% CI (-34.3 to -2.3), P = 0.03). Findings from this case-control study indicate that neuroimaging-based markers can help identify the disease-based dimensions that constitute MDD and predict treatment response.

The solvent effect and identification of a weakly emissive state in nonradiative dynamics of guanine nucleosides and nucleotides--a combined femtosecond broadband time-resolved fluorescence and transient absorption study.

A combined method of femtosecond broadband time-resolved fluorescence (fs-TRF) and transient absorption (fs-TA) was employed to investigate the excited state dynamics of 2'-deoxyguanosine (dG) and 2'-deoxyguanosine 5'-monophosphate (dGMP). Comparative fs-TRF and fs-TA measurements were conducted on dG and dGMP in neutral water, deuterated water, and methanol with excitation wavelengths of 245, 267 and 285 nm. Very similar results were observed with dG and dGMP. The data provide compelling evidence for the co-existence of two nonradiative pathways. One is the generally recognized Laππ* mediated channel, the other involves an unprecedented weakly emissive state which plays a significant role in the overall deactivation processes. The Laππ* channel features biphasic dynamics with time constants (τ1/τ2) of ~0.2/0.8 ps in water and ~0.25/1.0 ps in methanol. The biphasic decay arises due to a partial transfer with τ1 of the Laππ* population to the newly identified state followed by conversion in τ2 of the remaining Laππ* molecules into the electronic ground state. The channel mediated by the weakly emissive species shows solvent-dependent dynamics with time constants (τ3) of ~2.0 ps in water, ~2.3 ps in deuterated water, and ~4.1 ps in methanol. The species features absorption at UV wavelengths (~300-400 nm) and exhibits deeply red-shifted fluorescence (λmax ~ 520 nm) with polarization direction varied markedly from that of the Laππ* but close to the Lbππ*. This species acts as an effective quenching state to the radiative decay of the brightly emissive Laππ* and Lbππ*. It sets in promptly (<~50 fs) after the photoexcitation and is further populated through nonadiabatic coupling with the Laππ*. The overall involvement of this state is facilitated with excitation at high energy and is favoured in methanol over water. According to the spectral character and the solvent effect in particular the kinetic isotope effect, the species is tentatively associated to the πσ* state with charge transfer (CT) character which is considered to be preferentially stabilized by hydrogen-bonding between the guanine amino and surrounding solvent molecules. The result of this study leads to a dramatically different picture of guanine deactivation. It demonstrates a crucial role of the solvent in shaping the nonradiative dynamics of guanine nucleosides and nucleotides. The data presented are important for understanding the detailed photophysics of not only the monomeric guanine but also DNA assemblies that contain guanine in base pairs or have a guanine tetrad as the structural motif.

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.

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.

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.

Charge dynamics in solar cells with a blend of π-conjugated polymer-fullerene studied by transient photo-generated voltage.

The biphasic feature of transient photo-generated voltage (TPV) is investigated in organic solar cells (OSCs) with a blend active layer of poly(3-hexylthiophene) (P3HT) and phenyl C61 butyric acid methyl ester (PCBM). The positive and negative components in biphasic TPV are explained through PCBM only and P3HT only devices. The negative and positive components are ascribed to the dipole formation at the buried interface of P3HT/indium tin oxide (ITO) and PCBM/ITO respectively. Based on these findings, two fundamental phenomena are revealed as follows: (1) interfacial modification on the buried interface inverts the negative component in biphasic TPV to a positive component, which prevents the leakage current channel in the conventional OSC structure; and (2) the solvent chosen transforms the positive component in biphasic TPV into a negative signal, which blocks the leakage current channel in the inverted OSC structure. Consequently, the study of TPV polarity provides the justification of the interaction at the buried interface. Besides, the decay of TPV is found to be bi-exponential, which can be used as a tool to estimate the degree of charge balance in OSCs.