• Open Access close
• Embargo close
• Research data close
• Research software close
• Canadian Institutes of Health Resea... close

Abstract Background Major depressive disorder is common, debilitating, and affects feelings, thoughts, mood, and behaviors. Childhood and adolescence are critical periods for the development of depression and adolescence is marked by an increased incidence of mental health disorders. This protocol outlines the planned scope and methods for a systematic review update that will evaluate the benefits and harms of screening for depression in children and adolescents. Methods This review will update a previously published systematic review by Roseman and colleagues. Eligible studies are randomized controlled trials (RCTs) assessing formal screening in primary care to identify children or adolescents not already self-reporting symptoms of, diagnosed with, or treated for depression. If no or only a single RCT is available, we will consider controlled studies without random assignment. Studies of participants with characteristics associated with an elevated risk of depression will be analyzed separately. Outcomes of interest are symptoms of depression, classification of major depressive disorder based on a validated diagnostic interview, suicidality, health-related quality of life, social function, impact on lifestyle behavior (e.g., substance use, school performance, lost time at work, or school), false-positive results, overdiagnosis, overtreatment, labeling, and other harms such as those arising from treatment. We will search MEDLINE, Embase, PsycINFO, CINAHL, the Cochrane Library, and grey literature sources. Two reviewers will independently screen the titles and abstracts using the liberal accelerated method. Full-text screening will be performed independently by two reviewers using pre-specified eligibility criteria. Data extraction and risk of bias assessments will be performed independently by two reviewers. Pre-planned analyses, including subgroup and sensitivity analyses, are detailed within this protocol. Two independent reviewers will assess and finalize through consensus the certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach, and prepare GRADE evidence profiles and summary of findings tables for each outcome of interest. Discussion The systematic review will provide a current state of the evidence of benefits and harms of depression screening in children and adolescents. These findings will be used by the Canadian Task Force on Preventive Health Care to inform the development of recommendations on depression screening. Systematic review registration PROSPERO CRD42020150373

A major task in the history of neurophysiology has been to relate patterns of neural activity to ongoing external stimuli. More recently, this approach has branched out to relating current neural activity patterns to external stimuli or experiences that occurred in the past or future. Here, we aim to review the large body of methodological approaches used towards this goal, and to assess the assumptions each makes with reference to the statistics of neural data that are commonly observed. These methods primarily fall into two categories, those that quantify zero-lag relationships without examining temporal evolution, termed reactivation, and those that quantify the temporal structure of changing activity patterns, termed replay. However, no two studies use the exact same approach, which prevents an unbiased comparison between findings. These observations should instead be validated by multiple and, if possible, previously established tests. This will help the community to speak a common language and will eventually provide tools to study, more generally, the organization of neuronal patterns in the brain.This article is part of the Theo Murphy meeting issue ‘Memory reactivation: replaying events past, present and future’.

Additional optimization code (MATLAB). (TXT 2Â kb)

Abstract Background Patients and families want their healthcare to be delivered by healthcare providers that are both competent and compassionate. While compassion training has begun to emerge in healthcare education, there may be factors that facilitate or inhibit the uptake and implementation of training into practice. This review identified the attributes that explain the successes and/or failures of compassion training programs offered to practicing healthcare providers. Methods Realist review methodology for knowledge synthesis was used to consider the contexts, mechanisms (resources and reasoning), and outcomes of compassion training for practicing healthcare providers to determine what works, for whom, and in what contexts. Results Two thousand nine hundred ninety-one articles underwent title and abstract screening, 53 articles underwent full text review, and data that contributed to the development of a program theory were extracted from 45 articles. Contexts included the clinical setting, healthcare provider characteristics, current state of the healthcare system, and personal factors relevant to individual healthcare providers. Mechanisms included workplace-based programs and participatory interventions that impacted teaching, learning, and the healthcare organization. Contexts were associated with certain mechanisms to effect change in learners’ attitudes, knowledge, skills and behaviors and the clinical process. Conclusions In conclusion this realist review determined that compassion training may engender compassionate healthcare practice if it becomes a key component of the infrastructure and vision of healthcare organizations, engages institutional participation, improves leadership at all levels, adopts a multimodal approach, and uses valid measures to assess outcomes.

Abstract Background Assessing calibration���the agreement between estimated risk and observed proportions���is an important component of deriving and validating clinical prediction models. Methods for assessing the calibration of prognostic models for use with competing risk data have received little attention. Methods We propose a method for graphically assessing the calibration of competing risk regression models. Our proposed method can be used to assess the calibration of any model for estimating incidence in the presence of competing risk (e.g., a Fine-Gray subdistribution hazard model; a combination of cause-specific hazard functions; or a random survival forest). Our method is based on using the Fine-Gray subdistribution hazard model to regress the cumulative incidence function of the cause-specific outcome of interest on the predicted outcome risk of the model whose calibration we want to assess. We provide modifications of the integrated calibration index (ICI), of E50 and of E90, which are numerical calibration metrics, for use with competing risk data. We conducted a series of Monte Carlo simulations to evaluate the performance of these calibration measures when the underlying model has been correctly specified and when the model was mis-specified and when the incidence of the cause-specific outcome differed between the derivation and validation samples. We illustrated the usefulness of calibration curves and the numerical calibration metrics by comparing the calibration of a Fine-Gray subdistribution hazards regression model with that of random survival forests for predicting cardiovascular mortality in patients hospitalized with heart failure. Results The simulations indicated that the method for constructing graphical calibration curves and the associated calibration metrics performed as desired. We also demonstrated that the numerical calibration metrics can be used as optimization criteria when tuning machine learning methods for competing risk outcomes. Conclusions The calibration curves and numeric calibration metrics permit a comprehensive comparison of the calibration of different competing risk models.

This dataset contains the raw, unprocessed immunofluorescence, live-cell imaging and Western blot data for the manuscript entitled: Interactome rewiring following pharmacological targeting of BET bromodomains. All methods employed to generate this dataset can be found in the accompanying manuscript and supplemental material.

Additional file 5: Figure S5. A) qRT-PCR analysis of Spy1 levels in 8-week-old MMTV-Spy1 mice and their control littermates (cntl) 48 h after DMBA treatment in mice with and without DMBA. Levels of Flag-Spy1 are corrected for total levels of GAPDH. B) Representative western blot for p53 protein levels in MMTV-Spy1 8-week-old mice and their control littermates 48 h after DMBA treatment. Error bars represent SE. ***p

Abstract Background The ability to reliably identify the state (activated, repressed, or latent) of any molecular process in the tumor of a patient from an individual whole-genome gene expression profile obtained from microarray or RNA sequencing (RNA-seq) promises important clinical utility. Unfortunately, all previous bioinformatics tools are only applicable in large and diverse panels of patients, or are limited to a single specific pathway/process (e.g. proliferation). Methods Using a panel of 4510 whole-genome gene expression profiles from 10 different studies we built and selected models predicting the activation status of a compendium of 1733 different biological processes. Using a second independent validation dataset of 742 patients we validated the final list of 1773 models to be included in a de novo tool entitled absolute inference of patient signatures (AIPS). We also evaluated the prognostic significance of the 1773 individual models to predict outcome in all and in specific breast cancer subtypes. Results We described the development of the de novo tool entitled AIPS that can identify the activation status of a panel of 1733 different biological processes from an individual breast cancer microarray or RNA-seq profile without recourse to a broad cohort of patients. We demonstrated that AIPS is stable compared to previous tools, as the inferred pathway state is not affected by the composition of a dataset. We also showed that pathway states inferred by AIPS are in agreement with previous tools but use far fewer genes. We determined that several AIPS-defined pathways are prognostic across and within molecularly and clinically define subtypes (two-sided log-rank test false discovery rate (FDR) <5%). Interestingly, 74.5% (1291/1733) of the models are able to distinguish patients with luminal A cancer from those with luminal B cancer (Fisherâ s exact test FDR <5%). Conclusion AIPS represents the first tool that would allow an individual breast cancer patient to obtain a thorough knowledge of the molecular processes active in their tumor from only one individual gene expression (N-of-1) profile.