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Smartphone app-based therapies offer clear promise for reducing the gap in available mental health care for people at risk for or people with mental illness. To this end, as smartphone ownership has become widespread, app-based therapies have become increasingly common. However, the research on app-based therapies is lagging behind. In particular, although experts suggest that human support may be critical for increasing engagement and effectiveness, we have little systematic knowledge about the role that human support plays in app-based therapy. It is critical to address these open questions to optimally design and scale these interventions. The purpose of this study is to provide a scoping review of the use of human support or coaching in app-based cognitive behavioral therapy for emotional disorders, identify critical knowledge gaps, and offer recommendations for future research. Cognitive behavioral therapy is the most well-researched treatment for a wide range of concerns and is understood to be particularly well suited to digital implementations, given its structured, skill-based approach. We conducted systematic searches of 3 databases (PubMed, PsycINFO, and Embase). Broadly, eligible articles described a cognitive behavioral intervention delivered via smartphone app whose primary target was an emotional disorder or problem and included some level of human involvement or support (coaching). All records were reviewed by 2 authors. Information regarding the qualifications and training of coaches, stated purpose and content of the coaching, method and frequency of communication with users, and relationship between coaching and outcomes was recorded. Of the 2940 titles returned by the searches, 64 (2.18%) were eligible for inclusion. This review found significant heterogeneity across all of the dimensions of coaching considered as well as considerable missing information in the published articles. Moreover, few studies had qualitatively or quantitatively evaluated how the level of coaching impacts treatment engagement or outcomes. Although users tend to self-report that coaching improves their engagement and outcomes, there is limited and mixed supporting quantitative evidence at present. Digital mental health is a young but rapidly expanding field with great potential to improve the reach of evidence-based care. Researchers across the reviewed articles offered numerous approaches to encouraging and guiding users. However, with the relative infancy of these treatment approaches, this review found that the field has yet to develop standards or consensus for implementing coaching protocols, let alone those for measuring and reporting on the impact. We conclude that coaching remains a significant hole in the growing digital mental health literature and lay out recommendations for future data collection, reporting, experimentation, and analysis.

Serotonin-reuptake inhibitors (SRIs) are first-line pharmacotherapy for the treatment of body dysmorphic disorder (BDD), a common and severe disorder. However, prior research has not focused on or identified definitive predictors of SRI treatment outcomes. Leveraging precision medicine techniques such as machine learning can facilitate the prediction of treatment outcomes. The study used 10-fold cross-validation support vector machine (SVM) learning models to predict three treatment outcomes (i.e. response, partial remission, and full remission) for 97 patients with BDD receiving up to 14-weeks of open-label treatment with the SRI escitalopram. SVM models used baseline clinical and demographic variables as predictors. Feature importance analyses complemented traditional SVM modeling to identify which variables most successfully predicted treatment response. SVM models indicated acceptable classification performance for predicting treatment response with an area under the curve (AUC) of 0.77 (sensitivity = 0.77 and specificity = 0.63), partial remission with an AUC of 0.75 (sensitivity = 0.67 and specificity = 0.73), and full remission with an AUC of 0.79 (sensitivity = 0.70 and specificity = 0.79). Feature importance analyses supported constructs such as better quality of life and less severe depression, general psychopathology symptoms, and hopelessness as more predictive of better treatment outcome; demographic variables were least predictive. The current study is the first to demonstrate that machine learning algorithms can successfully predict treatment outcomes for pharmacotherapy for BDD. Consistent with precision medicine initiatives in psychiatry, the current study provides a foundation for personalized pharmacotherapy strategies for patients with BDD.

Body dysmorphic disorder (BDD) is a severe and undertreated condition. Although cognitive-behavioral therapy (CBT) is the first-line psychosocial treatment for this common disorder, how the intervention works is insufficiently understood. Specific pathways have been hypothesized, but only one small study has examined the precise nature of treatment effects of CBT, and no prior study has examined the effects of supportive psychotherapy (SPT). This study re-examined a large trial ( = 120) comparing CBT to SPT for BDD. Network intervention analyses were used to explore symptom-level data across time. We computed mixed graphical models at multiple time points to examine relative differences in direct and indirect effects of the two interventions. In the resulting networks, CBT and SPT appeared to differentially target certain symptoms. The largest differences included CBT increasing efforts to disengage from and restructure unhelpful thoughts and resist BDD rituals, while SPT was directly related to improvement in BDD-related insight. Additionally, the time course of differences aligned with the intended targets of CBT; cognitive effects emerged first and behavioral effects second, paralleling cognitive restructuring in earlier sessions and the emphasis on exposure and ritual prevention in later sessions. Differences in favor of CBT were most consistent for behavioral targets. CBT and SPT primarily affected different symptoms. To improve patient care, the field needs a better understanding of how and when BDD treatments and treatment components succeed. Considering patient experiences at the symptom level and over time can aid in refining or reorganizing treatments to better fit patient needs.

BackgroundRumination is a transdiagnostic correlate and risk factor for mental disorders. However, few studies have explored rumination and its components in everyday life, or their associations with other transdiagnostic processes, such as deficits in attention control, which may be an explanatory mechanism or consequence of rumination. Inspired by the Nolen-Hoeksema’s operationalization of rumination, we investigated the associations between five features of rumination and attention control.MethodWe conducted a study relying upon experience sampling methodology: forty participants answered six items four times per day over a two-week period. Using a multilevel vector autoregressive approach, we computed three networks representing temporal, contemporaneous, and between-subjects associations.ResultsThe results showed that negativity of thoughts temporally drives all other features of rumination and was the only feature impoverishing attention control over time. Negativity was also the only feature negatively associated with attention control within the same time frame. In contrast, brooding was the only rumination feature to be associated with attention control in the between-subject network (i.e., similar to cross-sectional approach).ConclusionThese results highlight negativity as a driving force of rumination and as a potent pathway in the interplay between rumination’s features and attention control. Although these results appear inconsistent with the hypothesis that impoverished attention control drives rumination, they fully align with the resource allocation hypothesis that engaging in negative thoughts depletes attentional resources.

The histone variant macroH2A occupies large repressive domains throughout the genome; however, mechanisms underlying its precise deposition remain poorly understood. Here, we characterize de novo chromatin deposition of macroH2A2 using temporal genomic profiling in murine-derived fibroblasts devoid of all macroH2A isoforms. We find that macroH2A2 is first pervasively deposited genome wide at both steady-state domains and adjacent transcribed regions, the latter of which are subsequently pruned, establishing mature macroH2A2 domains. Pruning of macroH2A2 can be counteracted by chemical inhibition of transcription. Further, locus-specific transcriptional manipulation reveals that gene activation depletes pre-existing macroH2A2, while silencing triggers ectopic macroH2A2 accumulation. We demonstrate that the FACT (facilitates chromatin transcription) complex is required for macroH2A2 pruning within transcribed chromatin. Taken together, we have identified active chromatin as a boundary for macroH2A domains through a transcription-associated ‘pruning’ mechanism that establishes and maintains the faithful genomic localization of macroH2A variants.

Oligodendrocytes (OLs) are the myelin-forming cells of the central nervous system. They are derived from differentiation of oligodendrocyte progenitors through a process requiring cell cycle exit and histone modifications. Here we identify the histone arginine methyl-transferase PRMT5, a molecule catalyzing symmetric methylation of histone H4R3, as critical for developmental myelination. PRMT5 pharmacological inhibition, CRISPR/cas9 targeting, or genetic ablation decrease p53-dependent survival and impair differentiation without affecting proliferation. Conditional ablation of Prmt5 in progenitors results in hypomyelination, reduced survival and differentiation. Decreased histone H4R3 symmetric methylation is followed by increased nuclear acetylation of H4K5, and is rescued by pharmacological inhibition of histone acetyltransferases. Data obtained using purified histones further validate the results obtained in mice and in cultured oligodendrocyte progenitors. Together, these results identify PRMT5 as critical for oligodendrocyte differentiation and developmental myelination by modulating the cross-talk between histone arginine methylation and lysine acetylation. Myelin-forming cells derive from oligodendrocyte progenitors. Here the authors identify histone arginine methyl-transferase PRMT5 as critical for developmental myelination by modulating the cross-talk between histone arginine methylation and lysine acetylation, to favor differentiation.

Histone variants are key players in shaping chromatin structure, and, thus, in regulating fundamental cellular processes such as chromosome segregation and gene expression. Emerging evidence points towards a role for histone variants in contributing to tumor progression, and, recently, the first cancer-associated mutation in a histone variant-encoding gene was reported. In addition, genetic alterations of the histone chaperones that specifically regulate chromatin incorporation of histone variants are rapidly being uncovered in numerous cancers. Collectively, these findings implicate histone variants as potential drivers of cancer initiation and/or progression, and, therefore, targeting histone deposition or the chromatin remodeling machinery may be of therapeutic value. Here, we review the mammalian histone variants of the H2A and H3 families in their respective cellular functions, and their involvement in tumor biology.

Resistance to regeneration of insulin-producing pancreatic β cells is a fundamental challenge for type 1 and type 2 diabetes. Recently, small molecule inhibitors of the kinase DYRK1A have proven effective in inducing adult human β cells to proliferate, but their detailed mechanism of action is incompletely understood. We interrogated our human insulinoma and β cell transcriptomic databases seeking to understand why β cells in insulinomas proliferate, while normal β cells do not. This search reveals the DREAM complex as a central regulator of quiescence in human β cells. The DREAM complex consists of a module of transcriptionally repressive proteins that assemble in response to DYRK1A kinase activity, thereby inducing and maintaining cellular quiescence. In the absence of DYRK1A, DREAM subunits reassemble into the pro-proliferative MMB complex. Here, we demonstrate that small molecule DYRK1A inhibitors induce human β cells to replicate by converting the repressive DREAM complex to its pro-proliferative MMB conformation.

While multiple mechanisms of BRAF V600 -mutant melanoma resistance to targeted MAPK signaling inhibitors (MAPKi) have been reported, the epigenetic regulation of this process remains undetermined. Here, using a CRISPR–Cas9 screen targeting chromatin regulators, we discover that haploinsufficiency of the histone deacetylase SIRT6 allows melanoma cell persistence in the presence of MAPKi. Haploinsufficiency, but not complete loss of SIRT6 promotes IGFBP2 expression via increased chromatin accessibility, H3K56 acetylation at the IGFBP2 locus, and consequent activation of the IGF-1 receptor (IGF-1R) and downstream AKT signaling. Combining a clinically applicable IGF-1Ri with BRAFi overcomes resistance of SIRT6 haploinsufficient melanoma cells in vitro and in vivo. Using matched melanoma samples derived from patients receiving dabrafenib + trametinib, we identify IGFBP2 as a potential biomarker for MAPKi resistance. Our study has not only identified an epigenetic mechanism of drug resistance, but also provides insights into a combinatorial therapy that may overcome resistance to standard-of-care therapy for BRAF V600 -mutant melanoma patients. The epigenetic mechanisms of melanoma drug resistance are poorly understood. Here, the authors develop a CRISPR-Cas9 screen targeting epigenetic regulators and discover that SIRT6 haploinsufficiency induces BRAF V600E melanoma cell resistance to MAPK inhibitors via IGF signalling.

Anxiety and depressive disorders are common psychiatric conditions with high rates of co-occurrence. Although traditional cognitive-behavioral therapy (CBT) protocols targeting individual anxiety and depressive disorder diagnoses have been shown to be effective, such “single-diagnosis” approaches pose challenges for providers who treat patients with multiple comorbidities and for large-scale dissemination of and training in evidence-based psychological treatments. To help meet this need, newer “transdiagnostic” CBT interventions targeting shared underlying features across anxiety, depressive, and related disorders have been developed in recent years. Here, we provide a rationale for and description of the transdiagnostic CBT model, followed by an overview of key therapeutic strategies included in transdiagnostic CBT protocols for patients with anxiety disorders and comorbid depression. We conclude with a brief review of the empirical evidence in support of transdiagnostic CBT for people with anxiety and depressive disorders and identify directions for future research. [Psychiatr Ann. 2021;51(5):226–230.]