Psychodynamic therapy: a poorly defined concept with questionable evidence

R34 MH086668 - NIMH NIH HHS; R01 AT007257 - NCCIH NIH HHS; R21 MH101567 - NIMH NIH HHS; R34 MH099311 - NIMH NIH HHS; R21 MH102646 - NIMH NIH HHS; K23 MH100259 - NIMH NIH HHS; R01 MH099021 - NIMH NIH HH

Canvass: a crowd-sourced, natural-product screening library for exploring biological space

NCATS thanks Dingyin Tao for assistance with compound characterization. This research was supported by the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH). R.B.A. acknowledges support from NSF (CHE-1665145) and NIH (GM126221). M.K.B. acknowledges support from NIH (5R01GM110131). N.Z.B. thanks support from NIGMS, NIH (R01GM114061). J.K.C. acknowledges support from NSF (CHE-1665331). J.C. acknowledges support from the Fogarty International Center, NIH (TW009872). P.A.C. acknowledges support from the National Cancer Institute (NCI), NIH (R01 CA158275), and the NIH/National Institute of Aging (P01 AG012411). N.K.G. acknowledges support from NSF (CHE-1464898). B.C.G. thanks the support of NSF (RUI: 213569), the Camille and Henry Dreyfus Foundation, and the Arnold and Mabel Beckman Foundation. C.C.H. thanks the start-up funds from the Scripps Institution of Oceanography for support. J.N.J. acknowledges support from NIH (GM 063557, GM 084333). A.D.K. thanks the support from NCI, NIH (P01CA125066). D.G.I.K. acknowledges support from the National Center for Complementary and Integrative Health (1 R01 AT008088) and the Fogarty International Center, NIH (U01 TW00313), and gratefully acknowledges courtesies extended by the Government of Madagascar (Ministere des Eaux et Forets). O.K. thanks NIH (R01GM071779) for financial support. T.J.M. acknowledges support from NIH (GM116952). S.M. acknowledges support from NIH (DA045884-01, DA046487-01, AA026949-01), the Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program (W81XWH-17-1-0256), and NCI, NIH, through a Cancer Center Support Grant (P30 CA008748). K.N.M. thanks the California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board for support. B.T.M. thanks Michael Mullowney for his contribution in the isolation, elucidation, and submission of the compounds in this work. P.N. acknowledges support from NIH (R01 GM111476). L.E.O. acknowledges support from NIH (R01-HL25854, R01-GM30859, R0-1-NS-12389). L.E.B., J.K.S., and J.A.P. thank the NIH (R35 GM-118173, R24 GM-111625) for research support. F.R. thanks the American Lebanese Syrian Associated Charities (ALSAC) for financial support. I.S. thanks the University of Oklahoma Startup funds for support. J.T.S. acknowledges support from ACS PRF (53767-ND1) and NSF (CHE-1414298), and thanks Drs. Kellan N. Lamb and Michael J. Di Maso for their synthetic contribution. B.S. acknowledges support from NIH (CA78747, CA106150, GM114353, GM115575). W.S. acknowledges support from NIGMS, NIH (R15GM116032, P30 GM103450), and thanks the University of Arkansas for startup funds and the Arkansas Biosciences Institute (ABI) for seed money. C.R.J.S. acknowledges support from NIH (R01GM121656). D.S.T. thanks the support of NIH (T32 CA062948-Gudas) and PhRMA Foundation to A.L.V., NIH (P41 GM076267) to D.S.T., and CCSG NIH (P30 CA008748) to C.B. Thompson. R.E.T. acknowledges support from NIGMS, NIH (GM129465). R.J.T. thanks the American Cancer Society (RSG-12-253-01-CDD) and NSF (CHE1361173) for support. D.A.V. thanks the Camille and Henry Dreyfus Foundation, the National Science Foundation (CHE-0353662, CHE-1005253, and CHE-1725142), the Beckman Foundation, the Sherman Fairchild Foundation, the John Stauffer Charitable Trust, and the Christian Scholars Foundation for support. J.W. acknowledges support from the American Cancer Society through the Research Scholar Grant (RSG-13-011-01-CDD). W.M.W.acknowledges support from NIGMS, NIH (GM119426), and NSF (CHE1755698). A.Z. acknowledges support from NSF (CHE-1463819). (Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH); CHE-1665145 - NSF; CHE-1665331 - NSF; CHE-1464898 - NSF; RUI: 213569 - NSF; CHE-1414298 - NSF; CHE1361173 - NSF; CHE1755698 - NSF; CHE-1463819 - NSF; GM126221 - NIH; 5R01GM110131 - NIH; GM 063557 - NIH; GM 084333 - NIH; R01GM071779 - NIH; GM116952 - NIH; DA045884-01 - NIH; DA046487-01 - NIH; AA026949-01 - NIH; R01 GM111476 - NIH; R01-HL25854 - NIH; R01-GM30859 - NIH; R0-1-NS-12389 - NIH; R35 GM-118173 - NIH; R24 GM-111625 - NIH; CA78747 - NIH; CA106150 - NIH; GM114353 - NIH; GM115575 - NIH; R01GM121656 - NIH; T32 CA062948-Gudas - NIH; P41 GM076267 - NIH; R01GM114061 - NIGMS, NIH; R15GM116032 - NIGMS, NIH; P30 GM103450 - NIGMS, NIH; GM129465 - NIGMS, NIH; GM119426 - NIGMS, NIH; TW009872 - Fogarty International Center, NIH; U01 TW00313 - Fogarty International Center, NIH; R01 CA158275 - National Cancer Institute (NCI), NIH; P01 AG012411 - NIH/National Institute of Aging; Camille and Henry Dreyfus Foundation; Arnold and Mabel Beckman Foundation; Scripps Institution of Oceanography; P01CA125066 - NCI, NIH; 1 R01 AT008088 - National Center for Complementary and Integrative Health; W81XWH-17-1-0256 - Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program; P30 CA008748 - NCI, NIH, through a Cancer Center Support Grant; California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board; American Lebanese Syrian Associated Charities (ALSAC); University of Oklahoma Startup funds; 53767-ND1 - ACS PRF; PhRMA Foundation; P30 CA008748 - CCSG NIH; RSG-12-253-01-CDD - American Cancer Society; RSG-13-011-01-CDD - American Cancer Society; CHE-0353662 - National Science Foundation; CHE-1005253 - National Science Foundation; CHE-1725142 - National Science Foundation; Beckman Foundation; Sherman Fairchild Foundation; John Stauffer Charitable Trust; Christian Scholars Foundation)Published versionSupporting documentatio

Identification of a novel polyprenylated acylphloroglucinol‑derived SIRT1 inhibitor with cancer‑specific anti-proliferative and invasion-suppressing activities

SIRT1, a class III histone deacetylase, plays a critical role in regulating cancer cell growth, migration and invasion, which makes it a potential target for cancer therapeutics. In this study, we screened derivatives of several groups of natural products and identified a novel SIRT1 inhibitor JQ-101, a synthetic derivative of the polyprenylated acylphloroglucinol (PPAP) natural products, with an IC(50) for SIRT1 of 30 µM in vitro, with 5-fold higher activity for SIRT1 vs. SIRT2. Exposure of tumor cells to JQ-101 significantly enhanced acetylation of p53 and histone H4K16 at known sites of SIRT1 deacetylation, validating SIRT1 as its cellular target. JQ-101 suppressed cancer cell growth and survival by targeting SIRT1, and also exhibited selective cytotoxicity towards a panel of human tumor cell lines, while producing no toxicity in two normal human cell types at comparable concentrations. JQ-101 induced both apoptosis and cell senescence, and suppressed cancer cell invasion in vitro. In summary, we have identified JQ-101 as a new SIRT1 inhibitor which may have potential application in cancer treatment through its ability to induce tumor cell apoptosis and senescence and suppress cancer cell invasion.CA164245 - NCI NIH HHS; R01 CA101992 - NCI NIH HHS; R21 CA129046 - NCI NIH HHS; R21 CA141036 - NCI NIH HHS; P50 GM067041 - NIGMS NIH HHS; UL1RR025771 - NCRR NIH HHS; CA101992 - NCI NIH HHS; UL1 RR025771 - NCRR NIH HHS; GM-073855 - NIGMS NIH HHS; CA129046 - NCI NIH HHS; R21 CA164245 - NCI NIH HHS; GM-067041 - NIGMS NIH HHS; CA141036 - NCI NIH HHS; R01 GM073855 - NIGMS NIH HH

Episodic future thinking in generalized anxiety disorder

Research on future-oriented cognition in generalized anxiety disorder (GAD) has primarily focused on worry, while less is known about the role of episodic future thinking (EFT), an imagery-based cognitive process. To characterize EFT in this disorder, we used the experimental recombination procedure, in which 21 GAD and 19 healthy participants simulated positive, neutral and negative novel future events either once or repeatedly, and rated their phenomenological experience of EFT. Results showed that healthy controls spontaneously generated more detailed EFT over repeated simulations. Both groups found EFT easier to generate after repeated simulations, except when GAD participants simulated positive events. They also perceived higher plausibility of negative-not positive or neutral-future events than did controls. These results demonstrate a negativity bias in GAD individuals' episodic future cognition, and suggest their relative deficit in generating vivid EFT. We discuss implications for the theory and treatment of GAD.R01 MH060941 - NIMH NIH HHS; R01 MH078308 - NIMH NIH HHS; R01AG08441 - NIA NIH HHS; R01 AT007257 - NCCIH NIH HHS; R01MH60941 - NIMH NIH HHS; R01 AG008441 - NIA NIH HHS; R34 MH099311 - NIMH NIH HHS; R21MH102646 - NIMH NIH HHS; R01AT007257 - NCCIH NIH HHS; R21 MH102646 - NIMH NIH HHS; R34MH078308 - NIMH NIH HH

Defective phagocytic corpse processing results in neurodegeneration and can be rescued by TORC1 activation

This work was supported by NIH Grants R01 GM094452 (K.M.) and F31 GM099425 (J.I.E.), BU Alzheimer's Disease Core Center NIH Grant P30 AG13846, Boston University Undergraduate Research Opportunities Program grants (J.A.T., V.S.), and NIH Grant R01 AG044113 to M.B.F. We thank the Bloomington Stock Center, TRiP at Harvard Medical School, the Kyoto Drosophila Genetic Resource Center, Estee Kurant, Eric Baehrecke, Marc Freeman, and Mary Logan for fly strains. We thank Todd Blute for assistance with electron microscopy and the Developmental Studies Hybridoma Bank for antibodies. (R01 GM094452 - NIH; F31 GM099425 - NIH; R01 AG044113 - NIH; P30 AG13846 - BU Alzheimer's Disease Core Center NIH Grant; Boston University Undergraduate Research Opportunities Program)https://www.jneurosci.org/content/36/11/3170.longPublished versionPublished versio

Interpersonal Emotion Regulation Questionnaire (IERQ): scale development and psychometric characteristics

Despite the popularity of emotion regulation in the contemporary literature, research has almost exclusively focused on only intrapersonal processes, whereas much less attention has been placed in interpersonal emotion regulation processes. In order to encourage research on interpersonal emotion regulation, we present a series of 4 studies to develop the Interpersonal Emotion Regulation Questionnaire (IERQ). The final scale consists of 20 items with 4 factors containing 5 items each. The 4 factors are: Enhancing Positive Affect; Perspective Taking; Soothing; and Social Modeling. The scale shows excellent psychometric characteristics. Implications for future research are discussed.R01 MH078308 - NIMH NIH HHS; R34 MH086668 - NIMH NIH HHS; R01 AT007257 - NCCIH NIH HHS; R21 MH101567 - NIMH NIH HHS; R34 MH099311 - NIMH NIH HHS; R21 MH102646 - NIMH NIH HHS; K23 MH100259 - NIMH NIH HHS; R01 MH099021 - NIMH NIH HH

Initial severity of depression and efficacy of cognitive-behavioural therapy: individual-participant data meta-analysis of pill-placebo-controlled trials

BACKGROUND: The influence of baseline severity has been examined for antidepressant medications but has not been studied properly for cognitive-behavioural therapy (CBT) in comparison with pill placebo. AIMS: To synthesise evidence regarding the influence of initial severity on efficacy of CBT from all randomised controlled trials (RCTs) in which CBT, in face-to-face individual or group format, was compared with pill-placebo control in adults with major depression. METHOD: A systematic review and an individual-participant data meta-analysis using mixed models that included trial effects as random effects. We used multiple imputation to handle missing data. RESULTS: We identified five RCTs, and we were given access to individual-level data (n = 509) for all five. The analyses revealed that the difference in changes in Hamilton Rating Scale for Depression between CBT and pill placebo was not influenced by baseline severity (interaction P = 0.43). Removing the non-significant interaction term from the model, the difference between CBT and pill placebo was a standardised mean difference of -0.22 (95% CI -0.42 to -0.02, P = 0.03, I2 = 0%). CONCLUSIONS: Patients suffering from major depression can expect as much benefit from CBT across the wide range of baseline severity. This finding can help inform individualised treatment decisions by patients and their clinicians.R01 MH060998 - NIMH NIH HHS; R34 MH086668 - NIMH NIH HHS; R01 AT007257 - NCCIH NIH HHS; R21 MH101567 - NIMH NIH HHS; K02 MH001697 - NIMH NIH HHS; R01 MH060713 - NIMH NIH HHS; R34 MH099311 - NIMH NIH HHS; R21 MH102646 - NIMH NIH HHS; K23 MH100259 - NIMH NIH HHS; R01 MH099021 - NIMH NIH HH

Effect of treatments for depression on quality of life: a meta-analysis

Published in final edited form as: Cogn Behav Ther. 2017 June; 46(4): 265–286. doi:10.1080/16506073.2017.1304445.Cognitive-behavioral therapy (CBT) and selective serotonin reuptake inhibitors (SSRIs) are the two first-line treatments for depression, but little is known about their effects on quality of life (QOL). A meta-analysis was conducted to examine changes in QOL in adults with major depressive disorder who received CBT (24 studies examining 1969 patients) or SSRI treatment (13 studies examining 4286 patients) for their depression. Moderate improvements in QOL from pre to post-treatment were observed in both CBT (Hedges' g = .63) and SSRI (Hedges' g = .79) treatments. The effect size remained stable over the course of the follow-up period for CBT. No data were available to examine follow-ups in the SSRI group. QOL effect sizes decreased linearly with publication year, and greater improvements in depression were significantly associated with greater improvements in QOL for CBT, but not for SSRIs. CBT and SSRIs for depression were both associated with moderate improvements in QOL, but are possibly caused by different mechanisms.This work was supported in part from NIH/NCCIH [grant number R01AT007257], NIH/NIMH [grant numbers R01MH099021; R34MH099311; R34MH086668; R21MH102646; R21MH101567; K23MH100259]. (R01AT007257 - NIH/NCCIH; R01MH099021 - NIH/NIMH; R34MH099311 - NIH/NIMH; R34MH086668 - NIH/NIMH; R21MH102646 - NIH/NIMH; R21MH101567 - NIH/NIMH; K23MH100259 - NIH/NIMH

A complex network perspective on clinical science

Contemporary classification systems for mental disorders assume that abnormal behaviors are expressions of latent disease entities. An alternative to the latent disease model is the complex network approach. Instead of assuming that symptoms arise from an underlying disease entity, the complex network approach holds that disorders exist as systems of interrelated elements of a network. This approach also provides a framework for the understanding of therapeutic change. Depending on the structure of the network, change can occur abruptly once the network reaches a critical threshold (the tipping point). Homogeneous and highly connected networks often recover more slowly from local perturbations when the network approaches the tipping point, potentially making it possible to predict treatment change, relapse, and recovery. In this article, we discuss the complex network approach as an alternative to the latent disease model and its implications for classification, therapy, relapse, and recovery.R34 MH086668 - NIMH NIH HHS; R01 AT007257 - NCCIH NIH HHS; R21 MH101567 - NIMH NIH HHS; R34 MH099311 - NIMH NIH HHS; R21 MH102646 - NIMH NIH HHS; K23 MH100259 - NIMH NIH HHS; R01 MH099021 - NIMH NIH HH

Metabolic network percolation quantifies biosynthetic capabilities across the human oral microbiome

The biosynthetic capabilities of microbes underlie their growth and interactions, playing a prominent role in microbial community structure. For large, diverse microbial communities, prediction of these capabilities is limited by uncertainty about metabolic functions and environmental conditions. To address this challenge, we propose a probabilistic method, inspired by percolation theory, to computationally quantify how robustly a genome-derived metabolic network produces a given set of metabolites under an ensemble of variable environments. We used this method to compile an atlas of predicted biosynthetic capabilities for 97 metabolites across 456 human oral microbes. This atlas captures taxonomically-related trends in biomass composition, and makes it possible to estimate inter-microbial metabolic distances that correlate with microbial co-occurrences. We also found a distinct cluster of fastidious/uncultivated taxa, including several Saccharibacteria (TM7) species, characterized by their abundant metabolic deficiencies. By embracing uncertainty, our approach can be broadly applied to understanding metabolic interactions in complex microbial ecosystems.T32GM008764 - NIGMS NIH HHS; T32 GM008764 - NIGMS NIH HHS; R01 DE024468 - NIDCR NIH HHS; R01 GM121950 - NIGMS NIH HHS; DE-SC0012627 - Biological and Environmental Research; RGP0020/2016 - Human Frontier Science Program; NSFOCE-BSF 1635070 - National Science Foundation; HR0011-15-C-0091 - Defense Advanced Research Projects Agency; R37DE016937 - NIDCR NIH HHS; R37 DE016937 - NIDCR NIH HHS; R01GM121950 - NIGMS NIH HHS; R01DE024468 - NIDCR NIH HHS; 1457695 - National Science FoundationPublished versio