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

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Brexpiprazole as an augmentation agent to antidepressants in treatment resistant major depressive disorder

Introduction: Approximately 50% of adults with major depressive disorder (MDD) who receive a first-line antidepressant treatment, at an appropriate dose, do not achieve an adequate response. Brexpiprazole is a novel serotonin-dopamine activity modulator in the second generation/atypical antipsychotic class that was approved by the United States Food & Drug Administration in 2015 for use as an adjunctive agent in the treatment of MDD inadequately responsive to antidepressant treatment. In general, second generation/atypical antipsychotics are widely used in the treatment of treatment resistant depression with brexpiprazole providing preliminary evidence for broad-spectrum efficacy across multiple domains affected by MDD, providing a basis for further elucidating its mechanistic effects to inform novel drug discovery. Areas covered: The review herein presents the evidence base for the use of brexpiprazole as an augmentation agent to antidepressants in individuals with treatment resistant MDD, including its efficacy, safety, and tolerability profile. Expert opinion: Brexpiprazole has been demonstrated to be effective and safe to use as an augmentation agent to antidepressant treatment among individuals with treatment resistant MDD due to its considerably improved tolerability profile when compared to other second generation/atypical antipsychotics; however, it is important to exercise clinical judgment when selecting disparate augmentation agents on a case-by-case basis weighing individual risks versus benefit

Inflamed moods: A review of the interactions between inflammation and mood disorders

Mood disorders have been recognized by the World Health Organization (WHO) as the leading cause of disability worldwide. Notwithstanding the established efficacy of conventional mood agents, many treated individuals continue to remain treatment refractory and/or exhibit clinically significant residual symptoms, cognitive dysfunction, and psychosocial impairment. Therefore, a priority research and clinical agenda is to identify pathophysiological mechanisms subserving mood disorders to improve therapeutic efficacy.During the past decade, inflammation has been revisited as an important etiologic factor of mood disorders. Therefore, the purpose of this synthetic review is threefold: 1) to review the evidence for an association between inflammation and mood disorders, 2) to discuss potential pathophysiologic mechanisms that may explain this association and 3) to present novel therapeutic options currently being investigated that target the inflammatory-mood pathway.Accumulating evidence implicates inflammation as a critical mediator in the pathophysiology of mood disorders. Indeed, elevated levels of pro-inflammatory cytokines have been repeatedly demonstrated in both major depressive disorder (MDD) and bipolar disorder (BD) patients. Further, the induction of a pro-inflammatory state in healthy or medically ill subjects induces 'sickness behavior' resembling depressive symptomatology.Potential mechanisms involved include, but are not limited to, direct effects of pro-inflammatory cytokines on monoamine levels, dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, pathologic microglial cell activation, impaired neuroplasticity and structural and functional brain changes.Anti-inflammatory agents, such as acetyl-salicylic acid (ASA), celecoxib, anti-TNF-alpha agents, minocycline, curcumin and omega-3 fatty acids, are being investigated for use in mood disorders. Current evidence shows improved outcomes in mood disorder patients when anti-inflammatory agents are used as an adjunct to conventional therapy; however, further research is needed to establish the therapeutic benefit and appropriate dosage. (C) 2014 Elsevier Inc. All rights reserved.Univ Toronto, Univ Hlth Network, MDPU, Toronto, ON, CanadaUniv Western Ontario, Schulich Sch Med & Dent, London, ON, CanadaUniversidade Federal de São Paulo, Dept Psychiat, Interdisciplinary Lab Clin Neurosci LINC, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Psychiat, Program Recognit & Intervent Individuals Risk Men, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Psychiat, Interdisciplinary Lab Clin Neurosci LINC, São Paulo, BrazilUniversidade Federal de São Paulo, Dept Psychiat, Program Recognit & Intervent Individuals Risk Men, São Paulo, BrazilWeb of Scienc

Selfish brain and neuroprogression in bipolar disorder

Bipolar disorder is associated with increases in mortality rates due to metabolic complications when compared to the general population. the selfish brain theory postulates that the CNS modulates energy metabolism in the periphery in order to prioritize its own demand and offers an heurist value framework to understand how and why metabolic abnormalities develop in the course of BD. Mood episodes, especially those of manic polarity are neurotoxic, because of the acute release of the neurotransmitters dopamine and glutamate, oxidative species, inflammatory cytokines and the deprivation of neuroprotective factors, such as neurotrophins. the cell loss and malfunctioning require from the brain an extra effort to repair itself, which will demand energetic supplies. Application of selfish brain theory in BD can potentially offer new insights about how to prevent and treat metabolic complications in BD. (c) 2012 Elsevier Inc. All rights reserved.Universidade Federal de São Paulo, Dept Psychiat, LINC, São Paulo, BrazilUniversidade Federal de São Paulo, Program Recognit & Intervent Individuals At Risk, São Paulo, BrazilUniv Toronto, MDPU, Univ Hlth Network, Toronto, ON, CanadaUniversidade Federal de São Paulo, Dept Psychiat, LINC, São Paulo, BrazilUniversidade Federal de São Paulo, Program Recognit & Intervent Individuals At Risk, São Paulo, BrazilWeb of Scienc

Implications of epigenetic modulation for novel treatment approaches in patients with schizophrenia

Schizophrenia is a heterogeneous and complex mental disorder with high rates of disability, non-recovery, and relapse. the primary pharmacological treatments for schizophrenia are antipsychotics. Notwithstanding the efficacy of antipsychotics in ameliorating positive symptoms and reducing relapse rates, cognitive deficits and negative symptoms are not sufficiently treated with available pharmaceutical agents. Moreover, schizophrenia is associated with consistent, replicable, and clinically significant deficits in cognition. the importance of cognitive deficits in schizophrenia is emphasized by reports indicating that the severity of cognitive deficits is predictive of treatment compliance, adherence, and risk of relapse among first-episode individuals. Taken together, this review highlights epigenetic modulations involving histone deacetylase (HDAC) inhibitors as a potential avenue for novel treatment toward improvements in cognition and functional outcomes in patients with schizophrenia. the combination of epigenetic modulation with pharmacological interventions that engage multiple disparate physiological systems implicated in schizophrenia are discussed, and may represent a more effective strategy in ameliorating cognitive deficits and mitigating symptoms for improved functionality. (C) 2013 Elsevier B.V. All rights reserved.Univ Hlth Network, Mood Disorders Psychopharmacol Unit, Toronto, ON M5T 2S8, CanadaUniv Toronto, Inst Med Sci, Toronto, ON, CanadaUniv Toronto, Dept Psychiat, Toronto, ON, CanadaQueens Univ, Ctr Neurosci Studies, Kingston, ON, CanadaUniversidade Federal de São Paulo, Interdisciplinary Lab Clin Neurosci LINC, Dept Psychiat, São Paulo, BrazilUniv Toronto, Dept Pharmacol, Toronto, ON, CanadaUniversidade Federal de São Paulo, Interdisciplinary Lab Clin Neurosci LINC, Dept Psychiat, São Paulo, BrazilWeb of Scienc