ImportanceArtificial intelligence (AI) is changing health and health care on an unprecedented scale. Though the potential benefits are massive, so are the risks. The JAMA Summit on AI discussed how health and health care AI should be developed, evaluated, regulated, disseminated, and monitored.ObservationsHealth and health care AI is wide-ranging, including clinical tools (eg, sepsis alerts or diabetic retinopathy screening software), technologies used by individuals with health concerns (eg, mobile health apps), tools used by health care systems to improve business operations (eg, revenue cycle management or scheduling), and hybrid tools supporting both business operations (eg, documentation and billing) and clinical activities (eg, suggesting diagnoses or treatment plans). Many AI tools are already widely adopted, especially for medical imaging, mobile health, health care business operations, and hybrid functions like scribing outpatient visits. All these tools can have important health effects (good or bad), but these effects are often not quantified because evaluations are extremely challenging or not required, in part because many are outside the US Food and Drug Administration's regulatory oversight. A major challenge in evaluation is that a tool's effects are highly dependent on the human-computer interface, user training, and setting in which the tool is used. Numerous efforts lay out standards for the responsible use of AI, but most focus on monitoring for safety (eg, detection of model hallucinations) or institutional compliance with various process measures, and do not address effectiveness (ie, demonstration of improved outcomes). Ensuring AI is deployed equitably and in a manner that improves health outcomes or, if improving efficiency of health care delivery, does so safely, requires progress in 4 areas. First, multistakeholder engagement throughout the total product life cycle is needed. This effort would include greater partnership of end users with developers in initial tool creation and greater partnership of developers, regulators, and health care systems in the evaluation of tools as they are deployed. Second, measurement tools for evaluation and monitoring should be developed and disseminated. Beyond proposed monitoring and certification initiatives, this will require new methods and expertise to allow health care systems to conduct or participate in rapid, efficient, and robust evaluations of effectiveness. The third priority is creation of a nationally representative data infrastructure and learning environment to support the generation of generalizable knowledge about health effects of AI tools across different settings. Fourth, an incentive structure should be promoted, using market forces and policy levers, to drive these changes.Conclusions and RelevanceAI will disrupt every part of health and health care delivery in the coming years. Given the many long-standing problems in health care, this disruption represents an incredible opportunity. However, the odds that this disruption will improve health for all will depend heavily on the creation of an ecosystem capable of rapid, efficient, robust, and generalizable knowledge about the consequences of these tools on health. This Special Communication discusses how health and health care artificial intelligence (AI) should be developed, evaluated, regulated, disseminated, and monitored.

Exonuclease EXO1 performs multiple roles in DNA replication and DNA damage repair (DDR). However, EXO1 loss is well-tolerated, suggesting the existence of compensatory mechanisms that could be exploited in DDR-deficient cancers. Using CRISPR screening, we find EXO1 loss as synthetic lethal with many DDR genes somatically inactivated in cancers, including Fanconi Anaemia (FA) pathway and BRCA1-A complex genes. We also identify the spliceosome factor and tumour suppressor ZRSR2 as synthetic lethal with loss of EXO1 and show that ZRSR2-deficient cells are attenuated for FA pathway activation, exhibiting cisplatin sensitivity and radial chromosome formation. Furthermore, FA or ZRSR2 deficiencies depend on EXO1 nuclease activity and can be potentiated in combination with PARP inhibitors or ionizing radiation. Finally, we uncover dysregulated replication-coupled repair as the driver of synthetic lethality between EXO1 and FA pathway attributable to defective fork reversal, elevated replication fork speeds, post-replicative single stranded DNA exposure and DNA damage. These findings implicate EXO1 as a synthetic lethal vulnerability and promising drug target in a broad spectrum of DDR-deficient cancers unaddressed by current therapies.

Importance:Artificial intelligence (AI) is changing health and health care on an unprecedented scale. Though the potential benefits are massive, so are the risks. The JAMA Summit on AI discussed how health and health care AI should be developed, evaluated, regulated, disseminated, and monitored. Observations:Health and health care AI is wide-ranging, including clinical tools (eg, sepsis alerts or diabetic retinopathy screening software), technologies used by individuals with health concerns (eg, mobile health apps), tools used by health care systems to improve business operations (eg, revenue cycle management or scheduling), and hybrid tools supporting both business operations (eg, documentation and billing) and clinical activities (eg, suggesting diagnoses or treatment plans). Many AI tools are already widely adopted, especially for medical imaging, mobile health, health care business operations, and hybrid functions like scribing outpatient visits. All these tools can have important health effects (good or bad), but these effects are often not quantified because evaluations are extremely challenging or not required, in part because many are outside the US Food and Drug Administration's regulatory oversight. A major challenge in evaluation is that a tool's effects are highly dependent on the human-computer interface, user training, and setting in which the tool is used. Numerous efforts lay out standards for the responsible use of AI, but most focus on monitoring for safety (eg, detection of model hallucinations) or institutional compliance with various process measures, and do not address effectiveness (ie, demonstration of improved outcomes). Ensuring AI is deployed equitably and in a manner that improves health outcomes or, if improving efficiency of health care delivery, does so safely, requires progress in 4 areas. First, multistakeholder engagement throughout the total product life cycle is needed. This effort would include greater partnership of end users with developers in initial tool creation and greater partnership of developers, regulators, and health care systems in the evaluation of tools as they are deployed. Second, measurement tools for evaluation and monitoring should be developed and disseminated. Beyond proposed monitoring and certification initiatives, this will require new methods and expertise to allow health care systems to conduct or participate in rapid, efficient, and robust evaluations of effectiveness. The third priority is creation of a nationally representative data infrastructure and learning environment to support the generation of generalizable knowledge about health effects of AI tools across different settings. Fourth, an incentive structure should be promoted, using market forces and policy levers, to drive these changes. Conclusions and Relevance:AI will disrupt every part of health and health care delivery in the coming years. Given the many long-standing problems in health care, this disruption represents an incredible opportunity. However, the odds that this disruption will improve health for all will depend heavily on the creation of an ecosystem capable of rapid, efficient, robust, and generalizable knowledge about the consequences of these tools on health.

BACKGROUND AND OBJECTIVES:Idiopathic/isolated REM sleep behavior disorder (iRBD) has been strongly linked to neurodegenerative synucleinopathies such as Parkinson disease, dementia with Lewy bodies, and multiple system atrophy. However, there have been increasing reports of RBD as a presenting feature of serious and treatable autoimmune syndromes, particularly IGLON5. This study's objective was to investigate the frequency of autoantibodies in a large cohort of participants with iRBD. METHODS:Participants were enrolled in the North American Prodromal Synucleinopathy cohort with polysomnography-confirmed iRBD, free of parkinsonism and dementia. Plasma samples were systematically screened for the autoantibodies IGLON5, DPPX, LGI1, and CASPR2 using plasma IgG cell-based assay. Positive or equivocal results were confirmed by repeat testing, plus tissue-based indirect immunofluorescence assay for IGLON5. RESULTS:Of 339 samples analyzed, 3 participants (0.9%) had confirmed positive IGLON5 autoantibodies in the cell-based assay, which were confirmed by the tissue-based assay. An additional participant was positive for CASPR2 with low titer by cell-based assay only (of lower clinical certainty). These cases exhibited a variety of symptoms including dream enactment, cognitive decline, autonomic dysfunction, and motor symptoms. In 1 IGLON5 case and the CASPR2 case, evolution was suggestive of typical synucleinopathy, suggesting the possibility that findings were incidental. However, 2 participants with IGLON5 died before diagnosis was clinically suspected, with a final clinical picture highly suggestive of autoimmune disease. DISCUSSION:Our finding that nearly 1% of a large iRBD cohort may have a serious but potentially treatable autoantibody syndrome has important clinical implications. In particular, it raises the question of whether autoantibody testing for IGLON-5-IgG should be widely implemented for participants with iRBD, considering the difficulty in diagnosis of autoimmune diseases, their response to treatment, and the potential for rapid disease progression. However, any routine testing protocol will also have to consider costs and potential adverse effects of false-positive findings. TRIAL REGISTRATION INFORMATION:NCT03623672.

The capabilities of artificial intelligence (AI) have accelerated over the past year, and they are beginning to impact healthcare in a significant way. Could this new technology help address issues that have been difficult and recalcitrant problems for quality and safety for decades? While we are early in the journey, it is clear that we are in the midst of a fundamental shift in AI capabilities. It is also clear these capabilities have direct applicability to healthcare and to improving quality and patient safety, even as they introduce new complexities and risks. Previously, AI focused on one task at a time: for example, telling whether a picture was of a cat or a dog, or whether a retinal photograph showed diabetic retinopathy or not. Foundation models (and their close relatives, generative AI and large language models) represent an important change: they are able to handle many different kinds of problems without additional datasets or training. This review serves as a primer on foundation models’ underpinnings, upsides, risks and unknowns—and how these new capabilities may help improve healthcare quality and patient safety.

Importance:Interest in artificial intelligence (AI) has reached an all-time high, and health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities.Observations:While AI as a concept has existed since the 1950s, all AI is not the same. Capabilities and risks of various kinds of AI differ markedly, and on examination 3 epochs of AI emerge. AI 1.0 includes symbolic AI, which attempts to encode human knowledge into computational rules, as well as probabilistic models. The era of AI 2.0 began with deep learning, in which models learn from examples labeled with ground truth. This era brought about many advances both in people's daily lives and in health care. Deep learning models are task-specific, meaning they do one thing at a time, and they primarily focus on classification and prediction. AI 3.0 is the era of foundation models and generative AI. Models in AI 3.0 have fundamentally new (and potentially transformative) capabilities, as well as new kinds of risks, such as hallucinations. These models can do many different kinds of tasks without being retrained on a new dataset. For example, a simple text instruction will change the model's behavior. Prompts such as "Write this note for a specialist consultant" and "Write this note for the patient's mother" will produce markedly different content.Conclusions and Relevance:Foundation models and generative AI represent a major revolution in AI's capabilities, ffering tremendous potential to improve care. Health care leaders are making decisions about AI today. While any heuristic omits details and loses nuance, the framework of AI 1.0, 2.0, and 3.0 may be helpful to decision-makers because each epoch has fundamentally different capabilities and risks.

A series of biotin-functionalized transition metal complexes was prepared by iClick reaction from the corresponding azido complexes with a novel alkyne-functionalized biotin derivative ([Au(triazolatoR,R′)(PPh3)], [Pt(dpb)(triazolatoR,R′)], [Pt(triazolatoR,R′)(terpy)]PF6, and [Ir(ppy)(triazolatoR,R′)(terpy)]PF6 with dpb = 1,3-di(2-pyridyl)benzene, ppy = 2-phenylpyridine, and terpy = 2,2′:6′,2′′-terpyridine and R = C6H5, R′ = biotin). The complexes were compared to reference compounds lacking the biotin moiety. The binding affinity toward avidin and streptavidin was evaluated with the HABA assay as well as isothermal titration calorimetry (ITC). All compounds exhibit the same binding stoichiometry of complex-to-avidin of 4:1, but the ITC results show that the octahedral Ir(III) compound exhibits a higher binding affinity than the square-planar Pt(II) complex. The antibacterial activity of the compounds was evaluated on a series of Gram-negative and Gram-positive bacterial strains. In particular, the neutral Au(I) and Pt(II) complexes showed significant antibacterial activity against Staphylococcus aureus and Enterococcus faecium at very low micromolar concentrations. The cytotoxicity against a range of eukaryotic cell lines was studied and revealed that the octahedral Ir(III) complex was non-toxic, while the square-planar Pt(II) and linear Au(I) complexes displayed non-selective micromolar activity.

To describe the prevalence, demographic characteristics, and cognitive function of individuals who believe that selective serotonin reuptake inhibitor (SSRI) use caused or exacerbated symptoms of REM-sleep behavior disorder (RBD) compared to idiopathic RBD (iRBD) in the North American Prodromal Synucleinopathy (NAPS) consortium registry.

Increasing evidence suggests slow-wave sleep (SWS) dysfunction in Parkinson’s disease (PD) is associated with faster disease progression, cognitive impairment, and excessive daytime sleepiness. Beta oscillations (8–35 Hz) in the basal ganglia thalamocortical (BGTC) network are thought to play a role in the development of cardinal motor signs of PD. The role cortical beta oscillations play in SWS dysfunction in the early stage of parkinsonism is not understood, however. To address this question, we used a within-subject design in a nonhuman primate (NHP) model of PD to record local field potentials from the primary motor cortex (MC) during sleep across normal and mild parkinsonian states. The MC is a critical node in the BGTC network, exhibits pathological oscillations with depletion in dopamine tone, and displays high amplitude slow oscillations during SWS. The MC is therefore an appropriate recording site to understand the neurophysiology of SWS dysfunction in parkinsonism. We observed a reduction in SWS quantity (p = 0.027) in the parkinsonian state compared to normal. The cortical delta (0.5–3 Hz) power was reduced (p = 0.038) whereas beta (8–35 Hz) power was elevated (p = 0.001) during SWS in the parkinsonian state compared to normal. Furthermore, SWS quantity positively correlated with delta power (r = 0.43, p = 0.037) and negatively correlated with beta power (r = −0.65, p < 0.001). Our findings support excessive beta oscillations as a mechanism for SWS dysfunction in mild parkinsonism and could inform the development of neuromodulation therapies for enhancing SWS in people with PD.

Journal Article Accepted manuscript Cognitive, motor, and autonomic function among individuals with serotonergic versus isolated REM sleep behavior disorder Get access Neha V Reddy, ScB, Neha V Reddy, ScB Department of Neurology, University of Minnesota, Minneapolis, Minnesota, USA https://orcid.org/0000-0001-5327-2876 Search for other works by this author on: Oxford Academic Google Scholar Meaghan Berns, Meaghan Berns Department of Neurology, University of Minnesota, Minneapolis, Minnesota, USA Search for other works by this author on: Oxford Academic Google Scholar Rachael Berns, Rachael Berns Department of Neurology, University of Minnesota, Minneapolis, Minnesota, USA Search for other works by this author on: Oxford Academic Google Scholar Hannah Olson, Hannah Olson Department of Neurology, University of Minnesota, Minneapolis, Minnesota, USA Search for other works by this author on: Oxford Academic Google Scholar Erija Cui, Erija Cui Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, Minnesota, USA Search for other works by this author on: Oxford Academic Google Scholar Mitchell G Miglis, Mitchell G Miglis Department of Neurology, Stanford University, Palo Alto, California, USA https://orcid.org/0000-0001-8488-0313 Search for other works by this author on: Oxford Academic Google Scholar Ronald Postuma, Ronald Postuma Department of Neurology, McGill University, Montreal, Quebec Canada Search for other works by this author on: Oxford Academic Google Scholar Bradley Boeve, Bradley Boeve Department of Neurology, Mayo Clinic-Rochester, Rochester, Minnesota, USA https://orcid.org/0000-0002-4153-8187 Search for other works by this author on: Oxford Academic Google Scholar Yo-El Ju, Yo-El Ju Department of Neurology, Washington University, St. Louis, Missouri, USA Search for other works by this author on: Oxford Academic Google Scholar NAPS Investigators, NAPS Investigators Search for other works by this author on: Oxford Academic Google Scholar ... Show more Michael Howell Michael Howell Department of Neurology, University of Minnesota, Minneapolis, Minnesota, USA Corresponding Author: Michael Howell, howel020@umn.edu Search for other works by this author on: Oxford Academic Google Scholar Sleep, zsae192, https://doi.org/10.1093/sleep/zsae192 Published: 19 August 2024 Article history Received: 05 June 2024 Published: 19 August 2024

Cryptosporidium is an obligate intracellular parasite that invades and replicates within a single cell type – epithelium, that lines the intestine of an infected host. Within these epithelial cells, the parasite remodels the cytosketeton, siphoning nutrients, and modulating host signalling pathways to hijack cellular processes and evade immune detection. To better understand how the parasite interacts with the host cell, we developed a high-throughput imaging screen to look for genetic dependencies. Our screen employed an arrayed CRISPR library, targeting all protein coding genes in the human genome, with a microscopy-based readout of parasite and host cellular markers. This approach allowed us to systematically measure the impact of every human protein coding gene on the growth and development of the parasite. Although several host metabolic pathways were identified to influence parasite growth, cholesterol biosynthesis attracted our attention due to the appearance of contrasting effects. Loss of genes early in the cholesterol synthesis pathway decreased parasite growth, while loss of genes late in the pathway led to increased parasite growth and development. Using a variety of genetic and chemical approaches, we then determined that the basis of these opposing results is the level of the intermediate metabolite squalene. Further, chemical modulation of squalene levels in vivo shows promise as an effective treatment option for Cryptosporidium infection. This unique data set furthers our understanding of intestinal epithelial cell biology and has the potential to establish new lines of host-pathogen research. This work was supported by the Francis Crick Institute-which receives its core funding from Cancer Research UK, the UK Medical Research Council, and the Wellcome Trust.

Study objectives: Rapid eye movement (REM) sleep behavior disorder (RBD) is strongly associated with phenoconversion to an overt synucleinopathy, e.g., Parkinson's disease (PD), Lewy Body Dementia (LBD), and related disorders. Comorbid traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) - henceforth "neurotrauma" (NT) - increase the odds of RBD by similar to 2.5-fold and is associated with an increased rate of service-connected PD in Veterans. Thus, RBD and NT are both independently associated with PD; however, it is unclear how NT influences neurological function in patients with RBD. Methods: Participants >= 18 years with overnight-polysomnogram-confirmed RBD were enrolled between 8/2018 to 4/2021 through the North American Prodromal Synucleinopathy (NAPS) Consortium. Standardized assessments for RBD, TBI, and PTSD history, as well as cognitive, motor, sensory and autonomic function were completed. This cross-sectional analysis compared cases (n=24; RBD+NT) to controls (n=96; RBD), matched for age (similar to 60 years), sex (15% female), and years of education (similar to 15 years). Results: RBD+NT reported earlier RBD symptom onset (37.5 +/- 11.9 vs. 52.2 +/- 15.1 years of age) and a more severe RBD phenotype. Similarly, RBD+NT reported more severe anxiety and depression, greater frequency of hypertension, and significantly worse cognitive, motor, and autonomic function compared to RBD. No differences in olfaction or color vision were observed. Conclusion: This cross-sectional, matched case:control study shows individuals with RBD+NT have significantly worse neurological measures related to common features of an overt synucleinopathy. Confirmatory longitudinal studies are ongoing; however, these results suggest RBD+NT may be associated with more advanced neurological symptoms related to an evolving neurodegenerative process.

Generation and characterization of A3A and A3B knockouts in TIIP cells.

Supplementary Table S4: APC/C mutation frequencies in TCGA.

Supplementary Table S2: Gene ontology enrichment analysis from whole-genome RNAi screen identifies APC/C subunits.

Supplementary Figure S1: Using reversine to cause chromosome segregation errors and p53-dependent aneuploidy tolerance. Supplementary Figure S2: Validation of APC/C subunits using siRNA deconvolution experiments in RPE1 and HCT116 cells, and how subunit knock-down rescues proliferation when the spindle assembly checkpoint is impaired. Supplementary Figure S3: APC/C subunit knock-down reduces segregation errors caused by SAC defects. Supplementary Figure S4: CRISPR-mediated disruption of TP53 and CDC27, and resistance to Mps1 inhibitors. Supplementary Figure S5: Functional testing of CDC27 mutations in Schizosaccharomyces pombe. Supplementary Figure S6: Cell division, microtubule attachment errors and tetraploidy. Supplementary Figure S7: CRISPR-mediated disruption of TP53 in RPE1 cells. Supplementary Figure S8: APC/C partial inhibition does not rescue structural anaphase defects caused by pre-mitotic replicative stress. Supplementary Figure S9: Spindle microtubule stability using photoactivation. Supplementary Figure S10: Adaptation of RPE1/p53ko populations to Mps1 inhibitors is accompanied by a mitotic delay.

Objective: To characterize the prevalence of neurogenic orthostatic hypotension (nOH) in REM sleep behavior disorder (RBD) and associations with other prodromal features of the disease. Background: Rapid eye movement (REM) sleep behavior disorder (RBD) is a prodromal synucleinopathy for most patients, and the majority of individuals with isolated RBD (iRBD) will phenoconvert to clinically manifest synucleinopathy within a decade. Autonomic dysfunction is common in iRBD, however the prevalence of neurogenic orthostatic hypotension (nOH) in iRBD is unknown. We aimed to prospectively evaluate the prevalence of nOH in a large multicenter cohort of participants with iRBD. Design/Methods: Participants >18 years of age with overnight polysomnogram-confirmed iRBD were enrolled from the North American Prodromal Synucleinopathy (NAPS) Consortium. Participants were excluded if they were on antihypertensives or other medications that might contribute to OH. All participants underwent 3-min orthostatic stand testing with blood pressure (BP) and heart rate (HR) measurements. The ΔHR/Δ systolic BP ratio was calculated for all participants. nOH was defined as OH with a ΔHR/ΔSBP ratio of <0.5. All participants also completed a battery of questionnaires including the Scales for Outcomes in Parkinson's Disease – Autonomic Dysfunction (SCOPA-AUT), as well as detailed cognitive, motor, and sensory testing. Results: 334 iRBD participants met eligibility criteria. OH was identified in 87 (26%) participants, of which 69 (21%) met criteria for nOH (ΔHR/ΔSBP 0.31±0.24) and 18 (5%) met criteria non-neurogenic OH (ΔHR/ΔSBP 0.70±0.27). There was no difference in participant age, age of RBD onset, or years of education across groups. Pupillary and sexual dysfunction as measured by SCOPA-AUT subdomain scores, as well as olfaction, were all significantly worse in those with nOH and non-neurogenic OH compared to those without OH. Conclusions: nOH is common in those with RBD. These data support the rationale to routinely include orthostatic stand testing in the evaluation of patients with iRBD. Disclosure: Dr. Miglis has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Infinite MD. Dr. Miglis has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Guidepoint, LLC. Dr. Miglis has received personal compensation in the range of $500-$4,999 for serving as a Consultant for 2nd MD. Dr. Miglis has received personal compensation in the range of $500-$4,999 for serving as a Consultant for MED-IQ. Dr. Miglis has received personal compensation in the range of $5,000-$9,999 for serving as an Expert Witness for Van Cott and Talamante. Dr. Miglis has received publishing royalties from a publication relating to health care. Dr. Lim has received personal compensation in the range of $10,000-$49,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Applied Cognition. Dr. Lim has stock in Applied Cognition. The institution of Dr. Lim has received research support from NIH. The institution of Dr. Lim has received research support from VA. The institution of Dr. Lim has received research support from Department of Defense. The institution of Dr. Lim has received research support from National Science Foundation. Dr. Lim has received publishing royalties from a publication relating to health care. Dr. Zitser-Koren has nothing to disclose. Dr. During has nothing to disclose. Donald Biwise has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Eisai. Donald Biwise has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Merck. Donald Biwise has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Huxley. Donald Biwise has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Ferring. Donald Biwise has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Idorsia. The institution of Dr. Huddleston has received research support from NIH-NINDS 1K23NS105944-01A1. The institution of Dr. Huddleston has received research support from Michael J. Fox Foundation (MJFF-010556). The institution of Dr. Huddleston has received research support from Lewy Body Dementia Association Research Center of Excellence (Emory). The institution of Dr. Huddleston has received research support from American Parkinson's Disease Association. Dr. Howell has received personal compensation in the range of $500-$4,999 for serving as a Consultant for neurodiem. Dr. Howell has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Alzecure. Dr. Howell has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for medlink. Dr. Howell has stock in Sleep Performance Institute. Dr. Howell has received publishing royalties from a publication relating to health care. Dr. St. Louis has received publishing royalties from a publication relating to health care. Dr. St. Louis has received publishing royalties from a publication relating to health care. The institution of Jean-Francois Gagnon has received research support from Canadian Institute of Health Research. The institution of Jean-Francois Gagnon has received research support from Canada Research Chair. The institution of Jean-Francois Gagnon has received research support from National Institutes of Health. Dr. Videnovic has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Jazz. Dr. Videnovic has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Alexion pharmaceuticals . Dr. Avidan has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Eisai. Dr. Avidan has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Avadel. Dr. Avidan has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Harmony. Dr. Avidan has received personal compensation in the range of $0-$499 for serving as a Consultant for Merck. Dr. Avidan has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Eisai. Dr. Avidan has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Harmony. Dr. Avidan has received publishing royalties from a publication relating to health care. Dr. Schenck has received personal compensation for serving as an employee of Physicians' Educational Resource, LLC.. Dr. Schenck has received personal compensation for serving as an employee of Eisai Global Medical Affairs. Dr. Postuma has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Roche, Biogen, Takeda, Theranexus, GE, Jazz, Curasen, Paladin, Inception Sciences, Phytopharmics, Vaxxinity, Merck. Dr. Postuma has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Biogen/Partners. The institution of Dr. Postuma has received research support from CIHR, Weston Foundation, Webster Foundation, Roche, MJFF, Parkinson Canada, FRSQ, NIH. Dr. Boeve has received personal compensation in the range of $10,000-$49,999 for serving as an officer or member of the Board of Directors for Rainwater Charitable Foundation. The institution of Dr. Boeve has received research support from Alector. The institution of Dr. Boeve has received research support from GE Healthcare. The institution of Dr. Boeve has received research support from Transposon. The institution of Dr. Boeve has received research support from Cognition Therapeutics. Dr. Boeve has received publishing royalties from a publication relating to health care. The institution of Dr. Ju has received research support from National Institutes of Health. Dr. Elliott has nothing to disclose.