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This study was a retrospective chart review performed to examine and describe physician practice patterns in managing attention deficit/hyperactivity disorder (ADHD) across Europe. Physicians treating ADHD in the UK, France, Germany, Italy, the Netherlands and Spain were recruited. Each physician abstracted medical records of five patients (aged 6-17 years at time of review) with a documented diagnosis of ADHD made between January 2004 and June 2007. Data provided by the physician via the abstraction included (a) physician characteristics, (b) patient characteristics, (c) ADHD diagnosis and (d) ADHD outcomes (adherence, symptom control and satisfaction). A total of 779 patients met study inclusion criteria. In the overall population, patients' mean (SD) age at time of diagnosis was 8.9 (2.6) years. The predominant treatment choice was long-acting methylphenidate, which was prescribed to more than 56 % of patients. According to physicians, only 30.8 % of patients showed 'complete symptom control' on current treatment and only 31.8 % of physicians reported being 'very satisfied' with their patients' current treatment. Physicians' assessments of complete symptom control and physician satisfaction with treatment were low, indicating unmet needs with current ADHD management in Europe., Shire Development, LLC, Depto. de Medicina Legal, Psiquiatría y Patología, Fac. de Medicina, TRUE, pub

Ravi K Goyal,1 Jingchuan Zhang,2 Keith L Davis,1 Martina Sluga-O’Callaghan,1 Peter A Kaufman3 1Health Economics, RTI Health Solutions, Research Triangle Park, NC, USA; 2Eisai Inc., Nutley, NJ, USA; 3Larner College of Medicine, Division of Hematology/Oncology, University of Vermont Cancer Center, Burlington, VT, USACorrespondence: Peter A Kaufman, Larner College of Medicine, Division of Hematology/Oncology, University of Vermont Cancer Center, 111 Colchester Avenue, EP2, Burlington, VT, 05401, USA, Email Peter.Kaufman@uvmhealth.orgIntroduction: Eribulin was approved by the FDA in 2010 for the treatment of metastatic breast cancer (MBC) in the United States (US). More recently, several immuno-oncology (IO) and antibody–drug conjugate (ADC) regimens have been approved for MBC. We assessed the treatment patterns and clinical outcomes in MBC patients treated with eribulin following treatment with an IO or ADC in US clinical practice.Materials and Methods: In a retrospective patient medical chart review study, patients with MBC, aged ≥ 18 years, who initiated eribulin therapy between March 1, 2019, and September 30, 2020, treated with either prior IO or ADC in the metastatic setting were included. Patient demographics, treatment characteristics, and clinical outcomes were analyzed descriptively. Real-world progression-free survival (rwPFS) and overall survival (OS) were estimated using Kaplan–Meier analyses.Results: In the study population (N=143), median age at eribulin initiation was 62 years; 64% were Caucasian, and 67% had triple-negative MBC (TNBC). Eribulin therapy was used in the second to fifth line of therapy in the metastatic setting; median treatment duration was 7.2 months. The overall response rate for eribulin was 59.4%. Median rwPFS and OS from eribulin initiation were 21.4 months (95% CI, 12.9-not estimable [NE]) and 24.2 months (95% CI, 17.5-NE), respectively. In patients with TNBC, median rwPFS and OS from eribulin initiation were 12.0 months (95

While we instantaneously recognize a face as attractive, it is much harder to explain what exactly defines personal attraction. This suggests that attraction depends on implicit processing of complex, culturally and individually defined features. Generative adversarial neural networks (GANs), which learn to mimic complex data distributions, can potentially model subjective preferences unconstrained by pre-defined model parameterization. Here, we present generative brain-computer interfaces (GBCI), coupling GANs with brain-computer interfaces. GBCI first presents a selection of images and captures personalized attractiveness reactions toward the images via electroencephalography. These reactions are then used to control a GAN model, finding a representation that matches the features constituting an attractive image for an individual. We conducted an experiment (N = 30) to validate GBCI using a face-generating GAN and producing images that are hypothesized to be individually attractive. In double-blind evaluation of the GBCI-produced images against matched controls, we found GBCI yielded highly accurate results. Thus, the use of EEG responses to control a GAN presents a valid tool for interactive information-generation. Furthermore, the GBCI-derived images visually replicated known effects from social neuroscience, suggesting that the individually responsive, generative nature of GBCI provides a powerful, new tool in mapping individual differences and visualizing cognitive-affective processing.

We investigate inferring individual preferences and the contradiction of individual preferences with group preferences through direct measurement of the brain. We report an experiment where brain activity collected from 31 participants produced in response to viewing images is associated with their self-reported preferences. First, we show that brain responses present a graded response to preferences, and that brain responses alone can be used to train classifiers that reliably estimate preferences. Second, we show that brain responses reveal additional preference information that correlates with group preference, even when participants self-reported having no such preference. Our analysis of brain responses carries significant implications for researchers in general, as it suggests an individual's explicit preferences are not always aligned with the preferences inferred from their brain responses. These findings call into question the reliability of explicit and behavioral signals. They also imply that additional, multimodal sources of information may be necessary to infer reliable preference information.

This study (ReCORD-FL) sought to construct a historical control cohort to augment single-arm trials in relapsed/refractory follicular lymphoma (r/r FL). A retrospective study in 10 centers across North America and Europe was conducted. Adults with grade 1-3A FL were required to be r/r after >= 2 therapy lines including an anti-CD20 and an alkylator. After first becoming r/r, patients were required to initiate >= 1 additional therapy line, which defined the study index date. Endpoints were observed from start of each therapy line (including index line) until death, last follow-up, or December 31, 2020. Endpoints were complete response (CR) rate, overall response rate (ORR), time to next treatment or death (TNT-D), event-free survival (EFS), and overall survival (OS). One hundred eighty-seven patients were identified. Most patients' (80.2%) index therapy occurred in third line (3L) (range, 3L-6L). Median follow-up from FL diagnosis was 9 years (range, 1-21 years). CR and ORR to the index therapy were 39.0% and 70.6%, respectively. Median (95% confidence interval) EFS from index was 14.6 (11.0-18.0) months; median OS from index was 10.6 years. Outcomes worsened across successive treatment lines and for patients who were double refractory (r/r to both an anti-CD20 monoclonal antibody and an alkylator) or POD24 (progressed <= 24 months after front-line anti-CD20) at index. Findings demonstrate the unmet need of FL patients with multiply relapsed, double refractory, or POD24 disease. Based on robustness of the historical data collected and comparability with a previous study (SCHOLAR-5), ReCORD-FL presents a valuable source of control data for comparative studies in r/r FL.

Despite recent advances in deep neural models for semantic image editing, present approaches are dependent on explicit human input. Previous work assumes the availability of manually curated datasets for supervised learning, while for unsupervised approaches the human inspection of discovered components is required to identify those which modify worthwhile semantic features. Here, we present a novel alternative: the utilization of brain responses as a supervision signal for learning semantic feature representations. Participants $(N=30)$ in a neurophysiological experiment were shown artificially generated faces and instructed to look for a particular semantic feature, such as 'old' or 'smiling', while their brain responses were recorded via electroencephalography (EEG). Using supervision signals inferred from these responses, semantic features within the latent space of a generative adversarial network (GAN) were learned and then used to edit semantic features of new images. We show that implicit brain supervision achieves comparable semantic image editing performance to explicit manual labeling. This work demonstrates the feasibility of utilizing implicit human reactions recorded via brain-computer interfaces for semantic image editing and interpretation.