Effectiveness of psychoeducational interventions for family carers of people with psychosis: A systematic review and meta-analysis.

Psychoeducational interventions for family carers of people with psychosis are effective for improving compliance and preventing relapse. Whether carers benefit from these interventions has been little explored. This systematic review investigated the effectiveness of psychoeducation for improving carers' outcomes, and potential treatment moderators. We searched for randomised controlled trials (RCTs) published in English or Chinese in eight databases. Carers' outcomes included wellbeing, quality of life, global morbidities, burden, and expressed emotion. Thirty-two RCTs were included, examining 2858 carers. Intervention duration ranged from 4 to 52weeks, and contact times ranged from 6 to 42hours. At post intervention, findings were equivocal for carers' wellbeing (SMD 0.103, 95% CI -0.186 to 0.392). Conversely, psychoeducation was superior in reducing carers' global morbidities (SMD -0.230, 95% CI -0.386 to -0.075), perceived burden (SMD -0.434, 95% CI -0.567 to -0.31), negative caregiving experiences (SMD -0.210, 95% CI -0.396 to -0.025) and expressed emotion (SMD -0.161, 95% CI -0.367 to -0.045). The lack of available data precluded meta-analysis of outcomes beyond short-term follow-up. Meta-regression revealed no significant associations between intervention modality, duration, or contact time and outcomes. Further research should focus on improving carers' outcomes in the longer-term and identifying factors to optimise intervention design

Effectiveness of psychoeducational interventions for family carers of people

Psychoeducational interventions for family carers of people with psychosis are effective for improving compliance and preventing relapse. Whether carers benefit from these interventions has been little explored. This systematic review investigated the effectiveness of psychoeducation for improving carers' outcomes, and potential treatment moderators. We searched for randomised controlled trials (RCTs) published in English or Chinese in eight databases. Carers' outcomes included wellbeing, quality of life, global morbidities, burden, and expressed emotion. Thirty-two RCTs were included, examining 2858 carers. Intervention duration ranged from 4 to 52 weeks, and contact times ranged from 6 to 42 hours. At post intervention, findings were equivocal for carers' wellbeing (SMD 0.103, 95% CI − 0.186 to 0.392). Conversely, psychoeducation was superior in reducing carers' global morbidities (SMD − 0.230, 95% CI − 0.386 to − 0.075), perceived burden (SMD − 0.434, 95% CI − 0.567 to − 0.31), negative caregiving experiences (SMD − 0.210, 95% CI − 0.396 to − 0.025) and expressed emotion (SMD − 0.161, 95% CI − 0.367 to − 0.045). The lack of available data precluded meta-analysis of outcomes beyond short-term follow-up. Meta-regression revealed no significant associations between intervention modality, duration, or contact time and outcomes. Further research should focus on improving carers' outcomes in the longer-term and identifying factors to optimise intervention design

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation