Clinical predictors of therapeutic response to antipsychotics in schizophrenia

The search for clinical outcome predictors for schizophrenia is as old as the field of psychiatry. However, despite a wealth of large, longitudinal studies into prognostic factors, only very few clinically useful outcome predictors have been identified. The goal of future treatment is to either affect modifiable risk factors, or use nonmodifiable factors to parse patients into therapeutically meaningful subgroups. Most clinical outcome predictors are nonspecific and/or nonmodifiable. Nonmodifiable predictors for poor odds of remission include male sex, younger age at disease onset, poor premorbid adjustment, and severe baseline psychopathology. Modifiable risk factors for poor therapeutic outcomes that clinicians can act upon include longer duration of untreated illness, nonadherence to antipsychotics, comorbidities (especially substance-use disorders), lack of early antipsychotic response, and lack of improvement with non-clozapine antipsychotics, predicting clozapine response. It is hoped that this limited capacity for prediction will improve as pathophysiological understanding increases and/or new treatments for specific aspects of schizophrenia become available

Role of social media and the Internet in pathways to care for adolescents and young adults with psychotic disorders and non-psychotic mood disorders

AIMS: Although psychosis often occurs during adolescence, there has been little research on how the ubiquitously used Internet and social media could impact pathways to care. We examined how youth with psychotic spectrum disorders (PSD) versus non-psychotic mood disorders (NPMD) use online resources in the early illness stages. METHODS: Social media use and pathways to care data were collected using a semi-structured interview from 80 youth (PSD = 40 and NPMD = 40) aged 12-21 years within 2 years of symptom onset. RESULTS: A total of 97.5% of participants (mean age = 18.3 years) regularly used social media, spending approximately 2.6 +/- 2.5 h per day online. There were 22.4% of our sample (PSD = 19.4%, NPMD = 25.0%, P = 0.56) who reported waiting to reach out for help believing that symptoms would disappear. A total of 76.5% (PSD = 67.5%, NPMD = 85.0%, P = 0.06) noticed social media habit changes during symptom emergence. Thirty per cent reported discussing their symptoms on social media (PSD = 22.5%, NPMD = 37.5%, P = 0.14). NPMD patients sought information most on how to stop symptoms (40.0% vs. 13.5%, P = 0.01), while PSD youth were more commonly interested in what caused their symptoms (21.6% vs. 15.0%, P = 0.45). More PSD patients (42.9% vs. 25.0%, P = 0.10) would prefer to receive mental health information via the Internet. Altogether, 63.6% (PSD = 64.9%, NPMD = 62.5%, P = 0.83) were amenable to clinicians proactively approaching them via social media during symptom emergence. A total of 74.3% (PSD = 78.4%, NPMD = 70.0%, P = 0.40) liked the idea of obtaining help/advice from professionals via social media. CONCLUSIONS: The Internet and social media provide an unparalleled opportunity to supplement and potentially transform early intervention services, and acceptance of this approach appears to be high

Analysis and Observations from the First Amazon Picking Challenge

This paper presents a overview of the inaugural Amazon Picking Challenge along with a summary of a survey conducted among the 26 participating teams. The challenge goal was to design an autonomous robot to pick items from a warehouse shelf. This task is currently performed by human workers, and there is hope that robots can someday help increase efficiency and throughput while lowering cost. We report on a 28-question survey posed to the teams to learn about each team's background, mechanism design, perception apparatus, planning and control approach. We identify trends in this data, correlate it with each team's success in the competition, and discuss observations and lessons learned based on survey results and the authors' personal experiences during the challenge

Recovering 6D Object Pose: A Review and Multi-modal Analysis

A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modality. Interpreting the depth data, the study in this paper presents thorough multi-modal analyses. It discusses the above-mentioned challenges for full 6D object pose estimation in RGB-D images comparing the performances of several 6D detectors in order to answer the following questions: What is the current position of the computer vision community for maintaining "automation" in robotic manipulation? What next steps should the community take for improving "autonomy" in robotics while handling objects? Our findings include: (i) reasonably accurate results are obtained on textured-objects at varying viewpoints with cluttered backgrounds. (ii) Heavy existence of occlusion and clutter severely affects the detectors, and similar-looking distractors is the biggest challenge in recovering instances' 6D. (iii) Template-based methods and random forest-based learning algorithms underlie object detection and 6D pose estimation. Recent paradigm is to learn deep discriminative feature representations and to adopt CNNs taking RGB images as input. (iv) Depending on the availability of large-scale 6D annotated depth datasets, feature representations can be learnt on these datasets, and then the learnt representations can be customized for the 6D problem

Delayed identification and diagnosis of Huntington\u27s disease due to psychiatric symptoms

Huntington\u27s disease (HD) is a progressive neurodegenerative illness that affects 2-9/100.000 of the general population. The usual onset is at around age 35-40 years, but there were cases with onset above 55 years. The disease manifests clinically with many neurological and psychiatric symptoms, leading in advanced phases to dementia, but cognitive symptoms are frequently present much earlier in the disease course. HD is caused by an expanded polyglutamine stretch in the N-terminal part of a 350 kDa protein called huntingtin (HTT). This stretch is encoded by a trinucleotide CAG repetition in exon 1 of HTT. An expansion of greater than 36 repeats results in HD. The number of repeats is inversely correlated with the age of onset of motor symptoms, and disease onset during childhood or adolescence is associated with more than 60 CAG repeats. Mood disturbances may be one of the earliest symptoms of HD and may precede the onset of the motor pheno-type for almost 10 years. Neuropsychiatric symptoms may delay the appropriate diagnosis of HD and have major implications for disease management, prognosis and quality of life for patients and families. This case study is about a 58 years old female patient with late identification of Huntington\u27s disease after two admissions to psychiatric inpatient units, for the treatment of behavioral disturbances