Mallingstuart1893

The bodily self is key to emotional embodiment, which is important for social functioning and emotion regulation. There is a paucity of research systematically assessing how basic and bodily self-disturbances relate to multimodal hallucinations. This study hypothesised that participants with greater hallucination-proneness would report greater degrees of basic and bodily self-disturbance and would demonstrate more ambiguous and less discrete mapping of emotional embodiment. Stage one screened non-clinical participants' degree of hallucination-proneness. Stage two participants completed seven further questionnaires. Hierarchical linear regression modelled the influence of hallucination-proneness and covariates on measures of basic and bodily self-disturbance and sensed presence. Stage two participants also completed a computerised body mapping task (EmBODY) which assessed emotional embodiment. Topographical maps were generated to compare patterns of embodiment between high and low hallucination-proneness groups. 55 respondents participated in stage two, with 18 participants from the high or low hallucination-proneness groups completing EmBODY. In the hierarchical regression analyses, the addition of a measure of hallucination proneness in the final step only increased predictive power where the dependent variable assessed sensed presence (p = 0.035 and p = 0.009, respectively). The EmBODY data revealed that participants with low hallucination-proneness consistently reported more bodily activation across 14 emotional states, whereas the high hallucination-proneness group reported more deactivation. In conclusion, hallucination-proneness was most strongly associated with sensed presence experiences. Patterns of embodiment appeared similar between the two groups, despite consistent differences in activation and deactivation. These findings are exploratory and need to be confirmed in a larger sample.

Historically, individuals managing serious mental illness (SMI) have often been excluded from research, typically because of concern that these individuals may not be able to understand and provide truly informed consent. As treatment has improved, the assumption that individuals managing SMI may not be capable of consent needs to be re-examined. Systematic exclusion from research may limit empirically tested treatments available for people managing SMI, and may contribute to the health care disparities seen in this population.

This article examines this issue by documenting current rates of research exclusion for high disease burden conditions, based on empirical review of studies in ClinicalTrials.gov.

Current rates of exclusion from studies for psychiatric conditions were assessed through systematic review of relevant clinical trials on ClinicalTrials.gov.

Subjects in this inquiry are either articles accessed in the literature reviews, or descriptions of studies in public data on ClinicalTrials.govzable sampling.

Evidence indicates that it is ethically and scientifically more appropriate to exclude based on capacity to consent than membership in the group of individuals managing SMI. The discussion outlines techniques researchers can use for more equitable and generalizable sampling.While first-episode schizophrenia has received extensive attention in the literature, few studies have focused on the first episode of affective psychoses. Considering the lack of structured data regarding this diagnostic grouping commonly used in clinical settings, our aim was to scope the literature on first-episode affective psychoses to consolidate current knowledge and to identify areas to be targeted in future studies. We also planned to investigate the relevance of the "affective psychosis" concept regarding diagnostic categories and specific needs of intervention. We conducted a search on the Embase, Medline, PubMed, PsycINFO and Web Of Science databases until October 2020. We selected studies and synthesized the key findings into a narrative review regarding major topics of early intervention research diagnostic categorization, premorbid factors, intervention, duration of untreated illness, neurobiology and neurocognition. After screening 961 titles and abstracts and 193 full-text papers, we selected 77 studies for inclusion. Our results showed heterogeneity in diagnosis-related grouping under the concept of affective psychoses, especially variability regarding the inclusion of schizoaffective disorder. Nonetheless, this concept still encompasses patients with different psychopathological and neurocognitive profiles from the non-affective patients requiring specialized intervention. This study thus provided support for the relevance of this concept as well as a need for further investigation.NRXN1 is involved in synaptogenesis and have been implicated in Autism spectrum disorders. However, many rare inherited missense variants of NRXN1 have not been thoroughly evaluated. Here, functional analyses in vitro and in Drosophila of three NRXN1 missense mutations, Y282H, L893V, and I1135V identified in ASD patients in our previous study were performed. Our results showed these three mutations interfered protein degradation compared with NRXN1-WT protein. Expressing human NRXN1 in Drosophila could lead to abnormal circadian rhythm and sleep behavior, and three mutated proteins caused milder phenotypes, indicating the mutations may change the function of NRXN1 slightly. These findings highlight the functional role of rare NRXN1 missense variants identified in autism patients, and provide clues for us to better understand the pathogenesis of abnormal circadian rhythm and sleep behavior of other organisms, including humans.The most common metastatic sites of pancreatic cancer are the liver, lymph nodes, peritoneum and lung. Here we report two cases of BRCA mutated pancreatic cancer that developed unusual metastasis while treatment with maintenance Olaparib and leading to rapid death. We hereby review the literature and address the possibility of a different nature and tumour biology of BRCA mutated cancer treated with PARP inhibitors.Unsupervised abnormality detection is an appealing approach to identify patterns that are not present in training data without specific annotations for such patterns. In the medical imaging field, methods taking this approach have been proposed to detect lesions. The appeal of this approach stems from the fact that it does not require lesion-specific supervision and can potentially generalize to any sort of abnormal patterns. The principle is to train a generative model on images from healthy individuals to estimate the distribution of images of the normal anatomy, i.e., a normative distribution, and detect lesions as out-of-distribution regions. Restoration-based techniques that modify a given image by taking gradient ascent steps with respect to a posterior distribution composed of a normative distribution and a likelihood term recently yielded state-of-the-art results. However, these methods do not explicitly model ascent directions with respect to the normative distribution, i.e. normative ascent direction, which is essential for successful restoration. In this work, we introduce a novel approach for unsupervised lesion detection by modeling normative ascent directions. We present different modelling options based on the defined ascent directions with local Gaussians. We further extend the proposed method to efficiently utilize 3D information, which has not been explored in most existing works. We experimentally show that the proposed method provides higher accuracy in detection and produces more realistic restored images. The performance of the proposed method is evaluated against baselines on publicly available BRATS and ATLAS stroke lesion datasets; the detection accuracy of the proposed method surpasses the current state-of-the-art results.Judging swallowing kinematic impairments via videofluoroscopy represents the gold standard for the detection and evaluation of swallowing disorders. However, the efficiency and accuracy of such a biomechanical kinematic analysis vary significantly among human judges affected mainly by their training and experience. Here, we showed that a novel machine learning algorithm can with high accuracy automatically detect key anatomical points needed for a routine swallowing assessment in real-time. We trained a novel two-stage convolutional neural network to localize and measure the vertebral bodies using 1518 swallowing videofluoroscopies from 265 patients. Our network model yielded high accuracy as the mean distance between predicted points and annotations was 4.20 ± 5.54 pixels. In comparison, human inter-rater error was 4.35 ± 3.12 pixels. Furthermore, 93% of predicted points were less than five pixels from annotated pixels when tested on an independent dataset from 70 subjects. Our model offers more choices for speech language pathologists in their routine clinical swallowing assessments as it provides an efficient and accurate method for anatomic landmark localization in real-time, a task previously accomplished using an off-line time-sinking procedure.The assessment of myocardial perfusion has become increasingly important in the early diagnosis of coronary artery disease. Currently, the process of perfusion assessment is time-consuming and subjective. Although automated methods by threshold processing have been proposed, they cannot obtain an accurate perfusion assessment. Thus, there is a great clinical demand to obtain a rapid and accurate assessment of myocardial perfusion through a standard procedure using an automated algorithm. In this work, we present a spatio-temporal multi-task network cascade (ST-MNC) to provide an accurate and robust assessment of myocardial perfusion. The proposed network captures patch-based spatio-temporal representations for each pixel through a spatio-temporal encoder-decoder network. Mallotoxin Then the multi-task network cascade uses spatio-temporal representations as shared features to predict various perfusion parameters and myocardial ischemic regions. Extensive experiments on CT images of 232 subjects demonstrate ST-MNC could produce a good approximation for perfusion parameters and an accurate classification for ischemic regions. These results show that our proposed method can provide a fast and accurate assessment of myocardial perfusion.Traditional soaking method takes days to remove cassava cyanide. Ten minutes of ultrasonic pretreatment (UPT) was found to be a new effective method to eliminate both cyanogenic glycosides and hydrogen cyanide in cassava. Here, the parameters of UPT were optimized and the underlying mechanisms were investigated. 40.36% and 24.95% of hydrogen cyanide and cyanogenic glycosides in cassava juice were eliminated under 10 min of UPT (45℃, 81 W). UPT before boiling enhanced the total cyanide elimination to 41.94%. The degradation patterns of hydrogen cyanide and cyanogenic glycosides were different. Ultrasound directly eliminated hydrogen cyanide and indirectly degraded cyanogenic glycosides through promoting enzymatic hydrolysis. The β-glucosidase activity was increased by 17.99% induced by ultrasound. This was supported by the movement of hydrophobic residual and the rearrangement of the secondary structure of the molecular as found in fluorescence, CD, FTIR, DSC and TG analysis. This study revealed that UPT acted as a fast and simple technical way in improving cassava safety.