Identifying clinical clusters with distinct trajectories in first-episode psychosis through an unsupervised machine learning technique

The extreme variability in symptom presentation reveals that individuals diagnosed with a first-episode psychosis (FEP) may encompass different sub-populations with potentially different illness courses and, hence, different treatment needs. Previous studies have shown that sociodemographic and family environment factors are associated with more unfavorable symptom trajectories. The aim of this study was to examine the dimensional structure of symptoms and to identify individuals’ trajectories at early stage of illness and potential risk factors associated with poor outcomes at follow-up in non-affective FEP. One hundred and forty-four non-affective FEP patients were assessed at baseline and at 2-year follow-up. A Principal component analysis has been conducted to identify dimensions, then an unsupervised machine learning technique (fuzzy clustering) was performed to identify clinical subgroups of patients. Six symptom factors were extracted (positive, negative, depressive, anxiety, disorganization and somatic/cognitive). Three distinct clinical clusters were determined at baseline: mild; negative and moderate; and positive and severe symptoms, and five at follow-up: minimal; mild; moderate; negative and depressive; and severe symptoms. Receiving a low-dose antipsychotic, having a more severe depressive symptomatology and a positive family history for psychiatric disorders were risk factors for poor recovery, whilst having a high cognitive reserve and better premorbid adjustment may confer a better prognosis. The current study provided a better understanding of the heterogeneous profile of FEP. Early identification of patients who could likely present poor outcomes may be an initial step for the development of targeted interventions to improve illness trajectories and preserve psychosocial functioning

Cognitive clusters in first-episode psychosis

Impairments in a broad range of cognitive domains have been consistently reported in some individuals with first-episode psychosis (FEP). Cognitive deficits can be observed during the prodromal stage. However, the course of cognitive deficits is still unclear. The aim of this study was to identify cognitive subgroups over time and to compare their sociodemographic, clinical and functional profiles. A total of 114 patients with Schizophrenia Spectrum Disorders were included in the present study. We assessed subjects through psychiatric scales and eight neuropsychological tests at baseline and at two-year follow-up visit. We performed the Partition Around Medoids algorithm with all cognitive variables. Furthermore, we performed a logistic regression to identify the predictors related to the different cognitive clusters at follow-up. Two distinct subgroups were found: the first cluster characterized by cognitive impairment and a second cluster had relatively intact cognition in comparison with norms. Up to 54.7% of patients with cognitive deficits at baseline tended to improve during the first two years of treatment. Patients with intact cognition at follow-up had a higher socioeconomic status, later age of onset, lower negative symptoms and a higher cognitive reserve (CR) at baseline. CR and age of onset were the baseline variables that predicted cognitive impairment. This research allows us to obtain a better understanding of the heterogeneous profile of psychotic disorders. Identifying the characteristics of patients who will present a cognitive impairment could improve early detection and intervention. These results suggest that enhancing CR could contribute to improving the course of the illness. © 2021 Elsevier B.V

Cognitive clusters in first-episode psychosis

Impairments in a broad range of cognitive domains have been consistently reported in some individuals with first-episode psychosis (FEP). Cognitive deficits can be observed during the prodromal stage. However, the course of cognitive deficits is still unclear. The aim of this study was to identify cognitive subgroups over time and to compare their sociodemographic, clinical and functional profiles. A total of 114 patients with Schizophrenia Spectrum Disorders were included in the present study. We assessed subjects through psychiatric scales and eight neuropsychological tests at baseline and at two-year follow-up visit. We performed the Partition Around Medoids algorithm with all cognitive variables. Furthermore, we performed a logistic regression to identify the predictors related to the different cognitive clusters at follow-up. Two distinct subgroups were found: the first cluster characterized by cognitive impairment and a second cluster had relatively intact cognition in comparison with norms. Up to 54.7% of patients with cognitive deficits at baseline tended to improve during the first two years of treatment. Patients with intact cognition at follow-up had a higher socioeconomic status, later age of onset, lower negative symptoms and a higher cognitive reserve (CR) at baseline. CR and age of onset were the baseline variables that predicted cognitive impairment. This research allows us to obtain a better understanding of the heterogeneous profile of psychotic disorders. Identifying the characteristics of patients who will present a cognitive impairment could improve early detection and intervention. These results suggest that enhancing CR could contribute to improving the course of the illness

Cognitive clusters in first-episode psychosis

Impairments in a broad range of cognitive domains have been consistently reported in some individuals with first-episode psychosis (FEP). Cognitive deficits can be observed during the prodromal stage. However, the course of cognitive deficits is still unclear. The aim of this study was to identify cognitive subgroups over time and to compare their sociodemographic, clinical and functional profiles. A total of 114 patients with Schizophrenia Spectrum Disorders were included in the present study. We assessed subjects through psychiatric scales and eight neuropsychological tests at baseline and at two-year follow-up visit. We performed the Partition Around Medoids algorithm with all cognitive variables. Furthermore, we performed a logistic regression to identify the predictors related to the different cognitive clusters at follow-up. Two distinct subgroups were found: the first cluster characterized by cognitive impairment and a second cluster had relatively intact cognition in comparison with norms. Up to 54.7% of patients with cognitive deficits at baseline tended to improve during the first two years of treatment. Patients with intact cognition at follow-up had a higher socioeconomic status, later age of onset, lower negative symptoms and a higher cognitive reserve (CR) at baseline. CR and age of onset were the baseline variables that predicted cognitive impairment. This research allows us to obtain a better understanding of the heterogeneous profile of psychotic disorders. Identifying the characteristics of patients who wil

The Transcriptome Analysis of Strongyloides stercoralis L3i Larvae Reveals Targets for Intervention in a Neglected Disease

BackgroundStrongyloidiasis is one of the most neglected diseases distributed worldwide with endemic areas in developed countries, where chronic infections are life threatening. Despite its impact, very little is known about the molecular biology of the parasite involved and its interplay with its hosts. Next generation sequencing technologies now provide unique opportunities to rapidly address these questions.Principal FindingsHere we present the first transcriptome of the third larval stage of S. stercoralis using 454 sequencing coupled with semi-automated bioinformatic analyses. 253,266 raw sequence reads were assembled into 11,250 contiguous sequences, most of which were novel. 8037 putative proteins were characterized based on homology, gene ontology and/or biochemical pathways. Comparison of the transcriptome of S. strongyloides with those of other nematodes, including S. ratti, revealed similarities in transcription of molecules inferred to have key roles in parasite-host interactions. Enzymatic proteins, like kinases and proteases, were abundant. 1213 putative excretory/secretory proteins were compiled using a new pipeline which included non-classical secretory proteins. Potential drug targets were also identified.ConclusionsOverall, the present dataset should provide a solid foundation for future fundamental genomic, proteomic and metabolomic explorations of S. stercoralis, as well as a basis for applied outcomes, such as the development of novel methods of intervention against this neglected parasite

Social Media, Gender and the Mediatisation of War: Exploring the German Armed Forces’ Visual Representation of the Afghanistan Operation on Facebook

Studies on the mediatisation of war point to attempts of governments to regulate the visual perspective of their involvements in armed conflict – the most notable example being the practice of ‘embedded reporting’ in Iraq and Afghanistan. This paper focuses on a different strategy of visual meaning-making, namely, the publication of images on social media by armed forces themselves. Specifically, we argue that the mediatisation of war literature could profit from an increased engagement with feminist research, both within Critical Security/Critical Military Studies and within Science and Technology Studies that highlight the close connection between masculinity, technology and control. The article examines the German military mission in Afghanistan as represented on the German armed forces’ official Facebook page. Germany constitutes an interesting, and largely neglected, case for the growing literature on the mediatisation of war: its strong antimilitarist political culture makes the representation of war particularly delicate. The paper examines specific representational patterns of Germany’s involvement in Afghanistan and discusses the implications which arise from what is placed inside the frame of visibility and what remains out of its view