individualized diagnostic and prognostic models for patients with psychosis risk syndromes a meta analytic view on the state of the art

Abstract Background The Clinical High Risk(CHR) paradigm has facilitated research into the underpinnings of help-seeking individuals at risk for developing psychosis, aiming at predicting and possibly preventing transition to the overt disorder. Statistical methods like machine learning(ML) and Cox regression have provided the methodological basis for this research by enabling the construction of diagnostic, i.e., distinguishing CHR from healthy individuals, and prognostic models, i.e., predicting a future outcome, based on different data modalities, including clinical, neurocognitive, and neurobiological data. However, their translation to clinical practice is still hindered by the high heterogeneity both of CHR populations and methodologies applied. Methods We systematically reviewed the literature on diagnostic and prognostic models built on Cox regression and ML. Furthermore, we conducted a meta-analysis on prediction performances investigating heterogeneity of methodological approaches and data modality. Results Forty-four articles were included covering 3707 individuals for prognostic and 1052 for diagnostic studies(572 CHR and 480 healthy controls). CHR patients could be classified against healthy controls with 78% sensitivity and 77% specificity. Across prognostic models, sensitivity reached 67% and specificity 78%. ML models outperformed those applying Cox regression by 10% sensitivity. There was a publication bias for prognostic studies, yet no other moderator effects. Conclusions Our results may be driven by substantial clinical and methodological heterogeneity currently affecting several aspects of the CHR field and limiting the clinical implementability of the proposed models. We discuss conceptual and methodological harmonization strategies to facilitate more reliable and generalizable models for future clinical practice

T179. DO INDIVIDUALS IN A CLINICAL HIGH-RISK STATE FOR PSYCHOSIS DIFFER FROM HEALTHY CONTROLS IN THEIR CORTICAL FOLDING PATTERNS?

Background Volumetric brain differences between persons meeting criteria for a clinical high-risk state for psychosis (CHR) and healthy controls (HC) have been previously reported, yet little is known about potential abnormalities in surface-based morphological measures. Gyrification (i.e., the amount of cortical convolution) remains relatively stable across the lifespan and is minimally influenced by ubiquitous confounding factors (e.g., drug use, medication, or stress). Recently, a multi-site analysis conducted in 104 CHR persons found global increases in cortical gyrification compared to HC (Sasabayashi et al. 2017). If replicated, gyrification abnormalities in CHR could potentially serve as early neuromarkers of elevated risk, and thus could eventually be used to identify objectively and efficiently the CHR state. Methods A total of 124 CHR and 264 HC subjects were recruited as part of the PRONIA consortium (www.pronia.eu), a large-scale international longitudinal study currently consisting of 10 European sites. Cortical surfaces were reconstructed from structural MRI images using a volume-based, newly introduced technique called the Projection-Based-Thickness (PBT) as available in the SPM-based-toolbox CAT12. Local gyrification was quantified automatically across the whole brain as absolute mean curvature for each vertex of the brain surface mesh consisting of thousands of individual measurement points. Vertex-wise differences of curvature values were calculated applying a General Linear Model, corrected for age, gender and site effects. Results were investigated at corrected and uncorrected levels. Results We found no significant differences in vertex-wise gyrification between CHR and HC at either corrected or uncorrected levels (p>0.05). Further investigations of potential confounding site effects also did not reveal differences. Discussion Our preliminary findings suggest that CHR subjects do not show whole-brain gyrification abnormalities when compared with healthy subjects. These negative results agree with literature suggesting that cortical convolution might be more affected by neurodevelopmental or genetic factors, and thus deviations from normal patterns might not be detectable in heterogeneous samples of at-risk subjects wherein the etiology and ultimate prognosis is unknown. In order to better investigate differences in cortical folding and address the role of gyrification as neuroanatomical biomarker for psychosis, future investigations should focus on subgroups within CHR populations (e.g. patients groups defined by basic symptoms, ultra-high risk, or familial risk) in addition to specific analyses of individuals with higher neurodevelopmental (e.g., obstetric complications) or genetic (e.g., polygenic risk) loadings

Lickometry: A novel and sensitive method for assessing functional deficits in rats after stroke

The need for sensitive, easy to administer assessments of long-term functional deficits is crucial in pre-clinical stroke research. In the present study, we introduce lickometry (lick microstructure analysis) as a precise method to assess sensorimotor deficits up to 40 days after middle cerebral artery occlusion in rats. Impairments in drinking efficiency compared to controls, and a compensatory increase in the number of drinking clusters were observed. This highlights the utility of this easy to administer task in assessing subtle, long-term deficits, which could be likened to oral deficits in patients

HIV-1 gp120 N-linked glycosylation differs between plasma and leukocyte compartments

<p>Abstract</p> <p>Background</p> <p>N-linked glycosylation is a major mechanism for minimizing virus neutralizing antibody response and is present on the Human Immunodeficiency Virus (HIV) envelope glycoprotein. Although it is known that glycosylation changes can dramatically influence virus recognition by the host antibody, the actual contribution of compartmental differences in N-linked glycosylation patterns remains unclear.</p> <p>Methodology and Principal Findings</p> <p>We amplified the <it>env </it>gp120 C2-V5 region and analyzed 305 clones derived from plasma and other compartments from 15 HIV-1 patients. Bioinformatics and Bayesian network analyses were used to examine N-linked glycosylation differences between compartments. We found evidence for cellspecific single amino acid changes particular to monocytes, and significant variation was found in the total number of N-linked glycosylation sites between patients. Further, significant differences in the number of glycosylation sites were observed between plasma and cellular compartments. Bayesian network analyses showed an interdependency between N-linked glycosylation sites found in our study, which may have immense functional relevance.</p> <p>Conclusion</p> <p>Our analyses have identified single cell/compartment-specific amino acid changes and differences in N-linked glycosylation patterns between plasma and diverse blood leukocytes. Bayesian network analyses showed associations inferring alternative glycosylation pathways. We believe that these studies will provide crucial insights into the host immune response and its ability in controlling HIV replication <it>in vivo</it>. These findings could also have relevance in shielding and evasion of HIV-1 from neutralizing antibodies.</p

HIV-1 integrase polymorphisms are associated with prior antiretroviral drug exposure

In a recent summary of integrase sequences, primary integrase inhibitor mutations were rare. In a review of integrase inhibitor-naïve Australian HIV-1 sequences, primary mutations were not identified, although the accessory mutation G140S was detected. A link with previous antiretroviral therapy, intra-subtype B divergence across the integrase gene and transmission of integrase polymorphisms were also noted. Based on these findings, we would recommend ongoing surveillance of integrase mutations, and integrase region sequencing for patients prior to commencement of integrase inhibitors

The progression of disorder-specific brain pattern expression in schizophrenia over 9 years.

Age plays a crucial role in the performance of schizophrenia vs. controls (SZ-HC) neuroimaging-based machine learning (ML) models as the accuracy of identifying first-episode psychosis from controls is poor compared to chronic patients. Resolving whether this finding reflects longitudinal progression in a disorder-specific brain pattern or a systematic but non-disorder-specific deviation from a normal brain aging (BA) trajectory in schizophrenia would help the clinical translation of diagnostic ML models. We trained two ML models on structural MRI data: an SZ-HC model based on 70 schizophrenia patients and 74 controls and a BA model (based on 561 healthy individuals, age range = 66 years). We then investigated the two models' predictions in the naturalistic longitudinal Northern Finland Birth Cohort 1966 (NFBC1966) following 29 schizophrenia and 61 controls for nine years. The SZ-HC model's schizophrenia-specificity was further assessed by utilizing independent validation (62 schizophrenia, 95 controls) and depression samples (203 depression, 203 controls). We found better performance at the NFBC1966 follow-up (sensitivity = 75.9%, specificity = 83.6%) compared to the baseline (sensitivity = 58.6%, specificity = 86.9%). This finding resulted from progression in disorder-specific pattern expression in schizophrenia and was not explained by concomitant acceleration of brain aging. The disorder-specific pattern's progression reflected longitudinal changes in cognition, outcomes, and local brain changes, while BA captured treatment-related and global brain alterations. The SZ-HC model was also generalizable to independent schizophrenia validation samples but classified depression as control subjects. Our research underlines the importance of taking account of longitudinal progression in a disorder-specific pattern in schizophrenia when developing ML classifiers for different age groups

Influenza epidemiology in patients admitted to sentinel Australian hospitals in 2018: the Influenza Complications Alert Network (FluCAN)

TThe Influenza Complications Alert Network (FluCAN) is a sentinel hospital-based surveillance program that operates at sites in all jurisdictions in Australia. This report summarises the epidemiology of hospitalisations with laboratory-confirmed influenza during the 2018 influenza season. In this observational surveillance system, cases were defined as patients admitted to any of the 17 sentinel hospitals with influenza confirmed by nucleic acid detection. Data were also collected on a frequencymatched control group of influenza-negative patients admitted with acute respiratory infection. During the period 3 April to 31 October 2018 (the 2018 influenza season), 769 patients were admitted with confirmed influenza to one of 17 FluCAN sentinel hospitals. Of these, 30% were elderly (≥65 years), 28% were children (<16 years), 6.4% were Aboriginal and Torres Strait Islander peoples, 2.2% were pregnant and 66% had chronic comorbidities. A small proportion of FluCAN admissions were due to influenza B (13%). Estimated vaccine coverage was 77% in the elderly (≥65 years), 45% in nonelderly adults with medical comorbidities and 26% in children (<16 years) with medical comorbidities. The estimated vaccine effectiveness (VE) in the target population was 52% (95% CI: 37%, 63%). There were a smaller number of hospital admissions detected with confirmed influenza in this national observational surveillance system in 2018 than in 2017, with the demographic profile reflecting the change in circulating subtype from A/H3N2 to A/H1N1.We acknowledge the support of the Australian Government Department of Health and the Victorian Department of Health and Human Services for funding this system

Influenza epidemiology, vaccine coverage and vaccine effectiveness in sentinel Australian hospitals in 2013: the Influenza Complications Alert Network

The National Influenza Program aims to reduce serious morbidity and mortality from influenza by providing public funding for vaccination to at-risk groups. The Influenza Complications Alert Network (FluCAN) is a sentinel hospital-based surveillance program that operates at 14 sites in all states and territories in Australia. This report summarises the epidemiology of hospitalisations with confirmed influenza, estimates vaccine coverage and influenza vaccine protection against hospitalisation with influenza during the 2013 influenza season. In this observational study, cases were defined as patients admitted to one of the sentinel hospitals, with influenza confirmed by nucleic acid testing. Controls were patients who had acute respiratory illnesses who were test-negative for influenza. Vaccine effectiveness was estimated as 1 minus the odds ratio of vaccination in case patients compared with control patients, after adjusting for known confounders. During the period 5 April to 31 October 2013, 631 patients were admitted with confirmed influenza at the 14 FluCAN sentinel hospitals. Of these, 31% were more than 65 years of age, 9.5% were Indigenous Australians, 4.3% were pregnant and 77% had chronic co-morbidities. Influenza B was detected in 30% of patients. Vaccination coverage was estimated at 81% in patients more than 65 years of age but only 49% in patients aged less than 65 years with chronic comorbidities. Vaccination effectiveness against hospitalisation with influenza was estimated at 50% (95% confidence interval: 33%, 63%,

O8.5. SIGNS OF ADVERSITY - A NOVEL MACHINE LEARNING APPROACH TO CHILDHOOD TRAUMA, BRAIN STRUCTURE AND CLINICAL PROFILES

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