Validation of the bag-mediated filtration system for environmental surveillance of poliovirus in Nairobi, Kenya.

AIMS: This study compared the bag-mediated filtration system (BMFS) and standard WHO two-phase separation methods for poliovirus (PV) environmental surveillance, examined factors impacting PV detection and monitored Sabin-like (SL) PV type 2 presence with withdrawal of oral polio vaccine type 2 (OPV2) in April 2016. METHODS AND RESULTS: Environmental samples were collected in Nairobi, Kenya (Sept 2015-Feb 2017), concentrated via BMFS and two-phase separation methods, then assayed using the WHO PV isolation algorithm and intratypic differentiation diagnostic screening kit. SL1, SL2 and SL3 were detected at higher rates in BMFS than two-phase samples (P < 0·05). In BMFS samples, SL PV detection did not significantly differ with volume filtered, filtration time or filter shipment time (P > 0·05), while SL3 was detected less frequently with higher shipment temperatures (P = 0·027). SL2 was detected more frequently before OPV2 withdrawal in BMFS and two-phase samples (P < 1 × 10-5 ). CONCLUSIONS: Poliovirus was detected at higher rates with the BMFS, a method that includes a secondary concentration step, than using the standard WHO two-phase method. SL2 disappearance from the environment was commensurate with OPV2 withdrawal. SIGNIFICANCE AND IMPACT OF THE STUDY: The BMFS offers comparable or improved PV detection under the conditions in this study, relative to the two-phase method.

Early identification of bipolar disorder among young adults - a 22-year community birth cohort.

OBJECTIVE: We set forth to build a prediction model of individuals who would develop bipolar disorder (BD) using machine learning techniques in a large birth cohort. METHODS: A total of 3748 subjects were studied at birth, 11, 15, 18, and 22 years of age in a community birth cohort. We used the elastic net algorithm with 10-fold cross-validation to predict which individuals would develop BD at endpoint (22 years) at each follow-up visit before diagnosis (from birth up to 18 years). Afterward, we used the best model to calculate the subgroups of subjects at higher and lower risk of developing BD and analyzed the clinical differences among them. RESULTS: A total of 107 (2.8%) individuals within the cohort presented with BD type I, 26 (0.6%) with BD type II, and 87 (2.3%) with BD not otherwise specified. Frequency of female individuals was 58.82% (n = 150) in the BD sample and 53.02% (n = 1868) among the unaffected population. The model with variables assessed at the 18-year follow-up visit achieved the best performance: AUC 0.82 (CI 0.75-0.88), balanced accuracy 0.75, sensitivity 0.72, and specificity 0.77. The most important variables to detect BD at the 18-year follow-up visit were suicide risk, generalized anxiety disorder, parental physical abuse, and financial problems. Additionally, the high-risk subgroup of BD showed a high frequency of drug use and depressive symptoms. CONCLUSIONS: We developed a risk calculator for BD incorporating both demographic and clinical variables from a 22-year birth cohort. Our findings support previous studies in high-risk samples showing the significance of suicide risk and generalized anxiety disorder prior to the onset of BD, and highlight the role of social factors and adverse life events.

Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group.

Despite decades of research, the pathophysiology of bipolar disorder (BD) is still not well understood. Structural brain differences have been associated with BD, but results from neuroimaging studies have been inconsistent. To address this, we performed the largest study to date of cortical gray matter thickness and surface area measures from brain magnetic resonance imaging scans of 6503 individuals including 1837 unrelated adults with BD and 2582 unrelated healthy controls for group differences while also examining the effects of commonly prescribed medications, age of illness onset, history of psychosis, mood state, age and sex differences on cortical regions. In BD, cortical gray matter was thinner in frontal, temporal and parietal regions of both brain hemispheres. BD had the strongest effects on left pars opercularis (Cohen's d=-0.293; P=1.71 × 10-21), left fusiform gyrus (d=-0.288; P=8.25 × 10-21) and left rostral middle frontal cortex (d=-0.276; P=2.99 × 10-19). Longer duration of illness (after accounting for age at the time of scanning) was associated with reduced cortical thickness in frontal, medial parietal and occipital regions. We found that several commonly prescribed medications, including lithium, antiepileptic and antipsychotic treatment showed significant associations with cortical thickness and surface area, even after accounting for patients who received multiple medications. We found evidence of reduced cortical surface area associated with a history of psychosis but no associations with mood state at the time of scanning. Our analysis revealed previously undetected associations and provides an extensive analysis of potential confounding variables in neuroimaging studies of BD.

Hippocampal subfield volumes in mood disorders.

Volume reduction and shape abnormality of the hippocampus have been associated with mood disorders. However, the hippocampus is not a uniform structure and consists of several subfields, such as the cornu ammonis (CA) subfields CA1-4, the dentate gyrus (DG) including a granule cell layer (GCL) and a molecular layer (ML) that continuously crosses adjacent subiculum (Sub) and CA fields. It is known that cellular and molecular mechanisms associated with mood disorders may be localized to specific hippocampal subfields. Thus, it is necessary to investigate the link between the in vivo hippocampal subfield volumes and specific mood disorders, such as bipolar disorder (BD) and major depressive disorder (MDD). In the present study, we used a state-of-the-art hippocampal segmentation approach, and we found that patients with BD had reduced volumes of hippocampal subfields, specifically in the left CA4, GCL, ML and both sides of the hippocampal tail, compared with healthy subjects and patients with MDD. The volume reduction was especially severe in patients with bipolar I disorder (BD-I). We also demonstrated that hippocampal subfield volume reduction was associated with the progression of the illness. For patients with BD-I, the volumes of the right CA1, ML and Sub decreased as the illness duration increased, and the volumes of both sides of the CA2/3, CA4 and hippocampal tail had negative correlations with the number of manic episodes. These results indicated that among the mood disorders the hippocampal subfields were more affected in BD-I compared with BD-II and MDD, and manic episodes had focused progressive effect on the CA2/3 and CA4 and hippocampal tail.

Areas of controversy in neuroprogression in bipolar disorder.

OBJECTIVE: We aimed to review clinical features and biological underpinnings related to neuroprogression in bipolar disorder (BD). Also, we discussed areas of controversy and future research in the field. METHOD: We systematically reviewed the extant literature pertaining to neuroprogression and BD by searching PubMed and EMBASE for articles published up to March 2016. RESULTS: A total of 114 studies were included. Neuroimaging and clinical evidence from cross-sectional and longitudinal studies show that a subset of patients with BD presents a neuroprogressive course with brain changes and unfavorable outcomes. Risk factors associated with these unfavorable outcomes are number of mood episodes, early trauma, and psychiatric and clinical comorbidity. CONCLUSION: Illness trajectories are largely variable, and illness progression is not a general rule in BD. The number of manic episodes seems to be the clinical marker more robustly associated with neuroprogression in BD. However, the majority of the evidence came from cross-sectional studies that are prone to bias. Longitudinal studies may help to identify signatures of neuroprogression and integrate findings from the field of neuroimaging, neurocognition, and biomarkers.

Identifying neuroanatomical signatures of anorexia nervosa: a multivariate machine learning approach.

BACKGROUND: There are currently no neuroanatomical biomarkers of anorexia nervosa (AN) available to make clinical inferences at an individual subject level. We present results of a multivariate machine learning (ML) approach utilizing structural neuroanatomical scan data to differentiate AN patients from matched healthy controls at an individual subject level. METHOD: Structural neuroimaging scans were acquired from 15 female patients with AN (age = 20, s.d. = 4 years) and 15 demographically matched female controls (age = 22, s.d. = 3 years). Neuroanatomical volumes were extracted using the FreeSurfer software and input into the Least Absolute Shrinkage and Selection Operator (LASSO) multivariate ML algorithm. LASSO was 'trained' to identify 'novel' individual subjects as either AN patients or healthy controls. Furthermore, the model estimated the probability that an individual subject belonged to the AN group based on an individual scan. RESULTS: The model correctly predicted 25 out of 30 subjects, translating into 83.3% accuracy (sensitivity 86.7%, specificity 80.0%) (p < 0.001; χ 2 test). Six neuroanatomical regions (cerebellum white matter, choroid plexus, putamen, accumbens, the diencephalon and the third ventricle) were found to be relevant in distinguishing individual AN patients from healthy controls. The predicted probabilities showed a linear relationship with drive for thinness clinical scores (r = 0.52, p < 0.005) and with body mass index (BMI) (r = -0.45, p = 0.01). CONCLUSIONS: The model achieved a good predictive accuracy and drive for thinness showed a strong neuroanatomical signature. These results indicate that neuroimaging scans coupled with ML techniques have the potential to provide information at an individual subject level that might be relevant to clinical outcomes.

Changes in the corpus callosum in women with late-stage bipolar disorder.

OBJECTIVE: This study investigated the differences in corpus callosum (CC) volumes between women with early-stage and late-stage bipolar I (BP I) disorder using the criteria previously described in the literature. METHOD: We compared women with early- and late-stage BP I using criteria described in the Staging Systems Task Force Report of the International Society for Bipolar Disorders. We included 20 patients with early stage and 21 patients with late-stage BP I and a group of 25 healthy controls. Patients and controls underwent structural magnetic resonance imaging. Information on the clinical features of bipolar disorder was collected using a standardized questionnaire. Anatomical volumes of five regions of CC were compared between the three groups. RESULTS: Women with late-stage BP I disorder had reduced posterior CC volumes compared with early-stage bipolar I patients and controls (F = 6.05; P = 0.004). The difference was significant after controlling for age, comorbidity with post-traumatic stress disorder, psychotic symptoms during mood episodes, and current use of medication. CONCLUSION: The posterior CC was significantly decreased in volume in women with late-stage bipolar disorder. These findings suggest that CC may be an anatomical target of neuroprogression in the course of bipolar disorder in women.

Predictive classification of individual magnetic resonance imaging scans from children and adolescents.

Neuroimaging techniques are increasingly being explored as potential tools for clinical prediction in psychiatry. There are a wide range of approaches which can be applied to make individual predictions for various aspects of disorders such as diagnostic status, symptom severity scores, identification of patients at risk of developing disorders and estimation of the likelihood of response to treatment. This selective review highlights a popular group of pattern recognition techniques, support vector machines (SVMs) for use with structural magnetic resonance imaging scans. First, however, we outline various practical issues, limitations and techniques which need to be considered before SVM's can be applied. We begin with a discussion on the practicalities of scanning children and adolescent participants and the importance of acquiring high quality images. Scan processing required for inter-subject comparisons is then discussed. We then briefly discuss feature selection and other considerations when applying pattern recognition techniques. Finally, SVMs are described and various studies highlighted to indicate the potential of these techniques for child and adolescent psychiatric research.

Effect of management policy upon 120 Type 1 diabetic pregnancies: policy decisions in practice.

AIMS: To evaluate the outcome of two specific changes in the policy of managing pregnancy in Type 1 diabetes over a 5-year period. The need for change had been identified following an audit in the previous 5-year period: firstly, the need for intensive effort to improve the uptake of pre-pregnancy counselling; secondly, a modest relaxation of the targets for blood glucose control during labour to minimize the risk of maternal hypoglycaemia. METHODS: Data were collated from maternal and neonatal case notes from all women with Type 1 diabetes mellitus managed by Newcastle obstetric services between August 1989 and July 1994 (n = 80), comparing data with 40 such women looked after between November 1985 and July 1989. RESULTS: The age of the women, blood glucose control during pregnancy, gestation at delivery, and birth weight were similar in the 5-year period under study to those previously reported for the first study period. Mean blood glucose in labour for Period 2 was 5.5 +/- 0.6 mmol/l, exactly 1.0 mmol/l higher than the mean blood glucose achieved in labour for Period 1. As a consequence, only 22.5% women (18/80) experienced one or more episodes of blood glucose less than 3.0 mmol/l compared with 40.0% women in Period 1 (16/40) (P < 0.01). There was no effect of maternal blood glucose on neonatal blood glucose provided the former was within the range 4-8 mmol/l during labour. However, if maternal blood glucose was over 10 mmol/l, the infant's blood glucose was always low (1.3 +/- 0.8 vs. 2.5 +/- 1.5 P < 0.02). Macrosomia (over the 90th percentile for gestational age) was observed in 43.1% of infants in Period 1, and the mean birth weight was not different from Period 1. In the initial 5-year period 27.5% (11/40) women received specific pre-pregnancy care for their diabetes, compared with 21.3% (17/80) in Period 2 despite the intensive programme of education. There were six cases of congenital abnormality and two antepartum deaths (10% adverse outcome). CONCLUSIONS: The target range for blood glucose control in labour of 4-7 mmol/l minimizes maternal hypoglycaemia in labour and the data indicate that an upper limit of 8 mmol/l would not increase the risk of neonatal hypoglycaemia. Fresh thought is required about the matter of preventing congenital abnormalities by achieving better pre-pregnancy and peri-conception blood glucose control.