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Around 30% of individuals with schizophrenia remain symptomatic and significantly impaired despite antipsychotic treatment and are considered to be treatment resistant. Clinicians are currently unable to predict which patients are at higher risk of treatment resistance.
To determine whether genetic liability for schizophrenia and/or clinical characteristics measurable at illness onset can prospectively indicate a higher risk of treatment-resistant psychosis (TRP).
In 1070 individuals with schizophrenia or related psychotic disorders, schizophrenia polygenic risk scores (PRS) and large copy number variations (CNVs) were assessed for enrichment in TRP. Regression and machine-learning approaches were used to investigate the association of phenotypes related to demographics, family history, premorbid factors and illness onset with TRP.
Younger age at onset (odds ratio 0.94, P = 7.79 × 10−13) and poor premorbid social adjustment (odds ratio 1.64, P = 2.41 × 10−4) increased risk of TRP in univariate regression analyses. These factors remained associated in multivariate regression analyses, which also found lower premorbid IQ (odds ratio 0.98, P = 7.76 × 10−3), younger father's age at birth (odds ratio 0.97, P = 0.015) and cannabis use (odds ratio 1.60, P = 0.025) increased the risk of TRP. Machine-learning approaches found age at onset to be the most important predictor and also identified premorbid IQ and poor social adjustment as predictors of TRP, mirroring findings from regression analyses. Genetic liability for schizophrenia was not associated with TRP.
People with an earlier age at onset of psychosis and poor premorbid functioning are more likely to be treatment resistant. The genetic architecture of susceptibility to schizophrenia may be distinct from that of treatment outcomes.
Background: Electroconvulsive therapy (ECT) involves the induction of a generalized seizure with an electrical current and has been used worldwide when treating medically refractory psychiatric illness. Here we describe a patient with no prior history or risk factors for epilepsy who developed temporal lobe epilepsy after chronic treatment of ECT. Methods: A 16-year-old right-handed boy with severe refractory depression received ECT treatment every 10 days for 8 months. Six months into his ECT treatment, the patient developed seizures and was admitted to a pediatric epilepsy monitoring unit. Results: Initial clinical events included lightheadedness, diaphoresis, and nausea with associated kaleidoscopic vision changes. Seizures progressed to confusion, fear and paranoia by the time the patient was admitted for monitoring. Long-term video EEG captured many focal seizures with impaired awareness, all originating from both temporal lobes. MRI was normal. ECT was terminated and the patient started on carbamazepine. He has been seizure free for the past 2 years on medication Conclusions: While rare, we present a case of a patient with no prior risk factors for epilepsy who developed temporal lobe epilepsy after chronic ECT treatment. Although ECT is an indispensable treatment for many medically refractory psychiatric illnesses, we suggest caution in young patient undergoing ECT.
Background: There are few published reports on the safety and efficacy of stereoelectroencephalography (SEEG) in the presurgical evaluation of pediatric drug-resistant epilepsy. Our objective was to describe institutional experience with pediatric SEEG in terms of (1) insertional complications, (2) identification of the epileptogenic zone and (3) seizure outcome following SEEG-tailored resections. Methods: Retrospective review of 29 patients pediatric drug resistant epilepsy patients who underwent presurgical SEEG between 2005 – 2018. Results: 29 pediatric SEEG patients (15 male; 12.4 ± 4.6 years old) were included in this study with mean follow-up of 6.0 ± 4.1 years. SEEG-related complications occurred in 1/29 (3%)—neurogenic pulmonary edema. A total of 190 multi-contact electrodes (mean of 7.0 ± 2.5per patient) were implanted across 30 insertions which captured 437 electrographic seizures (mean 17.5 ± 27.6 per patient). The most common rationale for SEEG was normal MRI with surface EEG that failed to identify the EZ (16/29; 55%). SEEG-tailored resections were performed in 24/29 (83%). Engel I outcome was achieved following resections in 19/24 cases (79%) with 5.9 ± 4.0 years of post-operative follow-up. Conclusions: Stereoelectroencephalography in presurgical evaluation of pediatric drug-resistant epilepsy is a safe and effective way to identify the epileptogenic zone permitting SEEG-tailored resection.
Objective: Post-stroke cognitive impairment is common, but mechanisms and risk factors are poorly understood. Frailty may be an important risk factor for cognitive impairment after stroke. We investigated the association between pre-stroke frailty and acute post-stoke cognition. Methods: We studied consecutively admitted acute stroke patients in a single urban teaching hospital during three recruitment waves between May 2016 and December 2017. Cognition was assessed using the Mini-Montreal Cognitive Assessment (min=0; max=12). A Frailty Index was used to generate frailty scores for each patient (min=0; max=100). Clinical and demographic information were collected, including pre-stroke cognition, delirium, and stroke-severity. We conducted univariate and multiple-linear regression analyses with covariates forced in (covariates included were: age, sex, stroke severity, stroke-type, pre-stroke cognitive impairment, delirium, previous stroke/transient ischemic attack) to investigate the association between pre-stroke frailty and post-stroke cognition. Results: Complete data were available for 154 stroke patients. Mean age was 68 years (SD=11; range=32–97); 93 (60%) were male. Median mini-Montreal Cognitive Assessment score was 8 (IQR=4–12). Mean Frailty Index score was 18 (SD=11). Pre-stroke cognitive impairment was apparent in 13/154 (8%) patients. Pre-stroke frailty was significantly associated with lower post-stroke cognition (Standardized-Beta=−0.40; p<0.001) and this association was independent of covariates (Unstandardized-Beta=−0.05; p=0.005). Additional significant variables in the multiple regression model were age (Unstandardized-Beta=−0.05; p=0.002), delirium (Unstandardized-Beta=−2.81; p<0.001), pre-stroke cognitive impairment (Unstandardized-Beta=−2.28; p=0.001), and stroke-severity (Unstandardized-Beta=−0.20; p<0.001). Conclusions: Pre-stroke frailty may be a moderator of post-stroke cognition, independent of other well-established post-stroke cognitive impairment risk factors. (JINS, 2019, 25, 501–506)
Rare copy number variants (CNVs) are associated with risk of neurodevelopmental disorders characterised by varying degrees of cognitive impairment, including schizophrenia, autism spectrum disorder and intellectual disability. However, the effects of many individual CNVs in carriers without neurodevelopmental disorders are not yet fully understood, and little is known about the effects of reciprocal copy number changes of known pathogenic loci.
We aimed to analyse the effect of CNV carrier status on cognitive performance and measures of occupational and social outcomes in unaffected individuals from the UK Biobank.
We called CNVs in the full UK Biobank sample and analysed data from 420 247 individuals who passed CNV quality control, reported White British or Irish ancestry and were not diagnosed with neurodevelopmental disorders. We analysed 33 pathogenic CNVs, including their reciprocal deletions/duplications, for association with seven cognitive tests and four general measures of functioning: academic qualifications, occupation, household income and Townsend Deprivation Index.
Most CNVs (24 out of 33) were associated with reduced performance on at least one cognitive test or measure of functioning. The changes on the cognitive tests were modest (average reduction of 0.13 s.d.) but varied markedly between CNVs. All 12 schizophrenia-associated CNVs were associated with significant impairments on measures of functioning.
CNVs implicated in neurodevelopmental disorders, including schizophrenia, are associated with cognitive deficits, even among unaffected individuals. These deficits may be subtle but CNV carriers have significant disadvantages in educational attainment and ability to earn income in adult life.
We identified a pseudo-outbreak of Mycobacterium avium in an outpatient bronchoscopy clinic following an increase in clinic procedure volume. We terminated the pseudo-outbreak by increasing the frequency of automated endoscope reprocessors (AER) filter changes from quarterly to monthly. Filter changing schedules should depend on use rather than fixed time intervals.
The fact that the symmetric difference is a group operation in a Boolean algebra is, of course, well known. Not so well known is the fact observed by Ellis  that it possesses some of the desirable properties of a metric distance function. Specifically, if * denotes this operation, it is easy to verify that
Mental disorders may emerge as the result of interactions between observable symptoms. Such interactions can be analyzed using network analysis. Several recent studies have used network analysis to examine eating disorders, indicating a core role of overvaluation of weight and shape. However, no studies to date have applied network models to binge-eating disorder (BED), the most prevalent eating disorder.
We constructed a cross-sectional graphical LASSO network in a sample of 788 individuals with BED. Symptoms were assessed using the Eating Disorders Examination Interview. We identified core symptoms of BED using expected influence centrality.
Overvaluation of shape emerged as the symptom with the highest centrality. Dissatisfaction with weight and overvaluation of weight also emerged as highly central symptoms. On the other hand, behavioral symptoms such as binge eating, eating in secret, and dietary restraint/restriction were less central. The network was stable, allowing for reliable interpretations (centrality stability coefficient = 0.74).
Overvaluation of shape and weight emerged as core symptoms of BED. This trend is consistent with past network analyses of eating disorders more broadly, as well as literature that suggests a primary role of shape and weight concerns in BED. Although DSM-5 diagnostic criteria for BED does not currently include a cognitive criterion related to body image or shape/weight overvaluation, our results provide support for including shape/weight overvaluation as a diagnostic specifier.
The aim of this study was to characterise changes in lean soft tissue (LST) and examine the contributions of energy intake, physical activity and breast-feeding practices to LST changes at 3 and 9 months postpartum. We examined current weight, LST (via dual-energy X-ray absorptiometry), dietary intake (3-d food diary), physical activity (Baecke questionnaire) and breast-feeding practices (3-d breast-feeding diary) in forty-nine women aged 32·9 (sd 3·8) years. Changes in LST varied from −2·51 to +2·50 kg with twenty-nine women gaining LST (1·1 (sd 0·7) kg, P<0·001) and twenty women losing LST (−0·9 (sd 0·8) kg, P<0·001). Energy intake (133 (SD 42) v. 109 (SD 33) kJ/kg, P=0·019) and % kJ from fat at 3 months postpartum was higher in women who gained LST at 9 months postpartum (gained LST=34 (sd 5) % kJ; lost LST=29 (sd 4) % kJ, P=0·002). Women who gained LST reported breast-feeding their infants more frequently (gained LST=8 (sd 3) feeds/d; lost LST=5 (sd 1) feeds/d, P=0·014) and for more time per d (gained LST=115 (sd 78) min/d; lost LST=59 (sd 34) min/d, P=0·016) at 9 months postpartum. Energy intake and % kJ from fat at 3 months were significant predictors of LST gain (β=0·08 (se 0·04) and 0·24 (se 0·09), respectively). This suggests that gain in LST may be associated with more frequent and longer episodes of breast-feeding at 9 months postpartum as well as dietary intake early in the postpartum period.
Light sheet fluorescence microscopy (LSFM) allows for high-resolution three-dimensional imaging with minimal photo-damage. By viewing the sample from different directions, different regions of large specimens can be imaged optimally. Moreover, owing to their good spatial resolution and high signal-to-noise ratio, LSFM data are well suited for image deconvolution. Here we present the Huygens Fusion and Deconvolution Wizard, a unique integrated solution for restoring LSFM images, and show that improvements in signal and resolution of 1.5 times and higher are feasible.
The Arizona Department of Health Services identified unusually high levels of influenza activity and severe complications during the 2015–2016 influenza season leading to concerns about potential increased disease severity compared with prior seasons. We estimated state-level burden and severity to compare across three seasons using multiple data sources for community-level illness, hospitalisation and death. Severity ratios were calculated as the number of hospitalisations or deaths per community case. Community influenza-like illness rates, hospitalisation rates and mortality rates in 2015–2016 were higher than the previous two seasons. However, ratios of severe disease to community illness were similar. Arizona experienced overall increased disease burden in 2015–2016, but not increased severity compared with prior seasons. Timely estimates of state-specific burden and severity are potentially feasible and may provide important information during seemingly unusual influenza seasons or pandemic situations.
The purpose of this volume is to present the theory of Markov and semi-Markov processes in a discrete-time, finite-state framework. Given this background, hidden versions of these processes are introduced and related estimation and filtering results developed. The approach is similar to the earlier book, Elliott et al. (1995). That is, a central tool is the Radon–Nikodym theorem and related changes of probability measure. In the discrete-time, finite-state framework that we employ these have simple interpretations following from Bayes’ theorem.
Markov chains and hidden Markov chains have found many applications in fields from finance, where the chains model different economic regimes, to genomics, where gene and protein structure is modelled as a hidden Markov model. Semi-Markov chains and hidden semi-Markov chains will have similar, possibly more realistic, applications. The genomics applications are modelled by discrete observations of these hidden chains.
Recent books in the area include in particular Koski (2001) and Barbu and Limnios (2008). Koski includes many examples, not much theory and little on semi-Markov Models. Barbu and Limnios say that the estimation of discrete-time semi-Markov systems is almost absent from the literature. They present an alternative specification from the one adopted in this book and so we give alternative methods in a rigorous framework. They provide limited applications in genomics.
This book carefully constructs relevant processes and proves required results. The filters and related parameter estimation methods we obtain for semi-Markov chains include new results. The occupation times in any state of a Markov chain are geometrically distributed; semi-Markov chains can have occupation times which are quite general and not necessarily geometrically distributed.
Works on semi-Markov processes include Barbu and Limnios (2008), C， inlar (1975), Harlamov (2008), Howard (1971), Janssen and Manca (2010), and Koski (2001) from Chapter 11 onwards. C， inlar (1975) considers a countable state space.
Hidden Markov models have found extensive applications in speech processing and genomics. References for these applications include Ferguson (1980), who considers more general occupation times. This problem was also investigated by Levinson (1986a,b), Ramesh and Wilpon (1992), and in the papers Guédon (1992) and Guédon and Cocozza-Thivent (1990). Genomic applications are treated in the thesis of Burge (1997) and the book Burge and Karlin (1997).