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Hypnotic suggestibility in dissociative and related disorders: A meta-analysis
No full textElevated responsiveness to verbal suggestions is hypothesized to represent a predisposing factor for dissociative disorders (DDs) and related conditions. However, the magnitude of this effect has not been estimated in these populations nor has the potential moderating influence of methodological limitations on effect size variability across studies. This study assessed whether patients with DDs, trauma- and stressor-related disorders (TSDs), and functional neurological disorder (FND) display elevated hypnotic suggestibility. A systematic literature search identified 20 datasets. A random-effects meta-analysis revealed that patients displayed greater hypnotic suggestibility than controls, Hedges’s g=0.92 [0.66, 1.18]. This effect was observed in all subgroups but was most pronounced in the DDs. Although there was some evidence for publication bias, a bias-corrected estimate of the group effect remained significant, g=0.57 [0.30, 0.85]. Moderation analyses did not yield evidence for a link between effect sizes and methodological limitations. These results demonstrate that DDs and related conditions are characterized by elevated hypnotic suggestibility and have implications for the mechanisms, risk factors, and treatment of dissociative psychopathology. Keywords: dissociation; hypnotizability; post-traumatic stress disorder; suggestion; trauma<br/
Sodium-glucose cotransporter 1 inhibition and gout: Mendelian randomization study
No full textObjective. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) reduce serum urate, but their efficacy depends on renal function which is often impaired in patients with gout. SGLT1 is primarily expressed in the small intestine and its inhibition may be a more suitable target for gout. We aimed to investigate the association of genetically proxied SGLT1i with gout risk, serum urate levels and cardiovascular safety using Mendelian randomization (MR).Methods. Leveraging data from a genome-wide association study of 344,182 individuals in the UK Biobank, we identified a missense variant in the SLC5A1 gene that associated with glycated haemoglobin (HbA1c) to proxy SGLT1i. Outcome genetic data comprised 13,179 gout cases and 750,634 controls, 457,690 individuals for serum urate levels, and up to 977,323 individuals for cardiovascular safety outcomes. We applied the Wald ratio method and investigated potential genetic confounding using colocalization.Results. The rs17683430 missense variant was selected to instrument SGLT1i. Genetically proxied SGLT1i was associated with 75% reduction in gout risk (OR 0.25; 95%CI 0.06, 0.99; p=0.048) and 32.0μmol/L reduction in serum urate (95%CI -56.7, -7.3; p=0.01), per 6.7mmol/mol reduction in HbA1c. SGLT1i was associated with increased levels of low-density lipoprotein cholesterol (0.37mmol/L; 95%CI 0.17, 0.56; p=0.0002) but not coronary heart disease, stroke, or chronic kidney disease. Colocalization did not suggest that the results are attributable to genetic confounding.Conclusion. SGLT1 inhibition may represent a novel therapeutic option for preventing gout in patients with or without comorbid diabetes. Randomised trials are needed to formally investigate efficacy and safety.Keywords: Sodium-glucose cotransporter, gout, urate, cholesterol, SGLT1, glycated haemoglobin, diabetes.<br/
How do we meaningfully involve people in primary care research from marginalised communities?
No full textA Proof System for Cyber-physical Systems with Shared-Variable Concurrency
No full textCyber-physical system (CPS) is about the interplay of discrete behaviors and continuous behaviors. The combination of the physical and the cyber may cause hardship for the modeling and verication of CPS. Hence, a language based on shared variables was proposed to realize the interaction in CPS. In this paper, we formulate a proof system for this language. To handle the parallel composition with shared variables, we extend classical Hoare triples and bring the trace model into our proof system. The introduction of the trace may complicate ourspecication slightly, but it can realize a compositional proof when the program is executing. Meanwhile, this introduction can set up a bridge between our proof system and denotational semantics. Throughout this paper, we also present some examples to illustrate the usage of our proof system intuitively.Keywords: Cyber-physical System (CPS) · Shared Variables · Trace Model · Hoare Logic
Challenging an algorithm built for autism classification using movement data by applying it to data from conditions with similar kinematic styles
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Obstetric Outcomes in Women with Rheumatic Disease and COVID-19 in the Context of Vaccination Status
No full textObjective:To describe obstetric outcomes based on COVID-19 vaccination status, in women with rheumatic and musculoskeletal diseases (RMDs) who developed COVID-19 during pregnancy. Methods:Data regarding pregnant women entered into the COVID-19 Global Rheumatology Alliance registry from 24 March 2020 to 25 February 2022 were analysed. Obstetric outcomes were stratified by number of COVID-19 vaccine doses received prior to COVID-19 infection in pregnancy. Descriptive differences between groups were tested using the chi -square or Fisher’s exact test. Results: There were 73 pregnancies in 73 women with RMD and COVID-19. Overall, 24.7% (18) of pregnancies were ongoing, while of the 55 completed pregnancies 90.9% (50) of pregnancies resulted in livebirths. At the time of COVID-19 diagnosis, 60.3% (n=44) of women were unvaccinated, 4.1% (n=3) had received one vaccine dose while 35.6% (n=26) had two or more doses. Although 83.6% (n=61) of women required no treatment for COVID-19, 20.5% (n=15) required hospital admission. COVID-19 resulted in delivery in 6.8% (n=3) of unvaccinated women and 3.8% (n=1) of fully vaccinated women. There was a greater number of preterm births (PTB) in unvaccinated women compared to fully vaccinated 29.5% (n=13) vs 18.2%(n=2). Conclusion:In this descriptive study, unvaccinated pregnant women with RMD and COVID-19 had a greater number of PTB compared with those fully vaccinated against COVID-19. Additionally, the need for COVID-19 pharmacological treatment was uncommon in pregnant women with RMD regardless of vaccination status. These results support active promotion of COVID-19 vaccination in women with RMD who are pregnant or planning a pregnancy.<br/
Towards an atomistic understanding of polymorphism in molecular solids
No full textAbstract Understanding and controlling polymorphism in molecular solids is a major unsolved problem in crystal engineering. While the ability to calculate accurate lattice energies with atomistic modelling provides valuable insight into the associated energy scales, existing methods cannot connect energy differences to the delicate balances of intra- and intermolecular forces that ultimately determine polymorph stability ordering. We report herein a protocol for applying Quantum Chemical Topology (QCT) to study the key intra- and intermolecular interactions in molecular solids, which we use to compare the three known polymorphs of succinic acid including the recently-discovered γ form. QCT provides a rigorous partitioning of the total energy into contributions associated with topological atoms, and a quantitative and chemically intuitive description of the intra- and intermolecular interactions. The newly-proposed Relative Energy Gradient (REG) method ranks atomistic energy terms (steric, electrostatic and exchange) by their importance in constructing the total energy profile for a chemical process. We find that the conformation of the succinic acid molecule is governed by a balance of large and opposing electrostatic interactions, while the H-bond dimerisation is governed by a combination of electrostatics and sterics. In the solids, an atomistic energy balance emerges that governs the contraction, towards the equilibrium geometry, of a molecular cluster representing the bulk crystal. The protocol we put forward is as general as the capabilities of the underlying quantum-mechanical model and it can provide novel perspectives on polymorphism in a wide range of chemical systems.<br/
Bias-Variance Decompositions for Margin Losses
No full textWe introduce a novel bias-variance decomposition for a range of strictly convex margin losses, including the logistic loss (minimized by the classic LogitBoost algorithm), as well as the squared margin loss and canonical boosting loss. Furthermore, we show that, for all strictly convex margin losses, the expected risk decomposes into the risk of a “central” model and a term quantifying variation in the functional margin with respect to variations in the training data. These decompositions provide a diagnostic tool for practitioners to understand model overfitting/underfitting, and have implications for additive ensemble models—for example, when our bias-variance decomposition holds, there is a corresponding “ambiguity” decomposition, which can be used to quantify model diversity.<br/
Combined statistical decision limits based on two GH-2000 scores for the detection of growth hormone misuse
No full textThe GH-2000 biomarker method, based on the measurements of insulin-like growth factor-I (IGF-I) and the amino-terminal pro-peptide of type III collagen (P-III-NP), has been developed as a powerful technique for the detection of growth hormone (GH) misuse by athletes. IGF-I and P-III-NP are combined in gender specific formulas to create the GH-2000 score, which is used to determine whether GH has been administered. To comply with World Anti-Doping Agency regulations, each analyte must be measured by two methods. IGF-I and P-III-NP can be measured by a number of approved methods, each leading to its own GH-2000 score. Single decision limits for each GH-2000 score have been introduced and developed by Bassett, Erotokritou-Mulligan, Holt, Boehning and their co-authors in a series of papers. These have been incorporated into the guidelines of the World Anti-Doping Agency. A joint decision limit was constructed based on the sample correlation between the two GH-2000 scores generated from an available sample in order to increase the sensitivity of the biomarker method. This paper takes this idea further into a fully developed statistical approach. It constructs combined decision limits when two GH-2000 scores from different assay combinations are used to decide whether an athlete has been misusing GH. The combined decision limits are directly related to tolerance regions and constructed using a Bayesian approach. It is also shown to have highly satisfactory frequentist properties. The new approach meets the required false-positive rate with a pre-specified level of certainty