Evaluating the Patient-Reported Outcomes Measurement Information System scales in acute intermittent porphyria.

PurposeAcute intermittent porphyria (AIP) is a rare inborn error of heme biosynthesis characterized by life-threatening acute attacks. Few studies have assessed quality of life (QoL) in AIP and those that have had small sample sizes and used tools that may not have captured important domains.MethodsBaseline data from the Porphyrias Consortium's Longitudinal Study were obtained for 259 patients, including detailed disease and medical history data, and the following Patient-Reported Outcomes Measurement Information System (PROMIS) scales: anxiety, depression, pain interference, fatigue, sleep disturbance, physical function, and satisfaction with social roles. Relationships between PROMIS scores and clinical and biochemical AIP features were explored.ResultsPROMIS scores were significantly worse than the general population across all domains, except depression. Each domain discriminated well between asymptomatic and symptomatic patients with symptomatic patients having worse scores. Many important clinical variables like symptom frequency were significantly associated with domain scores in univariate analyses, showing responsiveness of the scales, specifically pain interference and fatigue. However, most regression models only explained ~20% of the variability observed in domain scores.ConclusionPain interference and fatigue were the most responsive scales in measuring QoL in this AIP cohort. Future studies should assess whether these scales capture longitudinal disease progression and treatment response

Towards Logical Specification of Statistical Machine Learning

We introduce a logical approach to formalizing statistical properties of machine learning. Specifically, we propose a formal model for statistical classification based on a Kripke model, and formalize various notions of classification performance, robustness, and fairness of classifiers by using epistemic logic. Then we show some relationships among properties of classifiers and those between classification performance and robustness, which suggests robustness-related properties that have not been formalized in the literature as far as we know. To formalize fairness properties, we define a notion of counterfactual knowledge and show techniques to formalize conditional indistinguishability by using counterfactual epistemic operators. As far as we know, this is the first work that uses logical formulas to express statistical properties of machine learning, and that provides epistemic (resp. counterfactually epistemic) views on robustness (resp. fairness) of classifiers.Comment: SEFM'19 conference paper (full version with errors corrected

Regulatory domain or CpG site variation in SLC12A5, encoding the chloride transporter KCC2, in human autism and schizophrenia

Many encoded gene products responsible for neurodevelopmental disorders (NDs) like autism spectrum disorders (ASD), schizophrenia (SCZ), intellectual disability (ID), and idiopathic generalized epilepsy (IGE) converge on networks controlling synaptic function. An increase in KCC2 (SLC12A5) Cl− transporter activity drives the developmental GABA excitatory-inhibitory sequence, but the role of KCC2 in human NDs is essentially unknown. Here, we report two rare, non-synonymous (NS), functionally-impairing variants in the KCC2 C-terminal regulatory domain (CTRD) in human ASD (R952H and R1049C) and SCZ (R952H) previously linked with IGE and familial febrile seizures, and another novel NS KCC2 variant in ASD (R1048W) with highly-predicted pathogenicity. Exome data from 2517 simplex families in the ASD Simon Simplex Collection (SSC) revealed significantly more KCC2 CTRD variants in ASD cases than controls, and interestingly, these were more often synonymous and predicted to disrupt or introduce a CpG site. Furthermore, full gene analysis showed ASD cases are more likely to contain rare KCC2 variants affecting CpG sites than controls. These data suggest genetically-encoded dysregulation of KCC2-dependent GABA signaling may contribute to multiple human NDs

Glycogenosis is common in nonalcoholic fatty liver disease and is independently associated with ballooning, but lower steatosis and lower fibrosis

Background/aims: Glycogen synthesis and storage are normal hepatocyte functions. However, glycogenosis, defined as excess hepatocyte glycogen visible by routine H&E light microscopy, has not been well characterized in nonalcoholic fatty liver disease (NAFLD). Methods: Glycogenosis in NAFLD liver biopsies was graded as "none", "focal" (in <50% of hepatocytes), or "diffuse" (in ≥50% of hepatocytes). Clinical and pathological variables associated with glycogenosis were assessed. 2047 liver biopsies were prospectively analysed. Results: In adults and children, any glycogenosis was present in 54% of cases; diffuse glycogenosis was noted in approximately 1/3 of cases. On multiple logistic regression analysis, adults with glycogenosis tended to be older (P = .003), female (P = .04), have higher serum glucose (P = .01), and use insulin (P = .02). Adults tended to have lower steatosis scores (P = .006) and lower fibrosis stages (P = .005); however, unexpectedly, they also tended to have more hepatocyte injury including ballooning (P = .003). On multiple logistic regression analysis, paediatric patients with glycogenosis were more likely to be Hispanic (P = .03), have lower body weight (P = .002), elevated triglycerides (P = .001), and a higher fasting glucose (P = .007). Paediatric patients with glycogenosis also had less steatosis (P < .001) than those without. Conclusions: Glycogenosis is common in adult and paediatric NAFLD, and is associated with clinical features of insulin resistance. Glycogenosis is important to recognize histologically because it may be misinterpreted as ballooning, and when diffuse, confusion with glycogen storage disorders or glycogenic hepatopathy must be avoided. The newly observed dichotomous relationship between glycogenosis and increased liver cell injury but decreased steatosis and fibrosis requires further study

Alcohol Activates the Hedgehog Pathway and Induces Related Procarcinogenic Processes in the Alcohol-Preferring Rat Model of Hepatocarcinogenesis

Alcohol consumption promotes hepatocellular carcinoma (HCC). The responsible mechanisms are not well understood. Hepatocarcinogenesis increases with age and is enhanced by factors that impose a demand for liver regeneration. Because alcohol is hepatotoxic, habitual alcohol ingestion evokes a recurrent demand for hepatic regeneration. The alcohol-preferring (P) rat model mimics the level of alcohol consumption by humans who habitually abuse alcohol. Previously, we showed that habitual heavy alcohol ingestion amplified age-related hepatocarcinogenesis in P-rats, with over 80% of alcohol-consuming P rats developing HCCs after 18 months of alcohol exposure, compared to only 5% of water-drinking controls

A Consensus Definitive Classification of Scavenger Receptors and Their Roles in Health and Disease

Scavenger receptors constitute a large family of proteins that are structurally diverse and participate in a wide range of biological functions. These receptors are expressed predominantly by myeloid cells and recognize a diverse variety of ligands including endogenous and modified host-derived molecules and microbial pathogens. There are currently eight classes of scavenger receptors, many of which have multiple names, leading to inconsistencies and confusion in the literature. To address this problem, a workshop was organized by theUnited StatesNational Institute of Allergy and Infectious Diseases, National Institutes of Health, to help develop a clear definition of scavenger receptors and a standardized nomenclature based on that definition. Fifteen experts in the scavenger receptor field attended the workshop and, after extensive discussion, reached a consensus regarding the definition of scavenger receptors and a proposed scavenger receptor nomenclature. Scavenger receptors were defined as cell surface receptors that typically bind multiple ligands and promote the removal of nonself or altered-self targets. They often function by mechanisms that include endocytosis, phagocytosis, adhesion, and signaling that ultimately lead to the elimination of degraded or harmful substances. Based on this definition, nomenclature and classification of these receptors into 10 classes were proposed. This classification was discussed at three national meetings and input from participants at these meetings was requested. The following manuscript is a consensus statement that combines the recommendations of the initial workshop and incorporates the input received from the participants at the three national meetings

A leaky umbrella has little value: evidence clearly indicates the serotonin system is implicated in depression.

A recent “umbrella” review examined various biomarkers relating to the serotonin system, and concluded there was no consistent evidence implicating serotonin in the pathophysiology of depression. We present reasons for why this conclusion is overstated, including methodological weaknesses in the review process, selective reporting of data, over-simplification, and errors in the interpretation of neuropsychopharmacological findings. We use the examples of tryptophan depletion and serotonergic molecular imaging, the two research areas most relevant to the investigation of serotonin, to illustrate this

A leaky umbrella has little value:evidence clearly indicates the serotonin system is implicated in depression

A recent “umbrella” review examined various biomarkers relating to the serotonin system, and concluded there was no consistent evidence implicating serotonin in the pathophysiology of depression. We present reasons for why this conclusion is overstated, including methodological weaknesses in the review process, selective reporting of data, over-simplification, and errors in the interpretation of neuropsychopharmacological findings. We use the examples of tryptophan depletion and serotonergic molecular imaging, the two research areas most relevant to the investigation of serotonin, to illustrate this

Paracrine Hedgehog Signaling Drives Metabolic Changes in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) typically develop in cirrhosis, a condition characterized by Hedgehog (Hh) pathway activation and accumulation of Hh-responsive myofibroblasts (MF). Although Hh signaling generally regulates stromal-epithelial interactions that support epithelial viability, the role of Hh-dependent MF in hepatocarcinogenesis is unknown. Here we used human HCC samples, a mouse HCC model, and hepatoma cell/MF co-cultures to examine the hypothesis that Hh signaling modulates MF metabolism to generate fuels for neighboring malignant hepatocytes. The results identify a novel paracrine mechanism whereby malignant hepatocytes produce HH-ligands to stimulate glycolysis in neighboring MF, resulting in release of MF-derived lactate that the malignant hepatocytes use as an energy source. This discovery reveals new diagnostic and therapeutic targets that might be exploited to improve the outcomes of cirrhotic patients with HCC

Automatic Detection of Cyberbullying in Social Media Text

While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overload on the Web requires intelligent systems to identify potential risks automatically. The focus of this paper is on automatic cyberbullying detection in social media text by modelling posts written by bullies, victims, and bystanders of online bullying. We describe the collection and fine-grained annotation of a training corpus for English and Dutch and perform a series of binary classification experiments to determine the feasibility of automatic cyberbullying detection. We make use of linear support vector machines exploiting a rich feature set and investigate which information sources contribute the most for this particular task. Experiments on a holdout test set reveal promising results for the detection of cyberbullying-related posts. After optimisation of the hyperparameters, the classifier yields an F1-score of 64% and 61% for English and Dutch respectively, and considerably outperforms baseline systems based on keywords and word unigrams.Comment: 21 pages, 9 tables, under revie