Inductive Data Types Based on Fibrations Theory in Programming

Traditional methods including algebra and category theory have some deficiencies in analyzing semantics properties and describing inductive rules of inductive data types, we present a method based on Fibrations theory aiming at those questions above. We systematically analyze some basic logical structures of inductive data types about a fibration such as re-indexing functor, truth functor and comprehension functor, make semantics models of non-indexed fibration, single-sorted indexed fibration and many-sorted indexed fibration respectively. On this basis, we thoroughly discuss semantics properties of fibred, single-sorted indexed and many-sorted indexed inductive data types, and abstractly describe their inductive rules with universality. Furthermore, we briefly introduce applications of the three inductive dana types for analyzing semantics properties and describing inductive rules based on Fibrations theory via some examples. Compared with traditional methods, our works have the following three advantages. Firstly, brief descriptions and flexible expansibility of Fibrations theory can analyze semantics properties of inductive data types accurately, whose semantics are computed automatically. Secondly, superior abstractness of Fibrations theory does not rely on particular computing environments to depict inductive rules of inductive data types with universality. Thirdly, its rigorousness and consistence provide sound basis for testing and maintenance of software development

Myelin Activates FAK/Akt/NF-κB Pathways and Provokes CR3-Dependent Inflammatory Response in Murine System

Inflammatory response following central nervous system (CNS) injury contributes to progressive neuropathology and reduction in functional recovery. Axons are sensitive to mechanical injury and toxic inflammatory mediators, which may lead to demyelination. Although it is well documented that degenerated myelin triggers undesirable inflammatory responses in autoimmune diseases such as multiple sclerosis (MS) and its animal model, experimental autoimmune encephalomyelitis (EAE), there has been very little study of the direct inflammatory consequences of damaged myelin in spinal cord injury (SCI), i.e., there is no direct evidence to show that myelin debris from injured spinal cord can trigger undesirable inflammation in vitro and in vivo. Our data showed that myelin can initiate inflammatory responses in vivo, which is complement receptor 3 (CR3)-dependent via stimulating macrophages to express pro-inflammatory molecules and down-regulates expression of anti-inflammatory cytokines. Mechanism study revealed that myelin-increased cytokine expression is through activation of FAK/PI3K/Akt/NF-κB signaling pathways and CR3 contributes to myelin-induced PI3K/Akt/NF-κB activation and cytokine production. The myelin induced inflammatory response is myelin specific as sphingomyelin (the major lipid of myelin) and myelin basic protein (MBP, one of the major proteins of myelin) are not able to activate NF-κB signaling pathway. In conclusion, our results demonstrate a crucial role of myelin as an endogenous inflammatory stimulus that induces pro-inflammatory responses and suggest that blocking myelin-CR3 interaction and enhancing myelin debris clearance may be effective interventions for treating SCI

Trafficking-Deficient G572R-hERG and E637K-hERG Activate Stress and Clearance Pathways in Endoplasmic Reticulum

Background: Long QT syndrome type 2 (LQT2) is the second most common type of all long QT syndromes. It is well-known that trafficking deficient mutant human ether-a-go-go-related gene (hERG) proteins are often involved in LQT2. Cells respond to misfolded and trafficking-deficient proteins by eliciting the unfolded protein response (UPR) and Activating Transcription Factor (ATF6) has been identified as a key regulator of the mammalian UPR. In this study, we investigated the role of ER chaperone proteins (Calnexin and Calreticulin) in the processing of G572R-hERG and E637K-hERG mutant proteins. Methods: pcDNA3-WT-hERG, pcDNA3-G572R-hERG and pcDNA3-E637K-hERG plasmids were transfected into U2OS and HEK293 cells. Confocal microscopy and western blotting were used to analyze subcellular localization and protein expression. Interaction between WT or mutant hERGs and Calnexin/Calreticulin was tested by coimmunoprecipitation. To assess the role of the ubiquitin proteasome pathway in the degradation of mutant hERG proteins, transfected HEK293 cells were treated with proteasome inhibitors and their effects on the steady state protein levels of WT and mutant hERGs were examined. Conclusion: Our results showed that levels of core-glycosylated immature forms of G572R-hERG and E637K-hERG in association with Calnexin and Calreticulin were higher than that in WT-hERG. Both mutant hERG proteins could activate the UPR by upregulating levels of active ATF6. Furthermore, proteasome inhibition increased the levels of core-glycosylated immature forms of WT and mutant hERGs. In addition, interaction between mutant hERGs and Calnexin/Calreticulin wa

Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study

BackgroundEarly and reliable identification of patients with sepsis who are at high risk of mortality is important to improve clinical outcomes. However, 3 major barriers to artificial intelligence (AI) models, including the lack of interpretability, the difficulty in generalizability, and the risk of automation bias, hinder the widespread adoption of AI models for use in clinical practice. ObjectiveThis study aimed to develop and validate (internally and externally) a conformal predictor of sepsis mortality risk in patients who are critically ill, leveraging AI-assisted prediction modeling. The proposed approach enables explaining the model output and assessing its confidence level. MethodsWe retrospectively extracted data on adult patients with sepsis from a database collected in a teaching hospital at Beth Israel Deaconess Medical Center for model training and internal validation. A large multicenter critical care database from the Philips eICU Research Institute was used for external validation. A total of 103 clinical features were extracted from the first day after admission. We developed an AI model using gradient-boosting machines to predict the mortality risk of sepsis and used Mondrian conformal prediction to estimate the prediction uncertainty. The Shapley additive explanation method was used to explain the model. ResultsA total of 16,746 (80%) patients from Beth Israel Deaconess Medical Center were used to train the model. When tested on the internal validation population of 4187 (20%) patients, the model achieved an area under the receiver operating characteristic curve of 0.858 (95% CI 0.845-0.871), which was reduced to 0.800 (95% CI 0.789-0.811) when externally validated on 10,362 patients from the Philips eICU database. At a specified confidence level of 90% for the internal validation cohort the percentage of error predictions (n=438) out of all predictions (n=4187) was 10.5%, with 1229 (29.4%) predictions requiring clinician review. In contrast, the AI model without conformal prediction made 1449 (34.6%) errors. When externally validated, more predictions (n=4004, 38.6%) were flagged for clinician review due to interdatabase heterogeneity. Nevertheless, the model still produced significantly lower error rates compared to the point predictions by AI (n=1221, 11.8% vs n=4540, 43.8%). The most important predictors identified in this predictive model were Acute Physiology Score III, age, urine output, vasopressors, and pulmonary infection. Clinically relevant risk factors contributing to a single patient were also examined to show how the risk arose. ConclusionsBy combining model explanation and conformal prediction, AI-based systems can be better translated into medical practice for clinical decision-making

Deep learning-based signal quality assessment in wearable ECG monitoring

Wearable electrocardiogram (ECG) monitoring is an effective method of screening for occult arrhythmia. However, signals from the wearable ECG monitoring device are often disturbed by various artifacts and noises originating from daily activities and which can significantly affect peak detection and ECG morphological feature extraction, leading to frequent false alarms for arrhythmia detection. Therefore, it is crucial to exclude ECG fragments with poor signal quality. In this study, we developed three xResNet-based ECG signal quality assessment models, trained on the Brno University of Technology ECG Quality Database (BUT QDB). The first model is designed for quality assessment in arrhythmia screening tasks, and it can distinguish between ECG data in which the PQRST waves or only QRS complexes are visible from data in which these cannot be used for further analysis with a sensitivity (Se) of 98.87% and specificity (Sp) of 99.83%. The second model is used for quality assessment in arrhythmia diagnosis tasks, and it detects ECGs with visible PQRST waves with a Se of 97.15% and Sp of 95.95%. The third model classifies ECGs into data with PQRST visible, with only QRS visible, or unsuitable for analysis and achieves an accuracy (Acc) of 96.62%, 93.66%, and 98.97%, respectively. The results indicate that the proposed models can accurately evaluate the ECG signal quality during wearable monitoring, meeting the analysis requirements for arrhythmia