Acute generalized exanthematous pustulosis: A retrospective study of 51 cases in Taiwan

AbstractBackground/ObjectiveAcute generalized exanthematous pustulosis (AGEP) is a severe cutaneous adverse drug reaction characterized by fever and numerous sterile non-follicular pustules. It is mainly attributed to drugs, although other factors have been implicated. The objective of this study was to evaluate the clinical and histological features of AGEP in a Taiwanese population.MethodsIn this retrospective study, we reviewed patients diagnosed with AGEP with a EuroSCAR (RegiSCAR) validation score more than 4 (>4, probable to definite cases), between 1992 and 2012 at the Chang Gung Memorial Hospital in Taiwan. Demographic, clinical and laboratory data, pathologic findings, and disease causality were analyzed.ResultsA total of 51 patients were included in this study, with 34 (66.7%) patients being diagnosed with AGEP with drug causality, and 17 (33.3%) patients being diagnosed with AGEP without drug causality. Cases of AGEP with drug causality showed an older average age, and a significantly higher rate of previous drug hypersensitivity history compared to cases of AGEP without drug causality (p = 0.0018). None of the patients had a history of psoriasis or had developed psoriasis at the 1-year follow-up. A total of 12 cases (23.5%) had systemic involvement, including liver and kidneys. Penicillin or aminopenicillin (17.6%) and cephalosporins (17.6%) were the most common causative drug groups related to AGEP. In AGEP patients without drug causality, three cases of pathogen infections were identified (1 case of mycoplasma, Coxsackie virus, and Epstein-Barr virus, respectively).ConclusionWe found that beta-lactam antibiotics were the major drug class responsible for inducing AGEP in a Taiwanese population, but that some infectious pathogens may also contribute to AGEP development

Traditional Chinese Medicine Diagnosis “ Yang-Xu Zheng

Pathogenesis of sepsis includes complex interaction between pathogen activities and host response, manifesting highly variable signs and symptoms, possibly delaying diagnosis and timely life-saving interventions. This study applies traditional Chinese medicine (TCM) Zheng diagnosis in patients with severe sepsis and septic shock to evaluate its adaptability and use as an early predictor of sepsis mortality. Three-year prospective observational study enrolled 126 septic patients. TCM Zheng diagnosis, Acute Physiology and Chronic Health Evaluation (APACHE) II score, and blood samples for host response cytokines measurement (tumor necrosis factor-α, Interleukin-6, Interleukin-8, Interleukin-10, Interleukin-18) were collected within 24 hours after admission to Intensive Care Unit. Main outcome was 28-day mortality; multivariate logistic regression analysis served to determine predictive variables of the sepsis mortality. APACHE II score, frequency of Nutrient-phase heat, and Qi-Xu and Yang-Xu Zhengs were significantly higher in nonsurvivors. The multivariate logistic regression analysis identified Yang-Xu Zheng as the outcome predictor. APACHE II score and levels of five host response cytokines between patients with and without Yang-Xu Zheng revealed significant differences. Furthermore, cool extremities and weak pulse, both diagnostic signs of Yang-Xu Zheng, were also proven independent predictors of sepsis mortality. TCM diagnosis “Yang-Xu Zheng” may provide a new mortality predictor for septic patients

Revealing the Blind Spot of Sentence Encoder Evaluation by HEROS

Existing sentence textual similarity benchmark datasets only use a single number to summarize how similar the sentence encoder's decision is to humans'. However, it is unclear what kind of sentence pairs a sentence encoder (SE) would consider similar. Moreover, existing SE benchmarks mainly consider sentence pairs with low lexical overlap, so it is unclear how the SEs behave when two sentences have high lexical overlap. We introduce a high-quality SE diagnostic dataset, HEROS. HEROS is constructed by transforming an original sentence into a new sentence based on certain rules to form a \textit{minimal pair}, and the minimal pair has high lexical overlaps. The rules include replacing a word with a synonym, an antonym, a typo, a random word, and converting the original sentence into its negation. Different rules yield different subsets of HEROS. By systematically comparing the performance of over 60 supervised and unsupervised SEs on HEROS, we reveal that most unsupervised sentence encoders are insensitive to negation. We find the datasets used to train the SE are the main determinants of what kind of sentence pairs an SE considers similar. We also show that even if two SEs have similar performance on STS benchmarks, they can have very different behavior on HEROS. Our result reveals the blind spot of traditional STS benchmarks when evaluating SEs.Comment: ACL 2023 repl4nlp (representation learning for NLP) workshop poster paper. Dataset at https://huggingface.co/datasets/dcml0714/Hero

Integrin-mediated membrane blebbing is dependent on the NHE1 and NCX1 activities.

Integrin-mediated signal transduction and membrane blebbing have been well studied to modulate cell adhesion, spreading and migration^1-6^. However, the relationship between membrane blebbing and integrin signaling has not been explored. Here we show that integrin-ligand interaction induces membrane blebbing and membrane permeability change. We found that sodium-proton exchanger 1 (NHE1) and sodium-calcium exchanger 1 (NCX1) are located in the membrane blebbing sites and inhibition of NHE1 disrupts membrane blebbing and decreases membrane permeability change. However, inhibition of NCX1 enhances cell blebbing to cause cell swelling which is correlated with an intracellular sodium accumulation induced by NHE17. These data suggest that sodium influx induced by NHE1 is a driving force for membrane blebbing growth, while sodium efflux induced by NCX1 in a reverse mode causes membrane blebbing retraction. Together, these data reveal a novel function of NHE1 and NCX1 in membrane permeability change and blebbing and provide the link for integrin signaling and membrane blebbing

TNFAIP3, a negative regulator of the TLR signaling pathway, is a potential predictive biomarker of response to antidepressant treatment in major depressive disorder

AbstractInflammation and abnormalities in Toll-like receptor (TLR) expression and activation have been linked to major depressive disorder (MDD). However, negative regulators of TLR pathways have not been previously investigated in this context. Here, we sought to investigate the association of depression severity, measured by the 17-item Hamilton Depression Rating Scale (HAMD-17), with mRNA expression levels of negative regulators of the TLR pathway, including SOCS1, TOLLIP, SIGIRR, MyD88s, NOD2 and TNFAIP3, in peripheral blood mononuclear cells (PBMCs) from 100 patients with MDD and 53 healthy controls, before and after treatment with antidepressants. Positive regulators of the TLR4 pathway, including Pellino 1, TRAF6 and IRAK1, were also investigated. Among all patients, MyD88s, and TNFAIP3 mRNAs were expressed at lower levels in PBMCs from patients with MDD. Multiple linear regression analyses revealed that TNFAIP3 mRNA expression before treatment was inversely correlated with severity of depression and effectively predicted improvement in HAMD-17 scores. Among 79 treatment-completers, only TNFAIP3 mRNA was significantly increased by treatment with antidepressants for 4 weeks. Treatment of human monocytes (THP-1) and mouse microglia (SIM-A9) cell lines with fluoxetine significantly increased TNFAIP3 mRNA expression and suppressed IL-6 levels. The suppressive effect of fluoxetine on IL-6 was attenuated by knockdown of TNFAIP3 expression. These findings suggest that both dysfunction of the negative regulatory system in patients with MDD and antidepressant treatment exert anti-inflammatory effects, at least in part through increased expression of the TNFAIP3 gene. They also indicate that modulating expression of the TNFAIP3 gene to rebalance TLR-mediated inflammatory signaling may be potential therapeutic strategy for treating MDD

Interpretable Self-Attention Temporal Reasoning for Driving Behavior Understanding

Performing driving behaviors based on causal reasoning is essential to ensure driving safety. In this work, we investigated how state-of-the-art 3D Convolutional Neural Networks (CNNs) perform on classifying driving behaviors based on causal reasoning. We proposed a perturbation-based visual explanation method to inspect the models' performance visually. By examining the video attention saliency, we found that existing models could not precisely capture the causes (e.g., traffic light) of the specific action (e.g., stopping). Therefore, the Temporal Reasoning Block (TRB) was proposed and introduced to the models. With the TRB models, we achieved the accuracy of $\mathbf{86.3\%}$, which outperform the state-of-the-art 3D CNNs from previous works. The attention saliency also demonstrated that TRB helped models focus on the causes more precisely. With both numerical and visual evaluations, we concluded that our proposed TRB models were able to provide accurate driving behavior prediction by learning the causal reasoning of the behaviors.Comment: Submitted to IEEE ICASSP 2020; Pytorch code will be released soo

A Computational Framework for Boundary-Value Problem Based Simulations

A framework is presented for step-by-step implementation of weighted-residualmethods (MWR) for simulations that require the solution ofboundary-value problems. A set of Matlab-based functions ofthe computationally common MWR solution steps has beendeveloped and is used in the application of eigenfunction expansion,collocation, and Galerkin-projection discretizations oftime-dependent, distributed-parameter system models. Fourindustrially relevant examples taken from electronic materialsand chemical processing applications are used to demonstrate thesimulation approach developed