Association of atopy with disease severity in children with Mycoplasma pneumoniae pneumonia

BackgroundMycoplasma pneumoniae pneumonia (MPP) is common among children, but the impact of atopy on MPP severity in children is unknown. This study investigated whether atopic vs. nonatopic children had greater MPP severity.MethodsRetrospective analysis was conducted on 539 (ages 3–14 years) patients who were hospitalized in the First Affiliated Hospital of Anhui Medical University for MPP between January 2018 and December 2021, 195 were atopic and 344 were nonatopic. Of them, 204 had refractory MPP, and 335 had general MPP. And of atopic children, 94 had refractory MPP, and 101 had general MPP. Data on demographic and clinical characteristics, laboratory findings, clinical treatments were analyzed.ResultsSignificantly more boys with MPP were atopic than nonatopic (P < 0.05). More atopic (than nonatopic) children presented with prolonged fever and hospitalization, severe extra-pulmonary complications, asthma attaking, steroid and oxygen treatment, and increased IgE levels (all P < 0.05). In atopic (vs. nonatopic) children with MPP, the incidence of sputum plugs under the fiberoptic bronchoscopy and lobar pneumonia was significantly increased and required bronchoscopy-assisted and steroid therapy. Compared with nonatopic children, more atopic children developed refractory MPP (P < 0.05). Prolonged fever and hospitalization, severe extra-pulmonary complications, lymphocyte count, procalcitonin and lactate dehydrogenase levels, and percentages of atopy were all significantly higher (P < 0.05) among children with refractory MPP vs. general MPP. Moreover, Prolonged fever and hospitalization, lymphocyte count, procalcitonin and lactate dehydrogenase levels, and the treantment of steroid were all significantly higher (P < 0.05) among atopic children with refractory MPP vs. general MPP. Spearman correlation analysis showed strong associations between atopy and male sex, length of hospital stay, fever duration, IgE level, wheezing, lobar pneumonia, refractory MPP, and treatment with oxygen, hormones or bronchoscopy (P < 0.05).ConclusionsAtopy may be a risk factor for and was positively correlated with the severity of MPP in children

Balancing Gender Bias in Job Advertisements with Text-Level Bias Mitigation

Despite progress toward gender equality in the labor market over the past few decades, gender segregation in labor force composition and labor market outcomes persists. Evidence has shown that job advertisements may express gender preferences, which may selectively attract potential job candidates to apply for a given post and thus reinforce gendered labor force composition and outcomes. Removing gender-explicit words from job advertisements does not fully solve the problem as certain implicit traits are more closely associated with men, such as ambitiousness, while others are more closely associated with women, such as considerateness. However, it is not always possible to find neutral alternatives for these traits, making it hard to search for candidates with desired characteristics without entailing gender discrimination. Existing algorithms mainly focus on the detection of the presence of gender biases in job advertisements without providing a solution to how the text should be (re)worded. To address this problem, we propose an algorithm that evaluates gender bias in the input text and provides guidance on how the text should be debiased by offering alternative wording that is closely related to the original input. Our proposed method promises broad application in the human resources process, ranging from the development of job advertisements to algorithm-assisted screening of job applications

Gendered STEM Workforce in the United Kingdom:The Role of Gender Bias in Job Advertising

Evidence submitted to the ‘Diversity in STEM’ Inquiry, Science and Technology Committee, House of Commons, UK Parliamen

Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving

With widening deployments of natural language processing (NLP) in daily life, inherited social biases from NLP models have become more severe and problematic. Previous studies have shown that word embeddings trained on human-generated corpora have strong gender biases that can produce discriminative results in downstream tasks. Previous debiasing methods focus mainly on modeling bias and only implicitly consider semantic information while completely overlooking the complex underlying causal structure among bias and semantic components. To address these issues, we propose a novel methodology that leverages a causal inference framework to effectively remove gender bias. The proposed method allows us to construct and analyze the complex causal mechanisms facilitating gender information flow while retaining oracle semantic information within word embeddings. Our comprehensive experiments show that the proposed method achieves state-of-the-art results in gender-debiasing tasks. In addition, our methods yield better performance in word similarity evaluation and various extrinsic downstream NLP tasks

Gendered STEM Workforce in the United Kingdom: The Role of Gender Bias in Job Advertising

Evidence submitted to the ‘Diversity in STEM’ Inquiry, Science and Technology Committee, House of Commons, UK Parliamen

Balancing Gender Bias in Job Advertisements with Text-Level Bias Mitigation

Despite progress towards gender equality in the labor market over the past few decades, gender segregation in labor force composition and labor market outcomes persists. Evidence has shown that job advertisements may express gender preferences, which may selectively attract potential job candidates to apply for a given post and thus reinforce gendered labor force composition and outcomes. Removing gender-explicit words from job advertisements does not fully solve the problem as certain implicit traits are more closely associated with men, such as ambitiousness, while others are more closely associated with women, such as considerateness. However, it is not always possible to find neutral alternatives for these traits, making it hard to search for candidates with desired characteristics without entailing gender discrimination. Existing algorithms mainly focus on the detection of the presence of gender biases in job advertisements without providing a solution to how the text should be (re)worded. To address this problem, we propose an algorithm that evaluates gender bias in the input text and provides guidance on how the text should be debiased by offering alternative wording that is closely related to the original input. Our proposed method promises broad application in the human resources process, ranging from the development of job advertisements to algorithm-assisted screening of job applications

Balancing Gender Bias in Job Advertisements With Text-Level Bias Mitigation

Despite progress toward gender equality in the labor market over the past few decades, gender segregation in labor force composition and labor market outcomes persists. Evidence has shown that job advertisements may express gender preferences, which may selectively attract potential job candidates to apply for a given post and thus reinforce gendered labor force composition and outcomes. Removing gender-explicit words from job advertisements does not fully solve the problem as certain implicit traits are more closely associated with men, such as ambitiousness, while others are more closely associated with women, such as considerateness. However, it is not always possible to find neutral alternatives for these traits, making it hard to search for candidates with desired characteristics without entailing gender discrimination. Existing algorithms mainly focus on the detection of the presence of gender biases in job advertisements without providing a solution to how the text should be (re)worded. To address this problem, we propose an algorithm that evaluates gender bias in the input text and provides guidance on how the text should be debiased by offering alternative wording that is closely related to the original input. Our proposed method promises broad application in the human resources process, ranging from the development of job advertisements to algorithm-assisted screening of job applications

Graphene loaded double ridge plasmon Terahertz waveguide

In this paper, a single-mode graphene loaded double ridge plasmon waveguide (GDRW) with long propagation length and strong mode confinement is proposed. The characteristics of the guided modes are investigated in detail, and tunable single-mode transmission with good performance can be realized either by changing the Fermi energy level or by optimizing the key structural parameters. High figures of merit and much lower crosstalk are obtained due to the dramatically suppressed interference between two parallel placed GDRWs, enabling thereby more tightly stacking in terahertz integrated circuits. Further investigation on fabrication errors, such as the horizontal misalignment of the two symmetric ridges, fabrication distortion of rectangular ridges, and variation of ridge tip curvature radius, indicates that the proposed structure has enough fabrication error tolerance. The results are greatly expected to facilitate the application of high-density terahertz integrated circuits

Photo‐degradable micelles capable of releasing of carbon monoxide under visible light irradiation

Carbon monoxide (CO) has emerged as a potential therapeutic agent for the treatment of many diseases. However, the therapeutic outcome is highly dependent on the dosages and administration sites. Hence, there is mounting interest in the development of CO-releasing materials to accomplish site-specific and dose-controlled delivery of CO. Herein, a micellar nanoparticle platform for the photo-mediated release of CO by using amphiphilic triblock copolymers bearing CO-releasing moieties of 3-hydroxylflavone (3-HF) derivatives within the middle blocks is developed. These micelles are relatively stable without CO leakage but undergo visible light-mediated CO release and simultaneous main chain scission. Moreover, these micellar nanoparticles are cytocompatible regardless of light irradiation, which shows unique anti-inflammatory performance only after light irradiation as a result of photo-triggered CO release. This work may represent the first example of main-chain degradable micellar nanocarriers with controlled CO-releasing performance for potential anti-inflammatory applications

Decreased Interleukin-10 Responses in Children with Severe Mycoplasma pneumoniae Pneumonia.

Several cytokines may play roles in the immunological pathogenesis of mycoplasmal pneumonia caused by Mycoplasma pneumoniae. In this study, we investigated serum cytokine profiles in children with mycoplasmal pneumonia. The serum levels of interleukin (IL)-8, IL-10, and IL-18 were examined using ELISA kits in 34 patients with M. pneumoniae infection (Group 1, 11 with severe mycoplasmal pneumonia; Group 2, 13 with mild mycoplasmal pneumonia; Group 3, 10 with asthma) and 32 age-matched, non-infected controls. The serum levels of IL-8, IL-10, and IL-18 increased significantly in patients with mycoplasmal pneumonia compared with those in controls (P<0.01). The serum levels of IL-10 decreased significantly in Group 1 compared with those in Group 2 (P<0.01). The serum levels of IL-18 increased significantly in Group 1 compared with those in Group 2 (P<0.01). The serum levels of IL-10 and IL-18 decreased significantly in 10 M. pneumoniae-infected patients with asthma compared with those in 24 M. pneumoniae-infected patients without asthma (P<0.01). We examined the level of interleukins (IL-8, IL-10 and IL-18) after the patients started therapy. The data showed that IL-18 were lower after therapy (P<0.01). Collectively, our data suggested that these cytokines may be involved in the pathogenesis of mycoplasmal pneumonia