RESUMEN
Heme oxygenase HO-1 (HMOX) regulates cellular inflammation and apoptosis, but its role in regulation of autophagy in Mycoplasma bovis infection is unknown. The objective was to determine how the HO-1/CO- Protein kinase RNA-like endoplasmic reticulum kinase (PERK)-Ca2+- transcription factor EB (TFEB) signaling axis induces autophagy and regulates clearance of M. bovis by bovine mammary epithelial cells (bMECs). M. bovis inhibited autophagy and lysosomal biogenesis in bMECs and suppressed HO-1 protein and expression of related proteins, namely nuclear factor erythroid 2-related factor 2 (Nrf2) and Kelch-like ECH-associated protein 1 (keap1). Activation of HO-1 and its production of carbon monoxide (CO) were required for induction of autophagy and clearance of intracellular M. bovis. Furthermore, when HO-1 was deficient, CO sustained cellular autophagy. HO-1 activation increased intracellular calcium (Ca2+) and cytosolic localization activity of TFEB via PERK. Knockdown of PERK or chelation of intracellular Ca2+ inhibited HO-1-induced M. bovis autophagy and clearance. M. bovis infection affected nuclear localization of lysosomal TFEB in the MiT/TFE transcription factor subfamily, whereas activation of HO-1 mediated dephosphorylation and intranuclear localization of TFEB, promoting autophagy, lysosomal biogenesis and autophagic clearance of M. bovis. Nuclear translocation of TFEB in HO-1 was critical to induce M. bovis transport and survival of infected bMECs. Furthermore, the HO-1/CO-PERK-Ca2+-TFEB signaling axis induced autophagy and M. bovis clearance, providing a viable approach to treat persistent M. bovis infections.
Asunto(s)
Autofagia , Factores de Transcripción Básicos con Cremalleras de Leucinas y Motivos Hélice-Asa-Hélice , Calcio , Núcleo Celular , Retículo Endoplásmico , Células Epiteliales , Glándulas Mamarias Animales , Mycoplasma bovis , Animales , Bovinos , Femenino , Factores de Transcripción Básicos con Cremalleras de Leucinas y Motivos Hélice-Asa-Hélice/metabolismo , Factores de Transcripción Básicos con Cremalleras de Leucinas y Motivos Hélice-Asa-Hélice/genética , Calcio/metabolismo , Monóxido de Carbono/metabolismo , Núcleo Celular/metabolismo , eIF-2 Quinasa/metabolismo , eIF-2 Quinasa/genética , Retículo Endoplásmico/metabolismo , Células Epiteliales/microbiología , Células Epiteliales/metabolismo , Hemo-Oxigenasa 1/metabolismo , Hemo-Oxigenasa 1/genética , Lisosomas/metabolismo , Glándulas Mamarias Animales/microbiología , Glándulas Mamarias Animales/metabolismo , Infecciones por Mycoplasma/microbiología , Infecciones por Mycoplasma/veterinaria , Infecciones por Mycoplasma/metabolismo , Factor 2 Relacionado con NF-E2/metabolismo , Factor 2 Relacionado con NF-E2/genética , Transducción de SeñalRESUMEN
The literature has acknowledged the correlation between aggressive humor style and cyberbullying perpetration; however, little is known about how this occurs. In this study, we sought to gain an understanding of how and when someone with an aggressive humor style may develop into a perpetrator of cyberbullying. We propose that whether an individual's aggressive humor style results in cyberbullying perpetration depends on online social norms of tolerance for aggressive humor. When online normative tolerance for aggressive humor is high, individuals' aggressive humor style is positively correlated with their moral disengagement, which, in turn, increases their intention to commit cyberbullying. When online normative tolerance for aggressive humor is low, the effect of individuals' aggressive humor style on their moral disengagement is attenuated, which, in turn, weakens the relationship between aggressive humor style and cyberbullying perpetration. A total of 305 Chinese university students were recruited to participate in the experiment, and we found support for this hypothesis across the experiment. Several theoretical and practical implications are discussed.
RESUMEN
OBJECTIVE: This research was aimed to evaluate the impact of high-quality nursing (HQN) plus respiratory training on treatment compliance, pulmonary function (PF) and quality of life (QoL) of patients with chronic obstructive pulmonary disease (COPD). METHODS: We retrospectively analyzed 89 COPD patients who were treated at the affiliated Nanhua Hospital from February 2019 to February 2021. Among them, 40 cases received drug treatment and breathing training as the control group, and 49 cases were supplemented with HQN as the experimental group on the basis of the control group. The changes in PF, quality of life and compliance were compared between the two groups. RESULTS: Vital capacity (VC) and alveolar ventilation (VA) increased in both cohorts after treatment (P < 0.05), and increased more significantly in experimental group compared with control group (P < 0.05). Experimental group also presented markedly higher total effective rate and noticeably lower scores of symptoms, activities and disease impact on daily life than control group (P < 0.05). CONCLUSIONS: HQN plus respiratory training can effectively improve the PF, efficacy and QoL of patients with COPD.
RESUMEN
Academic institutions need to maintain publication lists for thousands of faculty and other scholars. Automated tools are essential to minimize the need for direct feedback from the scholars themselves who are practically unable to commit necessary effort to keep the data accurate. In relying exclusively on clustering techniques, author disambiguation applications fail to satisfy key use cases of academic institutions. Algorithms can perfectly group together a set of publications authored by a common individual, but, for them to be useful to an academic institution, they need to programmatically and recurrently map articles to thousands of scholars of interest en masse. Consistent with a savvy librarian's approach for generating a scholar's list of publications, identity-driven authorship prediction is the process of using information about a scholar to quantify the likelihood that person wrote certain articles. ReCiter is an application that attempts to do exactly that. ReCiter uses institutionally-maintained identity data such as name of department and year of terminal degree to predict which articles a given scholar has authored. To compute the overall score for a given candidate article from PubMed (and, optionally, Scopus), ReCiter uses: up to 12 types of commonly available, identity data; whether other members of a cluster have been accepted or rejected by a user; and the average score of a cluster. In addition, ReCiter provides scoring and qualitative evidence supporting why particular articles are suggested. This context and confidence scoring allows curators to more accurately provide feedback on behalf of scholars. To help users to more efficiently curate publication lists, we used a support vector machine analysis to optimize the scoring of the ReCiter algorithm. In our analysis of a diverse test group of 500 scholars at an academic private medical center, ReCiter correctly predicted 98% of their publications in PubMed.