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Mycobacterium bovis is a major cause of animal tuberculosis that is also highly dangerous to human health. Autophagic isolation and degradation of intracellular pathogens are employed by host cells as primary innate immune defence mechanisms to control intercellular M. bovis infection. In this study, RNA-Seq technology was used to obtain the total mRNA from bone marrow-derived macrophages (BMDMs) infected with M. bovis at 6 and 24 h after infection. One of the differential genes, GBP2b, was also investigated. Analysis of the significant pathway involved in GBP2b-coexpressed mRNA demonstrated that GBP2b was associated with autophagy and autophagy-related mammalian target of rapamycin (mTOR) signalling and AMP-activated protein kinase (AMPK) signalling. The results of in vivo and in vitro experiments showed significant up-regulation of GBP2b during M. bovis infection. For in vitro validation, small interfering RNA-GBP2b plasmids were transfected into BMDMs and RAW264.7 cells lines to down-regulate the expression of GBP2b. The results showed that the down-regulation of GBP2b impaired autophagy via the AMPK/mTOR/ULK1 pathway. Further studies revealed that the activation of AMPK signalling was essential for the regulation of autophagy during M. bovis infection, and the down-regulation of GBP2b promoted the intracellular survival of M. bovis. These findings were validated on two types of macrophages, which extended the knowledge about the involvement of GBP2b in the autophagic process. Based on these observations, GBP2b should be developed as a promising molecular target for intervening on host–pathogen interactions to develop novel therapeutic strategies to control M. bovis infections in humans and animals.
Abstract Purpose This study aimed to predict the progression-free survival (PFS) of the patients who were diagnosed with hypopharyngeal cancer and received postoperative chemoradiotherapy by using multi-omics method which integrating clinical factors, dosimetric and radiomic features. Materials and methods This study retrospectively collected the pretreatment T1-weighted MR imaging data of 88 hypopharyngeal cancer patients with postoperative chemoradiotherapy, including 56 cases from one center (training and testing cohorts) and 32 cases from another center (external validation cohort), and the gross tumor volumes (GTV) were countered for all cases. A Python-based library, pyradiomics was used to extract the radiomics features from each GTV. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most important features for classifier establishment. On the other hand, complete radiotherapy data are retained for 48 patients among them, and the planning tumor volumes (PTV) were countered for radiotherapy planning. The dose distribution features extracted by using pyradiomics and the dosimetric parameters were combined with the radiomics features to establish the classifiers. The probabilities of positive sample calculated from the best classifier, the radiomics and multi-omics signatures were obtained for establish the Cox proportional hazards models. Results The ensemble learning (EL) model was selected as the superior model with the higher area under the receiver operating characteristic curve (AUC) values than other classifier during the radiomics-only analysis, and the EL model with stacking technique showed the best performance, yielding AUC values of 0.93, 0.79, and 0.78 for the training, testing, and external validation cohorts, respectively. Furthermore, the multi-omics analysis integrating radiomics and dosiomics improved the effectiveness of the EL model with AUC values of 0.98 and 0.88 for the training and testing cohorts, respectively. Furthermore, the C-index of the Cox proportional hazards models resulted in a 0.099 improvement in the testing cohort when employing the multi-omics signature versus the radiomics signature. Conclusion Regarding the patients with hypopharyngeal cancer receiving postoperative chemoradiotherapy, the multi-omics-based prognostic prediction could achieve a more robust predictive capability than the radiomics-only study. This approach warrants further validation through prospective studies.
Conclusions. The combination of surface-enhanced laser desorption/ionization (SELDI) with bioinformatics tools could help find serum proteome biomarkers and establish a predictive model for early detection of hypopharyngeal squamous cell carcinoma (HSCC). Objectives. Proteomic profiling of serum using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) is an emerging technique to identify new biomarkers in biological fluids and to establish clinically useful diagnostic computational models. We used it to find new potential biomarkers and to establish a predictive model for early detection of HSCC. Materials and methods. One hundred serum samples including 48 from HSCC patients and 52 from normal controls which were divided into a training set and a blind testing set were treated on WCX2 and IMAC3 protein chip, and serum protein or peptide patterns were detected by SELDI-TOF-MS. The data of spectra were analyzed by Biomarker Wizard software to screen serum proteome biomarkers of HSCC. A decision tree classification algorithm and blind validation were determined by Biomarker Pattern Software (BPS). Results. Ranging from 2 to 30 kDa, 45 potential biomarkers could differentiate HSCC patients from normal controls (p < 0.05). Among them four candidate protein peaks with m/z values of 7796, 4216, 5927, and 5361Da were selected to establish a predictive model by BPS with sensitivity of 94% and specificity of 89%. A sensitivity of 92% and specificity of 82% were validated in the blind testing set.
Abstract Objective The value of adjuvant therapy in resected laryngeal cancer remains controversial. This large SEER‐based cohort study aimed to investigate the existing parameters of lymph node status that could predict survival outcomes and the prognostic value of adjuvant treatment in resected laryngeal carcinoma. Methods Population‐based data from the US Surveillance, Epidemiology, and End Results (SEER‐18) Program on patients after laryngectomy and lymphadenectomy (2004‐2015) were analyzed. The optimal cut‐off values for examined lymph nodes number (ELNs) and metastatic lymph nodes ratio (MLNR) were determined using the X‐tile program. Associations of ELNs and MLNR with overall survival were investigated through Cox regression analysis. A survival‐predicting model was then constructed to stratified patients. The prognostic value of adjuvant therapy was evaluated in different subgroups. Results A total of 2122 patients with resected laryngeal cancer were analyzed. A novel survival‐predicting model was proposed based on ELNs, MLNR, and other clinicopathological characteristics. Patients were stratified into three subgroups with the increasing risk of death. Only patients in the high‐risk group who receiving adjuvant treatment had a significantly better survival outcome than those receiving surgery alone. Conclusion A new survival‐predicting model was established in this study, which was superior in assessing the survival outcomes of patients with resected laryngeal cancer. Notably, this model was also able to assist in the decision making of adjuvant therapy for patients and physicians.
Healthcare services and management plays an essential role in human society. Nevertheless, there is little attention to the issues of healthcare services, concerning multiple stakeholders involved, complicated relationships, and high management difficulty. This paper aims to explore the approach to optimize management of stakeholders, from the perspective of the service ecosystem. Based on the construction of stakeholders' map in the healthcare service ecosystem, potential stakeholders are identified preliminarily. Moreover, drawing on Mitchell score-based approach, the stakeholders are classified. Eventually, with the distinguishing of leading, core and supporting populations based upon the classification results, the healthcare service ecosphere in the Chinese context is developed. This research provides a research framework for the positioning of stakeholder, formulation of management strategies and formation of interest balance mechanism in the healthcare service ecosystem. In addition, it also provides theoretical support and practical guidance for the optimization of healthcare services.
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