Global research trends of Mycoplasma pneumoniae pneumonia in children: a bibliometric analysis

BackgroundMycoplasma pneumoniae pneumonia (MPP), attributable to Mycoplasma pneumoniae (MP), represents a predominant form of community-acquired pneumonia in pediatric populations, thereby posing a significant threat to pediatric health. Given the burgeoning volume of research literature associated with pediatric MPP in recent years, it becomes imperative to undertake a bibliometric analysis aimed at delineating the current research landscape and emerging trends, thereby furnishing a framework for subsequent investigations.MethodsA comprehensive literature search targeting pediatric MPP was conducted in the Web of Science Core Collection. After the removal of duplicate entries through Endnote software, the remaining articles were subject to scientometric analysis via Citespace software, VOSviewer software and R language, focusing on variables such as publication volume, contributing nations, institutions and authors, references and keywords.ResultsA total of 1,729 articles pertinent to pediatric MPP were included in the analysis. China and the United States emerged as the nations with the highest publication output. Italian scholar Susanna Esposito and Japanese scholar Kazunobu Ouchi were the most influential authors in the domain of pediatric MPP. Highly-cited articles primarily focused on the epidemiological investigation of pediatric MPP, the clinical characteristics and treatment of macrolide-resistant MPP, and biomarkers for refractory Mycoplasma pneumoniae pneumonia (RMPP). From the corpus of 1,729 articles, 636 keywords were extracted and categorized into ten clusters: Cluster #0 centered on molecular-level typing of macrolide-resistant strains; Cluster #1 focused on lower respiratory tract co-infections; Clusters #2 and #6 emphasized other respiratory ailments caused by MP; Cluster #3 involved biomarkers and treatment of RMPP; Clusters #4 and #9 pertained to extrapulmonary complications of MPP, Clusters #5 and #7 addressed etiological diagnosis of MPP, and Cluster #8 explored pathogenic mechanisms.ConclusionsThe past few years have witnessed extensive attention directed towards pediatric MPP. Research in pediatric MPP principally revolves around diagnostic techniques for MP, macrolide resistance, complications of MPP, treatment and diagnosis of RMPP, and elucidation of pathogenic mechanisms. The present study provides pediatric clinicians and researchers with the research status and focal points in this field, thereby guiding the orientation of future research endeavors

Binary carbon-based additives in LiFePO4 cathode with favorable lithium storage

A pairwise coupling of 0D Super-P (SP), 1D carbon nanotubes (CNTs), and 2D graphene nanosheets (GNs) into binary carbon-based conductive additives was used here for the LiFePO4 cathode in lithium-ion batteries. For comparison, the LiFePO4 cathode with SP, CNT, or GN unitary conductive agent was also examined. Electrochemical test results suggest that the cathodes with binary conducting additives present greatly improved electrochemical performance than the traditional cathode system (only SP used). Especially, the LiFePO4 cathode containing 3% CNT component exhibits the highest specific capacity and the best cycling stability among all the cathodes with binary conducting additives, indicating that an appropriate amount of CNTs is critical in enhancing the conductivity and practical capacity output. However, an excess of CNTs leads to entangling with each other, hampering the uniform distribution of active materials and resulting in poor electrode performance. Furthermore, the combination of CNT and GN can effectively improve the capacity and cycling stability of the LiFePO4 cathodes due to the synergistic effect of 3D conductive networks constructed by the two

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset