Software tools for conducting bibliometric analysis in science: An up-to-date review

Tissue engineering (TE) is defined as a multidisciplinary scientific discipline with the main objective to develop artificial bioengineered living tissues to regenerate damaged or lost tissues. Since its appearance in 1988, TE has globally spread to improve current therapeutic approaches, entailing a revolution in clinical practice. The aim of this study is to analyze global research trends on TE publications to realize the scenario of TE research from 1991 to 2016 by using document retrieval from Web of Science database and bibliometric analysis. Document type, language, source title, authorship, countries and filiation centers, and citation count were evaluated in 31,859 documents. Obtained results suggest a great multidisciplinary role of TE due to a wide spectrum—up to 51—of scientific research areas identified in the corpus of literature, being predominant technological disciplines as Material Sciences or Engineering, followed by biological and biomedical areas, as Cell Biology, Biotechnology, or Biochemistry. Distribution of authorship, journals, and countries revealed a clear imbalance, in which a minority is responsible for a majority of documents. Such imbalance is notorious in authorship, where a 0.3% of authors are involved in half of the whole production.

Classic Hodgkin lymphoma (cHL) constitutes a B cell-derived neoplasm defined by a scarce tumoral population, termed Hodgkin and Reed–Sternberg (HRS) cells, submerged into a histologically heterogeneous microenvironment. The paucity of HRS cells has historically hampered genetic studies, rendering the identification of the recurrent genetic lesions and molecular pathways deregulated in this lymphoma difficult. The advent of high-throughput sequencing methods such as next-generation sequencing (NGS) could sensibly optimize the identification of the mutational landscape of cHL. However, there is no current consensus either in the design of panels for targeted NGS or in its most relevant clinical applications. In this work, we systematically review the current state of NGS studies of cHL, stressing the need for standardization both in the candidate genes to be analyzed and the bioinformatic pipelines. As different institutions have developed and implemented their own customized NGS-based protocols, to compare and systematically review the major findings of this ongoing research area could be of added value for centers that routinely perform diagnostic, monitoring and genotyping strategies in cHL samples. The results of this systematic review should contribute to the interdepartmental harmonization and achievement of a consensus in the current clinical applications of NGS studies of cHL.

Genomic analysis and digitalization of medical records have led to a big data scenario within hematopathology. Artificial intelligence and machine learning tools are increasingly used to integrate clinical, histopathological, and genomic data in lymphoid neoplasms. In this study, we identified global trends, cognitive, and social framework of this field from 1990 to 2020. Metadata were obtained from the Clarivate Analytics Web of Science database in January 2021. A total of 525 documents were assessed by document type, research areas, source titles, organizations, and countries. SciMAT and VOSviewer package were used to perform scientific mapping analysis. Geographical distribution showed the USA and People’s Republic of China as the most productive countries, reporting up to 190 (36.19%) of all documents. A third-degree polynomic equation predicts that future global production in this area will be three-fold the current number, near 2031. Thematically, current research is focused on the integration of digital image analysis and genomic sequencing in Non-Hodgkin lymphomas, prediction of chemotherapy response and validation of new prognostic models. These findings can serve pathology departments to depict future clinical and research avenues, but also, public institutions and administrations to promote synergies and optimize funding allocation.

The prognostic impact of the presence of Epstein–Barr virus (EBV) in classical Hodgkin lymphoma (cHL) is controversial. Previous studies reported heterogeneous results, rendering difficult the clinical validation of EBV as a prognostic biomarker in this lymphoma. The objective of this study was to evaluate the survival impact of the expression of EBV Latent-Membrane Protein 1 (EBV-LMP1) in tumoral Hodgkin–Reed–Sternberg (HRS) cells of primary diagnostic samples of cHL. Formalin-Fixed Paraffin-Embedded (FFPE) lymph node samples from 88 patients with cHL were analyzed. Patients were treated with the standard first-line chemotherapy (CT) with Adriamycin, Bleomycin, Vinblastine and Dacarbazine (ABVD) followed by radiotherapy. The Kaplan–Meier method and the Cox proportional hazards model were used for carrying out the survival analysis. In order to investigate whether the influence of EBV was age-dependent, analyses were performed both for patients of all ages and for age-stratified subgroups. In bivariate analysis, the expression of EBV was associated with older age (p = 0.011), mixed cellularity subtype cHL (p < 0.001) and high risk International Prognostic Score (IPS) (p = 0.023). Overall survival (OS) and progression-free survival (PFS) were associated with the presence of bulky disease (p = 0.009) and advanced disease at diagnosis (p = 0.016). EBV-positive cases did not present a significantly lower OS and PFS in comparison with EBV-negative cases, for all ages and when stratifying for age. When adjusted for covariates, absence of bulky disease at diagnosis (HR: 0.102, 95% CI: 0.02–0.48, p = 0.004) and limited disease stages (I–II) (HR: 0.074, 95% CI: 0.01–0.47, p = 0.006) were associated with a significant better OS. For PFS, limited-disease stages also retained prognostic impact in the multivariate Cox regression (HR: 0.145, 95% CI: 0.04–0.57, p = 0.006). These results are of importance as the early identification of prognostic biomarkers in cHL is critical for guiding and personalizing therapeutic decisions. The prognostic role of EBV in cHL could be modulated by the type of CT protocol employed and interact with the rest of presenting features.

Classic Hodgkin lymphoma (cHL) constitutes the most frequent lymphoma in young adults. Its histopathology is unique as a scattered tumor population, termed Hodgkin Reed-Sternberg (HRS) cells is diluted in a prominent tumor microenvironment (TME) composed of T lymphocytes, B lymphocytes, macrophages, neutrophils, eosinophils and histiocytes. Traditionally, the identification of prognostic biomarkers in the cHL TME has required visual inspection and manual counting by pathologists. The advent of whole-slide imaging (WSI) and digital image analysis methods could significantly contribute to improve this essential objective in cHL research, as a 10-20% of patients are still refractory or relapsed after conventional chemotherapy. In this work, we have digitized a total of 255 diagnostic cHL slides and quantified the proportion of HRS cells (CD30), B cells (CD20) and T cells (CD3) by digital image analysis. Data obtained where then correlated with the overall survival (OS) and progression free survival (PFS) of cHL patients. Quantification of HRS cells, B cells and T cells reflects the biological heterogeneity of the different cHL histological subtypes analyzed. A percentage of 2.00% of HRS cells statistically significantly discriminated between patients achieving a complete metabolic response (CMR) and refractory or relapsed (R/R) patients both for the OS (P=0.001) and PFS (P=0.005). Furthermore, patients with a percentage of T cells below the 26.70% in the TME showed a statistically significantly shorter OS (P=0.019) and PFS (P=0.041) in comparison with patients above this threshold. A subgroup of patients with a low content of T cells and high content of HRS cells exhibited a special aggressive clinical course. Currently, there is the need to implement quantitative and easy scalable methods to enhance clinical translation, as the cHL TME plays a central role in the clinical course of the disease. The results of this study could contribute to the identification of prognostic biomarkers specifically looking at the cHL TME and their inclusion in future clinical trials.