Estimating the global burden of Epstein–Barr virus‑related cancers

Background: More than 90% of the adult population globally is chronically infected by the Epstein–Barr virus (EBV). It is well established that EBV is associated with a number of malignancies, and advances in knowledge of EBV-related malignancies are being made every year. Several studies have analysed the global epidemiology and geographic distribution of EBV-related cancers. However, most have only described a single cancer type or subtype in isolation or limited their study to the three or four most common EBV-related cancers. This review will present an overview on the spectrum of cancers linked to EBV based on observations of associations and proportions in the published literature while also using these observations to estimate the incidence and mortality burden of some of these cancers. Method: We have reviewed the literature on defining features, distribution and outcomes across six cancers with a relatively large EBV-related case burden: Nasopharyngeal carcinoma (NPC), Gastric carcinoma (GC), Hodgkin lymphoma (HL), Burkitt lymphoma (BL), Diffuse large B-cell lymphoma (DLBCL) and Extranodal NK/T-cell lymphoma, Nasal type (ENKTL-NT). We retrieved published region-specific EBV-related case proportions for NPC, GC, HL and BL and performed meta-analyses on pooled region-specific studies of EBV-related case proportions for DLBCL and ENKTL-NT. We match these pooled proportions with their respective regional incidence and mortality numbers retrieved from a publicly available cancer database. Additionally, we also reviewed the literature on several other less common EBV-related cancers to summarize their key characteristics herein. Conclusion: We estimated that EBV-related cases from these six cancers accounted for 239,700–357,900 new cases and 137,900–208,700 deaths in 2020. This review highlights the significant global impact of EBV-related cancers and extends the spectrum of disease that could benefit from an EBV-specific therapeutic

The medicinal plant Tabebuia impetiginosa potently reduces pro-inflammatory cytokine responses in primary human lymphocytes

Bark from the Handroanthus impetiginosus (Mart. ex DC.) Mattos (Bignoniaceae) tree has long been used in traditional South American healing practises to treat inflammation. However, its anti-inflammatory activity has not been closely examined. Here we use chemical extraction, qualitative phytochemical examination, toxicity testing and quantitative examination of anti-inflammatory activity on human cells ex vivo. All extracts were found to be nontoxic. We found different extracts exhibited unique cytokine profiles with some extracts outperforming a positive control used in the clinic. These results verify the immunomodulatory activity of Handroanthus impetiginosus (Mart. ex DC.) Mattos (Bignoniaceae) tree bark-derived compounds. Collectively, combining a lack of toxicity and potency in human immune cells supports further fractionation and research

The geographic distribution, venom components, pathology and treatments of stonefish (Synanceia spp.) venom

Stonefish are regarded as one of the most venomous fish in the world. Research on stonefish venom has chiefly focused on the in vitro and in vivo neurological, cardiovascular, cytotoxic and nociceptive effects of the venom. The last literature review on stonefish venom was published over a decade ago, and much has changed in the field since. In this review, we have generated a global map of the current distribution of all stonefish (Synanceia) species, presented a table of clinical case reports and provided up-to-date information about the development of polyspecific stonefish antivenom. We have also presented an overview of recent advancements in the biomolecular composition of stonefish venom, including the analysis of transcriptomic and proteomic data from Synanceia horrida venom gland. Moreover, this review highlights the need for further research on the composition and properties of stonefish venom, which may reveal novel molecules for drug discovery, development or other novel physiological uses

Nitrogen fertilizer rate but not form affects the severity of Fusarium wilt in banana

Nitrogen (N) fertilizers are routinely applied to bananas (Musa spp.) to increase production but may exacerbate plant diseases like Fusarium wilt of banana (FWB), which is the most economically important disease. Here, we characterized the effects of N rate and form on banana plant growth, root proteome, bacterial and fungal diversity in the rhizosphere, the concentration of Fusarium oxysporum f.sp. cubense (Foc) in the soil, and the FWB severity. Banana plants (Musa subgroup ABB) were grown under greenhouse conditions in soil with ammonium or nitrate supplemented at five N rates, and with or without inoculation with Foc. The growth of non-inoculated plants was positively correlated with the N rate. In bananas inoculated with Foc, disease severity increased with the N rate, resulting in the Foc-inoculated plant growth being greatest at intermediate N rates. The abundance of Foc in the soil was weakly related to the treatment conditions and was a poor predictor of disease severity. Fungal diversity was consistently affected by Foc inoculation, while bacterial diversity was associated with changes in soil pH resulting from N addition, in particular ammonium. N rate altered the expression of host metabolic pathways associated with carbon fixation, energy usage, amino acid metabolism, and importantly stress response signaling, irrespective of inoculation or N form. Furthermore, in diseased plants, Pathogenesis-related protein 1, a key endpoint for biotic stress response and the salicylic acid defense response to biotrophic pathogens, was negatively correlated with the rate of ammonium fertilizer but not nitrate. As expected, inoculation with Foc altered the expression of a wide range of processes in the banana plant including those of defense and growth. In summary, our results indicate that the severity of FWB was negatively associated with host defenses, which was influenced by N application (particularly ammonium), and shifts in microbial communities associated with ammonium-induced acidification

Secreted and surface proteome and transcriptome of Opisthorchis felineus

Introduction: Opisthorchis felineus, Opisthorchis viverrini, and Clonorchis sinensis are the most medically important species of fish-borne zoonotic trematodes. O. felineus is endemic to the river plains of Western Siberia and Eastern Europe, and it is estimated that more than 1.6 million people could be infected with this parasite. Chronic opisthorchiasis may lead to significant gastrointestinal and hepatobiliary pathology. This study aimed to identify and characterize proteins from the secreted and tegumental proteomes of O. felineus. Methods: Adult flukes were collected from experimentally infected hamsters and cultured in vitro in serum-free media. We extracted proteins from different compartments of the O. felineus secretome, including (i) soluble excretory/secretory (ES) products; (ii) secreted 15K-extracellular vesicles (EVs); and (iii) tegument. Results: We also generated a transcriptome using long-read sequencing, and when this was combined with high-resolution mass spectrometry, sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) separation, and protein digestion, we identified 686, 894, 389, 324, and 165 proteins from the ES, 15K-EV, and the three sequentially extracted tegument (TEG) protein fractions, respectively. We conducted in-depth gene ontology and protein family analyses on the identified proteins and discussed comparisons against similar proteome data sets acquired for the Southeast Asian liver fluke O. viverrini and the Chinese liver fluke C. sinensis. Discussion: The information from this study will form a biologically relevant data set of O. felineus proteins that could be used to develop diagnostic and therapeutic tools to manage the human cost of O. felineus infection and its associated comorbidities.This project has been funded by the Australian National Health and Medical Research council e-ASIA Joint Research Program APP1185434. AL is supported by a Level Three NHMRC Investigator Grant APP2008450. OF and EK are supported by a RFBR Grant 19-515-70004. JS is supported by a Ramon y Cajal fellowship (RYC2021-032443-I) from the Ministerio de Ciencia e Innovacion in Spain.S

Excretory/secretory proteome of females and males of the hookworm Ancylostoma ceylanicum

The dynamic host-parasite mechanisms underlying hookworm infection establishment and maintenance in mammalian hosts remain poorly understood but are primarily mediated by hookworm\u27s excretory/secretory products (ESPs), which have a wide spectrum of biological functions. We used ultra-high performance mass spectrometry to comprehensively profile and compare female and male ESPs from the zoonotic human hookwor

Progressive Semisupervised Learning of Multiple Classifiers

International audienceSemisupervised learning methods are often adopted to handle datasets with very small number of labeled samples. However, conventional semisupervised ensemble learning approaches have two limitations: 1) most of them cannot obtain satisfactory results on high dimensional datasets with limited labels and 2) they usually do not consider how to use an optimization process to enlarge the training set. In this paper, we propose the progressive semisupervised ensemble learning approach (PSEMISEL) to address the above limitations and handle datasets with very small number of labeled samples. When compared with traditional semisupervised ensemble learning approaches, PSEMISEL is characterized by two properties: 1) it adopts the random subspace technique to investigate the structure of the dataset in the subspaces and 2) a progressive training set generation process and a self evolutionary sample selection process are proposed to enlarge the training set. We also use a set of nonparametric tests to compare different semisupervised ensemble learning methods over multiple datasets. The experimental results on 18 real-world datasets from the University of California, Irvine machine learning repository show that PSEMISEL works well on most of the real-world datasets, and outperforms other state-of-the-art approaches on 10 out of 18 datasets