How useful is the assessment of lymphatic vascular density in oral carcinoma prognosis?

Abstract Background Lymphatic vessels are major routes for metastasis in head and neck squamous cell carcinoma (HNSCC), but lymphatic endothelial cells (LECs) are difficult to recognize in tumor histological sections. D2-40 stains podoplanin, a molecule expressed in LECs, however, the potential prognostic usefulness of this molecule is not completely understood in HNSCC. We aimed to investigate the value of assessing peritumoral and intratumoral lymphatic vessel density (LVD) as prognostic marker for HNSCC. Methods Thirty-one cases of HNSCC were stained for D2-40 and CD31. LVD and blood vessel density (BVD) were assessed by counting positive reactions in 10 hotspot areas at ×200 magnification. Results D2-40 was specific for lymphatic vessels and did not stain blood vascular endothelial cells. LECs showed more tortuous and disorganized structure in intratumoral lymphatic vessels than in peritumoral ones. No statistical differences were observed between peritumoral-LVD and intratumoral-LVD or between peritumoral-BVD and intratumoral-BVD. Tumor D2-40 staining was positively associated with lymphatic vessel invasion (p = 0.011). Conclusion LVD is a powerful marker for HNSCC prognosis. We found significant differences in peritumoral and intratumoral D2-40 immunoreactivity, which could have important implications in future therapeutic strategies and outcome evaluation

Profile of Central and Effector Memory T Cells in the Progression of Chronic Human Chagas Disease

Chagas disease is a parasitic infection caused by protozoan Trypanosoma cruzi that affects approximately 11 million people in Latin America. The involvement of the host's immune response on the development of severe forms of Chagas disease has not been fully elucidated. Studies on the immune response against T. cruzi infection show that the immunoregulatory mechanisms are necessary to prevent the deleterious effect of excessive immune response stimulation and consequently the fatal outcome of the disease. A recall response against parasite antigens observed in in vitro peripheral blood cell culture clearly demonstrates that memory response is generated during infection. Memory T cells are heterogeneous and differ in both the ability to migrate and exert their effector function. This heterogeneity is reflected in the definition of central (TCM) and effector memory (TEM) T cells. Our results suggest that a balance between regulatory and effectors T cells may be important for the progression and development of the disease. Furthermore, the high percentage of central memory CD4+ T cells in indeterminate patients after stimulation suggests that these cells may modulate host's inflammatory response by controlling cell migration to tissues and their effector role during chronic phase of the disease

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio