Surgical approach to medullary thyroid carcinoma associated with multiple endocrine neoplasia type 2

We briefly review the surgical approaches to medullary thyroid carcinoma associated with multiple endocrine neoplasia type 2 (medullary thyroid carcinoma/multiple endocrine neoplasia type 2). The recommended surgical approaches are usually based on the age of the affected carrier/patient, tumor staging and the specific rearranged during transfection codon mutation. We have focused mainly on young children with no apparent disease who are carrying a germline rearranged during transfection mutation. Successful management of medullary thyroid carcinoma in these cases depends on early diagnosis and treatment. Total thyroidectomy should be performed before 6 months of age in infants carrying the rearranged during transfection 918 codon mutation, by the age of 3 years in rearranged during transfection 634 mutation carriers, at 5 years of age in carriers with level 3 risk rearranged during transfection mutations, and by the age of 10 years in level 4 risk rearranged during transfection mutations. Patients with thyroid tumor >5 mm detected by ultrasound, and basal calcitonin levels >40 pg/ml, frequently have cervical and upper mediastinal lymph node metastasis. In the latter patients, total thyroidectomy should be complemented by extensive lymph node dissection. Also, we briefly review our data from a large familial medullary thyroid carcinoma genealogy harboring a germline rearranged during transfection Cys620Arg mutation. All 14 screened carriers of the rearranged during transfection Cys620Arg mutation who underwent total thyroidectomy before the age of 12 years presented persistently undetectable serum levels of calcitonin (<2 pg/ml) during the follow-up period of 2–6 years. Although it is recommended that preventive total thyroidectomy in rearranged during transfection codon 620 mutation carriers is performed before the age of 5 years, in this particular family the surgical intervention performed before the age of 12 years led to an apparent biochemical cure

A HIF1α Regulatory Loop Links Hypoxia and Mitochondrial Signals in Pheochromocytomas

Pheochromocytomas are neural crest–derived tumors that arise from inherited or sporadic mutations in at least six independent genes. The proteins encoded by these multiple genes regulate distinct functions. We show here a functional link between tumors with VHL mutations and those with disruption of the genes encoding for succinate dehydrogenase (SDH) subunits B (SDHB) and D (SDHD). A transcription profile of reduced oxidoreductase is detected in all three of these tumor types, together with an angiogenesis/hypoxia profile typical of VHL dysfunction. The oxidoreductase defect, not previously detected in VHL-null tumors, is explained by suppression of the SDHB protein, a component of mitochondrial complex II. The decrease in SDHB is also noted in tumors with SDHD mutations. Gain-of-function and loss-of-function analyses show that the link between hypoxia signals (via VHL) and mitochondrial signals (via SDH) is mediated by HIF1α. These findings explain the shared features of pheochromocytomas with VHL and SDH mutations and suggest an additional mechanism for increased HIF1α activity in tumors

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

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 understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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 lost