The role of TNF-αand NFkβin an experimental model of intestinal carcinogenesis with 1,2-dimethyhydrazine

ABSTRACT Purpose: To analyze the potential of tumor necrosis factor-α (TNF-α) and factor nuclear kappa B (NF-κB) as colorectal cancer (CRC) biomarkers in an experimental model of intestinal carcinogenesis with 1,2-dimethyhydrazine (1,2-DMH). Methods: Twenty-four male Wistar rats were divided into two groups: sham and 1,2-DMH. First, 1,2-DMH (20 mg/kg/week) was administered for 15 consecutive weeks. In the 25th week, proctocolectomy was conducted. Histopathological analysis, immunohistochemistry, and gene expression of TNF-α and NF-κB were performed. Statistical analysis was performed using GraphPad Prism. The location of aberrant crypt foci (ACF) was analyzed by Kruskal-Wallis’ test. For analyses with two groups with parametric data, the t-test was used; for non-parametric data, the Mann-Whitney’s test was used. P < 0.05 was considered significant. Results: The number of ACF and macroscopic lesions was significantly higher (p < 0.5) in the 1,2-DMH group compared to the sham group, and most ACF were concentrated in the distal segment of the colon. There was a statistically significant increase (p < 0.5) in protein and gene expression of TNF-α and NF-κB in the 1,2-DMH group compared to the sham group. Conclusions: Our results provide supportive evidence that TNF-α and NF-κB pathways are strongly involved in CRC development in rats and might be used as early biomarkers of CRC pathogenesis in experimental studies

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

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

Development and mechanical-functional validation of 3D-printed laparoscopic forceps

ABSTRACT Introduction: 3-dimensional printing has enabled the development of unique and affordable additive manufacturing, including the prototyping and production of surgical forceps. Objective: demonstrate the development, 3D printing and mechanical-functional validation of a laparoscopic grasping forceps. Methods: the clamp was designed using a computer program and printed in 3 dimensions with polylactic acid (PLA) filament and added 5 screws for better leverage. Size and weight measurements were carried out, as well as mechanicalfunctional grip and rotation tests in the laboratory with a validated simulator. Results: Called “Easylap”, the clamp weighed 48 grams, measured 43cm and was printed in 8 pieces, taking an average of 12 hours to produce. It allowed the simulation of the functional characteristics of laparoscopic pressure forceps, in addition to the rotation and rack locking mechanism. However, its strength is reduced due to the material used. Conclusion: It is possible to develop plastic laparoscopic grasping forceps through 3-dimensional printing

Treatment of Severe Refractory Hematuria due to Radiation-Induced Hemorrhagic Cystitis with Dexamethasone

Treatment of pelvic neoplasms with radiotherapy may develop sequelae, especially RHC. An 85-year-old male patient was admitted to a hospital emergency with gross hematuria leading to urinary retention and was diagnosed with RHC. The urinary bladder was probed, unobstructed, and maintained in continuous three-way saline irrigation. During 45 days of hospitalization, the patient underwent two cystoscopic procedures for urinary bladder flocculation, whole blood transfusions, and one platelet apheresis. None of these interventions led to clinical resolution. As the patient hematological condition was deteriorating, dexamethasone (4 mg i.v., bolus of 6/6, 12/12, and 24 h during five days) and epoetin alpha (1000 IU, 1 ml, s.c., for four weeks) were administered which led to the remission of the urinary bleeding. Dexamethasone therapy may be considered for RHC, when conventional treatments are not effective or are not possible, avoiding more aggressive interventions such as cystectomy