Schiff bases: A short review of their antimicrobial activities

AbstractSchiff bases are aldehyde- or ketone-like compounds in which the carbonyl group is replaced by an imine or azomethine group. They are widely used for industrial purposes and also exhibit a broad range of biological activities. This short review compiles examples of the most promising antimalarial, antibacterial, antifungal, and antiviral Schiff bases. An overview of synthetic methodologies used for the preparation of Schiff bases is also described

Allopurinol Reduces the Lethality Associated with Acute Renal Failure Induced by Crotalus durissus terrificus Snake Venom: Comparison with Probenecid

In Brazil, among registered snake bites, those by the genus Crotalus originate the highest mortality rate. The rattlesnake Crotalus durissus terrificus is the most frequently implicated in these accidents. The kidney is a particularly vulnerable organ to the venom of this rattlesnake. In fact, the most serious complication of Crotalus snake bite is the renal dysfunction, and among the fatal cases of Crotalus bites in Brazil 5% are patients treated with antivenom. The hyperuricemia has been observed in human accidents with snake venoms, but this parameter has not received any special attention as a relevant factor in the etiology of renal dysfunction caused by these venoms. This study examined the effects of treatments with low-cost and low-risk uricostatic (allopurinol) and uricosuric (probenecid) drugs on the envenomation by C. d. terrificus, showing that allopurinol and probenecid mitigated certain nephrotoxic effects, as well as the survival of envenomed mice was improved through the effects of allopurinol on reduction of oxidative stress and intracellular formation of uric acid. This new knowledge provides consistent evidences linking uric acid with the renal dysfunction induced by rattlesnake bites and that the allopurinol deserves to be clinically evaluated as an approach complementary to anti-snake venom serotherapy

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