BRAFV600E mutations in malignant melanoma are associated with increased expressions of BAALC

<p>Abstract</p> <p>Bachground</p> <p>Activating <it>BRAF </it>mutations are present in approximately 50% of melanomas. Although different downstream target genes of the most common mutant V600E have been identified, the contribution of activating <it>BRAF </it>mutations to malignant transformation needs further clarification.</p> <p>Methods</p> <p>Microarray gene analysis was performed for human melanoma cell lines harboring BRAF^V600Emutations in comparison to cell lines without this mutation.</p> <p>Results</p> <p>This analysis revealed a more than two fold down-regulation of 43 and an increase of 39 gene products. <it>BAALC </it>(<it>Brain and acute Leukaemia, cytoplasmatic</it>) was most prominently regulated, since it was up-regulated in mutated cell lines by a mean of 11.45. Real time PCR analyses with RNA from melanoma cell lines (n = 30) confirmed the <it>BRAF</it>-activation dependent up-regulation of <it>BAALC</it>.</p> <p>Conclusion</p> <p><it>BAALC</it>, which has been associated with cell dedifferentiation and migration, may function as a downstream effector of activating <it>BRAF </it>mutations during melanomagenesis.</p

The carcinogenic potential of tacrolimus ointment beyond immune suppression: a hypothesis creating case report

BACKGROUND: Since tacrolimus ointment was approved by the U.S. Food and Drug Administration (FDA) as a promising treatment for atopic dermatitis, it has been approved in more than 30 additional countries, including numerous European Union member nations. Moreover, in the current clinical routine the use of this drug is no longer restricted to the approved indication, but has been extended to a wide variety of inflammatory skin diseases including some with the potential of malignant transformation. So far, the side-effects reported from the topical use of tacrolimus have been relatively minor (e.g. burning, pruritus, erythema). Recently, however, the FDA reviewed the safety of topical tacrolimus, which resulted in a warning that the use of calcineurin inhibitors may be associated with an increased risk of cancer. CASE PRESENTATION: Oral lichen planus (OLP) was diagnosed in a 56-year-old women in February 1999. After several ineffective local and systemic therapeutic measures an off-label treatment of this recalcitrant condition using Tacrolimus 0.1% ointment was initiated in May 2002. After a few weeks of treatment most of the lesions ameliorated, with the exception of the plaques on the sides of the tongue. Nevertheless, the patient became free of symptoms which, however, reoccurred once tacrolimus was weaned, as a consequence treatment was maintained. In April 2005, the plaques on the left side of the tongue appeared increasingly compact and a biopsy specimen confirmed the suspected diagnosis of an oral squamous cell carcinoma. CONCLUSION: The suspected causal relationship between topical use of tacrolimus and the development of a squamous cell carcinoma prompted us to test the notion that the carcinogenicity of tacrolimus may go beyond mere immune suppression. To this end, tacrolimus has been shown to have an impact on cancer signalling pathways such as the MAPK and the p53 pathway. In the given case, we were able to demonstrate that these pathways had also been altered subsequent to tacrolimus therapy

Susceptibility of adult cat fleas (Siphonaptera: Pulicidae) to insecticides and status of insecticide resistance mutations at the Rdl and knockdown resistance loci

This is an Open Access article. © 2015 The Author(s). Published by Springer Berlin Heidelberg.The susceptibility of 12 field-collected isolates and 4 laboratory strains of cat fleas, Ctenocephalides felis was determined by topical application of some of the insecticides used as on-animal therapies to control them. In the tested field-collected flea isolates the LD50 values for fipronil and imidacloprid ranged from 0.09 to 0.35 ng/flea and 0.02 to 0.19 ng/flea, respectively, and were consistent with baseline figures published previously. The extent of variation in response to four pyrethroid insecticides differed between compounds with the LD50 values for deltamethrin ranging from 2.3 to 28.2 ng/flea, etofenprox ranging from 26.7 to 86.7 ng/flea, permethrin ranging from 17.5 to 85.6 ng/flea, and d-phenothrin ranging from 14.5 to 130 ng/flea. A comparison with earlier data for permethrin and deltamethrin implied a level of pyrethroid resistance in all isolates and strains. LD50 values for tetrachlorvinphos ranged from 20.0 to 420.0 ng/flea. The rdl mutation (conferring target-site resistance to cyclodiene insecticides) was present in most field-collected and laboratory strains, but had no discernible effect on responses to fipronil, which acts on the same receptor protein as cyclodienes. The kdr and skdr mutations conferring target-site resistance to pyrethroids but segregated in opposition to one another, precluding the formation of genotypes homozygous for both mutations.Peer reviewedFinal Published versio

Describing complex interactions of social-ecological systems for tipping point assessments: an analytical framework

Humans play an interconnecting role in social-ecological systems (SES), they are part of these systems and act as agents of their destruction and regulation. This study aims to provide an analytical framework, which combines the concept of SES with the concept of tipping dynamics. As a result, we propose an analytical framework describing relevant dynamics and feedbacks within SES based on two matrixes: the “tipping matrix” and the “cross-impact matrix.” We take the Southwestern Amazon as an example for tropical regions at large and apply the proposed analytical framework to identify key underlying sub-systems within the study region: the soil ecosystem, the household livelihood system, the regional social system, and the regional climate system, which are interconnected through a network of feedbacks. We consider these sub-systems as tipping elements (TE), which when put under stress, can cross a tipping point (TP), resulting in a qualitative and potentially irreversible change of the respective TE. By systematically assessing linkages and feedbacks within and between TEs, our proposed analytical framework can provide an entry point for empirically assessing tipping point dynamics such as “tipping cascades,” which means that the crossing of a TP in one TE may force the tipping of another TE. Policy implications: The proposed joint description of the structure and dynamics within and across SES in respect to characteristics of tipping point dynamics promotes a better understanding of human-nature interactions and critical linkages within regional SES that may be used for effectively informing and directing empirical tipping point assessments, monitoring or intervention purposes. Thereby, the framework can inform policy-making for enhancing the resilience of regional SES

.Using machine learning to identify important predictors of COVID-19 infection prevention behaviors during the early phase of the pandemic

Before vaccines for coronavirus disease 2019 (COVID-19) became available, a set of infection-prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine-learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection-prevention behavior in 56,072 participants across 28 countries, administered in March to May 2020. The machine-learning model predicted 52% of the variance in infection-prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individuallevel injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically derived predictors were relatively unimportant

Using Machine Learning to Identify Important Predictors of COVID-19 Infection Prevention Behaviors During the Early Phase of the Pandemic

Before vaccines for COVID-19 became available, a set of infection prevention behaviors constituted the primary means to mitigate the virus spread. Our study aimed to identify important predictors of this set of behaviors. Whereas social and health psychological theories suggest a limited set of predictors, machine learning analyses can identify correlates from a larger pool of candidate predictors. We used random forests to rank 115 candidate correlates of infection prevention behavior in 56,072 participants across 28 countries, administered in March-May 2020. The machine- learning model predicted 52% of the variance in infection prevention behavior in a separate test sample—exceeding the performance of psychological models of health behavior. Results indicated the two most important predictors related to individual- level injunctive norms. Illustrating how data-driven methods can complement theory, some of the most important predictors were not derived from theories of health behavior—and some theoretically-derived predictors were relatively unimportant