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Abstract: Triple-negative breast cancer (TNBC) accounts for about 10-20% of all breast cancer cases and is associated with an unfavorable prognosis. Until recently, treatment options for TNBC were limited to chemotherapy. A new successful systemic treatment is immunotherapy with immune checkpoint inhibitors, but new tumor-specific biomarkers are needed to improve patient outcomes. Cannabinoids show antitumor activity in most preclinical studies in TNBC models and do not appear to have adverse effects on chemotherapy. Clinical data are needed to evaluate efficacy and safety in humans. Importantly, the endocannabinoid system is linked to the immune system and immunosuppression. Therefore, cannabinoid receptors could be a potential biomarker for immune checkpoint inhibitor therapy or a novel mechanism to reverse resistance to immunotherapy. In this article, we provide an overview of the currently available information on how cannabinoids may influence standard therapy in TNBC.
Keywords: triple-negative breast cancer, breast cancer, cancer treatment, cannabinoids
Published in DiRROS: 18.09.2024; Views: 11; Downloads: 6
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Abstract: Introduction: Recognizing and extracting key information from textual data plays an important role in intelligent systems by maintaining up-to-date knowledge, reinforcing informed decision-making, question-answering, and more. It is especially apparent in the food domain, where critical information guides the decisions of nutritionists and clinicians. The information extraction process involves two natural language processing tasks named entity recognition—NER and named entity linking—NEL. With the emergence of large language models (LLMs), especially ChatGPT, many areas began incorporating its knowledge to reduce workloads or simplify tasks. In the field of food, however, we noticed an opportunity to involve ChatGPT in NER and NEL. Methods: To assess ChatGPT's capabilities, we have evaluated its two versions, ChatGPT-3.5 and ChatGPT-4, focusing on their performance across both NER and NEL tasks, emphasizing food-related data. To benchmark our results in the food domain, we also investigated its capabilities in a more broadly investigated biomedical domain. By evaluating its zero-shot capabilities, we were able to ascertain the strengths and weaknesses of the two versions of ChatGPT. Results: Despite being able to show promising results in NER compared to other models. When tasked with linking entities to their identifiers from semantic models ChatGPT's effectiveness falls drastically. Discussion: While the integration of ChatGPT holds potential across various fields, it is crucial to approach its use with caution, particularly in relying on its responses for critical decisions in food and bio-medicine.
Keywords: ChatGPT, food data, named-entity recognition, named-entity linking
Published in DiRROS: 16.09.2024; Views: 34; Downloads: 18
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Abstract: This study examines the generalization ability of algorithm performance prediction models across various bench-mark suites. Comparing the statistical similarity between the problem collections with the accuracy of performance prediction models that are based on exploratory landscape analysis features, we observe that there is a positive correlation between these two measures. Specifically, when the high-dimensional feature value distributions between training and testing suites lack statistical significance, the model tends to generalize well, in the sense that the testing errors are in the same range as the training errors. Two experiments validate these findings: one involving the standard benchmark suites, the BBOB and CEC collections, and another using five collections of affine combinations of BBOB problem instances.
Keywords: meta-learning, single-objective optimization, module importance
Published in DiRROS: 16.09.2024; Views: 43; Downloads: 25
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Keywords: bilevel optimization, evolutionary algorithms, pessimistic approach
Published in DiRROS: 16.09.2024; Views: 38; Downloads: 293
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Abstract: Research on the metabolic physiology of groundwater species, particularly regarding oxygen consumption rates (OCR), has made significant advancement, revealing valuable insights into the adaptations of exclusively groundwater-dwelling (stygobitic) species. However, a comprehensive understanding of how these metabolic rates scale with body mass and respond to temperature changes remains elusive. This study aims to bridge this gap by reviewing published data on OCR across a variety of groundwater organisms to elucidate patterns of metabolic rates in relation to body size and temperature. We employed a combination of literature review and quantitative analyses, focusing on the allometric scaling of OCR with body weight and the effect of temperature on metabolic rates. Our findings indicate that OCR scales with body weight in an allometric pattern, with an inter-species slope of 0.80, suggesting non-isometric scaling. Furthermore, our analysis showed that stygobitic species’ metabolic rates are less responsive to warming than those of non-stygobitic species at low to moderate temperatures. However, at higher temperatures, metabolic rates in stygobitic species decline faster than in non-stygobitic taxa, highlighting a potential vulnerability to global climate change. This study contributes to our understanding of the metabolic strategies of groundwater species, underscoring the need for further research to fully grasp the eco-evolutionary implications of these findings for groundwater conservation.
Keywords: allometry, Arrhenius equation, ectotherms, metabolism, oxygen consumption rate, respiration, stygobiont
Published in DiRROS: 13.09.2024; Views: 84; Downloads: 66
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