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Numerous studies have shown that in novel noun generalization tasks, the simultaneous presentation of multiple learning examples increases the percentage of generalizations that are based on less salient properties, compared to the presentation of a single learning example. In this research with preschoolers ( = 300) we demonstrate that this effect can be modulated by dimensional distinctiveness, i.e.

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Microbial biopesticides: A one health perspective on benefits and risks.

One Health

June 2025

Department of Molecular Microbiology and Immunology, Johns Hopkins Malaria Research Institute, Johns Hopkins University, Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA.

Controlling insect pests that destroy crop and spread diseases will become increasingly crucial for addressing the food demands of a growing global population and the expansion of vector-borne diseases. A key challenge is the development of a balanced approach for sustainable food production and disease control in 2050 and beyond. Microbial biopesticides, derived from bacteria, viruses, fungi, protozoa, or nematodes, offer potentially significant benefits for promoting One Health and contributing to several United Nations Sustainable Development Goals (SDGs).

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Inflammation and Immune Escape in Ovarian Cancer: Pathways and Therapeutic Opportunities.

J Inflamm Res

January 2025

Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, People's Republic of China.

Ovarian cancer (OC) remains one of the most lethal gynecological malignancies, largely due to its late-stage diagnosis and high recurrence rates. Chronic inflammation is a critical driver of OC progression, contributing to immune evasion, tumor growth, and metastasis. Inflammatory cytokines, including IL-6, TNF-α, and IL-8, as well as key signaling pathways such as nuclear factor kappa B (NF-kB) and signal transducer and activator of transcription 3 (STAT3), are upregulated in OC, promoting a tumor-promoting environment.

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Background: Sepsis is a severe complication in leukemia patients, contributing to high mortality rates. Identifying early predictors of sepsis is crucial for timely intervention. This study aimed to develop and validate a predictive model for sepsis risk in leukemia patients using machine learning techniques.

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Background: While epidemiological data suggest a connection between atopic dermatitis (AD) and COVID-19, the molecular mechanisms underlying this relationship remain unclear.

Objective: To investigate whether COVID-19-related CpGs may contribute to AD development and whether this association is mediated through the regulation of specific genes' expression.

Methods: We combined Mendelian randomization and transcriptome analysis for data-driven explorations.

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