Publications by authors named "M Miteva"

Frontal Fibrosing Alopecia (FFA) is a primary lymphocytic cicatricial alopecia predominantly affecting postmenopausal Caucasian women. It is characterized by a progressive frontotemporal hairline recession that presents as a scarring hairless band and is often accompanied by eyebrow and body hair loss. Although initially described in postmenopausal women, FFA has been observed in a broader demographic, including premenopausal women and occasionally men.

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Frontal Fibrosing Alopecia (FFA) poses a distinct dermatological challenge with epithelial-mesenchymal transition (EMT) at its core, driving follicular cell transformation and fibrotic changes. Genetic studies highlight significant associations, while environmental triggers, such as implicated cosmetic products (sunblock, personal hair care products, and moisturizers), introduce complexity. Managing FFA proves daunting due to its chronic and unpredictable nature.

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The ATP-binding cassette (ABC) and solute carrier (SLC) transporters play pivotal roles in cellular transport mechanisms, influencing a wide range of physiological processes and impacting various medical conditions. Recent advancements in structural biology and computational modeling have provided significant insights into their function and regulation. This review provides an overview of the current knowledge of human ABC and SLC transporters, emphasizing their structural and functional relationships, transport mechanisms, and the contribution of computational approaches to their understanding.

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The development of fluorescence-based methods for bioassays and medical diagnostics requires the design and synthesis of specific markers to target biological microobjects. However, biomolecular recognition in real cellular systems is not always as selective as desired. A new concept for creating fluorescent biomolecular probes, utilizing a fluorogenic dye and biodegradable, biocompatible nanomaterials, is demonstrated.

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Antimicrobial resistance (AMR) represents today a major challenge for global public health, compromising the effectiveness of treatments against a multitude of bacterial infections. In recent decades, artificial intelligence (AI) has emerged as a promising technology for the identification and development of new antibacterial agents. This review focuses on AI methodologies applied to discover new antibacterial candidates.

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