Search

Adenomyosis (ADM) is a multifaceted uterine pathology characterized by the ectopic infiltration of endometrial tissue into the myometrium, affecting approximately 20% of women in the reproductive age group seeking gynecological care. This condition manifests as a range of debilitating symptoms, including dysmenorrhea, menorrhagia, impaired fertility, and heightened susceptibility to miscarriage and obstetric complications. Substantial research has been dedicated to exploring its underlying molecular mechanisms and developing non-invasive precision medical therapies. ADM is primarily characterized by a dysregulation in sex steroid hormone homeostasis, particularly estrogen and progesterone. However, emerging evidence suggests that additional endocrine mediators and disruptors may play contributory roles in the etiology of ADM. Genetic and epigenetic alterations of endocrine signaling pathways have been implicated as prevailing mechanisms underlying the development and progression of the disease. The present review aims to provide an updated and comprehensive overview of the current understanding of the pathophysiology of ADM, with a particular emphasis on the dysregulated hormonal milieu and the potential involvement of endocrine disruptors. By elucidating these intricate molecular mechanisms, this review seeks to pave the way for novel research directions in the development of targeted therapeutic strategies for ADM management.

Current understanding of fibrosis remains incomplete despite the increasing burden of related diseases. Preclinical models are used to dissect the pathogenesis and dynamics of fibrosis, and to evaluate anti-fibrotic therapies. These studies require objective and accurate measurements of fibrosis. Existing histological quantification methods are operator-dependent, organ-specific, and/or need advanced equipment. Therefore, we developed a robust, minimally operator-dependent, and tissue-transposable digital method for fibrosis quantification. The proposed method involves a novel algorithm for more specific and more sensitive detection of collagen fibers stained by picrosirius red (PSR), a computer-assisted segmentation of histological structures, and a new automated morphological classification of fibers according to their compactness. The new algorithm proved more accurate than classical filtering using principal color component (red-green-blue; RGB) for PSR detection. We applied this new method on established mouse models of liver, lung, and kidney fibrosis and demonstrated its validity by evidencing topological collagen accumulation in relevant histological compartments. Our data also showed an overall accumulation of compact fibers concomitant with worsening fibrosis and evidenced topological changes in fiber compactness proper to each model. In conclusion, we describe here a robust digital method for fibrosis analysis allowing accurate quantification, pattern recognition, and multi-organ comparisons useful to understand fibrosis dynamics.