The normal function of ovaries, along with the secretion of sex hormones, is among the most important endocrine factors that maintain the female sexual characteristics and promote follicular development and ovulation. Premature ovarian insufficiency (POI) is a common cause in the etiology of female infertility. It is defined as the loss of ovarian function before the age of 40. The characteristics of POI are menstrual disorders, including amenorrhea and delayed menstruation, accompanied by a raised gonadotrophin level and decreased estradiol level. Inflammatory aging is a new concept in the research field of aging. It refers to a chronic and low-degree proinflammatory state which occurs with increasing age. Inflammatory aging is closely associated with multiple diseases, as excessive inflammation can induce the inflammatory lesions in certain organs of the body. In recent years, studies have shown that inflammatory aging plays a significant role in the pathogenesis of POI. This paper begins with the pathogenesis of inflammatory aging and summarizes the relationship between inflammatory aging and premature ovarian insufficiency in a comprehensive way, as well as discussing the new diagnostic and therapeutic methods of POI.

Clarifying the molecular mechanisms by which primordial follicles are initiated is crucial for the prevention and treatment of female infertility and ovarian dysfunction. The Hippo pathway has been proven to have a spatiotemporal correlation with the size of the primordial follicle pool in mice in our previous work. But the role and underlying mechanisms of the Hippo pathway in primordial follicle activation remain unclear. Here, the localization and expression of the core components were examined in primordial follicles before and after activation. And the effects of the Hippo pathway on primordial follicle activation were determined by genetically manipulating yes-associated protein 1 (Yap1), the key transcriptional effector. Furthermore, an AKT specific inhibitor (MK2206) was added to determine the interaction between the Hippo pathway and AKT, an important signaling regulator of ovarian function. Results showed that the core components of the Hippo pathway were localized in both primordial and primary follicles and the expression levels of them changed significantly during the initiation of primordial follicles. Yap1 knockdown suppressed primordial follicle activation, while its overexpression led to the opposite trend. MK2206 downregulated the ratio of P-MST/MST1 and upregulated the ratio of P-YAP1/YAP1 significantly, whereas Yap1-treatment had no influence on AKT. In addition, YAP1 upregulation partially rescued the suppression of the primordial follicle activation induced by MK2206. Our findings revealed that the Hippo-YAP1 regulates primordial follicular activation, which is mediated by AKT signaling in mice, thus providing direct and new evidence to highlight the role of Hippo signaling in regulating ovarian follicles development.

The goal of spectral imaging is to capture the spectral signature of a target. Traditional scanning method for spectral imaging suffers from large system volume and low image acquisition speed for large scenes. In contrast, computational spectral imaging methods have resorted to computation power for reduced system volume, but still endure long computation time for iterative spectral reconstructions. Recently, deep learning techniques are introduced into computational spectral imaging, witnessing fast reconstruction speed, great reconstruction quality, and the potential to drastically reduce the system volume. In this article, we review state-of-the-art deep-learning-empowered computational spectral imaging methods. They are further divided into amplitude-coded, phase-coded, and wavelength-coded methods, based on different light properties used for encoding. To boost future researches, we’ve also organized publicly available spectral datasets.

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