Premature ovarian failure (POF) is one of the most common complications among female patients with tumors treated with chemotherapy and requires advanced treatment strategies. Human amniotic epithelial cell (hAEC)-based therapy mediates tissue regeneration in a variety of diseases, and increasing evidence suggests that the therapeutic efficacy of hAECs mainly depends on paracrine action. This study aimed to identify exosomes derived from hAECs and explored the therapeutic potential in ovaries damaged by chemotherapy and the underlying molecular mechanism. hAEC-derived exosomes exhibited a cup- or sphere-shaped morphology with a mean diameter of 100 nm and were positive for Alix, CD63 , and CD9. hAEC exosomes increased the number of follicles and improved ovarian function in POF mice. During the early stage of transplantation, hAEC exosomes significantly inhibited granulosa cell apoptosis, protected the ovarian vasculature from damage, and were involved in maintaining the number of primordial follicles in the injured ovaries. Enriched microRNAs (miRNAs) existed in hAEC exosomes, and target genes were enriched in phosphatidylinositol signaling and apoptosis pathways. Studies in vitro demonstrated that hAEC exosomes inhibited chemotherapy-induced granulosa cell apoptosis via transferring functional miRNAs, such as miR-1246. Our findings demonstrate that hAEC-derived exosomes have the potential to restore ovarian function in chemotherapy-induced POF mice by transferring miRNAs.

Nosema bombycis is a destructive, obligate intracellular parasite of the Bombyx mori. In this study, a single-chain variable fragment (scFv) dependent technology is developed for the purpose of inhibiting parasite proliferation in insect cells. The scFv-G4, which we prepared from a mouse G4 monoclonal antibody, can target the N. bombycis spore wall protein 12 (NbSWP12). Indirect immunofluorescence assays showed that NbSWP12 located mainly on the outside of the N. bombycis cytoskeleton, although some of it co-localized with β-tubulin in the meront-stage of parasites. When meront division began, NbSWP12 became concentrated at both ends of each meront. Western blotting showed that scFv-G4 could express in Sf9-III cells and recognized native NbSWP12. The transgenic Sf9-III cell line showed better resistance than the controls when challenged with N. bombycis, indicating that NbSWP12 is a promising target in this parasite and this scFv dependent strategy could be a solution for construction of N. bombycis-resistant Bombyx mori.

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The microsporidian Nosema bombycis is an obligate intracellular parasite of Bombyx mori, that lost its intact tricarboxylic acid cycle and mitochondria during evolution but retained its intact glycolysis pathway. N. bombycis hexokinase (NbHK) is not only a rate-limiting enzyme of glycolysis but also a secretory protein. Indirect immunofluorescence assays and recombinant HK overexpressed in BmN cells showed that NbHK localized in the nucleus and cytoplasm of host cell during the meront stage. When N. bombycis matured, NbHK tended to concentrate at the nuclei of host cells. Furthermore, the transcriptional profile of NbHK implied it functioned during N. bombycis’ proliferation stages. A knock-down of NbHK effectively suppressed the proliferation of N. bombycis indicating that NbHK is an important protein for parasite to control its host.

Continuous wearable sensor data in high resolution contain physiological and behavioral information that can be utilized to predict human health and wellbeing, establishing the foundation for developing early warning systems to eventually improve human health and wellbeing. We propose a deep neural network framework, the Locally Connected Long Short-Term Memory Denoising AutoEncoder (LC-LSTM-DAE), to automatically extract features from passively collected raw sensor data and perform personalized prediction of self-reported mood, health, and stress scores with high precision. We enabled personalized learning of features by finetuning the general representation model with participant-specific data. The framework was evaluated using wearable sensor data and wellbeing labels collected from college students (total 6391 days from N=239). Sensor data include skin temperature, skin conductance, and acceleration; wellbeing labels include self-reported mood, health and stress scored 0 - 100. Compared to the prediction performance based on hand-crafted features, the proposed framework achieved higher precision with a smaller number of features. We also provide statistical interpretation and visual explanation to the automatically learned features and the prediction models. Our results show the possibility of predicting self-reported mood, health, and stress accurately using an interpretable deep learning framework, ultimately for developing real-time health and wellbeing monitoring and intervention systems that can benefit various populations.

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