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Biomaterial scaffolds have been used successfully to promote the regenerative repair of small endometrial lesions in small rodents, providing partial restoration of gestational function. The use of rabbits in this study allowed us to investigate a larger endometrial tissue defect and myometrial injury model. A gelatin/polycaprolactone (GT/PCL) gradient-layer biofilm was sutured at the defect to guide the reconstruction of the original tissue structure. Twenty-eight days postimplantation, the uterine cavity had been restored to its original morphology, endometrial growth was accompanied by the formation of glands and blood vessels, and the fragmented myofibers of the uterine smooth muscle had begun to resemble the normal structure of the lagomorph uterine cavity, arranging in a circular luminal pattern and a longitudinal serosal pattern. In addition, the repair site supported both embryonic implantation into the placenta and normal embryonic development. Four-dimensional label-free proteomic analysis identified the cell adhesion molecules, phagosome, ferroptosis, rap1 signaling pathways, hematopoietic cell lineage, complement and coagulation cascades, tricarboxylic acid cycle, carbon metabolism, and hypoxia inducible factor (HIF)-1 signaling pathways as important in the endogenous repair process of uterine tissue injury, and acetylation of protein modification sites upregulated these signaling pathways. Copyright 2024, Mary Ann Liebert, Inc., publishers.

Background: Abnormal stromal-epithelial cell communication is a pathogenic mechanism in endometriosis, and metformin can modulate it. Insulin-like growth factor binding protein-1 (IGFBP1) plays a role in endometriosis, but the exact mechanism is unknown. IGFBP1 is reportedly a downstream target of metformin in some diseases. We aimed to investigate the role of IGFBP1 in endometriosis development, whether it is associated with abnormal communication, and whether metformin affects IGFBP1 expression. Methods: Patients who underwent surgical treatment for endometriosis or other diseases were enrolled. Ten patients with ovarian-type endometriosis and eight patients each who underwent surgical treatment for other lesions with or without endometriosis were selected, and their tissues taken for cell proliferation, western blotting, polymerase chain reaction, and knockdown experiments. Results: Ectopic and eutopic stromal cells (EcSCs and EuSCs) lost their ability to inhibit epithelial cell proliferation, and IGFBP1 expression was downregulated in both groups of stromal cells compared to that in normal stromal cells (NSCs; 1.09 vs. 0.25, p =.0002 1.09 vs. 0.57, p =.0029). In an EcSC IGFBP1 overexpression model, the ability of EcSCs to inhibit epithelial cell proliferation was enhanced (EdU positivity decreased from 38% to 25%, p =.0001). Furthermore, adenosine 5’-monophosphate-activated protein kinase (AMPK) phosphorylation was downregulated in EcSCs and EuSCs compared to that in NSCs (0.99 vs. 0.42, p =.0006/0.99 vs. 0.57, p = 0.0032). Treatment of EcSCs with metformin increased AMPK phosphorylation (0.47 vs. 1.04, p =.0107) while upregulating IGFBP1 expression (0.69 vs. 1.01, p =.0164), whereas pre-treatment with an AMPK phosphorylation inhibitor abrogated metformin-induced IGFBP1 upregulation. Conclusions: IGFBP1 mediates aberrant stromal-epithelial communication in endometriosis. Metformin can upregulate IGFBP1 expression in EcSCs by activating AMPK, and upregulated IGFB

Fashion understanding is a hot topic in computer vision, with many applications having a great business value in the market. It remains a difficult challenge for computer vision due to the immense diversity of garments and a wide range of scenes and backgrounds. In this work, we try to remove the background of fashion images to boost data quality and ultimately increase model performance. Thanks to the fashion image consisting of evident persons in full garments visible, we can utilize Salient Object Detection (SOD) to achieve the background removal of fashion data to our expectations. The fashion image with removing the background is claimed as the “rembg” image, contrasting with the original one in the fashion dataset. We conduct comparative experiments between these two types of images on multiple aspects of model training, including model architectures, model initialization, compatibility with other training tricks and data augmentations, and target task types. Our experiments suggested that background removal can significantly work for fashion data in simple and shallow networks that are not susceptible to overfitting. It can improve model accuracy by up to 5% in the classification of FashionStyle14 when training models from scratch. However, background removal does not perform well in the deep network due to its incompatibility with other regularization techniques like batch normalization, pre-trained initialization, and data augmentations introducing randomness. The loss of background pixels invalidates many existing training tricks in the model training, adding the risk of overfitting for deep models., Modeförståelse är ett hett ämne inom datorseende, med många applikationer som har ett stort affärsvärde på marknaden. Det är fortfarande en svår utmaning för datorseende på grund av den enorma mångfalden av plagg och ett brett utbud av scener och bakgrunder. I det här arbetet försöker vi ta bort bakgrunden från modebilder för att öka datakvaliteten och i slutändan öka modellens prestanda. Tack vare modebilden som består av synliga personer i helt synliga plagg, kan vi använda framträdande objektivdetektion för att uppnå bakgrundsborttagning av modedata enligt våra förväntningar. Modebilden med att ta bort bakgrunden hävdas vara “rembg”-bilden, i kontrast till den ursprungliga i modedatasetet. Vi genomför jämförande experiment mellan dessa två typer av bilder på flera aspekter av modellträning, inklusive modellarkitekturer, modellinitiering , kompatibilitet med andra träningsknep och dataökningar och måluppgiftstyper. Våra experiment antydde att bakgrundsborttagning avsevärt kan fungera för modedata i enkla och ytliga nätverk som inte är mottagliga för överanpassning. Det kan förbättra modellens noggrannhet med upp till 5 % i klassificeringen av FashionStyle14 när man tränar modeller från grunden. Bakgrundsborttagning fungerar dock inte bra i det djupa nätverket på grund av dess inkompatibilitet med andra regulariseringstekniker som batchnormalisering, förtränad initialisering och dataförstärkningar som introducerar slumpmässighet. Förlusten av bakgrundspixlar ogiltigförklarar många befintliga träningsknep i modellträningen, lägg till risken för övermontering för djupa modeller.