CAO, J. | bioRxiv Science

bioRxiv2026

MXRA7 Alleviates Epididymitis from Exercise-Induced Fatigue by Inhibiting Pyroptosis

AimsTo explore exercise-induced fatigue (EIF)s effects on the male reproductive system and MXRA7s regulatory role herein. MethodsWe recruited EIF volunteers for semen/serum tests, established a mouse EIF model via weight-loaded swimming to assess epididymal segmental injury, and constructed pyroptosis models of PC-1/DC-2 cells. Public database transcriptomic analysis identified MXRA7 expression and enriched pathways in epididymitis; MXRA7s function was verified via its knockdown/overexpression in DC-2 cells. PKC-MXRA7 association was explored by phosphorylation assays and CO-IP, and sperm incubation experiments evaluated MXRA7s effect on sperm function. ResultsEIF impaired human sperm motility, reduced mouse sperm quality and induced epididymitis with segment-specific pyroptosis. MXRA7 expression differed in PC-1/DC-2 cells and correlated with pyroptosis; it was phosphorylated by PKC, inhibited the NF-{kappa}B pathway to alleviate inflammation, and mitigated pyroptosis-induced sperm motility damage. ConclusionEIF induces epididymal epithelial pyroptosis and epididymitis, and MXRA7 exerts a protective effect mainly in caudal epididymal cells by alleviating pyroptosis, thus reducing sperm quality damage.

molecular biology↗

bioRxiv2023

Cell Instance Segmentation via Multi-Scale Non-local Correlation

For cell instance segmentation on Electron Microscopy (EM) images, state-of-the-art methods either conduct pixel-wise classification or follow a detection and segmentation manner. However, both approaches suffer from the enormous cell instances of EM images where cells are tightly close to each other and show inconsistent morphological properties and/or homogeneous appearances. This fact can easily lead to over-segmentation and under-segmentation problems for model prediction, i.e., falsely splitting and merging adjacent instances. In this paper, we propose a novel approach incorporating non-local correlation in the embedding space to make pixel features distinct or similar to their neighbors and thus address the over- and under-segmentation problems. We perform experiments on five different EM datasets where our proposed method yields better results than several strong baselines. More importantly, by using non-local correlation, we observe fewer false separations within one cell and fewer false fusions between cells.

bioinformatics↗