Attractive deep morphology-aware active contour network for vertebral body contour extraction with extensions to heterogeneous and semi-supervised scenarios.

Automatic vertebral body contour extraction (AVBCE) from heterogeneous spinal MRI is indispensable for the comprehensive diagnosis and treatment of spinal diseases. However, AVBCE is challenging due to data heterogeneity, image characteristics complexity, and vertebral body morphology variations, which may cause morphology errors in semantic segmentation. Deep active contour-based (deep ACM-based) methods provide a promising complement for tackling morphology errors by directly parameterizing the contour coordinates. Extending the target contours' capture range and providing morphology-aware parameter maps are crucial for deep ACM-based methods. For this purpose, we propose a novel Attractive Deep Morphology-aware actIve contouR nEtwork (ADMIRE) that embeds an elaborated contour attraction term (CAT) and a comprehensive contour quality (CCQ) loss into the deep ACM-based framework. The CAT adaptively extends the target contours' capture range by designing an all-to-all force field to enable the target contours' energy to contribute to farther locations. Furthermore, the CCQ loss is carefully designed to generate morphology-aware active contour parameters by simultaneously supervising the contour shape, tension, and smoothness. These designs, in cooperation with the deep ACM-based framework, enable robustness to data heterogeneity, image characteristics complexity, and target contour morphology variations. Furthermore, the deep ACM-based ADMIRE is able to cooperate well with semi-supervised strategies such as mean teacher, which enables its function in semi-supervised scenarios. ADMIRE is trained and evaluated on four challenging datasets, including three spinal datasets with more than 1000 heterogeneous images and more than 10000 vertebrae bodies, as well as a cardiac dataset with both normal and pathological cases. Results show ADMIRE achieves state-of-the-art performance on all datasets, which proves ADMIRE's accuracy, robustness, and generalization ability.

Lacticaseibacillus paracasei K56 Attenuates High-Fat Diet-Induced Obesity by Modulating the Gut Microbiota in Mice.

This study investigated the effects of Lacticaseibacillus paracasei K56 (L. paracasei K56) on body weight, body composition, and glycolipid metabolism in mice with high-fat diet-induced obesity and explored the underlying mechanisms. Male C57BL/6J mice were fed a high-fat diet for 8 weeks to induce obesity; then, the obese mice were gavaged with or without L. paracasei K56 for 10 weeks. The body weight, body composition, fat mass, blood lipid, blood glucose, and hormones of the mice were evaluated. Moreover, the fatty acid synthesis (FAS) and peroxisome proliferator-activated receptor γ (PPAR-γ) expressions in the liver were detected via Western blotting. 16S rRNA gene sequencing was adopted to determine the gut microbiota alterations. The high-fat diet successfully induced obesity, as indicated by the abnormal increase in body weight, visceral fat, fat mass, blood lipids, fasting blood glucose, and insulin-resistance. Moreover, the FAS expression in the liver was significantly increased, whereas the PPAR-γ expression was significantly decreased. The relative abundance of Proteobacteria, Actinobacteria and Patescibacteria was also significantly increased, and that of Verrucomicrobia was significantly decreased. However, these indicators of mice supplemented with L. paracasei K56 were significantly opposite to those of obese mice. The Ruminococcuaceae_UCG-013, Akkermansia, Prevotellaceae_UCG-001, Muribaculum, and Lachnospiraceae_NK4A136 groups were significantly negatively correlated with body weight, blood lipids, and blood glucose-related indicators, whereas Coriobacteriaceae_UCG-002, Enterorhabdus, Raoultibacter, Acinetobacter, Romboutsia, Leuconostoc, and Erysipelatoclostridium were significantly positively correlated with these indicators. L. paracasei K56 might be a promising probiotic strain that could effectively slow down the body weight gain, reduce fat accumulation, alleviate insulin-resistance, and restore pancreatic ß-cell function in obese mice by regulating the gut microbiota.

Bifidobacterium lactis BL-99 protects mice with osteoporosis caused by colitis via gut inflammation and gut microbiota regulation.

Patients diagnosed with inflammatory bowel disease or related conditions also frequently suffer from osteoporosis as a consequence of changes in the intestinal microenvironment and consequent dysbiosis. We hypothesized that anti-inflammatory probiotic treatment would be sufficient to alleviate intestinal inflammation and thereby prevent the development of osteoporosis. To that end, the ability of Bifidobacterium lactis BL-99 administration to protect against bone loss in an experimental model of dextran sodium sulfate-induced ulcerative colitis (UC) was analyzed, and the underlying molecular mechanisms were interrogated in detail. The results of these analyses revealed that BL-99 administration suppressed colitis-associated weight loss (P < 0.05), disease activity index scores, and the production of proinflammatory cytokines (TNF-α, IL-1ß, IL-6, and IL-17) (P < 0.05). Colon tissue pathological sections similarly revealed BL-99-mediated reductions in tissue injury severity. Micro-computed tomography (Micro-CT) analyses further exhibited significant improvements in percent bone volume (BV/TV) as well as trabecular number and thickness in BL-99-treated animals (P < 0.05). Such probiotic supplementation also resulted in pronounced changes in the composition of the gut microbiota. Moreover, BL-99 intervention markedly increased the expression of intestinal barrier-related proteins (Claudin-1, MUC2, ZO-1, and Occludin). Together, these results suggest that BL-99 can be utilized as a beneficial probiotic preparation to prevent the incidence of osteoporosis in UC patients owing to its ability to shape the intestinal microflora and to suppress inflammatory cytokine production.