A microfluidic-based gut-on-a-chip model containing the gut microbiota of patients with depression reveals physiological characteristics similar to depression.

The diverse commensal microbiome of the human intestine has been considered to play a central role in depression. However, no host-microbiota co-culture system has been developed for depression, which hinders the controlled study of the interaction between depression and gut microbiota. We designed and manufactured a microfluidic-based gut-on-a-chip model containing the gut microbiota of patients with depression (depression-on-gut-chip, DoGC), which enables the extended co-culture of viable aerobic human intestinal epithelial cells and anaerobic gut microbiota, and allows the direct study of interactions between human gut microbiota and depression. We introduced representative gut microbiota from individuals with depression into our constructed DoGC model, successfully recapitulating the gut microbiota structure of depressed patients. This further led to the manifestation of physiological characteristics resembling depression, such as reduced gut barrier function, chronic low-grade inflammatory responses and decreased neurotransmitter 5-HT levels. Metabolome analysis of substances in the DoGC revealed a significant increase in lipopolysaccharides and tyrosine, while hyodeoxycholic acid, L-proline and L-threonine were significantly reduced, indicating the occurrence of depression. The proposed DoGC can serve as an effective platform for studying the gut microbiota of patients with depression, providing important cues for their roles in the pathology of this condition and acting as a powerful tool for personalized medicine.

Microgravity stress alters bacterial community assembly and co-occurrence networks during wheat seed germination.

Bacterial interactions occurring on and around seeds are integral to plant fitness, health and productivity. Although seed- and plant-associated bacteria are sensitive to environmental stress, the effects of microgravity, as present during plant cultivation in space, on microbial assembly during seed germination are not clear. Here, we characterized the bacterial microbiome assembly process and mechanisms during seed germination of two wheat varieties under simulated microgravity by 16S rRNA gene amplicon sequencing and metabolome analysis. We found that the bacterial community diversity, and network complexity and stability were significantly decreased under simulated microgravity. In addition, the effects of simulated microgravity on the plant bacteriome of the two wheat varieties tended to be consistent in seedlings. At this stage, the relative abundance of Oxalobacteraceae, Paenibacillaceae, Xanthomonadaceae, Lachnospiraceae, Sphingomonadaceae and Ruminococcaceae decreased, while the relative abundance of Enterobacteriales increased under simulated microgravity. Analysis of predicted microbial function revealed that simulated microgravity exposure leads to lower sphingolipid signaling and calcium signaling pathways. We also found that simulated microgravity drove the strengthening of deterministic processes in microbial community assembly. Importantly, some specific metabolites exhibited significant changes under simulated microgravity, suggesting that bacteriome assembly is mediated, at least in part, by metabolites altered by microgravity. The data we present here moves us closer to a holistic understanding of the plant bacteriome under microgravity stress at plant emergence, and provides a theoretical basis for the precise utilization of microorganisms in microgravity to improve plant adaptation to the challenge of cultivation in space.

Deep learning model improves radiologists' performance in detection and classification of breast lesions.

OBJECTIVE: Computer-aided diagnosis using deep learning algorithms has been initially applied in the field of mammography, but there is no large-scale clinical application. METHODS: This study proposed to develop and verify an artificial intelligence model based on mammography. Firstly, mammograms retrospectively collected from six centers were randomized to a training dataset and a validation dataset for establishing the model. Secondly, the model was tested by comparing 12 radiologists' performance with and without it. Finally, prospectively enrolled women with mammograms from six centers were diagnosed by radiologists with the model. The detection and diagnostic capabilities were evaluated using the free-response receiver operating characteristic (FROC) curve and ROC curve. RESULTS: The sensitivity of model for detecting lesions after matching was 0.908 for false positive rate of 0.25 in unilateral images. The area under ROC curve (AUC) to distinguish the benign lesions from malignant lesions was 0.855 [95% confidence interval (95% CI): 0.830, 0.880]. The performance of 12 radiologists with the model was higher than that of radiologists alone (AUC: 0.852 vs. 0.805, P=0.005). The mean reading time of with the model was shorter than that of reading alone (80.18 s vs. 62.28 s, P=0.032). In prospective application, the sensitivity of detection reached 0.887 at false positive rate of 0.25; the AUC of radiologists with the model was 0.983 (95% CI: 0.978, 0.988), with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 94.36%, 98.07%, 87.76%, and 99.09%, respectively. CONCLUSIONS: The artificial intelligence model exhibits high accuracy for detecting and diagnosing breast lesions, improves diagnostic accuracy and saves time.