Low first-trimester Hb concentration increases the risk of LBW, preterm birth and SGA.

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Background Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Methods Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, > 14,680 WSIs, from > 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. Results Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. Conclusions This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.

Aims/hypothesis Rapamycin impaired glucose tolerance and insulin sensitivity. Our previous study demonstrated that rapamycin significantly increases the expression of gastric ghrelin, which is critical in the regulation of glucose metabolism. Here, we investigated whether ghrelin contributes to derangements of glucose metabolism induced by rapamycin. Methods The effects of rapamycin on glucose metabolism were examined in mice receiving ghrelin receptor antagonist or with ghrelin receptor gene deletion. Changes in Glut4, JNK, and pS6 were investigated by immnuofluorescent staining or Western. Related hormones were detected by radioimmuno-assay kits. Results Rapamycin impaired glucose metabolism and insulin sensitivity not only in normal C57BL/6J mice but also in both obese mice induced by high fat diet and db/db mice. This was accompanied by elevation of plasma acylated ghrelin. Rapamycin significantly increased the levels of plasma acylated ghrelin in normal C57BL/6J mice, high fat diet induced obese mice, and db/db mice. Elevation in plasma acylated ghrelin and derangements of glucose metabolism upon administration of rapamycin was significantly correlated. The deterioration in glucose homeostasis induced by rapamycin was blocked by D-Lys3-GHRP-6, a ghrelin receptor antagonist, or by deletion of ghrelin receptor gene. Ghrelin receptor antagonism and ghrelin receptor gene deletion blocked the up-regulation of JNK activity, and GLUT4 expression and translocation in the gastrocnemius muscle induced by rapamycin. Conclusions The current study demonstrates that ghrelin contributes to derangements of glucose metabolism induced by rapamycin via altering the expression and translocation of GLUT4 in muscles.