ABSTRACT
Iodine is an essential trace element in the human body, and the gastrointestinal tract is the main way for the body to intake iodine. The intestinal tract contains trillions of microorganisms that have important impacts on the substance-energy metabolism and the genetic information processing in the human body. Gut microbiota or their metabolites can act on the thyroid through the circulatory system (namely the " gut-thyroid axis" ), thus potentially regulating iodine metabolism in thyroid. This article reviews the effects of gut microbiota on intestinal iodine uptake, as well as the effects of gut microbiota and their metabolites on the expression and activity of sodium iodide symporter (NIS) in thyroid cells, thus exploring the potential regulatory mechanisms of gut microbiota that involved in thyroid iodine metabolism. Potential factors affecting thyroid iodine metabolism by gut microbiota include the direct and the indirect factors. The direct factors include lipopolysaccharides, short-chain fatty acids, microbial peptides, and microbial proteins, which may affect the expression or activity of NIS in thyroid by regulating the nuclear factor kappa-B pathway, histone acetylation modifications, or antigen-antibody reactions. The indirect factors include the altered cellular environment that effected by gut microbiota which can further affect the transport of iodine ions in thyroid cells by manners like regulating the levels of thyroid-specific transcription factors and regulating the signal pathways mediated by thyroid-stimulating hormone and its receptor.
ABSTRACT
Objective:To investigate the relationship between the expression levels of chemokines in serum of patients with differentiated thyroid carcinoma (DTC) and the progression of DTC.Methods:From January to April in 2017, blood samples of 76 patients (25 males, 51 females, median age: 39 years) with DTC after surgery in Nuclear Medicine Department of Tenth People′s Hospital Affiliated to Tongji University were collected retrospectively for detecting the expression levels of 40 chemokines. Patients were divided into different groups according to (1) with or without metastasis: the non-metastasis group ( n=13) and the metastasis group ( n=63); (2) degree of gradual dedifferentiation: without metastasis group ( n=13), lymph node metastasis group ( n=48), highly malignant group ( n=11) and radioactive iodine refractory (RAIR) with distant metastasis group ( n=4); (3) frequency of 131I treatment in follow-up for nearly 2 years: single treatment group ( n=51) and multiple treatment group ( n=25). Differences in chemokine levels among groups were compared. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of differential chemokines′ levels on DTC metastasis and multiple 131I treatment. Independent-sample t test, Mann-Whitney U test and one-way analysis of variance were used to analyze the data. Results:Compared with the non-metastatic group, the expression levels of Eotaxin-3 ((25.94±6.05) vs (21.76±5.71) ng/L), interferon-γ (IFN-γ; (116.04±28.98) vs (98.71±26.18) ng/L), macrophage-derived chemokine (MDC; (1 468.08±401.74) vs (1 082.94±423.30) ng/L) and thymus expressd chemokine (TECK; (505.22(419.80, 563.36) vs 402.89(347.43, 442.97) ng/L) in metastatic group were decreased, and the differences were statistically significant ( t values: 2.376, 2.131, 3.007, U=215.000, all P<0.05). The area under the ROC curve of IFN-γ+ MDC+ TECK for predicting DTC metastasis was 0.844(95% CI: 0.755-0.932, P<0.001), and the sensitivity was 79.37%(50/63). Only the differences of MDC among without metastasis group, lymph node metastasis group, highly malignant group and RAIR with distant metastasis group were significant ((1 468.08±401.74), (1 121.59±454.20), (976.07±281.04), (922.68±342.41) ng/L; F=3.564, P<0.05), and the expression was gradually decreased with the degree of dedifferentiation. Only IL-8 was significantly increased in the multiple treatment group compared with the single treatment group (28.20(23.22, 32.51) vs 30.51(26.98, 35.57) ng/L; U=801.000, P<0.05). The area under the ROC curve of IL-8 for predicting multiple 131I treatment was 0.648(95% CI: 0.523-0.773, P<0.05), and the sensitivity was 100%(25/25). Conclusions:Decreased expression of IFN-γ, MDC and TECK may be potential markers for predicting metastasis in DTC. MDC is likely to be a potential molecular target for detecting the dedifferentiation degree of DTC, decreased expression of which may indicate the increased malignancy of tumor. IL-8 may be used to predict whether patients need multiple 131I treatments.
ABSTRACT
Objective To develop a diagnostic model based on deep neural network for intelligent discrimination of thyroid function. Methods A total of 1616 patients ( 283 males, 1333 females, average age:52 years) who underwent thyroid imaging between May 2016 and June 2018 were selected. According to the clinical diagnosis, the 1616 cases included 299 normal thyroid cases, 876 hyperthyroidism cases and 441 hypothyroidism cases. Feature extraction and learning training were performed on 1000 training set sam-ples by two deep neural network models ( AlexNet;deep convolution generative adversarial networks ( DCGAN) ) using deep learning algorithm. Performance verifications were implemented on 616 test set samples. The con-sistency between the verification results of the two models and the clinical diagnosis was analyzed by Kappa test. Meanwhile, the time advantage of the intelligent diagnosis models was analyzed. Results The average diagnostic time of AlexNet model was 1 s/case, and the classification accuracy for normal thyroid, hyperthy-roidism, hypothyroidism were 82.29％(79/96), 94.62％(369/390), 100％(130/130), respectively. The Kappa value between results of AlexNet model and clinical diagnosis was 0.886 ( P<0.05) . The average di-agnostic time of DCGAN model was 1 s/case, and the classification accuracy for normal thyroid, hyperthy-roidism, hypothyroidism were 85.42％(82/96), 95.64％(373/390), 99.23％(129/130), respectively. The Kappa value between results of DCGAN model and clinical diagnosis was 0.904 ( P<0.05) . Conclusion The deep neural network intelligent diagnosis model can quickly determine the functional status of thyroid gland in thyroid imaging, and it has a high recognition accuracy, thus providing a new method for thyroid image review.