Integrated microbiome and metabolome analysis reveals a distinct microbial and metabolic signature in Graves' disease and hypothyroidism.

Recent studies reveal that imbalanced microbiota is related to thyroid diseases. However, studies on the alterations in fecal metabolites in Graves' disease and clinical hypothyroidism patients are insufficient. Here, we identified 21 genera and 53 metabolites that were statistically significant among Graves' disease patients, hypothyroidism patients, and controls integrating microbiome and untargeted metabolome analysis. Disease groups revealed a decreased abundance in butyrate-producing microbiota and an increased abundance in potentially pathogenic microbiota. Lipids molecules were the major differential metabolites identified in all fecal samples. Network analysis recognized that microbiota may affect thyroid function by targeting specific metabolites. We further identified specific microbiota and metabolites that could distinguish Graves' disease patients, hypothyroidism patients, and controls. Our study reveals a distinct microbial and metabolic signature in hypothyroidism patients and Graves' disease patients and further validates the potential role of microbiota in thyroid diseases, providing new ideas for future research into the etiology and clinical intervention of thyroid diseases.

The value of deep neural networks in the pathological classification of thyroid tumors.

BACKGROUND: To explore the distinguishing diagnostic value and clinical application potential of deep neural networks (DNN) for pathological images of thyroid tumors. METHODS: A total of 799 pathological thyroid images of 559 patients with thyroid tumors were retrospectively analyzed. The pathological types included papillary thyroid carcinoma (PTC), medullary thyroid carcinoma (MTC), follicular thyroid carcinoma (FTC), adenomatous goiter, adenoma, and normal thyroid gland. The dataset was divided into a training set and a test set. Resnet50, Resnext50, EfficientNet, and Densenet121 were trained using the training set data and tested with the test set data to determine the diagnostic efficiency of different pathology types and to further analyze the causes of misdiagnosis. RESULTS: The recall, precision, negative predictive value (NPV), accuracy, specificity, and F1 scores of the four models ranged from 33.33% to 100.00%. The area under curve (AUC) ranged from 0.822 to 0.994, and the Kappa coefficient ranged from 0.7508 to 0.7713. However, the performance of diagnosing FTC, adenoma, and adenomatous goiter was slightly inferior to other types of pathological tissues. CONCLUSION: The DNN model achieved satisfactory results in the task of classifying thyroid tumors by learning thyroid pathology images. These results indicate the potential of the DNN model for the efficient diagnosis of thyroid tumor histopathology.

Differential Performances in Lesions and Radiotracer of 18F-FDG PET/CT between Multiple Myeloma and Unknown Osteolytic Metastasis.

PURPOSE: The aim of the study was to investigate the differential performances in lesions and 18F-FDG radiotracer distribution detected by PET/CT between multiple myeloma and unknown osteolytic metastasis. METHODS: A retrospective study was performed on 18F-FDG PET/CT imaging of 63 patients with multiple bone destructions without extraosseous primary malignant tumors. By pathological diagnosis, 20 patients were confirmed to have multiple myeloma and 43 patients to have unknown osteolytic metastasis. The whole body was categorized into 8 sites: skull, spine, ribs, pelvis, sternum, clavicle, scapula and limb bone. The length of lesion cross-sections, cortical bone damage, SUVmax and radiotracer distribution were comprehensively compared to differentiate these two diseases. RESULTS: The cross-section lengths and SUVmax of the lesions in 5 sites (e.g., skull, spine, ribs, pelvis, and limb bone) were significantly shorter and lower in the multiple myeloma group than those of the unknown osteolytic metastasis group (P < 0.05). The 18F-FDG was more uniformly distributed in the lesion sites of the skull, spine, ribs, pelvis, scapula, and limb bone in the multiple myeloma group (P < 0.05). In the spine and rib lesion sites, the multiple myeloma group was more likely to show noncortical bone damage than the unknown osteolytic metastasis group (P < 0.05). CONCLUSION: Differential observations in lesions and 18F-FDG distribution between multiple myeloma and unknown osteolytic metastasis were detected by comprehensively comparing the length of lesion cross-sections, cortical bone damage, SUVmax, and the distribution of radiotracer on18F-FDG PET/CT imaging.

Alterations of Gut Microbiome and Metabolite Profiles Associated With Anabatic Lipid Dysmetabolism in Thyroid Cancer.

Background: Currently, the high morbidity of individuals with thyroid cancer (TC) is an increasing health care burden worldwide. The aim of our study was to investigate the relationship among the gut microbiota community, metabolites, and the development of differentiated thyroid cancer. Methods: 16S rRNA gene sequencing and an integrated LC-MS-based metabolomics approach were performed to obtain the components and characteristics of fecal microbiota and metabolites from 50 patients with TC and 58 healthy controls (HCs). Results: The diversity and richness of the gut microbiota in the TC patients were markedly decreased. The composition of the gut microbiota was significantly altered, and the Bacteroides enterotype was the dominant enterotype in TC patients. Additionally, the diagnostic validity of the combined model (three genera and eight metabolites) and the metabolite model (six metabolites) were markedly higher than that of the microbial model (seven genera) for distinguishing TC patients from HCs. LEfSe analysis demonstrated that genera (g_Christensenellaceae_R-7_group, g_Eubacterium_coprostanoligenes_group) and metabolites [27-hydroxycholesterol (27HC), cholesterol] closely related to lipid metabolism were greatly reduced in the TC group. In addition, a clinical serum indicator (total cholesterol) and metabolites (27HC and cholesterol) had the strongest influence on the sample distribution. Furthermore, functional pathways related to steroid biosynthesis and lipid digestion were inhibited in the TC group. In the microbiota-metabolite network, 27HC was significantly related to metabolism-related microorganisms (g_Christensenellaceae_R-7_group). Conclusions: Our research explored the characteristics of the gut microecology of patients with TC. The findings of this study will help to discover risk factors that affect the occurrence and development of TC in the intestinal microecology.

Differential diagnostic value of the ResNet50, random forest, and DS ensemble models for papillary thyroid carcinoma and other thyroid nodules.

Objective To explore the differential diagnostic efficiency of the residual network (ResNet)50, random forest (RF), and DS ensemble models for papillary thyroid carcinoma (PTC) and other pathological types of thyroid nodules.Methods This study retrospectively analyzed 559 patients with thyroid nodules and collected thyroid pathological images and auxiliary examination results (laboratory and ultrasound results) to construct datasets. The pathological image dataset was used to train a ResNet50 model, the text dataset was used to train a random forest (RF) model, and a DS ensemble model was constructed from the results of the two models. The differential diagnostic values of the three models for PTC and other types of thyroid nodules were then compared.Results The DS ensemble model had the highest sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (85.87%, 97.18%, 93.77%, and 0.982, respectively).Conclusions Compared with Resnet50 and the RF models trained only on imaging data or text information, respectively, the DS ensemble model showed better diagnostic value for PTC.

Gut microbiota changes and its potential relations with thyroid carcinoma.

Introduction: Emerging evidence suggests that the essence of life is the ecological balance of the neural, endocrine, metabolic, microbial, and immune systems. Gut microbiota have been implicated as an important factor affecting thyroid homeostasis. Objectives: This study aims to explore the relationship between gut microbiota and the development of thyroid carcinoma. Methods: Stool samples were collected from 90 thyroid carcinoma patients (TCs) and 90 healthy controls (HCs). Microbiota were analyzed using 16S ribosomal RNA gene sequencing. A cross-sectional study of an exploratory cohort of 60 TCs and 60 HCs was conducted. The gut microbiota signature of TCs was established by LEfSe, stepwise logistic regression, lasso regression, and random forest model analysis. An independent cohort of 30 TCs and 30 HCs was used to validate the findings. Functional prediction was achieved using Tax4Fun and PICRUSt2. TC patients were subsequently divided into subgroups to analyze the relationship between microbiota and metastatic lymphadenopathy. Results: In the exploratory cohorts, TCs had reduced richness and diversity of gut microbiota compared to HCs. No significant difference was found between TCs and HCs on the phylum level, though 70% of TCs had increased levels of Proteobacteria-types based on dominant microbiota typing. A prediction model of 10 genera generated with LEfSe analysis and lasso regression distinguished TCs from HCs with areas under the curves of 0.809 and 0.746 in the exploration and validation cohorts respectively. Functional prediction suggested that the microbial changes observed in TCs resulted in a decline in aminoacyl-tRNA biosynthesis, homologous recombination, mismatch repair, DNA replication, and nucleotide excision repair. A four-genus microbial signature was able to distinguish TC patients with metastatic lymphadenopathy from those without metastatic lymphadenopathy. Conclusion: Our study shows that thyroid carcinoma patients demonstrate significant changes in gut microbiota, which will help delineate the relationship between gut microbiota and TC pathogenesis.