Nonalcoholic Fatty Liver Disease: Changes in Gut Microbiota and Blood Lipids.

Background and Aims: The global prevalence of nonalcoholic fatty liver disease (NAFLD) is 25%. This study aimed to explore differences in the gut microbial community and blood lipids between normal livers and those affected by NAFLD using 16S ribosomal deoxyribonucleic acid sequencing. Methods: Gut microbiome profiles of 40 NAFLD and 20 non-NAFLD controls were analyzed. Information about four blood lipids and 13 other clinical features was collected. Patients were divided into three groups by ultrasound and FibroScan, those with a normal liver, mild FL (FL1), and moderate-to-severe FL (FL2). FL1 and FL2 patients were divided into two groups, those with either hyperlipidemia or non-hyperlipidemia based on their blood lipids. Potential keystone species within the groups were identified using univariate analysis and a specificity-occupancy plot. Significant difference in biochemical parameters ion NAFLD patients and healthy individuals were identified by detrended correspondence analysis and canonical correspondence analysis. Results: Decreased gut bacterial diversity was found in patients with NAFLD. Firmicutes/Bacteroidetes decreased as NAFLD progressed. Faecalibacterium and Ruminococcus 2 were the most representative fatty-related bacteria. Glutamate pyruvic transaminase, aspartate aminotransferase, and white blood cell count were selected as the most significant biochemical indexes. Calculation of areas under the curve identified two microbiomes combined with the three biochemical indexes that identified normal liver and FL2 very well but performed poorly in diagnosing FL1. Conclusions: Faecalibacterium and Ruminococcus 2, combined with glutamate pyruvic transaminase, aspartate aminotransferase, and white blood cell count distinguished NAFLD. We speculate that regulating the health of gut microbiota may release NAFLD, in addition to providing new targets for clinicians to treat NAFLD.

Deep learning to assist composition classification and thyroid solid nodule diagnosis: a multicenter diagnostic study.

OBJECTIVES: This study aimed to propose a deep learning (DL)-based framework for identifying the composition of thyroid nodules and assessing their malignancy risk. METHODS: We conducted a retrospective multicenter study using ultrasound images from four hospitals. Convolutional neural network (CNN) models were constructed to classify ultrasound images of thyroid nodules into solid and non-solid, as well as benign and malignant. A total of 11,201 images of 6784 nodules were used for training, validation, and testing. The area under the receiver-operating characteristic curve (AUC) was employed as the primary evaluation index. RESULTS: The models had AUCs higher than 0.91 in the benign and malignant grading of solid thyroid nodules, with the Inception-ResNet AUC being the highest at 0.94. In the test set, the best algorithm for identifying benign and malignant thyroid nodules had a sensitivity of 0.88, and a specificity of 0.86. In the human vs. DL test set, the best algorithm had a sensitivity of 0.93, and a specificity of 0.86. The Inception-ResNet model performed better than the senior physicians (p < 0.001). The sensitivity and specificity of the optimal model based on the external test set were 0.90 and 0.75, respectively. CONCLUSIONS: This research demonstrates that CNNs can assist thyroid nodule diagnosis and reduce the rate of unnecessary fine-needle aspiration (FNA). CLINICAL RELEVANCE STATEMENT: High-resolution ultrasound has led to increased detection of thyroid nodules. This results in unnecessary fine-needle aspiration and anxiety for patients whose nodules are benign. Deep learning can solve these problems to some extent. KEY POINTS: • Thyroid solid nodules have a high probability of malignancy. • Our models can improve the differentiation between benign and malignant solid thyroid nodules. • The differential performance of one model was superior to that of senior radiologists. Applying this could reduce the rate of unnecessary fine-needle aspiration of solid thyroid nodules.

A new xAI framework with feature explainability for tumors decision-making in Ultrasound data: comparing with Grad-CAM.

BACKGROUND AND OBJECTIVE: The value of implementing artificial intelligence (AI) on ultrasound screening for thyroid cancer has been acknowledged, with numerous early studies confirming AI might help physicians acquire more accurate diagnoses. However, the black box nature of AI's decision-making process makes it difficult for users to grasp the foundation of AI's predictions. Furthermore, explainability is not only related to AI performance, but also responsibility and risk in medical diagnosis. In this paper, we offer Explainer, an intrinsically explainable framework that can categorize images and create heatmaps highlighting the regions on which its prediction is based. METHODS: A dataset of 19341 thyroid ultrasound images with pathological results and physician-annotated TI-RADS features is used to train and test the robustness of the proposed framework. Then we conducted a benign-malignant classification study to determine whether physicians perform better with the assistance of an explainer than they do alone or with Gradient-weighted Class Activation Mapping (Grad-CAM). RESULTS: Reader studies show that the Explainer can achieve a more accurate diagnosis while explaining heatmaps, and that physicians' performances are improved when assisted by the Explainer. Case study results confirm that the Explainer is capable of locating more reasonable and feature-related regions than the Grad-CAM. CONCLUSIONS: The Explainer offers physicians a tool to understand the basis of AI predictions and evaluate their reliability, which has the potential to unbox the "black box" of medical imaging AI.

Inhibition of CCR8 attenuates Ang â ¡-induced vascular smooth muscle cell injury by suppressing the MAPK/NF-κB pathway.

Objectives: Hyperinsulinemia, secondary to insulin resistance, may lead to vascular smooth muscle cell dysfunction. In the present research, we aimed to investigate the effect of Chemokine receptor 8 (CCR8) on angiotensin II (Ang II)-induced dysfunction of vascular smooth muscle cells (VSMCs) and to explore the underlying molecular mechanism. Materials and Methods: The expression of CCR8 was analyzed in diabetics and normal people by RT-PCR and ELISA. CCK-8 assay and transwell were used to explore cell proliferation and migration, and ELISA was used to measure the content of IL-6 and TNF-α. Reactive oxygen species (ROS) kit was employed to measure ROS generation. Results: The results revealed that CCR8 was highly expressed in diabetics and Ang Ⅱ-induced VSMCs. Further studies found that interfering with the expression of CCR8 significantly reduced the production of ROS and the levels of inflammatory factors in AngⅡ-induced VSMCs. Interfering with CCR8 increased the glucose uptake induced by AngⅡ+IR. More importantly, inhibition of CCR8 alleviated Ang II-induced dysfunction of VSMCs. Inhibition of CCR8 inactivated the MAPK/NF-κB signaling pathway. Conclusion: Inhibition of CCR8 attenuates Ang II-induced VSMCs injury by inhibiting the MAPK/NF-κB pathway. CCR8 may be a new biomarker related to hypertension and insulin resistance and is a new target for the treatment of human cardiovascular diseases.

Metanephric adenoma: association between the imaging features of contrast-enhanced ultrasound and clinicopathological characteristics.

BACKGROUND: This study aimed to improve the understanding of metanephric adenoma (MA) by retrospective analysis of contrast-enhanced ultrasound (CEUS) findings and clinicopathological characteristics of MAs. METHODS: Gray-scale ultrasound (US) and CEUS findings of 7 adult MA patients, confirmed by postoperative pathology, were summarized via collection of clinicopathological and ultrasonographic imaging data, including tumor location, size, echo intensity, color flow, presence or absence of calcification, and liquefactive necrosis, contrast-enhanced pattern, enhancement characteristics, and contrast wash-out compared with adjacent parenchyma, and the presence or absence of a pseudocapsule. Histopathological analyses, including hematoxylin and eosin (HE) and immunohistochemical (IHC) staining, were conducted with the EnVision method. RESULTS: All 7 participants were female, aged 29-73 years (mean age, 54 years), with flank pain (3/7). All tumors were solid (7/7) with sizes of 2.0-5.0 cm (mean diameter, 3.07 cm), including 4 in the left kidney, 3 in the right kidney, 2 in the renal pelvis, and 5 in the renal parenchyma. On the gray-scale US, MA was shown as hypoechoic (4/7), slightly hyperechoic (2/7), isoechoic (1/7), and with a defined border. The morphology was regular and rounded (7/7), internal echogenicity was homogeneous (5/7), and no calcification was seen (7/7). The CEUS showed clear boundaries (7/7), homogeneous isodensity (5/7), with calcification (0/7), necrosis (2/11), heterogeneous hyperattenuation (2/7), pseudocapsule (2/7), and medullary phase fast wash-out (7/7). The surgical methods were radical nephrectomy (4/7) and partial nephrectomy (3/7). The duration of follow-up period for all participants was 3-74 months, and no local or distant recurrences were found. The IHC staining showed that most tumor cells were positive for WT1, cytokeratins AE1/AE3, vimentin, and CD57, and exhibited focal positivity for CK7, while negative for CD10, AMACR, and CK720. The proliferative index (Ki-67) was 2-3%. CONCLUSIONS: On gray-scale US, MA appears as a solid nodule with a well-defined boundary, regular morphology, and homogeneous echogenicity; CEUS shows slow progression and slightly lower homogeneous enhancement and fast wash-out in the medullary phase. These findings may provide insight into the progression of MA and aid in the development of diagnostic and therapeutic strategies.

Elastography for the differential diagnosis of malignant versus benign testicular lesions: a meta-analysis.

PURPOSE: The aim of this study was to evaluate the value of elastography in the differential diagnosis of benign versus malignant testicular lesions. METHODS: The PubMed, Cochrane Library, and Embase databases were searched for relevant studies. The diagnostic accuracy of elastography was evaluated using pooled sensitivity, specificity, likelihood ratio, post-test probability, diagnostic odds ratio, and by summarizing the area under the hierarchical summary receiver operating characteristic (HSROC) curve. RESULTS: Seven studies with 568 lesions were included. The pooled sensitivity and specificity were 87% (95% confidence interval [CI], 81% to 92%) and 81% (95% CI, 65% to 90%), respectively. The pooled estimates of the positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 4.48 (95% CI, 2.37 to 8.47), 0.16 (95% CI, 0.10 to 0.25), and 28.11 (95% CI, 11.39 to 69.36), respectively. The area under the HSROC curve was 90% (95% CI, 88% to 93%). CONCLUSION: Elastography is useful for assessing the stiffness of testicular lesions and for differentiating benign from malignant lesions. Elastography can be an effective supplement to conventional ultrasonography.