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Hashimoto’s thyroiditis (HT) is the main cause of hypothyroidism. Here the authors develop a deep learning model for diagnosis of HT on a large multi-site dataset including image and video data.

Objective: Large volume radiological text data have been accumulated since the incorporation of electronic health record (EHR) systems in clinical practice. We aimed to determine whether deep natural language processing algorithms could aid radiologists in improving thyroid cancer diagnosis. Methods: Sonographic EHR data were obtained from the EHR database. Pathological reports were used as the gold standard for diagnosing thyroid cancer. We developed thyroid cancer diagnosis based on natural language processing (THCaDxNLP) to interpret unstructured sonographic text reports for thyroid cancer diagnosis. We used the area under the receiver operating characteristic curve (AUROC) as the primary metric to measure the performance of the THCaDxNLP. We compared the performance of thyroid ultrasound radiologists aided with THCaDxNLP vs. those without THCaDxNLP using 5 independent test sets. Results: We obtained a total number of 788,129 sonographic radiological reports. The number of thyroid sonographic data points was 132,277, 18,400 of which were thyroid cancer patients. Among the 5 test sets, the numbers of patients per set were 439, 186, 82, 343, and 171. THCaDxNLP achieved high performance in identifying thyroid cancer patients (the AUROC ranged from 0.857–0.932). Thyroid ultrasound radiologists aided with THCaDxNLP achieved significantly higher performances than those without THCaDxNLP in terms of accuracy (93.8% vs. 87.2%; one-sided t-test, adjusted P = 0.003), precision (92.5% vs. 86.0%; P = 0.018), and F1 metric (94.2% vs. 86.4%; P = 0.007). Conclusions: THCaDxNLP achieved a high AUROC for the identification of thyroid cancer, and improved the accuracy, sensitivity, and precision of thyroid ultrasound radiologists. This warrants further investigation of THCaDxNLP in prospective clinical trials.

Investigating river bars and their vegetation dynamics in response to upstream damming is important for riverine flood management and ecological assessment. However, our mechanical understanding of the damming-induced changes in river bar and vegetation, such as bar area, morphology, and leaf area index (LAI), remains limited for large river systems. Leveraging satellite images and in situ observed hydrogeomorphic data from, we improve a machine learning-based LAI inversion model to quantify variations in river bar morphology, vegetation distribution, and LAI in the Middle Yangtze River (MYR) following the operation of the Three Gorges Dam (TGD). Then we analyze the mechanisms controlling the bar and vegetation dynamics based on high-resolution river cross-sectional profiles as well as daily discharge, water levels, and sediment in both the pre- and post-TGD periods. Our results indicate that the river bar area decreased by approximately 10% from 2003 to 2020, while the vegetation area and average LAI of these bars increased by >50% and >20%, respectively. Moreover, the plant community on most river bars tended to expand from the bar tail to the bar head and from the edge to the center. The main factor driving vegetation expansion in the MYR after the TGD’s operation was the reduction in bar submergence frequency (by 55%), along with a slight bar erosion. Further analysis revealed that the standard deviation of annual discharge decreased by approximately 37%, and the frequency of vegetation-erosive flow decreased by approximately 74%. Our data highlight the potential impact of large dams downstream flow regimes and vegetation encroachement. Such findings further the understanding of the biogeomorphological impacts of large dams on the river bar vegetation and have important implications for riverine plant flux estimatin, flood management and ecological restoration in dammed river systems.

N6-methyladenosine (m6A) is a common transcriptomic modification in cancer. Recently, it has been found to be involved in the regulation of non-small cell lung cancer (NSCLC) formation and metastasis. Interleukin 37 (IL-37) plays a crucial protective role in lung cancer. In our previous studies, we found that IL-37 is a potential novel tumor suppressor by inhibiting IL-6 expression to suppress STAT3 activation and decreasing epithelial-to-mesenchymal transition. Moreover, we found that treatment of IL-37 in lung cancer cells induced widespread and dynamic RNA m6A methylation. The effects of RNA m6A methylation of IL-37 treatment require further study. However, the functions of RNA m6A methylation of IL-37 treatment still await elucidation. Using MeRIP-seq and RNA-seq, we uncovered a unique m6A methylation profile in the treatment of IL-37 on the A549 cell line. We also showed the expression of m6A writers METTL3, METTL14, and WTAP and erasers ALKBH5 and FTO in A549 cells and lung cancer tissues after the treatment of IL-37. This study showed that IL-37 could lead to changes in m6A methylation level and related molecule expression level in A546 cells and may downregulate the proliferation by inhibiting Wnt5a/5b pathway in A549 cells. We conclude that IL-37 suppresses tumor growth through regulation of RNA m6A methylation in lung cancer cells.

Calcium hexaluminate (CA6)-based porous ceramic was prepared with αalumina powders and nanometer-sized calcium carbonate using a direct foaming technique with the addition of calcium aluminate cement and microsilica. The microsilica leads to the acceleration of CA6 formation. Moreover, the shrinkage, densification, and elastic modulus at room temperature were enhanced with increasing microsilica content. But the hot elastic modulus showed obvious reduction between 1100°C and 1200°C if the microsilica content was over 0.5 wt%. Furthermore, αAl2O3 appeared in the specimens with and without microsilica after treatment at 1550°C, and the proportion of CA6 grains having a high aspect ratio increased after microsilica addition. [ABSTRACT FROM AUTHOR]

The present study reports the novel case of an 81-year-old male with prostatic adenocarcinoma (PAC), whose histopathological study revealed a pure urothelial carcinoma (UC) that originated, however, from the prostatic glandular epithelium. The levels of serum prostate-specific antigen (PSA) were extraordinarily high in this patient. An MRI scan indicated a prostatic neoplasm and no malignant changes were observed in the bladder or other areas of the urinary tract. Hematoxylin and eosin-stained sections revealed a diagnosis of pure UC with no other form of differentiation (typical adenocarcinoma or squamous differentiation). The immunohistochemical findings were positive for PSA and P504S, and negative for CK7, CK20, 34βE12 and p63. A diagnosis of primary PAC (solid carcinoma) originating from the prostate was made based on the clinical, histopathological and immunohistochemical observations. This case was susceptible to diagnostic errors, however, a novel subtype of primary PAC was identified and termed the UC-like subtype. [ABSTRACT FROM AUTHOR]