Comprehensive pan-cancer analysis of the C2ORF40 expression: Infiltration associations and prognostic implications.

In recent years, C2ORF40 has been identified as a tumor suppressor gene with multiple functions, including roles in cell proliferation, migration, and senescence. To explore the role of the C2ORF40 gene in different tumors, we used multiple databases for analysis. Compared to adjacent normal tissues, C2ORF40 is downregulated in a variety of malignant tumors, including tumors such as breast cancer, colorectal cancer, bladder cancer, hepatocellular carcinoma and prostate cancer. Notably, low expression of the gene is significantly associated with poor overall survival and relapse-free survival rates. In specific cancers including colon cancer and prostate cancer, the expression of C2ORF40 is correlated with the infiltration of CAFs. C2ORF40 is also involved in biological processes such as cell apoptosis and regulation of protein stability. In conclusion, C2ORF40 can hold promise as a prognostic marker for pan-cancer analysis.

Macromolecular crowding in chiral assembly of ellipsoidal nanoparticles.

Anisotropic colloidal particles have the ability to self-assemble into cholesteric structures. We used molecular dynamics to simulate the self-assembly of ellipsoidal particles with the objective to establish a general framework to reveal the primary factors driving chiral interactions. To characterize these interactions, we introduce a characteristic parameter following the crowding factor (CF) theory. Our simulations and statistical analysis showed good agreement with the CF theory; at the early stages of the assembly process, the ellipsoidal particles go through a critical aggregation point followed by further clustering toward nematic order. Furthermore, we demonstrate that under high CF conditions, small initial clusters may induce a chiral twist, which subsequently forms a cholesteric structure with no directional preference in higher organization states.

Whole-cycle management of women with epilepsy of child-bearing age: ontology construction and application.

BACKGROUND: The effective management of epilepsy in women of child-bearing age necessitates a concerted effort from multidisciplinary teams. Nevertheless, there exists an inadequacy in the seamless exchange of knowledge among healthcare providers within this context. Consequently, it is imperative to enhance the availability of informatics resources and the development of decision support tools to address this issue comprehensively. MATERIALS AND METHODS: The development of the Women with Epilepsy of Child-Bearing Age Ontology (WWECA) adhered to established ontology construction principles. The ontology's scope and universal terminology were initially established by the development team and subsequently subjected to external evaluation through a rapid Delphi consensus exercise involving domain experts. Additional entities and attribute annotation data were sourced from authoritative guideline documents and specialized terminology databases within the respective field. Furthermore, the ontology has played a pivotal role in steering the creation of an online question-and-answer system, which is actively employed and assessed by a diverse group of multidisciplinary healthcare providers. RESULTS: WWECA successfully integrated a total of 609 entities encompassing various facets related to the diagnosis and medication for women of child-bearing age afflicted with epilepsy. The ontology exhibited a maximum depth of 8 within its hierarchical structure. Each of these entities featured three fundamental attributes, namely Chinese labels, definitions, and synonyms. The evaluation of WWECA involved 35 experts from 10 different hospitals across China, resulting in a favorable consensus among the experts. Furthermore, the ontology-driven online question and answer system underwent evaluation by a panel of 10 experts, including neurologists, obstetricians, and gynecologists. This evaluation yielded an average rating of 4.2, signifying a positive reception and endorsement of the system's utility and effectiveness. CONCLUSIONS: Our ontology and the associated online question and answer system hold the potential to serve as a scalable assistant for healthcare providers engaged in the management of women with epilepsy (WWE). In the future, this developmental framework has the potential for broader application in the context of long-term management of more intricate chronic health conditions.

Selective Heterogeneous Fenton Degradation of Formaldehyde Using the Fe-ZSM-5 Catalyst.

As a toxic Volatile Organic Pollutant (TVOC), formaldehyde has a toxic effect on microorganisms, consequently inhibiting the biochemical process of formaldehyde wastewater treatment. Therefore, the selective degradation of formaldehyde is of great significance in achieving high-efficiency and low-cost formaldehyde wastewater treatment. This study constructed a heterogeneous Fe-ZSM-5/H2O2 Fenton system f or the selective degradation of target compounds. By immobilizing Fe3+ onto the surface of a ZSM-5 molecular sieve, Fe-ZSM-5 was prepared successfully. XRD, BET and FT-IR spectral studies showed that Fe-ZSM-5 was mainly composed of micropores. The influences of different variables on formaldehyde-selective heterogeneous Fenton degradation performance were studied. The 93.7% formaldehyde degradation and 98.2% selectivity of formaldehyde compared with glucose were demonstrated in the optimized Fenton system after 360 min. Notably, the resultant selective Fenton oxidation system had a wide range of pH suitability, from 3.0 to 10.0. Also, the Fe-ZSM-5 was used in five consecutive cycles without a significant drop in formaldehyde degradation efficiency. The use of reactive oxygen species scavengers indicated that the hydroxyl radical was the primary active species responsible for degrading formaldehyde. Furthermore, great degradation performance was acquired with high concentrations of formaldehyde for this system, and the degradation efficiency was more than 95.0%.

Weakly Supervised Framework for Cancer Region Detection of Hepatocellular Carcinoma in Whole-Slide Pathologic Images Based on Multiscale Attention Convolutional Neural Network.

Visual inspection of hepatocellular carcinoma cancer regions by experienced pathologists in whole-slide images (WSIs) is a challenging, labor-intensive, and time-consuming task because of the large scale and high resolution of WSIs. Therefore, a weakly supervised framework based on a multiscale attention convolutional neural network (MSAN-CNN) was introduced into this process. Herein, patch-based images with image-level normal/tumor annotation (rather than images with pixel-level annotation) were fed into a classification neural network. To further improve the performances of cancer region detection, multiscale attention was introduced into the classification neural network. A total of 100 cases were obtained from The Cancer Genome Atlas and divided into 70 training and 30 testing data sets that were fed into the MSAN-CNN framework. The experimental results showed that this framework significantly outperforms the single-scale detection method according to the area under the curve and accuracy, sensitivity, and specificity metrics. When compared with the diagnoses made by three pathologists, MSAN-CNN performed better than a junior- and an intermediate-level pathologist, and slightly worse than a senior pathologist. Furthermore, MSAN-CNN provided a very fast detection time compared with the pathologists. Therefore, a weakly supervised framework based on MSAN-CNN has great potential to assist pathologists in the fast and accurate detection of cancer regions of hepatocellular carcinoma on WSIs.

Association of fresh vegetable and salt-preserved vegetable consumptions with estimated glomerular filtration rate.

OBJECTIVE: This study aimed to investigate the relationship between the consumption of fresh and salt-preserved vegetables and the estimated glomerular filtration rate (eGFR), which requires further research. METHODS: For this purpose, the data of those subjects who participated in the 2011-2012 and 2014 surveys of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and had biomarker data were selected. Fresh and salt-preserved vegetable consumptions were assessed at each wave. eGFR was assessed using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation based on plasma creatinine. Furthermore, a linear mixed model was used to evaluate associations between fresh/salt-preserved vegetables and eGFR. RESULTS: The results indicated that the median baseline and follow-up eGFRs were 72.47 mL/min/1.73 m² and 70.26 mL/min/1.73 m², respectively. After applying adjusted linear mixed model analysis to the data, the results revealed that compared to almost daily intake, occasional consumption of fresh vegetables was associated with a lower eGFR (ß=-2.23, 95% CI: -4.23, -0.23). Moreover, rare or no consumption of salt-preserved vegetables was associated with a higher eGFR (ß = 1.87, 95% CI: 0.12, 3.63) compared to individuals who consumed salt-preserved vegetables daily. CONCLUSION: Fresh vegetable consumption was direct, whereas intake of salt-preserved vegetables was inversely associated with eGFR among the oldest subjects, supporting the potential benefits of diet-rich fresh vegetables for improving eGFR.

Phototriggered Complex Motion by Programmable Construction of Light-Driven Molecular Motors in Liquid Crystal Networks.

Recent developments in artificial molecular machines have enabled precisely controlled molecular motion, which allows several distinct mechanical operations at the nanoscale. However, harnessing and amplifying molecular motion along multiple length scales to induce macroscopic motion are still major challenges and comprise an important next step toward future actuators and soft robotics. The key to addressing this challenge relies on effective integration of synthetic molecular machines in a hierarchically aligned structure so numerous individual molecular motions can be collected in a cooperative way and amplified to higher length scales and eventually lead to macroscopic motion. Here, we report the complex motion of liquid crystal networks embedded with molecular motors triggered by single-wavelength illumination. By design, both racemic and enantiomerically pure molecular motors are programmably integrated into liquid crystal networks with a defined orientation. The motors have multiple functions acting as cross-linkers, actuators, and chiral dopants inside the network. The collective rotary motion of motors resulted in multiple types of motion of the polymeric film, including bending, wavy motion, fast unidirectional movement on surfaces, and synchronized helical motion with different handedness, paving the way for the future design of responsive materials with enhanced complex functions.

Narrow linewidth parity-time symmetric Brillouin fiber laser based on a dual-polarization cavity with a single micro-ring resonator.

A narrow linewidth parity-time (PT) symmetric Brillouin fiber laser (BFL) based on dual-polarization cavity (DPC) with single micro-ring resonator (MRR) is proposed and experimentally investigated. A 10 km single-mode fiber provides SBS gain, while a DPC consisting of optical coupler, polarization beam combiner and a MRR, is used to achieve PT symmetry. Due to the reciprocity of light propagation in the MRR, the PT symmetry BFL based on DPC implements two identical feedback loops that are connected to one another, one with a Brillouin gain coefficient and the other with a loss coefficient of the same magnitude, to break a PT symmetric. Compared with existing BFL studies, this design does not call for frequency matching of compound cavities structures or without ultra-narrow bandwidth bandpass filters. In the experiment, the 3-dB linewidth of PT symmetry BFL based on DPC with single MRR is 11.95 Hz with the threshold input power of 2.5 mW, according to the measured linewidth of 239 Hz at the -20 dB power point. And a 40 dB maximum mode suppression ratio are measured. Furthermore, the PT symmetry BFL's wavelength is tuned between 1549.60 and 1550.73 nm. This design with single longitudinal mode output can be applied to high coherent communication systems.

Programming and Dynamic Control of the Circular Polarization of Luminescence from an Achiral Fluorescent Dye in a Liquid Crystal Host by Molecular Motors.

Circular polarized light is utilized in communication and display technologies and a major challenge is to develop systems that can be switched between left and right circular polarized luminescence with high degrees of polarization and enable multiple addressable stable states. Luminescent dyes in Liquid Crystal (LC) cholesteric phases are attractive systems to generate, amplify and modulate circularly polarized luminescence (CPL). In the present study, we employ light-driven molecular motors as photo-controlled chiral dopants in LCs to switch the handedness of the LC and the circular polarization of luminescence from an achiral dye embedded in the mesogenic material. Tuning of the color of the CPL and the retention time of the photoprogrammed helicity is demonstrated making use of a variety of motors and dyes. The flexibility offered by the design based on inherently chiral unidirectional rotary motors provides full control over CPL non-invasively by light, opening possibilities for pixilated displays with externally addressable polarization.

Computer-Aided Pathologic Diagnosis of Nasopharyngeal Carcinoma Based on Deep Learning.

The pathologic diagnosis of nasopharyngeal carcinoma (NPC) by different pathologists is often inefficient and inconsistent. We have therefore introduced a deep learning algorithm into this process and compared the performance of the model with that of three pathologists with different levels of experience to demonstrate its clinical value. In this retrospective study, a total of 1970 whole slide images of 731 cases were collected and divided into training, validation, and testing sets. Inception-v3, which is a state-of-the-art convolutional neural network, was trained to classify images into three categories: chronic nasopharyngeal inflammation, lymphoid hyperplasia, and NPC. The mean area under the curve (AUC) of the deep learning model is 0.936 based on the testing set, and its AUCs for the three image categories are 0.905, 0.972, and 0.930, respectively. In the comparison with the three pathologists, the model outperforms the junior and intermediate pathologists, and has only a slightly lower performance than the senior pathologist when considered in terms of accuracy, specificity, sensitivity, AUC, and consistency. To our knowledge, this is the first study about the application of deep learning to NPC pathologic diagnosis. In clinical practice, the deep learning model can potentially assist pathologists by providing a second opinion on their NPC diagnoses.

Understanding the structural diversity of chitins as a versatile biomaterial.

Chitin is one of the most abundant biopolymers, and it has adopted many different structural conformations using a combination of different natural processes like biopolymerization, crystallization and non-equilibrium self-assembly. This leads to a number of striking physical effects like complex light scattering and polarization as well as unique mechanical properties. In doing so, chitin uses a fine balance between the highly ordered chain conformations in the nanofibrils and random disordered structures. In this opinion piece, we discuss the structural hierarchy of chitin, its crystalline states and the natural biosynthesis processes to create such specific structures and diversity. Among the examples we explored, the unified question arises from the generation of completely different bioarchitectures like the Christmas tree-like nanostructures, gyroids or helicoidal geometries using similar dynamic non-equilibrium growth processes. Understanding the in vivo development of such structures from gene expressions, enzymatic activities as well as the chemical matrix employed in different stages of the biosynthesis will allow us to shift the material design paradigms. Certainly, the complexity of the biology requires a collaborative and multi-disciplinary research effort. For the future's advanced technologies, using chitin will ultimately drive many innovations and alternatives using biomimicry in materials science. This article is part of the theme issue 'Bio-derived and bioinspired sustainable advanced materials for emerging technologies (part 1)'.

Assessment of breast arteries and lymph nodes by 3D MR angiography enhancement imaging: feasibility and pilot clinical results.

BACKGROUND: Conventional dynamic contrast enhanced (DCE) magnetic resonance (MR) hardly achieves a good imaging performance of arteries and lymph nodes in the breast area. Therefore, a new imaging method is needed for the assessment of breast arteries and lymph nodes. METHODS: We performed prospective research. The research included 52 patients aged from 25 to 64 between June 2019 and April 2020. The isotropic e-THRIVE sequence scanned in the coronal direction after DCE-THRIVE. Reconstructed images obtained by DCE-THRIVE and the coronal e-THRIVE were compared mainly in terms of the completeness of the lateral thoracic artery, thoracodorsal artery, and lymph nodes. We proposed a criterion for evaluating image quality. According to the criterion, images were assigned a score from 1 to 5 according to the grade from low to high. Two board-certified doctors evaluated images individually, and their average score was taken as the final result. The chi-square test was used to assess the difference. RESULTS: The coronal e-THRIVE score is 4.60, which is higher than the DCE-THRIVE score of 3.48, there are significant differences between the images obtained by two sequences (P = 1.2712e-8). According to the score of images, 44 patients (84.61%) had high-quality images on the bilateral breast. Only 3 patients' (5.77%) images were not ideal on both sides. The improved method is effective for most patients to get better images. CONCLUSIONS: The proposed coronal e-THRIVE scan can get higher quality reconstruction images than the conventional method to visualize the course of arteries and the distribution of lymph nodes in most patients, which will be helpful for the clinical follow-up treatment.

Photo-responsive Helical Motion by Light-Driven Molecular Motors in a Liquid-Crystal Network.

Controlling sophisticated motion by molecular motors is a major goal on the road to future actuators and soft robotics. Taking inspiration from biological motility and mechanical functions common to artificial machines, responsive small molecules have been used to achieve macroscopic effects, however, translating molecular movement along length scales to precisely defined linear, twisting and rotary motions remain particularly challenging. Here, we present the design, synthesis and functioning of liquid-crystal network (LCN) materials with intrinsic rotary motors that allow the conversion of light energy into reversible helical motion. In this responsive system the photochemical-driven molecular motor has a dual function operating both as chiral dopant and unidirectional rotor amplifying molecular motion into a controlled and reversible left- or right-handed macroscopic twisting movement. By exploiting the dynamic chirality, directionality of motion and shape change of a single motor embedded in an LC-network, complex mechanical motions including bending, walking and helical motion, in soft polymer materials are achieved which offers fascinating opportunities toward inherently photo-responsive materials.

Formylation boosts the performance of light-driven overcrowded alkene-derived rotary molecular motors.

Artificial molecular motors and machines constitute a critical element in the transition from individual molecular motion to the creation of collective dynamic molecular systems and responsive materials. The design of artificial light-driven molecular motors operating with high efficiency and selectivity constitutes an ongoing fundamental challenge. Here we present a highly versatile synthetic approach based on Rieche formylation that boosts the quantum yield of the forward photoisomerization reaction while reaching near-perfect selectivity in the steps involved in the unidirectional rotary cycle and drastically reducing competing photoreactions. This motor is readily accessible in its enantiopure form and operates with nearly quantitative photoconversions. It can easily be functionalized further and outperforms its direct predecessor as a reconfigurable chiral dopant in cholesteric liquid crystal materials.

TCM Prescription Generation via Knowledge Source Guidance Network Combined with Herbal Candidate Mechanism.

Traditional Chinese medicine (TCM) prescriptions have made great contributions to the treatment of diseases and health preservation. To alleviate the shortage of TCM resources and improve the professionalism of automatically generated prescriptions, this paper deeply explores the connection between symptoms and herbs through deep learning technology, and realizes the automatic generation of TCM prescriptions. Particularly, this paper considers the significance of referring to similar underlying prescriptions as herbal candidates in the TCM prescribing process. Moreover, this paper incorporates the idea of referring to the potential guidance information of corresponding prescriptions when model extracts symptoms representations. To provide a reference for inexperienced young TCM doctors when they prescribe, this paper proposes a dual-branch guidance strategy combined with candidate attention model (DGSCAM) to automatically generate TCM prescriptions based on symptoms text. The format of the data used this paper is the "symptoms-prescription" data pair. The specific method is as follows. First, DGSCAM realizes the extraction of key information of prescription-guided symptoms through a dual-branch network. Then, herbal candidates in the form of prescriptions that can treat symptoms are proposed in view of the compatibility knowledge of TCM prescriptions. To our knowledge, this is the first attempt to use prescriptions as herbal candidates in the prescription generation process. We conduct extensive experiments on a mixed public and clinical dataset, achieving 37.39% precision, 25.04% recall, and 29.99% F1 score, with an average doctor score of 7.7 out of 10. The experimental results show that our proposed model is valid and can generate more specialized TCM prescriptions than the baseline models. The DGSCAM developed by us has broad application scenarios and greatly promotes the research on intelligent TCM prescribing.

MSTS-Net: malignancy evolution prediction of pulmonary nodules from longitudinal CT images via multi-task spatial-temporal self-attention network.

PURPOSE: Longitudinal CT images contain the law of lesion growth and evolution over time. Therefore, our purpose is to explore the growth and evolution law of pulmonary lesions in the time dimension to improve the performance of predicting the malignant evolution of pulmonary nodules. METHODS: In this paper, we propose a Multi-task Spatial-Temporal Self-attention network (MSTS-Net) to predict the malignancy growth trend of pulmonary nodules from different periods. More specifically, the model achieves lesion segmentation task and lesion prediction task by sharing the same encoder. Segmentation task boosts the performance of the prediction task. In addition, a Static Context Spatial Self-attention Module and a Dynamic Adaptive Temporal Self-Attention Module are introduced to capture both static spatial coherence patterns between consecutive slices of lesions in the same period and temporal dynamics across different time points. RESULTS: We repeatedly evaluated the proposed method on the National Lung Screening Trial dataset and the Shanxi Cancer Hospital dataset. The final experimental results show that our MSTS-Net has an area under the ROC curve score of 0.919. CONCLUSION: In the computer-aided prediction of the malignant evolution of pulmonary nodules, combining the characteristics of the temporal dimension of pulmonary nodules with CT data can effectively improve the accuracy of prediction. The MSTS-Net we developed has high predictive value and broad prospects for clinical application.

Ultra-Narrow Bandwidth Microwave Photonic Filter Implemented by Single Longitudinal Mode Parity Time Symmetry Brillouin Fiber Laser.

In this paper, a novel microwave photonic filter (MPF) based on a single longitudinal mode Brillouin laser achieved by parity time (PT) symmetry mode selection is proposed, and its unparalleled ultra-narrow bandwidth as low as to sub-kHz together with simple and agile tuning performance is experimentally verified. The Brillouin fiber laser ring resonator is cascaded with a PT symmetric system to achieve this MPF. Wherein, the Brillouin laser resonator is excited by a 5 km single mode fiber to generate Brillouin gain, and the PT symmetric system is configured with Polarization Beam Splitter (PBS) and polarization controller (PC) to achieve PT symmetry. Thanks to the significant enhancement of the gain difference between the main mode and the edge mode when the polarization state PT symmetry system breaks, a single mode oscillating Brillouin laser is generated. Through the selective amplification of sideband modulated signals by ultra-narrow linewidth Brillouin single mode laser gain, the MPF with ultra-narrow single passband performance is obtained. By simply tuning the central wavelength of the stimulated Brillouin scattering (SBS) pumped laser to adjust the Brillouin oscillation frequency, the gain position of the Brillouin laser can be shifted, thereby achieving flexible tunability. The experimental results indicate that the MPF proposed in this paper achieves a single pass band narrow to 72 Hz and the side mode rejection ratio of more than 18 dB, with a center frequency tuning range of 0-20 GHz in the testing range of vector network analysis, which means that the MPF possesses ultra high spectral resolution and enormous potential application value in the domain of ultra fine microwave spectrum filtering such as radar imaging and electronic countermeasures.

Deep Multi-Magnification Similarity Learning for Histopathological Image Classification.

Precise classification of histopathological images is crucial to computer-aided diagnosis in clinical practice. Magnification-based learning networks have attracted considerable attention for their ability to improve performance in histopathological classification. However, the fusion of pyramids of histopathological images at different magnifications is an under-explored area. In this paper, we proposed a novel deep multi-magnification similarity learning (DSML) approach that can be useful for the interpretation of multi-magnification learning framework and easy to visualize feature representation from low-dimension (e.g., cell-level) to high-dimension (e.g., tissue-level), which has overcome the difficulty of understanding cross-magnification information propagation. It uses a similarity cross entropy loss function designation to simultaneously learn the similarity of the information among cross-magnifications. In order to verify the effectiveness of DMSL, experiments with different network backbones and different magnification combinations were designed, and its ability to interpret was also investigated through visualization. Our experiments were performed on two different histopathological datasets: a clinical nasopharyngeal carcinoma and a public breast cancer BCSS2021 dataset. The results show that our method achieved outstanding performance in classification with a higher value of area under curve, accuracy, and F-score than other comparable methods. Moreover, the reasons behind multi-magnification effectiveness were discussed.

Integrative Histology-Genomic Analysis Predicts Hepatocellular Carcinoma Prognosis Using Deep Learning.

Cancer prognosis analysis is of essential interest in clinical practice. In order to explore the prognostic power of computational histopathology and genomics, this paper constructs a multi-modality prognostic model for survival prediction. We collected 346 patients diagnosed with hepatocellular carcinoma (HCC) from The Cancer Genome Atlas (TCGA), each patient has 1-3 whole slide images (WSIs) and an mRNA expression file. WSIs were processed by a multi-instance deep learning model to obtain the patient-level survival risk scores; mRNA expression data were processed by weighted gene co-expression network analysis (WGCNA), and the top hub genes of each module were extracted as risk factors. Information from two modalities was integrated by Cox proportional hazard model to predict patient outcomes. The overall survival predictions of the multi-modality model (Concordance index (C-index): 0.746, 95% confidence interval (CI): ±0.077) outperformed these based on histopathology risk score or hub genes, respectively. Furthermore, in the prediction of 1-year and 3-year survival, the area under curve of the model achieved 0.816 and 0.810. In conclusion, this paper provides an effective workflow for multi-modality prognosis of HCC, the integration of histopathology and genomic information has the potential to assist clinical prognosis management.

[The effect of Hashimoto's thyroiditis on the diagnostic efficacy of ultrasound-guided fine needle aspiration cytology for thyroid nodules ≥ 1 cm].

Objective:To explore the diagnostic efficacy of ultrasound-guided fine needle aspiration cytology（US-FNAC） for thyroid nodules ≥1 cm, and the effect of Hashimoto's thyroiditis（HT） on it. Methods:The clinical data of 1027 cases of thyroid nodules ≥ 1 cm were retrospectively analyzed. Two-dimensional ultrasound, US-FNAC and BRAFV600E gene testing were performed. The postoperative pathological results were used as the criterion. The two dimensional ultrasound examination, clinical characteristics, follow-up results, and BRAFV600E were used to diagnosis for unoperated patients. The diagnostic efficiency of US-FNAC in HT（+） group and HT（-） group was compared, and the factors affecting the diagnostic efficiency were analyzed. Results:Of the 1027 nodules, the cytological results were nondiagnostic/unsatisfactory in 73 nodules（7.1%）, benign in 282（27.5%）, atypia of undetermined significance/follicular lesion of undetermined significance in 230（22.4%）, follicular neoplasm/suspicious for a follicular neoplasm in 20（1.9%）, suspicious for malignancy in 120（11.7%）, and malignant in 302（29.4%）. 515 cases underwent surgery. Among them, 495 were malignant and 20 were benign. 512 cases continued to be followed up without surgery, and the BRAFV600E of them were wild type. Combined with the two dimensional ultrasound examination, clinical features, and follow-up results, they were judged to be benign. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, false positive rate and the false negative rate the of US-FNAC were 98.7%, 98.4%, 99.3%, 99.5%, 97.5%, 0.7% and 1.6%, respectively. The accuracy, sensitivity and negative predictive value of the HT（+） group were 95.5%, 95.4% and 82.8%, respectively, which were lower than that of HT（-） group （99.5%, 99.4%, 99.2% ）（P=0.001, 0.018, P<0.001）. The false negative rate of the HT（+） group was 4.6%, higher than 0.6% of the HT（-） group（P=0.018）, and HT was an risk factor for increased FNR（OR=7.596, 95%CI: 1.452-39.740）. Conclusion:US-FNAC is an effective method for the diagnosis of thyroid nodules and it has high sensitivity and specificity in ≥ 1 cm nodules. However, the combination of HT reduces the diagnostic accuracy and HT is a risk factor for increased false negative rate.