Deep learning based ultrasound analysis facilitates precise distinction between parotid pleomorphic adenoma and Warthin tumor.

Background: Pleomorphic adenoma (PA), often with the benign-like imaging appearances similar to Warthin tumor (WT), however, is a potentially malignant tumor with a high recurrence rate. It is worse that pathological fine-needle aspiration cytology (FNAC) is difficult to distinguish PA and WT for inexperienced pathologists. This study employed deep learning (DL) technology, which effectively utilized ultrasound images, to provide a reliable approach for discriminating PA from WT. Methods: 488 surgically confirmed patients, including 266 with PA and 222 with WT, were enrolled in this study. Two experienced ultrasound physicians independently evaluated all images to differentiate between PA and WT. The diagnostic performance of preoperative FNAC was also evaluated. During the DL study, all ultrasound images were randomly divided into training (70%), validation (20%), and test (10%) sets. Furthermore, ultrasound images that could not be diagnosed by FNAC were also randomly allocated to training (60%), validation (20%), and test (20%) sets. Five DL models were developed to classify ultrasound images as PA or WT. The robustness of these models was assessed using five-fold cross-validation. The Gradient-weighted Class Activation Mapping (Grad-CAM) technique was employed to visualize the region of interest in the DL models. Results: In Grad-CAM analysis, the DL models accurately identified the mass as the region of interest. The area under the receiver operating characteristic curve (AUROC) of the two ultrasound physicians were 0.351 and 0.598, and FNAC achieved an AUROC of only 0.721. Meanwhile, for DL models, the AUROC value for discriminating between PA and WT in the test set was from 0.828 to 0.908. ResNet50 demonstrated the optimal performance with an AUROC of 0.908, an accuracy of 0.833, a sensitivity of 0.736, and a specificity of 0.904. In the test set of cases that FNAC failed to provide a diagnosis, DenseNet121 demonstrated the optimal performance with an AUROC of 0.897, an accuracy of 0.806, a sensitivity of 0.789, and a specificity of 0.824. Conclusion: For the discrimination of PA and WT, DL models are superior to ultrasound and FNAC, thereby facilitating surgeons in making informed decisions regarding the most appropriate surgical approach.

3D Spine Model Reconstruction Based on RGBD Images of Unclothed Back Surface.

Most 3D spine reconstruction methods require X-ray images as input, which usually leads to high cost and radiation damage. Therefore, these methods are hardly ever applied to large scale scoliosis screening or spine pose monitoring during treatment. We propose a novel, low-cost, easy-to-operate and none-radioactive 3D spine model reconstruction method, which is based on human back surface information without requiring X-ray images as input. Our method fits a pre-built Spine Priors Model (SPrM) to human back surface information and reconstructs the main part of spine with 17 vertebrae: lumbar vertebrae L1-L5 and thoracic vertebrae T1-T12. The Spine Priors Model is constructed according to human spine priors, including Statistical Spine Shape Model (SSSM), Spine Pose Model (SPM) and Spine Biomechanical Simplified Model (SBSM). The spine-related information on back surface, including back surface spinous curve and local symmetry nearby spinous curve is extracted from the RGBD images of human back surface. We formulate the spine optimization constraints from spine-related feature on back surface and spine priors, then optimize the spine model by gradient descent to get the optimal personalized shape parameters and pose parameters of the Spine Priors Model (SPrM). We assess our reconstruction by scoliosis Cobb angle error, and the result is comparable to current X-ray based methods.

Unraveling the Pivotal Network of Ultrasound and Somatic Mutations in Triple-Negative and Non-Triple-Negative Breast Cancer.

Purpose: The emergence of genomic targeted therapy has improved the prospects of treatment for breast cancer (BC). However, genetic testing relies on invasive and sophisticated procedures. Patients and Methods: Here, we performed ultrasound (US) and target sequencing to unravel the possible association between US radiomics features and somatic mutations in TNBC (n=83) and non-TNBC (n=83) patients. Least absolute shrinkage and selection operator (Lasso) were utilized to perform radiomic feature selection. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was utilized to identify the signaling pathways associated with radiomic features. Results: Thirteen differently represented radiomic features were identified in TNBC and non-TNBC, including tumor shape, textual, and intensity features. The US radiomic-gene pairs were differently exhibited between TNBC and non-TNBC. Further investigation with KEGG verified radiomic-pathway (ie, JAK-STAT, MAPK, Ras, Wnt, microRNAs in cancer, PI3K-Akt) associations in TNBC and non-TNBC. Conclusion: The pivotal network provided the connections of US radiogenomic signature and target sequencing for non-invasive genetic assessment of precise BC treatment.

Diverse diterpenoids from Callicarpa rubella Lindl. as natural inhibitors of macrophage foam cell formation.

Ten undescribed diterpenoids namely rubellawus E-N of structural types pimarane (1, 3-4), nor-abietane (2), nor-pimarane (5-6), isopimarane (7-9), and nor-isopimarane (10), along with eleven known compounds, were isolated and identified from the aerial parts of Callicarpa rubella Lindl. The structures of the isolated compounds were confirmed by comprehensive spectroscopic analyses and quantum chemical computations. Pharmacologically, almost all the compounds exhibited a potential inhibitory effect on oxidized low-density lipoprotein-induced macrophage foam cell formation, which suggests that these compounds may be promising candidates in the treatment of atherosclerosis.

Unsaturated bond recognition leads to biased signal in a fatty acid receptor.

Individual free fatty acids (FAs) play important roles in metabolic homeostasis, many through engagement with more than 40G protein-coupled receptors. Searching for receptors to sense beneficial omega-3 FAs of fish oil enabled the identification of GPR120, which is involved in a spectrum of metabolic diseases. Here, we report six cryo-electron microscopy structures of GPR120 in complex with FA hormones or TUG891 and Gi or Giq trimers. Aromatic residues inside the GPR120 ligand pocket were responsible for recognizing different double-bond positions of these FAs and connect ligand recognition to distinct effector coupling. We also investigated synthetic ligand selectivity and the structural basis of missense single-nucleotide polymorphisms. We reveal how GPR120 differentiates rigid double bonds and flexible single bonds. The knowledge gleaned here may facilitate rational drug design targeting to GPR120.

Biomimetic nanoparticles drive the mechanism understanding of shear-wave elasticity stiffness in triple negative breast cancers to predict clinical treatment.

In clinical practice, we noticed that triple negative breast cancer (TNBC) patients had higher shear-wave elasticity (SWE) stiffness than non-TNBC patients and a higher α-SMA expression was found in TNBC tissues than the non-TNBC tissues. Moreover, SWE stiffness also shows a clear correlation to neoadjuvant response efficiency. To elaborate this phenomenon, TNBC cell membrane-modified polylactide acid-glycolic acid (PLGA) nanoparticle was fabricated to specifically deliver artesunate to regulate SWE stiffness through inhibiting CAFs functional status. As tested in MDA-MB-231 and E0771 orthotopic tumor models, CAFs functional status inhibited by 231M-ARS@PLGA nanoparticles (231M-AP NPs) had reduced the SWE stiffness as well as attenuated hypoxia of tumor as tumor soil loosening agent which amplified the antitumor effects of paclitaxel and PD1 inhibitor. Single-cell sequencing indicated that the two main CAFs (extracellular matrix and wound healing CAFs) that produces extracellular matrix could influence the tumor SWE stiffness as well as the antitumor effect of drugs. Further, biomimetic nanoparticles inhibited CAFs function could attenuate tumor hypoxia by increasing proportion of inflammatory blood vessels and oxygen transport capacity. Therefore, our finding is fundamental for understanding the role of CAFs on affecting SWE stiffness and drugs antitumor effects, which can be further implied in the potential clinical theranostic predicting in neoadjuvant therapy efficacy through non-invasive analyzing of SWE imaging.

A novel rapid web investigation method for ecological agriculture patterns in China.

The investigation of Ecological Agriculture (EA) patterns can reveal the differences, aggregation, and diversity of agricultural development, providing specific paths in agricultural development and environmental protection to achieve the Sustainable Development Goals. Although field surveys, literature analysis, and the method using administrative statistics can be employed to investigate EA records and determine EA distributions comprehensively, they still rely on manual operations that are generally unable to support the rapid and large-scale identification of EA patterns required by current agricultural sustainable researches. To address this issue, this paper proposes a novel and rapid approach for Ecological Agriculture Pattern Investigation Based on Web-text (WEAPI), with the ability to automatically acquire EA pattern records, including pattern type, occurrence time, precise location, and other relevant information. The proposed method is employed in a national-scale case study to investigate trends in Chinese Ecological Agriculture (CEA). Results of the study reveal WEAPI's ability to detect new trends in CEA via the latest news and the corresponding distributions. The WEAPI method can also exhibit the unknown patterns of the current Chinese agricultural development. Further validation experiments demonstrate that the proposed method achieves over 95 % precision in the pattern parse processes and an 87 % coverage rate at the town level of the official CEA pattern list. Moreover, WEAPI can provide dynamic changing analyses on the annual evolution of the EA patterns in each type. Despite limitations under sparse records in partial classes, the results reveal WEAPI as a promising and powerful tool for agricultural research and agricultural development planning.

Preoperative Prediction of Central Cervical Lymph Node Metastasis in Fine-Needle Aspiration Reporting Suspicious Papillary Thyroid Cancer or Papillary Thyroid Cancer Without Lateral Neck Metastasis.

Purpose: No non-invasive method can accurately determine the presence of central cervical lymph node (CCLN) metastasis in papillary thyroid cancer (PTC) until now. This study aimed to investigate factors significantly associated with CCLN metastasis and then develop a model to preoperatively predict CCLN metastasis in fine-needle aspiration (FNA) reporting suspicious papillary thyroid cancer (PTC) or PTC without lateral neck metastasis. Patients and Methods: Consecutive inpatients who were diagnosed as suspicious PTC or PTC in FNA and underwent partial or total thyroidectomy and CCLN dissection between May 1st, 2016 and June 30th, 2018 were included. The total eligible patients were randomly divided into a training set and an internal validation set with the ratio of 7:3. Univariate analysis and multivariate analysis were conducted in the training set to investigate factors associated with CCLN metastasis. The predicting model was built with factors significantly correlated with CCLN metastasis and validated in the validation set. Results: A total of 770 patients were eligible in this study. Among them, 268 patients had histologically confirmed CCLN metastasis, while the remaining patients did not. Factors including age, BRAF mutation, multifocality, size, and capsule involvement were found to be significantly correlated with the CCLN metastasis in univariate and multivariate analysis. A model used to predict the presence CCLN metastasis based on these factors and US CCLN status yielded AUC, sensitivity, specificity and accuracy of 0.933 (95%CI: 0.905-0.960, p < 0.001), 0.816, 0.966 and 0.914 in the training set and 0.967 (95%CI: 0.943-0.991, p < 0.001), 0.897, 0.959 and 0.936 in the internal validation set. Conclusion: Age, BRAF mutation, multifocality, size, and capsule involvement were independent predictors of CCLN metastasis in FNA reporting suspicious PTC or PTC without lateral neck metastasis. A simple model was successfully built and showed excellent discrimination to distinguish patients with or without CCLN metastasis.

Application of preoperative ultrasound features combined with clinical factors in predicting HER2-positive subtype (non-luminal) breast cancer.

BACKGROUND: Human epidermal growth factor receptor2+ subtype breast cancer has a high degree of malignancy and a poor prognosis. The aim of this study is to develop a prediction model for the human epidermal growth factor receptor2+ subtype (non-luminal) of breast cancer based on the clinical and ultrasound features related with estrogen receptor, progesterone receptor, and human epidermal growth factor receptor2. METHODS: We collected clinical data and reviewed preoperative ultrasound images of enrolled breast cancers from September 2017 to August 2020. We divided the data into in three groups as follows. Group I: estrogen receptor ± , Group II: progesterone receptor ± and Group III: human epidermal growth factor receptor2 ± . Univariate and multivariate logistic regression analyses were used to analyze the clinical and ultrasound features related with biomarkers among these groups. A model to predict human epidermal growth factor receptor2+ subtype was then developed based on the results of multivariate regression analyses, and the efficacy was evaluated using the area under receiver operating characteristic curve, accuracy, sensitivity, specificity. RESULTS: The human epidermal growth factor receptor2+ subtype accounted for 138 cases (11.8%) in the training set and 51 cases (10.1%) in the test set. In the multivariate regression analysis, age ≤ 50 years was an independent predictor of progesterone receptor + (p = 0.007), and posterior enhancement was a negative predictor of progesterone receptor + (p = 0.013) in Group II; palpable axillary lymph node, round, irregular shape and calcifications were independent predictors of the positivity for human epidermal growth factor receptor-2 in Group III (p = 0.001, p = 0.007, p = 0.010, p < 0.001, respectively). In Group I, shape was the only factor related to estrogen receptor status in the univariate analysis (p < 0.05). The area under receiver operating characteristic curve, accuracy, sensitivity, specificity of the model to predict human epidermal growth factor receptor2+ subtype breast cancer was 0.697, 60.14%, 72.46%, 58.49% and 0.725, 72.06%, 64.71%, 72.89% in the training and test sets, respectively. CONCLUSIONS: Our study established a model to predict the human epidermal growth factor receptor2-positive subtype with moderate performance. And the results demonstrated that clinical and ultrasound features were significantly associated with biomarkers.

Application of deep learning to predict underestimation in ductal carcinoma in situ of the breast with ultrasound.

BACKGROUND: To develop an ultrasound-based deep learning model to predict postoperative upgrading of pure ductal carcinoma in situ (DCIS) diagnosed by core needle biopsy (CNB) before surgery. METHODS: Of the 360 patients with DCIS diagnosed by CNB and identified retrospectively, 180 had lesions upstaged to ductal carcinoma in situ with microinvasion (DCISM) or invasive ductal carcinoma (IDC) postoperatively. Ultrasound images obtained from the hospital database were divided into a training set (n=240) and validation set (n=120), with a ratio of 2:1 in chronological order. Four deep learning models, based on the ResNet and VggNet structures, were established to classify the ultrasound images into postoperative upgrade and pure DCIS. We obtained the area under the receiver operating characteristic curve (AUROC), specificity, sensitivity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) to estimate the performance of the predictive models. The robustness of the models was evaluated by a 3-fold cross-validation. RESULTS: Clinical features were not significantly different between the training set and the test set (P value >0.05). The area under the receiver operating characteristic curve of our models ranged from 0.724 to 0.804. The sensitivity, specificity, and accuracy of the optimal model were 0.733, 0.750, and 0.742, respectively. The three-fold cross-validation results showed that the model was very robust. CONCLUSIONS: The ultrasound-based deep learning prediction model is effective in predicting DCIS that will be upgraded postoperatively.

Prediction of Sentinel Lymph Node Metastasis in Breast Ductal Carcinoma In Situ Diagnosed by Preoperative Core Needle Biopsy.

PURPOSE: The positivity of sentinel lymph node (SLN) metastasis is relatively low in ductal carcinoma in situ (DCIS) patients. The aim of this study was to investigate factors associated with SLN metastasis and build a model to predict the potential risk of SLN metastasis in patients with a preoperative diagnosis of DCIS. PATIENTS AND METHODS: Core needle biopsy-proved DCIS patients who underwent SLN biopsy and breast surgery were retrospectively reviewed and selected. Univariate analysis was used to identify the variables correlated with SLN metastasis. A model to predict SLN metastasis was developed using a multivariate logistic regression in the training set and then validated in an internal set. RESULTS: A total of 407 patients with a preoperative diagnosis of DCIS were included. Upstaging to invasive/microinvasive cancer occurred in 225 patients after surgery. SLN metastasis was found in 42 patients, including 32 patients upstaging to invasive disease, 8 to microinvasive disease, and 2 pure DCIS. Tumor size based on US examination, axillary ultrasound finding, multifocality, surgery, upstaging, and Ki-67 expression were significantly related to SLN metastasis. The model incorporating tumor size, axillary ultrasound finding and multifocality yielded an AUC of 0.805 (95% CI: 0.715-0.895, p<0.001) in the training set, and 0.729 (95% CI: 0.547-0.911, p=0.013) in the testing set. CONCLUSION: A simple model was developed to predict SLN metastasis in patients with a preoperative diagnosis of DCIS. With good discriminatory power, this model should be helpful for surgeons to decide if SLN biopsy could be safely avoided in certain patients.

The Role of Contrast-Enhanced Ultrasound in the Diagnosis and Pathologic Response Prediction in Breast Cancer: A Meta-analysis and Systematic Review.

PURPOSE: To determine the overall performance of contrast-enhanced ultrasound (CEUS) in differentiating between benign and malignant breast lesions and in predicting the pathologic response to neoadjuvant chemotherapy (NAC) in patients with breast cancer (BC). MATERIALS AND METHODS: Articles published up to April 2019 were systematically searched in Medline, Web of Science, and China National Knowledge Infrastructure. The sensitivities and specificities across studies, the calculations of positive and negative likelihood ratios (LR+ and LR-), diagnostic odds ratio (OR), and constructed summary receiver operating characteristic curves were determined. Methodologic quality was assessed using the QUADAS (Quality Assessment of Diagnostic Accuracy Studies) tool. Subgroup analyses and metaregression were performed on prespecified study-level characteristics. RESULTS: Fifty-one studies involving 4875 patients with 5246 breast lesions and 10 studies involving 462 patients with BC receiving NAC were included. Methodologic quality was relatively high, and no publication bias was detected. The overall sensitivity, specificity, diagnostic OR, LR+, and LR- for CEUS were 0.88 (95% confidence interval [CI], 0.86-0.89), 0.82 (95% CI, 0.80-0.83), 30.55 (95% CI, 21.40-43.62), 4.29 (95% CI, 3.51-5.25), and 0.16 (95% CI, 0.13-0.21), respectively, showing statistical heterogeneity. Multivariable metaregression analysis showed contrast mode to be the most significant source of heterogeneity. The overall sensitivity, specificity, LR+, LR, and diagnostic OR of CEUS imaging in predicting the overall pathologic response to NAC in patients with BC were 0.89 (95% CI, 0.83-0.93), 0.83 (95% CI, 0.78-0.88), 4.49 (95% CI, 3.04-6.62), 0.16 (95% CI, 0.10-0.24,), and 32.21 (95% CI, 16.74-62.01), respectively, showing mild heterogeneity. CONCLUSION: Our data confirmed the excellent performance of breast CEUS in differentiating between benign and malignant breast lesions as well as pathologic response prediction in patients with BC receiving NAC.

Evolutionary balance between LRR domain loss and young NBS-LRR genes production governs disease resistance in Arachis hypogaea cv. Tifrunner.

BACKGROUND: Cultivated peanut (Arachis hypogaea L.) is an important oil and protein crop, but it has low disease resistance; therefore, it is important to reveal the number, sequence features, function, and evolution of genes that confer resistance. Nucleotide-binding site-leucine-rich repeats (NBS-LRRs) are resistance genes that are involved in response to various pathogens. RESULTS: We identified 713 full-length NBS-LRRs in A. hypogaea cv. Tifrunner. Genetic exchange events occurred on NBS-LRRs in A. hypogaea cv. Tifrunner, which were detected in the same subgenomes and also found in different subgenomes. Relaxed selection acted on NBS-LRR proteins and LRR domains in A. hypogaea cv. Tifrunner. Using quantitative trait loci (QTL), we found that NBS-LRRs were involved in response to late leaf spot, tomato spotted wilt virus, and bacterial wilt in A. duranensis (2 NBS-LRRs), A. ipaensis (39 NBS-LRRs), and A. hypogaea cv. Tifrunner (113 NBS-LRRs). In A. hypogaea cv. Tifrunner, 113 NBS-LRRs were classified as 75 young and 38 old NBS-LRRs, indicating that young NBS-LRRs were involved in response to disease after tetraploidization. However, compared to A. duranensis and A. ipaensis, fewer LRR domains were found in A. hypogaea cv. Tifrunner NBS-LRR proteins, partly explaining the lower disease resistance of the cultivated peanut. CONCLUSIONS: Although relaxed selection acted on NBS-LRR proteins and LRR domains, LRR domains were preferentially lost in A. hypogaea cv. Tifrunner compared to A. duranensis and A. ipaensis. The QTL results suggested that young NBS-LRRs were important for resistance against diseases in A. hypogaea cv. Tifrunner. Our results provid insight into the greater susceptibility of A. hypogaea cv. Tifrunner to disease compared to A. duranensis and A. ipaensis.

Assistive Technology Models Commonly Used Abroad / 中国康复理论与实践

Assistive technology model is the guideline and theoretical basis of assistive technology selection and adaptation, and in-depth understanding of assistive technology model can help disabled people choose the appropriate assistive technolo-gy.This paper introduced four assistive technology models which were commonly used abroad,and they were compared and analyzed from the applicable objects, suitable environment, target, evaluation project, with or without any assess-ment steps and tools,and several other aspects.

Mutation of an X chromosome in aggressive angiomyxoma: Report of a case and review of the literature.

► Cytogenetic analysis performed on peripheral blood showed a similar abnormal chromosomal complement in tumor tissue. ► Thus, mutation of an X chromosome appears to be confined to the neoplasm. ► This anomaly has not been previously described in aggressive angiomyxoma.