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The outbreak of COVID-19 plunged the world into unprecedented difficulties, bringing life to a terrible standstill around the world and claiming thousands of lives. It seriously endangers human life and health. Thus, a high-efficiency COVID-19 and community-acquired pneumonia (CAP) detection method based on computed tomography (CT) images were developed to combat the pandemic. Sixty-one confirmed COVID-19 cases were consecutively enrolled together with 27 confirmed community-acquired pneumonia patients and 87 normal control patients with no finding of COVID-19 and CAP from Ruian People’s Hospital, from January 2020 to March 2020. The final cohort of 175 patients (1611 CT slices) was divided into training and test sets in an 8:2 and equal ratio. Three senior radiologists assessed clinical Characteristics of COVID-19 patients and abnormalities in chest CT images independently. The proposed model, EfficientNet-B1 was applied for the classification task, which consists of 16 mobile inverted bottleneck convolutions (MBConv), 2 convolutional (Conv) layers, 1 global average-pooling layer, and 1 fully connected layer, obtained a test accuracy of 94.24% and 93.64% for binary and three-class classification, respectively. It also achieved a sensitivity of 92.35%, a specificity of 95.93%, a positive predictive value of 95.26%, negative predictive value of 93.39% with an area under the receiver operating characteristic curve of 0.9414 for COVID-19 and CAP classification. Fever (58 [95.1%]) with a mean axillary temperature of 37.3 ± 0.8 °C, cough (52 [85.2%]), and sputum production (35 [57.4%]) are the most common symptoms of COVID-19 patients at the onset of illness. More importantly, more than 50% of COVID-19 images show ground-glass opacities. The proposed model achieves state-of-the-art performance and can be applied as a supplementary tool for radiologists, which is beneficial for pandemic control.

(1) Background: As a enrichment plant, ramie can be used for the phytoremediation of cadmium (Cd)-contaminated soil. However, it is worth exploring the role of plant growth regulators and foliar fertilizers in the process of plant growth and development and Cd adsorption. (2) Methods: By measuring the agronomic traits, Cd content of aboveground and underground ramie, calculating the Cd transfer coefficient (TF) and Cd bioconcentration factors (BCF), and the correlation between various indicators. This study examined the effects of plant growth regulators and foliar fertilizers on ramie’s capacity for Cd accumulation and transportation, (3) Results: Plant growth regulators and foliar fertilizers increased the Cd content of the aboveground ramie, reduced the Cd content of the underground ramie, and increased the TF. Among them, GA-1 increased the Cd content of the aboveground ramie to 3 times more than that of the control and reduced the Cd content of the underground ramie by 54.76%. Salicylic acid (SA) increased the Cd content of the aboveground ramie to 3 times more than that of the control. The combination of GA and foliar fertilizer reduced the Cd content of the aboveground and underground ramie and the TF and BCF of the underground ramie. After the hormones were sprayed, the TF of ramie had a significant positive correlation with the Cd content of the aboveground ramie; the BCF of the aboveground ramie had a significant positive correlation with the Cd content and TF of the aboveground ramie. (4) Conclusions: The results indicate that Brassinolide (BR), gibberellin (GA), ethephon (ETH), polyamines (PAs), and salicylic acid (SA) have different effects on the enrichment and transport of Cd in ramie. This study provided an effective method to improve the capacity for ramie to adsorb heavy metals during cultivation.

Media-adventitia (MA) border delineates the outer appearance of arterial wall in intravascular ultrasound (IVUS) image. The detection of MA border is a challenging topic due to many difficulties such as complicated intravascular structures, intrinsic artifacts and image noises. We propose a classification-based MA border detection method with an embedded feature selection technique. The feature selection technique is based on Fractional-order Darwinian particle swarm optimization (FODPSO) algorithm. By employing feature selection, 293-dimension features including multi-scale features, gray-scale features and morphological feature are reducing to 37-dimension. The border detection method with feature selection is tested on a public dataset extracted from in-vivo pullbacks of human coronary arteries, which contains 77 IVUS images. Three indicators, Jaccard (JACC), Hausdorff Distance (HD) and Percentage of Area Difference (PAD), are measured for quantitative evaluation. Detection with 293-dimension features obtains JACC 0.79, HD 1.41 and PAD 0.16, while detection with 37-dimension features obtains JACC 0.83, HD 1.27 and PAD 0.12, indicating that the FODPSO-based feature selection method improves MA border detection by JACC 0.04, HD 0.14 and PAD 0.04. Furthermore, the proposed border detection method acquires better performances compared with two other automatic methods conducted on the same dataset available in literature.

Background Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with 27 confirmed general pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground-glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray-level co-occurrence matrix (GLCM) features, 15 gray-level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High-dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each feature was the average weights calculated by ReliefF in n times. Features with relevance larger than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using tenfold cross-validation. Results and conclusions The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

Purpose: Translocator protein (TSPO), an outer mitochondrial membrane protein, is regarded as a key biomarker for neuroinflammation in a variety of neurodegenerative diseases. In this study, we aim to evaluate two highly specific TSPO radiotracers [18F]VUIIS1009A and [18F]VUIIS1009B in a mild cerebral ischemic rat model, and to compare their in vivo performance to the well-established TSPO probe [18F]DPA-714 for neuroinflammation imaging. With multiple graphic analytical methods tested and macro parameters determined, we propose to find a suitable and best quantification method to profile neuroinflammation and measure TSPO density with the three TSPO radiotracers.Methods: Cerebral ischemia rat model was created and imaged using [18F]VUIIS1009A, [18F]VUIIS1009B and [18F]DPA-714. Displacement studies using non-radioactive analogs were performed to evaluate the binding specificities of [18F]VUIIS1009A and [18F]VUIIS1009B individually. Imaging analysis using arterial plasma input functions (AIFs) was employed to generate Logan plots and parametric images of total distribution volume (VT) for each radiotracer. Reference Logan model using contralateral brain as a reference region was introduced to generate parametric images for binding potential (BPND). Results: When compared to [18F]DPA-714, [18F]VUIIS1009B demonstrated higher binding potential (BPND) and distribution volume ratio (DVR). Parameter images of BPND and VT also indicate [18F]VUIIS1009B has a superior imaging profile with higher BPND and DVR when compared with other two radiotracers in TSPO imaging. Correlation analysis between BPND for [18F]VUIIS1009B and [18F]DPA-714 also indicates [18F]VUIIS1009B is more sensitive than [18F]DPA-714 in TSPO density measurement.Conclusions: This study demonstrates the superiority of [18F]VUIIS1009B to [18F]VUIIS1009A and [18F]DPA-714 in the neuroinflammation imaging. It also demonstrates that [18F]VUIIS1009B PET imaging coupled with parameter mapping (VT and BPND) and graphic analysis using Logan analysis and reference Logan analysis holds great promise for neuroinflammation characterization and TSPO density measurement.

Background: Thyroid nodules are commonly found at palpation amounting to 4–7% of the asymptomatic population and 50% of the cases are found at autopsy. Only a small proportion of thyroid nodules are malignant. The major challenge is the differential diagnosis of benign or malignant thyroid nodules, so we aim to develop the computer-assisted diagnostic method based on computed tomography (CT) images for thyroid lesions. Methods: In this study, we retrospectively collected 52 benign and 46 malignant thyroid nodules from 90 patients in CT examinations, together with the pathologist findings and radiology diagnosis. The first-order statistic and gray-level co-occurrence matrix features were extracted from thyroid computed tomography images. These texture features were used to assess the malignancy risk of the thyroid nodules. Several classification algorithms, including support vector machine, linear discriminant analysis, random forest, and bootstrap aggregating, were applied in the prediction. Leave-one-out cross-validation was used to evaluate the performance of thyroid cancer recognition. Results: In thyroid cancer identification based on a computed tomography image, we found the system using 17 texture features and support vector machine performed well. The accuracy, area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, and negative predictive value, were 0.8673, 0.9105, 0.9130, 0.8269, 0.8235 and 0.9146, respectively. Conclusions: The proposed computer-aided diagnosis system provides a good assessment of the malignancy-risk of the thyroid nodules, which may help radiologists to improve the accuracy and efficiency of thyroid diagnosis.

Label-free quantitative proteomics based on mass spectrometry plays an essential role in large-scale analysis of complex proteomes. Meanwhile, quantitative proteomics is not only a way for data processing, but also an important approach for exploring protein functions and interactions in a large-scale manner. An effective method combining quantitation and qualification should be built. To systematically overcome this challenge, we proposed a new label-free quantitative method using spectral counting in the proposed method, the count of shared peptides was considered as an optimized factor to accurately appraise abundance of Isoforms for complex proteomes. Large-scale functional annotations for complex proteomes were extracted by g:Profiler and were assigned to functional clusters. To test the effect of the methods, three groups of mitochondrial proteins including mouse heart mitochondrial dataset, mouse liver mitochondrial dataset and human heart mitochondrial dataset were selected for analysis. According to the biochemical properties of mitochondrial proteins, all functional annotations were assigned to various signalling pathway or functional clusters. We came to draw a conclusion that the strategy with shared peptides overcame inaccurate and overestimated results for low-abundant isoforms to improve accuracy, and quantitative proteomics coupled with biomedical knowledge can thoroughly comprehend functions and relationships for complex proteomes, and contribute to providing a new method for large-scale comparative or diseased proteomics.