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Surgery Surgery

Radiopharmaceutical and Eu3+ doped gadolinium oxide nanoparticles mediated triple-excited fluorescence imaging and image-guided surgery.

In Journal of nanobiotechnology

Cerenkov luminescence imaging (CLI) is a novel optical imaging technique that has been applied in clinic using various radionuclides and radiopharmaceuticals. However, clinical application of CLI has been limited by weak optical signal and restricted tissue penetration depth. Various fluorescent probes have been combined with radiopharmaceuticals for improved imaging performances. However, as most of these probes only interact with Cerenkov luminescence (CL), the low photon fluence of CL greatly restricted it's interaction with fluorescent probes for in vivo imaging. Therefore, it is important to develop probes that can effectively convert energy beyond CL such as β and γ to the low energy optical signals. In this study, a Eu3+ doped gadolinium oxide (Gd2O3:Eu) was synthesized and combined with radiopharmaceuticals to achieve a red-shifted optical spectrum with less tissue scattering and enhanced optical signal intensity in this study. The interaction between Gd2O3:Eu and radiopharmaceutical were investigated using 18F-fluorodeoxyglucose (18F-FDG). The ex vivo optical signal intensity of the mixture of Gd2O3:Eu and 18F-FDG reached 369 times as high as that of CLI using 18F-FDG alone. To achieve improved biocompatibility, the Gd2O3:Eu nanoparticles were then modified with polyvinyl alcohol (PVA), and the resulted nanoprobe PVA modified Gd2O3:Eu (Gd2O3:Eu@PVA) was applied in intraoperative tumor imaging. Compared with 18F-FDG alone, intraoperative administration of Gd2O3:Eu@PVA and 18F-FDG combination achieved a much higher tumor-to-normal tissue ratio (TNR, 10.24 ± 2.24 vs. 1.87 ± 0.73, P = 0.0030). The use of Gd2O3:Eu@PVA and 18F-FDG also assisted intraoperative detection of tumors that were omitted by preoperative positron emission tomography (PET) imaging. Further experiment of image-guided surgery demonstrated feasibility of image-guided tumor resection using Gd2O3:Eu@PVA and 18F-FDG. In summary, Gd2O3:Eu can achieve significantly optimized imaging property when combined with 18F-FDG in intraoperative tumor imaging and image-guided tumor resection surgery. It is expected that the development of the Gd2O3:Eu nanoparticle will promote investigation and application of novel nanoparticles that can interact with radiopharmaceuticals for improved imaging properties. This work highlighted the impact of the nanoprobe that can be excited by radiopharmaceuticals emitting CL, β, and γ radiation for precisely imaging of tumor and intraoperatively guide tumor resection.

Shi Xiaojing, Cao Caiguang, Zhang Zeyu, Tian Jie, Hu Zhenhua

2021-Jul-16

Cerenkov luminescence imaging, Gd2O3:Eu, Image-guided surgery, Optical imaging, Radiopharmaceuticals

General General

Revealing the threat of emerging SARS-CoV-2 mutations to antibody therapies.

In Journal of molecular biology ; h5-index 65.0

The ongoing massive vaccination and the development of effective intervention offer the long-awaited hope to end the global rage of the COVID-19 pandemic. However, the rapidly growing SARS-CoV-2 variants might compromise existing vaccines and monoclonal antibody (mAb) therapies. Although there are valuable experimental studies about the potential threats from emerging variants, the results are limited to a handful of mutations and Eli Lilly and Regeneron mAbs. The potential threats from frequently occurring mutations on the SARS-CoV-2 spike (S) protein receptor-binding domain (RBD) to many mAbs in clinical trials are largely unknown. We fill the gap by developing a topology-based deep learning strategy that is validated with tens of thousands of experimental data points. We analyze 796,759 genome isolates from patients to identify 606 non-degenerate RBD mutations and investigate their impacts on 16 mAbs in clinical trials. Our findings, which are highly consistent with existing experimental results about Alpha, Beta, Gamma, Delta, Epsilon, and Kappa variants shed light on potential threats of 100 most observed mutations to mAbs not only from Eli Lilly and Regeneron but also from Celltrion and Rockefeller University that are in clinical trials. We unveil, for the first time, that high-frequency mutations R346K/S, N439K, G446V, L455F, V483F/A, F486L, F490L/S, Q493L, and S494P might compromise some of mAbs in clinical trials. Our study gives rise to a general perspective about how mutations will affect current vaccines.

Chen Jiahui, Gao Kaifu, Wang Rui, Wei Guo-Wei

2021-Jul-14

Antibody, clinical trial., deep learning, mutation, variant

General General

An evaluation of performance measures for arterial brain vessel segmentation.

In BMC medical imaging

BACKGROUND : Arterial brain vessel segmentation allows utilising clinically relevant information contained within the cerebral vascular tree. Currently, however, no standardised performance measure is available to evaluate the quality of cerebral vessel segmentations. Thus, we developed a performance measure selection framework based on manual visual scoring of simulated segmentation variations to find the most suitable measure for cerebral vessel segmentation.

METHODS : To simulate segmentation variations, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation. In 10 patients, we generated a set of approximately 300 simulated segmentation variations for each ground truth image. Each segmentation was visually scored based on a predefined scoring system and segmentations were ranked based on 22 performance measures common in the literature. The correlation of visual scores with performance measure rankings was calculated using the Spearman correlation coefficient.

RESULTS : The distance-based performance measures balanced average Hausdorff distance (rank = 1) and average Hausdorff distance (rank = 2) provided the segmentation rankings with the highest average correlation with manual rankings. They were followed by overlap-based measures such as Dice coefficient (rank = 7), a standard performance measure in medical image segmentation.

CONCLUSIONS : Average Hausdorff distance-based measures should be used as a standard performance measure in evaluating cerebral vessel segmentation quality. They can identify more relevant segmentation errors, especially in high-quality segmentations. Our findings have the potential to accelerate the validation and development of novel vessel segmentation approaches.

Aydin Orhun Utku, Taha Abdel Aziz, Hilbert Adam, Khalil Ahmed A, Galinovic Ivana, Fiebach Jochen B, Frey Dietmar, Madai Vince Istvan

2021-Jul-16

Average Hausdorff distance, Cerebral arteries, Cerebral vessel segmentation, Dice, Image processing (computer-assisted), Ranking, Segmentation, Segmentation measures

General General

Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction.

In Bioinformatics (Oxford, England)

MOTIVATION : To complement experimental efforts, machine learning-based computational methods are playing an increasingly important role to predict human-virus protein-protein interactions (PPIs). Furthermore, transfer learning can effectively apply prior knowledge obtained from a large source dataset/task to a small target dataset/task, improving prediction performance.

RESULTS : To predict interactions between human and viral proteins, we combine evolutionary sequence profile features with a Siamese convolutional neural network (CNN) architecture and a multi-layer perceptron. Our architecture outperforms various feature encodings-based machine learning and state-of-the-art prediction methods. As our main contribution, we introduce two transfer learning methods (i.e., 'frozen' type and 'fine-tuning' type) that reliably predict interactions in a target human-virus domain based on training in a source human-virus domain, by retraining CNN layers. Finally, we utilize the 'frozen' type transfer learning approach to predict human-SARS-CoV-2 PPIs, indicating that our predictions are topologically and functionally similar to experimentally known interactions.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Yang Xiaodi, Yang Shiping, Lian Xianyi, Wuchty Stefan, Zhang Ziding

2021-Jul-17

General General

Diffuse reflectance spectroscopy based rapid coal rank estimation: A machine learning enabled framework.

In Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

This research aims at studying the ability of using diffuse reflectance spectroscopy (DRS) for discriminating or classifying coal samples into different ranks. Spectral characteristics such as the shape of the spectral profile, slope, absorption intensity of coal samples of different ranks ranging from lignite A to semi-anthracite were studied in the Vis-NIR-SWIR (350-2500 nm) range. A number of classification algorithms (Logistic Regression, Random Forest, and SVM) were trained using the DRS dataset of coal samples. Class imbalances present in the dataset were handled using different approaches (SMOTE and Oversampling of minority classes), which improved the classification accuracy. Coal samples were initially classified into broad classes viz., lignite, sub-bituminous, bituminous, and anthracite with an accuracy of 0.98 and F1 score of 0.75. Later, the same samples were further classified into sub-class levels. The sub-class level classification also obtained good results with an accuracy of 0.77 and F1 score of 0.64. The results demonstrate the effectiveness of rapid coal classification systems based on DRS dataset in combination with different machine learning-based classification algorithms.

Begum Nafisa, Maiti Abhik, Chakravarty Debashish, Das Bhabani Sankar

2021-Jul-05

Coal rank, Diffuse reflectance spectroscopy, Logistic regression, Random forest classifier, Support vector machine

General General

Modeling the response of ecological service value to land use change through deep learning simulation in Lanzhou, China.

In The Science of the total environment

Land use (LU) changes caused by urbanization, climate, and anthropogenic activities alter the supply of ecosystem services (ES), which affects the ecological service value (ESV) of a given region. Existing LU simulation models extract neighborhood effects with only one data time slice, which ignores long-term dependence in neighborhood interactions. Previous studies on the dynamic relationship between LU change and ES in semi-arid areas is rare than that in humid coastal areas. Here, we selected a semi-arid city, Lanzhou, in Northwest China as the study area, to simulate LU changes in 2030 under natural growth (NG), ecological protection (EP), economic development (EP), and ecological protection-economic development (EPD) scenarios, using a novel deep learning method, named CL-CA. Convolutional neural network and long short term memory (CNN-LSTM) with cellular automata (CA) were utilized to extract the spatiotemporal neighborhood features. The overall simulation performance of the proposed model was larger than 0.92, which is surpassed that of LSTM-CA, artificial neural network (ANN)-CA, and recursive neural network (RNN)-CA. Ultimately, we utilized LU and ES to quantitatively evaluate the ESV changes. The results indicated that: (1) The variable trend of ESV in arid area is different from that in coastal humid areas. (2) Forest land and water were the main factors that affect the ESV change. (3) The EPD scenario was more suitable for sustainable urban development.

Liu Jiamin, Xiao Bin, Jiao Jizong, Li Yueshi, Wang Xiaoyun

2021-Jul-10

Deep learning, Ecological service value, Land use change, Lanzhou, Scenario simulation, Semi-arid region