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General General

Research Progress of Coronavirus Based on Bibliometric Analysis.

In International journal of environmental research and public health ; h5-index 73.0

BACKGROUND : COVID-19 has become one of the most serious global epidemics in the 21st Century. This study aims to explore the distribution of research capabilities of countries, institutions, and researchers, and the hotspots and frontiers of coronavirus research in the past two decades. In it, references for funding support of urgent projects and international cooperation among research institutions are provided.

METHOD : the Web of Science core collection database was used to retrieve the documents related to coronavirus published from 2003 to 2020. Citespace.5.6.R2, VOSviewer1.6.12, and Excel 2016 were used for bibliometric analysis.

RESULTS : 11,036 documents were retrieved, of which China and the United States have contributed the most coronavirus studies, Hong Kong University being the top contributor. Regarding journals, the JournalofVirology has contributed the most, while in terms of researchers, Yuen Kwok Yung has made the most contributions. The proportion of documents published by international cooperation has been rising for decades. Vaccines for SARS-CoV-2 are under development, and clinical trials of several drugs are ongoing.

CONCLUSIONS : international cooperation is an important way to accelerate research progress and achieve success. Developing corresponding vaccines and drugs are the current hotspots and research directions.

Zhai Fei, Zhai Yuxuan, Cong Chuang, Song Tingyan, Xiang Rongwu, Feng Tianyi, Liang Zhengxuan, Zeng Ya, Yang Jing, Yang Jie, Liang Jiankun

2020-May-26

COVID-19, SARS-CoV-2, bibliometrics, coronavirus

General General

Multitechnology Biofabrication: A New Approach for the Manufacturing of Functional Tissue Structures?

In Trends in biotechnology

Most available 3D biofabrication technologies rely on single-component deposition methods, such as inkjet, extrusion, or light-assisted printing. It is unlikely that any of these technologies used individually would be able to replicate the complexity and functionality of living tissues. Recently, new biofabrication approaches have emerged that integrate multiple manufacturing technologies into a single biofabrication platform. This has led to fabricated structures with improved functionality. In this review, we provide a comprehensive overview of recent advances in the integration of different manufacturing technologies with the aim to fabricate more functional tissue structures. We provide our vision on the future of additive manufacturing (AM) technology, digital design, and the use of artificial intelligence (AI) in the field of biofabrication.

Castilho Miguel, de Ruijter Mylène, Beirne Stephen, Villette Claire C, Ito Keita, Wallace Gordon G, Malda Jos

2020-May-25

3D bioprinting, artificial intelligence, convergency of technologies, digital design, functional tissue, hybrid fabrication

Pathology Pathology

Imaging Mass Spectrometry is an Accurate Tool in Differentiating Clear Cell Renal Cell Carcinoma and Chromophobe Renal Cell Carcinoma: A Proof-of-concept Study.

In The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society

Clear cell renal cell carcinoma (ccRCC) and chromophobe renal cell carcinoma (chRCC) are relatively common tumors that can have significant risk for mortality. Treatment and prognostication in renal cell carcinoma (RCC) are dependent upon correct histologic typing. ccRCC and chRCC are generally straightforward to diagnose based on histomorphology alone. However, high-grade ccRCC and chRCC can sometimes resemble each other morphologically, particularly in small biopsies. Multiple immunostains and/or colloidal iron stain are sometimes required to differentiate the two. Imaging mass spectrometry (IMS) allows simultaneous spatial mapping of thousands of biomarkers, using formalin-fixed paraffin-embedded tissue sections. In this study, we evaluate the ability of IMS to differentiate between World Health Organization/International Society for Urological Pathology grade 3 ccRCC and chRCC. IMS spectra from a training set of 14 ccRCC and 13 chRCC were evaluated via support vector machine algorithm with a linear kernel for machine learning, building a classification model. The classification model was applied to a separate validation set of 6 ccRCC and 6 chRCC, with 19 to 20, 150-μm diameter tumor foci in each case sampled by IMS. Most evaluated tumor foci were classified correctly as ccRCC versus chRCC (99% accuracy, kappa=0.98), demonstrating that IMS is an accurate tool in differentiating high-grade ccRCC and chRCC.

Lu Hsiang-Chih, Patterson Nathan Heath, Judd Audra M, Reyzer Michelle L, Sehn Jennifer K

2020-May-28

chromophobe renal cell carcinoma, clear cell renal cell carcinoma, imaging mass spectrometry, kidney neoplasms, renal cell cancer

General General

3D-Convolutional Neural Network with Generative Adversarial Network and Autoencoder for Robust Anomaly Detection in Video Surveillance.

In International journal of neural systems

As the surveillance devices proliferate, various machine learning approaches for video anomaly detection have been attempted. We propose a hybrid deep learning model composed of a video feature extractor trained by generative adversarial network with deficient anomaly data and an anomaly detector boosted by transferring the extractor. Experiments with UCSD pedestrian dataset show that it achieves 94.4% recall and 86.4% precision, which is the competitive performance in video anomaly detection.

Shin Wonsup, Bu Seok-Jun, Cho Sung-Bae

2020-May-28

3D CNN, Video anomaly detection, generative adversarial network, machine learning, transfer learning

General General

Machine-learning for Precision Breast Cancer Diagnosis and Prediction of the Nanoparticles Cellular internalization.

In ACS sensors

In the field of theranostics, diagnostic nanoparticles are de-signed to collect highly patient-selective disease profiles, which is then leveraged by a set of nanotherapeutics to improve the therapeutic results. Despite their early promise, high inter-patient and intra-tumoral heterogeneities make any rational design and analysis of these theranostics platforms remained extremely problematic. Recent advances in deep learning-based tools may help bridge this gap, using pattern recognition algo-rithms for better diagnostic precision and therapeutic outcome. Triple-negative breast cancer (TNBC) is a conundrum because of the complex molecular diversity, making its diagnosis and therapy challenging. To address these challenges, we propose a method to predict the cellular internalization of nanoparticles (NPs) against different cancer stages using artificial intelli-gence. Here we demonstrate for the first time that a combina-tion of machine learning (ML) algorithm and characteristic cellular uptake responses for individual cancer cell types can be successfully used to classify various cancer cell types. Utilizing this approach, we can optimize the nanomaterials to get an optimum structure-internalization response for a given particle. This methodology predicted the structure-internalization re-sponse of the evaluated nanoparticles with remarkable accuracy (Q2=0.9). We anticipate that it can reduce the effort by mini-mizing the number of nanoparticles that need to be tested and could be utilized as a screening tool for designing nanothera-peutics. Following this, we have proposed a diagnostic nano-material-based platform used to assemble a patient-specific cancer profile with the assistant of machine-learning (ML). The platform is comprised of eight carbon nanoparticles (CNPs) with multifarious surface chemistries that can differentiate healthy breast cells from cancerous cells and then subclassify TNBC cells vs. non-TNBC cells, within the TNBC group. Arti-ficial The artificial neural network (ANN) algorithm has been successfully used in identifying the type of cancer cells from 36 unknown cancer samples with an overall accuracy of >98%, providing potential applications in cancer diagnostics.

Alafeef Maha, Srivastava Indrajit, Pan Dipanjan

2020-May-28

General General

Reliability of Family Dogs' Sleep Structure Scoring Based on Manual and Automated Sleep Stage Identification.

In Animals : an open access journal from MDPI

Non-invasive polysomnography recording on dogs has been claimed to produce data comparable to those for humans regarding sleep macrostructure, EEG spectra and sleep spindles. While functional parallels have been described relating to both affective (e.g., emotion processing) and cognitive (e.g., memory consolidation) domains, methodologically relevant questions about the reliability of sleep stage scoring still need to be addressed. In Study 1, we analyzed the effects of different coders and different numbers of visible EEG channels on the visual scoring of the same polysomnography recordings. The lowest agreement was found between independent coders with different scoring experience using full (3 h-long) recordings of the whole dataset, and the highest agreement within-coder, using only a fraction of the original dataset (randomly selected 100 epochs (i.e., 100 × 20 s long segments)). The identification of drowsiness was found to be the least reliable, while that of non-REM (rapid eye movement, NREM) was the most reliable. Disagreements resulted in no or only moderate differences in macrostructural and spectral variables. Study 2 targeted the task of automated sleep EEG time series classification. Supervised machine learning (ML) models were used to help the manual annotation process by reliably predicting if the dog was sleeping or awake. Logistic regression models (LogREG), gradient boosted trees (GBT) and convolutional neural networks (CNN) were set up and trained for sleep state prediction from already collected and manually annotated EEG data. The evaluation of the individual models suggests that their combination results in the best performance: ~0.9 AUC test scores.

Gergely Anna, Kiss Orsolya, Reicher Vivien, Iotchev Ivaylo, Kovács Enikő, Gombos Ferenc, Benczúr András, Galambos Ágoston, Topál József, Kis Anna

2020-May-26

automatic staging, canine EEG, polysomnography reliability, sleep staging