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Radiology Radiology

Propofol rather than Isoflurane Accelerates the Interstitial Fluid Drainage in the Deep Rat Brain.

In International journal of medical sciences

Objective: Different anesthetics have distinct effects on the interstitial fluid (ISF) drainage in the extracellular space (ECS) of the superficial rat brain, while their effects on ISF drainage in the ECS of the deep rat brain still remain unknown. Herein, we attempt to investigate and compare the effects of propofol and isoflurane on ECS structure and ISF drainage in the caudate-putamen (CPu) and thalamus (Tha) of the deep rat brain. Methods: Adult Sprague-Dawley rats were anesthetized with propofol or isoflurane, respectively. Twenty-four anesthetized rats were randomly divided into the propofol-CPu, isoflurane-CPu, propofol-Tha, and isoflurane-Tha groups. Tracer-based magnetic resonance imaging (MRI) and fluorescent-labeled tracer assay were utilized to quantify ISF drainage in the deep brain. Results: The half-life of ISF in the propofol-CPu and propofol-Tha groups was shorter than that in the isoflurane-CPu and isoflurane-Tha groups, respectively. The ECS volume fraction in the propofol-CPu and propofol-Tha groups was much higher than that in the isoflurane-CPu and isoflurane-Tha groups, respectively. However, the ECS tortuosity in the propofol-CPu and propofol-Tha groups was much smaller than that in isoflurane-CPu and isoflurane-Tha groups, respectively. Conclusions: Our results demonstrate that propofol rather than isoflurane accelerates the ISF drainage in the deep rat brain, which provides novel insights into the selective control of ISF drainage and guides selection of anesthetic agents in different clinical settings, and unravels the mechanism of how general anesthetics function.

Zhao Guomei, Han Hongbin, Wang Wei, Jia Kaiying


Propofol, deep rat brain, extracellular space, interstitial fluid, isoflurane

General General

Deep Ensemble of Weighted Viterbi Decoders for Tail-Biting Convolutional Codes.

In Entropy (Basel, Switzerland)

Tail-biting convolutional codes extend the classical zero-termination convolutional codes: Both encoding schemes force the equality of start and end states, but under the tail-biting each state is a valid termination. This paper proposes a machine learning approach to improve the state-of-the-art decoding of tail-biting codes, focusing on the widely employed short length regime as in the LTE standard. This standard also includes a CRC code. First, we parameterize the circular Viterbi algorithm, a baseline decoder that exploits the circular nature of the underlying trellis. An ensemble combines multiple such weighted decoders, and each decoder specializes in decoding words from a specific region of the channel words' distribution. A region corresponds to a subset of termination states; the ensemble covers the entire states space. A non-learnable gating satisfies two goals: it filters easily decoded words and mitigates the overhead of executing multiple weighted decoders. The CRC criterion is employed to choose only a subset of experts for decoding purpose. Our method achieves FER improvement of up to 0.75 dB over the CVA in the waterfall region for multiple code lengths, adding negligible computational complexity compared to the circular Viterbi algorithm in high signal-to-noise ratios (SNRs).

Raviv Tomer, Schwartz Asaf, Be’ery Yair


deep learning, ensembles, error correcting codes, tail-biting convolutional codes, viterbi, machine learning

General General

The association of Coronavirus Disease-19 mortality and prior bacille Calmette-Guerin vaccination: a robust ecological analysis using unsupervised machine learning.

In Scientific reports ; h5-index 158.0

Population-level data have suggested that bacille Calmette-Guerin (BCG) vaccination may lessen the severity of Coronavirus Disease-19 (COVID-19) prompting clinical trials in this area. Some reports have demonstrated conflicting results. We performed a robust, ecologic analysis comparing COVID-19 related mortality (CRM) between strictly selected countries based on BCG vaccination program status utilizing publicly available databases and machine learning methods to define the association between active BCG vaccination programs and CRM. Validation was performed using linear regression and country-specific modeling. CRM was lower for the majority of countries with a BCG vaccination policy for at least the preceding 15 years (BCG15). CRM increased significantly for each increase in the percent population over age 65. A higher total population of a country and BCG15 were significantly associated with improved CRM. There was a consistent association between countries with a BCG vaccination for the preceding 15 years, but not other vaccination programs, and CRM. BCG vaccination programs continued to be associated with decreased CRM even for populations < 40 years old where CRM events are less frequent.

Brooks Nathan A, Puri Ankur, Garg Sanya, Nag Swapnika, Corbo Jacomo, Turabi Anas El, Kaka Noshir, Zemmel Rodney W, Hegarty Paul K, Kamat Ashish M


General General

Predicting the COVID-19 infection with fourteen clinical features using machine learning classification algorithms.

In Multimedia tools and applications

While the RT-PCR is the silver bullet test for confirming the COVID-19 infection, it is limited by the lack of reagents, time-consuming, and the need for specialized labs. As an alternative, most of the prior studies have focused on Chest CT images and Chest X-Ray images using deep learning algorithms. However, these two approaches cannot always be used for patients' screening due to the radiation doses, high costs, and the low number of available devices. Hence, there is a need for a less expensive and faster diagnostic model to identify the positive and negative cases of COVID-19. Therefore, this study develops six predictive models for COVID-19 diagnosis using six different classifiers (i.e., BayesNet, Logistic, IBk, CR, PART, and J48) based on 14 clinical features. This study retrospected 114 cases from the Taizhou hospital of Zhejiang Province in China. The results showed that the CR meta-classifier is the most accurate classifier for predicting the positive and negative COVID-19 cases with an accuracy of 84.21%. The results could help in the early diagnosis of COVID-19, specifically when the RT-PCR kits are not sufficient for testing the infection and assist countries, specifically the developing ones that suffer from the shortage of RT-PCR tests and specialized laboratories.

Arpaci Ibrahim, Huang Shigao, Al-Emran Mostafa, Al-Kabi Mohammed N, Peng Minfei


COVID-19, Classification algorithms, Diagnosis, Machine learning, Novel coronavirus, Prediction

General General

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches.

In Biotechnology and bioprocess engineering : BBE

As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.

Kim Hyunho, Kim Eunyoung, Lee Ingoo, Bae Bongsung, Park Minsu, Nam Hojung


artificial intelligence, data-driven, drug discovery, machine learning

General General

COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays.

In Neural computing & applications

COVID-19 has emerged as a global crisis with unprecedented socio-economic challenges, jeopardizing our lives and livelihoods for years to come. The unavailability of vaccines for COVID-19 has rendered rapid testing of the population instrumental in order to contain the exponential rise in cases of infection. Shortage of RT-PCR test kits and delays in obtaining test results calls for alternative methods of rapid and reliable diagnosis. In this article, we propose a novel deep learning-based solution using chest X-rays which can help in rapid triaging of COVID-19 patients. The proposed solution uses image enhancement, image segmentation, and employs a modified stacked ensemble model consisting of four CNN base-learners along with Naive Bayes as meta-learner to classify chest X-rays into three classes viz. COVID-19, pneumonia, and normal. An effective pruning strategy as introduced in the proposed framework results in increased model performance, generalizability, and decreased model complexity. We incorporate explainability in our article by using Grad-CAM visualization in order to establish trust in the medical AI system. Furthermore, we evaluate multiple state-of-the-art GAN architectures and their ability to generate realistic synthetic samples of COVID-19 chest X-rays to deal with limited numbers of training samples. The proposed solution significantly outperforms existing methods, with 98.67% accuracy, 0.98 Kappa score, and F-1 scores of 100, 98, and 98 for COVID-19, normal, and pneumonia classes, respectively, on standard datasets. The proposed solution can be used as one element of patient evaluation along with gold-standard clinical and laboratory testing.

Singh Rajeev Kumar, Pandey Rohan, Babu Rishie Nandhan


COVID-19, Chest X-rays, Deep learning, Ensemble learning, ExplainableAI, GANs