The global spread of SARS-CoV-2 requires an urgent need to find effective therapeutics for the treatment of COVID-19. We developed a data-driven drug repositioning framework, which applies both machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2. The retrospective study using the past SARS-CoV and MERS-CoV data demonstrated that our machine learning based method can successfully predict effective drug candidates : bioRxiv preprint against a specific coronavirus. Our in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19. Our in vitro assays revealed that CVL218 can exhibit effective inhibitory activity against SARS-CoV-2 replication without obvious cytopathic effect. In addition, we showed that CVL218 is able to suppress the CpG-induced IL-6 production in peripheral blood mononuclear cells, suggesting that it may also have anti-inflammatory effect that is highly relevant to the prevention immunopathology induced by SARS-CoV-2 infection. Further pharmacokinetic and toxicokinetic evaluation in rats and monkeys showed a high concentration of CVL218 in lung and observed no apparent signs of toxicity, indicating the appealing potential of this drug for the treatment of the pneumonia caused by SARS-CoV-2 infection. Moreover, molecular docking simulation suggested that CVL218 may bind to the N-terminal domain of nucleocapsid (N) protein of SARS-CoV-2, providing a possible model to explain its antiviral action. We also proposed several possible mechanisms to explain the antiviral activities of PARP1 inhibitors against SARS-CoV-2, based on the data present in this study and previous evidences reported in the literature. In summary, the PARP1 inhibitor CVL218 discovered by our data-driven drug repositioning framework can serve as a potential therapeutic agent for the treatment of COVID-19.

Objectives/Hypothesis: To develop a deep-learning-based computer-aided diagnosis system for distinguishing laryngeal neoplasms (benign, precancerous lesions, and cancer) and improve the clinician-based accuracy of diagnostic assessments of laryngoscopy findings. Study Design: Retrospective study. Methods: A total of 24,667 laryngoscopy images (normal, vocal nodule, polyps, leukoplakia and malignancy) were collected to develop and test a convolutional neural network (CNN)-based classifier. A comparison between the proposed CNNbased classifier and the clinical visual assessments (CVAs) by 12 otolaryngologists was conducted. Results: In the independent testing dataset, an overall accuracy of 96.24% was achieved; for leukoplakia, benign, malignancy, normal, and vocal nodule, the sensitivity and specificity were 92.8% vs. 98.9%, 97% vs. 99.7%, 89% vs. 99.3%, 99.0% vs. 99.4%, and 97.2% vs. 99.1%, respectively. Furthermore, when compared with CVAs on the randomly selected test dataset, the CNN-based classifier outperformed physicians for most laryngeal conditions, with striking improvements in the ability to distinguish nodules (98% vs. 45%, P < .001), polyps (91% vs. 86%, P < .001), leukoplakia (91% vs. 65%, P < .001), and malignancy (90% vs. 54%, P < .001). Conclusions: The CNN-based classifier can provide a valuable reference for the diagnosis of laryngeal neoplasms during laryngoscopy, especially for distinguishing benign, precancerous, and cancer lesions.

The present study investigated whether moderate amounts of computer-assisted speech training can improve the speech recognition performance of hearing-impaired children. Ten Mandarin-speaking children (3 hearing aid users and 7 cochlear implant users) participated in the study. Training was conducted at home using a personal computer for half an hour per day, 5 days per week, for a period of 10 weeks. Results showed significant improvements in subjects’ vowel, consonant, and tone recognition performance after training. The improved performance was largely retained for 2 months after training was completed. These results suggest that moderate amounts of auditory training, using a computer-based auditory rehabilitation tool with minimal supervision, can be effective in improving the speech performance of hearing-impaired children.

This paper describes a novel microbend fiber optic sensor system for respiratory monitoring and respiratory gating in the MRI environment. The system enables the noninvasive real-time monitoring and measurement of breathing rate and respiratory/body movement pattern of healthy subjects inside the MRI gantry, and has potential application in respiratory-gated image acquisition based on respiratory cues. The working principle behind this sensor is based on the microbending effect of an optical fiber on light transmission. The sensor system comprises of a 1.0-mm-thin graded-index multimode optical fiber-embedded plastic sensor mat, a photoelectronic transceiver, and a computer with a digital signal processing algorithm. In vitro testing showed that our sensor has a typical signal-to-noise ratio better than 28 dB. Clinical MRI trials conducted on 20 healthy human subjects showed good and comparable breathing rate detection (with an accuracy of ±2 bpm) and respiratory-gated image quality produced using the sensor system, with reference to current predicate hospital device/system. The MRI safe, ease of operation characteristics, low fabrication cost, and extra patient comfort offered by this system suggest its good potential in replacing predicate device/system and serve as a dual function in real-time respiratory monitoring and respiratory-gated image acquisition at the same time during MRI.

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