A CNN-transformer fusion network for COVID-19 CXR image classification.

The global health crisis due to the fast spread of coronavirus disease (Covid-19) has caused great danger to all aspects of healthcare, economy, and other aspects. The highly infectious and insidious nature of the new coronavirus greatly increases the difficulty of outbreak prevention and control. The early and rapid detection of Covid-19 is an effective way to reduce the spread of Covid-19. However, detecting Covid-19 accurately and quickly in large populations remains to be a major challenge worldwide. In this study, A CNN-transformer fusion framework is proposed for the automatic classification of pneumonia on chest X-ray. This framework includes two parts: data processing and image classification. The data processing stage is to eliminate the differences between data from different medical institutions so that they have the same storage format; in the image classification stage, we use a multi-branch network with a custom convolution module and a transformer module, including feature extraction, feature focus, and feature classification sub-networks. Feature extraction subnetworks extract the shallow features of the image and interact with the information through the convolution and transformer modules. Both the local and global features are extracted by the convolution module and transformer module of feature-focus subnetworks, and are classified by the feature classification subnetworks. The proposed network could decide whether or not a patient has pneumonia, and differentiate between Covid-19 and bacterial pneumonia. This network was implemented on the collected benchmark datasets and the result shows that accuracy, precision, recall, and F1 score are 97.09%, 97.16%, 96.93%, and 97.04%, respectively. Our network was compared with other researchers' proposed methods and achieved better results in terms of accuracy, precision, and F1 score, proving that it is superior for Covid-19 detection. With further improvements to this network, we hope that it will provide doctors with an effective tool for diagnosing Covid-19.

A tRNA-derived fragment from Chinese yew suppresses ovarian cancer growth via targeting TRPA1

Drug discovery from plants usually focuses on small molecules rather than such biological macromolecules as RNAs. Although plant transfer RNA (tRNA)-derived fragment (tRF) has been associated with the developmental and defense mechanisms in plants, its regulatory role in mammals remains unclear. By employing a novel reverse small interfering RNA (siRNA) screening strategy, we show that a tRF mimic (antisense derived from the 5′ end of tRNAHis(GUG) of Chinese yew) exhibits comparable anti-cancer activity with that of taxol on ovarian cancer A2780 cells, with a 16-fold lower dosage than that of taxol. A dual-luciferase reporter assay revealed that tRF-T11 directly targets the 3′ UTR of oncogene TRPA1 mRNA. Furthermore, an Argonaute-RNA immunoprecipitation (AGO-RIP) assay demonstrated that tRF-T11 can interact with AGO2 to suppress TRPA1 via an RNAi pathway. This study uncovers a new role of plant-derived tRFs in regulating endogenous genes. This holds great promise for exploiting novel RNA drugs derived from nature and sheds light on the discovery of unknown molecular targets of therapeutics. Graphical

A tRNA-derived fragment from Chinese yew suppresses ovarian cancer growth via targeting TRPA1.

Drug discovery from plants usually focuses on small molecules rather than such biological macromolecules as RNAs. Although plant transfer RNA (tRNA)-derived fragment (tRF) has been associated with the developmental and defense mechanisms in plants, its regulatory role in mammals remains unclear. By employing a novel reverse small interfering RNA (siRNA) screening strategy, we show that a tRF mimic (antisense derived from the 5' end of tRNAHis(GUG) of Chinese yew) exhibits comparable anti-cancer activity with that of taxol on ovarian cancer A2780 cells, with a 16-fold lower dosage than that of taxol. A dual-luciferase reporter assay revealed that tRF-T11 directly targets the 3' UTR of oncogene TRPA1 mRNA. Furthermore, an Argonaute-RNA immunoprecipitation (AGO-RIP) assay demonstrated that tRF-T11 can interact with AGO2 to suppress TRPA1 via an RNAi pathway. This study uncovers a new role of plant-derived tRFs in regulating endogenous genes. This holds great promise for exploiting novel RNA drugs derived from nature and sheds light on the discovery of unknown molecular targets of therapeutics.

Progression of myopia in a natural cohort of Chinese children during COVID-19 pandemic.

PURPOSE: To determine myopia progression in children during the COVID-19 and the related factors associated with myopia. METHODS: All subjects underwent three-timepoint ocular examinations that were measured in July 2019, January, and August 2020. We compared the changes in uncorrected visual acuity (UCVA), mydriatic spherical equivalent (SE), and axial length (AL) between two periods (before and during COVID-19). A questionnaire was performed to investigate risk factors for myopia. RESULTS: Compared with before the COVID-19, the mean (S.D.) myopia progression during the COVID-19 was significantly higher in right eyes (- 0.93 (0.65) vs. - 0.33 (0.47) D; p < 0.001). However, the differences in UCVA changes and the axial elongation between two periods were clinically insignificant. Through logistic regressive analysis, we found the difference of the SE changes was associated with the baseline AL (P = 0.028; 95% confidence interval [CI], 1.058, 2.632), online education (P = 0.02; 95% CI, 1.587, 8.665), and time of digital screen (p < 0.005; 95% CI, 1.587, 4.450). CONCLUSIONS: Children were at higher risk of myopia progression during COVID-19, which was associated with the baseline AL, the longtime online learning, and digital screen reading.

Current Evidence of 2019 Novel Coronavirus Disease (COVID-19) Ocular Transmission: A Systematic Review and Meta-Analysis.

OBJECTIVE: To estimate the prevalence rate of ocular symptoms and the positive rate of conjunctival swab samples of patients diagnosed with 2019 Novel Coronavirus Disease (COVID-19). METHODS: We performed a systematic review and meta-analysis. A comprehensive literature search was done based on PubMed, Embase, MedRxiv, and the Cochrane Library. The primary outcomes are the prevalence rate of conjunctivitis/conjunctival congestion and the positive rate of conjunctival swab samples. Rates were expressed as proportions with 95% confidence intervals (CIs). RESULTS: A total of 12 studies with 1930 participants were included for meta-analysis. The pooled prevalence rate of conjunctivitis/conjunctival congestion was 8% (95% CI: 5%-12%). 1% (95% CI: 1%-4%) of COVID-19 patients were diagnosed with conjunctivitis/conjunctival congestion as the initial symptom. The pooled positive rate of conjunctival swab samples was 3% (95% CI: 2%-5%). We also assessed other ocular symptoms reported in the 12 studies, including foreign body sensation, increased secretion, and eye itching. The pooled prevalence rates were 6% (95% CI: 3%-10%), 10% (95% CI: 8%-12%), and 9% (95% CI: 7%-10%), respectively. CONCLUSIONS: The evidence on the positive rate of conjunctival swab samples and the prevalence rates of ocular symptoms indicated that COVID-19 ocular transmission was possible but less likely.

Epidemiological characteristics of respiratory viruses in patients with acute respiratory infections during 2009-2018 in southern China.

BACKGROUND: Acute respiratory infections (ARIs) remain a significant public threat with high morbidity and mortality worldwide; viruses are significant pathogens that cause ARIs. This study was conducted to better understand the epidemiological characteristics of respiratory viruses circulating in southern China. METHODS: We collected 22,680 respiratory samples from ARI patients in 18 hospitals in southern China during 2009-2018; seven common respiratory viruses including Flu, RSV, PIV, hMPV, ADV, HCoV, and HBoV were screened using in-house real-time PCR. RESULTS: Of all samples, 9760 ARI cases (9760/22680, 43.03%) tested positive for the seven common respiratory viruses. The most detected virus was Flu (14.15%), followed by RSV (10.33%) and PIV (5.43%); Flu-A, PIV3, and HCoV-OC43 were the predominant subtypes. Although most of the viruses were detected in male inpatients, Flu was more likely detected in female outpatients. Flu infection was more likely to cause URTI (upper respiratory tract infection), whereas RSV infection was more likely to cause pneumonia and bronchitis. The prevalence of Flu was particularly high in 2009. The epidemic level was found notably high in 2014-2018 for RSV, in 2016-2018 for PIV, in the summer of 2018 for ADV, in the summer of 2016 and winter of 2018 for HCoV, and in the summer of 2011 and autumn of 2018 for HBoV. The co-detection rate of the seven viruses was 4.70%; RSV, PIV, and Flu were the most commonly co-detected viruses. CONCLUSIONS: This work demonstrates the epidemiological characteristics of seven common respiratory viruses in ARI patients in southern China.