Selective degradation of tRNASer(AGY) is the primary driver for mitochondrial seryl-tRNA synthetase-related disease.

Mitochondrial translation is of high significance for cellular energy homeostasis. Aminoacyl-tRNA synthetases (aaRSs) are crucial translational components. Mitochondrial aaRS variants cause various human diseases. However, the pathogenesis of the vast majority of these diseases remains unknown. Here, we identified two novel SARS2 (encoding mitochondrial seryl-tRNA synthetase) variants that cause a multisystem disorder. c.654-14T > A mutation induced mRNA mis-splicing, generating a peptide insertion in the active site; c.1519dupC swapped a critical tRNA-binding motif in the C-terminus due to stop codon readthrough. Both mutants exhibited severely diminished tRNA binding and aminoacylation capacities. A marked reduction in mitochondrial tRNASer(AGY) was observed due to RNA degradation in patient-derived induced pluripotent stem cells (iPSCs), causing impaired translation and comprehensive mitochondrial function deficiencies. These impairments were efficiently rescued by wild-type SARS2 overexpression. Either mutation caused early embryonic fatality in mice. Heterozygous mice displayed reduced muscle tissue-specific levels of tRNASers. Our findings elucidated the biochemical and cellular consequences of impaired translation mediated by SARS2, suggesting that reduced abundance of tRNASer(AGY) is a key determinant for development of SARS2-related diseases.

Use of a specialty endoscopy online platform for continuing medical education for clinical endoscopists during the COVID-19 pandemic.

BACKGROUND: To explore the use of a digestive endoscopy professional online platform by domestic endoscopists and its application effect on endoscopists' continuing medical education, analyse the related problems of continuing medical education using this method, and propose targeted improvement suggestions. METHODS: Based on the "Doctor's Circle" app, a questionnaire was sent to all members who successfully registered on the Hebei Biliary and Pancreatic Endoscopy Diagnosis and Treatment Alliance online platform. The questionnaire was available for 30 days. The questionnaire survey results were collected and counted for a grouping comparison. RESULTS: By the deadline, 703 completed questionnaires had been received. After the registered doctors joined the platform, 469 (66.7%) experienced a significant influence on their own endoscopic operation ability level, and 354 (50.3%) felt a significant improvement in their ability to diagnose biliary- and pancreatic-related diseases. The application effect of the platform on members' continuing medical education was affirmed by the vast majority of registered doctors. The clinical specialty of registered doctors, the length of time they joined the platform, the length of time they participated in the platform activities each time, and whether they played back course videos after the live broadcast of the course on the platform were the main factors affecting the application effect on continuing medical education (P < 0.05). Registered doctors who benefited significantly from the platform used it for 6-12 months, participated in activities for 1-2 hours each time, and often played back course videos. CONCLUSION: The new model of continuing medical education based on an online platform breaks through the constraints of traditional models and meets the individualized needs of every medical worker to improve their comprehension level. At present, the global outbreak of COVID-19 makes this learning mode increasingly popular among medical workers. We should constantly improve the organization of the content and methods of continuing medical education courses, make the online platform better serve the majority of medical workers, and effectively improve the comprehension levels of clinicians.

Nucleic acid detection of fecal samples from confirmed cases of COVID-19

To investigate the status of viral infection in the feces of confirmed COVID-19, cases fecal samples or anal swabs from 36 confirmed cases were collected, 2019-nCoV was detected by real-time fluorescence RT-PCR, and the infection rates of the cases were compared by statistical analysis software SPSS 19.0. Among the 36 samples, 20 (55.56%) were positive for 2019-nCoV. The positive rate of critical cases (2/3) and severe cases(6/9) were both 66.67%, the positive rate of common pneumonia was 62.50% (10/16), and the positive rate of mild pneumonia was 25.00% (2/8). The 36 confirmed COVID-19 cases included 22 males and 14 females, with a detection rate of 54.55% and 57.14%, respectively. The age distribution range of the cases was ranging between 17 and 86 years old, with an average age of 48.75 years. Among the 36 samples, 2 positive specimens in 5 anal swab specimens and 18 positive specimens in 31 fecal specimens were detected. There were no statistically significant differences in the positive detection rates among clinical types, genders, ages and specimen types, respectively. From these results, we deduced that fecal samples of confirmed COVID-19 cases contained 2019-nCoV, which can cause infection through potential fecal-oral transmission and be significant for clinical treatment and epidemiological study.

Brain Mechanisms of COVID-19-Sleep Disorders.

2020 and 2021 have been unprecedented years due to the rapid spread of the modified severe acute respiratory syndrome coronavirus around the world. The coronavirus disease 2019 (COVID-19) causes atypical infiltrated pneumonia with many neurological symptoms, and major sleep changes. The exposure of people to stress, such as social confinement and changes in daily routines, is accompanied by various sleep disturbances, known as 'coronasomnia' phenomenon. Sleep disorders induce neuroinflammation, which promotes the blood-brain barrier (BBB) disruption and entry of antigens and inflammatory factors into the brain. Here, we review findings and trends in sleep research in 2020-2021, demonstrating how COVID-19 and sleep disorders can induce BBB leakage via neuroinflammation, which might contribute to the 'coronasomnia' phenomenon. The new studies suggest that the control of sleep hygiene and quality should be incorporated into the rehabilitation of COVID-19 patients. We also discuss perspective strategies for the prevention of COVID-19-related BBB disorders. We demonstrate that sleep might be a novel biomarker of BBB leakage, and the analysis of sleep EEG patterns can be a breakthrough non-invasive technology for diagnosis of the COVID-19-caused BBB disruption.

Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach.

BACKGROUND: Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19. OBJECTIVE: We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection. METHODS: In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants' clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes: nonsevere, severe, healthy, and viral pneumonia. RESULTS: Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%). CONCLUSIONS: Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study's hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.

Isolation and genome sequence analyses of 2019-nCoV from COVID-19 patients in Fujian Province, China

To explore genomic characteriation of 2019-nCoV, throat swab specimens from COVID-19 patients were inoculated on Vero-E6 cells. Six days post-inoculation, supernatants of cell cultures were collected, tested, and sequenced on the Ion Torrent S5 Next-Generation Sequencing system. Two strains viruses were isolated from 12 specimens (eight of them were positive in ORF1ab gene and N gene, one of them was positive in N gene and three of them were negative in ORF1ab gene and N gene), as confirmed by online BLAST search at NCBI website. Genetic similarity of more than 99.9% were observed, from genomic level to structure genes including E, M, N and S genes, between the 2 isolates and the 2019-nCoV reference strain Wuhan-Hu-1 that isolated from Wuhan, Hubei province. Therefore, these results indicated that 2019-nCoV isolates in Fujian province have not mutated significantly yet so far.

Patients with Cancer Appear More Vulnerable to SARS-CoV-2: A Multicenter Study during the COVID-19 Outbreak.

The novel COVID-19 outbreak has affected more than 200 countries and territories as of March 2020. Given that patients with cancer are generally more vulnerable to infections, systematic analysis of diverse cohorts of patients with cancer affected by COVID-19 is needed. We performed a multicenter study including 105 patients with cancer and 536 age-matched noncancer patients confirmed with COVID-19. Our results showed COVID-19 patients with cancer had higher risks in all severe outcomes. Patients with hematologic cancer, lung cancer, or with metastatic cancer (stage IV) had the highest frequency of severe events. Patients with nonmetastatic cancer experienced similar frequencies of severe conditions to those observed in patients without cancer. Patients who received surgery had higher risks of having severe events, whereas patients who underwent only radiotherapy did not demonstrate significant differences in severe events when compared with patients without cancer. These findings indicate that patients with cancer appear more vulnerable to SARS-CoV-2 outbreak. SIGNIFICANCE: Because this is the first large cohort study on this topic, our report will provide much-needed information that will benefit patients with cancer globally. As such, we believe it is extremely important that our study be disseminated widely to alert clinicians and patients.This article is highlighted in the In This Issue feature, p. 747.