A Nomogram for Predicting Delayed Viral Shedding in Non-Severe SARS-CoV-2 Omicron Infection.

Purpose: The Omicron variant of SARS-CoV-2 has emerged as a significant global concern, characterized by its rapid transmission and resistance to existing treatments and vaccines. However, the specific hematological and biochemical factors that may impact the clearance of Omicron variant infection remain unclear. The present study aimed to identify easily accessible laboratory markers that are associated with prolonged virus shedding in non-severe patients with COVID-19 caused by the Omicron variant. Patients and Methods: A retrospective cohort study was conducted on 882 non-severe COVID-19 patients who were diagnosed with the Omicron variant in Shanghai between March and June 2022. The least absolute shrinkage and selection operator regression model was used for feature selection and dimensional reduction, and multivariate logistic regression analysis was performed to construct a nomogram for predicting the risk of prolonged SARS-CoV-2 RNA positivity lasting for more than 7 days. The receiver operating characteristic (ROC) curve and calibration curves were used to assess predictive discrimination and accuracy, with bootstrap validation. Results: Patients were randomly divided into derivation (70%, n = 618) and validation (30%, n = 264) cohorts. Optimal independent markers for prolonged viral shedding time (VST) over 7 days were identified as Age, C-reactive protein (CRP), platelet count, leukocyte count, lymphocyte count, and eosinophil count. These factors were subsequently incorporated into the nomogram utilizing bootstrap validation. The area under the curve (AUC) in the derivation (0.761) and validation (0.756) cohorts indicated good discriminative ability. The calibration curve showed good agreement between the nomogram-predicted and actual patients with VST over 7 days. Conclusion: Our study confirmed six factors associated with delayed VST in non-severe SARS-CoV-2 Omicron infection and constructed a Nomogram which may assist non-severely affected patients to better estimate the appropriate length of self-isolation and optimize their self-management strategies.

Alfalfa Xeno-miR168b Target CPT1A to Regulate Milk Fat Synthesis in Bovine Mammary Epithelial Cells.

It was shown that microRNAs (miRNAs) play an important role in the synthesis of milk fat; thus, this manuscript evaluated whether exogenous miRNA (xeno-miRNAs) from alfalfa could influence the milk fat content in dairy cows. At first, mtr-miR168b was screened from dairy cow milk and blood. Then, EdU staining, flow cytometry, Oil Red O staining, qRT-PCR, and WB were applied to explore the effect of xeno-miR168b on the proliferation, apoptosis, and lipid metabolism of bovine mammary epithelial cells (BMECs). Finally, in order to clarify the pathway that regulated the lipid metabolism of BMECs using xeno-miR168b, a double-luciferase reporter assay was used to verify the target gene related to milk fat. These results showed that overexpression of xeno-miR168b inhibited cell proliferation but promoted apoptosis, which also decreased the expression of several lipid metabolism genes, including PPARγ, SCD1, C/EBPß, and SREBP1, significantly inhibited lipid droplet formation, and reduced triglyceride content in BMECs. Furthermore, the targeting relationship between CPT1A and xeno-miR168b was determined and it was confirmed that CPT1A silencing reduced the expression of lipid metabolism genes and inhibited fat accumulation in BMECs. These findings identified xeno-miR168b from alfalfa as a cross-kingdom regulatory element that could influence milk fat content in dairy cows by modulating CPT1A expression.

COVIDSum: A linguistically enriched SciBERT-based summarization model for COVID-19 scientific papers.

The coronavirus disease (COVID-19) has claimed the lives of over 350,000 people and infected more than 173 million people worldwide, it triggers researchers from diverse fields are accelerating their research to help diagnostics, therapies, and vaccines. Researchers also publish their recent research progress through scientific papers. However, manually writing the abstract of a paper is time-consuming, and it increases the writing burden of the researchers. Abstractive summarization technique which automatically provides researchers reliable draft abstracts, can alleviate this problem. In this work, we propose a linguistically enriched SciBERT-based summarization model for COVID-19 scientific papers, named COVIDSum. Specifically, we first extract salient sentences from source papers and construct word co-occurrence graphs. Then, we adopt a SciBERT-based sequence encoder and a Graph Attention Networks-based graph encoder to encode sentences and word co-occurrence graphs, respectively. Finally, we fuse the above two encodings and generate an abstractive summary of each scientific paper. When evaluated on the publicly available COVID-19 open research dataset, the performance of our proposed model achieves significant improvement compared with other document summarization models.

Hits-based attentional neural model for abstractive summarization

Automatic abstractive summary generation is still an open problem in natural language processing field. Conventional encoder–decoder model based abstractive summarization methods often suffer from repetition and semantic irrelevance. Recent studies apply traditional attention or graph-based attention on the encoder–decoder model to tackle the problem, under the assumption that all the sentences in the original document are indistinguishable from each other. But in a document, the same words in different sentences are not equally important, i.e., the words in a trivial sentence are less important than the words in a salient sentence. Based on it, we develop a HITS-based attention mechanism in this paper, which fully leverages sentence-level and word-level information by considering sentences and words in the original document as authorities and hubs. Based on it, we present a novel abstractive summarization method, with Kullback–Leibler (KL) divergence to refine the attention value, meanwhile we propose a comparison mechanism in summary generation to further improve the summarization performance. When evaluated on the CNN/Daily Mail and NYT datasets, the experimental results demonstrate the improvement of summarization performance and show the performance of our proposed method is comparable with that of the other summarization methods. Besides, we also conduct experiments on CORD-19 dataset (COVID-19 Open Research Dataset) which is a biomedical domain dataset, and the experimental results show great performance of our proposed method compared with that of the other state-of-the-art summarization methods.

Optimization, Prevention and Control Measures of PET/CT Examination and Nursing Workflow During the Prevention and Control of Novel Coronavirus Pneumonia

The PET/CT examination in nuclear medicine subject has many procedures involved many links and workplaces, and the management requirements for patients are complex. Patients need to stay in a relatively closed environment for a long time after the injection of radio pharmaceuticals, so the risk of cross-infection between medical staff and patients, and between patients is high. In novel coronavirus pneumonia (NCP) during the prevention and control, it’s very important for patients carried out PET/CT examination to formulate emergency preplans for prevention, control and optimize the workflow, take necessary control measures, make the medical staff of personal protection. We have to do well for the patients of the correct health education, reduce exposure risk, reduce the cross infection, and guarantee the quality of nuclear medicine inspection and safety.