Janus Nanofibrous Patch with In Situ Grown Superlubricated Skin for Soft Tissue Repair with Inhibited Postoperative Adhesion.

The patch with a superlubricated surface shows great potential for the prevention of postoperative adhesion during soft tissue repair. However, the existing patches suffer from the destruction of topography during superlubrication coating and lack of pro-healing capability. Herein, we demonstrate a facile and versatile strategy to develop a Janus nanofibrous patch (J-NFP) with antiadhesion and reactive oxygen species (ROS) scavenging functions. Specifically, sequential electrospinning is performed with initiators and CeO2 nanoparticles (CeNPs) embedded on the different sides, followed by subsurface-initiated atom transfer radical polymerization for grafting zwitterionic polymer brushes, introducing superlubricated skin on the surface of single nanofibers. The poly(sulfobetaine methacrylate) brush-grafted patch retains fibrous topography and shows a coefficient of friction of around 0.12, which is reduced by 77% compared with the pristine fibrous patch. Additionally, a significant reduction in protein, platelet, bacteria, and cell adhesion is observed. More importantly, the CeNPs-embedded patch enables ROS scavenging as well as inhibits pro-inflammatory cytokine secretion and promotes anti-inflammatory cytokine levels. Furthermore, the J-NFP can inhibit tissue adhesion and promote repair of both rat skin wounds and intrauterine injuries. The present strategy for developing the Janus patch exhibits enormous prospects for facilitating soft tissue repair.

Explainable ensemble learning method for OCT detection with transfer learning.

The accuracy and interpretability of artificial intelligence (AI) are crucial for the advancement of optical coherence tomography (OCT) image detection, as it can greatly reduce the manual labor required by clinicians. By prioritizing these aspects during development and application, we can make significant progress towards streamlining the clinical workflow. In this paper, we propose an explainable ensemble approach that utilizes transfer learning to detect fundus lesion diseases through OCT imaging. Our study utilized a publicly available OCT dataset consisting of normal subjects, patients with dry age-related macular degeneration (AMD), and patients with diabetic macular edema (DME), each with 15 samples. The impact of pre-trained weights on the performance of individual networks was first compared, and then these networks were ensemble using majority soft polling. Finally, the features learned by the networks were visualized using Grad-CAM and CAM. The use of pre-trained ImageNet weights improved the performance from 68.17% to 92.89%. The ensemble model consisting of the three CNN models with pre-trained parameters loaded performed best, correctly distinguishing between AMD patients, DME patients and normal subjects 100% of the time. Visualization results showed that Grad-CAM could display the lesion area more accurately. It is demonstrated that the proposed approach could have good performance of both accuracy and interpretability in retinal OCT image detection.

Evaluation and clinical practice of pathogens and antimicrobial resistance genes of BioFire FilmArray Pneumonia panel in lower respiratory tract infections.

BACKGROUND: Existing panels for lower respiratory tract infections (LRTIs) are slow and lack quantification of important pathogens and antimicrobial resistance, which are not solely responsible for their complex etiology and antibiotic resistance. BioFire FilmArray Pneumonia (PN) panels may provide rapid information on their etiology. METHODS: The bronchoalveolar lavage fluid of 187 patients with LRTIs was simultaneously analyzed using a PN panel and cultivation, and the impact of the PN panel on clinical practice was assessed. The primary endpoint was to compare the consistency between the PN panel and conventional microbiology in terms of etiology and drug resistance, as well as to explore the clinical significance of the PN panel. The secondary endpoint was pathogen detection using the PN panel in patients with community-acquired pneumonia (CAP) or hospital-acquired pneumonia (HAP). RESULTS: Fifty-seven patients with HAP and 130 with CAP were included. The most common pathogens of HAP were Acinetobacter baumannii and Klebsiella pneumoniae, with the most prevalent antimicrobial resistance (AMR) genes being CTX-M and KPC. For CAP, the most common pathogens were Haemophilus influenzae and Staphylococcus aureus, with the most frequent AMR genes being CTX-M and VIM. Compared with routine bacterial culture, the PN panel demonstrated an 85% combined positive percent agreement (PPA) and 92% negative percent agreement (NPA) for the qualitative identification of 13 bacterial targets. PN detection of bacteria with higher levels of semi-quantitative bacteria was associated with more positive bacterial cultures. Positive concordance between phenotypic resistance and the presence of corresponding AMR determinants was 85%, with 90% positive agreement between CTX-M-type extended-spectrum beta-lactamase gene type and phenotype and 100% agreement for mecA/C and MREJ. The clinical benefit of the PN panel increased by 25.97% compared with traditional cultural tests. CONCLUSION: The bacterial pathogens and AMR identified by the PN panel were in good agreement with conventional cultivation, and the clinical benefit of the PN panel increased by 25.97% compared with traditional detection. Therefore, the PN panel is recommended for patients with CAP or HAP who require prompt pathogen diagnosis and resistance identification.

LncRNA KCNQ1OT1/miR-496/HMGB1 Signaling Axis Promotes Invasion and Migration of Non-small Cell Lung Cancer Cells.

Enhanced invasion and migration of non-small cell lung cancer (NSCLC) cells is the major cause of metastasis and poor prognosis in NSCLC. This study was conducted to investigate the role and mechanism of lncRNA KCNQ1OT1 in the proliferation, invasion, and migration of NSCLC cells. The expression of KCNQ1OT1 in NSCLC was analyzed in the StarBase database, and the target miRNA of KCNQ1OT1 as well as the target genes of the miRNA was predicted. Then, the mRNA expression levels of KCNQ1OT1, miR-496, and HMGB1 were detected in clinical tissue samples and cells by qRT-PCR assay. Besides, the protein levels of HMGB1 were detected by Western blot. MTT assay, transwell assay, and scratch assay were used to determine the proliferation, invasion, and migration ability of NSCLC cells, respectively. Correlation analysis was performed to assess the correlation between the expression of KCNQ1OT1, miR-496, and HMGB1 in clinical NSCLC samples. Dual-luciferase reporter gene assay was conducted to analyze the interaction between KCNQ1OT1 and miR-496 and between miR-496 and HMGB1. The database results showed that KCNQ1OT1 was highly expressed in NSCLC. Similarly, we found that the expression level of KCNQ1OT1 was significantly higher in NSCLC tissues and cells than that in the corresponding normal tissues and cells. The results of MTT assay, transwell assay, and scratch assay demonstrated that KCNQ1OT1 significantly enhanced the proliferation, invasion, and migration of NSCLC cells. Further mechanism exploration revealed that KCNQ1OT1 could sponge miR-496, and miR-496 directly targeted and regulated the expression of HMGB1. The expression of miR-496 and either KCNQ1OT1 or HMGB1 were negatively correlated in NSCLC, while the expression of KCNQ1OT1 and HMGB1 were positively correlated. Compared with normal paracancer tissues, miR-496 was much lower and HMGB1 was much higher expressed in NSCLC tissues. The results of cotransfection also further demonstrated that miR-496 inhibitor or sh-HMGB1 cotransfected with sh-KCNQ1OT1 could significantly decrease or increase the ability of sh-KCNQ1OT1 to inhibit the proliferation, invasion, and migration of H1299 cells, respectively. In conclusion, lncRNA KCNQ1OT1 promotes the invasion and migration of NSCLC cells through miR-496/HMGB1 signaling axis.

para-Selective, Direct C(sp2)-H Alkylation of Electron-Deficient Arenes by the Electroreduction Process.

Direct para-selective C(sp2)-H alkylation of electron-deficient arenes based on the electroreduction-enabled radical addition of alkyl bromides has been developed under mild conditions. In the absence of any metals and redox agents, the simple electrolysis system tolerates a variety of primary, secondary, and tertiary alkyl bromides and behaves as an important complement to the directed alkylation of the C(sp2)-H bond and the classic Friedel-Crafts alkylation. This electroreduction process provides a more straightforward, environmentally benign, and effective alkylation method for electron-deficient arenes.

Construction of m7G subtype classification on heterogeneity of sepsis.

Sepsis is a highly heterogeneous disease and a major factor in increasing mortality from infection. N7-Methylguanosine (m7G) is a widely RNA modification in eukaryotes, which involved in regulation of different biological processes. Researchers have found that m7G methylation contributes to a variety of human diseases, but its research in sepsis is still limited. Here, we aim to establish the molecular classification of m7G gene-related sepsis, reveal its heterogeneity and explore the underlying mechanism. We first identified eight m7G related prognostic genes, and identified two different molecular subtypes of sepsis through Consensus Clustering. Among them, the prognosis of C2 subtype is worse than that of C1 subtype. The signal pathways enriched by the two subtypes were analyzed by ssGSEA, and the results showed that the amino acid metabolism activity of C2 subtype was more active than that of C1 subtype. In addition, the difference of immune microenvironment among different subtypes was explored through CIBERSORT algorithm, and the results showed that the contents of macrophages M0 and NK cells activated were significantly increased in C2 subtype, while the content of NK cells resting decreased significantly in C2 subtype. We further explored the relationship between immune regulatory genes and inflammation related genes between C2 subtype and C1 subtype, and found that C2 subtype showed higher expression of immune regulatory genes and inflammation related genes. Finally, we screened the key genes in sepsis by WGCNA analysis, namely NUDT4 and PARN, and verified their expression patterns in sepsis in the datasets GSE131761 and GSE65682. The RT-PCR test further confirmed the increased expression of NUDTA4 in sepsis patients. In conclusion, sepsis clustering based on eight m7G-related genes can well distinguish the heterogeneity of sepsis patients and help guide the personalized treatment of sepsis patients.

Electricity-driven three-component reductive coupling reaction for the synthesis of diarylmethylamine.

A three-component reductive coupling reaction of aldehydes, amines and cyanopyridines under electrochemical conditions has been developed. The in situ generated imine and cyanopyridine simultaneously undergo single-electron reduction at the cathode, and afford diarylmethylamines through radical coupling without the participation of reducing agents. The one-pot electrolysis method can modularly obtain various secondary and tertiary amines and exhibits broad functional group compatibility. Mechanistic experiments verify the pivotal reduction step from imine to α-amino radical and reveal the key role of benzoic acid in reducing the reduction potential of imine and cyanopyridine.

Identification of novel cuproptosis-related lncRNA signatures to predict the prognosis and immune microenvironment of breast cancer patients.

Background: Cuproptosis is a new modality of cell death regulation that is currently considered as a new cancer treatment strategy. Nevertheless, the prognostic predictive value of cuproptosis-related lncRNAs in breast cancer (BC) remains unknown. Using cuproptosis-related lncRNAs, this study aims to predict the immune microenvironment and prognosis of BC patients. and develop new therapeutic strategies that target the disease. Methods: The Cancer Genome Atlas (TCGA) database provided the RNA-seq data along with the corresponding clinical and prognostic information. Univariate and multivariate Cox regression analyses were performed to acquire lncRNAs associated with cuproptosis to establish predictive features. The Kaplan-Meier method was used to calculate the overall survival rate (OS) in the high-risk and low-risk groups. High risk and low risk gene sets were enriched to explore functional discrepancies among risk teams. The mutation data were analyzed using the "MAFTools" r-package. The ties of predictive characteristics and immune status had been explored by single sample gene set enrichment analysis (ssGSEA). Last, the correlation between predictive features and treatment condition in patients with BC was analyzed. Based on prognostic risk models, we assessed associations between risk subgroups and immune scores and immune checkpoints. In addition, drug responses in at-risk populations were predicted. Results: We identified a set of 11 Cuproptosis-Related lncRNAs (GORAB-AS1, AC 079922.2, AL 589765.4, AC 005696.4, Cytor, ZNF 197-AS1, AC 002398.1, AL 451085.3, YTH DF 3-AS1, AC 008771.1, LINC 02446), based on which to construct the risk model. In comparison to the high-risk group, the low-risk patients lived longer (p < 0.001). Moreover, cuproptosis-related lncRNA profiles can independently predict prognosis in BC patients. The AUC values for receiver operating characteristics (ROC) of 1-, 3-, and 5-year risk were 0.849, 0.779, and 0.794, respectively. Patients in the high-risk group had lower OS than those in the low-risk group when they were divided into groups based on various clinicopathological variables. The tumor burden mutations (TMB) correlation analysis showed that high TMB had a worse prognosis than low-TMB, and gene mutations were found to be different in high and low TMB groups, such as PIK3CA (36% versus 32%), SYNE1 (4% versus 6%). Gene enrichment analysis indicated that the differential genes were significantly concentrated in immune-related pathways. The predictive traits were significantly correlated with the immune status of BC patients, according to ssGSEA results. Finally, high-risk patients showed high sensitivity in anti-CD276 immunotherapy and conventional chemotherapeutic drugs such as imatinib, lapatinib, and pazopanib. Conclusion: We successfully constructed of a cuproptosis-related lncRNA signature, which can independently predict the prognosis of BC patients and can be used to estimate OS and clinical treatment outcomes in BRCA patients. It will serve as a foundation for further research into the mechanism of cuproptosis-related lncRNAs in breast cancer, as well as for the development of new markers and therapeutic targets for the disease.

Unilateral pulmonary vein atresia presenting with recurrent hydrothorax in an adult: A case report.

UPVA (Unilateral pulmonary vein atresia) is the failure of connection between the common pulmonary vein and the left atrium. UPVA is a rare malformation of common pulmonary vein caused by embryonic development defects. Isolated UPVA is uncommon, the diagnosis commonly occurs during early childhood because of asthma, recurrent pneumonia or hemoptysis, but diagnosis in adults is unusual. Some patients can be asymptomatic until adulthood. In this report, we describe a case about UPVA presenting with recurrent hydrothorax in an adult. We gradually carried out routine diagnostic methods and eventually confirmed the rare UPVA according to the two common clinical manifestations of repeated pleural effusion and hilar soft tissue shadow.

Ultrasound Image Classification of Thyroid Nodules Based on Deep Learning.

A thyroid nodule, which is defined as abnormal growth of thyroid cells, indicates excessive iodine intake, thyroid degeneration, inflammation, and other diseases. Although thyroid nodules are always non-malignant, the malignancy likelihood of a thyroid nodule grows steadily every year. In order to reduce the burden on doctors and avoid unnecessary fine needle aspiration (FNA) and surgical resection, various studies have been done to diagnose thyroid nodules through deep-learning-based image recognition analysis. In this study, to predict the benign and malignant thyroid nodules accurately, a novel deep learning framework is proposed. Five hundred eight ultrasound images were collected from the Third Hospital of Hebei Medical University in China for model training and validation. First, a ResNet18 model, pretrained on ImageNet, was trained by an ultrasound image dataset, and a random sampling of training dataset was applied 10 times to avoid accidental errors. The results show that our model has a good performance, the average area under curve (AUC) of 10 times is 0.997, the average accuracy is 0.984, the average recall is 0.978, the average precision is 0.939, and the average F1 score is 0.957. Second, Gradient-weighted Class Activation Mapping (Grad-CAM) was proposed to highlight sensitive regions in an ultrasound image during the learning process. Grad-CAM is able to extract the sensitive regions and analyze their shape features. Based on the results, there are obvious differences between benign and malignant thyroid nodules; therefore, shape features of the sensitive regions are helpful in diagnosis to a great extent. Overall, the proposed model demonstrated the feasibility of employing deep learning and ultrasound images to estimate benign and malignant thyroid nodules.

Abnormal Cerebral Blood Flow and Volumetric Brain Morphometry in Patients With Obstructive Sleep Apnea.

Obstructive sleep apnea (OSA) is a serious breathing disorder, leading to myocardial infarction, high blood pressure, and stroke. Brain morphological changes have been widely reported in patients with OSA. The pathophysiological mechanisms of cerebral blood flow (CBF) changes associated with OSA are not clear. In this study, 20 patients with OSA and 36 healthy controls (HCs) were recruited, and then pseudo-continuous arterial spin labeling (pCASL) and voxel-based morphometry (VBM) methods were utilized to explore blood perfusion and morphological changes in the patients with OSA. Compared with the HC group, the OSA group showed increased CBF values in the right medial prefrontal cortex (mPFC), left precentral gyrus, and right insula and showed decreased CBF values in the right temporal pole (TP) and the right cerebellum_Crus2. Compared with the HC group, the patients with OSA showed decreased gray matter volume (GMV) in the right dorsal lateral prefrontal cortex (DLPFC), the right occipital pole, and the vermis. There were no significantly increased GMV brain regions found in patients with OSA. Pearson correlation analysis showed that the reduced GMV in the right DLPFC and the right occipital pole was both positively correlated with Mini-Mental State Examination (MMSE) (r = 0.755, p < 0.001; r = 0.686, p = 0.002) and Montreal Cognitive Assessment (MoCA) scores (r = 0.716, p = 0.001; r = 0.601, p = 0.008), and the reduced GMV in the right occipital pole was negatively correlated with duration of illness (r = -0.497, p = 0.036). Patients with OSA have abnormal blood perfusion metabolism and morphological changes in brain regions including the frontal lobe and the cerebellum and were closely related to abnormal behavior, psychology, and cognitive function, which play an important role in the pathophysiological mechanism of OSA.

Evaluating the Microsatellite Instability of Colorectal Cancer Based on Multimodal Deep Learning Integrating Histopathological and Molecular Data.

Microsatellite instability (MSI), an important biomarker for immunotherapy and the diagnosis of Lynch syndrome, refers to the change of microsatellite (MS) sequence length caused by insertion or deletion during DNA replication. However, traditional wet-lab experiment-based MSI detection is time-consuming and relies on experimental conditions. In addition, a comprehensive study on the associations between MSI status and various molecules like mRNA and miRNA has not been performed. In this study, we first studied the association between MSI status and several molecules including mRNA, miRNA, lncRNA, DNA methylation, and copy number variation (CNV) using colorectal cancer data from The Cancer Genome Atlas (TCGA). Then, we developed a novel deep learning framework to predict MSI status based solely on hematoxylin and eosin (H&E) staining images, and combined the H&E image with the above-mentioned molecules by multimodal compact bilinear pooling. Our results showed that there were significant differences in mRNA, miRNA, and lncRNA between the high microsatellite instability (MSI-H) patient group and the low microsatellite instability or microsatellite stability (MSI-L/MSS) patient group. By using the H&E image alone, one can predict MSI status with an acceptable prediction area under the curve (AUC) of 0.809 in 5-fold cross-validation. The fusion models integrating H&E image with a single type of molecule have higher prediction accuracies than that using H&E image alone, with the highest AUC of 0.952 achieved when combining H&E image with DNA methylation data. However, prediction accuracy will decrease when combining H&E image with all types of molecular data. In conclusion, combining H&E image with deep learning can predict the MSI status of colorectal cancer, the accuracy of which can further be improved by integrating appropriate molecular data. This study may have clinical significance in practice.

A Novel N-Gram-Based Image Classification Model and Its Applications in Diagnosing Thyroid Nodule and Retinal OCT Images.

Imbalanced classes and dimensional disasters are critical challenges in medical image classification. As a classical machine learning model, the n-gram model has shown excellent performance in addressing this issue in text classification. In this study, we proposed an algorithm to classify medical images by extracting their n-gram semantic features. This algorithm first converts an image classification problem to a text classification problem by building an n-gram corpus for an image. After that, the algorithm was based on the n-gram model to classify images. The algorithm was evaluated by two independent public datasets. The first experiment is to diagnose benign and malignant thyroid nodules. The best area under the curve (AUC) is 0.989. The second experiment is to diagnose the type of fundus lesion. The best result is that it correctly identified 86.667% of patients with dry age-related macular degeneration (AMD), 93.333% of patients with diabetic macular edema (DME), and 93.333% of normal individuals.

HAUS5 Is A Potential Prognostic Biomarker With Functional Significance in Breast Cancer.

Background: Breast cancer (BRCA) has become the most frequently appearing, lethal, and aggressive cancer with increasing morbidity and mortality. Previously, it was discovered that the HAUS5 protein is involved in centrosome integrity, spindle assembly, and the completion of the cytoplasmic division process during mitosis. By encouraging chromosome misdivision and aneuploidy, HAUS5 has the potential to cause cancer. The significance of HAUS5 in BRCA and the relationship between its expression and clinical outcomes or immune infiltration remains unclear. Methods: Pan-cancer was analyzed by TIMER2 web and the expression differential of HAUS5 was discovered. The prognostic value of HAUS5 for BRCA was evaluated with KM plotter and confirmed with Gene Expression Omnibus (GEO) dataset. Following that, we looked at the relationship between the high and low expression groups of HAUS5 and breast cancer clinical indications. Signaling pathways linked to HAUS5 expression were discovered using Gene Set Enrichment Analysis (GSEA). The relative immune cell infiltrations of each sample were assessed using the CIBERSORT algorithm and ESTIMATE method. We evaluated the Tumor Mutation Burden (TMB) value between the two sets of samples with high and low HAUS5 expression, as well as the differences in gene mutations between the two groups. The proliferation changes of BRCA cells after knockdown of HAUS5 were evaluated by fluorescence cell counting and colony formation assay. Result: HAUS5 is strongly expressed in most malignancies, and distinct associations exist between HAUS5 and prognosis in BRCA patients. Upregulated HAUS5 was associated with poor clinicopathological characteristics such as tumor T stage, ER, PR, and HER2 status. mitotic prometaphase, primary immunodeficiency, DNA replication, cell cycle related signaling pathways were all enriched in the presence of elevated HAUS5 expression, according to GSEA analysis. The BRCA microenvironment's core gene, HAUS5, was shown to be related with invading immune cell subtypes and tumor cell stemness. TMB in the HAUS5-low expression group was significantly higher than that in the high expression group. The mutation frequency of 15 genes was substantially different in the high expression group compared to the low expression group. BRCA cells' capacity to proliferate was decreased when HAUS5 was knocked down. Conclusion: These findings show that HAUS5 is a positive regulator of BRCA progression that contributes to BRCA cells proliferation. As a result, HAUS5 might be a novel prognostic indicator and therapeutic target for BRCA patients.

A Novel Single-Cell RNA Sequencing Data Feature Extraction Method Based on Gene Function Analysis and Its Applications in Glioma Study.

Critical in revealing cell heterogeneity and identifying new cell subtypes, cell clustering based on single-cell RNA sequencing (scRNA-seq) is challenging. Due to the high noise, sparsity, and poor annotation of scRNA-seq data, existing state-of-the-art cell clustering methods usually ignore gene functions and gene interactions. In this study, we propose a feature extraction method, named FEGFS, to analyze scRNA-seq data, taking advantage of known gene functions. Specifically, we first derive the functional gene sets based on Gene Ontology (GO) terms and reduce their redundancy by semantic similarity analysis and gene repetitive rate reduction. Then, we apply the kernel principal component analysis to select features on each non-redundant functional gene set, and we combine the selected features (for each functional gene set) together for subsequent clustering analysis. To test the performance of FEGFS, we apply agglomerative hierarchical clustering based on FEGFS and compared it with seven state-of-the-art clustering methods on six real scRNA-seq datasets. For small datasets like Pollen and Goolam, FEGFS outperforms all methods on all four evaluation metrics including adjusted Rand index (ARI), normalized mutual information (NMI), homogeneity score (HOM), and completeness score (COM). For example, the ARIs of FEGFS are 0.955 and 0.910, respectively, on Pollen and Goolam; and those of the second-best method are only 0.938 and 0.910, respectively. For large datasets, FEGFS also outperforms most methods. For example, the ARIs of FEGFS are 0.781 on both Klein and Zeisel, which are higher than those of all other methods but slight lower than those of SC3 (0.798 and 0.807, respectively). Moreover, we demonstrate that CMF-Impute is powerful in reconstructing cell-to-cell and gene-to-gene correlation and in inferring cell lineage trajectories. As for application, take glioma as an example; we demonstrated that our clustering methods could identify important cell clusters related to glioma and also inferred key marker genes related to these cell clusters.

Predicting lncRNA-Protein Interaction With Weighted Graph-Regularized Matrix Factorization.

Long non-coding RNAs (lncRNAs) are widely concerned because of their close associations with many key biological activities. Though precise functions of most lncRNAs are unknown, research works show that lncRNAs usually exert biological function by interacting with the corresponding proteins. The experimental validation of interactions between lncRNAs and proteins is costly and time-consuming. In this study, we developed a weighted graph-regularized matrix factorization (LPI-WGRMF) method to find unobserved lncRNA-protein interactions (LPIs) based on lncRNA similarity matrix, protein similarity matrix, and known LPIs. We compared our proposed LPI-WGRMF method with five classical LPI prediction methods, that is, LPBNI, LPI-IBNRA, LPIHN, RWR, and collaborative filtering (CF). The results demonstrate that the LPI-WGRMF method can produce high-accuracy performance, obtaining an AUC score of 0.9012 and AUPR of 0.7324. The case study showed that SFPQ, SNHG3, and PRPF31 may associate with Q9NUL5, Q9NUL5, and Q9UKV8 with the highest linking probabilities and need to further experimental validation.

Longitudinal virological changes and underlying pathogenesis in hospitalized COVID-19 patients in Guangzhou, China.

Prolonged viral RNA shedding and recurrence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in coronavirus disease 2019 (COVID-19) patients have been reported. However, the clinical outcome and pathogenesis remain unclear. In this study, we recruited 43 laboratory-confirmed COVID-19 patients. We found that prolonged viral RNA shedding or recurrence mainly occurred in severe/critical patients (P<0.05). The average viral shedding time in severe/critical patients was more than 50 days, and up to 100 days in some patients, after symptom onset. However, chest computed tomography gradually improved and complete absorption occurred when SARS-CoV-2 RT-PCR was still positive, but specific antibodies appeared. Furthermore, the viral shedding time significantly decreased when the A1,430G or C12,473T mutation occurred (P<0.01 and FDR<0.01) and increased when G227A occurred (P<0.05 and FDR<0.05). High IL1R1, IL1R2, and TNFRSF21 expression in the host positively correlated with viral shedding time (P<0.05 and false discovery rate <0.05). Prolonged viral RNA shedding often occurs but may not increase disease damage. Prolonged viral RNA shedding is associated with viral mutations and host factors.

Suspected SARS-CoV-2 infection with fever and coronary heart disease: A case report.

BACKGROUND: The coronavirus disease 2019 (COVID-19) is an emerging infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Suspected cases accounted for a large proportion in the early stage of the COVID-19 outbreak. The deviation of the nucleic acid test by throat swab (the current gold standard of COVID-19) caused by variation in sampling techniques and reagent kits and coupled with nonspecific clinical manifestations make confirmation of the suspected cases difficult. Proper management of the suspected cases of COVID-19 is crucial for disease control. CASE SUMMARY: A 65-year-old male presented with fever, lymphopenia, and chest computed tomography (CT) images similar to COVID-19 after percutaneous coronary intervention. The patient was diagnosed as having bacterial pneumonia with cardiogenic pulmonary edema instead of COVID-19. This was based on four negative results for throat swab detection of SARS-CoV-2 nucleic acid using reverse transcriptase-polymerase chain reaction assay and one negative result for serological antibody of SARS-CoV-2 with the serological assay. Additionally, the distribution of ground-glass opacities and thickened blood vessels from the CT images differed from COVID-19 features, which further supported the exclusion of COVID-19. CONCLUSION: Distinguishing COVID-19 patients from those with bacterial pneumonia with cardiogenic pulmonary edema can be difficult. Therefore, it requires serious identification.

LncRNA KCNQ1OT1 promotes cell proliferation, migration and invasion via regulating miR-129-5p/JAG1 axis in non-small cell lung cancer.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the most deadly cancer worldwide. LncRNA KCNQ1OT1 has been reported to be involved in the progression of various tumors, including NSCLC. However, the precise mechanism of KCNQ1OT1 in NSCLC requires further investigation. METHODS: The expression levels of KCNQ1OT1, miR-129-5p and JAG1 were detected by qRT-PCR or western blot. Kaplan-Meier survival analysis was used to assess the correlation between KCNQ1OT1 expression and the overall survival of NSCLC patients. CCK-8 assay was used to measure cell viability. Cell migration and invasion were detected by transwell assay. The targets of KCNQ1OT1 and miR-129-5p were predicted by bioinformatics, which was confirmed by dual-luciferase reporter assay or pull-down assay. RESULTS: KCNQ1OT1 expression was significantly enhanced, while miR-129-5p expression was dramatically reduced in NSCLC tissues and cells. Higher KCNQ1OT1 shortened overall survival and was positively associated with tumor stage and lymph node metastasis. KCNQ1OT1 knockdown inhibited proliferation, migration and invasion of NSCLC cells. Inhibition of miR-129-5p attenuated the inhibition of NSCLC cell viability, migration and invasion induced by KCNQ1OT1 knockdown. In addition, JAG1 was confirmed as a target of miR-129-5p. Knockdown of JAG1 reversed the effects of miR-129-5p knockdown on NSCLC progression. KCNQ1OT1 regulated JAG1 expression by sponging miR-129-5p in NSCLC cells. CONCLUSION: KCNQ1OT1 induced proliferation, migration and invasion of NSCLC cells by sponging miR-129-5p and regulating JAG1 expression, indicating that KCNQ1OT1 was a therapeutic target for NSCLC.