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The coronavirus disease 2019 (COVID-19) outbreak, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global ongoing pandemic. Timely, accurate and non-invasive SARS-CoV-2 detection in both symptomatic and asymptomatic patients, as well as determination of their immune status, will facilitate effective large-scale pandemic control measures to prevent the spread of COVID-19. Saliva is a biofluid whose anatomical source and location is of particularly strategic relevance to COVID-19 transmission and monitoring. This review focuses on the role of saliva as both a foe (a common mode of viral transmission via salivary droplets and potentially aerosols) and a friend (as a non-invasive diagnostic tool for viral detection and immune status surveillance) in combating COVID-19.

Periodontitis is an inflammatory disease, associated with a microbial dysbiosis. Early detection using salivary small extracellular vesicles (sEVs) biomarkers may facilitate timely prevention. sEVs derived from different species (i.e., humans, bacteria) are expected to circulate in saliva. This pilot study recruited 22 participants (seven periodontal healthy, seven gingivitis and eight periodontitis) and salivary sEVs were isolated using the size-exclusion chromatography (SEC) method. The healthy, gingivitis and periodontitis groups were compared in terms of salivary sEVs in the CD9+ sEV subpopulation, Gram-negative bacteria-enriched lipopolysaccharide (LPS+) outer membrane vesicles (OMVs) and global DNA methylation pattern of 5-methylcytosine (5mC), 5-hydroxymethylcytosine (5hmC) and N6-Methyladenosine (m6dA). It was found that LPS+ OMVs, global 5mC methylation and four periodontal pathogens (T. denticola, E. corrodens, P. gingivalis and F. nucleatum) that secreted OMVs were significantly increased in periodontitis sEVs compared to those from healthy groups. These differences were more pronounced in sEVs than the whole saliva and were more superior in distinguishing periodontitis than gingivitis, in comparison to healthy patients. Of note, global 5mC hypermethylation in salivary sEVs can distinguish periodontitis patients from both healthy controls and gingivitis patients with high sensitivity and specificity (AUC = 1). The research findings suggest that assessing global sEV methylation may be a useful biomarker for periodontitis.

This pilot study aims to investigate whether salivary small extracellular vesicle (sEV)-associated microRNAs could act as potential biomarkers for periodontal disease status. Twenty-nine participants (10 who were healthy, nine with gingivitis, 10 with stage III/IV periodontitis) were recruited and unstimulated whole saliva samples were collected. Salivary sEVs were isolated using the size-exclusion chromatography (SEC) method and characterised by morphology, EV-protein and size distribution using transmission electron microscopy (TEM), Western Blot and Nanoparticle Tracking Analysis (NTA), respectively. Ten mature microRNAs (miRNAs) in salivary sEVs and saliva were evaluated using RT-qPCR. The discriminatory power of miRNAs as biomarkers in gingivitis and periodontitis versus healthy controls was evaluated by Receiver Operating Characteristics (ROC) curves. Salivary sEVs were comparable to sEVs morphology, mode, size distribution and particle concentration in healthy, gingivitis and periodontitis patients. Compared to miRNAs in whole saliva, three significantly increased miRNAs (hsa-miR-140-5p, hsa-miR-146a-5p and hsa-miR-628-5p) were only detected in sEVs in periodontitis when compared to that of healthy controls, with a good discriminatory power (area under the curve (AUC) = 0.96) for periodontitis diagnosis. Our study demonstrated that salivary sEVs are a non-invasive source of miRNAs for periodontitis diagnosis. Three miRNAs that are selectively enriched in sEVs, but not whole saliva, could be potential biomarkers for periodontal disease status.

With the development of artificial intelligence education, the human-computer interaction and human-human interaction in virtual learning communities such as Zhihu and Quora have become research hotspots. This study has optimized the research dimensions of the virtual learning system in colleges and universities based on neural network algorithms and the value of digital intelligence in the humanities. This study aims to improve the efficiency and interactive quality of students’ online learning by optimizing the interactive system of virtual learning communities in colleges. Constructed an algorithmic model for a long short-term memory (LSTM) network based on the concept of digital humanities integration. The model uses attention mechanism to improve its ability to comprehend and process question-and-answer (Q&A) content. In addition, student satisfaction with its use was investigated. The Siamese LSTM model with the attention mechanism outperforms other methods when using Word2Vec for embedding and Manhattan distance as a similarity function. The performance of the Siamese LSTM model with the introduction of the attention mechanism improves by 9%. In the evaluation of duplicate question detection on the Quora dataset, our model outperformed the previously established high-performing models, achieving an accuracy of 91.6%. Students expressed greater satisfaction with the updated interactive platform. The model in this study is more suitable than other published models for processing the SemEval Task 1 dataset. Our Q&A system, which implements simple information extraction and a natural language understanding method to answer questions, is highly rated by students.

Objectives To explore the effects of a ‘Rebuilding Myself’ intervention on enhancing the adaptability of cancer patients to return to work. Methods A single-center, single-blind, randomized controlled trial design was used. Eligible patients who were receiving routine hospital treatment were recruited from the university-affiliated hospital in our city. Patients in the control group only received usual care, while patients in the intervention group received additional ‘Rebuilding Myself’ intervention. Adaptability to return to work, self-efficacy of returning to work, mental resilience, quality of life and work ability were measured at baseline, the 6th and 12th of the intervention. The general estimation equations were used to compare the overall changes of each outcome index between the two groups at different time points. Considering that there may be patient shedding and rejection, Per-Protocol and Intention-to-Treat analysis were used to analyze the data in this study. Results There were statistically significant differences between the two groups of patients in the cancer patients’ adaptability to return to work, self-efficacy to return to work, mental resilience, work abilities, the physical, emotional, cognitive function, fatigue, insomnia and overall health status dimensions of quality of life ( P  < 0.05). And no significant difference was found in other dimensions ( P  > 0.05). The group effect, time effect, and interaction effect of patients’ return to work adaptability and return to work self-efficacy were statistically significant in both groups ( P  < 0.05). Mental resilience, working ability, and quality of life had obvious time effect and interaction effect ( P  < 0.05). Conclusion This intervention could improve cancer patients’ adaptability to return to work, self-efficacy to return to work, mental resilience, work abilities and quality of life. And it can be further expanded to improve the adaptability of patients to return to work, then to help patients achieve comprehensive rehabilitation. Implications for cancer survivors The application of ‘Rebuilding Myself’ interventions can effectively improve the adaptability of cancer patients returning to work. Trial registration This study was registered at the Chinese Clinical Trial Registry (Registration number: ChiCTR2200057943) on 23 March, 2022.

Salivary small extracellular vesicles (sEV) are emerging as a potential liquid biopsy for oral diseases. However, technical difficulties for salivary sEV isolation remain a challenge. Twelve participants (five periodontally healthy, seven gingivitis patients) were recruited and salivary sEV were isolated by ultracentrifuge (UC-sEV) and size exclusion chromatography (SEC-sEV). The effect of UC and SEC on sEV yield, DNA methylation of five cytokine gene promoters (interleukin (IL)−6, tumor necrosis factor (TNF)-α, IL−1β, IL−8, and IL−10), and functional uptake by human primary gingival fibroblasts (hGFs) was investigated. The results demonstrated that SEC-sEV had a higher yield of particles and particle/protein ratios compared to UC-sEV, with a minimal effect on the detection of DNA methylation of five cytokine genes and functional uptake in hGFs (n = 3). Comparing salivary sEV characteristics between gingivitis and healthy patients, gingivitis-UC-sEV were increased compared to the healthy group; while no differences were found in sEV size, oral bacterial gDNA, and DNA methylation for five cytokine gene promoters, for both UC-sEV and SEC-sEV. Overall, the data indicate that SEC results in a higher yield of salivary sEV, with no significant differences in sEV DNA epigenetics, compared to UC.

Periodontitis is a chronic complex inflammatory disease associated with a destructive host immune response to microbial dysbiosis, leading to irreversible loss of tooth-supporting tissues. Regeneration of functional periodontal soft (periodontal ligament and gingiva) and hard tissue components (cementum and alveolar bone) to replace lost tissues is the ultimate goal of periodontal treatment, but clinically predictable treatments are lacking. Similarly, the identification of biomarkers that can be used to accurately diagnose periodontitis activity is lacking. A relatively novel category of molecules found in oral tissue, circular RNAs (circRNAs) are single-stranded endogenous, long, non-coding RNA molecules, with covalently circular-closed structures without a 5’ cap and a 3’ tail via non-classic backsplicing. Emerging research indicates that circRNAs are tissue and disease-specific expressed and have crucial regulatory functions in various diseases. CircRNAs can function as microRNA or RNA binding sites or can regulate mRNA. In this review, we explore the biogenesis and function of circRNAs in the context of the emerging role of circRNAs in periodontitis pathogenesis and the differentiation of periodontal cells. CircMAP3K11, circCDK8, circCDR1as, circ_0062491, and circ_0095812 are associated with pathological periodontitis tissues. Furthermore, circRNAs are expressed in periodontal cells in a cell-specific manner. They can function as microRNA sponges and can form circRNA–miRNA–mRNA networks during osteogenic differentiation for periodontal-tissue (or dental pulp)-derived progenitor cells.

Background: Periodontitis is a chronic inflammatory disease involving an interplay between bacteria, inflammation, host response genes, and environmental factors. The manifestation of epigenetic factors during periodontitis pathogenesis and periodontal inflammation is still not well understood, with limited reviews on histone modification with periodontitis management. This scoping review aims to evaluate current evidence of global and specific DNA methylation and histone modification in periodontitis and discuss the gaps and implications for future research and clinical practice. Methods: A scoping literature search of three electronic databases was performed in SCOPUS, MEDLINE (PubMed) and EMBASE. As epigenetics in periodontitis is an emerging research field, a scoping review was conducted to identify the extent of studies available and describe the overall context and applicability of these results. Results: Overall, 30 studies were evaluated, and the findings confirmed that epigenetic changes in periodontitis comprise specific modifications to DNA methylation patterns and histone proteins modification, which can either dampen or promote the inflammatory response to bacterial challenge. Conclusions: The plasticity of epigenetic modifications has implications for the future development of targeted epi-drugs and diagnostic tools in periodontitis. Such advances could be invaluable for the early detection and monitoring of susceptible individuals.

This paper investigates robust feature screening for ultra-high dimensional data in the presence of outliers and heterogeneity. Considering the susceptibility of likelihood methods to outliers, we propose a Sparse Robust Weighted Expectile Regression (SRoWER) method that combines the L2E criterion with expectile regression. By utilizing the IHT algorithm, our method effectively incorporates correlations of covariates and enables joint feature screening. The proposed approach demonstrates robustness against heavy-tailed errors and outliers in data. Simulation studies and a real data analysis are provided to demonstrate the superior performance of the SRoWER method when dealing with outlier-contaminated explanatory variables and/or heavy-tailed error distributions.

IntroductionThe development and migration of T cells in the thymus and peripheral tissues are crucial for maintaining adaptive immunity in mammals. However, the regulatory mechanisms underlying T cell development and thymocyte identity formation in pigs remain largely underexplored.MethodHere, by integrating bulk and single-cell RNA-sequencing data, we investigated regulatory signatures of porcine thymus and lymph node T cells.ResultsThe comparison of T cell subpopulations derived from porcine thymus and lymph nodes revealed that their transcriptomic differences were influenced more by tissue origin than by T cell phenotypes, and that lymph node cells exhibited greater transcriptional diversity than thymocytes. Through weighted gene co-expression network analysis (WGCNA), we identified the key modules and candidate hub genes regulating the heterogeneity of T cell subpopulations. Further, we integrated the porcine thymocyte dataset with peripheral blood mononuclear cell (PBMC) dataset to systematically compare transcriptomic differences between T cell types from different tissues. Based on single-cell datasets, we further identified the key transcription factors (TFs) responsible for maintaining porcine thymocyte identity and unveiled that these TFs coordinately regulated the entire T cell development process. Finally, we performed GWAS of cell type-specific differentially expressed genes (DEGs) and 30 complex traits, and found that the DEGs in thymus-related and peripheral blood-related cell types, especially CD4_SP cluster and CD8-related cluster, were significantly associated with pig productive and reproductive traits.DiscussionOur findings provide an insight into T cell development and lay a foundation for further exploring the porcine immune system and genetic mechanisms underlying complex traits in pigs.