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Background We hypothesized that adipose-derived mesenchymal stem cells (ADMSCs) may ameliorate sepsis-induced acute lung injury (ALI) and change microorganism populations in the gut microbiota, such as that of Firmicutes and Bacteroidetes. Methods A total of 60 male adult Sprague-Dawley (SD) rats were separated into three groups: the sham control (SC) group, the sepsis induced by cecal ligation and puncture (CLP) group, and the ADMSC treatment (CLP-ADMSCs) group, in which rats underwent the CLP procedure and then received 1 × 10 6 ADMSCs. Rats were sacrificed 24 h after the SC or CLP procedures. To study the role of ADMSCs during ALI caused by sepsis and examine the impact of ADMSCs on the gut microbiome composition, rat lungs were histologically evaluated using hematoxylin and eosin (H&E) staining, serum levels of pro-inflammatory factors were detected using enzyme-linked immunosorbent assay (ELISA), and fecal samples were collected and analyzed using 16S rDNA sequencing. Results The serum levels of inflammatory cytokines, tumor necrosis factor (TNF)-α and interleukin (IL)-6, were significantly increased in rats after the CLP procedure, but were significantly decreased in rats treated with ADMSCs. Histological evaluation of the rat lungs yielded results consistent with the changes in IL-6 levels among all groups. Treatment with ADMSCs significantly increased the diversity of the gut microbiota in rats with sepsis. The principal coordinates analysis (PCoA) results showed that there was a significant difference between the gut microbiota of the CLP-ADMSCs group and that of the CLP group. In rats with sepsis, the proportion of Escherichia – Shigella ( P  = 0.01) related to lipopolysaccharide production increased, and the proportion of Akkermansia ( P  = 0.02) related to the regulation of intestinal mucosal thickness and the maintenance of intestinal barrier function decreased. These changes in the gut microbiota break the energy balance, aggravate inflammatory reactions, reduce intestinal barrier functions, and promote the translocation of intestinal bacteria. Intervention with ADMSCs increased the proportion of beneficial bacteria, reduced the proportion of harmful bacteria, and normalized the gut microbiota. Conclusions Therapeutically administered ADMSCs ameliorate CLP-induced ALI and improves gut microbiota, which provides a potential therapeutic mechanism for ADMSCs in the treatment of sepsis.

Understanding learners’ emotions can help optimize instruction sand further conduct effective learning interventions. Most existing studies on student emotion recognition are based on multiple manifestations of external behavior, which do not fully use physiological signals. In this context, on the one hand, a learning emotion EEG dataset (LE-EEG) is constructed, which captures physiological signals reflecting the emotions of boredom, neutrality, and engagement during learning; on the other hand, an EEG emotion classification network based on attention fusion (ECN-AF) is proposed. To be specific, on the basis of key frequency bands and channels selection, multi-channel band features are first extracted (using a multi-channel backbone network) and then fused (using attention units). In order to verify the performance, the proposed model is tested on an open-access dataset SEED (N = 15) and the self-collected dataset LE-EEG (N = 45), respectively. The experimental results using five-fold cross validation show the following: (i) on the SEED dataset, the highest accuracy of 96.45% is achieved by the proposed model, demonstrating a slight increase of 1.37% compared to the baseline models; and (ii) on the LE-EEG dataset, the highest accuracy of 95.87% is achieved, demonstrating a 21.49% increase compared to the baseline models.

Because of its ability to objectively reflect people’s emotional states, electroencephalogram (EEG) has been attracting increasing research attention for emotion classification. The classification method based on spatial-domain analysis is one of the research hotspots. However, most previous studies ignored the complementarity of information between different frequency bands, and the information in a single frequency band is not fully mined, which increases the computational time and the difficulty of improving classification accuracy. To address the above problems, this study proposes an emotion classification method based on dynamic simplifying graph convolutional (SGC) networks and a style recalibration module (SRM) for channels, termed SGC-SRM, with multi-band EEG data as input. Specifically, first, the graph structure is constructed using the differential entropy characteristics of each sub-band and the internal relationship between different channels is dynamically learned through SGC networks. Second, a convolution layer based on the SRM is introduced to recalibrate channel features to extract more emotion-related features. Third, the extracted sub-band features are fused at the feature level and classified. In addition, to reduce the redundant information between EEG channels and the computational time, (1) we adopt only 12 channels that are suitable for emotion classification to optimize the recognition algorithm, which can save approximately 90.5% of the time cost compared with using all channels; (2) we adopt information in the θ, α, β, and γ bands, consequently saving 23.3% of the time consumed compared with that in the full bands while maintaining almost the same level of classification accuracy. Finally, a subject-independent experiment is conducted on the public SEED dataset using the leave-one-subject-out cross-validation strategy. According to experimental results, SGC-SRM improves classification accuracy by 5.51–15.43% compared with existing methods.

Corona Virus Disease (COVID-19) has become a global public health crisis, and the Omicron variant has rapidly taken over as soon as it was detected Serum circulating metabolites can provide extensive insights into the pathogenesis and diagnosis of many diseases. We included 336 omicron variant cases (OC), 216 recovered cases (RC), and 380 healthy controls (HC) for untargeted metabolomics analysis and analyzed their serum metabolic profiles by liquid chromatography-tandem mass spectrometry. Principal component analysis, orthogonal partial least squares discriminant analysis, t-test analysis and false discovery rate were used to characterize the serum metabolites of OC and RC. In addition, a noninvasive diagnostic model for OC was developed using Receiver operating characteristic analysis. Finally, a correlation analysis was performed using data from our published articles. The results showed that compared with HC, five metabolites, including DL-stachydrine, D-(+)-pipecolinic acid, furazolidone, L-arginine and 5α-dihydrotestosterone glucuronide were significantly elevated and one metabolite, prenylcysteine, was significantly decreased in the serum of OC, and that the increase in L-arginine and the decrease in prenylcysteine led to impaired urea cycling and a high risk of developing atherosclerosis, respectively. These metabolites were not fully restored to healthy human levels in recovered cases. In addition, we constructed a noninvasive diagnostic model for distinguishing Omicron variant patients from healthy individuals based on the six differential metabolites, and achieved high diagnostic efficacy in both the discovery and validation cohorts. Finally, the results of the correlation analysis showed a strong correlation between the alterations in the oropharyngeal microbiome and serum metabolome and the clinical indicators in the omicron variant cases. This study was the first to characterize serum metabolites in OC and RC based on a large clinical cohort, and successfully constructed and validated a noninvasive diagnostic model for Omicron variant patients.

Facial expression recognition (FER) has received increasing attention. However, multiple factors (e.g., uneven illumination, facial deflection, occlusion, and subjectivity of annotations in image datasets) probably reduce the performance of traditional FER methods. Thus, we propose a novel Hybrid Domain Consistency Network (HDCNet) based on a feature constraint method that combines both spatial domain consistency and channel domain consistency. Specifically, first, the proposed HDCNet mines the potential attention consistency feature expression (different from manual features, e.g., HOG and SIFT) as effective supervision information by comparing the original sample image with the augmented facial expression image. Second, HDCNet extracts facial expression-related features in the spatial and channel domains, and then it constrains the consistent expression of features through the mixed domain consistency loss function. In addition, the loss function based on the attention-consistency constraints does not require additional labels. Third, the network weights are learned to optimize the classification network through the loss function of the mixed domain consistency constraints. Finally, experiments conducted on the public RAF-DB and AffectNet benchmark datasets verify that the proposed HDCNet improved classification accuracy by 0.3–3.84% compared to the existing methods.

Oral microbiota is related to the severity and recovery of SARS-CoV-2 infection. This study aims to predict clinical classification after SARS-CoV-2 infection using oral microbiota before infection. Herein, we collected tongue-coating samples before infection and then monitored clinical information after infection. Oral microbiota was detected by MiSeq sequencing. We randomly assigned participants from Zhengzhou into discovery and validation cohorts to develop a predictive model and conducted cross-region verification using Xinyang and Hangzhou cohorts. Sixteen asymptomatic patients (AP), 257 mild patients (MP), 106 common patients (CP), and 7 severe patients (SP) were enrolled. Oral microbiota diversity was decreased in CP versus MP. At genus level, 11 microorganisms, including Rothia and Gemella , were increased, while 5 microorganisms, including Selenomonas and Lachnoanaerobaculum , were decreased in CP versus MP. Moreover, the classifier based on 15 optimal markers showed high prediction efficiency in discovery cohort (area under the curve [AUC]: 98.35%), validation cohort (AUC: 81.91%), Xinyang cohort (AUC: 74.34%), and Hangzhou cohort (AUC: 94.44%). Interestingly, a higher abundance of Selenomonas was associated with milder clinical symptoms. In conclusion, our study established a good model to predict clinical classification after SARS-CoV-2 infection using oral microbiota before infection, providing a novel strategy for precise prevention and treatment.

Oral fungal microbiota plays an important role in many diseases, however, the role of oral fungal microorganisms in the development of patients infected with Omicron has not been reported. A total of 963 tongue coating samples were prospectively included in this study, and finally 336 samples from patients infected Omicron variant (PIOV), 234 samples from recovered patients infected with Omicron (RP), 71 samples from patients infected original strain of Severe Acute Respiratory Syndrome Coronavirus 2 ( SARS-CoV-2 ) (PIOS), 299 samples from healthy controls (HC) completed internal transcribed spacer (ITS) sequencing after screening and quality control. By comparing the difference of oral fungal microorganisms between PIOV, RP and HC, we found that with the recovery of PIOV, their oral fungal microecological diversity increased gradually. Besides, at the species level, there were 24 oral fungal species such as Zanclospora_jonesii increased gradually, while there were 24 oral fungal species such as Saccharomyces_cerevisiae decreased gradually. In addition, by comparing PIOS and PIOV, we found that the alpha diversity of oral fungal microorganisms in PIOV was significantly lower than PIOS and the main species of the two groups were different. At the same time, we randomly divided PIOV and HC into training and validation set. Based on random forest model and five-fold cross-validation, we identified three optimal microbial markers of oral fungi and constructed a diagnostic model of PIOV. The area under the curve (AUC) value of PIOV group was 99.01% in discovery phase and 97.84% in verification phase. In summary, based on large-scale samples, this study is the first to elucidate the characteristics of oral fungal microbiota changes during PIOV recovery and establish a supplemental non-invasive diagnostic model for PIOV based on the oral fungal microbiome.

Cancer significantly contributes to the unfavorable prognosis of coronavirus disease 2019 (COVID-19) patients. The efficacy and safety of azvudine and nirmatrelvir/ritonavir (Paxlovid) in cancer patients with COVID-19 remain uncertain. Therefore, we designed a comprehensive retrospective study encompassing clinical data of 32,864 hospitalized COVID-19 patients, 691 of whom were cancer patients treated with azvudine and 200 were cancer patients treated with Paxlovid. After 2:1 propensity score matching, 397 patients in the azvudine group and 199 patients in the Paxlovid group were enrolled. Cox regression analysis revealed the risk of all-cause death (HR: 1.84, 95% CI: 1.059–3.182, P  = 0.030) and composite disease progression (HR: 1.70, 95% CI: 1.043–2.757, P  = 0.033) were greater in the Paxlovid group than in the azvudine group. Two sensitivity analyses confirmed the robustness of our findings. The safety analysis of adverse events revealed no statistically significant differences between the two groups. In conclusion, we carried out the first analysis to compare the efficacy and safety of azvudine and Paxlovid in cancer patients with COVID-19 and demonstrated that azvudine significantly reduced the risk of all-cause death and composite disease progression among cancer patients with COVID-19 compared with Paxlovid.

The oral bacteriome, gut bacteriome, and gut mycobiome are associated with coronavirus disease 2019 (COVID‐19). However, the oral fungal microbiota in COVID‐19 remains unclear. This article aims to characterize the oral mycobiome in COVID‐19 and recovered patients. Tongue coating specimens of 71 COVID‐19 patients, 36 suspected cases (SCs), 22 recovered COVID‐19 patients, 36 SCs who recovered, and 132 controls from Henan are collected and analyzed using internal transcribed spacer sequencing. The richness of oral fungi is increased in COVID‐19 versus controls, and beta diversity analysis reveals separate fungal communities for COVID‐19 and control. The ratio of Ascomycota and Basidiomycota is higher in COVID‐19, and the opportunistic pathogens, including the genera Candida, Saccharomyces, and Simplicillium, are increased in COVID‐19. The classifier based on two fungal biomarkers is constructed and can distinguish COVID‐19 patients from controls in the training, testing, and independent cohorts. Importantly, the classifier successfully diagnoses SCs with positive specific severe acute respiratory syndrome coronavirus 2 immunoglobulin G antibodies as COVID‐19 patients. The correlation between distinct fungi and bacteria in COVID‐19 and control groups is depicted. These data suggest that the oral mycobiome may play a role in COVID‐19. This research characterizes oral mycobiome in coronavirus disease 2019 (COVID‐19) patients and recovered COVID‐19 patients, constructs and validates the diagnostic model for COVID‐19, successfully diagnoses suspected cases with positive immunoglobulin G antibody as confirmed patients, and depicts the correlations between the oral microbiome and mycobiome.

Coptidis Rhizoma binds to the membrane receptors on hPDLSC/CMC, and the active ingredient Berberine (BER) that can be extracted from it may promote the proliferation and osteogenesis of periodontal ligament stem cells (hPDLSC). The membrane receptor that binds with BER on the cell surface of hPDLSC, the mechanism of direct interaction between BER and hPDLSC, and the related signal pathway are not yet clear. In this research, EGFR was screened as the affinity membrane receptor between BER and hPDLSC, through retention on CMC, competition with BER and by using a molecular docking simulation score. At the same time, the MAPK PCR Array was selected to screen the target genes that changed when hPDLSC was simulated by BER. In conclusion, BER may bind to EGFR on the cell membrane of hPDLSC so the intracellular ERK signalling pathways activate, and nuclear-related genes of FOS change, resulting in the effect of osteogenesis on PDLSC.