The feasibility and accuracy of the method for selecting the optimal size of double-lumen tube in thoracic surgery: a prospective, randomized controlled trial.

Double-lumen tubes (DLTs) are commonly used for one-lung ventilation (OLV) in thoracic surgery and the selection of an optimal size of DLTs is still a humongous task. The purpose of this study was to assess the feasibility and accuracy of the method for selecting an optimal size of DLTs in thoracic surgery. Sixty adult patients requiring a left side double-lumen tube (LDLT) for elective thoracoscopic surgery were included in this study. All patients were randomly allocated to the following two groups: Cuffs Collapsed group (CC group, n = 30) and Cuffs Inflated group (CI group, n = 30). In the Cuffs Collapsed group, the outer diameter of LDLT (the outer diameter of the tracheal and bronchial cuffs when they were collapsed as the outer diameter of the LDLT) matched with the inner diameter of the trachea and bronchus measured by the anesthesiologist on the chest CT slice; In the Cuffs Inflated group, the outer diameter of LDLT (the outer diameter of the tracheal and bronchial cuffs when they were inflated as the outer diameter of the LDLT) matched with the inner diameter of the trachea and bronchus measured by the anesthesiologist on the chest CT slice. The primary outcomes were the incidences of airway complications postoperative such as hoarseness and sore throat. The time of intubation and alignment, the incidences of LDLT displacement and adjustment, the peak airway pressure, the plateau airway pressure and the end-tidal carbon dioxide were also recorded. The incidences of airway complications postoperative such as sore throat and hoarseness were lower in the CI group than the CC group (P < 0.05), the intubation times was shorter in the CI group than the CC group (P < 0.05), while the peak airway pressure, the plateau airway pressure and the end-tidal carbon dioxide during two-lung ventilation and one-lung ventilation were no significant difference between two groups (P > 0.05). The method which matched the inner diameter of the trachea and bronchus measured on chest CT slice with the outer diameter of the tracheal and bronchial cuffs when they were inflated to select an appropriate size of LDLT can reduce the incidence of airway complications.Trials registration: Clinical Trials: gov. no. NCT05739318. Registered at https://classic.clinicaltrials.gov 22/02/2023.

YBX1 Promotes Esophageal Squamous Cell Carcinoma Progression via m5C­Dependent SMOX mRNA Stabilization.

The modification and recognition of 5-methylcytosine (m5C) are involved in the initiation and progression of various tumor types. However, the precise role and potential mechanism of Y-box-binding protein 1 (YBX1) in esophageal squamous cell carcinoma (ESCC) remains unclear. Here, it is found that YBX1 is frequently upregulated in ESCC compared with matched nontumor tissues. Gain- and loss-of-function assays show that YBX1 promoted the proliferation and metastasis of ESCC cells both in vitro and in vivo. Functional studies revealed that NOP2/Sun RNA methyltransferase family member 2 (NSUN2) is a critical RNA methyltransferase that facilitates YBX1-mediated ESCC progression. Mechanistically, integrated analysis based on RNA immunoprecipitation sequencing (RIP-seq) and m5C methylated RNA immunoprecipitation and sequencing (MeRIP-seq) assays identified spermine oxidase (SMOX) as a target gene containing an m5C site in its coding sequence (CDS) region, which coincided well with the binding site of YBX1. Overexpression of SMOX-WT but not SMOX-Mut partially restored the proliferation and invasion ability of ESCC cells curbed by YBX1 knockdown. Moreover, YBX1 activated the mTORC1 signaling pathway by stabilizing SMOX mRNA. The study reveals that YBX1 promotes ESCC development by stabilizing SMOX mRNA in an m5C-dependent manner, thus providing a valuable therapeutic target for ESCC.

Dynamic changes of Bacterial Microbiomes in Oropharynx during Infection and Recovery of COVID-19 Omicron Variant.

Oropharyngeal microbiomes play a significant role in the susceptibility and severity of COVID-19, yet the role of these microbiomes play for the development of COVID-19 Omicron variant have not been reported. A total of 791 pharyngeal swab samples were prospectively included in this study, including 297 confirmed cases of Omicron variant (CCO), 222 confirmed case of Omicron who recovered (CCOR), 73 confirmed cases of original strain (CCOS) and 199 healthy controls (HC). All samples completed MiSeq sequencing. The results showed that compared with HC, conditional pathogens increased in CCO, while acid-producing bacteria decreased. Based on six optimal oropharyngeal operational taxonomy units (OTUs), we constructed a marker microbial classifier to distinguish between patients with Omicron variant and healthy people, and achieved high diagnostic efficiency in both the discovery queue and the verification queue. At same time, we introduced a group of cross-age infection verification cohort and Omicron variant subtype XBB.1.5 branch, which can be accurately distinguished by this diagnostic model. We also analyzed the characteristics of oropharyngeal microbiomes in two subgroups of Omicron disease group-severity of infection and vaccination times, and found that the change of oropharyngeal microbiomes may affect the severity of the disease and the efficacy of the vaccine. In addition, we found that some genera with significant differences gradually increased or decreased with the recovery of Omicron variant infection. The results of Spearman analysis showed that 27 oropharyngeal OTUs were closely related to 6 clinical indexes in CCO and HC. Finally, we found that the Omicron variant had different characterization of oropharyngeal microbiomes from the original strain. Our research characterizes oropharyngeal microbiomes of Omicron variant cases and rehabilitation cases, successfully constructed and verified the non-invasive diagnostic model of Omicron variant, described the correlation between microbial OTUs and clinical indexes. It was found that the infection of Omicron variant and the infection of original strain have different characteristics of oropharyngeal microbiomes.

Low-intensity transcranial ultrasound stimulation modulates neural activities in mice under propofol anaesthesia.

BACKGROUND: Previous studies have reported that transcranial focused ultrasound stimulation can significantly decrease the time to emergence from intraperitoneal ketamine-xylazine anaesthesia in rats. However, how transcranial focused ultrasound stimulation modulates neural activity in anaesthetized rats is unclear. METHODS: In this study, to answer this question, we used low-intensity transcranial ultrasound stimulation (TUS) to stimulate the brain tissue of propofol-anaesthetized mice, recorded local field potentials (LFPs) in the mouse motor cortex and electromyography (EMG) signals from the mouse neck, and analysed the emergence and recovery time, mean absolute power, relative power and entropy of local field potentials. RESULTS: We found that the time to emergence from anaesthesia in the TUS group (20.3 ± 1.7 min) was significantly less than that in the Sham group (32 ± 2.6 min). We also found that compared with the Sham group, 20 min after low-intensity TUS during recovery from anaesthesia, (1) the absolute power of local field potentials in mice was significantly reduced in the [1-4 Hz] and [13-30 Hz] frequency bands and significantly increased in the [55-100 Hz], [100-140 Hz] and [140-200 Hz] frequency bands; (2) the relative power of local field potentials in mice was enhanced at [30-45 Hz], [100-140 Hz] and [140-200 Hz] frequency bands; (3) the entropy of local field potentials ([1-200 Hz]) was increased. CONCLUSION: These results demonstrate that low-intensity TUS can effectively modulate neural activities in both awake and anaesthetized mice and has a positive effect on recovery from propofol anaesthesia in mice.

NSUN2 stimulates tumor progression via enhancing TIAM2 mRNA stability in pancreatic cancer.

NSUN2 is a nuclear RNA methyltransferase which catalyzes 5-methylcytosine (m5C), a posttranscriptional RNA modification. Aberrant m5C modification has been implicated in the development of multiple malignancies. However, its function in pancreatic cancer (PC) needs to be elucidated. Herein, we determined that NSUN2 was overexpressed in PC tissues and related to aggressive clinical features. Silence of NSUN2 by lentivirus weakened the capability of proliferation, migration and invasion of PC cells in vitro and inhibited the growth and metastasis of xenograft tumors in vivo. Contrarily, overexpression of NSUN2 stimulated PC growth and metastasis. Mechanistically, m5C-sequencing (m5C-seq) and RNA-sequencing (RNA-seq) were carried out to identify downstream targets of NSUN2 and results showed that loss of NSUN2 led to decreased m5C modification level concomitant with reduced TIAM2 mRNA expression. Further validation experiments proved that NSUN2 silence accelerated the decay of TIAM2 mRNA in a YBX1-dependent manner. Additionally, NSUN2 exerted its oncogenic function partially through enhancing TIAM2 transcription. More importantly, disruption of the NSUN2/TIAM2 axis repressed the malignant phenotype of PC cells through blocking epithelial-mesenchymal transition (EMT). Collectively, our study highlighted the critical function of NSUN2 in PC and provided novel mechanistic insights into NSUN2/TIAM2 axis as promising therapeutic targets against PC.

Ubiquitin-proteasome system-based signature to predict the prognosis and drug sensitivity of hepatocellular carcinoma.

Background: Ubiquitin-proteasome system (UPS) is implicated in cancer occurrence and progression. Targeting UPS is emerging as a promising therapeutic target for cancer treatment. Nevertheless, the clinical significance of UPS in hepatocellular carcinoma (HCC) has not been entirely elucidated. Methods: Differentially expressed UPS genes (DEUPS) were screened from LIHC-TCGA datasets. The least absolute shrinkage and selection operator (LASSO) and stepwise multivariate regression analysis were conducted to establish a UPS-based prognostic risk model. The robustness of the risk model was further validated in HCCDB18, GSE14520, and GSE76427 cohorts. Subsequently, immune features, clinicopathologic characteristics, enrichment pathways, and anti-tumor drug sensitivity of the model were further evaluated. Moreover, a nomogram was established to improve the predictive ability of the risk model. Results: Seven UPS-based signatures (ATG10, FBXL7, IPP, MEX3A, SOCS2, TRIM54, and PSMD9) were developed for the prognostic risk model. Individuals with HCC with high-risk scores presented a more dismal prognosis than those with low-risk scores. Moreover, larger tumor size, advanced TNM stage, and tumor grade were observed in the high-risk group. Additionally, cell cycle, ubiquitin-mediated proteolysis, and DNA repair pathways were intimately linked to the risk score. In addition, obvious immune cell infiltration and sensitive drug response were identified in low-risk patients. Furthermore, both nomogram and risk score showed a significant prognosis-predictive ability. Conclusion: Overall, we established a novel UPS-based prognostic risk model in HCC. Our results will facilitate a deep understanding of the functional role of UPS-based signature in HCC and provide a reliable prediction of clinical outcomes and anti-tumor drug responses for patients with HCC.

Development of a model by LASSO to predict hospital length of stay (LOS) in patients with the SARS-Cov-2 omicron variant.

The length of stay (LOS) in hospital varied considerably in different patients with COVID-19 caused by SARS-CoV-2 Omicron variant. The study aimed to explore the clinical characteristics of Omicron patients, identify prognostic factors, and develop a prognostic model to predict the LOS of Omicron patients. This was a single center retrospective study in a secondary medical institution in China. A total of 384 Omicron patients in China were enrolled. According to the analyzed data, we employed LASSO to select the primitive predictors. The predictive model was constructed by fitting a linear regression model using the predictors selected by LASSO. Bootstrap validation was used to test performance and eventually we obtained the actual model. Among these patients, 222 (57.8%) were female, the median age of patients was 18 years and 349 (90.9%) completed two doses of vaccination. Patients on admission diagnosed as mild were 363 (94.5%). Five variables were selected by LASSO and a linear model, and those with P < 0.05 were integrated into the analysis. It shows that if Omicron patients receive immunotherapy or heparin, the LOS increases by 36% or 16.1%. If Omicron patients developed rhinorrhea or occur familial cluster, the LOS increased by 10.4% or 12.3%, respectively. Moreover, if Omicron patients' APTT increased by one unit, the LOS increased by 0.38%. Five variables were identified, including immunotherapy, heparin, familial cluster, rhinorrhea, and APTT. A simple model was developed and evaluated to predict the LOS of Omicron patients. The formula is as follows: Predictive LOS = exp(1*2.66263 + 0.30778*Immunotherapy + 0.1158*Familiar cluster + 0.1496*Heparin + 0.0989*Rhinorrhea + 0.0036*APTT).

Alterations in the human oral microbiota in systemic lupus erythematosus.

BACKGROUND: Alterations in oral microbiota in patients with systemic lupus erythematosus (SLE) is less evaluated. The aim of this study was to compare the characteristics of the oral microbiome in SLE patients and healthy controls, and construct an SLE classifier based on the oral microbiota. METHODS: We sequenced tongue-coating samples of individuals in treatment-naïve SLE (n = 182) and matched healthy controls (n = 280). We characterized the oral microbiome and constructed a microbial classifier in the derivation cohort and validated the results in the validation cohorts. Furthermore, the oral microbiome of posttreatment SLE (n = 73) was characterized. RESULTS: The oral microbial diversity of SLE was increased, and the microbial community was different between SLE and healthy controls. The genera Prevotella and Veillonella were enriched, while Streptococcus and Porphyromonas were reduced in SLE. In addition, an increase was noted in 27 predicted microbial functions, while a decrease was noted in 34 other functions. Thirty-nine operational taxonomy units (OTUs) were identified to be related with seven clinical indicators. Two OTUs were identified to construct a classifier, which yielded area under the curve values of 0.9166 (95% CI 0.8848-0.9483, p < 0.0001), 0.8422 (95% CI 0.7687-0.9157, p < 0.0001), and 0.8406 (95% CI 0.7677-0.9135, p < 0.0001) in the derivation, validation, and cross-regional validation groups, respectively. Moreover, as disease activity increased, Abiotrophia and Lactobacillales increased, while Phyllobacterium and unclassified Micrococcusaceae decreased. Finally, nine OTUs were selected to construct a classifier distinguishing posttreatment SLE patients from healthy controls, which achieved a diagnostic efficacy of 0.9942 (95% CI 0.9884-1, p < 0.0001). CONCLUSIONS: Our study comprehensively characterizes the oral microbiome of SLE and shows the potential of the oral microbiota as a non-invasive diagnostic biomarker in SLE.

The effect of the intratumoral microbiome on tumor occurrence, progression, prognosis and treatment.

In the past few decades, great progress has been achieved in the understanding of microbiome-cancer interactions. However, most of the studies have focused on the gut microbiome, ignoring how other microbiomes interact with tumors. Emerging evidence suggests that in many types of cancers, such as lung cancer, pancreatic cancer, and colorectal cancer, the intratumoral microbiome plays a significant role. In addition, accumulating evidence suggests that intratumoral microbes have multiple effects on the biological behavior of tumors, for example, regulating tumor initiation and progression and altering the tumor response to chemotherapy and immunotherapy. However, to fully understand the role of the intratumoral microbiome in cancer, further investigation of the effects and mechanisms is still needed. This review discusses the role of intratumoral bacteria in tumorigenesis and tumor progression, recurrence and metastasis, as well as their effect on cancer prognosis and treatment outcome, and summarizes the relevant mechanisms.

Metagenomic next-generation sequencing assists in the diagnosis of Cryptococcus pneumonia: Case series and literature review.

Background: Pulmonary cryptococcosis (PC) was once thought to occur only in patients with immune deficiencies, such as tested positive for the Human Immunodeficiency Virus (HIV). However, in recent years, it has been discovered that more than half of the patients with PC in our nation are individuals with normal immune function. As more and more PC cases are recorded, our diagnosis and treatment approaches, as well as our understanding of PC, are gradually improving. In reality, most PC patients still have a high incidence of misdiagnosis on their initial visit. It is primarily linked to the diverse clinical manifestations, atypical imaging findings, and inaccurate diagnostic approaches. Methods: The research was conducted from 2019 to 2020. We performed traditional microbiological testing and mNGS on sample from patients with fever of Pulmonary nodules or lung infections. Furthermore, we collected patients' baseline information, clinical features, laboratory and imaging examination results, diagnosis, treatment and outcome. In the end, we confirmed three cases of PC using biopsy and mNGS. Conclusion: Our data demonstrates that mNGS can be utilized as an auxiliary method for PC diagnosis. Early mNGS aids in the identification of pathogens, enabling early diagnosis and treatment, as well as a reduction in the rate of misdiagnosis and illness progression.

Immune response induced by novel coronavirus infection.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus (SARS-CoV)-2 has been prominent around the world since it was first discovered, affecting more than 100 million people. Although the symptoms of most infected patients are not serious, there is still a considerable proportion of patients who need hospitalization and even develop fatal symptoms such as cytokine storms, acute respiratory distress syndrome and so on. Cytokine storm is usually described as a collection of clinical manifestations caused by overactivation of the immune system, which plays an important role in tissue injury and multiorgan failure. The immune system of healthy individuals is composed of two interrelated parts, the innate immune system and the adaptive immune system. Innate immunity is the body's first line of defense against viruses; it can quickly perceive viruses through pattern recognition receptors and activate related inflammatory pathways to clear pathogens. The adaptive immune system is activated by specific antigens and is mainly composed of CD4+ T cells, CD8+ T cells and B cells, which play different roles in viral infection. Here, we discuss the immune response after SARS-CoV-2 infection. In-depth study of the recognition of and response of innate immunity and adaptive immunity to SARS-CoV-2 will help to prevent the development of critical cases and aid the exploration of more targeted treatments.

Comparison of clinical characteristics between SARS-CoV-2 Omicron variant and Delta variant infections in China.

Background: The continued 'evolution' of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the emergence of the Omicron variant after the Delta variant, resulting in a significant increase in the number of people with COVID-19. This increase in the number of cases continues to have a significant impact on lives. Therefore, a more detailed understanding of the clinical characteristics of Omicron infection is essential. Methods: Using medical charts, we extracted clinical information for 384 patients infected with the Omicron variant in Anyang City, Henan Province, China. Epidemiology and clinical characteristics were compared with a cohort of people infected with the Delta variant in Zhengzhou in 2021. Findings: Common initial symptoms at onset of illness were cough [240 (63%)], expectoration [112 (29%)], fever [96 (25%)], nasal congestion [96 (25%)] and myalgia or fatigue [30 (6%)]. In patients with the Omicron variant, levels of total cholesterol, low-density lipoprotein and creatinine increased in 52 (14%), 36 (9%) and 58 (15%) patients, respectively, compared with patients with the Delta variant [one (1%), one (1%) and two (2%)]. Levels of triglyceride and high-density lipoprotein also increased. In patients with the Omicron variant, the levels of specific gravity and the erythrocyte sedimentation rate were increased in 115 (30%) and 81 (21%) patients, and serum levels of complement 3 decreased in 93 (41%). Results: Compared with patients infected with Delta, no major differences in initial clinical symptoms were identified in patients infected with Omicron. However, dyslipidemia and kidney injury were much more severe in patients with the Omicron variant, and the erythrocyte sedimentation rate was increased. Due to decreased levels of complement 3, the immunity of patients with the Omicron variant was weak.

Characterization of oral and gut microbiome and plasma metabolomics in COVID-19 patients after 1-year follow-up.

BACKGROUND: Due to the outbreak and rapid spread of coronavirus disease 2019 (COVID-19), more than 160 million patients have become convalescents worldwide to date. Significant alterations have occurred in the gut and oral microbiome and metabonomics of patients with COVID-19. However, it is unknown whether their characteristics return to normal after the 1-year recovery. METHODS: We recruited 35 confirmed patients to provide specimens at discharge and one year later, as well as 160 healthy controls. A total of 497 samples were prospectively collected, including 219 tongue-coating, 129 stool and 149 plasma samples. Tongue-coating and stool samples were subjected to 16S rRNA sequencing, and plasma samples were subjected to untargeted metabolomics testing. RESULTS: The oral and gut microbiome and metabolomics characteristics of the 1-year convalescents were restored to a large extent but did not completely return to normal. In the recovery process, the microbial diversity gradually increased. Butyric acid-producing microbes and Bifidobacterium gradually increased, whereas lipopolysaccharide-producing microbes gradually decreased. In addition, sphingosine-1-phosphate, which is closely related to the inflammatory factor storm of COVID-19, increased significantly during the recovery process. Moreover, the predictive models established based on the microbiome and metabolites of patients at the time of discharge reached high efficacy in predicting their neutralizing antibody levels one year later. CONCLUSIONS: This study is the first to characterize the oral and gut microbiome and metabonomics in 1-year convalescents of COVID-19. The key microbiome and metabolites in the process of recovery were identified, and provided new treatment ideas for accelerating recovery. And the predictive models based on the microbiome and metabolomics afford new insights for predicting the recovery situation which benefited affected individuals and healthcare.

Characterization of the Human Oropharyngeal Microbiomes in SARS-CoV-2 Infection and Recovery Patients.

Respiratory tract microbiome is closely related to respiratory tract infections, while characterization of oropharyngeal microbiome in recovered coronavirus disease 2019 (COVID-19) patients is not studied. Herein, oropharyngeal swabs are collected from confirmed cases (CCs) with COVID-19 (73 subjects), suspected cases (SCs) (36), confirmed cases who recovered (21), suspected cases who recovered (36), and healthy controls (Hs) (140) and then completed MiSeq sequencing. Oropharyngeal microbial α-diversity is markedly reduced in CCs versus Hs. Opportunistic pathogens are increased, while butyrate-producing genera are decreased in CCs versus Hs. The classifier based on eight optimal microbial markers is constructed through a random forest model and reached great diagnostic efficacy in both discovery and validation cohorts. Notably, the classifier successfully diagnosed SCs with positive IgG antibody as CCs and is demonstrated from the perspective of the microbiome. Importantly, several genera with significant differences gradually increase and decrease along with recovery from COVID-19. Forty-four oropharyngeal operational taxonomy units (OTUs) are closely correlated with 11 clinical indicators of SARS-CoV-2 infection and Hs based on Spearman correlation analysis. Together, this research is the first to characterize oropharyngeal microbiota in recovered COVID-19 cases and suspected cases, to successfully construct and validate the diagnostic model for COVID-19 and to depict the correlations between microbial OTUs and clinical indicators.

m6A Regulator-Associated Modification Patterns and Immune Infiltration of the Tumor Microenvironment in Hepatocarcinoma.

Background: Immunotherapy elicits durable responses in many tumors. Nevertheless, the positive response to immunotherapy always depends on the dynamic interactions between the tumor cells and infiltrating lymphocytes in the tumor microenvironment (TME). Currently, the application of immunotherapy in hepatocellular carcinoma (HCC) has achieved limited success. The ectopic modification of N6-methyladenosine (m6A) is a common feature in multiple tumors. However, the relationship between m6A modification with HCC clinical features, prognosis, immune cell infiltration, and immunotherapy efficacy remains unclear. Materials and Methods: Here, we comprehensively evaluated m6A modification clusters based on 22 m6A regulators and systematically explored the relationship between m6A modification with tumor progression, prognosis, and immune cell infiltration characteristics. The m6Ascore was calculated by principal component analysis to quantify the m6A modifications of individual patients. Key regulators involved in immunoregulation in HCC were identified using immunohistochemistry and immunofluorescence. Results: Three distinct m6A modification clusters were identified. The m6A clusters were significantly associated with clinical features, prognosis, and immune cell infiltration. The three clusters were highly consistent with the three tumor immune phenotypes, i.e., immune-excluded, immune-inflamed, and immune-desert. Comprehensive bioinformatics analysis revealed that high m6Ascore was closely associated with tumor progression, poor prognosis, and immunotherapy non-response. m6A regulators were dysregulated in HCC tissues. Hence, they play a role as predictors of poor prognosis. Tissue microarray demonstrated that overexpressed YTHDF1 was associated with low CD3+ and CD8+ T cell infiltration in HCC. Conclusion: Our findings demonstrate that m6A modification patterns play a crucial role in the tumor immune microenvironment and the prognosis of HCC. High YTHDF1 expression is closely associated with low CD3+ and CD8+ T cell infiltration in HCC.

Alterations in the human oral and gut microbiomes and lipidomics in COVID-19.

OBJECTIVE: To characterise the oral microbiome, gut microbiome and serum lipid profiles in patients with active COVID-19 and recovered patients; evaluate the potential of the microbiome as a non-invasive biomarker for COVID-19; and explore correlations between the microbiome and lipid profile. DESIGN: We collected and sequenced 392 tongue-coating samples, 172 faecal samples and 155 serum samples from Central China and East China. We characterised microbiome and lipid molecules, constructed microbial classifiers in discovery cohort and verified their diagnostic potential in 74 confirmed patients (CPs) from East China and 37 suspected patients (SPs) with IgG positivity. RESULTS: Oral and faecal microbial diversity was significantly decreased in CPs versus healthy controls (HCs). Compared with HCs, butyric acid-producing bacteria were decreased and lipopolysaccharide-producing bacteria were increased in CPs in oral cavity. The classifiers based on 8 optimal oral microbial markers (7 faecal microbial markers) achieved good diagnostic efficiency in different cohorts. Importantly, diagnostic efficacy reached 87.24% in the cross-regional cohort. Moreover, the classifiers successfully diagnosed SPs with IgG antibody positivity as CPs, and diagnostic efficacy reached 92.11% (98.01% of faecal microbiome). Compared with CPs, 47 lipid molecules, including sphingomyelin (SM)(d40:4), SM(d38:5) and monoglyceride(33:5), were depleted, and 122 lipid molecules, including phosphatidylcholine(36:4p), phosphatidylethanolamine (PE)(16:0p/20:5) and diglyceride(20:1/18:2), were enriched in confirmed patients recovery. CONCLUSION: This study is the first to characterise the oral microbiome in COVID-19, and oral microbiomes and lipid alterations in recovered patients, to explore their correlations and to report the successful establishment and validation of a diagnostic model for COVID-19.

Gut Microbiome Distinguishes Patients With Epilepsy From Healthy Individuals.

Objective: The gut microecosystem is the largest microecosystem in the human body and has been proven to be linked to neurological diseases. The main objective of this study was to characterize the fecal microbiome, investigate the differences between epilepsy patients and healthy controls, and evaluate the potential efficacy of the fecal microbiome as a diagnostic tool for epilepsy. Design: We collected 74 fecal samples from epilepsy patients (Eps, n = 24) and healthy controls (HCs, n = 50) in the First Affiliated Hospital of Zhengzhou University and subjected the samples to 16S rRNA MiSeq sequencing and analysis. We set up a train set and a test set, identified the optimal microbial markers for epilepsy after characterizing the gut microbiome in the former and built a diagnostic model, then validated it in the validation group. Results: There were significant differences in microbial communities between the two groups. The α-diversity of the HCs was higher than that of the epilepsy group, but the Venn diagram showed that there were more unique operational taxonomic unit (OTU) in the epilepsy group. At the phylum level, Proteobacteria and Actinobacteriota increased significantly in Eps, while the relative abundance of Bacteroidota increased in HCs. Compared with HCs, Eps were enriched in 23 genera, including Faecalibacterium, Escherichia-Shigella, Subdoligranulum and Enterobacteriaceae-unclassified. In contrast, 59 genera including Bacteroides, Megamonas, Prevotella, Lachnospiraceae-unclassified and Blautia increased in the HCs. In Spearman correlation analysis, age, WBC, RBC, PLT, ALB, CREA, TBIL, Hb and Urea were positively correlated with most of the different OTUs. Seizure-type, course and frequency are negatively correlated with most of the different OTUs. In addition, twenty-two optimal microbial markers were identified by a fivefold cross-validation of the random forest model. In the established train set and test set, the area under the curve was 0.9771 and 0.993, respectively. Conclusion: Our study was the first to characterize the gut microbiome of Eps and HCs in central China and demonstrate the potential efficacy of microbial markers as a noninvasive biological diagnostic tool for epilepsy.

Potential Associations Between Microbiome and COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has plunged the world into a major crisis. The disease is characterized by strong infectivity, high morbidity, and high mortality. It is still spreading in some countries. Microbiota and their metabolites affect human physiological health and diseases by participating in host digestion and nutrition, promoting metabolic function, and regulating the immune system. Studies have shown that human microecology is associated with many diseases, including COVID-19. In this research, we first reviewed the microbial characteristics of COVID-19 from the aspects of gut microbiome, lung microbime, and oral microbiome. We found that significant changes take place in both the gut microbiome and airway microbiome in patients with COVID-19 and are characterized by an increase in conditional pathogenic bacteria and a decrease in beneficial bacteria. Then, we summarized the possible microecological mechanisms involved in the progression of COVID-19. Intestinal microecological disorders in individuals may be involved in the occurrence and development of COVID-19 in the host through interaction with ACE2, mitochondria, and the lung-gut axis. In addition, fecal bacteria transplantation (FMT), prebiotics, and probiotics may play a positive role in the treatment of COVID-19 and reduce the fatal consequences of the disease.