Background: The association between inflammatory biomarkers and individual delirium symptoms remains controversial in observational studies. We investigated the relationship between inflammatory biomarkers and the risk of developing delirium.Methods: A bidirectional two-sample Mendelian randomization (MR) was performed. Genetic instruments associated with peripheral tumor necrosis factor-a (TNF-a) C-reactive protein (CRP), interleukin (IL)-1α, IL-1β, IL-2, IL-8, IL-6, soluble IL-6 receptor alpha (sIL-6Rα), and soluble gp130 were identified in three different large summary genome-wide association studies (GWAS) conducted in the European population. Summary-level statistics for delirium not induced by alcohol and other psychoactive substances were obtained from the FinnGen consortium (2,612 cases and 325,306 controls). The estimated causal effects were performed using instruments' variants at the genome-wide significant level (P < 5e-8 and P < 5e-6), applying a linkage disequilibrium clumping approach with a threshold of r² < 0.001 for each of the exposures. Reverse causation was also performed. The inverse-variance weighted method (IVW), MR-Egger method, weighted median method, MR-Egger regression, and MR Pleiotropy RESidual Sum were used for MR analyses.Results: At the genome-wide significant level (P < 5e-8, r² < 0.001), genetically predicted sIL-6Rα was significantly associated with a decreased risk of delirium with less than three single-nucleotide polymorphisms (SNPs) in all three GWAS data sources (OR_Waldratio = 0.89, 95% CI: 0.79–0.96, P_Waldratio = 0.0016; OR_IVW = 0.88, 95% CI: 0.79–0.97, P_IVW = 0.008; OR_IVW = 0.88, 95% CI: 0.80–0.96, P_IVW = 0.004). The causal relationship between sIL-6Rα and delirium became non-significant when a more liberal threshold of P of < 5e-6 was applied (all P_IVW > 0.05). At the two genome-wide significance levels (P < 5e-8 and P < 5e-6), we found no evidence for the causal effects of peripheral TNF-α, CRP, IL-1α, IL-1β, IL-2, IL-6, IL-8, and soluble gp130 on delirium (all P > 0.05). The MR-Egger intercept and MR-PRESSO results indicated that no SNP had possible pleiotropy (all P > 0.05). Regarding the reverse, no evidence for an effect of delirium on these inflammatory biomarkers could be found (all P > 0.05).Conclusion: The results of this MR analysis did not support that peripheral TNF-α, CRP, IL-1α, IL-1β, IL-2, IL-6, sIL-6Rα, soluble gp130, and IL-8 were causally associated with delirium. More research is needed to explore the role of inflammatory factors in the pathogenesis of delirium.

Background: Anorexia nervosa (AN) is a psychological disorder, which is characterized by the misunderstanding of body image, food restriction, and low body weight. An increasing number of studies have reported that the pathophysiological mechanism of AN might be associated with the dysbiosis of gut microbiota. The purpose of our study was to explore the features of gut microbiota in patients with AN, hoping to provide valuable information on its pathogenesis and treatment. Methods: In this cross-sectional study, from August 2020 to June 2021, patients with AN who were admitted into Peking University Third Hospital and Peking University Sixth Hospital (n = 30) were recruited as the AN group, and healthy controls (HC) were recruited from a middle school and a university in Beijing (n = 30). Demographic data, Hamilton Depression Scale (HAMD) scores of the two groups, and length of stay of the AN group were recorded. Microbial diversity analysis of gut microbiota in stool samples from the two groups was analyzed by 16S ribosomal RNA (rRNA) gene sequencing. Results: The weight (AN vs. HC, [39.31 ± 7.90] kg vs. [56.47 ± 8.88] kg, P < 0.001) and body mass index (BMI, AN vs. HC, [14.92 ± 2.54] kg/m² vs. [20.89 ± 2.14] kg/m², P < 0.001) of patients with AN were statistically significantly lower than those of HC, and HAMD scores in AN group were statistically significantly higher than those of HC. For alpha diversity, there were no statistically significant differences between the two groups; for beta diversity, the two groups differed obviously regarding community composition. Compared to HC, the proportion of Lachnospiraceae in patients with AN was statistically significantly higher (AN vs. HC, 40.50% vs. 31.21%, Z = −1.981, P = 0.048), while that of Ruminococcaceae was lower (AN vs. HC, 12.17% vs. 19.15%, Z = −2.728, P = 0.007); the proportion of Faecalibacterium (AN vs. HC, 3.97% vs. 9.40%, Z = −3.638, P < 0.001) and Subdoligranulum (AN vs. HC, 4.60% vs. 7.02%, Z = −2.369, P = 0.018) were statistically significantly lower, while that of Eubacterium_hallii_group was significantly higher (AN vs. HC, 7.63% vs. 3.43%, Z = −2.115, P = 0.035). Linear discriminant effect (LEfSe) analysis (LDA score >3.5) showed that o_Lachnospirales, f_Lachnospiraceae, and g_Eubacterium_hallii_group (o, f and g represents order, family and genus respectively) were enriched in patients with AN. Microbial function of nutrient transport and metabolism in AN group were more abundant (P > 0.05). In AN group, weight and BMI were significantly negatively correlated with the abundance of Bacteroidota and Bacteroides, while positively correlated with Subdoligranulum. BMI was significantly positively correlated with Firmicutes; HAMD scores were significantly negatively correlated with Faecalibacterium. Conclusions: The composition of gut microbiota in patients with AN was different from that of healthy people. Clinical indicators have correlations with the abundance of gut microbiota in patients with AN.

Background: The immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are crucial in maintaining a delicate balance between protective effects and harmful pathological reactions that drive the progression of coronavirus disease 2019 (COVID-19). T cells play a significant role in adaptive antiviral immune responses, making it valuable to investigate the heterogeneity and diversity of SARS-CoV-2-specific T cell responses in COVID-19 patients with varying disease severity.Methods: In this study, we employed high-throughput T cell receptor (TCR) β repertoire sequencing to analyze TCR profiles in the peripheral blood of 192 patients with COVID-19, including those with moderate, severe, or critical symptoms, and compared them with 81 healthy controls. We specifically focused on SARS-CoV-2-associated TCR clonotypes.Results: We observed a decrease in the diversity of TCR clonotypes in COVID-19 patients compared to healthy controls. However, the overall abundance of dominant clones increased with disease severity. Additionally, we identified significant differences in the genomic rearrangement of variable (V), joining (J), and VJ pairings between the patient groups. Furthermore, the SARS-CoV-2-associated TCRs we identified enabled accurate differentiation between COVID-19 patients and healthy controls (AUC > 0.98) and distinguished those with moderate symptoms from those with more severe forms of the disease (AUC > 0.8). These findings suggest that TCR repertoires can serve as informative biomarkers for monitoring COVID-19 progression.Conclusions: Our study provides valuable insights into TCR repertoire signatures that can be utilized to assess host immunity to COVID-19. These findings have important implications for the use of TCR β repertoires in monitoring disease development and indicating disease severity.

Acute respiratory distress syndrome (ARDS) is a prevalent complication among critically ill patients, constituting around 10% of intensive care unit (ICU) admissions and mortality rates ranging from 35 to 46%. Hence, early recognition and prediction of ARDS are crucial for the timely administration of targeted treatment. However, ARDS is frequently underdiagnosed or delayed, and its heterogeneity diminishes the clinical utility of ARDS biomarkers. This study aimed to observe the incidence of ARDS among high-risk patients and develop and validate an ARDS prediction model using machine learning (ML) techniques based on clinical parameters. This prospective cohort study in China was conducted on critically ill patients to derivate and validate the prediction model. The derivation cohort, consisting of 400 patients admitted to the ICU of the Peking University Third Hospital(PUTH) between December 2020 and August 2023, was separated for training and internal validation, and an external data set of 160 patients at the FU YANG People's Hospital from August 2022 to August 2023 was employed for external validation. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were used to screen predictor variables. Multiple ML classification models were integrated to analyze and identify the best models. Several evaluation indexes were used to compare the model performance, including the area under the receiver-operating-characteristic curve (AUC) and decision curve analysis (DCA). SHapley Additive ex Planations (SHAP) is used to interpret ML models. 400 critically ill patients were included in the analysis, with 117 developing ARDS during follow-up. The final model included gender, Lung Injury Prediction Score (LIPS), Hepatic Disease, Shock, and combined Lung Contusion. Based on the AUC and DCA in the validation group, the logistic model demonstrated excellent performance, achieving an AUC of 0.836 (95% CI: 0.762–0.910). For external validation, comprising 160 patients, 44 of whom developed ARDS, the AUC was 0.799 (95% CI: 0.723–0.875), significantly outperforming the LIPS score alone. Combining the LIPS score with other clinical parameters in a logistic regression model provides a more accurate, clinically applicable, and user-friendly ARDS prediction tool than the LIPS score alone.

Background: The symptoms of coronavirus disease 2019 (COVID-19) range from moderate to critical conditions, leading to death in some patients, and the early warning indicators of the COVID-19 progression and the occurrence of its serious complications such as myocardial injury are limited.Methods: We carried out a multi-center, prospective cohort study in three hospitals in Wuhan. Genome-wide 5-hydroxymethylcytosine (5hmC) profiles in plasma cell-free DNA (cfDNA) was used to identify risk factors for COVID-19 pneumonia and develop a machine learning model using samples from 53 healthy volunteers, 66 patients with moderate COVID-19, 99 patients with severe COVID-19, and 38 patients with critical COVID-19.Results: Our warning model demonstrated that an area under the curve (AUC) for 5hmC warning moderate patients developed into severe status was 0.81 (95% CI 0.77–0.85) and for severe patients developed into critical status was 0.92 (95% CI 0.89–0.96). We further built a warning model on patients with and without myocardial injury with the AUC of 0.89 (95% CI 0.84–0.95).Conclusion: This is the first study showing the utility of 5hmC as an accurate early warning marker for disease progression and myocardial injury in patients with COVID-19. Our results show that phosphodiesterase 4D and ten-eleven translocation 2 may be important markers in the progression of COVID-19 disease.