Changes of parameters associated with anemia of inflammation in patients with stage Ⅲ periodontitis before and after periodontal initial therapy / 北京大学学报(医学版)

Objective:To investigate the differences and similarities of parameters associated with ane-mia of inflammation between patients with stage Ⅲ periodontitis and periodontally healthy volunteers,and to explore the influence of periodontal initial therapy on those indicators.Methods:Patients with stageⅢ periodontitis and periodontally healthy volunteers seeking periodontal treatment or prophylaxis at De-partment of Periodontology,Peking University School and Hospital of Stomatology from February 2020 to February 2023 were enrolled.Their demographic characteristics,periodontal parameters(including pro-bing depth,clinical attachment loss,bleeding index),and fasting blood were gathered before periodontal initial therapy.Three months after periodontal initial therapy,the periodontal parameters of the patients with stage Ⅲ periodontitis were re-evaluated and their fasting blood was collected again.Blood routine examinations(including white blood cells,red blood cells,hemoglobin,packed cell volume,mean cor-puscular volume of erythrocytes,and mean corpuscular hemoglobin concentration)were performed.And ferritin,hepcidin,erythropoietin(EPO)were detected with enzyme-linked immunosorbent assay(ELISA).All data analysis was done with SPSS 21.0,independent sample t test,paired t test,and analysis of co-variance were used for comparison between the groups.Results:A total of 25 patients with stage Ⅲperiodontitis and 25 periodontally healthy volunteers were included in this study.The patients with stageⅢ periodontitis were significantly older than those in periodontally healthy status[(36.72±7.64)years vs.(31.44±7.52)years,P=0.017].The patients with stage Ⅲ periodontitis showed lower serum he-moglobin[(134.92±12.71)g/L vs.(146.52±12.51)g/L,P=0.002]and higher serum ferritin[(225.08±103.36)μg/L vs.(155.19±115.38)μg/L,P=0.029],EPO[(41.28±12.58)IU/L vs.(28.38±10.52)IU/L,P＜0.001],and hepcidin[(48.03±34.44)μg/L vs.(27.42±15.00)μg/L,P=0.009]compared with periodontally healthy volunteers.After adjusting the age with the co-variance analysis,these parameters(hemoglobin,ferritin,EPO,and hepcidin)showed the same trends as independent-sample t test with statistical significance.Three months after periodontal initial therapy,all the periodontal parameters showed statistically significant improvement.The serum hemoglobin raised[(146.05±15.48)g/L vs.(133.77±13.15)g/L,P＜0.001],while the serum ferritin[(128.52± 90.95)μg/Lvs.(221.22±102.15)μg/L,P＜0.001],EPO[(27.66±19.67)IU/L vs.(39.63± 12.48)IU/L,P=0.004],and hepcidin[(32.54±18.67)μg/L vs.(48.18±36.74)μg/L,P=0.033]decreased compared with baseline.Conclusion:Tendency of iron metabolism disorder and ane-mia of inflammation was observed in patients with stage Ⅲ periodontitis,which can be attenuated by periodontal initial therapy.

Modeling the SARS-CoV-2 mutation based on geographical regions and time

The Coronavirus Disease 2019 (COVID-19) epidemic was first detected in late-December 2019. So far, it has caused 203,815,431 confirmed cases and 4,310,623 deaths in the world. We collected sequences from 150,659 COVID-19 patients. Based on the previous phylogenomic analysis, we found three major branches of the virus RNA genomic mutation located in Asia, America, and Europe which is consistent with other studies. We selected sites with high mutation frequencies from Asia, America, and Europe. There are only 13 common mutation sites in these three regions. It infers that the viral mutations are highly dependent on their location and different locations have specific mutations. Most mutations can lead to amino acid substitutions, which occurred in 3/5UTR, S/N/M protein, and ORF1ab/3a/8/10. Thus, the mutations may affect the pathogenesis of the virus. In addition, we applied an ARIMA model to predict the short-term frequency change of these top mutation sites during the spread of the disease. We tested a variety of settings of the ARIMA model to optimize the prediction effect of three patterns. This model can provide good help for predicting short-term mutation frequency changes.

Plasma cell-free DNA promise disease monitoring and tissue injury assessment of COVID-19

COVID-19 is a huge threat to global health. Due to the lack of definitive etiological therapeutics currently, effective disease monitoring is of high clinical value for better healthcare and management of the large number of COVID-19 patients. In this study, we recruited 37 COVID-19 patients, collected 176 blood samples upon diagnosis and during treatment, and analyzed cell-free DNA (cfDNA) in these samples. We report gross abnormalities in cfDNA of COVID-19 patients, including elevated GC content, altered molecule size and end motif patterns. More importantly, such cfDNA characteristics reflect patient-specific physiological conditions during treatment. Further analysis on tissue origin tracing of cfDNA reveals frequent tissue injuries in COVID-19 patients, which is supported by clinical diagnoses. Hence, we demonstrate the translational merit of cfDNA as valuable analyte for effective disease monitoring, as well as tissue injury assessment in COVID-19 patients.

The Trans-omics Landscape of COVID-19

System-wide molecular characteristics of COVID-19, especially in those patients without comorbidities, have not been fully investigated. We compared extensive molecular profiles of blood samples from 231 COVID-19 patients, ranging from asymptomatic to critically ill, importantly excluding those with any comorbidities. Amongst the major findings, asymptomatic patients were characterized by highly activated anti-virus interferon, T/natural killer (NK) cell activation, and transcriptional upregulation of inflammatory cytokine mRNAs. However, given very abundant RNA binding proteins (RBPs), these cytokine mRNAs could be effectively destabilized hence preserving normal cytokine levels. In contrast, in critically ill patients, cytokine storm due to RBPs inhibition and tryptophan metabolites accumulation contributed to T/NK cell dysfunction. A machine-learning model was constructed which accurately stratified the COVID-19 severities based on their multi-omics features. Overall, our analysis provides insights into COVID-19 pathogenesis and identifies targets for intervening in treatment.