Objective: To investigate the mechanism by which serum amyloid P component (SAP) alleviates periodontitis in mice, providing an experimental basis to establish SAP as a novel therapeutic agent for periodontitis. Methods: Ethical approval was obtained from the Institutional Animal Ethics Committee. Periodontitis models were established in wild-type (WT) mice and SAP-transgenic (SAP-Tg) mice, divided into four groups: WT control (WT group), WT periodontitis (WT+P group), SAP-Tg control (Tg group), and SAP-Tg periodontitis (Tg+P group). On day 7, the mice were euthanized, and periodontal tissues, teeth, and alveolar bone were collected. SAP protein expression was detected by enzyme-linked immunosorbent assay (ELISA). Micro-CT and HE staining were used to measure alveolar bone resorption (distance from the cementoenamel junction to the alveolar bone crest). Tartrate-resistant acid phosphatase (TRAP) staining was performed to assess osteoclast number, and immunohistochemistry (IHC) was employed to evaluate macrophage infiltration. The expression levels of inflammatory cytokines including interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α) were measured by qRT-PCR. Oral microorganism composition was analyzed using 16S ribosomal RNA (16S rRNA) gene sequencing. Additionally, macrophages from WT and SAP-Tg mice were isolated to establish an in vitro inflammation model, divided into WT+LPS and Tg+LPS groups. The expression of macrophage polarization-related genes including inducible nitric oxide synthase (iNOS), CD86, CD163, and CD206) were assessed by qRT-PCR. After the induction of osteoclast differentiation, TRAP staining was performed. Results: ELISA results demonstrated that periodontal tissues from Tg+P group mice exhibited higher levels of SAP expression compared to the WT+P group. Micro-CT and HE staining analyses revealed that the Tg+P group showed reduced alveolar bone resorption, indicated by a shorter distance between the cementoenamel junction and alveolar bone crest, compared to the WT+P group. Furthermore, TRAP staining results indicated a decrease in osteoclast numbers in the Tg+P group compared to the WT+P group. IHC and qRT-PCR results indicated reduced macrophage infiltration and decreased expression of IL-1β, IL-6, and TNF-α in the Tg+P group. Oral microorganism sequencing showed no significant difference in periodontitis-associated pathogenic bacteria between WT+P and Tg+P groups. In vitro experiments demonstrated that compared to the WT+LPS group, the Tg+LPS group exhibited downregulated M1 macrophage markers (iNOS and CD86) and upregulated M2 macrophage markers (CD163 and CD206). TRAP staining confirmed fewer osteoclasts in the Tg+LPS group. Conclusion SAP overexpression effectively alleviates periodontitis severity in mice by inhibiting M1 macrophage polarization, reducing pro-inflammatory cytokine expression, and suppressing osteoclast differentiation, thereby attenuating alveolar bone resorption.

Objective To analyze the risk of adverse drug events in pediatric clinical applications of tocilizumab versus inflixima.Methods Adverse event(AE)reporting data for tocilizumab versus infliximab in the U.S.Food and Drug Administration Adverse Event Reporting System database for the pediatric population from Q1 2013 to Q1 2023 were collected.AE risk signal mining was performed using the reporting odds ratio(ROR)method and the proportional reporting ratio(PRR)method.AEs were also classified and statistically analyzed according to the preferred system organ classification and preferred terminology(PT)of the International Dictionary of Medical Terminology.Results Data were extracted and cleaned to include 1 052 AE reports with 198 positive PT signals for tocilizumab as the suspected drug and 9 1 39 AE reports with 387 positive PT signals for infliximab as the suspected drug.The analyses suggested that the stronger positive risk signals for both drugs were focused on gastrointestinal disorders,infectious and invasive diseases,laboratory tests,musculoskeletal and connective tissue disorders,and blood,vascular,and lymphatic disorders.The risk signals for infliximab were focused on gastrointestinal disorders,infections,and infectious diseases,while the risk signals for tocilizumab were focused on the musculoskeletal muscle system.Conclusion Clinical use of both drugs in children has multi-system effects,tocilizumab may have effects on growth and development,and infliximab has effects on the gastrointestinal tract in children.

Background Cooking oil fumes are closely related to immune response, and adipose tissue also plays an important role in immune regulation. At present, the biological effect and mechanism of inflammation of adipose tissue induced by oil fume exposure are not clear yet. Objective To investigate the inflammatory effect of different exposure duration of cooking fumes on adipose tissue in mice and explore the role of Nod-like receptor pyrin domain 3 (NLRP3)/cysteinyl aspartate specific proteinase 1 (Caspase 1)/interleukin (IL)-1β signaling pathway. Methods Forty 8-week-old female C57BL/6J mice were randomly divided into 3-day control group (CON₃ group), 7-day control group (CON₇ group), 3-day oil fume exposure group (COF₃ group), and 7-day oil fume exposure group (COF₇ group), with 10 mice in each group. The mice were exposed to oil fumes in a cooking oil fume formation and exposure equipment (COFFEE) for 20 min, followed by a 10-min pause, 1 h a day for consecutive 3 d or 7 d. General condition of mice was observed and body weight was measured every day. After exposure, blood was sampled from the eyeball. Serum levels of IL-6, IL-27, and IL-1β were detected by enzyme-linked immunosorbent assay (ELISA). The adipose tissue of mice was collected and observed after hematoxylin-eosin (HE) staining. The percentages of CD4⁺ and CD8⁺T cells in adipose tissue were detected by flow cytometry. Real-time quantitative PCR (RT-qPCR) was used to detect the expression levels of nuclear factor-κB (NF-κB), NLRP3, Caspase 1, and IL-1β in adipose tissue. Western blot was used to detect the expression levels of NLRP3, Caspase 1, and IL-1β in adipose. Results Compared with the corresponding control group, serum IL-6, IL-27, and IL-1β contents in the COF₃ group and the COF₇ group were significantly increased (P<0.05) except IL-6 in the COF₃ group, and the levels in the COF₇ group were significantly higher than those in the COF₃ group (P<0.05). Vacuolar lipid droplets in adipocytes decreased, cytoplasm shrank, and inflammatory cells infiltrated in the COF₇ group after HE staining. The flow cytometry results showed that the proportions of CD4⁺ and CD8⁺T cells in adipocytes of the COF₃ group and the COF₇ group were increased compared to the corresponding control group, with a significant increase in the COF₇ group (P<0.05), and the CD4⁺/CD8⁺T ratio also significantly increased progressively in the two groups (P<0.05). The results of RT-qPCR showed that compared with the corresponding control group, the mRNA expression levels of NF-κB, NLRP3, Caspase 1, and IL-1β in adipose tissue of mice in the COF₃ group and the COF₇ group were significantly increased (P<0.05, P<0.01). The mRNA expression levels of mice in each exposure group gradually increased over time. The Western blot results showed that compared with the corresponding control group, the protein expressions of NLRP3 and Caspase 1 in the COF₃ group were significantly increased (P<0.01), and the expression of IL-1β protein also increased but without statistical significance. The protein expressions of NLRP3, Caspase 1, and IL-1β in the COF₇ group were significantly higher than those in the CON₇ group (P<0.05, P<0.01). Conclusion Acute exposure to cooking oil fumes can induce significant inflammatory response in adipose tissue, and the effect gradually increases with the extension of exposure time. The mechanism of action may be related to the activation of NLRP3 inflammasome signaling pathway.

Objective To retrieve,evaluate and summarize the best evidence of early mobilization in awake patients on extracorporeal membrane oxygenation,and to provide a reference for clinical practice.Methods UpToDate,BMJ Best Practice,Registered Nurses'Association of Ontario,National Guideline Clearinghouse,National Institute for Health and Care Excellence,Yimaitong,Joanna Briggs Institute Library,Cochrane Library,CINAHL,PubMed,SinoMed,CNKI,Wanfang Database,Vip Database and Extracorporeal Life Support Organization Website were researched to collect the literature,including clinical guidelines,expert consensuses,evidence summaries,systematic reviews,and well-designed original studies.The time limit for retrieval was until June 2023.The quality of literature and the level of evidence were evaluated by the evaluation criteria and evidence grading system of J BI Evidence-Based Health Care Center.Results 14 pieces of the literature were included,including 2 clinical guidelines,4 expert consensuses,5 systematic reviews,2 cohort studies and 1 case series.Totally 33 pieces of evidence were summarized,covering 7 aspects:adaptation conditions for the implementation of awake ECMO,team composition,comprehensive assessment,pre-mobilization preparation,mobilization content,prevention and control of adverse events,and effect evaluation.Conclusion The study summarizes the best evidence of early mobilization in awake patients on extracorporeal membrane oxygenation.It is suggested that medical institutions establish a professional team for the early mobilization of awake ECMO patients,apply the best evidence to standardize the early mobilization process,and formulate an individualized mobilization program.

AIM To investigate the clinical effects of Feining Paidu Decoction combined with conventional treatment on patients with Mycoplasma pneumoniae lobar pneumonia.METHODS Ninety patients were randomly assigned into control group(45 cases)for 2-week intervention of conventional treatment,and observation group(45 cases)for 2-week intervention of both Feining Paidu Decoction and conventional treatment.The changes in clinical effects,TCM syndrome scores,inflammatory indices(WBC,N,CRP,ESR,PCT),inflammatory cytokines(TNF-α,IL-6,IL-8),coagulation indices(PLT,TT,PT,APTT,Fib,D-D),pulmonary imaging intergal and incidence of adverse reactions were detected.RESULTS The observation group demonstrated higher total effective rate than the control group(P<0.05).After the treatment,the two groups displayed decreased TCM syndrome scores,inflammatory indices,inflammatory cytokines,PLT,pulmonary imaging intergal(P<0.05),especially for the observation group(P<0.05);the observation group exhibited prolonged TT,PT,APTT(P<0.05),and decreased Fib,D-D(P<0.05),which were more obvious than those in the control group(P<0.05).No significant difference in incidence of adverse reactions was found between the two groups(P>0.05).CONCLUSION For the patients with Mycoplasma pneumoniae lobar pneumonia,Feining Paidu Decoction combined with conventional treatment can safely and effectively alleviate clinical symptoms,and improve inflammatory responses,coagulation functions.

Objective To investigate the risk factors of in-hospital mortality and establish a risk prediction model for patients receiving venoarterial extracorporeal membrane oxygenation(VA-ECMO).Methods We retrospectively collected the data of 302 patients receiving VA-ECMO in ICU of 3 hospitals in Guangdong Province between January,2015 and January,2022 using a convenience sampling method.The patients were divided into a derivation cohort(201 cases)and a validation cohort(101 cases).Univariate and multivariate logistic regression analyses were used to analyze the risk factors for in-hospital death of these patients,based on which a risk prediction model was established in the form of a nomogram.The receiver operator characteristic(ROC)curve,calibration curve and clinical decision curve were used to evaluate the discrimination ability,calibration and clinical validity of this model.Results The in-hospital mortality risk prediction model was established based the risk factors including hypertension(OR=3.694,95%CI:1.582-8.621),continuous renal replacement therapy(OR=9.661,95%CI:4.103-22.745),elevated Na2+ level(OR=1.048,95%CI:1.003-1.095)and increased hemoglobin level(OR=0.987,95%CI:0.977-0.998).In the derivation cohort,the area under the ROC curve(AUC)of this model was 0.829(95%CI:0.770-0.889),greater than those of the 4 single factors(all AUC<0.800),APACHE Ⅱ Score(AUC=0.777,95%CI:0.714-0.840)and the SOFA Score(AUC=0.721,95%CI:0.647-0.796).The results of internal validation showed that the AUC of the model was 0.774(95%CI:0.679-0.869),and the goodness of fit test showed a good fitting of this model(χ2=4.629,P>0.05).Conclusion The risk prediction model for in-hospital mortality of patients on VA-ECMO has good differentiation,calibration and clinical effectiveness and outperforms the commonly used disease severity scoring system,and thus can be used for assessing disease severity and prognostic risk level in critically ill patients.

Objective To develop an intelligent model for differential diagnosis of atrioventricular nodal re-entrant tachycardia(AVNRT)and atrioventricular re-entrant tachycardia(AVRT)using 12-lead wearable electrocardiogram devices.Methods A total of 356 samples of 12-lead supraventricular tachycardia(SVT)electrocardiograms recorded by wearable devices were randomly divided into training and validation sets using 5-fold cross validation to establish the intelligent classification model,and 101 patients with the diagnosis of SVT undergoing electrophysiological studies and radiofrequency ablation from October,2021 to March,2023 were selected as the testing set.The changes in electrocardiogram parameters before and during induced tachycardia were compared.Based on multiscale deep neural network,an intelligent diagnosis model for classifying SVT mechanisms was constructed and validated.The 3-lead electrocardiogram signals from Ⅱ,Ⅲ,and V1 were extracted to build new classification models,whose diagnostic efficacy was compared with that of the 12-lead model.Results Of the 101 patients with SVT in the testing set,68 were diagnosed with AVNRT and 33 were diagnosed with AVRT by electrophysiological study.The pre-trained model achieved a high area under the precision-recall curve(0.9492)and F1 score(0.8195)for identifying AVNRT in the validation set.The total F1 scores of the lead Ⅱ,Ⅲ,V1,3-lead and 12-lead intelligent diagnostic models in the testing set were 0.5597,0.6061,0.3419,0.6003 and 0.6136,respectively.Compared with the 12-lead classification model,the lead-Ⅲ model had a net reclassification index improvement of-0.029(P=0.878)and an integrated discrimination index improvement of-0.005(P=0.965).Conclusion The intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.

Objective To investigate the risk factors of in-hospital mortality and establish a risk prediction model for patients receiving venoarterial extracorporeal membrane oxygenation(VA-ECMO).Methods We retrospectively collected the data of 302 patients receiving VA-ECMO in ICU of 3 hospitals in Guangdong Province between January,2015 and January,2022 using a convenience sampling method.The patients were divided into a derivation cohort(201 cases)and a validation cohort(101 cases).Univariate and multivariate logistic regression analyses were used to analyze the risk factors for in-hospital death of these patients,based on which a risk prediction model was established in the form of a nomogram.The receiver operator characteristic(ROC)curve,calibration curve and clinical decision curve were used to evaluate the discrimination ability,calibration and clinical validity of this model.Results The in-hospital mortality risk prediction model was established based the risk factors including hypertension(OR=3.694,95%CI:1.582-8.621),continuous renal replacement therapy(OR=9.661,95%CI:4.103-22.745),elevated Na2+ level(OR=1.048,95%CI:1.003-1.095)and increased hemoglobin level(OR=0.987,95%CI:0.977-0.998).In the derivation cohort,the area under the ROC curve(AUC)of this model was 0.829(95%CI:0.770-0.889),greater than those of the 4 single factors(all AUC<0.800),APACHE Ⅱ Score(AUC=0.777,95%CI:0.714-0.840)and the SOFA Score(AUC=0.721,95%CI:0.647-0.796).The results of internal validation showed that the AUC of the model was 0.774(95%CI:0.679-0.869),and the goodness of fit test showed a good fitting of this model(χ2=4.629,P>0.05).Conclusion The risk prediction model for in-hospital mortality of patients on VA-ECMO has good differentiation,calibration and clinical effectiveness and outperforms the commonly used disease severity scoring system,and thus can be used for assessing disease severity and prognostic risk level in critically ill patients.

Objective To develop an intelligent model for differential diagnosis of atrioventricular nodal re-entrant tachycardia(AVNRT)and atrioventricular re-entrant tachycardia(AVRT)using 12-lead wearable electrocardiogram devices.Methods A total of 356 samples of 12-lead supraventricular tachycardia(SVT)electrocardiograms recorded by wearable devices were randomly divided into training and validation sets using 5-fold cross validation to establish the intelligent classification model,and 101 patients with the diagnosis of SVT undergoing electrophysiological studies and radiofrequency ablation from October,2021 to March,2023 were selected as the testing set.The changes in electrocardiogram parameters before and during induced tachycardia were compared.Based on multiscale deep neural network,an intelligent diagnosis model for classifying SVT mechanisms was constructed and validated.The 3-lead electrocardiogram signals from Ⅱ,Ⅲ,and V1 were extracted to build new classification models,whose diagnostic efficacy was compared with that of the 12-lead model.Results Of the 101 patients with SVT in the testing set,68 were diagnosed with AVNRT and 33 were diagnosed with AVRT by electrophysiological study.The pre-trained model achieved a high area under the precision-recall curve(0.9492)and F1 score(0.8195)for identifying AVNRT in the validation set.The total F1 scores of the lead Ⅱ,Ⅲ,V1,3-lead and 12-lead intelligent diagnostic models in the testing set were 0.5597,0.6061,0.3419,0.6003 and 0.6136,respectively.Compared with the 12-lead classification model,the lead-Ⅲ model had a net reclassification index improvement of-0.029(P=0.878)and an integrated discrimination index improvement of-0.005(P=0.965).Conclusion The intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.

Objective To analyze the characteristics of adult body composition and obesity status of three ethnic groups:Ewenki,Daur and Mongolian Buryat.Methods The bioelectrical impedance analysis(BIA)was used to measure 18 body composition components in three adults ethnic groups:245(male 124,female 121)Ewenki,207(male 90,female 117)Daur,and 181(male 74,female 107)Mongolian Buryat.The data were processed using Excel 2016 and SPSS 24.0 statistical software.Results The result of correlation analysis showed that visceral fat level was significantly and positively correlated with age(P＜0.01).Stature,total body muscle mass,estimated bone mass and trunk muscle mass were all significantly and negatively correlated with age(P＜0.01)in males and females of the three ethnic groups.The percent body fat,percent left upper limb fat and percent trunk fat were positively correlated with age in Ewenki males(P＜0.05 or P＜0.01)and the percent body fat,body mass index(BMI),percent limb fat and percent trunk fat were positively correlated with age in Ewenki females(P＜0.05 or P＜0.01).Body weight,BMI,percent left upper limb fat,left upper limb muscle mass,bilateral lower limb fat and muscle mass were all negatively correlated with age in Daur males(P＜0.05 or P＜0.01).Body weight,upper limb muscle mass and left lower limb muscle mass were negatively correlated with age(P＜0.05 or P＜0.01)in Mongolian Buryat males and percent trunk fat was positively correlated with age(P＜0.05)in Mongolian Buryat females.Comparison between ethnic groups showed that most of the body composition index values of the Ewenki and Mongolian Buryat divisions were closest to each other,and the body fat content was higher than that of the Daur.BMI,percent body fat and visceral fat level were all manifested in the Mongolian Buryat Department＞Ewenki＞Daur.Cluster analysis showed that Ewenki,Daur and Mongolian Buryat were closer to the northern groups and further from the southern groups.Conclusion The Ewenki is most similar to the Mongolian Buryat in body composition characteristics all three ethnic groups has a more serious obesity problem.