Patients with periodontal disease often require combined periodontal-orthodontic interventions to restore periodontal health, function, and aesthetics, ensuring both patient satisfaction and long-term stability. Managing these patients involving orthodontic tooth movement can be particularly challenging due to compromised periodontal soft and hard tissues, especially in severe cases. Therefore, close collaboration between orthodontists and periodontists for comprehensive diagnosis and sequential treatment, along with diligent patient compliance throughout the entire process, is crucial for achieving favorable treatment outcomes. Moreover, long-term orthodontic retention and periodontal follow-up are essential to sustain treatment success. This expert consensus, informed by the latest clinical research and practical experience, addresses clinical considerations for orthodontic treatment of periodontal patients, delineating indications, objectives, procedures, and principles with the aim of providing clear and practical guidance for clinical practitioners.
Humans ; Consensus ; Orthodontics, Corrective/standards* ; Periodontal Diseases/complications* ; Tooth Movement Techniques/methods* ; Practice Guidelines as Topic

Objective:To obtain a prevalent respiratory syncytial virus (RSV) clinical isolate in Beijing and analyze the genotype and biological characteristics of the strain.Methods:A nasopharyngeal secretion specimen was collected from a child with RSV infection in Beijing in 2023 and used for viral isolation. Viral nucleic acid was amplified using qRT-PCR. The isolated virus was identified by transmission electron microscopy, indirect immunofluorescence assay, and plaque formation assay. A phylogenetic analysis was conducted based on the whole-genome sequencing results. Virus titers were determined, and replication characteristics were analyzed. The efficacy of the isolated strain for in vitro screening of antiviral drugs was validated. Results:A clinical RSV isolate, named hRSV/C-Tan/BJ 202301, was successfully isolated, which could form syncytia in Hep-2 cells. Spherical, filamentous, and irregular virus particles were observed by electron microscopy. Immunofluorescence detection showed green fluorescence in Hep-2 cells, and plaque assay showed round plaques, which were similar to the Long strain in morphology. Genomic sequence analysis showed that it belonged to ON1 genotype. It exhibited similar cell growth kinetics characteristics with the Long strain and could be used for antiviral drug screening in vitro. Conclusions:In this study, one RSV strain is successfully isolated and identified. The biological characteristics and the phylogenetic relationship of this strain reflect the characteristics of the circulating strains in Beijing, which provides experimental material for RSV vaccine development and antiviral drug screening in China.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

BACKGROUND: Lung cancer is currently the most prevalent malignancy and the leading cause of cancer deaths worldwide. Although the early stage non-small cell lung cancer (NSCLC) presents a relatively good prognosis, a considerable number of lung cancer cases are still detected and diagnosed at locally advanced or late stages. Surgical treatment combined with perioperative multimodality treatment is the mainstay of treatment for locally advanced NSCLC and has been shown to improve patient survival. Following the standard methods of neoadjuvant therapy, perioperative management, postoperative adjuvant therapy, and other therapeutic strategies are important for improving patients' prognosis and quality of life. However, controversies remain over the perioperative management of NSCLC and presently consensus and standardized guidelines are lacking for addressing critical clinical issues in multimodality treatment. METHODS: The working group consisted of 125 multidisciplinary experts from thoracic surgery, medical oncology, radiotherapy, epidemiology, and psychology. This guideline was developed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system. The clinical questions were collected and selected based on preliminary open-ended questionnaires and subsequent discussions during the Guideline Working Group meetings. PubMed, Web of Science, Cochrane Library, Scopus, and China National Knowledge Infrastructure (CNKI) were searched for available evidence. The GRADE system was used to evaluate the quality of evidence and grade the strengths of recommendations. Finally, the recommendations were developed through a structured consensus-building process. RESULTS: The Guideline Development Group initially collected a total of 62 important clinical questions. After a series of consensus-building conferences, 24 clinical questions were identified and corresponding recommendations were ultimately developed, focusing on neoadjuvant therapy, perioperative management, adjuvant therapy, postoperative psychological rehabilitation, prognosis assement, and follow-up protocols for NSCLC. CONCLUSIONS This guideline puts forward reasonable recommendations focusing on neoadjuvant therapy, perioperative management, adjuvant therapy, postoperative psychological rehabilitation, prognosis assessment, and follow-up protocol of NSCLC. It standardizes perioperative multimodality treatment and provides guidance for clinical practice among thoracic surgeons, medical oncologists, and radiotherapists, aiming to reduce postoperative recurrence, improve patient survival, accelerate recovery, and minimize postoperative complications such as atelectasis.
Humans ; Carcinoma, Non-Small-Cell Lung/therapy* ; Lung Neoplasms/therapy* ; Combined Modality Therapy ; Perioperative Care

Objective To identify and analyze risk factors for acute renal failure (ARF) following lung transplantation and to develop a predictive model. Methods Data for this study were obtained from the United Network for Organ Sharing (UNOS) database, encompassing patients who underwent unilateral or bilateral lung transplantation between 2015 and 2022. We analyzed both preoperative and postoperative clinical characteristics of the patients. A combined approach utilizing random forest and least absolute shrinkage and selection operator (LASSO) regression was employed to identify key factors associated with the incidence of ARF post-transplantation, based on which a nomogram model was developed. The predictive performance of the constructed model was evaluated in both training and validation sets, using receiver operating characteristic (ROC) curves and area under the curve (AUC) metrics to verify and compare model effectiveness. Results A total of 15 110 lung transplantation patients were included in the study, consisting of6 041 males and 9 069 females, with a median age of 62.00 years (interquartile range: 54.00 to 67.00). The analysis revealed statistically significant differences between postoperative renal dialysis and non-dialysis patients regarding preoperative lung diagnosis, estimated glomerular filtration rate (eGFR), mechanical ventilation, preoperative ICU treatment, extracorporeal membrane oxygenation (ECMO) support, infections occurring within two weeks prior to transplantation, Karnofsky Performance Status (KPS) score, waitlist duration, double-lung transplantation, and ischemia time (P<0.05). Five key variables associated with ARF after lung transplantation were identified through random forest and LASSO regression: recipients’ eGFR, preoperative ICU treatment, ECMO support, bilateral lung transplantation, and ischemia time. A nomogram model was subsequently established. Model evaluation demonstrated that the constructed predictive model achieved high accuracy in both training and validation sets, with favorable AUC values, confirming its validity and reliability. Conclusion This study identifies common risk factors for ARF following lung transplantation and introduces an effective predictive model with potential clinical applications.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Objective To explore the relationship between the expression levels of serum long chain non coding RNA Kinectin 1 antisense RNA 1(LncRNA KTN1-AS1)and retinoblastoma binding protein 4(RBBP4)with postoperative recurrence and metastasis in esophageal cancer patients.Methods From May 2018 to May 2021,119 patients with esophageal cancer who underwent surgical treatment in our hospital were included as the study group.They were separated into an occurrence group(n=51)and a non occurrence group(n=68)based on whether there was recurrence or metastasis during a 3-year follow-up.Additionally,100 patients with benign esophageal tumors treated at the same stage were selected as the control group.ELISA method was applied to detect the expression levels of serum LncRNA KTN1-AS1 and RBBP4.Multivariate Logistic regression was applied to analyze the influencing factors of postoperative recurrence and metastasis in esophageal cancer patients.ROC curve was applied to analyze the predictive value of serum LncRNA KTN1-AS1 and RBBP4 for postoperative recurrence and metastasis in esophageal cancer patients.Results The expression levels of serum LncRNA KTN1-AS1 and RBBP4 in the study group were obviously higher than those in the control group(P＜0.05).The expression levels of serum LncRNA KTN1-AS1 and RBBP4,and the proportions of mucosal/submucosal infiltration depth,and low differentiation degree and proportion of lymph node metastasis in the occurrence group were obviously higher than those in the non occurrence group(P＜0.05).Serum LncRNA KTN1-AS1,RBBP4,proportion of lymph node metastasis,degree of differentiation,and depth of infiltration were influencing factors for postoperative recurrence and metastasis in esophageal cancer patients(P＜0.05).The area under the curve(AUC)of the combined prediction of serum LncRNA KTN1 AS1 and RBBP4 for postoperative recurrence and metastasis in esophageal cancer patients was 0.912,which was better than their individual predictions(Zcombination LncRNA KTN1 AS1=2.470,Zcombination-RBBp4=1.994,P=0.014,P=0.046),the sensitivity and specificity of the combined prediction were 90.20％and 85.29％,respectively.Conclusion The expression levels of serum LncRNA KTN1-AS1 and RBBP4 in esophageal cancer patients are obviously increased,which is closely related to postoperative recurrence and metastasis.The combined detection of the two has good predictive value for postoperative recurrence and metastasis in patients.

Objective To explore the relationship between the expression levels of serum long chain non coding RNA Kinectin 1 antisense RNA 1(LncRNA KTN1-AS1)and retinoblastoma binding protein 4(RBBP4)with postoperative recurrence and metastasis in esophageal cancer patients.Methods From May 2018 to May 2021,119 patients with esophageal cancer who underwent surgical treatment in our hospital were included as the study group.They were separated into an occurrence group(n=51)and a non occurrence group(n=68)based on whether there was recurrence or metastasis during a 3-year follow-up.Additionally,100 patients with benign esophageal tumors treated at the same stage were selected as the control group.ELISA method was applied to detect the expression levels of serum LncRNA KTN1-AS1 and RBBP4.Multivariate Logistic regression was applied to analyze the influencing factors of postoperative recurrence and metastasis in esophageal cancer patients.ROC curve was applied to analyze the predictive value of serum LncRNA KTN1-AS1 and RBBP4 for postoperative recurrence and metastasis in esophageal cancer patients.Results The expression levels of serum LncRNA KTN1-AS1 and RBBP4 in the study group were obviously higher than those in the control group(P＜0.05).The expression levels of serum LncRNA KTN1-AS1 and RBBP4,and the proportions of mucosal/submucosal infiltration depth,and low differentiation degree and proportion of lymph node metastasis in the occurrence group were obviously higher than those in the non occurrence group(P＜0.05).Serum LncRNA KTN1-AS1,RBBP4,proportion of lymph node metastasis,degree of differentiation,and depth of infiltration were influencing factors for postoperative recurrence and metastasis in esophageal cancer patients(P＜0.05).The area under the curve(AUC)of the combined prediction of serum LncRNA KTN1 AS1 and RBBP4 for postoperative recurrence and metastasis in esophageal cancer patients was 0.912,which was better than their individual predictions(Zcombination LncRNA KTN1 AS1=2.470,Zcombination-RBBp4=1.994,P=0.014,P=0.046),the sensitivity and specificity of the combined prediction were 90.20％and 85.29％,respectively.Conclusion The expression levels of serum LncRNA KTN1-AS1 and RBBP4 in esophageal cancer patients are obviously increased,which is closely related to postoperative recurrence and metastasis.The combined detection of the two has good predictive value for postoperative recurrence and metastasis in patients.