Periodontitis constitutes the primary cause of tooth loss among adults in China. The disease is characterized by the high morbidity, which significantly impairs both oral and systemic health. As the key insights of initial periodontal therapy, subgingival scaling and root planing (SRP) have been considered as simple, effective, and cost-efficient treatment approaches for managing periodontal inflammation. Substantial published evidences indicated that the majority of patients diagnosed with periodontitis could achieve lifelong retention of their natural teeth through standardized initial periodontal therapy, rigorous plaque control, and long-term supportive periodontal treatment. However, the proficiency in periodontal diagnosis and treatment among general dental practitioners in China is heterogeneous, with a notable scarcity of periodontists and a suboptimal level of SRP standardization. Drawing upon over four decades of clinical expertise from Peking University School and Hospital of Stomatology as well as comprehensive literature reviews, this paper meticulously outlines the standardized procedures for SRP, aiming at guiding clinicians to perform standardized SRP step by step, and thereby enhancing clinical efficacy of periodontal treatment, promoting periodontal health, and ultimately improving patients′ quality of life.

This study investigated the effects of Rehmanniae Radix Praeparata on striatal neuronal apoptosis in rats with attention deficit hyperactivity disorder(ADHD) based on the B-cell lymphoma-2(Bcl-2)/Bcl-2-associated X protein(Bax)/caspase-3 signaling pathway. Twenty-four 3-week-old male spontaneously hypertensive rats(SHR) were randomly divided into a model group, a methylphenidate group(2 mg·kg~(-1)·d~(-1)), and a Rehmanniae Radix Praeparata group(2.4 mg·kg~(-1)·d~(-1)). Age-matched male Wistar Kyoto(WKY) rats were used as the normal control group, with 8 rats in each group. The rats were administered by gavage for 28 days. Body weight and food intake were recorded for each group. The open field test and elevated plus maze test were used to assess hyperactivity and impulsive behaviors. Nissl staining was used to detect changes in striatal neurons and Nissl bodies. Terminal deoxynucleotidyl transferase dUTP nick end labeling(TUNEL) fluorescence staining was used to detect striatal cell apoptosis. Western blot was employed to detect the expression levels of Bcl-2, Bax, and caspase-3 proteins in the striatum. The results showed that compared with the model group, Rehmanniae Radix Praeparata significantly reduced the total movement distance, average movement speed, and central area residence time in the open field test, and significantly reduced the ratio of open arm entries, open arm stay time, and head dipping in the elevated plus maze test. Furthermore, it increased the number of Nissl bodies in striatal neurons, significantly downregulated the apoptosis index, significantly increased Bcl-2 protein expression and the Bcl-2/Bax ratio, and reduced Bax and caspase-3 protein expression. In conclusion, Rehmanniae Radix Praeparata can reduce hyperactivity and impulsive behaviors in ADHD rats. Its mechanism may be related to the regulation of the Bcl-2/Bax/caspase-3 signaling pathway in the striatum, enhancing the anti-apoptotic capacity of striatal neurons.
Animals ; Male ; Apoptosis/drug effects* ; Rats ; Drugs, Chinese Herbal/administration & dosage* ; Caspase 3/genetics* ; Proto-Oncogene Proteins c-bcl-2/genetics* ; bcl-2-Associated X Protein/genetics* ; Rehmannia/chemistry* ; Attention Deficit Disorder with Hyperactivity/physiopathology* ; Signal Transduction/drug effects* ; Neurons/cytology* ; Rats, Inbred SHR ; Rats, Inbred WKY ; Humans ; Corpus Striatum/cytology* ; Plant Extracts

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

Periodontitis constitutes the primary cause of tooth loss among adults in China. The disease is characterized by the high morbidity, which significantly impairs both oral and systemic health. As the key insights of initial periodontal therapy, subgingival scaling and root planing (SRP) have been considered as simple, effective, and cost-efficient treatment approaches for managing periodontal inflammation. Substantial published evidences indicated that the majority of patients diagnosed with periodontitis could achieve lifelong retention of their natural teeth through standardized initial periodontal therapy, rigorous plaque control, and long-term supportive periodontal treatment. However, the proficiency in periodontal diagnosis and treatment among general dental practitioners in China is heterogeneous, with a notable scarcity of periodontists and a suboptimal level of SRP standardization. Drawing upon over four decades of clinical expertise from Peking University School and Hospital of Stomatology as well as comprehensive literature reviews, this paper meticulously outlines the standardized procedures for SRP, aiming at guiding clinicians to perform standardized SRP step by step, and thereby enhancing clinical efficacy of periodontal treatment, promoting periodontal health, and ultimately improving patients′ quality of life.

Objective To analyze the risk factors for increased drainage volume after open transforaminal lumbar interbody fusion(TLIF),and to establish a predictive model and then validate it.Methods The clinical data of 680 patients who underwent open TLIF at the General Hospital of Northern Theater Command from January 2016 to December 2019 were collected and the patients were randomly divided into the training group(n=476)and the validation group(n=204).Taking the predictive factors screened out by LASSO regression analysis as independent variables,a multivariate Logistic regression predictive model was constructed.The model was internally validated through the receiver operating characteristic(ROC)curve,Hosmer-Lemeshow goodness-of-fit test,and calibration curve,and its clinical utility was assessed via decision curve analysis(DCA).Results LASSO regression analysis screened out four predictive variables:age,number of surgical segments,operative duration,and intraoperative blood loss.The multivariate Logistic regression predictive model demonstrated that age≥60 years,number of surgical segments≥4,operative duration≥2 hours,and intraoperative blood loss≥200 mL were independent influencing factors for the increased postoperative drainage volume in patients undergoing TLIF(P<0.05).ROC curve analysis revealed an area under the curve(AUC)of 0.816(95%CI:0.798 to 0.867)in the training group and 0.783(95%CI:0.685 to 0.823)in the validation group,indicating that the predictive model had good discriminatory ability.Additionally,the Hosmer-Lemeshow goodness-of-fit test and calibration curve indicated that the predictive model had a good degree of fit,and the predicted probability was basically consistent with the actual probability,demonstrating a good calibration.The DCA results confirmed that this predictive model could be applied in clinical practice.Conclusion The risk factors for increased drainage volume after open TLIF include age,number of surgical segments,operative duration,and intraoperative blood loss.The predictive model established based on these factors demonstrates good performance,and it can be applied in clinical guidance for the selection of drainage tube removal time after TLIF.

Purpose To establish a nomogram based on conventional ultrasound combined with contrast-enhanced ultrasound(CEUS)for predicting the probability of cervical central lymph node metastasis(CLNM)in clinical lymph node-negative(CN0)papillary thyroid carcinoma(PTC)patients.Materials and Methods A retrospective study was performed on 359 patients with single CN0 PTC,all of whom underwent thyroid surgery and prophylactic central compartment neck dissection in the First Affiliated Hospital of Shihezi University from September 2015 to March 2022.According to the postoperative pathological results,there were 116 cases with CLNM(+)and other 243 cases with CLNM(-).The indicators of gender,age,conventional ultrasound and CEUS were recorded,and multivariate stepwise Logistic regression was performed to screen out risk predictors to construct prediction models for CLNM in CN0 PTC.The receiver operating characteristic curves of prediction models were drawn,and the area under the curve(AUC)was further compared.The preferable prediction model was selected to establish the risk probability nomogram,and the prediction performance and clinical applicability of the nomogram model were assessed.Results Multivariate analysis showed that gender,age,the maximum diameter of nodule,capsule invasion and enhancement pattern on CEUS were risk factors for CLNM in CN0 PTC(all P<0.05).The AUC of prediction model 1 including the above five indicators was 0.753,and the AUC of prediction model 2 excluding CEUS indicator was 0.704.There were statistically significant difference in AUCs between the two models(Z=2.473,P=0.013).Prediction model 1 was selected to construct a risk probability nomogram for predicting CLNM in CN0 PTC.The nomogram had a C-index of 0.753 and showed well consistency on the calibration curve.Clinical decision curve analysis indicated that the nomogram could achieve ideal net benefit when the threshold probability was between 10.7%to 81.5%.Conclusion Gender,age,the maximum diameter of nodule,capsule invasion and enhancement pattern on CEUS may be the risk predictors for CLNM in CN0 PTC.The nomogram model based on the above indicators can predict the probability of CLNM effectively,and the CEUS indicators can substantially improve the prediction performance of the model.

Objective To establish a dynamic prediction model of fatal massive hemorrhage in trauma based on the vital signs time series data and machine learning algorithms.Methods Retrospectively analyze the vital signs time series data of 7522 patients with trauma in the Medical Information Mart for Intensive Care-Ⅳ(MIMIC-Ⅳ)database from 2008 to 2019.According to the occurrence of posttraumatic fatal massive hemorrhage,the patients were divided into two groups:fatal massive hemorrhage group(n=283)and non-fatal massive hemorrhage group(n=7239).Six machine learning algorithms,including logistic regression(LR),support vector machine(SVM),random forests(RF),adaptive boosting(AdaBoost),gated recurrent unit(GRU),and GRU-D were used to develop a dynamic prediction models of fatal massive hemorrhage in trauma.The probability of fatal massive hemorrhage in the following 1,2,and 3 h was dynamically predicted.The performance of the models was evaluated by accuracy,sensitivity,specificity,positive predictive value,negative predictive value,Youden index,and area under receiver operating characteristic curve(AUC).The models were externally validated based on the trauma database of the Chinese PLA General Hospital.Results In the MIMIC-Ⅳ database,the set of dynamic prediction models based on the GRU-D algorithm was the best.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.946±0.029,0.940±0.032,and 0.943±0.034,respectively,and there was no significant difference(P=0.905).In the trauma dataset,GRU-D model achieved the best external validation effect.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.779±0.013,0.780±0.008,and 0.778±0.009,respectively,and there was no significant difference(P=0.181).This set of models was deployed in a public web calculator and hospital emergency department information system,which is convenient for the public and medical staff to use and validate the model.Conclusion A set of dynamic prediction models has been successfully developed and validated,which is greatly significant for the early diagnosis and dynamic prediction of fatal massive hemorrhage in trauma.

ObjectiveTo observe the effects of Xibining （XBN） and adipose stem cell exosome （ADSC-Exos） in the cases of separate or joint application on cartilage degeneration and mitochondrial autophagy and explore its mechanism of action to improve knee osteoarthritis （KOA）. MethodSD rats were divided into a sham operation group （sham group）， a model group， an ADSC-Exos group （Exos group）， an XBN group， and an ADSC-Exos+XBN group （Exos+XBN group）. KOA model was established by using anterior cruciate ligament transection （ACLT）. The pain sensitivity status of rats was evaluated， and the degeneration degree of the knee joint and cartilage tissue was detected by Micro-CT and pathological staining. The expression of p62 and LC3B was observed by immunofluorescence， and the serum levels of TNF-α， IL-1β， IL-6， and IL-15 in rats were detected by ELISA. The Western blot was used to detect the protein expression levels of MMP-3， MMP-13， ADAMTS5， ColⅡ， TIMP， ACAN， PINK1， Parkin， p62， and LC3A/B. ResultCompared with the sham group， rats in the model group showed decreased cold-stimulated foot-shrinkage thresholds and mechanical pain sensitivity thresholds， varying degrees of abrasion and loss of cartilage tissue， degeneration of cartilage tissue， elevated serum IL-1β， IL-6， IL-15， and TNF-α levels （P<0.01）， and increased protein expression of MMP-3， MMP-13， and ADAMTS5 in cartilage tissue. In addition， the protein expression of ColⅡ， TIMP1， and ACAN was decreased （P<0.01）. Compared with the model group， rats in each treatment group showed higher cold-stimulated foot-shrinkage thresholds and mechanical pain sensitivity thresholds， reduced cartilage tissue degeneration， lower serum levels of IL-1β， IL-6， IL-15， and TNF-α （P<0.05，P<0.01）， decreased protein expression of MMP-3， MMP-13， and ADAMTS5， and higher protein expression of Cold， TIMP1， and ACAN in cartilage tissue （P<0.05，P<0.01）. Moreover， the changes were the most obvious in the Exos+XBN group. ConclusionBoth ADSCs-Exos and XBN can increase the level of mitochondrial autophagy in chondrocytes and delay cartilage tissue degeneration by promoting the expression of the PINK1/Parkin signaling pathway， and the combination of the two can enhance the therapeutic effect.

Purpose To explore the application value of clinical-ultrasound parameter model in secondary hyperparathyroidism(SHPT).Materials and Methods A total of 86 patients(134 lesions)with renal insufficiency who underwent maintenance hemodialysis in the First Affiliated Hospital of Shihezi University from October 2020 to August 2022 were included and divided into group 1 according to the level of parathyroid hormone(iPTH)(iPTH＜300 pg/ml),group 2(iPTH 300-800 pg/ml)and group 3(iPTH≥800 pg/ml),all patients underwent gray-scale parathyroid ultrasound and acoustic palpation tissue quantitative imaging examinations.The characteristics of glandular gray-scale ultrasound and virtual touch tissue imaging quantification parameters between different groups,combined with relevant clinical indicators,established a clinical-ultrasound parameter model,used multiple linear regression to analyze the correlation between the model and iPTH,explored the independent risk factors of iPTH,and evaluated this model to evaluate SHPT the value of.Results There were significant differences in dialysis age,phosphorus,alkaline phosphatase,serum creatinine,corrected calcium and phosphorus product,lesion size,number,echo,shear wave velocity(SWV)max,SWVcen,and SWVmean among the three groups(F/x2/H=6.396-53.524,all P＜0.05).Dialysis age,phosphorus,alkaline phosphatase,and SWVratio were independent influencing factors of iPTH level(β=0.514,0.422,0.226,-0.368,all P＜0.005).The area under the curve,sensitivity,specificity and accuracy of the model for diagnosing SHPT and predicting surgical treatment with iPTH levels of 300 pg/ml and 800 pg/ml were 0.967,95.00％,100.00％,97.73％and 0.824,77.42％,71.43％and 90.00％,respectively.Conclusion Dialysis age,phosphorus,alkaline phosphatase and SWVratio are independent influencing factors of iPTH level,and the clinical-ultrasound parameter model is of great value in accurately assessing the severity of SHPT.

Objective: To understand the incidence of chronic obstructive pulmonary disease (COPD) in the Suzhou cohort, and explore the risk factors for the development of COPD in Suzhou, and provide a scientific basis for COPD prevention. Methods: This study was based on the China Kadoorie Biobank project in Wuzhong District, Suzhou. After excluding individuals with airflow obstruction and self-reported chronic bronchitis, emphysema, or pulmonary heart disease at baseline, 45 484 individuals were finally included in the analysis. Cox proportional risk models were used to analyze risk factors of COPD and calculate hazard ratios and 95% confidence interval (CI) in the Suzhou cohort. The effect modifications of smoking on the association between other risk factors and COPD were evaluated. Results: Complete follow-up was available through December 31, 2017. Participants were followed up for a median of 11.12 years, and 524 individuals were diagnosed with COPD during the follow-up period; the incidence was 105.54 per 100 000 person-years. Multivariate Cox proportional risk regression models showed that age (HR=3.78, 95%CI:3.32-4.30), former smoking (HR=2.00, 95%CI:1.24-3.22), current smoking (<10 cigarettes/day, HR=2.14, 95%CI:1.36-3.35;≥10 cigarettes/day, HR=2.69, 95%CI:1.60-4.54), history of respiratory disease (HR=2.08, 95%CI:1.33-3.26), daily sleep duration ≥10 hours (HR=1.41, 95%CI:1.02-1.95) were associated with increased risk of COPD. However, education level of primary school and above (primary or junior high school, HR=0.65, 95%CI:0.52-0.81; high school and above, HR=0.54, 95%CI:0.33-0.87), consuming fresh fruit daily (HR=0.59, 95%CI:0.42-0.83) and consuming spicy food weekly (HR=0.71, 95%CI:0.53-0.94) were associated with reduced risk of COPD. Conclusions: The incidence of COPD is low in Suzhou. Older age, smoking, history of respiratory disease, and long sleep duration were risk factors for the development of COPD in the Suzhou cohort.
Humans ; Incidence ; Pulmonary Disease, Chronic Obstructive/epidemiology* ; Risk Factors ; Smoking/epidemiology* ; Tobacco Smoking