Digital intraoral scanning is a hot topic in the field of oral digital technology.In recent years,digital intra-oral scanning has gradually become the mainstream technology in orthodontics,prosthodontics,and implant dentistry.The precision of digital intraoral scanning and the accuracy and stitching of data collection are the keys to the success of the impression.However,the operators are less familiar with the intraoral scanning characteristics,imaging process-ing,operator scanning method,oral tissue specificity of the scanned object,and restoration design.Thus far,no unified standard and consensus on digital intraoral scanning technology has been achieved at home or abroad.To deal with the problems encountered in oral scanning and improve the quality of digital scanning,we collected common expert opin-ions and sought to expound the causes of scanning errors and countermeasures by summarizing the existing evidence.We also describe the scanning strategies under different oral impression requirements.The expert consensus is that due to various factors affecting the accuracy of digital intraoral scanning and the reproducibility of scanned images,adopting the correct scanning trajectory can shorten clinical operation time and improve scanning accuracy.The scanning trajec-tories mainly include the E-shaped,segmented,and S-shaped methods.When performing fixed denture restoration,it is recommended to first scan the abutment and adjacent teeth.When performing fixed denture restoration,it is recommend-ed to scan the abutment and adjacent teeth first.Then the cavity in the abutment area is excavated.Lastly,the cavity gap was scanned after completing the abutment preparation.This method not only meets clinical needs but also achieves the most reliable accuracy.When performing full denture restoration in edentulous jaws,setting markers on the mucosal tissue at the bottom of the alveolar ridge,simultaneously capturing images of the vestibular area,using different types of scanning paths such as Z-shaped,S-shaped,buccal-palatal and palatal-buccal pathways,segmented scanning of dental arches,and other strategies can reduce scanning errors and improve image stitching and overlap.For implant restora-tion,when a single crown restoration is supported by implants and a small span upper structure restoration,it is recom-mended to first pre-scan the required dental arch.Then the cavity in the abutment area is excavated.Lastly,scanning the cavity gap after installing the implant scanning rod.When repairing a bone level implant crown,an improved indi-rect scanning method can be used.The scanning process includes three steps:First,the temporary restoration,adjacent teeth,and gingival tissue in the mouth are scanned;second,the entire dental arch is scanned after installing a standard scanning rod on the implant;and third,the temporary restoration outside the mouth is scanned to obtain the three-di-mensional shape of the gingival contour of the implant neck,thereby increasing the stability of soft tissue scanning around the implant and improving scanning restoration.For dental implant fixed bridge repair with missing teeth,the mobility of the mucosa increases the difficulty of scanning,making it difficult for scanners to distinguish scanning rods of the same shape and size,which can easily cause image stacking errors.Higher accuracy of digital implant impres-sions can be achieved by changing the geometric shape of the scanning rods to change the optical curvature radius.The consensus confirms that as the range of scanned dental arches and the number of data concatenations increases,the scanning accuracy decreases accordingly,especially when performing full mouth implant restoration impressions.The difficulty of image stitching processing can easily be increased by the presence of unstable and uneven mucosal mor-phology inside the mouth and the lack of relatively obvious and fixed reference objects,which results in insufficient ac-curacy.When designing restorations of this type,it is advisable to carefully choose digital intraoral scanning methods to obtain model data.It is not recommended to use digital impressions when there are more than five missing teeth.

Objective To observe the value of pseudo-continuous arterial spin labeling(PCASL)MRI for evaluating renal function in patients with renal occupying lesions.Methods Totally 56 patients with single renal occupying lesion were retrospectively enrolled.The left and right side kidneys were divided into normal renal function group(normal group,30 ml/min)and damaged renal function group(damaged group,＜30 ml/min)according to glomerular filtration rate(GFR)measured with 99Tcm-DTPA dynamic renal imaging,respectively.The total renal blood flow(tRBF)and cortical renal blood flow(cRBF)were calculated using total nephrometry and cortical nephrometry based on PCASL MRI,respectively,then GFR,tRBF and cRBF were compared between groups on the same side.Receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated to evaluate the efficacy of tRBF and cRBF for assessing unilateral renal injury.Pearson correlation analysis was performed to observe the correlations of tRBF and cRBF with GFR.Results GFR,tRBF and cRBF in left/right damaged group were all significantly lower than those in ipsilateral normal group(all P＜0.05).AUC of tRBF and cRBF for assessing left renal injury was 0.823 and 0.813,respectively,being not significantly different(P＞0.05).AUC of tRBF and cRBF for assessing right renal injury was 0.940 and 0.922,respectively,being not significantly different(P＞0.05).No obvious correlation of bilateral tRBF nor cRBF with GFR was found(all P＞0.05).Conclusion PCASL MRI could effectively evaluate renal function in patients with renal occupying lesion,and the efficacy of total nephrometry was comparable to that of cortical nephrometry.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.