Influences of standardized clinical probing on peri-implant soft tissue seal in a situation of peri-implant mucositis: A histomorphometric study in dogs.

BACKGROUND: Clinical probing is commonly recommended to evaluate peri-implant conditions. In a situation of peri-implant mucositis or peri-implantitis, the peri-implant seal healing from the disruption of soft tissue caused by probing has not yet been studied. This study aimed to investigate soft tissue healing after standardized clinical probing around osseointegrated implants with peri-implant mucositis in a dog model. METHODS: Three transmucosal implants in each hemi-mandible of six dogs randomly assigned to the peri-implant healthy group or peri-implant mucositis group were probed randomly in the mesial or distal site as probing groups (PH or PM), the cross-sectional opposite sites as unprobed control groups. Histomorphometric measurements of implant shoulder (IS)-most coronal level of alveolar bone contact to the implant surface (BCI), apical termination of the junctional epithelium (aJE)-BCI, mucosal margin (MM)-BCI, and MM-aJE were performed at 1 day, 1 week, and 2 weeks after probing. Apoptosis, proliferation, proinflammatory cytokines, and matrix metalloproteinases (MMPs) of peri-implant soft tissue were estimated by immunofluorescent analysis. RESULTS: In the PM group, apical migration of junctional epithelium was revealed by significantly decreased aJE-BCI from 1 day to 2 weeks in comparison to unprobed sites (p < 0.05), while no significant differences were found in the PH group. Immunofluorescent analysis showed higher levels of interleukin-1ß (IL-1ß), IL-6, tumor necrosis factor-α (TNF-α), MMP-1, and MMP-8, together with exaggerated apoptosis and proliferation of peri-implant soft tissue in the PM group. CONCLUSION: Within the limitations, standardized clinical probing might lead to apical migration of the junctional epithelium in a situation of peri-implant mucositis.

Development of a Predictive Model for Screening Patients with Psoriasis at Increased Risk of Psoriatic Arthritis.

INTRODUCTION: This study aimed to develop a predictive model based on ultrasound variables which can be used to screen patients with psoriasis who are prone to progress to psoriatic arthritis (PsA) in clinical practice. METHODS: This is a cross-sectional study conducted in a single center from October 2018 to November 2020. All subjects (non-PsA group, PsA group, and control group) underwent an ultrasound examination and their ultrasound abnormalities were recorded. On the basis of statistical analysis and clinical experts' advice, several variables were selected for modelling. We used logistic regression to establish the prediction model. To assess the discrimination and accuracy of this model, internal validation and external validation were performed. RESULTS: A total of 852 patients with psoriasis but without PsA, 261 patients with PsA, and 86 healthy volunteers were included. Ultimately, the predictive model consisted of six variables, namely hand joint power Doppler (PD) signals (grade 0: OR 2.94, 95% CI 1.94-4.47; grade ≥ 1: OR 109.30, 95% CI 14.35-832.27; P < 0.001), wrist joint synovial thickening (grade 1: OR 1.29, 95% CI 0.69-2.43; grade 2: OR 4.30, 95% CI 1.92-9.65; grade 3: OR 11.05, 95% CI 1.01-120.64; P = 0.001), knee joint PD signals (grade 0: OR 1.01, 95% CI 0.56-1.80; grade ≥ 1: OR 14.77, 95% CI 3.99-54.69; P < 0.001), toe joint PD signals (grade 0: OR 1.18, 95% CI 0.78-1.79; grade ≥ 1: OR 5.74, 95% CI 2.84-11.63; P < 0.001), quadriceps tendon and patellar tendon enthesitis (OR 1.95, 95% CI 1.36-2.78, P < 0.001), Achilles tendon and plantar aponeurosis enthesitis (OR 1.63, 95% CI 1.14-2.32, P = 0.007). C-index for the predictive model was 0.80 (95% CI 0.76-0.83). After bootstrapping validation (1000 times), it was confirmed to be 0.79. The external validation showed the accuracy of the predictive model is 0.87 (95% CI 0.69-0.95). CONCLUSION: This study succeeded in developing a predictive model with a high degree of accuracy to predict the risk of PsA in patients with psoriasis.

Psoriatic arthritis often occurs in the population of patients with psoriasis. It brings a huge burden and pain to patients. At present, the diagnosis for psoriatic arthritis is very challenging. Numerous research studies have begun to focus on identifying patients with psoriasis at increased risk of psoriatic arthritis. Among a lot of modalities, ultrasound has been considered as a sensitive and convenient tool for screening early psoriatic arthritis. Our study successfully established a predictive model based on ultrasound variables to screen patients with psoriasis at high risk of transiting to psoriatic arthritis. After internal and external validation, it showed great accuracy and generalizability. We recommend that clinicians perform ultrasound screening of patients with psoriasis in clinical routine and get their risk value of transiting to psoriatic arthritis by using this model. For those patients with a high risk of progression to psoriatic arthritis, clinicians should refer them to a rheumatology department as soon as possible so that they could have access to early and effective management which might bring them good clinical and imaging outcomes.

Nomogram Model for Dynamic and Individual Prediction of Cardiac Response and Survival for Light Chain Amyloidosis in 737 Patients With Cardiac Involvement.

OBJECTIVE: Light chain amyloidosis (AL) with cardiac involvement is associated with poor prognosis. The existing prognostic assessment system does not consider treatment-related factors, and there is currently no effective system for predicting the response. The purpose of this study was to build an individualized, dynamic assessment model for cardiac response and overall survival (OS) for AL patients with cardiac involvement. METHODS: The records of 737 AL patients with cardiac involvement were collected through cooperation with 18 hospitals in the Chinese Registration Network for Light-chain Amyloidosis (CRENLA). We used univariate and multivariate analyses to evaluate the prognostic factors for OS and cardiac response. Then, two nomogram models were developed to predict OS and cardiac response in AL patients with cardiac involvement. RESULTS: A nomogram including four independent factors from the multivariate Cox proportional hazards analysis-Mayo staging, courses of treatment, hematologic response, and cardiac response-was constructed to calculate the possibility of achieving survival by adding all the points associated with four variables. The higher the score, the more likely death would occur. The other nomogram model included the courses of treatment, hematological response, and different treatment regimens, and was correlated with cardiac response. The higher the score, the more likely a cardiac response would occur. CONCLUSION: In conclusion, based on the large Chinese cohort of patients with AL and cardiac involvement, we identified nomogram models to predict cardiac response and OS. These models are more individualized and dynamic, and therefore, they have important clinical application value.

A prognostic staging system for light-chain amyloidosis using hepatic and renal indicator data from 1,064 Chinese patients.

BACKGROUND: Light-chain (AL) amyloidosis frequently involves severe multiple end-organ damage, thus affecting prognosis. As the current disease staging system is based only on cardiac indicators, we propose a new staging system based on multiple organ indicators to supplement the existing system. METHODS: Patients with AL amyloidosis (n=1,064) from 18 Chinese hospitals were enrolled and divided into test and validation cohorts (4:1). Multivariate analyses were performed to identify the clinical and laboratory factors for inclusion in the new staging system. RESULTS: A score of 1 was assigned for each of the following-the difference between the involved and uninvolved free light chains ≥100 mg/L, estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2, total bilirubin (Tbil) ≥18 µmol/L, cardiac troponin I ≥0.06 µg/L, and N-terminal pro-brain natriuretic peptide (NT-proBNP) ≥3,600 pg/mL-to divide the patients into five disease stages (0 to IV). There were 220 (20.7%), 291 (27.3%), 251 (23.6%), 178 (16.7%), and 124 (11.7%) patients with stage 0, I, II, III, and IV disease, respectively. Patients with stage II, III, and IV disease had a median overall survival (OS) of 56.9 months [95% confidence interval (CI), 33.9-not reached (NR)], 18.6 months (95% CI, 33.9-NR), and 6.5 months (95% CI, 8.0-24.6) (P<0.001), respectively. The 3-year survival estimates for patients with stages 0, I, II, III, and IV were 90.7%, 71.4%, 59.4%, 39.0%, and 22.1%, respectively. CONCLUSIONS: The new staging system has been developed that incorporates plasma cell-related characteristics in addition to cardiac, renal, and hepatic function parameters. It enhances the risk stratification of patients with AL amyloidosis and is useful when multiple organs are involved.