Background: Mechanical plaque removal has been commonly accepted to be the basis for periodontal treatment. This study aims to compare the effectiveness of ultrasonic and manual subgingival scaling at different initial probing pocket depths (PPD) in periodontal treatment.Methods: English-language databases (PubMed, Cochrane Central Register of Controlled Trials, EMBASE, Medline, and ClinicalTrials.gov, by January, 2019) were searched. Weighted mean differences in primary outcomes, PPD and clinical attachment loss (CAL) reduction, were estimated by random effects model. Secondary outcomes, bleeding on probing (BOP), gingival recession (GR), and post-scaling residual dental calculus, were analyzed by comparing the results of each study. The quality of RCTs was appraised with the Cochrane Collaboration risk of bias tool. The GRADE approach was used to assess quality of evidence.Results: Ten randomized controlled trials were included out of 1434 identified. Initial PPD and follow-up periods formed subgroups. For 3-months follow-up: (1) too few shallow initial pocket studies available to draw a conclusion; (2) the heterogeneity of medium depth studies was so high that could not be merged to draw a conclusion; (3) deep pocket studies showed no statistical differences in PPD and CAL reduction between ultrasonic and manual groups. For 6-months follow-up: (1) too few shallow initial PPD studies to draw a conclusion; (2) at medium pocket depth, PPD reduction showed manual subgingival scaling better than ultrasound. No statistical differences were observed in CAL reduction between the two approaches; (3) for deep initial PPD studies, both PPD and CAL reduction showed manual subgingival scaling better. GR results indicated no statistical differences at medium and deep initial pocket studies between the two methods. BOP results showed more reduction at deep pocket depths with manual subgingival scaling. No conclusion could be drawn about residual dental calculus.Conclusion: When initial PPD was 4-6 mm, PPD reduction proved manual subgingival scaling was superior, but CAL results showed no statistical differences between the two means. When initial PPD was ≥6 mm, PPD and CAL reductions suggested that manual subgingival scaling was superior.

Background: Machine learning based auto-segmentation of 3D images has been developed rapidly in recent years. However, the application of this new method in the research of patients with unilateral cleft lip and palate (UCLP) is very limited. In this study, a machine learning algorithm utilizing 3D U-net was used to automatically segment the maxilla, fill the cleft and evaluate the alveolar bone graft in UCLP patients. Cleft related factors and the surgery impact on the development of maxilla were analyzed. Methods: Preoperative and postoperative computed tomography images of 32 patients (64 images) were obtained. The deep-learning-based protocol was used to segment the maxilla and defect, followed by manual refinement. Paired t-tests and Mann-Whitney tests were performed to reveal the changes of the maxilla after surgery. Two-factor, two-level analysis for repeated measurement was used to examine the different trends of growth on the cleft and non-cleft sides of the maxilla. Pearson and Spearman correlations were used to explore the relationship between the defect and the changes of the maxillary cleft side. Results: One-year after the alveolar bone grafting surgery, different growth amount was found on the cleft and non-cleft sides of maxilla. The maxillary length (from 34.64 ± 2.48 to 35.67 ± 2.45 mm) and the alveolar length (from 36.58 ± 3.21 to 37.63 ± 2.94 mm) increased significantly only on the cleft side while the maxillary anterior width (from 11.61 ± 1.61 to 12.01 ± 1.41 mm) and posterior width (from 29.63 ± 2.25 to 30.74 ± 2.63 mm) increased significantly only on the non-cleft side after surgery. Morphology of the cleft was found to be related to the pre-surgical maxillary dimension on the cleft side, while its correlation with the change of the maxilla after surgery was low or not statistically significant. Conclusion: The auto-segmentation of the maxilla and the cleft could be performed very efficiently and accurately with the machine learning method. Asymmetric growth was found on the cleft and non-cleft sides of the maxilla after alveolar bone graft in UCLP patients. The morphology of the cleft mainly contributed to the pre-operation variance of the maxilla but had little impact on the maxilla growth after surgery.

Background/Objectives: Current approaches to monitoring obstructive sleep apnea (OSA) risk primarily focus on structural or functional abnormalities, often neglecting systemic metabolic and physiological factors. Resource-intensive methods, such as polysomnography (PSG), limit their routine applicability. This study aimed to evaluate composite nutritional-inflammatory indices derived from routine blood markers to identify feasible indices for OSA management and explore their association with OSA risk. Methods: Data from 9622 adults in the NHANES (2015–2020) and GWAS datasets were analyzed using logistic regression, restricted cubic splines, machine learning, and Mendelian randomization (MR). These techniques were employed to identify nutritional-inflammatory indices associated with OSA risk. Random forest modeling identified body mass index (BMI) and albumin (ALB) as key components of the advanced lung cancer inflammation index (ALI). Causal relationships between ALI components and OSA were validated using MR. Results: ALI was significantly associated with OSA, with individuals in the highest ALI tertile exhibiting a 59% higher likelihood of OSA (OR = 1.59, 95% CI: 1.38–1.84; p < 0.001). BMI and ALB were identified as key contributors to ALI and confirmed as causal risk factors for OSA (BMI: OR = 1.91, 95% CI: 1.80–2.02; ALB: OR = 1.11, 95% CI: 1.04–1.19). Age, gender, and the neutrophil-to-lymphocyte ratio (NLR) were also significant predictors. Conclusions: This study identifies ALI as a potential composite index for assessing OSA risk. Integrating statistical modeling, machine learning, and causal inference techniques highlights the utility of nutritional-inflammatory indices in improving OSA monitoring and management in clinical practice.

Obstructive sleep apnoea and hypopnoea syndrome (OSAHS) affects cognitive and behavioural functions in children, with adenotonsillar hypertrophy being the most common risk factor. Thus, adenotonsillectomy is the primary treatment for paediatric OSAHS [1]. However, two large multicentre randomised controlled trials (RCTs) did not show additional improvement in the primary cognitive test after adenotonsillectomy compared to watchful waiting with supportive care (WWSC) [2, 3], contradicting some prior findings [4, 5]. These discrepancies partly stemmed from varying study designs, assessment methods and follow-up durations. Redline et al. [3] specifically noted that both RCTs used the apnoea–hypopnoea index (AHI) as the primary inclusion or stratification criterion. The cognitive impact of OSAHS may not be well-represented by AHI levels, indicating the need for additional indices to assess the risk in children.