Conectar
by Viet Anh Nguyen, Viet Hoang, Thi Quynh Trang Vuong, Thi Nga Phung, Nghi Phan Bich Hoang
ObjectivesChairside bonding of auxiliaries directly to aligners can avoid remanufacturing trays, but optimal protocols may be substrate-specific across modern thermoformed and 3D-printed materials. This study aimed to compare bond strength and failure mode across six representative aligner materials using a universal primer-orthodontic adhesive combination and a one-step aligner adhesive, with and without sandblasting.
Materials and methodsPolyethylene terephthalate glycol-modified (PETG), thermoplastic polyurethane (TPU), and glycol-modified polycyclohexylenedimethylene terephthalate (PCTG), together with three 3D-printed resins (TA-28, TC-85DAC, DCA), were prepared as 0.76-mm plates (n = 64). Specimens received alumina sandblasting or no treatment, then were bonded with either of two bonding strategies (n = 16). After thermocycling, bond strength was tested, and failures were scored by ARI. Two- and three-way ANOVA and proportional-odds modeling assessed effects (α = 0.05).
ResultsBond strength showed significant main effects of material and sandblasting, with significant material–sandblasting and material–primer interactions. The primer main effect was not significant. Post hoc tests confirmed substrate-specific rankings. PETG with Bond Aligner (non-sandblasted) reached 26.71 MPa, while DCA with universal primer (sandblasted) reached 22.36 MPa. Sandblasting generally increased bond strength, with some exceptions. Failure mode was material-dependent and not completely parallel with bond strength.
ConclusionsBonding efficacy depends on the aligner substrate. For thermoformed trays, a one-step aligner adhesive is preferable, with sandblasting contraindicated for PETG but advantageous for more elastic TPU and PCTG. For 3D-printed trays, a universal primer-orthodontic adhesive combination performs more consistently, with sandblasting benefiting DCA and TA-28, whereas TC-85DAC performs slightly better without it.
Severe mental disorders are associated with increased risk of metabolic dysfunction. Identifying those subgroups at higher risk may help to inform more effective early intervention. The objective of this study was to compare metabolic profiles across three proposed pathophysiological subtypes of common mood disorders (‘hyperarousal-anxious depression’, ‘circadian-bipolar spectrum’ and ‘neurodevelopmental-psychosis’).
751 young people (aged 16–25 years; mean age 19.67±2.69) were recruited from early intervention mental health services between 2004 and 2024 and assigned to two mood disorder subgroups (hyperarousal-anxious depression (n=656) and circadian-bipolar spectrum (n=95)). We conducted cross-sectional assessments and between-group comparisons of metabolic and immune risk factors. Immune-metabolic markers included body mass index (BMI), fasting glucose (FG), fasting insulin, Homeostasis Model Assessment-Insulin Resistance (HOMA2-IR), C reactive protein and blood lipids.
Individuals in the circadian-bipolar spectrum subgroup had significantly elevated FG (F=5.75, p=0.04), HOMA2-IR (F=4.86, p=0.03) and triglycerides (F=4.98, p=0.03) as compared with those in the hyperarousal-anxious depression subgroup. As the larger hyperarousal-anxious depression subgroup is the most generic type, and weight gain is also a characteristic of the circadian-bipolar subgroup, we then differentiated those with the hyperarousal-anxious subtype on the basis of low versus high BMI (2 vs ≥25 kg/m2, respectively). The ‘circadian-bipolar’ group had higher FG, FI and HOMA2-IR than those in the hyperarousal-anxious-depression group with low BMI.
Circadian disturbance may be driving increased rates of metabolic dysfunction among youth with emerging mood disorders, while increased BMI also remains a key determinant. Implications for assessment and early interventions are discussed.
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