Objective Evaluate the accuracy and variability of predicted versus achieved maxillary lateral incisor movements with optimised and conventional attachments in CAT. Methods This retrospective cohort study included 56 patients (90 lateral incisors; 46 conventional; 44 optimised) treated with Invisalign Lite. Predicted and achieved movements were assessed using 3D superimposition for three linear (mesiodistal, buccolingual, occlusogingival) and three angular (tip, torque, rotation) parameters. Prediction error (one-sample t-tests), between-group comparisons (linear mixed-effects models), and clinical equivalence (two one-sided tests, TOST) with predefined margins (± 0.25 mm and ± 0.5°) were calculated. Results Torque (mean difference of 1.07°; SD 4.06, 90% CI: 0.37 to 1.78, p = 0.013) indicated reduced expression. Other parameters showed smaller mean differences (mesiodistal − 0.08 mm, buccolingual − 0.08 mm) and notable variability (SD 6.25° for rotation). Within-group discrepancies were observed in occlusogingival (0.25 mm, SD 0.40) and rotational (1.95°, SD 5.28) movements in the conventional group, and in buccolingual (− 0.22 mm, SD 0.55) and occlusogingival (− 0.17 mm, SD 0.47) movements in the optimised group. Between-group comparisons showed significant differences for buccolingual (− 0.21 mm, p = 0.040) and occlusogingival (− 0.42 mm, p < 0.001). Mesiodistal was the only parameter demonstrating clinical equivalence between groups (TOST p = 0.004). Dose–response analyses showed greater discrepancies across all parameters with increased planned movements. Conclusions Small mean discrepancies but considerable variability, particularly for angular movements, were noted. Mesiodistal movement showed clinically acceptable agreement between attachment types, while no consistent advantage of one attachment design was observed across all movements. Increased planned movement was associated with greater discrepancy.
Background: Osseointegration, first proposed by Dr. Brånemark, revolutionized dental implantology. However, traditional implants often present challenges in cases of severe bone atrophy, sinus enlargement, or systemic conditions such as uncontrolled diabetes. Basal or cortical implants, with their unique design enabling anchorage in the basal cortical bone, eliminate the need for bone augmentation and allow immediate functional loading. Objectives: To present a case series demonstrating the clinical application and outcomes of basal implant placement with immediate loading in patients with compromised bone conditions. Results: Two female patients aged 24 and 25 years underwent basal implant placement with immediate loading. The procedures, including socket preparation, implant placement, impression taking, and crown fabrication, were completed with minimal invasiveness and rapid recovery. Multi-cortical engagement principles were applied, achieving stable anchorage and functional restoration without bone grafting. Both cases demonstrated successful outcomes with immediate function and patient satisfaction. Conclusion: Basal implantology, based on multi-cortical engagement and immediate loading, provides a timeefficient, cost-effective, and reliable solution for dental rehabilitation in patients with inadequate bone volume for conventional implants. This approach offers predictable results even in anatomically or medically compromised cases.
Background:Dental implant placement demanded an always a guide to high precision, especially in anatomically sensitive zones such as the posterior mandible. The integration of robotic assistance emerged as a promising technique to enhance surgical accuracy. Aim:This pilot study evaluated the precision of dental implant placement using a robotic surgery system in comparison with digitally planned trajectories. Materials and Methods:Ten partially edentulous patients, each desiring a single implant in the posterior mandibular region, were enrolled. Preoperatively cone-beam computed tomography (CBCT) and digital intraoral scanning reports were allowed to merged in CAD software for virtual implant positioning. Implant placement by the robotic arm guided by preloaded surgical plans and real-time navigation feedback was followed by postoperative regimen of CBCT imaging, to assess deviations between planned and actual implant positions. Results:The mean deviation (implant platform) was 0.71 mm, while the apex deviation averaged 0.68 mm. The angular deviation from planned to placed implants was 1.56°. These results suggested high fidelity. Conclusion:Robotic-assisted implant procedure demonstrated promising accuracy. Technology offers potential benefits in precision and predictability, particularly in challenging posterior mandibular sites.
Global food demand is expected to increase between 55 and 70% by 2050. Plant breeders and geneticists are constantly under pressure to develop high-yielding climate-resilient varieties using novel approaches. The quest for simplifying complex traits and efforts for developing high-yielding varieties during the twenty-first century led to a paradigm shift from phenotypic-based selection to genome-based breeding. On one hand, the development and utilization of diverse genetic resources, and advances in genomics on the other hand provided a kick start for the understanding the genetics of economically important complex traits at a faster pace. Further, the next-generation sequencing revolutionized our understanding of the genome architecture. As a result, there has been an increasing demand for statistical and bioinformatics tools to analyse and manage the enormous amount of data generated from sequencing of genomes, transcriptomes, proteome and metabolomes. In this chapter, we review the intervention of bioinformatics and computational tools for deploying the tremendous wealth of data for plant genetics and breeding research.
Alzheimer’s Disease (AD) is a progressive, irreversible, and neuro-degenerative disease with a long pre-clinical period, affecting brain cells and leading to memory loss, misperception, learning problems, and improper decisions. Given its significance, presently no treatment options are available, although disease advancement can be retarded through medication. Unfortunately, AD is diagnosed at a very late stage, after irreversible damage to the brain cells has occurred, when there is no scope to prevent further cognitive decline. Non-invasive neuroimaging procedures capable of detecting AD at the preliminary stages are crucial for providing treatment and retarding disease progression and have proven to be a promising area of research. We conducted a comprehensive assessment of papers employing machine learning to predict AD using neuroimaging data. Most of the studies employed brain images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, consisting of magnetic resonance imaging (MRI) and positron emission tomography (PET) images. The most widely used method, the support vector machine (SVM), has a mean accuracy of 75.4
