Importance: The prediction of nasal tip position in terms of projection, rotation, and length is a major challenge in rhinoplasty. Studies using preoperative and postoperative photographs lack accuracy owing to variable position, and computer-simulated models lack clinical applicability.
Objectives: (1) To describe an accurate and reproducible technique to study the effect of surgical manipulations on the nasal tip; and (2) to describe the effect on the nasal tip cartilages of the lateral crural steal (LCS).
Design, Setting, And Participants: Cadaveric study in a tertiary hospital center using 10 cadaveric specimens.
Interventions: Heads were placed in a Mayfield head holder, and a 12.2-megapixel camera was fixed on a tripod in a perfectly still position and focused on the surgical field during all surgical manipulations. An external rhinoplasty approach was performed for all specimens, and a 4-mm LCS was achieved.
Main Outcomes And Measures: Measures include tip projection, tip rotation, and nasal length using preoperative and postoperative photographs.
Results: Our method was successfully performed on all specimens: LCS resulted in a significant mean increase in projection using the Goode ratio (mean, 0.05; P = .005) and rotation (mean, 13.2°; P = .005). However, absolute tip projection variation was inconsistent, ranging from -1.0 mm to 0.6 mm. Nasal length was significantly shortened in all cases (mean, 1.3 mm, P = .005).
Conclusions And Relevance: We describe the first technique for precise anatomical study of tip position in rhinoplasty on cadaveric specimens. This technique was successfully applied to 10 consecutive nasal tips. We have shown a significant increase in projection using the Goode ratio and rotation with LCS. However, the effect on absolute projection is inconsistent.
Level Of Evidence: NA.
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http://dx.doi.org/10.1001/jamafacial.2013.1416 | DOI Listing |
J Pain Res
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Fracture surgeries are frequently accompanied by severe pain, necessitating efficacious pain management strategies to enhance postoperative recovery. Nerve block techniques, which are critical in mitigating pain, involve the targeted administration of local anesthetics to disrupt nerve signal transmission, thereby achieving significant analgesia. Traditionally, these techniques rely on anatomical landmarks and the clinician's expertise, which can introduce variability and potential risks.
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Vaginal agenesis, a rare and complex congenital anomaly predominantly linked to Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome or complete androgen insensitivity syndrome (CAIS), demands innovative and highly individualized treatment strategies to achieve anatomical and functional restoration. While non-surgical options like vaginal dilation remain foundational, the advent of custom-made stents has redefined the paradigm of care, emerging as a transformative tool in both post-surgical and non-surgical management. Bridging the expertise of prosthodontics and gynecology, personalized stents not only enhance healing and maintain patency but also elevate patient comfort and compliance.
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CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, North Kargar Ave., Tehran, Iran.
The term "neuroenhancement" describes the enhancement of cognitive function associated with deficiencies resulting from a specific condition. Nevertheless, there is currently no agreed-upon definition for the term "neuroenhancement", and its meaning can change based on the specific research being discussed. As humans, our continual pursuit of expanding our capabilities, encompassing both cognitive and motor skills, has led us to explore various tools.
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March 2025
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States of America.
High resolution peripheral quantitative computed tomography (HRpQCT) offers detailed bone geometry and microarchitecture assessment, including cortical porosity, but assessing chronic kidney disease (CKD) bone images remains challenging. This proof-of-concept study merges deep learning and machine learning to 1) improve automatic segmentation, particularly in cases with severe cortical porosity and trabeculated endosteal surfaces, and 2) maximize image information using machine learning feature extraction to classify CKD-related skeletal abnormalities, surpassing conventional DXA and CT measures. We included 30 individuals (20 non-CKD, 10 stage 3 to 5D CKD) who underwent HRpQCT of the distal and diaphyseal radius and tibia and contributed data to develop and validate four different AI models for each anatomical site.
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