Automated localization of mandibular landmarks in the construction of mandibular median sagittal plane.

OBJECTIVE: To use deep learning to segment the mandible and identify three-dimensional (3D) anatomical landmarks from cone-beam computed tomography (CBCT) images, the planes constructed from the mandibular midline landmarks were compared and analyzed to find the best mandibular midsagittal plane (MMSP). METHODS: A total of 400 participants were randomly divided into a training group (n = 360) and a validation group (n = 40). Normal individuals were used as the test group (n = 50). The PointRend deep learning mechanism segmented the mandible from CBCT images and accurately identified 27 anatomic landmarks via PoseNet. 3D coordinates of 5 central landmarks and 2 pairs of side landmarks were obtained for the test group. Every 35 combinations of 3 midline landmarks were screened using the template mapping technique. The asymmetry index (AI) was calculated for each of the 35 mirror planes. The template mapping technique plane was used as the reference plane; the top four planes with the smallest AIs were compared through distance, volume difference, and similarity index to find the plane with the fewest errors. RESULTS: The mandible was segmented automatically in 10 ± 1.5 s with a 0.98 Dice similarity coefficient. The mean landmark localization error for the 27 landmarks was 1.04 ± 0.28 mm. MMSP should use the plane made by B (supramentale), Gn (gnathion), and F (mandibular foramen). The average AI grade was 1.6 (min-max: 0.59-3.61). There was no significant difference in distance or volume (P > 0.05); however, the similarity index was significantly different (P < 0.01). CONCLUSION: Deep learning can automatically segment the mandible, identify anatomic landmarks, and address medicinal demands in people without mandibular deformities. The most accurate MMSP was the B-Gn-F plane.

Acceptability, Effectiveness, and Roles of mHealth Applications in Supporting Cancer Pain Self-Management: Integrative Review.

BACKGROUND:  Cancer pain remains highly prevalent and persistent throughout survivorship, and it is crucial to investigate the potential of leveraging the advanced features of mobile health (mHealth) apps to empower individuals to self-manage their pain. OBJECTIVE:  This review aims to comprehensively understand the acceptability, users' experiences, and effectiveness of mHealth apps in supporting cancer pain self-management. METHODS:  We conducted an integrative review following Souza and Whittemore and Knafl's 6 review processes. Literature was searched in PubMed, Scopus, CINAHL Plus with Full Text, PsycINFO, and Embase, from 2013 to 2023. Keywords including "cancer patients," "pain," "self-management," "mHealth applications," and relevant synonyms were used in the search. The Johns Hopkins research evidence appraisal tool was used to evaluate the quality of eligible studies. A narrative synthesis was conducted to analyze the extracted data. RESULTS:  A total of 20 studies were included, with the overall quality rated as high (n=15) to good (n=5). Using mHealth apps to monitor and manage pain was acceptable for most patients with cancer. The internal consistency of the mHealth in measuring pain was 0.96. The reported daily assessment or engagement rate ranged from 61.9% to 76.8%. All mHealth apps were designed for multimodal interventions. Participants generally had positive experiences using pain apps, rating them as enjoyable and user-friendly. In addition, 6 studies reported significant improvements in health outcomes, including enhancement in pain remission (severity and intensity), medication adherence, and a reduced frequency of breakthrough pain. The most frequently highlighted roles of mHealth apps included pain monitoring, tracking, reminders, education facilitation, and support coordination. CONCLUSIONS:  mHealth apps are effective and acceptable in supporting pain self-management. They offer a promising multi-model approach for patients to monitor, track, and manage their pain. These findings provide evidence-based insights for leveraging mHealth apps to support cancer pain self-management. More high-quality studies are needed to examine the effectiveness of digital technology-based interventions for cancer pain self-management and to identify the facilitators and barriers to their implementation in real-world practice.

Testing Biological and Psychological Pathways of Emotion Regulation as a Primary Mechanism of Action in Yoga Interventions for Chronic Low Back Pain: Protocol for a Randomized Controlled Trial.

BACKGROUND: Interventions that promote adaptive emotion regulation (ER) skills reduce pain in patients with chronic pain; however, whether the effects of yoga practice on chronic low back pain (CLBP) are due to improvements in ER remains to be examined. OBJECTIVE: This study will test whether the effects of yoga on CLBP (improved pain severity and interference) are mediated by improved ER, the extent to which effects are related to specific aspects of ER, and the role of pain sensitization as a mediator or moderator of effects. In this study, pain sensitization will be assessed by quantitative sensory testing and gene expression profiles to examine whether pain sensitization moderates yoga's effects on pain or whether yoga and ER abilities reduce pain sensitization, leading to decreased pain severity and interference. METHODS: For this 2-arm parallel group blinded randomized controlled trial, we will enroll 204 adults with CLBP who will be randomized to receive the yoga (n=102) or a control stretching and strengthening (n=102) intervention, which are delivered via web-based synchronous biweekly 75-minute sessions over 12 weeks. Participants are encouraged to practice postures or exercises for 25 minutes on other days using accessible prerecorded practice videos that are sent to participants digitally. Participants will be assessed at 5 time points: baseline, midintervention (6 weeks), postintervention (12 weeks), and 3- and 6-month follow-ups. Assessments of ER, pain severity and interference, pain sensitivity including somatosensory and gene expression profiles, and physical strength and flexibility will be conducted at each visit. The fidelity of the interventions is assessed using a manualized checklist to evaluate recorded group sessions to ensure consistent instructor delivery. RESULTS: The primary outcome will be the mean change in pain severity as measured by the Brief Pain Inventory-Short Form at 12 weeks. The primary mechanism of action is ER measured by change in the Difficulties in Emotion Regulation Scale total score. Secondary outcomes include pain sensitivity, physical strength and flexibility, pain interference, and quality of life. A mediation path analysis and series of moderated mediation path analyses will be conducted to test the study hypotheses. As of January 2024, we have enrolled 138 participants. We expect the study to be completed by May 2025. CONCLUSIONS: The study will provide important data for evaluating whether improvements in ER are responsible for reduced pain perception and pain sensitivity as well as increased quality of life in the context of chronic pain. The study findings have important implications for determining the mechanism of action for yoga and possibly other mind-body interventions as nonpharmacological therapies for pain management. The results of the study will inform the content, delivery, and measures for intervention trials involving yoga as a modality for relieving pain and improving function. TRIAL REGISTRATION: ClinicalTrials.gov NCT04678297; https://clinicaltrials.gov/study/NCT04678297. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/56016.

Attenuated angiogenesis and macrophage infiltration during adipose tissue regeneration in a megavolume human fat grafting nude mouse model.

BACKGROUND: Survival and regeneration mechanisms of large (>250 mL) fat grafts remain incompletely understood. In fat grafts from volunteers with megavolume fat transfer breast augmentation, neovascularization and inflammatory cell infiltration decreased within 7 days according to histological analysis. We further investigated this phenomenon using a nude mouse model. METHODS: To simulate clinical contexts, chambers containing 1 mL human fat were implanted into nude mice. Chambers allowed selective transfer of tissue fluid from recipient nude mice into chambers, but not capillaries or macrophages. Seven days later, fat was removed from the chamber and reimplanted into a new nude mouse in the open-chambered fat group (OCFG, n=45). Adipose samples from volunteers and explanted grafts from OCFG were subjected to histological analyses. Graft weight, vascularization, and immune response were also compared between the OCFG and conventional direct fat grafting (control group (CG)). RESULTS: Percent tissue integrity, percent fibrosis, adipocyte viability, and neovascularization did not significantly differ between volunteer samples and OCFG grafts at day 7. On day 90, OCFG retention rate was decreased relative to CG and the fibrosis area was larger in the OCFG than in the CG. However, the macrophage and capillary counts were lower in the OCFG group relative to CG at days 7 and 14 after transplantation. CONCLUSIONS: The present study provides histological analyses of megavolume fat grafts sampled from clinical breast augmentation tissues and a xenograft nude mouse model. However, these preliminary results in a small clinical cohort should be further assessed in large allogeneic animal models.