To evaluate the impact of thoracic spine degeneration in adult spinal deformity (ASD) patients on radiographic outcomes. Primary ASD patients undergoing thoracolumbar fusion with T9–L1 upper instrumented vertebra (UIV) and S1/ilium lower instrumented vertebra were included. Thoracic spine degeneration was assessed radiographically using Kellgren-Lawrence (KL) grading and segmented into T1–T5, T5–T9, and T9–L1 arcs per Lafage criteria. Arc degeneration was defined as ≥ 2 levels with KL grade 3 + in an arc and thoracic spine degeneration as ≥ 1 degenerated arc. Proximal zone degeneration was KL grade 3 + in the two levels above the UIV and distal zone degeneration was KL grade 3 + in unfused thoracic levels outside the proximal zone. Patients with no degenerated levels served as controls. Among 272 patients (mean age 65.1years, 74
BACKGROUND:Artificial intelligence (AI) is a rapidly growing technological advancement that can be used to transform surgical practice. Applications range from robotic assistance and computer vision, to perioperative guidance and administrative tasks. Despite the promising potential of AI in surgery, limitations to its adoption remains an issue. OBJECTIVE:The aim of this article is to produce a framework for the incentivization of widespread adoption of artificial intelligence in surgery; taking into consideration the current landscape of AI in surgery, barriers to its adoption and challenges. METHODS:A literature search was carried out of peer-reviewed articles, policy documents, and funding program reports that are relevant to the use of AI in healthcare. CONCLUSION:Strategic financial and non-financial incentives frameworks, coupled with a continuous demonstration of clinical and economic value, are required for AI to transition from a promising innovation to a routine component of surgical practice.
BACKGROUND: Curcumin, a natural compound found in turmeric (Curcuma longa), demonstrates anticancer properties; however, it is characterized by poor bioavailability and stability. This study investigates the stability, antioxidant activity, and anticancer effects of two monocarbonyl analogs of curcumin (MACs), C66 and B2BrBC, in in vitro breast cancer models. METHODS: Stability and antioxidant activity of C66 and B2BrBC were assessed using spectrophotometric assays. Their effects on breast cancer cells (MCF-7, BT-474, MDA-MB-231) were evaluated through MTT assay, wound-healing assay, and caspase-3 fluorescence microscopy. EMT modulation was examined via RT-qPCR, Western blot, and immunofluorescence analyses. A MILLIPLEX protein assay was used to analyze cancer metastasis-related protein expression. RESULTS: C66 and B2BrBC demonstrated enhanced stability compared to curcumin. Both compounds significantly reduced breast cancer cell viability and migration, with B2BrBC showing higher potency. They effectively suppressed EMT, reversing EMT-inducer effects on epithelial and mesenchymal markers. C66 and B2BrBC modulated the expression of several metastasis-related proteins, including DKK1, OPG, and GDF15, in a cell line-dependent manner. CONCLUSIONS: C66 and B2BrBC exhibit improved stability and potent anticancer effects in breast cancer cells, effectively inhibiting cell viability, migration, and EMT. These compounds show promise as potential therapeutic agents for breast cancer, warranting further investigation in in vivo models.
To assess the complementary value of transverse plane descriptors (orientation of the regional planes of deformation (ORPD) and local apical vertebral rotations (AVR)) integrated into the new modular three-tiered, four-modifier SRS-Lenke-Aubin 3D classification, relative to conventional 2D radiographic parameters and current Lenke 2D classification in adolescent idiopathic scoliosis (AIS). Transverse plane deformities of 285 surgically treated AIS cases reconstructed in 3D were quantified using ORPD and AVR, independently assessed for the proximal thoracic (PT), main thoracic (MT), and thoracolumbar/lumbar (TL/L) regions. Correlation analyses evaluated relationships between standard 2D parameters (Cobb angles, thoracic kyphosis (TK), lumbar lordosis (LL)) and transverse plane indices (ORPD, AVR). The distribution of ORPD and AVR subclasses was examined, as well as the associations between conventional Lenke lumbar and thoracic sagittal profile modifiers, and their corresponding 3D transverse plane modifiers. Complementary analyses also included 3D displacement of the apex relative to the end-vertebrae line (DAEVL). Nearly all ORPD–AVR subclass combinations were observed across regions, confirming the system’s ability to capture diverse deformity patterns. ORPD and AVR were independent in PT and MT but correlated in TL/L (r = 0.69). Cobb angle correlated moderately with ORPD in MT (r = 0.43) and strongly in TL/L (r = 0.67), while correlations with AVR were moderate in MT (r = 0.50) and TL/L (r = 0.59). TK correlated negatively with MT ORPD (r = –0.58), whereas LL showed no association with TL/L ORPD. DAEVL correlated strongly with Cobb across all regions but only weakly to moderately with ORPD. Associations between Lenke 2D modifiers and ORPD were strong in TL/L (V = 0.59) and moderate in MT (V = 0.37). Multivariate models showed that Cobb and TK explained 44
Objective performance indicators (OPIs) derived from robotic surgery are showing potential for automated skill assessment, but their high dimensionality, data sparsity, and lack of functional context limit their clinical utility and interpretability. Here, we introduce and validate a semantic taxonomy that automatically classifies surgical instruments into functional roles—such as ‘Dominant’, ‘Active Retractor’, and ‘Passive Retractor’—based on their kinematic signatures. Applied to 462 cholecystectomies, hernia repairs, and sleeve gastrectomies, this framework drastically reduced data dimensionality. In predictive modeling for surgical experience and task efficiency, taxonomy-structured OPIs achieved superior performance to conventional metrics while requiring substantially fewer features to reach optimal results (mean, 12.4 vs. 19.5; P = 0.025). By providing functional context, this approach streamlines kinematic analysis, creating a more scalable and interpretable foundation for objective skill assessment, actionable feedback, and data-driven surgical training, ultimately enhancing surgical quality and safety.
