There is limited data available on short-term outcomes on a cut-block positioning robotic system. The purpose of this study was to compare 12-month clinical outcomes between robotic-assisted (raTKA) and conventional total knee arthroplasty (cTKA) with multiple outcomes and surgical centers. This was a non-randomized controlled trial of patients who received either raTKA (n = 120) or cTKA (n = 101) at 6 different surgical centers. Variables of interest included occurrence of soft tissue release, complications and revisions at minimum one-year follow-up. Satisfaction, pain (numeric rating scale [NRS]), 5-dimensional European Quality of Life (EQ-5D-5 L) questionnaire (index and visual analog scale [VAS]), Oxford Knee Score (OKS), and the Forgotten Joint Score (FJS-12) were collected pre-operatively, and at six weeks, three months, and 12 months post-operative. There were significantly less soft tissue releases with raTKA (28/120, 23.3
BACKGROUND:Cementless implants have seen a resurgence in knee arthroplasty to improve long-term bone fixation. The purpose of this study was to report the results of the US Investigational Device Exemption study comparing the mobile bearing cementless and cemented unicompartmental knee arthroplasty (UKA). METHODS:A single-blind, multicenter, randomized controlled trial was performed to compare a medial cementless and cemented UKA implant. There were 378 patients allocated 2:1 to receive the cementless (n = 241) or cemented (n = 137) device between November 2013 and November 2018. The primary end points were identified to demonstrate noninferiority of the cementless device in terms of survivorship, radiographic results, and Knee Society Clinical Assessment and Functional Scores. RESULTS:There was no statistical difference in Kaplan-Meier 2-year survivorship between the cementless (94.0%, 95% confidence interval [CI] 89.9 to 96.8) and cemented cohorts (97.5%, 95% CI: 93.0 to 99.5, P = 0.19); similar survival persisted at five years (91.7%, 95% CI: 86.4 to 95.0 versus 95.6%, 95% CI: 88.4 to 98.4, P = 0.24). Examination of radiographic success revealed noninferior performance of the cementless prosthesis (93.8 versus 97.4%, P = 0.19). Knee Society function (89.9 ± 13.0 versus 89.8 ± 14.0, P = 0.95) and clinical assessment (95.5 ± 8.5 versus 95.6 ± 6.9, P = 0.92) scores at two years demonstrated no differences between groups. CONCLUSIONS:The cementless mobile bearing UKA demonstrated noninferior clinical and radiographic outcomes compared to the cemented device in this US Investigational Device Exemption study. Cementless fixation may be an attractive option for appropriately selected patients.
This study investigates the structure-property relationships and machine learning prediction capabilities for twenty quantum materials that exhibit exotic electronic and magnetic phenomena at cryogenic temperatures. The materials exhibit lattice constants ranging from 3.4 to 4.3 angstroms, electron mobilities ranging from 800-2100 cm²/V·s, band gaps of 0-0.7 eV, band sensitivities of 20-130 (×10⁻ ⁶ ), and critical temperatures ranging from 6-32 K. Statistical analysis reveals exceptionally strong positive correlations (0.93-0.97) between lattice constant, electron mobility, magnetic susceptibility, and critical temperature, while the band gap exhibits strong negative correlations (-0.84 to - 0.93) with all other parameters. These relationships reflect fundamental quantum mechanical principles: expanded lattices facilitate electron wave function delocalization, simultaneously enhancing mobility, magnetic response, and phase transition temperatures. Materials with zero band gaps consistently exhibit superior properties, reaching mobilities greater than 2000 cm²/V·s and susceptibilities greater than 110 (×10⁻ ⁶ ). Elastic net regression successfully uses these relationships to predict magnetic susceptibility, achieving a training R² of 0.99 with an RMSE of 3.34. However, the study suffers from important experimental limitations, in particular the insufficiently large experimental set of only two samples, making the reported experimental R² of 0.88 statistically unreliable. Validation curve analysis identifies the optimal regularization strength (alpha ≈ 0.001- 0.01), where model performance peaks. The dataset reveals clear material hierarchies, with a peak performance of 4.3 Å, suitable for advanced quantum applications, and small lattice constants that severely limit the functionality. This research demonstrates that quantum materials follow predictable structure-property relationships suitable for machine learning prediction, although robust validation requires significantly larger datasets with appropriately sized test sets that implement cross-validation procedures. Key words: Quantum materials, elastic net recoil, structure-property relationships, electron mobility, magnetic susceptibility, critical temperature, machine learning prediction
Orthopaedic medical device manufacturing often utilizes manufacturing materials or processing aids that can remain on device surfaces as residues and result in leachable chemicals to which patients can be exposed upon contact with the device. Manufacturing steps that modify a device surface through electrochemical means such as electropolishing can remove manufacturing material residues and be considered as surface “reset” in the manufacturing process. In this report, a retrospective analysis of chemical characterization data gathered for medical devices subjected to electropolishing steps during the manufacturing process is summarized. Review of the extractables data from metallic medical devices demonstrated that electropolishing operations effectively remove the residues originating from manufacturing materials utilized upstream of electropolishing. Based on this retrospective review, electropolishing can serve as a surface “reset” point in the manufacturing process in biological safety evaluations, thereby reducing the overall testing requirements, including animal testing, in alignment with the ISO 10993 framework.
Floods, One of the most destructive natural catastrophes in the globe, it damages infrastructure and has major social effects, especially in disaster-prone areas such as Indonesia. This research addresses the vital requirement for precise and prompt flood detection and monitoring systems through advanced remote sensing and machine learning technologies. This study evaluates the performance of deep learning frameworks, especially for semantic segmentation of flood-affected areas, DeepLabv3 uses a variety of pre-trained backbone models, such as ResNet50, EfficientNet-B4, and MobileNet using the Flood Net dataset of 398 reference samples. In addition, a comparative analysis of state-of-the-art frameworks, including SegNet, UNet, and FCN32, demonstrates their effectiveness in automatic flood area detection, with SegNet achieving the best performance at 88% accuracy. The research methodology includes key parameters including rainfall intensity, flooded area size, water depth measurements, and segmentation accuracy parameters including F1-score, recall, accuracy, and mIoU. The incorporation of synthetic aperture radar (SAR) technology and satellite-based remote sensing provides extensive monitoring capabilities over large geographical areas. This multi-faceted approach, which combines integrated techniques such as unmanned aerial vehicles, deep learning architectures, and stochastic forest regression, provides a strong foundation for improving disaster recovery, flood forecasting systems, and risk reduction strategies in vulnerable communities. Keywords: Flood Detection, Semantic Segmentation, Deep Learning, Remote Sensing, DeepLabv3, UAV Surveillance, Disaster Management, SAR Images, Machine Learning, Computer Vision
