Expandable Intravertebral Implant in Cancer-Related Vertebral Compression Fractures: A Retrospective Review of 36 Implantations

The purpose of this retrospective review was to evaluate SpineJack implantation in cancer-related vertebral compression fractures in 13 consecutive patients (mean age, 62.8 years ± 18.8). A total of 36 devices were inserted at 20 levels (13 [65%] lumbar and 7 [35%] thoracic vertebrae), with a mean Spinal Instability Neoplastic Score of 9.1 ± 2.1. Vertebral height restoration was observed in 10 levels (50%), with a mean height restoration of 5.6 mm ± 2.2 (interquartile range [IQR], 4-7.5). A total of 6 cement leakages were observed in 3 (23%) patients without clinical consequences. No severe adverse events were observed. One adjacent fracture occurred. Average pain scores on the visual analog scale significantly improved from 5.5 ± 1.8 (IQR, 4-7) preoperatively to 1.5 ± 2.2 (IQR, 0-3.3) at 1 month (P < .01) and to 1.5 ± 1.3 (IQR, 0.3-2.8) at 6 months (P < .01). In this small cohort, SpineJack offered pain relief in cancer-related fractures without an observed increase in adverse events.

Advancing breast cancer diagnosis: token vision transformers for faster and accurate classification of histopathology images

The vision transformer (ViT) architecture, with its attention mechanism based on multi-head attention layers, has been widely adopted in various computer-aided diagnosis tasks due to its effectiveness in processing medical image information. ViTs are notably recognized for their complex architecture, which requires high-performance GPUs or CPUs for efficient model training and deployment in real-world medical diagnostic devices. This renders them more intricate than convolutional neural networks (CNNs). This difficulty is also challenging in the context of histopathology image analysis, where the images are both limited and complex. In response to these challenges, this study proposes a TokenMixer hybrid-architecture that combines the strengths of CNNs and ViTs. This hybrid architecture aims to enhance feature extraction and classification accuracy with shorter training time and fewer parameters by minimizing the number of input patches employed during training, while incorporating tokenization of input patches using convolutional layers and encoder transformer layers to process patches across all network layers for fast and accurate breast cancer tumor subtype classification. The TokenMixer mechanism is inspired by the ConvMixer and TokenLearner models. First, the ConvMixer model dynamically generates spatial attention maps using convolutional layers, enabling the extraction of patches from input images to minimize the number of input patches used in training. Second, the TokenLearner model extracts relevant regions from the selected input patches, tokenizes them to improve feature extraction, and trains all tokenized patches in an encoder transformer network. We evaluated the TokenMixer model on the BreakHis public dataset, comparing it with ViT-based and other state-of-the-art methods. Our approach achieved impressive results for both binary and multi-classification of breast cancer subtypes across various magnification levels (40×, 100×, 200×, 400×). The model demonstrated accuracies of 97.02% for binary classification and 93.29% for multi-classification, with decision times of 391.71 and 1173.56 s, respectively. These results highlight the potential of our hybrid deep ViT-CNN architecture for advancing tumor classification in histopathological images. The source code is accessible: https://github.com/abimouloud/TokenMixer .

Magnetic properties of different phases iron oxide nanoparticles prepared by micro emulsion-hydrothermal method

In this study, we report the synthesis of iron oxide nanoparticles (FeONPs) using micro-emulsion-hydrothermal method. By adjusting the synthesis temperature, we successfully produced FeO nanorods and nanospheres. In addition, the 2-octanol, and the surfactant cetyltrimethylammonium bromide served as a solvent in the synthesis process. Using iron nitrate hexahydrate as the salt precursor allowed for the formation of FeONPs with varied sizes, shapes and phases. The synthesized materials were extensively characterized using XRD, SEM, TEM, EDS, FTIR, and XPS techniques. However, the structural analysis revealed rhombohedral (hematite), and (magnetite) crystal structures in the materials synthesized at different temperature and durations, with particles ranged in size from 12 to 97 nm. More importantly, the magnetic characterization, performed with a vibrating sample magnetometer and SQUID magnetometer, indicated that the NPs showed not clear superparamagnetic behavior. In conclusion, this work demonstrates the synthesis of FeONPs with controlled size, shape and phase using microemulsion hydrothermal technique, with detailed characterization offering valuable insights into their magnetic and structural properties.