Deep Learning Model for Grading and Localization of Lumbar Disc Herniation on Magnetic Resonance Imaging.

BACKGROUND: Methods for grading and localization of lumbar disc herniation (LDH) on MRI are complex, time-consuming, and subjective. Utilizing deep learning (DL) models as assistance would mitigate such complexities. PURPOSE: To develop an interpretable DL model capable of grading and localizing LDH. STUDY TYPE: Retrospective. SUBJECTS: 1496 patients (M/F: 783/713) were evaluated, and randomly divided into training (70%), validation (10%), and test (20%) sets. FIELD STRENGTH/SEQUENCE: 1.5T MRI for axial T2-weighted sequences (spin echo). ASSESSMENT: The training set was annotated by three spinal surgeons using the Michigan State University classification to train the DL model. The test set was annotated by a spinal surgery expert (as ground truth labels), and two spinal surgeons (comparison with the trained model). An external test set was employed to evaluate the generalizability of the DL model. STATISTICAL TESTS: Calculated intersection over union (IoU) for detection consistency, utilized Gwet's AC1 to assess interobserver agreement, and evaluated model performance based on sensitivity and specificity, with statistical significance set at P < 0.05. RESULTS: The DL model achieved high detection consistency in both the internal test dataset (grading: mean IoU 0.84, recall 99.6%; localization: IoU 0.82, recall 99.5%) and external test dataset (grading: 0.72, 98.0%; localization: 0.71, 97.6%). For internal testing, the DL model (grading: 0.81; localization: 0.76), Rater 1 (0.88; 0.82), and Rater 2 (0.86; 0.83) demonstrated results highly consistent with the ground truth labels. The overall sensitivity of the DL model was 87.0% for grading and 84.0% for localization, while the specificity was 95.5% and 94.4%. For external testing, the DL model showed an appreciable decrease in consistency (grading: 0.69; localization: 0.66), sensitivity (77.2%; 76.7%), and specificity (92.3%; 91.8%). DATA CONCLUSION: The classification capabilities of the DL model closely resemble those of spinal surgeons. For future improvement, enriching the diversity of cases could enhance the model's generalization. TECHNICAL EFFICACY: Stage 2.

Making graphene oxide (GO)-cladded SiO2 spheres (SiO2 @GO) as inorganic fillers for dental restorative resin composites.

OBJECTIVE: Graphene oxide (GO) is of great interest in dentistry as the functional filler, mainly owing to its ability to inhibit the formation of cariogenic bacteria and possess low cytotoxicity to different cells, such as human dental pulp cells, HeLa cells, etc. However, its typical brown color limits the practical application. METHODS: Here, the refractive-index-matched monodisperse SiO2 were used as the supporting substrates to synthesize GO-cladded SiO2 spheres (xSiO2 @ yGO) through a mild electrostatic self-assembly process, where x and y represent the amount of SiO2 and GO in the reaction mixture, respectively. The morphology and the optical performance of the obtained xSiO2 @ yGO particles were modulated by varying the mass ratio of SiO2 and GO (5:1, 10:1, 50:1, and 100:1). All developed hybrid particles were silanized and formulated with dimethacrylate-based resins. These were tested for curing depth, polymerization conversion, mechanical performance, in vitro cell viability, and antibacterial activity. RESULTS: Of all xSiO2 @ yGO materials, increasing the mass ratio to 100:1 made the 100SiO2 @GO particles appear light brown and possess the lowest light absorbance from 300 to 800 nm. The results of CIEL*a*b* system showed that all these hybrid particles exhibited obvious discoloration compared with SiO2 and GO, where 100SiO2 @GO possessed the smallest color difference. Furthermore, following the results of curing depth, polymerization conversion, and mechanical performance of dental composites, the optimal filler composition was 100SiO2 @GO at 5 wt% filler loading. The resultant 100SiO2 @GO-filled composite produced the highest flexural strength (115 ± 12 MPa) and the lowest bacterial concentration (6.7 × 108 CFU/mL) than those of the resin matrix (78 ± 11 MPa; 9.2 × 108 CFU/mL) and 5 wt% SiO2-filled composite (106 ± 9 MPa; 9.1 × 108 CFU/mL), respectively, without affecting in vitro cell viability. SIGNIFICANCE: The facile and mild synthesis of xSiO2 @ yGO hybrid particles provided a convenient way to tune their optical property. The optimal 100SiO2 @GO particles could be considered as the promising antibacterial filler to be applied in dental care and therapy.

Response to Comments on "Ultrastructure reveals ancestral vertebrate pharyngeal skeleton in yunnanozoans".

He et al. dispute our anatomical interpretations on the structures of cellular chambers and microfibrils in yunnanozoan branchial arches and put forward alternative interpretations on these structures. Zhang and Pratt argue that the microfibrils we identified in yunnanozoans are more likely modern organic contamination. Here we provide additional evidence to support our interpretations and dismiss the alternative interpretations.

Ultrastructure reveals ancestral vertebrate pharyngeal skeleton in yunnanozoans.

Pharyngeal arches are a key innovation that likely contributed to the evolution of the jaws and braincase of vertebrates. It has long been hypothesized that the pharyngeal (branchial) arch evolved from an unjointed cartilaginous rod in vertebrate ancestors such as that in the nonvertebrate chordate amphioxus, but whether such ancestral anatomy existed remains unknown. The pharyngeal skeleton of controversial Cambrian animals called yunnanozoans may contain the oldest fossil evidence constraining the early evolution of the arches, yet its correlation with that of vertebrates is still disputed. By examining additional specimens in previously unexplored techniques (for example, x-ray microtomography, scanning and transmission electron microscopy, and energy dispersive spectrometry element mapping), we found evidence that yunnanozoan branchial arches consist of cellular cartilage with an extracellular matrix dominated by microfibrils, a feature hitherto considered specific to vertebrates. Our phylogenetic analysis provides further support that yunnanozoans are stem vertebrates.