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IntroductionThe diagnosis and treatment of ankylosing spondylitis (AS) is a difficult task, especially in less developed countries without access to experts. To address this issue, a comprehensive artificial intelligence (AI) tool was created to help diagnose and predict the course of AS.MethodsIn this retrospective study, a dataset of 5389 pelvic radiographs (PXRs) from patients treated at a single medical center between March 2014 and April 2022 was used to create an ensemble deep learning (DL) model for diagnosing AS. The model was then tested on an additional 583 images from three other medical centers, and its performance was evaluated using the area under the receiver operating characteristic curve analysis, accuracy, precision, recall, and F1 scores. Furthermore, clinical prediction models for identifying high-risk patients and triaging patients were developed and validated using clinical data from 356 patients.ResultsThe ensemble DL model demonstrated impressive performance in a multicenter external test set, with precision, recall, and area under the receiver operating characteristic curve values of 0.90, 0.89, and 0.96, respectively. This performance surpassed that of human experts, and the model also significantly improved the experts' diagnostic accuracy. Furthermore, the model's diagnosis results based on smartphone-captured images were comparable to those of human experts. Additionally, a clinical prediction model was established that accurately categorizes patients with AS into high-and low-risk groups with distinct clinical trajectories. This provides a strong foundation for individualized care.DiscussionIn this study, an exceptionally comprehensive AI tool was developed for the diagnosis and management of AS in complex clinical scenarios, especially in underdeveloped or rural areas that lack access to experts. This tool is highly beneficial in providing an efficient and effective system of diagnosis and management.

The excellent photothermal and photoelectrical property of Au nanodumbbells make themselves serve as pore generators on the surface of GO sheets, which both accelerate the reduction and cut the large pieces of GO sheets into small pieces.

The objective of this study was to utilize machine learning techniques to analyze perioperative factors and identify blood glucose levels that can predict the occurrence of surgical site infection following posterior lumbar spinal surgery. Methods: A total of 4019 patients receiving lumbar internal fixation surgery from an institute were enrolled between June 2012 and February 2021. First, the filtered data were randomized into the test and verification groups. Second, in the test group, specific variables were screened using logistic regression analysis, Lasso regression analysis, support vector machine, and random forest. Specific variables obtained using the four methods were intersected, and a dynamic model was constructed. ROC and calibration curves were constructed to assess model performance. Finally, internal model performance was verified in the verification group using ROC and calibration curves. Results: The data from 4019 patients were collected. In total, 1327 eligible cases were selected. By combining logistic regression analysis with three machine learning algorithms, this study identified four predictors associated with SSI, namely Modic changes, sebum thickness, hemoglobin, and glucose. Using this information, a prediction model was developed and visually represented. Then, we constructed ROC and calibration curves using the test group; the area under the ROC curve was 0.988. Further, calibration curve analysis revealed favorable consistency of nomogram-predicted values compared with real measurements. The C-index of our model was 0.986 (95% CI 0.981-0.994). Finally, we used the validation group to validate the model internally; the AUC was 0.987. Calibration curve analysis revealed favorable consistency of nomogram-predicted values compared with real measurements. The C-index was 0.982 (95% CI 0.974-0.999). Conclusion: Logistic regression analysis and machine learning were employed to select four risk factors: Modic changes, sebum thickness, hemoglobin, and glucose. Then, a dynamic prediction model was constructed to help clinicians simplify the monitoring and prevention of SSI.

In this work, the rationally-designed sharp corners on Au nanorods tremendously improved the catalytic activity, particularly in the presence of visible light irradiation, towards the hydrogenation of 4-nitrophenol to 4-aminophenol. A strikingly increased rate constant of 50.6 g s L was achieved in M-Au-3, which was 41.8 times higher than that of parent Au nanorods under dark conditions. The enhanced activities were proportional to the extent of the protruding sharp corners. Furthermore, remarkably enhanced activities were achieved in novel ternary Au/RGO/TiO sheets, which were endowed with a 52.0 times higher rate constant than that of straight Au nanorods. These remarkably enhanced activities were even higher than those of previously reported 3-5 nm Au and 3 nm Pt nanoparticles. It was systematically observed that there are three aspects to the synergistic effects between Au and RGO sheets: (i) electron transfer from RGO to Au, (ii) a high concentration of p-nitrophenol close to dumbbell-like Au nanorods on RGO sheets, and (iii) increased local reaction temperature from the photothermal effect of both dumbbell-like Au nanorods and RGO sheets.

This study reports a new sinter-resistant catalyst system, consisting of Pt nanoparticles on Fe2O3rhombohedrons isolated by wrinkled graphene sheets.

New N‐doped reduced graphene oxide (N‐RGO) meshes are facile fabricated by selective etching of 3–5 nm nanopores, with controllable doping of N dopants at an ultrahigh N/C ratio up to 15.6 at%, from pristine graphene oxide sheets in one‐pot hydrothermal reaction. The N‐RGO meshes are illustrated to be an efficient metal‐free catalyst toward hydrogenation of 4‐nitrophenol, with new catalytic behaviors emerging in following three aspects: (i) tunable kinetics following pseudofirst order from commonly observed pseudozero order; (ii) strikingly improved activity with 26‐fold increased rate constant (1.0 s−1 g−1 L); (iii) no induction time required prior to reaction due to depressed back conversion, and dramatically decreased apparent activation energy (Ea) (17 kJ mol−1). The origin of these new catalytic properties can be assigned to the synergetic effects between graphitic N doping and structural defects arising from nanopores. Deeper understanding unveils that the concentration of graphitic N is inverse proportion to Ea, while the pyrrolic N has no impact on this reaction, and oxygenate groups hampers it. The porous nature allows the N‐RGO meshes to conduct catalyze reactions in continuous flow fashion.

We report a solventless bilayer strategy for grafting hydrophilic poly(methacrylic acid) (PMAA) onto metal meshes. The bilayer nanocoating consisted of ultrathin PMAA brushes grafted onto a highly crosslinked primer that binds to each mesh wire. The enrichment of hydrophilic functionalities on the PMAA-grafted mesh surface improved the surface hydration and underwater oil repellency over the PMAA-crosslinked mesh, which contributed to the formation and stabilization of continuous water films between mesh wires to reject oil penetration in repeated uses. The vapor-based nanocoating process maximized the retention of pore openings for high-flux oil–water separation. The retained permeability, together with the enhanced oil repellency, enabled the PMAA-grafted mesh to achieve excellent gravity-driven oil–water separation, with an oil rejection rate higher than 99%, water flux exceeding 50,000 L m–2 h–1, and flux decay less than 3% after 20 cycles of reuse. More importantly, the PMAA-grafted mesh preserved high separation efficiency and flux stability under conditions of high salinity and extreme pH.