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Prosthetic joint infection (PJI) is a prevalent and severe complication characterized by high diagnostic challenges. Currently, a unified diagnostic standard incorporating both computed tomography (CT) images and numerical text data for PJI remains unestablished, owing to the substantial noise in CT images and the disparity in data volume between CT images and text data. This study introduces a diagnostic method, HGT, based on deep learning and multimodal techniques. It effectively merges features from CT scan images and patients’ numerical text data via a Unidirectional Selective Attention (USA) mechanism and a graph convolutional network (GCN)-based Feature Fusion network. We evaluated the proposed method on a custom-built multimodal PJI dataset, assessing its performance through ablation experiments and interpretability evaluations. Our method achieved an accuracy (ACC) of 91.4% and an area under the curve (AUC) of 95.9%, outperforming recent multimodal approaches by 2.9% in ACC and 2.2% in AUC, with a parameter count of only 68 M. Notably, the interpretability results highlighted our model’s strong focus and localization capabilities at lesion sites. This proposed method could provide clinicians with additional diagnostic tools to enhance accuracy and efficiency in clinical practice.

In the present work, NiCo2O4 nanostructures are fabricated in three dimensions (3D) on nickel foam by the hydrothermal method. The nanomaterial was characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). The nanostructures exhibit nanoneedle-like morphology grown in 3D with good crystalline quality. The nanomaterial is composed of nickel, cobalt and oxygen atoms. By using the favorable porosity of the nanomaterial and the substrate itself, a sensitive glucose sensor is proposed by immobilizing glucose oxidase. The presented glucose sensor has shown linear response over a wide range of glucose concentrations from 0.005 mM to 15 mM with a sensitivity of 91.34 mV/decade and a fast response time of less than 10 s. The NiCo2O4 nanostructures-based glucose sensor has shown excellent reproducibility, repeatability and stability. The sensor showed negligible response to the normal concentrations of common interferents with glucose sensing, including uric acid, dopamine and ascorbic acid. All these favorable advantages of the fabricated glucose sensor suggest that it may have high potential for the determination of glucose in biological samples, food and other related areas.

In this study, we have developed a sensitive and selective glucose sensor using novel CuO nanosheets which were grown on a gold coated glass substrate by a low temperature growth method. X-ray differaction (XRD) and scanning electron microscopy (SEM) techniques were used for the structural characterization of CuO nanostructures. CuO nanosheets are highly dense, uniform, and exhibited good crystalline array structure. X-ray photoelectron spectroscopy (XPS) technique was applied for the study of chemical composition of CuO nanosheets and the obtained information demonstrated pure phase CuO nanosheets. The novel CuO nanosheets were employed for the development of a sensitive and selective non-enzymatic glucose sensor. The measured sensitivity and a correlation coefficient are in order 5.20 × 102 µA/mMcm2 and 0.998, respectively. The proposed sensor is associated with several advantages such as low cost, simplicity, high stability, reproducibility and selectivity for the quick detection of glucose.

In this paper, we show that the possibility of using polyethylene glycol (EG) as a hydrogen source and it is used to assist the hydrothermal synthesis of ZnO nanorods (ZNRs). EG doping in ZNRs has been found to significantly improve their optical and chemical sensing characteristics toward glutamate. The EG was found to have no role on the structural properties of the ZNRs. However, the x-ray photoelectron spectroscopy (XPS) suggests that the EG could induce donor impurities effect in ZnO. Photoluminescence (PL) and UV-Vis. spectra demonstrated this doping effect. Mott-Schottky analysis at the ZNRs/electrolyte interface was used to investigate the charge density for the doped ZNRs and showed comparable dependence on the used amount of EG. Moreover, the doped ZNRs were used in potentiometric measurements for glutamate for a range from 10−6 M to 10−3 M and the potential response of the sensor electrode was linear with a slope of 91.15 mV/decade. The wide range and high sensitivity of the modified ZNRs based glutamate biosensor is attributed to the doping effect on the ZNRs that is dictated by the EG along with the high surface area-to-volume ratio. The findings in the present study suggest new avenues to control the growth of n-ZnO nanostructures and enhance the performance of their sensing devices.