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Abstract Hepatocellular cancer (HCC) remained a life-threatening carcinoma. Agents for HCC imaging and therapy were expected to possess different intratumoral retention time. To construct an agent with different intratumoral retention time when applied for tumor imaging or therapy remained great values. A lasialoglycoprotein receptor (ASGPR) targeted lactobionic acid derivative (LABO) was constructed for fluorescent imaging and photodynamic therapy of HCC. 18F labeled LABO (18F-LABO) was developed for PET imaging of HCC. LABO and 18F-LABO showed similar molecular structure. LABO exhibited characteristic of viscosity and concentration-induced intratumoral in-situ self-assembly to expand the intratumoral retention. LABO was non-fluorescent at free stage, but emitted NIR fluorescence and generated irradiation-induced ROS after self-assembly for fluorescent imaging and photodynamic therapy. ASGPR specificity of LABO and 18F-LABO was confirmed using HepG2 cell. Biodistribution and fluorescent imaging confirmed the different tumor retention time of LABO and 18F-LABO when used for photodynamic therapy and PET imaging. PET imaging and photodynamic therapy were performed on HepG2 tumor bearing mice, which revealed that 18F-LABO/LABO could specifically accumulated in the HepG2 tumor for tumor location/inhibition. LABO/18F-LABO with excellent HCC specificity but different intratumoral behaviors showed great values for the PET/NIR imaging and photodynamic therapy for HCC. Graphical Abstract

In the realms of Internet of Things (IoT), satellite communication, and related scenarios, automatic modulation recognition is crucial for accurate signal demodulation. In complex communication environments, accurately identifying diverse modulation types is a challenging task. This paper introduces an automatic modulation recognition approach leveraging a joint neural network framework. The method integrates a flow-based collaborative training module for signal enhancement, a deep learning mechanism for feature extraction, and a two-dimensional sparse weighting mechanism. This method enhances the input signal through enhancement processing and strengthens attention to different dimensional features via a weighting mechanism, thereby suppressing irrelevant features with lower weights. The network architecture is optimized in terms of layer depth and connectivity to enhance modulation identification accuracy and model stability under non-ideal conditions. Experimental evaluations conducted on the RML2016.10a dataset across varying SNR demonstrate the method’s robustness in low SNR environments and its effective recognition performance for high-order modulated signals compared to baseline models.

Abstract Background In clinical medicine, fetal heart rate (FHR) monitoring using cardiotocography (CTG) is one of the most commonly used methods for assessing fetal acidosis. However, as the visual interpretation of CTG depends on the subjective judgment of the clinician, this has led to high inter-observer and intra-observer variability, making it necessary to introduce automated diagnostic techniques. Methods In this study, we propose a computer-aided diagnostic algorithm (Hybrid-FHR) for fetal acidosis to assist physicians in making objective decisions and taking timely interventions. Hybrid-FHR uses multi-modal features, including one-dimensional FHR signals and three types of expert features designed based on prior knowledge (morphological time domain, frequency domain, and nonlinear). To extract the spatiotemporal feature representation of one-dimensional FHR signals, we designed a multi-scale squeeze and excitation temporal convolutional network (SE-TCN) backbone model based on dilated causal convolution, which can effectively capture the long-term dependence of FHR signals by expanding the receptive field of each layer’s convolution kernel while maintaining a relatively small parameter size. In addition, we proposed a cross-modal feature fusion (CMFF) method that uses multi-head attention mechanisms to explore the relationships between different modalities, obtaining more informative feature representations and improving diagnostic accuracy. Results Our ablation experiments show that the Hybrid-FHR outperforms traditional previous methods, with average accuracy, specificity, sensitivity, precision, and F1 score of 96.8, 97.5, 96, 97.5, and 96.7%, respectively. Conclusions Our algorithm enables automated CTG analysis, assisting healthcare professionals in the early identification of fetal acidosis and the prompt implementation of interventions.

In this study, we propose a novel approach to achieve an ultra-narrow bandwidth and high transmittance filter in the visible range. By employing TiO2 as the waveguide layer material, the coupling energy in the waveguide layer is enhanced, leading to a reduction in the full width half maximum (FWHM). Additionally, a double-layer dielectric film structure composed of SiO2 and Al2O3 is employed on the Ag grating to suppress random scattering leakage in the short wavelength range, resulting in reduced sidebands and improved transmittance of the resonant peak. To minimize wave loss between the metal grating and the waveguide layer, a SiO2 dielectric layer is introduced. By adjusting the metal grating period, the resonance peak position can be shifted across the entire visible range. The proposed approach offers a metal grating-based guided-mode resonance (GMR) transmissive filter with low sidebands, high transmittance, ultra-narrow bandwidth, and tunability in the entire visible range. Experimental validation of the proposed design is conducted, and the results demonstrate the effectiveness of this approach in achieving superior filter performance in the visible spectral range.