Lipid nanoparticles (LNPs) exhibit significant potential as highly efficient carriers for nucleic acid therapeutics in the treatment of pulmonary diseases. Nebulized inhalation delivery, which directly targets the lungs through respiratory tract, represents an ideal pulmonary administration route. However, the clinical translation of nebulized LNP-based delivery still faces several critical challenges. Intense shear forces during nebulization impair the structural integrity and stability of LNPs, leading to nucleic acid leakage. The mucin network within the pulmonary mucus layer forms a physical barrier that restricts LNP diffusion, while non-specific phagocytosis by alveolar macrophages further decreases delivery efficiency. In recent years, various design strategies have been developed to address these limitations. Modifying LNP component ratios, optimizing buffer formulations, and functionalizing LNP components have collectively improved stability, enhanced mucus penetration, reduced macrophage uptake, and increased cellular uptake by epithelial cells. This systematic review analyzes the key challenges of nebulized LNP delivery, summarizes recent breakthrough, and outlines future research directions, thereby providing theoretical insights for developing efficient pulmonary nucleic acid delivery systems.

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Malignant tumours represent a significant public health issue posing a grave threat to human life and health, garnering considerable attention in the field of biomedical science in recent years. Early screening and diagnosis of tumours provide patients with valuable treatment time, constituting a crucial measure in tumour prevention and management. Surface-enhanced Raman spectroscopy (SERS), with its advantages of ultra-high sensitivity, high precision, and multiplexing capabilities, has been widely applied in the detection of tumour markers. This paper examines SERS-based biosensors for three distinct tumour markers: prostate-specific antigen (PSA) for prostate cancer screening, alpha-fetoprotein (AFP) for primary liver cancer detection, and chromogranin A (CgA) for neuroendocrine tumour diagnosis. Compared to SERS technology, alternative early-stage tumour marker detection methods—such as chemiluminescent immunoassays, enzyme-linked immunosorbent assays, and real-time quantitative PCR—face limitations in widespread clinical adoption due to their higher costs, longer analysis times, and greater operational complexity. To address current clinical application challenges, future advancements in SERS-based biosensor detection of tumour markers will primarily be achieved through innovative improvements to the biosensor substrate.

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With the acceleration of the global energy transition process and the wide popularization of smart homes, household electricity consumption data is showing high-frequency and multi-dimensional characteristics. Hourly electricity consumption prediction has gradually become a key technical link to support the load dispatching of smart grids and help users achieve energy-saving management. Machine learning algorithms, with their powerful nonlinear fitting capabilities and efficient feature learning abilities, provide effective solutions for temporal power consumption prediction. This paper proposes the HLOA-CNN-BIGRU regression algorithm. Firstly, correlation analysis and violin plot analysis are carried out. Then, through comparative experiments of multiple machine learning algorithms, it is found that the KELM-LSTM-Transformer algorithm shows significant advantages in all indicators: Its MSE is 0.002, which is much lower than the minimum value of 0.006 in other algorithms. The RMSE is 0.047, significantly lower than the minimum value of 0.075 in other algorithms. The MAE is 0.038, which is significantly better than the minimum value of 0.056 in other algorithms. The MAPE is 2.28%, which is lower than the minimum value of 3.49% in other algorithms. R² is 99.4%, which is higher than the maximum value of 98.7% in other algorithms. Among traditional algorithms, ExtraTrees has a relatively superior overall performance, with R² reaching 98.7%, RMSE 0.075, and MAPE 3.58%, outperforming algorithms such as AdBoost and Random Forest. The overall performance of GBDT is relatively weak, with R² being only 96.8%, and MSE, RMSE, MAE, and MAPE are all the highest among all traditional algorithms. The R² of CatBoost is 97.8%, and its various error indicators are also higher than those of algorithms such as ExtraTrees and AdBoost. Overall, the KELM-LSTM-Transformer algorithm comprehensively outperforms other compared machine learning algorithms in terms of prediction accuracy and fitting effect, with stronger performance, providing important technical support for the efficient operation of smart grids and the refined management of household energy.

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To address low accuracy and poor robustness in tomato maturity detection under complex agricultural environments (light variations, occlusion, multi-scale targets), this study proposes a lightweight multi-scale differential fusion network Tomato Net-MSF. Its core innovation is scale-customized feature enhancement modules: (1) Small-scale KSFA module (multi-core selection + spatial-spectral attention) suppresses background and improves small-target recognition; (2) Medium-scale C3k2l module (2*2 simplified convolution + dual-bottleneck stacking) enhances fusion stability; (3) Large-scale LSConv module (global perception + dynamic aggregation) refines target contours. Experiments on Laboro Tomato dataset show: mAP50 0.75 (8% higher than baseline YOLOv11), F1 0.72 (6% higher), and 50FPS inference speed (20ms/frame) on RK3588NPU (5-fold acceleration). The model optimizes multi-scale detection balance, integrating high precision, robustness and edge deployment capability, providing an efficient solution for real-time tomato maturity detection in complex scenarios.

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