With the intensification of population aging, Alzheimer's disease (AD), as the main type of dementia, has attracted increasing attention due to its high incidence and heavy disease burden. The pathogenesis of AD remains unclear, and existing drug therapies still face significant limitations in terms of efficacy, accessibility, and economic burden. Therefore, non-pharmacological interventions have become a research hotspot. Gamma rhythm, a neural oscillation at 30~100 Hz in the brain, is closely related to cognitive function, and its attenuation is an important feature of cognitive impairment in AD. Gamma rhythmic stimulation enhances brain gamma oscillations through neural entrainment, showing great potential in AD intervention. This study systematically retrieved relevant literature from China National Knowledge Infrastructure database and Web of Science Core Collection from January 1, 2015 to June 30, 2025. It reviewed the researches of gamma rhythmic stimulation in the treatment of AD. The results show that gamma rhythmic stimulation exerts neuroprotective and cognitive-improving effects through multiple pathways, such as reducing amyloid β-protein (Aβ) deposition and tau hyperphosphorylation. Among them, audiovisual combined multimodal stimulation has entered phase III clinical trials, with good safety and compliance in home-based interventions. However, several key issues remain to be addressed in this field, including inconsistent stimulation parameters, incompletely clarified mechanisms, insufficient reproducibility of clinical effects, and lack of a standardized verification system. In the future, it is necessary to establish a neural entrainment verification standard based on electroencephalography/quantitative electroencephalography (EEG/qEEG), standardize stimulation parameters, develop precise and wearable closed-loop devices, promote multimodal synergistic interventions, and deepen research on molecular and circuit mechanisms. These efforts will accelerate the translational application of gamma rhythmic stimulation from basic research to standardized clinical practice for AD.

Laser induced breakdown spectroscopy (LIBS) has attracted extensive attention in the field of biomedical detection due to its advantages of rapid, in situ, micro-damage and multi-element synchronous analysis. However, its inherent spectral noise, matrix effect and other problems restrict the analysis accuracy. In recent years, the powerful nonlinear fitting and feature extraction capabilities of neural network models provide effective solutions to overcome these bottlenecks. This paper systematically reviews the research progress of LIBS technology combined with neural network model in biomedical field, focusing on the key research achievements of this combined strategy in tumor diagnosis and classification, cancer staging and treatment monitoring, bacterial and pathogen identification, as well as traditional Chinese medicine analysis and traceability. Studies have shown that by combining artificial neural network, convolutional neural network, spiking neural network and other models, it can extract deep features from complex LIBS spectra, significantly improve the accuracy and robustness of classification and quantitative analysis. It is concluded that the integration of LIBS and neural network forms a synergistic effect of “rapid detection+intelligent analysis”, which shows great clinical application potential and lays a solid technical foundation for the development of intelligent, high-precision point-of-care diagnosis system in the future.

Pulmonary embolism (PE) is an acute cardiopulmonary disease characterized by high morbidity and mortality, making early detection and prompt treatment crucial. Although computed tomography pulmonary angiography (CTPA) serves as the “gold standard” for diagnosing PE due to its speed, non-invasiveness, and high sensitivity, it still presents several challenges in clinical practice. These include inconsistent image quality, the risk of missing small emboli, cumbersome diagnostic workflows, and insufficient precision in quantitative analysis. This article delves into the clinical value of artificial intelligence (AI) throughout the entire CTPA workflow. It systematically reviews research progress on AI applications, ranging from pre-scan optimization, image processing, automatic segmentation of pulmonary artery tree, to intelligent detection and quantitative analysis of pulmonary embolism, and prognosis risk assessment. It also discusses the current difficulties and challenges of implementing AI in clinical CTPA practice and explores future directions toward multimodal, intelligent, and portable solutions. This review aims to provide a reference for the research and clinical application of AI in the diagnosis of PE using CTPA, and to facilitate the innovation of CTPA and the improvement of medical service quality.

Intraoperative gamma cameras are key devices for acquiring real-time molecular imaging information in radioguided surgery, playing an important role in clinical applications such as precise tumor resection, sentinel lymph node localization, and residual lesion assessment. Their performance directly affects the accuracy and reliability of radioguided surgical procedures. To address the limitations of existing intraoperative gamma cameras, which generally rely on mechanical collimators and consequently suffer from reduced sensitivity, increased system weight, and restricted field of view, this study proposes and develops a handheld intraoperative gamma camera based on self-collimation theory, and evaluates its key performance characteristics and representative applications. The proposed gamma camera employs a detector array composed of three-dimensionally sparse, interlaced Ce: GAGG crystal and glass elements, enabling self-collimating imaging without the use of conventional mechanical collimators. Experimental results demonstrate that the system achieves a sensitivity of (7.55±2.97)×10³ cps/MBq within a 100 mm×100 mm field of view at source-to-detector distances ranging from 30 to 100 mm. Spatial resolutions of 4 mm and 10 mm are achieved at distances of 5 cm and 10 cm, respectively. The three-dimensional localization errors of lymph node models are less than 2 mm in all three dimensions. In mouse sentinel lymph node imaging experiments, clear visualization and accurate localization of lymph nodes are achieved even when the activity level is significantly lower than the typical activity of human sentinel lymph nodes during clinical radioguided surgery. These results demonstrate that the self-collimation–based handheld intraoperative gamma camera, by eliminating the need for mechanical collimators, achieves a favorable balance between high sensitivity and spatial resolution, exhibits excellent low-activity imaging performance, and shows strong potential for providing effective intraoperative imaging in radioguided surgery.

Yttrium-90 microsphere selective internal radiation therapy (⁹⁰Y-SIRT) is an emerging internal irradiation modality for liver cancer in China. Endowed with the advantages of high energy, short half-life and limited penetration range, it has been widely applied in the management of hepatic malignancies including primary liver cancer and colorectal liver metastases, and can be combined with other pharmaceutical agents or therapeutic approaches to augment treatment efficacy. This review is dedicated to summarizing the practical experience and optimization strategies for nuclear medicine technical support throughout the entire course of ⁹⁰Y radionuclide therapy, encompassing drug transportation, dispensing, radioactive waste collection and disposal, radiation protection, as well as the scanning parameters for pre-and post-procedural imaging assessment. It is designed to provide fellow clinicians with a set of safe, efficient and reproducible standardized operating protocols, thereby ensuring the precision of both therapeutic implementation and clinical evaluation.

This paper addresses the problems of cumbersome processes and high costs in the management of temporary procurement of medical consumables in public general hospitals. It proposes an optimization scheme centered on an information-based procurement platform and a centralized management strategy, aiming to reduce the costs of temporary medical consumables, streamline processes, and mitigate procurement risks. Based on the hospital information systems of general hospital and its affiliated hospitals, a unified consumable management platform has been established, and data sharing and cross-hospital allocation of consumables have been achieved. As a result, the product categories of medical consumables and in vitro diagnostic reagents in both the general hospital and the affiliated hospitals have seen significant growth. The procurement quantity and expenditure for high-value and reagent consumables in the general hospital have significantly decreased. Specifically, the procurement quantity in the anesthesiology department dropped by 94.1%, and the procurement expenditure decreased by 85.0%. The informatization construction and collaborative management have enhanced the transparency of temporary medical consumables procurement and the ability to control costs, providing a scientific and centralized management solution for medical institutions to address emergency demands.