Radar-based non-contact health monitoring technology (RBNHMT) has emerged as a transformative paradigm in continuous health sensing, enabling non-invasive and continuous monitoring of physiological parameters and behavioral patterns by transmitting electromagnetic waves, analyzing the reflected signals, and detecting subtle bodily movements—ranging from millimeter-scale chest wall displacements due to respiration to micro-scale vibrations associated with cardiac activity—ultimately transforming them into quantifiable health data. Distinguished by its non-contact operation, inherent privacy preservation, and adaptability to diverse scenarios, RBNHMT exhibits stronger resistance to environmental interference than conventional contact-based monitoring, and has solidified its position as a prominent and dynamic research focus in the field of non-contact health monitoring. Currently, significant and multifaceted progress has been made across several key areas. In human activity recognition (HAR), systems leveraging micro-Doppler signatures or point cloud sequences achieve high-precision detection of gait, gestures, and fall events, with state-of-the-art deep learning-based models achieving accuracy rates exceeding 99% in controlled experimental settings. For vital sign and sleep monitoring, it not only tracks respiratory and heart rates continuously but also extracts clinically relevant metrics such as heart rate variability (HRV) for autonomic nervous system assessment and estimates blood pressure through indirect methods like pulse transit time analysis, while maintaining robustness in dynamic settings through advanced motion compensation algorithms. In sleep monitoring, it further enables sleep posture classification and apnea event detection. In emotion and stress recognition, it provides a non-intrusive approach for psychological assessment by analyzing autonomic-response physiological signal patterns or behavioral features. Furthermore, its applications in auxiliary medical diagnosis have expanded to promising interdisciplinary areas such as non-contact heart sound auscultation, radar-based screening for obstructive sleep apnea (OSA), and emerging research into breast cancer detection using microwave and millimeter-wave imaging techniques. However, several challenges impede its practical deployment. Signal quality is significantly compromised by multipath interference in complex indoor environments and clutter from static objects, and by motion artifacts in dynamic scenarios where gross body movements obscure the subtle physiological signals. Algorithmically, separating signals from multiple targets in close proximity and calibrating for substantial individual physiological differences, such as body habitus, baseline vital signs, remain difficult and limit generalizability. Hardware design also faces the challenge of balancing power consumption, cost, integration, and performance, often requiring trade-offs that constrain miniaturization, battery life, or measurement sensitivity. Future advancement, therefore, requires collaborative and targeted innovation across multiple dimensions. Algorithmically, developing adaptive signal processing models based on emerging paradigms such as few-shot learning (for user-specific calibration with minimal data) and reinforcement learning (for dynamic noise suppression) is essential. At the hardware level, highly integrated radar SoCs with embedded processing capabilities and advanced packaging technologies are crucial for achieving the dual goals of device miniaturization and cost reduction without sacrificing performance. At the system level, fusing radar data with complementary modalities such as infrared and acoustic sensing can create a synergistic, multi-modal framework that significantly enhances perceptual robustness and reliability in complex, real-world environments. This review provides a comprehensive synthesis that systematically summarizes the relevant theoretical foundations and application progress, and offers an in-depth analysis of the current technical bottlenecks. It aims to provide a clear development path and a foundational academic reference for the in-depth integration and practical application of RBNHMT in critical scenarios including rehabilitation engineering, smart elderly care, in-vehicle health monitoring, and beyond, thereby offering innovative technical support for the vision of universal, proactive, and personalized health management.

Hepatocellular carcinoma(HCC), the third leading cause of cancer-related death worldwide, is characterized by high mortality and recurrence rates. Common treatments include hepatectomy, liver transplantation, ablation therapy, interventional therapy, radiotherapy, systemic therapy, and traditional Chinese medicine(TCM). While exhibiting specific advantages, these approaches are associated with varying degrees of adverse effects. To alleviate patients' suffering and burdens, it is crucial to explore additional treatments and elucidate the pathogenesis of HCC, laying a foundation for the development of new TCM-based drugs. With emerging research on gut microbiota, it has been revealed that microbiota plays a vital role in the development of HCC by influencing intestinal barrier function, microbial metabolites, and immune regulation. TCM, with its multi-component, multi-target, and multi-pathway characteristics, has been increasingly recognized as a vital therapeutic treatment for HCC, particularly in patients at intermediate or advanced stages, by prolonging survival and improving quality of life. Recent global studies demonstrate that TCM exerts anti-HCC effects by modulating gut microbiota, restoring intestinal barrier function, regulating microbial composition and its metabolites, suppressing inflammation, and enhancing immune responses, thereby inhibiting the malignant phenotype of HCC. This review aims to elucidate the mechanisms by which gut microbiota contributes to the development and progression of HCC and highlight the regulatory effects of TCM, addressing the current gap in systematic understanding of the "TCM-gut microbiota-HCC" axis. The findings provide theoretical support for integrating TCM with western medicine in HCC treatment and promote the transition from basic research to precision clinical therapy through microbiota-targeted drug development and TCM-based interventions.
Humans ; Gastrointestinal Microbiome/drug effects* ; Carcinoma, Hepatocellular/microbiology* ; Liver Neoplasms/microbiology* ; Drugs, Chinese Herbal/administration & dosage* ; Animals ; Medicine, Chinese Traditional

Diffuse pediatric-type high-grade glioma (pHGG), H3- and isocitrate dehydrogenase (IDH)-wild-type, is a World Health Organization (WHO) grade 4 diffuse glioma with malignant histological features, which typically affects in children and adolescents. This multidisciplinary treatment case involves a 24-year-old young female patient initially diagnosed via biopsy. After concurrent chemoradiotherapy reduced the tumor size, complete resection was achieved, followed by adjuvant therapy with temozolomide and regorafenib. After recurrence, the tumor was resected again, and a second course of radiotherapy was administered, but the disease ultimately progressed rapidly, with an overall survival exceeding 32 months. pHGGs are highly malignant, aggressive, and associated with poor prognosis. Accurate diagnosis and targeted intervention require close collaboration among a multidisciplinary team, which highlights the critical value of multidisciplinary manage-ment in clinical practice.

In recent years, with the increasing societal focus on drug quality and safety, quality issues have become a major challenge faced by the pharmaceutical industry, directly impacting consumer health and market trust. By combining multispectral imaging technology with machine learning, it is possible to achieve rapid, non-destructive, and precise detection of traditional Chinese medicine(TCM) preparations, thereby revolutionizing traditional detection methods and developing more convenient and automated solutions. This paper provides a comprehensive review of the current applications of rapid, non-destructive detection techniques based on machine learning algorithms in the field of TCM preparations. It analyzed the principles and advantages of commonly used rapid, non-destructive detection techniques, offering a reference for the application and promotion of these technologies in TCM preparation detection. Additionally, this paper explored various data preprocessing techniques, operational processes, and machine learning algorithms to enhance data utilization efficiency. Finally, it focused on the challenges of applying machine learning in TCM preparation detection and offered corresponding recommendations, providing guidance for the future integration of machine learning with rapid, non-destructive detection techniques in practical production.
Machine Learning ; Drugs, Chinese Herbal/analysis* ; Medicine, Chinese Traditional/methods* ; Humans ; Quality Control

The construction method and simulation parameter settings for the discrete element model of Jianwei Xiaoshi Granules, as the primary material of Jianwei Xiaoshi Tablets, are not yet clear. The accuracy of the simulation model significantly influences the dynamic response characteristics between granules. Therefore, it is necessary to calibrate the parameters to improve the accuracy of the simulation parameters. Using the repose angle of Jianwei Xiaoshi Granules as the response value, the response surface methodology was employed to optimize and calibrate the discrete element parameters. Physical experiments were conducted to determine the physical properties of Jianwei Xiaoshi Granules. Based on the Hertz-Mindlin with Johnson-Kendall-Roberts(JKR) V2 model and virtual simulation methods, a repose angle determination model was constructed in EDEM software. The repose angle was measured using image analysis and numerical fitting methods. The Plackett-Burman experiment was used to screen the initial parameters for significance in the discrete element simulation. The significant parameters were then subjected to a steepest ascent experiment to determine the optimal parameter range. Furthermore, based on the Box-Behnken experiment, a second-order regression equation between significant parameters and repose angle was established, with the repose angle of 37.64° in the physical experiment as the target value. The regression equation was optimized and solved. The significance screening experiment revealed that the granule-granule static friction coefficient, granule-granule rolling friction, and granule-steel plate rolling friction of Jianwei Xiaoshi Granules significantly influenced the simulated repose angle. The optimal parameter combination was found to be 0.330, 0.222, and 0.229. The simulation results with this optimal parameter combination showed that there was no significant difference between the simulated repose angle and the repose angle obtained in the physical experiment, with a relative error of 0.05%, which further validated the reliability of the calibrated discrete element parameters for Jianwei Xiaoshi Granules.
Drugs, Chinese Herbal/chemistry* ; Calibration ; Computer Simulation

Identification of critical material attributes(CMAs) is a key issue in the quality control of large-scale TCM products like Jianwei Xiaoshi Tablets. This study focuses on the granules of Jianwei Xiaoshi Tablets, using tablet tensile strength as the primary quality attribute. A method for identifying the CMAs and a design space for the granules were established, along with a predictive model for the granule CMAs based on Fourier transform near-infrared spectroscopy(FT-NIR). First, granules of Jianwei Xiaoshi Tablets with different properties were prepared using a partial factorial design method from the design of experiments(DOE). The powder properties of the granules were measured. An orthogonal partial least squares(OPLS) model was established to correlate the powder properties with tensile strength. Based on the characteristics of the comprehensive variables extracted by OPLS, the independent variables with the greatest explanatory power for tensile strength were identified. FT-NIR technology was then employed to establish a predictive model for the granule CMAs. The final CMAs identified were hygroscopicity, moisture content, D_(50), collapse angle, mass flow rate, and tapped density. The coefficients of determination of the prediction set(R■) and relative percentage deviation(RPD) of the prediction set for flowability, D_(50), and moisture content were 0.891, 0.994, and 0.998; and 2.97, 12.4, and 20.7, respectively. The established OPLS model clearly identified the impact of various factors on tensile strength, demonstrating good fit results. The model exhibited high prediction accuracy and can be used for the rapid and accurate determination of CMAs in granules of Jianwei Xiaoshi Tablets.
Drugs, Chinese Herbal/chemistry* ; Tablets/chemistry* ; Tensile Strength ; Quality Control ; Spectroscopy, Fourier Transform Infrared ; Spectroscopy, Near-Infrared

Objective To retrospectively investigate the efficacy of pelvic floor muscle training(PFMT)using APP-based home device in the real-world scenario.Methods A retrospective survey was conducted among 171 000 Chinese female users over 18 years old who freely registered APP and performed PFMT using a domestic manufactured home device from Oct 1,2019 to Mar 31,2021.The Patient Global Impression of Improvement(PGI-I)questionnaires concerning pelvic floor general condition,sexual satisfaction and other three major symptoms related to pelvic floor muscle laxity such as stress urinary incontinence(SUI),pelvic organ prolapse(POP),and vaginal laxity(VL)were broadcast public online for users to self-assess the improvement.The vaginal muscle strength values prior and post training recorded by APP were also reported.All data were collected anonymously for further stratified analysis.Results A total of 984 valid questionnaires were collected by systematic sampling.The PGI-I scores of pelvic floor general condition(P=0.000 1),sexual satisfaction(P=0.009),SUI(P=0.000 1),POP(P=0.044)and VL(P=0.034)were statistically significant in users who reported to use the device for 3 months or more compared with those less than 3 months.In addition,the increase of vaginal muscle strength was related to the improvement of PGI-I scores in SUI and POP with statistical significance.There were no significant difference in subgroups such as age,education,parity,and delivery mode.Conclusion The subjective benefit of pelvic floor function-related symptoms and the improvement of vaginal muscle strength could be observed after PFMT for three months or more using APP-based home device in Chinese women in the real-world scenario.

AIM: To compare the clinical effect of small incision lenticule extraction(SMILE)and implantable collamer lens(ICL)implantation on efficacy, safety and visual quality in patients with high myopia complicated with low astigmatism.METHODS: Retrospective study. A total of 80 cases(159 eyes)of high myopia and low astigmatism treated in the hospital from January 2021 to January 2022 were included, and they were divided into 46 cases(91 eyes)in SMILE group and 34 cases(68 eyes)in ICL implantation group by means of different surgical methods. The spherical equivalent, uncorrected visual acuity(UCVA), best corrected visual acuity(BCVA), higher order aberrations of cornea and optical quality index, effectiveness and safety index were compared between the two groups before surgery and at 3 and 6 mo after surgery, and the surgical complications were counted.RESULTS: The spherical equivalent, UCVA and BCVA of both groups were all improved at 6 mo postoperatively(all P<0.05). At 6 mo after surgery, there were no differences in UCVA, BCVA, spherical equivalent, effectiveness and safety index between both groups(all P>0.05). The root mean square values of total higher order aberration, spherical aberration, coma, vertical coma and horizontal coma in the SMILE group were higher than those in the ICL implantation group, and Strehl ratio(SR)in the ICL implantation group was higher than that in the SMILE group at 3 and 6 mo after surgery(all P<0.05). CONCLUSION: SMILE and ICL implantation have good surgical efficacy in the treatment of patients with high myopia and low astigmatism, and both methods can achieve good postoperative visual acuity and objective visual quality. Furthermore, the ICL implantation has better postoperative visual quality than SMILE.

Bovine coronavirus(BCoV)can cause diarrhea in calves,as well as respiratory diseases,and lead to the death of calves in severe cases,resulting in great economic losses to the cattle pro-duction.BCoV is highly prevalent and widely spread.So far in China there is no independently de-veloped vaccine against BCoV on the market.In this study,the most conserved N gene of BCoV was first selected to construct a prokaryotic expression vector,and the N protein was expressed and purified;subsequently,BALB/c mice were immunized with BCoV N protein combined with Freund's adjuvant,and the antibody potency,the proportion of splenic T-lymphocyte subpopulation and the cytokine release of the immunized mice were detected by ELISA and flow cytometry.The results showed that a soluble BCoV N protein about 55 kDa in size was successfully obtained.The ELISA test showed that the antibody potency of serum IgG and IgA was 1∶51 200 and 1∶3 200,respectively,in mice immunized with BCoV N protein combined with Freund's adjuvant.The flow cytometry test showed that the percentage of CD4+/CD8+T-lymphocytc subsets was extremely higher in the immunized mice(P＜0.01),and the release of TNF-α was significantly higher(P＜0.05),producing a cellular immune response biased towards the Th1 type in comparison with that in control mice.It is indicated that the soluble expression of BCoV N protein can be successfully a-chieved by the prokaryotic expression system,and the obtained BCoV N protein presents good im-munogenicity which induces strong humoral,mucosal and cellular immune responses in the immu-nized mice.Our study provides an important technical support for the development of a safe and ef-fective BCoV subunit vaccine.

Bovine coronavirus(BCoV)can cause diarrhea in calves,as well as respiratory diseases,and lead to the death of calves in severe cases,resulting in great economic losses to the cattle pro-duction.BCoV is highly prevalent and widely spread.So far in China there is no independently de-veloped vaccine against BCoV on the market.In this study,the most conserved N gene of BCoV was first selected to construct a prokaryotic expression vector,and the N protein was expressed and purified;subsequently,BALB/c mice were immunized with BCoV N protein combined with Freund's adjuvant,and the antibody potency,the proportion of splenic T-lymphocyte subpopulation and the cytokine release of the immunized mice were detected by ELISA and flow cytometry.The results showed that a soluble BCoV N protein about 55 kDa in size was successfully obtained.The ELISA test showed that the antibody potency of serum IgG and IgA was 1∶51 200 and 1∶3 200,respectively,in mice immunized with BCoV N protein combined with Freund's adjuvant.The flow cytometry test showed that the percentage of CD4+/CD8+T-lymphocytc subsets was extremely higher in the immunized mice(P＜0.01),and the release of TNF-α was significantly higher(P＜0.05),producing a cellular immune response biased towards the Th1 type in comparison with that in control mice.It is indicated that the soluble expression of BCoV N protein can be successfully a-chieved by the prokaryotic expression system,and the obtained BCoV N protein presents good im-munogenicity which induces strong humoral,mucosal and cellular immune responses in the immu-nized mice.Our study provides an important technical support for the development of a safe and ef-fective BCoV subunit vaccine.