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.

OBJECTIVE To optimize the extraction process of flavonoids from Potentilla discolor .METHODS Deep eutectic solvents （DESs） were used as the extraction solvent to prepare test sample solutions. The contents of rutin， quercitrin， and kaempferol were determined by ultra-performance liquid chromatography. The analytic hierarchy process combined with the criteria importance through intercriteria correlation weighting method was employed to assign comprehensive weights to the contents of the three flavonoids， and calculate the comprehensive score. The optimal DESs were screened based on the comprehensive score.Mean while， using the comprehensive score as the response variable and the liquid-to-solid ratio， ultrasonic time， and urltrasonic temperature as the influencing factors， the extraction technology of flavonoids from P.discolor was optimized by Box-Behnken response surface methodology and subsequently validated.RESULTS The comprehensive weight coefficients of rutin， quercitrin， and kaempferol were 0.646 7， 0.207 5， and 0.145 8， respectively. The optimal DESs were choline chloride-lactic acid （molar ratio 1∶4） with a water content of 40%. The optimal extraction conditions were determined as follows： liquid-to-solid ratio of 25∶1， ultrasonic time of 40 min， and ultrasonic temperature of 50 ℃. The validation results showed that the average comprehensive score of three batches of samples was 99.28， with an RSD of 0.54% （ n ＝3）.CONCLUSIONS The optimal extraction technology of flavonoids from P. discolor based on DESs obtained in this study was stable and feasible.

To evaluate long-term survival and clinical outcomes of patients with knee osteo-arthritis undergoing total knee arthroplasty (TKA) through long-term follow-up.

Objective To investigate the expression of calumenin(CALU)in gastric cancer and its effect on metastasis and invasion of gastric cancer,and analyze its relationship with the prognosis of gastric cancer patients.Methods The Cancer Genome Atlas(TCGA)database was used to analyze the expression level of CALU in gastric cancer and its impact on patient prognosis.A total of 102 pairs of gastric cancer and paracancerous tissue samples were collected from 189 gastric cancer patients who underwent partial gastrectomy in First Affiliated Hospital of Army Medical University from January 2018 to December 2022.The expression of CALU in gastric cancer and paracancerous tissues was detected by immunohistochemical assay,and the relationship of its expression with clinicopathological parameters was statistically analyzed.After gastric cancer cells with CALU knockdown and overexpression were constructed,and the efficiencies of knockdown and overexpression were evaluated by Western blotting as well as RT-qPCR.Transwell assay was applied to determine the effect of CALU on the migration and invasion abilities of gastric cancer cells.Results Bioinformation analysis found that CALU was significantly highly expressed in gastric cancer tissues(P<0.05),and its expression level was negatively correlated with the prognosis of patients(P<0.05).Immunohistochemical results showed that the expression level of CALU was obviously highly in gastric cancer tissues than the paracancerous tissues(P<0.01),and its level was positively correlated with the depth of infiltration(P<0.01),lymph node metastasis(P<0.01),and TNM stage(P<0.05).Statistical analysis revealed that the clinical data of 102 patients showed that CALU expression was positively correlated with the TNM stage(P=0.021)and T stage(P<0.001)and N stage(P=0.028).CALU knockdown significantly inhibited the migration and invasion abilities of gastric cancer cells(P<0.01),while over-expression obtained the opposite results.Conclusion CALU is highly expressed in gastric cancer tissues and promotes metastasis and invasion of gastric cancer and thus leads to poor prognosis in patients.

Objective To develop a machine learning-based risk prediction model for postoperative acute kidney injury(AKI)and a model for mortality in obese patients admitted to intensive care unit(ICU)in order to improve early warning and prognostic evaluation to support clinical decision-making.Methods Data of obese postoperative ICU patients were retrospectively retrieved from the MIMIC-Ⅳ and eICU databases for statistical analysis.Ultimately,2 520 patients(670 from MIMIC-Ⅳ and 1 850 from eICU databases)were included to build the risk prediction models for AKI and mortality.The data included demographic information,vital signs,laboratory findings,surgical types,comorbidities,and medication use.After data cleaning and preprocessing,Boruta feature selection was applied,followed by the construction of prediction models using 7 machine learning algorithms,that is,Gradient Boosting Machine(GBM),Generalized Linear Model(GLM),k-Nearest Neighbors(KNN),Na?ve Bayes(NB),Neural Network(NNET),Support Vector Machine(SVM),and XGBoost.Model performance was evaluated through cross-validation and external validation.Results In the risk prediction models of AKI,the SVM model achieved the highest AUC value of 0.80 in the testing set and 0.71 in the external validation test.For the risk prediction models of mortality,the GBM model outperformed others in the prediction,attaining an AUC value of 0.91 in the testing set.Conclusion Risk predictive models for postoperative AKI and mortality in obese ICU patients are successfully constructed,and are valuable tools for clinicians to optimize early intervention and improve clinical outcomes for the patients.

Objective To investigate the characteristics of immune exhaustion in sepsis and analyze its association with gut microbiota translocation.Methods A total of 130 mice were randomly divided into a cecal ligation and puncture(CLP)group(n=100)and a Sham group(n=30)Mouse model of sepsis was established with CLP procedure.Flow cytometry was used to analyze the proportions of peripheral blood CD4+T and CD8+T cells and programmed cell death protein 1(PD-1)positive T cell subsets in mice.Bacterial colonization in organs such as the heart,liver and kidneys was quantified by plating homogenates of the organs.Pathological changes in immune organs were observed with HE staining.The expression and localization of CD4?,CD8?,and PD-1?cells in immune organs were detected with immunohistochemical staining,and Image J software was employed for subsequent quantification of the number of the positive cells.Results HE staining demonstrated that immune organs exhibited varying degrees of pathological damages with disease progression.Compared with the Sham mice,the CLP mice exhibited significantly increased bacterial colonization in parenchymal organs and peripheral blood(P<0.05),notably in the liver,which showed the most severe infection.In the CLP group,the proportion of CD4+T lymphocytes in peripheral blood at days 1,3,and 5 postoperatively was decreased by 56%,70.57%,and 87.42%,respectively,when compared with the Sham group(P<0.001).The proportion of CD8+T lymphocytes was decreased by 48.33%relative to the Sham group only at day 5(P<0.001).In contrast,the proportion of CD4+T cell subsets expressing PD-1 was increased to 673.08,423.08,and 600 times that of the Sham group,respectively,at the same postoperative time points(P<0.001).Immunohistochemical results showed that,in the CLP group,the proportion of CD4+T cells in the thymus,spleen,and mesenteric lymph nodes was increased to 7.65,2.66,and 3.7 times that of the Sham group,respectively,at the early-stage peak(P<0.001),and then these proportions were decreased by 82.8%(P<0.001),41.9%(P<0.01),and 60.15%(P<0.001),respectively,at the late-stage trough when compared with the early-stage peak in the corresponding organs.The proportion of CD8+positive cells was increased in the early stage and then decreased insignificantly,while the proportion of PD-1+positive cells was increased continuously,and reached 6.24,13.9,and 20.96 times that of the Sham group at the peak in the thymus,spleen,and mesenteric lymph nodes respectively(P<0.001),with their expression regions showing a rough overlap with those of CD4+cells.Conclusion During sepsis,the inflammatory response can cause severe damage to immune organs and persistent exhaustion of CD4?T lymphocytes,leading to declined defenses against infection,which may be the main causes for exacerbated gut microbiota translocation and then systemic infection.

In this study,a sensitive lateral flow immunoassay(LFIA)platform based on carbon dots-mesoporous silica nanocomposite(CD-MSNs)fluorescent probes was constructed for high-performance detection of inflammatory marker interleukin-6(IL-6).Green fluorescent carbon dots(CDs)were prepared by hydrothermal method with 3,9-perylenic acid and 3-aminopropyltriethoxysilane(APTES)as raw materials,and highly fluorescent CD-MSNs composites were then constructed by encapsulating the prepared CDs in mesoporous silica nanoparticles(MSNs).Fluorescent probes were prepared by covalent coupling of CD-MSNs with IL-6 antibody.Fluorescent immunochromatographic test strips were constructed by spraying IL-6 capture antibody and goat anti-mouse IgG on nitrocellulose membrane as detection line(T-line)and quality control line(C-line),respectively.The fluorescence immunoassay analyzer was used to quantitatively detect the fluorescence intensity of T-line,and the experimental results showed that the LFIA platform based on this probe had a good linear relationship in IL-6 concentration range of 102-106 pg/mL,and the detection limit was 64 pg/mL,which was two orders of magnitude more sensitive than that of the traditional colloidal gold test strips.This method effectively solved the issue of insufficient sensitivity of traditional LFIA technique,and provided a rapid and highly sensitive detection method for early diagnosis of inflammatory diseases.

Objective:To explore the value of 3D reconstruction combined with watershed analysis in thoracoscopic anatomical partial lobectomy for peripheral early stage lung cancer.Methods:A retrospective cohort study was used to analyze the case data of 152 patients with early peripheral lung cancer who received targeted treatment in Hai′an People′s Hospital of Jiangsu Province from January 2022 to April 2023. All patients were treated with thoracoscopic anatomical partial lung resection. According to different preoperative analysis methods, they were divided into an observation group ( n=79) and a control group ( n=73). The observation group underwent three-dimensional reconstruction combined with venous drainage analysis, and the control group underwent routine analyze. The surgical indicators, pulmonary function indicators, C-reactive protein (CRP), interleukin-6 (IL-6) and complications were compared between the two groups. Normally distributed measurement data were expressed as mean±standard deviation ( ± s), independent samples t-test was used for comparison between groups, and paired-sample t-test was used for comparison within groups. Comparison of enumeration data between groups was performed using the chi-square test. Results:The operation time, postoperative drainage time, postoperative hospital stay, and intraoperative blood loss in the observation group were (76.57±11.94) min, (2.15±0.24) d, (4.32±0.75) d, and (27.63±3.54) mL respectively. The control group was (112.64±13.67) min, (3.76±0.31) d, (5.16±0.81) d, (53.26±4.61) mL, respectively. The observation group was smaller than the control group, and the differences between the two groups were statistically significant ( P<0.05). At discharge, the observation group had maximum ventilation per minute as a percentage of predicted value (MVV% pred), forced expiratory volume in 1 second as a percentage of predicted value (FEV1% pred), and forced vital capacity as a percentage of predicted value (FVC% pred) were (83.33±7.51)%, (85.22±7.44)%, (84.16±7.26)%, and those of the control group were (77.19±7.46)%, (76.47±7.36)%, and (79.38±7.20)%, respectively. The observation group was greater than that of the control group, and the differences between the two groups were statistically significant ( P<0.05). The CRP and IL-6 in the observation group were(47.53±6.34) mg/L, (32.21±5.14) mg/L, (144.53±20.84) ng/L, (180.82±21.17) ng/L, respectively on the 1st and 3rd days after surgery, while those in the control group were (57.84±6.71) mg/L, (38.77±5.23)mg/L, (155.14±22.83) ng/L, (200.97±25.52) ng/L, the observation group was lower than that of the control group, and the difference was statistically significant ( P<0.05). The incidence rate of complications in the observation group was 5.06%, and that in the control group was 10.96%, There was no statistically significant difference between the two groups ( P>0.05). Conclusion:Preoperative 3D reconstruction combined with venous drainage analysis can better promote the recovery of patients with early peripheral lung cancer and retain more lung function after thoracoscopic anatomical partial pulmonary resection.

With reference to the Information and Documentation-Resource Description (GB/T 3792-2021) and Bibliographical Description for Ancient Chinese Books (GB/T 3792.7-2008) and other cataloging standards and rules, drawing on the practical experience of cataloging ancient TCM books, Bibliographical Cataloging for Ancient TCM Books was formulated. This standard specifies the entry items and their order of ancient TCM books, cataloging identifier, cataloging text, cataloging information source, and cataloging item details. The standard can provide standardized and unified guiding principles and methods for the work of ancient TCM books, and promote the sharing and utilization of ancient TCM books.

Objective To investigate the factors influencing the prognosis of patients with cere-bral infarction(CI)and nursing risk management measures.Methods A retrospective analysis was conducted in 80 CI patients as study subjects.All patients underwent risk management,and their prognosis was assessed,with 58 patients in good prognosis group and 22 patients in poor prognosis group.General patient data and disease-related characteristics were collected,and differences were ana-lyzed using the Logistic regression equation to identify factors influencing patient prognosis.A receiver operating characteristic(ROC)curve was plotted to analyze the predictive value of these factors for poor prognosis.Results There were statistically significant differences between the poor prognosis group and the good prognosis group in terms of age,baseline National Institutes of Health Stroke Scale(NIHSS)score,and the presence of comorbid hypertension(P＜0.05).Logistic regression analysis revealed that these indicators were all influencing factors for poor prognosis in patients,with area under the curve val-ues ranging from 0.676 to 0.723,indicating high sensitivity and specificity.Conclusion Multiple factors,such as age,baseline NIHSS score,and comorbid hypertension,influence poor prognosis in CI patients and have certain predictive value for poor prognosis.