Abstract:Nanozymes catalyzes the conversion of tumor-associated substances into toxic reactive oxygen species(ROS),which induce oxidative stress and promote cancer cell death.However,in the complex tumor microenvironment,factors such as the design,activity,reaction substrates,and the microenvironment all impact the efficiency of ROS generation.To address these challenges,relat-ed research has focused on optimizing active factors and developing nanozyme cascade catalytic systems that can trigger multiple cascade processes within tumors to efficiently and stably generate more therapeutic substances while minimizing side effects.This paper discus-ses effective strategies for the design of cascade catalytic systems,summarizes representative cases of nanozyme cascade catalytic sys-tems in cancer therapy,and reveals their potential catalytic mechanisms.Finally,the paper outline the challenges faced by these sys-tems in biomedical applications and discuss their future prospects.

Abstract:The skin of diabetic foot ulcer(DFU)patients is prone to damage and difficult to heal,posing significant challenges in treatment.Hydrogels formed by cross-linking plant polysaccharides not only possess the biocompatibility and biodegradability of poly-saccharides,but also have the high water absorption and water retention capacity of hydrogels,and are expected to become green dress-ings for the treatment of DFU.This review systematically expounds the mechanism of action and application of plant polysaccharide hy-drogels to provid a research basis for the treatment of DFU.

Abstract:To accurately monitor arterial diameter parameters,we designed a wearable system for automatic arterial identification and diameter monitoring based on flexible ultrasound technology.Firstly,based on the energy-variance product(EVP)feature in com-bined with the motion patterns of the anterior and posterior arterial walls during pulsation,the system automatically detected the arterial walls from single-frame ultrasound echoes and measured the diameter.Then,based on a transducer position probability distribution model,a layered scanning method from the center to the array periphery was used to realize the rapid automatic localization of the arte-rial center,and reduce the requirements of operating accuracy during patch application.Test results demonstrated that the system could achieve automatic arterial identification and localization,with an average diameter detection error of less than 5.6％.Additionally,through extraction and analysis of the waveform features of arterial diameter,this system can provide quantitative or qualitative evalua-tion of physiological parameters such as cardiac cycle,which can be used for long-term continuous monitoring of arterial diameter pa-rameters.

Abstract:In order to improve the sleep disorders and explore the neural mechanism of sleep regulation,we designed four acoustic with electrical multimodal stimulation modes to explore the influence of stimulation parameters on the sleep quality and response chan-ges of characteristic brain regions of the subjects.Firstly,through traceability analysis of the electroencephalogram(EEG)data of the subjects,time series related to sleep in 11 brain regions were extracted.Then,the energy changes of different brain regions in the five frequency bands of δ,θ,α,σ,and β were obtained through power spectral density(PSD).Meanwhile,the experiment used subjec-tive scales to evaluate the sleep quality of the subjects in the past month.Finally,the finger tapping experiment was used to explore the impact of stimulation on the brain's programmed memory.The results showed that each mode of stimulation had no effect on the partici-pants'programmed memory,M1 and M3 optimized the emotional regulation of the subjects during sleep,reduced the degree of sleepi-ness,and at the same time,M1 activated sleep-related brain regions,improving sleep quality.The research can provide new ideas for multimodal sleep modulation mechanisms.

Abstract:To quantitatively assess the physical fatigue state of ground crew during maintenance operations,we fused multimodal sig-nals such as surface electromyography(sEMG),electrocardiogram(ECG),photoplethysmography(PPG)and proposed a physical fatigue assessment method for ground crew maintenance work.Firstly,10 ground crew members were selected to perform the action of lunge with upper hands operation,and their physiological signals were collected.Then,the BorgCR-10 scale was taken as the reference scale for fa-tigue level,a model from physiological characteristics to fatigue level was established using the support vector machine.The results showed that the linear kernel support vector regression(SVR)model had the best prediction results in the 12 s sliding window case,and it also had the advantages of real-time performance and accuracy.This study confirms the effectiveness of multimodal signal fusion in fa-tigue assessment,and can provide technical support for optimizing the efficiency of ground crew maintenance operations.

Abstract:In order to solve the inconsistent feature distribution between domains of multi-site resting-state functional magnetic resonance imaging(rs-fMRI)data,we proposed an unsupervised cross-domain adaptive depression recognition algorithm to make full use of multi-scale timing information,balance the feature distribution difference between the source domain and the target domain of multi-site rs-fMRI data.First,the inter-domain distribution difference was reduced through generative adversarial network,The do-main alignment mechanism with statistical feature matched with adversarial learning was employed to achieve unsupervised cross-do-main feature alignment between two domains.Then,an attention-guided multi-scale spatiotemporal graph convolution module was de-signed to extract and fuse multi-scale feature information.Based on the analysis of 681 subjects in the REST-meta-MDD dataset,the accuracy,area under the receiver operating characteristic curve,sensitivity,specificity and precision of this algorithm reached 67.57％,65.16％,89.19％,66.22％and 65.00％,respectively.The experimental results show that the algorithm has excellent recog-nition performance and can provide certain technical support for the clinical auxiliary diagnosis of major depressive disorder.

Abstract:With the intensification of societal aging,cognitive decline among the elderly has become increasingly severe.The prev-alence of neurodegenerative diseases such as Stroke,Parkinson's disease,and Alzheimer's disease continues to rise,bringing immense pressure to patients and their families.Cognitive rehabilitation training has emerged as an effective therapeutic approach,playing a cru-cial role in alleviating and improving cognitive impairments in the elderly.Acoustic,optical,and electrical stimulation technologies,which modulate neural activity through sound,light,and electrical currents,have shown promise in promoting cognitive recovery.This paper provides a comprehensive discussion of the applications and research progress of acoustic stimulation,optical stimulation,and e-lectrical stimulation in cognitive rehabilitation.

Abstract:The nacre of shells is a natural material composed of organic matter and calcium carbonate,has excellent biocompati-bility and degradability.The water-soluble organic matter in the nacre of shells has great potentialities in promoting bone formation,en-hancing enamel mineralization,accelerating skin wound healing and improving memory function,due to its excellent properties,such as anti-hemolysis,antioxidation and antibacterial.This paper reviews the composition characteristics,biological properties,extraction methods of water-soluble organic matter in the nacre of shells,as well as the research progress in the biomedical field,and looks for-ward to its application prospects.

Abstract:In order to solve the problems that the traditional moxibustion process is cumbersome,relies on manual operation and low efficiency,we designed a new type of intelligent moxibustion device based on STC89C52 microcontroller.The distance from the moxibustion head to the acupuncture point was measured by the HC-SR04 ultrasonic ranging sensor.When the moxibustion distance is less than the set threshold,the buzzer alarm will be triggered.The MLX90614 non-contact infrared temperature sensor monitored the body surface temperature of the acupoint in real time,and compared the set expected temperature,automatically adjusted the moxa stick to the appropriate position.The measured temperature and distance were showed on the the display screen,and broadcasted per 60 s through the speech synthesis module.The experimental results showed that the intelligent moxibustion device could autonomously reg-ulate the movement of the moxa stick,adjusted the distance between the moxibustion head and the skin acupoints,and promoted the temperature of the skin acupoints approach to the set expected temperature with an error of less than 1℃.The device is convenient to operate,stable in operation,has strong practicability,which is conducive to the popularization and application of moxibustion.

Abstract:To explore the relationships between metabolic disturbances in patients with Alzheimer's disease(AD)and different stages of the disease,and to study the potential of plasma in the prevention,diagnosis and treatment of AD,we employed three ma-chine learning methods to establish classification models for plasma and cerebrospinal fluid at different stages of the disease respective-ly,and conducted enrichment pathway analysis by screening relevant features as differential metabolites.The results indicated that ab-normalities in amino acid metabolism,lipid metabolism,energy metabolism and mitochondrial function were found in both body fluids of the patients,and these abnormalities occurred in the early stage of mild cognitive impairment.This research not only investigates the potential of plasma in the diagnosis of AD,but also explores the pathogenesis of different stages of AD from a metabolic perspective.

Abstract:To address the problem that most of the existing murmur detection methods are limited to a single task and fail to fully u-tilize the correlation between heart murmur signals,we proposed a multi-task heart murmur model based on progressive layered extrac-tion(PLE),which could simultaneously complete the detection of small-segment heart murmurs and the automatic segmentation of heart sound signals.The model could effectively integrate the spatiotemporal information of heart sound signals by sharing the underlying features,thereby improving the accuracy of murmur detection and heart sound segmentation.On the CirCor Digiscope dataset 2022,the average recall rate and average F1 score of the model for small segment murmur detection reached 0.8033 and 0.6096,respectively,and the average recall rate and average F1 score for heart sound segmntation reached 0.9160 and 0.9100,respectively.The results showed that this model could provide new method and idea for the detection and analysis of heart murmur,offer potential technical sup-port for the early screening and diagnosis of cardiovascular diseases.

Abstract:To quantitatively measure the mechanical properties of the sclera,we established a sclera shear wave optical coherence elastography(OCE)research platform based on OCE technology,using the piezoelectric stack as the shear wave excitation module and spectral domain optical coherence tomography(SD-OCT)as the motion detection module.Firstly,two scanning modes,M-mode and M-B mode were developed to track the shear wave propagation induced by the piezoelectric stack,achieving non-destructive and high-resolution quantitative analysis of the mechanical properties of different regions of the sclera.Then,experiments on silicone gel mimics,isolated eyeballs and sclera collagen cross-linking were designed.Finally,by systematically comparing the OCE measurement results with the traditional uniaxial mechanical tensile tests,the feasibility and effectiveness of the platform were verified.The experiments on the silicone gel imitation showed that as the Shore hardness of the silicone gel imitation increased,the shear wave velocity also in-creased accordingly.The in vitro eyeball experiment showed that the shear wave velocities on the sclera presented regional differences.Among them,the hardness was the greatest at the equatorial part,followed by the anterior part and the smallest at the posterior part.The scleral collagen cross-linking experiment showed that this method could distinguish the differences in hardening effects caused by different durations of ultraviolet radiation.This research not only provides a new method for evaluating the region-specific mechanical properties of the sclera,but also offers an important technique for studying the pathogenesis of diseases related to scleral biomechanics,such as myopia and glaucoma.

Abstract:To systematically evaluate the transdermal penetration behavior and effects of full molecular weight sodium hyaluronate(FMW-SH),we took FMW-SH as the research object and determined the molecular weight distribution of FMW-SH by high perform-ance gel chromatography.Using a 3D skin model,we evaluated its transdermal penetration efficiency.Multiple analytical approaches were employed to assess FMW-SH's biological effects,including ELISA for typeⅠcollagen synthesis,scratch assay for keratinocyte mi-gration,immunofluorescence for carboxymethyl lysine(CML)clearance and qPCR for sensitive gene mRNA expression.The results showed that the weight-average molecular weight of FMW-SH was 547.7 kDa,the polydispersion coefficient was 17.679,and the cu-mulative transdermal rate within 24 h reached 26.7％,which was significantly better than that of high molecular weight SH and oligo-meric SH.Cytotoxicity tests confirmed the safety of 0.01％～0.5％FMW-SH concentrations.Significant biological activities were ob-served,0.1％～0.5％FMW-SH enhanced typeⅠcollagen synthesis(P<0.05)and keratinocyte migration rate(P<0.05).0.2％FMW-SH reduced CML expression by 52.92％,outperforming aminoguanidine remarkably.0.0005％FMW-SH effectively suppressed IL-4/IL-13induced upregulation of CAII and NELL2 mRNA(P<0.01).FMW-SH can increase the content of typeⅠcollagen,promote ke-ratinocyte migration,eliminate CML and down-regulate the expression of sensitive genes.

Abstract:To alleviate the difficulties in the early identification of microthrombi and the lack of dynamic monitoring methods,we developed a high-sensitivity impedance monitoring system based on interdigital electrode(IDE)for label-free,real-time monitoring of microthrombus formation.Firstly,the finite element simulations were combined with in vitro thrombus formation experiments,thrombin was used to induce fibrinogen to form microthrombi,and the fluorescence intensity of fibrin was measured to quantify the formation process of microthrombi.Then,the influences of different red blood cell(RBC)concentrations,spatial distributions and thrombus sizes on the system impedance characteristics were comprehensively analyzed,and the real-time impedance changes and impedance frequen-cy responses during the dynamic development process of microthrombus were studied by using the IDE impedance monitoring system.Experimental results demonstrated that RBC aggregated on the IDE surface or bridging across positive and negative electrodes disrupted current conduction and increased system impedance.Low-frequency impedance was more sensitive to conductivity changes induced by microthrombi.High-frequency impedance exhibited only minor variations during thrombus formation.Real-time impedance monitoring further showed that the impedance-time curve followed the same trend as fibrin fluorescence intensity,characterized by a rapid initia-tion,gradual progression and plateau phase.The research provides an experimental approach for label-free,real-time monitoring of microthrombi,with potential applications in blood disease diagnosis and antithrombotic drug screening.

Abstract:To address the issue of subjectivity and susceptibility to environmental interference in murmur grading diagnosis through manual auscultation,we constructed a heart murmur grade recognition model based on omni-dimensional dynamic convolution(ODConv)and feature fusion.Firstly,the ODConv module was adopted to capture rich context information.Secondly,time-domain and time-frequency domain features were integrated to improve the recognition performance of murmur levels.Finally,the experiments on the amplitude attenuation and enhancement of the first heart sound(S1)and the second heart sound(S2)were designed to verify the influence of the relative loudness of S1,S2 and the murmur on the murmur level recognition of the model.The verification results on the CirCor DigiScope dataset showed that this model not only referred to the relative loudness information of the murmur with S1 and S2 in the classification of murmur levels,but also achieved recall rate of 93.39%,53.70%and 73.46%in the Absent,Soft and Loud cate-gories,as well as F1 score of 94.07%,51.28%and 73.34%,respectively.The proposed model can accurately identify heart murmur severity,and provide crucial technical support for the automated recognition of heart murmur levels.

Abstract:The blood glucose levels of patients with diabetes mellitus(DM)are influenced by multiple factors such as diet,exer-cise,insulin and stress,making it rather difficult to control.Accurate prediction of blood glucose changes can not only help to predict the risks of hypo-and hyperglycemia,but also optimize treatment plans and improve the quality of life of patients.Deep learning(DL)is widely applied in blood glucose prediction due to its powerful temporal modeling capabilities.The development of continuous blood glucose monitoring(CGM)and wearable sensors provides data support for DL.This paper systematically reviews the research progress of DL in the field of blood glucose prediction,introduces the common data sets and evaluation metrics and compares the performance of different models.Finally,the future development directions such as personalized prediction,multimodal fusion and interpretability mod-els are prospected to provide references for blood glucose prediction research.

Abstract:For Rician noise existed in magnetic resonance imaging(MRI)of fetal brain,we designed a kernel regression denoising method.Firstly,classical kernel regression(CKR)was used to acquire gradient information.Then,the covariance matrix representing the local characteristics of the MRI image was constructed by the gradient information and the adaptive kernel regression(AKR)to a-chieve adaptive denoising of MRI data.The quantitative analysis results of MRI data of 9 sets of fetal brain MRI data and 12 sets of a-dult brain MRI data with different Rician noise levels showed that the AKR denoising algorithm could reduce the root mean square error(RMSE)by approximately 28.64%～57.57%,the peak signal-to-noise ratio(PSNR)was increased by approximately 11.67%～45.50%,and the structural similarity index measure(SSIM)was increased by approximately 7.95%～72.50%.The qualitative analysis results of the simulation data and the real MRI of fetal brain indicate that this algorithm can effectively remove the noise in MRI data of fetal and adult brain,and can maintain the global features of the images.

Abstract:Aiming at the problems that the collection process of pharmacokinetic data for ex vivo monitoring is easily affected by in-sufficient sample size,delayed data collection and significant differences among individuals,we developed a four-channel in vivo fluo-rescence monitoring system with real-time data acquisition by integrating hardware components and the LabView platform.Based on the principle of laser-induced fluorescence,this system utilized the unique gain mechanism of photomultiplier tube(PMT)to amplify weak optical signals.The sensitivity and effectiveness of the system were confirmed by one-way analysis of variance(P<0.05)on the back-ground light and the number of fluorescent photons in 0.25 μg/mL fluorescein isothiocyanate(FITC)solution.In vivo experiment fur-ther demonstrated that this system could not only effectively achieve real-time monitoring of fluorescence signals in major metabolic or-gans such as the kidneys,but also minimized data errors caused by ex vivo experiment and improve data accuracy.It can provide more comprehensive data for subsequent research on drug efficacy and therapeutic mechanisms.

Abstract:Addressing the problem of bulky size,high power consumption and high cost associated with traditional bioelectrical im-pedance detection devices,we designed a portable,low-cost and single-channel bioelectrical impedance measurement device.Firstly,this device centered around the AD5933 impedance conversion chip,integrated various functions such as wired data transmission,wire-less communication,and real-time local data display.It could perform single-frequency and multi-frequency scanning of bioimpedance spectra within the frequency range of 2～100 kHz,achieving impedance monitoring and impedance spectrum analysis.Finally,we con-ducted experiments on the measurement accuracy of the three components,and the consistency of human body electrical impedance measurement respectively.The results showed that the impedance measurement error of this device within the frequency range of 2～100 kHz was less than 0.6%,with small size,light weight and low power consumption.It could meet the clinical requirements for dynamic monitoring of human body impedance.This research can provide a new solution for the monitoring and evaluation of non-invasive physi-ological signals.

Abstract:Fluorescence in vivo imaging technology,with its non-invasive,high sensitivity and high spatiotemporal resolution char-acteristics,holds an important position in the field of basic medical diagnosis.Nanocrystals due to their unique photophysical proper-ties,have become a class of highly promising fluorescent probes.However,the strong scattering and absorption of photons by biological tissues significantly limit the penetration depth of traditional visible light band fluorescence imaging,restricting its application in the de-tection of deep-seated lesions.In recent years,the development of near-infrared(650～1700 nm)nanocrystal fluorescent probes has brought new breakthroughs in the fields of biological detection,diagnosis,treatment and postoperative monitoring.This article systemat-ically reviews the latest progress of different emission nanocrystal fluorescent probes in the field of in vivo multi-scale imaging,expound the application prospects of near-infrared emitting nanocrystals in high-resolution non-invasive imaging of deep tissues.At the same time,it explores the potential value of these probes in the entire chain of disease diagnosis and treatment,and analyzes the challenges currently faced by nanocrystal fluorescent probes in terms of biocompatibility,safety and clinical translation,while looking forward to future research directions.