Abstract:Biomedical engineering(BME)is a comprehensive subject that applies the principles and methods of engineering to solve biomedical problems and improve the level of human health.This subject is dedicating to exploring the mysteries of the human body and providing high-level scientific methods and engineering and technical means for disease prevention,treatment,rehabilitation and health of the human body,This paper expounds the current research directions and industry development prospects of BME,analy-zes the general situation of BME education and industry development in China,in order to provide reference significance for the devel-opment of the BME field.

Abstract:To enhance the efficiency and accuracy of posture assessment and meet users'demands for early identification and cor-rection of poor posture,we developed an intelligent online detection system for poor posture assessment integrating WeChat mini-pro-grams with deep learning algorithms.Firstly,the front-end design was WeChat mini-program,which could provide functions such as image upload and display of posture detection results.Secondly,the back-end processing used the OpenPose algorithm to identify hu-man key points,and the X-Cobb model assessed the degree of scoliosis to achieve posture and spine detection.Finally,integrating the front-end and back-end parts,an intelligent online detection system for poor posture assessment was built.The results showed that the system successfully identified 11 common poor postures.Moreover,it achieved the upload of images and the return of assessment results within 20 s,significantly enhancing user convenience.This research can achieve effective detection and early intervention of poor pos-ture,and provide an innovative technical means for solving problems related to poor posture.

Abstract:In response to the preoperative planning requirements for microwave ablation of thyroid cancer,we developed a simula-tion planning system based on computed tomography(CT)images to assist the physician in formulating personalized treatment plans.The system adopted the MFC and VTK libraries to implement interface development and image processing,including five main mod-ules:image preprocessing,3D reconstruction,path planning,dynamic simulation and generation of preoperative plans.The system im-ported DICOM data to denoise,enhance and segment CT images,and provided doctors with an intuitive display of tumor location with the help of a 3D reconstruction model.The test results showed that this system could effectively assist the physician in delineating tumor boundaries,designing the optimal insertion path and planning safe ablation path,thereby improved the preoperative assessment and treatment accuracy of microwave ablation for thyroid cancer.This system has potential clinical application value in minimally invasive treatment of thyroid cancer and lays the foundation for the future combination of real-time navigation and deep learning technologies.

Abstract:In order to solve and optimize the problem that the classification results were biased towards the majority class due to unbalanced data in the process of pattern recognition,we took University of California,Irvine(UCI)myocardial infarction dataset as the research object,constructed a Nomogram prediction model.Firstly,three imbalance-handling methods,including K-fold cross-sam-pling voting(K-CSV),synthetic minority over-sampling technique_norminal continuous(SMOTE_NC)and random undersampling(RUS)were used to combine with mutual information,support vector machine weights,Spearman correlation analysis and variance ex-pansion facto to remove multicollinearity features.Secondly,univariate and multivariate Logistic regression were used to screen for inde-pendent risk factors,and constructe the Nomogram prediction model.The results showed that the original imbalanced data,the area un-der the receiver operating characteristic curve(AUC)value and average precision(AP)value of the model was 0.85 and 0.64,re-spectively.After RUS processing,the AUC and AP was 0.87 and 0.86 respectively,and the type Ⅱ error rate was 11.54％.After pro-cessing with SMOTE_NC,the AUC and AP were 0.96,but the accuracy rate dropped to 79.89％and the type Ⅱ error rate increased to 29.73％.The AUC and AP values of K-CSV were both 0.90,and the type Ⅱ error rate was 10.53％.The results of Cox regression anal-ysis showed that the selected features were significantly correlated with the prognosis of patients(P<0.01),indicating that the estab-lished model has high reliability in survival risk prediction.

Abstract:Organoid culture has long been confronted with bottlenecks such as complex operation,poor uniformity,high cost and low throughput.Droplet microfluidics equipped with novel hydrogel materials provides a new strategy due to its advantages of high throughput,good repeatability and retention of heterogeneity.This article systematically reviews the progress of hydrogel materials based on droplet microfluidic technology and their applications in organoids.Then,it analyzes the future development directions of integrated integration of droplet microfluidic platforms,dynamic responsiveness regulation of hydrogel materials,and supporting high-throughput analysis technologies to provide new research ideas and technical platforms for new drug development,precision medicine and regenera-tive medicine.

Abstract:In order to achieve the synergistic optimization of the mechanical properties and electrical conductivity of polyvinyl alco-hol(PVA)hydrogels,we used PVA and sodium carboxymethyl cellulose(CMC)as hydrophilic matrices and combined with Fe3+ions to construct a multiple cross-linked network.Through ion complexing method,the PVA-CMC/Fe3+multi-network hydrogel was pre-pared.Systematic characterization confirmed that Fe3+could not only enhance the mechanical strength,as a cross-linker but also signifi-cantly improve the electrical conductivity.The test results showed that the optimized PVA-CMC-0.2Fe3+hydrogel exhibited a tensile strength of 112.66 kPa and an electrical conductivity of 3.03 S/m.This hydrogel has high sensitivity and stability,can be assembled as flexible sensors for monitoring human joint movements and electrocardiogram(ECG)signals.

Abstract:In response to the physical mechanism and vulnerable areas unknown of lung injury caused by blast shock,we employed the numerical simulation study on the mechanical response of human lung under the action of blast shock.Firstly,a finite element mod-el of the human chest was established.Then,based on the numerical simulation method,the finite element numerical analysis software LS-DYNA was used to study the pressure evolution law of the explosion flow field,the mechanical response of the chest cavity and the stress distribution law of the lung after the shock wave.The results showed that under blast loading,the chest wall collided at high speed with the thoracic organs,and the compression wave was generated in the lung.The transient pressure of the lung was the main mechanism of lung injury.Under the action of shock wave,the lung tissue near the ribs,heart and vertebrae was more prone to injury.Shear stress formed between the ribs'edge and the lung tissue was a key factor in the development of streak hemorrhage.The research verifies the validity of the chest model and can reveal the mechanical mechanism of blast shock waves causing lung injury.

Abstract:Regarding the problem of time-consuming of Ki-67 immunohistochemical staining and weak pairing between H&E and Ki-67 images in breast cancer,we proposed a multi-level pathology-guided supervised generative adversarial network(MPAS-GAN)to generate high-quality virtual Ki-67 images from H&E counterparts to assess the expression distribution of the Ki-67 biomarker.Firstly,the multi-level pathology-guided supervision framework was introduced in MPAS-GAN to solve the tissue misalignment at the macro-feature level,through the confidence-weighted optimal transport alignment.Finally,the diagnostic information at the key patho-logical semantic level was restored through the consistency constraint of Ki-67 pathological information,and the nuclear morphology at the basic cell structure level was retained through the consistency constraint of pathological cell structure.Experimental results on the public MIST and IHC4BC datasets demonstrated that MPAS-GAN significantly outperformed existing state-of-the-art methods across structural similarity index measure(SSIM),peak signal-to-noise ratio(PSNR),Fréchet inception distance(FID),learned percep-tual image patch similarity(LPIPS)metrics.Furthermore,it achieved the highest consistency in the quantitative correlation analysis of Ki-67 positive regions.This research can generate visually realistic and pathologically reliable virtual Ki-67 images,which can effec-tively solve the problem of weakly paired medical image translation,and is expected to provide a more efficient and reliable tool for the digital pathological diagnosis of breast cancer.

Abstract:Regarding the problem for precise localization of refractory epileptic nidus,we developed an electrical impedance tomo-graphy(EIT)and electroencephalogram(EEG)synchronous detection system to verify the feasibility and accuracy of EIT in the localiza-tion of epileptogenic zone(EZ).The system employed the same set of electrodes to achieve simultaneous,synchronous and co-channel acquisition of impedance and EEG signals.The dual-domain collaborative strategy of"hardware low-pass+software comb"was used to eliminate mutual interference between devices,and ensure the accuracy of signal acquisition.The test results on 20 healthy adult male SD rats showed that the average deviation between EIT-localized and EEG-topography centroids was 0.13 mm,with a 6.37%rel-ative error.Moreover,the area was confirmed by histopathology to have typical features of epileptic lesions,indicating that EIT has the feasibility of accurately locating EZ.The research can offer a new method for precise EZ localization and significance for improving the success rate of epilepsy treatment.

Abstract:To address the time complexity bottleneck caused by the traditional K-means algorithm in the template deconvolution stage,when Kilosort4 processes spike data.We proposed an optimization method based on mini batch K-means clustering algorithm.Firstly,the K-means++algorithm was used to initialize cluster centers.Then,during the iterative process,a dynamic data subset was extracted,local clustering was performed and the size of the subset was adaptively adjusted according to the size of the data set.Final-ly,the cluster centers were updated incrementally until convergence.Experimental results demonstrated that the optimized Kilosort4 al-gorithm achieved approximately 8%improvement in processing speed compared to the original algorithm.This algorithm can significant-ly reduce computational complexity while maintaining clustering quality.This research can provide more efficient tools for neuroscience studies.

Abstract:To overcome the limitations of traditional fatigue detection methods based on behavioral or electrical signals,we pro-posed a fatigue monitoring method for ballistocardiogram(BCG)based on optical fiber Fabry-Perot pressure sensors.A"dual-channel denoising"strategy combining wavelet decomposition with high-pass and band-pass filtering was employed,and an adaptive beat-by-beat feature extraction algorithm was designed to enhance signal quality.Three volunteers were recruited complete the N-back task un-der sleep deprivation conditions,their electrocardiogram(ECG)and BCG signals were collected for comparative analysis.The results showed that the heart rate variability(HRV)indicators of ECG did not change significantly under the fatigue state,while the character-istic parameters of BCG showed a downward trend in the slope of the ascending line and fluctuations in the reflex index,indicated weakened cardiac contractile function and unstable autonomic nerve regulation.The proposed method demonstrates higher sensitivity to mild fatigue,with strong resistance to electromagnetic interference and good stability.It can provide a new approach for fatigue monito-ring in aviation and transportation safety applications.

Abstract:To address the limitation of existing deep learning methods that often treat electrocardiogram(ECG)signals as isolated segments and ignore temporal context information,we proposed a continuous temporal modeling(CTM)framework based on single-lead ECG.In the feature extraction phase,a convolutional neural network(CNN)was employed to capture local morphological patterns.Furthermore,a spectral channel attention(SCA)module based on the fast Fourier transform(FFT)was introduced to adaptively recal-ibrate channel weights,thereby enhancing features in key frequency bands.In the temporal modeling phase,a bidirectional long short-term memory(BiLSTM)network was adopted to aggregate continuous feature sequences.This approach explicitly modeled the physio-logical evolution surrounding apnea events,utilizing temporal context to correct classification biases resulting from a reliance solely on local features.Experimental results on the public PhysioNet Apnea-ECG dataset demonstrated that the accuracy,sensitivity and speci-ficity of the model was 90.12%,86.82%and 92.18%,respectively.Comparative experiments indicated that incorporating this CTM framework into various backbone models yielded an average performance improvement of approximately 7%over non-continuous base-lines,fully validating the effectiveness of CTM in capturing long-term dependencies.

Abstract:Electrophysiological signals are electrical signals generated by cell activity within living organisms,which are closely as-sociated with physiological functions and widely utilized in medical diagnosis and health monitoring.Epidermal electrodes,owing to their non-invasive nature and usability,are particularly suitable for long-term electrophysiological monitoring.This article systematical-ly reviews the research progress of epidermal electrodes used for electrophysiological monitoring,introduces the classification of epider-mal electrodes,the materials used for manufacturing the electrodes and their electrochemical properties,analyzes the influence of elec-trode structure and shape on performance and looks forward to the future research directions and application prospects of epidermal elec-trodes,to provide a reference for the development and application of epidermal electrodes.

Abstract:To address the deficiencies in cross-subject generalizability and robustness of existing epileptic electroencephalogram(EEG)prediction models,we proposed a transfer learning-based epileptic prediction model with multi-scale spatio-temporal features(TLEP-MST)by integrating signal analysis and deep learning technology.Firstly,the raw data was analyzed through independent com-ponent analysis(ICA)to remove artifacts,and the temporal feature extraction module and wavelet convolutional layer were used to ex-tract the time-frequency information in the EEG signals.Then,the adaptive attention mechanism was applied to perform multi-channel weight assignment of the time-frequency information,and obtain spatial features of the EEG signals.Finally,transfer learning was in-corporated to reduce data distribution discrepancies between source and target domains,enhancing the model generalization perform-ance.The model was experimented on the public dataset CHB-MIT.In the cross-validation,the accuracy rate,specificity and false positive rate of the model was 91.88%,96.49%and 0.0369/h,respectively.In patient-specific experiments,the specificity improved from 67.04%to 85.06%,and the false positive rate decreased from 0.4194/h to 0.3485/h after introducing transfer learning.This re-search is of great value in predicting the EEG of epilepsy across subjects.

Abstract:Parkinson's disease(PD)is a common neurodegenerative disorder.Its pathogenesis remains unclear,and challenges persist in achieving precise detection and effective rehabilitation.Traditional clinical rating scales are limited by subjectivity and low sensitivity in early stages.In recent years,research on the neural mechanisms underlying sensorimotor integration(SMI)deficits in PD has advanced,and novel detection technologies based on surface electromyography(sEMG),inertial measurement units(IMU)and e-lectroencephalogram(EEG)have provided new tools for objective assessment,while also driving technological innovation in rehabilita-tion interventions.This paper systematically reviews the SMI deficits in PD,along with research progress in neural detection technolo-gies and rehabilitation strategies within this framework.It focuses on detection methods grounded in SMI and closed-loop neuromodula-tion interventions,and discusses their application prospects and future directions in personalized and precise rehabilitation for PD.

Abstract:To address the problem that the transit time(TT)method is susceptible to noise and reflected waves in the detection of carotid local pulse wave velocity(CLPWV),resulting in low accuracy of CLPWV estimation,we proposed an intelligent estimation method for the CLPWV estimation based on the time-frequency image of pulse wave.Firstly,a carotid local pulse wave propagation da-tabase(CLPWPD)in 4374 virtual adults with different ages and hemodynamic parameters was established.The central statistical mo-ments features and texture energy extraction features from the time-frequency image corresponding to the pulse waves were calculated.Then,each feature was combined separately with such parameters as age to get two comparison feature sets.Finally,different CLPWV prediction models were constructed based on five machine learning models.The results showed that the multi-layer perceptron(MLP)model performed the best overall,with the combination of energy feature set and MLP being less affected by noise and an average root mean squared error(RMSE)of 0.134.The research is expected to provide a new technical reference approach for the early and precise clinical screening and prevention of carotid artery atherosclerosis.

Abstract:Microneedles(MNs)as a minimally invasive and painless micro-needle array technology,form reversible microchannels by physically penetrating the skin's stratum corneum,breaking through the limitations of traditional transdermal drug delivery and sub-cutaneous injection.Their types including solid,hollow and dissolvable forms,with materials covering natural and synthetic polymers,inorganic materials,and composites.The fabrication processes involving 3D printing,laser micromachining and other technologies.In the biomedical field,MNs can efficiently and controllably deliver active ingredients such as small molecules,proteins and vaccines,enable continuous monitoring of biomarkers like glucose,and pierce bacterial biofilms in wound healing to synergistically exert antibac-terial,anti-inflammatory and tissue regeneration effects.However,MNs technology faces challenges including manufacturing costs,controllability of drug release and large-scale production.This review summarizes the types,materials,fabrication,applications and challenges of MNs to provide a reference for clinical translation of MNs.

Abstract:Stroke is a prevalent disease with an extremely high disability rate.Rehabilitation exoskeletons have been clinically proven to aid in limb function recovery for patients.Traditional exoskeleton drives primarily rely on electric motors and hydraulic sys-tems,and have problems such as bulkiness,poor compliance,and limited portability.To overcome the limitations of conventional exo-skeleton drive technologies,the preparation of shape memory alloy(SMA)with high strength-to-weight ratio and variable stiffness of-fers a novel solution.This paper systematically reviews the applications and challenges of SMA in rehabilitation exoskeletons.Firstly,based on the driving characteristics of SMA,the innovative design and integration methods of its driving force in upper limb and lower limb rehabilitation exoskeletons are elaborated respectively,and its mechanical properties and practical effects are analyzed.Subse-quently,addressing inherent challenges like hysteretic nonlinearity and thermal management,popular control strategies and heat dissi-pation solutions are further examined.Finally,future development directions for SMA-driven rehabilitation exoskeletons are summarized and projected,offering insights for designing novel rehabilitation exoskeletons.

Abstract:Forced oscillation technique(FOT)is a critical tool in pulmonary function testing.Aiming at the lack of a unified standard for electrical reactance in existing FOT equipment,which leads to significant differences in the electrical reactance results of different instruments,we proposed an easily reproducible design method for reactance standards that covers the human physiological range selected commercial FOT devices(e.g.,Canadian Thorasys tremoflo® C2,German Jaeger IOS)to test these standards and recor-ded absolute errors for each instrument.Furthermore,5 healthy subjects were recruited for validation tests.The results showed that cali-bration based on theoretical values significantly reduced the individual differences among the instruments for the same subjects,with an average reduction of 51.4%in standard deviation during the test.This method can lay a foundation for cross-device data interoperability and clinical standardization.

Abstract:Aiming at the problems of high similarity of morphological features of similar categories of cervical cell images and insuf-ficient generalization ability of the model caused by the limited size of the dataset,we proposed an improved Resnet34 model for the multi-classification task of cervical cell images.Firstly,an attention module was introduced into the residual blocks of Resnet34,and the activation function ReLU was replaced with PReLU,thereby enhancing the model's ability to capture details and its adaptability.Secondly,the transfer learning strategy was adopted to enhance the generalization performance of the model.The result showed that the accuracy of the improved model reached 96.78%,was 1.32%higher than that of the original Resnet34.The research can effectively capture the subtle differences in the image morphology of cervical cells belonging to similar categories,optimize the recognition perform-ance and generalization ability of the model,can provide reliable technical support for the early automated screening of cervical cancer.