Objective: To evaluate the application value of a digital technology-based multiparameter vital signs monitoring system in perioperative comprehensive full-cycle surveillance. Methods: A comprehensive multidimensional vital signs monitoring system was developed through the integration of medical-grade wireless wearable devices, incorporating patch-type ambulatory electrocardiographic monitor, continuous glucose monitoring sensor, pulse oximeter, wireless digital thermometer, smart wristband, and bioelectrical impedance analyzer. This system facilitates continuous real-time acquisition of multiple physiological parameters including electrocardiogram, blood glucose, oxygen saturation, body temperature, physical activity, and body composition indices. The acquired data were systematically integrated and analyzed through a four-level digital architecture consisting of nurse mobile interfaces, bedside patient terminals, centralized ward monitoring displays, and hospital management information systems. One patient with gastric cancer complicated by diabetes mellitus was selected for full-cycle digital monitoring from preoperative evaluation to hospital discharge. The technical performance of the monitoring system was assessed in terms of data acquisition continuity and timeliness of abnormal event alerts. Results: The monitoring system effectively identified early postoperative abnormalities, such as decreased oxygen saturation and blood glucose fluctuations, providing timely guidance for clinical intervention. The built-in algorithm enabled visualization of perioperative stress levels through heart rate variability indices and continuous glucose monitoring data. The patient demonstrated good compliance with early postoperative mobilization, and the satisfaction score for monitoring management was 4 points based on the Likert 5-point scale. Conclusions: The multiparameter vital signs monitoring system enhanced the precision of perioperative management through continuous and dynamic physiological status assessment. Its modular design aligns with the principles of enhanced recovery after surgery, offering a novel technological solution for intelligent perioperative management.
Humans ; Stomach Neoplasms/physiopathology* ; Vital Signs ; Monitoring, Physiologic/instrumentation* ; Diabetes Mellitus ; Wearable Electronic Devices ; Perioperative Period

OBJECTIVES: A ventricular assist device (VAD) is an electromechanical device used to assist cardiac blood circulation, which can be employed for the treatment of end-stage heart failure and is most commonly placed in the left ventricle. Despite enhancing perfusion performance, the implantation of left ventricular assist device (LVAD) transforms the local intraventricular flow and thus may increase the risk of thrombogenesis. This study aims to investigate fluid-particle interactions and thromboembolic risk under different LVAD configurations using three-dimensional (3D) reconstruction models, focusing on the effects of outflow tract orientation and blood flow rates. METHODS: A patient-specific end-diastolic 3D reconstruction model was initially constructed in stereo lithography (STL) format using Mimics software based on CT images. Transient numerical simulations were performed to analyze fluid-particle interactions and thromboembolic risks for LVAD with varying outflow tract orientations under 2 flow rates (4 L/min and 5 L/min), using particles of uniform size (2 mm), and a blood flow rate optimization protocol was implemented for this patient. RESULTS: When the LVAD flow rate was 5 L/min, helicity and flow stagnation of the blood flow increased the particle residence time (RT) and the risk of thrombogenesis of the aortic root. The percentage of particles traveling toward the brachiocephalic trunk was up to 20.33%. When the LVAD flow rate was 4 L/min, blood turbulence in the aorta was reduced, the RT of blood particles was shortened, and then the percentage of particles traveling toward the brachiocephalic trunk decreased to 10.54%. When the LVAD blood flow rate was 5 L/min and the direction of the outflow pipe was optimal, the RT of blood particles was shortened, and then the percentage of particles traveling toward the brachiocephalic trunk decreased to 11.22%. A 18-month follow-up observation of the patient revealed that the LVAD was in good working order and the patient had no complications related to the implantation of LVAD. CONCLUSIONS Implantation of LVAD results in a higher risk of cerebral infarction; When implanting LVAD with the same outflow tract direction, optimizing flow velocity and outflow tract can reduce the risk of cerebral infarction occurrence.
Heart-Assist Devices/adverse effects* ; Humans ; Heart Failure/physiopathology* ; Blood Flow Velocity ; Thromboembolism/prevention & control* ; Models, Cardiovascular ; Heart Ventricles/physiopathology* ; Imaging, Three-Dimensional

OBJECTIVES: To design a deep learning model that balances model complexity and performance to enable its integration into wearable ECG monitoring devices for automated diagnosis of atrial fibrillation. METHODS: This study was performed based on data from 84 patients with atrial fibrillation, 25 patients with atrial fibrillation, and 18 subjects without obvious arrhythmia collected from the publicly available datasets LTAFDB, AFDB, and NSRDB, respectively. A lightweight attention network based on depthwise separable convolution and fusion of channel-spatial information, namely DSC-AttNet, was proposed. Depthwise separable convolution was introduced to replace standard convolution and reduce model parameters and computational complexity to realize high efficiency and light weight of the model. The multilayer hybrid attention mechanism was embedded to compute the attentional weights of the channels and spatial information at different scales to improve the feature expression ability of the model. Ten-fold cross-validation was performed on LTAFDB, and external independent testing was conducted on AFDB and NSRDB datasets. RESULTS: DSC-AttNet achieved a ten-fold average accuracy of 97.33% and a precision of 97.30% on the test set, both of which outperformed the other 4 comparison models as well as the 3 classical models. The accuracy of the model on the external test set reached 92.78%, better than those of the 3 classical models. The number of parameters of DSC-AttNet was 1.01M, and the computational volume was 27.19G, both smaller than the 3 classical models. CONCLUSIONS This proposed method has a smaller complexity, achieves better classification performance, and has a better generalization ability for atrial fibrillation classification.
Atrial Fibrillation/diagnosis* ; Humans ; Electrocardiography ; Deep Learning ; Wearable Electronic Devices ; Neural Networks, Computer

OBJECTIVES: To evaluate the feasibility, efficacy and safety of Sentinel cerebral embolic protection device (CEPD) during transcatheter aortic valve replacement (TAVR). This study is a subgroup analysis of the China Moderate to Severe Valvular Heart Disease Registry, which has been registered at the Chinese Clinical Trial Registry (ChiCTR2300075006). METHODS: Patients undergoing TAVR with the Sentinel CEPD from October 2023 to September 2024 were retrospectively enrolled. A total of 80 patients were included, with a median age of 72 (68, 76) years, including 52 males (65.0%) and 28 females (35.0%); 62 patients (77.5%) with tricuspid valves, and 18 patients (22.5%) with bicuspid valves; 34 patients (42.5%) with type Ⅰ aortic arch, 24 patients (30.0%) with type Ⅱ aortic arch, 12 patients (15.0%) with type Ⅲ aortic arch, and 10 patients (12.5%) with bovine-type aortic arch. Clinical data of the patients were summarized and analyzed. The primary endpoints were success rate of Sentinel CEPD implantation, as well as all-cause death, symptomatic stroke, transient ischemic attack, and Sentinel CEPD access vessel complications during hospitalization and within 30 days postoperatively. RESULTS: In the 80 patients, self-expanding valves were used in 68 cases (85.0%) and balloon-expandable valves in 12 cases (15.0%). Seventy-nine patients (98.8%) successfully underwent TAVR with Sentinel CEPD deployment. Macroscopically visible debris was captured in 92.5% (74/80) by filters of Sentinel CEPD. Although the procedure time for Sentinel CEPD placement was slightly longer in patients with bovine-type aortic arch, there was no statistically significant difference in deployment time among different aortic arch types (P>0.05). During hospitalization and within 30 days postoperatively, only one case of transient ischemic attack occurred, and there was no all-cause mortality, symptomatic stroke, or access-site vascular complications related to the Sentinel CEPD observed. CONCLUSIONS The Sentinel CEPD demonstrates high feasibility across aortic arch types, potential efficacy in embolic capture, and excellent safety in TAVR.
Humans ; Transcatheter Aortic Valve Replacement/adverse effects* ; Male ; Female ; Aged ; Embolic Protection Devices ; Retrospective Studies ; Intracranial Embolism/prevention & control* ; Aged, 80 and over ; Aortic Valve Stenosis/surgery* ; Aortic Valve/surgery*

Lung cancer remains one of the most prevalent and lethal malignancies worldwide. The advancement of its precise diagnosis and therapeutic development urgently requires in vitro models that can highly recapitulate the pathophysiological characteristics of human tissues. Organ-on-a-chip has emerged as a novel technological platform that integrates microfluidic engineering, biomaterials, and other engineering strategies with organoid culture. This platform enables precise control over the cellular microenvironment, thereby closely mimicking the three-dimensional structure and physiological functions of human organs in vitro. Organ-on-a-chip systems demonstrate significant advantages in cancer research, developmental biology, and disease modeling, as they not only preserve the heterogeneity and pathological features of patient samples but also support co-culture of various cell types to reconstruct the tumor microenvironment (TME). However, standardized construction methods and integrated analytical strategies for this technology in lung cancer research remain to be further refined. This review systematically elaborates on the key technical principles of organ-on-a-chip and its recent advances in lung cancer modeling, drug screening, and immunotherapy research. It aims to provide a theoretical foundation and technical perspective for promoting the deeper application of organ-on-a-chip in precision medicine and translational research for lung cancer.
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Humans ; Lung Neoplasms/drug therapy* ; Organoids/drug effects* ; Lab-On-A-Chip Devices ; Animals ; Tumor Microenvironment

To explore the feasibility of using UAV-LiDAR for measuring the leaf area index (LAI) of crop canopies, we employed UAV-LiDAR to scan sugarcane canopies during the tillering and elongation stages, acquiring canopy point cloud data. Subsequently, features such as average row height, projected row area, point cloud density at different canopy layers, and the ratios between these parameters were extracted. Three feature selection methods-partial least squares regression (PLSR), XGBoost feature importance (XGBoost-FI), and random forest-recursive feature elimination (RF-RFE)-were adopted to evaluate and identify the optimal input variables for modeling. With these selected variables, LAI inversion models were developed based on random forest (RF) and adaptive boosting (AdaBoost) algorithms, and their performance was assessed. Among the extracted features, the projected row area S_p and the total row point count C_total exhibited strong correlations with LAI, with correlation coefficients of 0.73 and 0.72, respectively. The AdaBoost-based LAI inversion model, using the projected row area S_p, average height H_avg, mid-layer point cloud density C_m, and total row point count C_total as input variables, achieved the best performance, with a coefficient of determination (R_v²) of 0.713 and a root mean square error (RMSE_v) of 0.25 on the validation set. This study provides an effective method for high-throughput acquisition of LAI in field crops, offering valuable scientific support for sugarcane field management and breeding efforts.
Plant Leaves/growth & development* ; Saccharum/growth & development* ; Algorithms ; Unmanned Aerial Devices ; Remote Sensing Technology/methods* ; Crops, Agricultural/growth & development*

Accurately obtaining the crop quantity and density is not only crucial for the demand-based input of water and fertilizer in the field but also vital for ensuring the yield and quality of crops. Aerial photography by unmanned aerial vehicles (UAVs) can quickly acquire the distribution image information of crops over a large area. However, the accurate recognition of a single type of dense targets is a huge challenge for most recognition algorithms. Taking banana seedlings as an example in this study, we captured the images of banana plantations by UAVs from high altitudes to explore an efficient recognition method for dense targets. We proposed a strategy of "cut-recognition-stitch" and constructed a counting method based on the improved Faster R-CNN algorithm. First, the images containing highly dense targets were cropped into a large number of image tiles according to different sizes (simulating different flight altitudes), and the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm was adopted to improve the image quality. A banana seedling dataset containing 36 000 image tiles was constructed. Then, the Faster R-CNN network with optimized parameters was used to train the banana seedling recognition model. Finally, the recognition results were reversely stitched together, and a boundary deduplication algorithm was designed to correct the final counting results to reduce the repeated recognition caused by image cropping. The results show that the recognition accuracy of the Faster R-CNN with optimized parameters for banana image datasets of different sizes can reach up to 0.99 at most. The deduplication algorithm can reduce the average counting error for the original aerial images from 1.60% to 0.60%, and the average counting accuracy of banana seedlings reaches 99.4%. The proposed method effectively addresses the challenge of recognizing dense small objects in high-resolution aerial images, providing an efficient and reliable technical solution for intelligent crop density monitoring in precision agriculture.
Musa/growth & development* ; Crops, Agricultural/growth & development* ; Algorithms ; Neural Networks, Computer ; Unmanned Aerial Devices ; Seedlings/growth & development* ; Image Processing, Computer-Assisted/methods* ; Photography ; Agriculture/methods*

Tesla blood pumps demonstrate a reduced propensity for hemolysis and thrombosis compared with vane blood pumps. Considering the restricted driving force within the secondary flow channel of vane blood pumps, along with the low hydraulic efficiency of conventional Tesla blood pumps and their internal flow characteristics that significantly contribute to hemolysis and thrombosis, this study introduces a set of vanes atop the rotor of the Tesla blood pump. This forms a dual-fluid domain rotor, and an axial dual-outlet volute shell structure is adopted to realize the separation of the fluid domains. Through numerical simulations of the new structure, a comparative analysis was conducted in this study on the internal flow characteristics of double-outlet and single-outlet volute shells, and symmetric and asymmetric cross-sections of the same rotor. The results indicate that the flow field distribution is more uniform under the double-outlet volute shell structure, and overall energy dissipation is decreased. After implementing the double-outlet design, in the asymmetric cross-section, compared with the symmetric cross-section, the fluid velocity gradient and turbulent kinetic energy at the tongue of the septum are reduced, and the fluid velocity gradient at the convergence of the diffuser tube outlets are also decreased. The maximum scalar stress is lower, and the decline in head and efficiency is mitigated. Moreover, compared with the single-outlet volute shell, the hemolysis index in the asymmetric cross-section is reduced. In summary, this paper proposes a novel dual-outlet centrifugal disk blood pumps, which can provide a reference for the structural design and performance optimization of magnetically levitated centrifugal blood pumps.
Heart-Assist Devices ; Humans ; Equipment Design ; Hemolysis ; Computer Simulation

Brain-computer interface (BCI) has high application value in the field of healthcare. However, in practical clinical applications, convenience and system performance should be considered in the use of BCI. Wearable BCIs are generally with high convenience, but their performance in real-life scenario needs to be evaluated. This study proposed a wearable steady-state visual evoked potential (SSVEP)-based BCI system equipped with a small-sized electroencephalogram (EEG) collector and a high-performance training-free decoding algorithm. Ten healthy subjects participated in the test of BCI system under simplified experimental preparation. The results showed that the average classification accuracy of this BCI was 94.10% for 40 targets, and there was no significant difference compared to the dataset collected under the laboratory condition. The system achieved a maximum information transfer rate (ITR) of 115.25 bit/min with 8-channel signal and 98.49 bit/min with 4-channel signal, indicating that the 4-channel solution can be used as an option for the few-channel BCI. Overall, this wearable SSVEP-BCI can achieve good performance in real-life scenario, which helps to promote BCI technology in clinical practice.
Brain-Computer Interfaces ; Humans ; Evoked Potentials, Visual/physiology* ; Electroencephalography ; Wearable Electronic Devices ; Algorithms ; Signal Processing, Computer-Assisted ; Adult ; Male

Flexible conductive fibers have been widely applied in wearable flexible sensing. However, exposed wearable flexible sensors based on liquid metal (LM) are prone to abrasion and significant conductivity degradation. This study presented a high-sensitivity LM conductive fiber with integration of strain sensing, electrical heating, and thermochromic capabilities, which was fabricated by coating eutectic gallium-indium (EGaIn) onto spandex fibers modified with waterborne polyurethane (WPU), followed by thermal curing to form a protective polyurethane sheath. This fiber, designated as Spandex/WPU/EGaIn/Polyurethane (SWEP), exhibits a four-layer coaxial structure: spandex core, WPU modification layer, LM conductive layer, and polyurethane protective sheath. The SWEP fiber had a diameter of (458.3 ± 10.4) μm, linear density of (2.37 ± 0.15) g/m, and uniform EGaIn coating. The fiber had excellent conductivity with an average value of (3 716.9 ± 594.2) S/m. The strain sensing performance was particularly noteworthy. A 5 cm × 5 cm woven fabric was fabricated using polyester warp yarns and SWEP weft yarns. The fabric exhibited satisfactory moisture permeability [(536.06 ± 33.15) g/(m ²·h)] and maintained stable thermochromic performance after repeated heating cycles. This advanced conductive fiber development is expected to significantly promote LM applications in wearable electronics and smart textile systems.
Wearable Electronic Devices ; Polyurethanes/chemistry* ; Electric Conductivity ; Gallium/chemistry* ; Metals/chemistry*