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Continuous imaging of cardiac functions is highly desirable for the assessment of long-term cardiovascular health, detection of acute cardiac dysfunction and clinical management of critically ill or surgical patients 1 – 4 . However, conventional non-invasive approaches to image the cardiac function cannot provide continuous measurements owing to device bulkiness 5 – 11 , and existing wearable cardiac devices can only capture signals on the skin 12 – 16 . Here we report a wearable ultrasonic device for continuous, real-time and direct cardiac function assessment. We introduce innovations in device design and material fabrication that improve the mechanical coupling between the device and human skin, allowing the left ventricle to be examined from different views during motion. We also develop a deep learning model that automatically extracts the left ventricular volume from the continuous image recording, yielding waveforms of key cardiac performance indices such as stroke volume, cardiac output and ejection fraction. This technology enables dynamic wearable monitoring of cardiac performance with substantially improved accuracy in various environments. Innovations in device design, material fabrication and deep learning are described, leading to a wearable ultrasound transducer capable of dynamic cardiac imaging in various environments and under different conditions.

Recent advances in wearable ultrasound technologies have demonstrated the potential for hands-free data acquisition, but technical barriers remain as these probes require wire connections, can lose track of moving targets and create data-interpretation challenges. Here we report a fully integrated autonomous wearable ultrasonic-system-on-patch (USoP). A miniaturized flexible control circuit is designed to interface with an ultrasound transducer array for signal pre-conditioning and wireless data communication. Machine learning is used to track moving tissue targets and assist the data interpretation. We demonstrate that the USoP allows continuous tracking of physiological signals from tissues as deep as 164 mm. On mobile subjects, the USoP can continuously monitor physiological signals, including central blood pressure, heart rate and cardiac output, for as long as 12 h. This result enables continuous autonomous surveillance of deep tissue signals toward the internet-of-medical-things. A wearable ultrasound patch monitors subjects in motion using machine learning and wireless electronics.

Plant-based drinks have garnered significant attention as viable substitutes for traditional dairy milk, providing options for individuals who are lactose intolerant or allergic to dairy proteins, and those who adhere to vegan or vegetarian diets. In recent years, demand for plant-based drinks has expanded rapidly. Each variety has unique characteristics in terms of flavor, texture, and nutritional composition, offering consumers a diverse range of choices tailored to meet individual preferences and dietary needs. In this review, we aimed to provide a comprehensive overview of the various types of plant-based drinks and explore potential considerations including their nutritional compositions, health benefits, and processing technologies, as well as the challenges facing the plant-based drink processing industry. We delve into scientific evidence supporting the consumption of plant-based drinks, discuss their potential roles in meeting dietary requirements, and address current limitations and concerns regarding their use. We hope to illuminate the growing significance of plant-based drinks as sustainable and nutritious alternatives to dairy milk, and assist individuals in making informed choices regarding their dietary habits, expanding potential applications for plant-based drinks, and providing necessary theoretical and technical support for the development of a plant-based drink processing industry.

Coronary computed tomography angiography (CCTA) has emerged as the first-line noninvasive imaging test for patients at high risk of coronary artery disease (CAD). When combined with machine learning (ML), it provides more valid evidence in diagnosing major adverse cardiovascular events (MACEs). Radiomics provides informative multidimensional features that can help identify high-risk populations and can improve the diagnostic performance of CCTA. However, its role in predicting MACEs remains highly debated. We evaluated the diagnostic value of ML models constructed using radiomic features extracted from CCTA in predicting MACEs, and compared the performance of different learning algorithms and models, thereby providing clinical recommendations for the diagnosis, treatment, and prognosis of MACEs. We comprehensively searched 5 online databases, Cochrane Library, Web of Science, Elsevier, CNKI, and PubMed, up to September 10, 2024, for original studies that used ML models among patients who underwent CCTA to predict MACEs and reported clinical outcomes and endpoints related to it. Risk of bias in the ML models was assessed by the Prediction Model Risk of Bias Assessment Tool, while the radiomics quality score (RQS) was used to evaluate the methodological quality of the radiomics prediction model development and validation. We also followed the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) guidelines to ensure transparency of ML models included. Meta-analysis was performed using Meta-DiSc software (version 1.4), which included the I² score and Cochran Q test, along with StataMP 17 (StataCorp) to assess heterogeneity and publication bias. Due to the high heterogeneity observed, subgroup analysis was conducted based on different model groups. Ten studies were included in the analysis, 5 (50%) of which differentiated between training and testing groups, where the training set collected 17 kinds of models and the testing set gathered 26 models. The pooled area under the receiver operating characteristic (AUROC) curve for ML models predicting MACEs was 0.7879 in the training set and 0.7981 in the testing set. Logistic regression (LR), the most commonly used algorithm, achieved an AUROC of 0.8229 in the testing group and 0.7983 in the training group. Non-LR models yielded AUROCs of 0.7390 in the testing set and 0.7648 in the training set, while the random forest (RF) models reached an AUROC of 0.8444 in the training group. Study limitations included a limited number of studies, high heterogeneity, and the types of included studies. The performance of ML models for predicting MACEs was found to be superior to that of general models based on basic feature extraction and integration from CCTA. Specifically, LR-based ML diagnostic models demonstrated significant clinical potential, particularly when combined with clinical features, and are worth further validation through more clinical trials. PROSPERO CRD42024596364; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024596364.

Background Air pollution and cardiovascular-kidney-metabolic (CKM) syndrome have emerged as major global public health challenges. However, limited evidence exists on the association between air pollution exposure and CKM incidence and progression. Method We analyzed data from participants across CKM stages 0–5 enrolled in the China Health and Retirement Longitudinal Study. Ambient air pollutant concentrations were obtained from the China High Air Pollutants database. Binary logistic regression and weighted quantile sum (WQS) regression models were applied to assess the impacts of individual and combined air pollutants on CKM incidence. Cumulative exposure models were employed to study the long-term effects of pollutants on CKM progression. CKM stages were modeled as ordered categorical outcomes using proportional odds models to examine the association between pollutant levels and CKM progression. Results A total of 4,026 individuals were included in the main analysis. Each interquartile range increase in particulate matter with aerodynamic diameters ≤ 1 μm (PM1; OR  = 1.008, 95% CI : 1.001–1.015, p  < 0.05), ≤ 2.5 μm (PM2.5; OR  = 1.009, 95% CI : 1.002–1.015, p  < 0.01 ), ≤ 10 μm (PM10; OR  = 1.010, 95% CI : 1.004–1.016, p  < 0.01), nitrogen dioxide (NO2; OR  = 1.011, 95% CI :1.004–1.018, p  < 0.01), and ozone (O3; OR  = 1.014, 95% CI : 1.006–1.021, p  < 0.01) was significantly associated with increased CKM risk. The combined effect of multiple pollutants, reflected by the WQS index, was associated with a 77.7% higher risk of CKM incidence ( OR  = 1.777, 95% CI : 1.143–2.762, p  < 0.05). PM10 ( OR  = 1.160, 95% CI : 1.059–1.270, p  < 0.05) and sulfur dioxide (SO2; OR  = 1.122, 95% CI : 1.018–1.236, p  < 0.05) were significant contributors to CKM progression. In the cumulative effect analysis of 2,146 individuals, 5-year cumulative PM10 exposure was positively associated with CKM progression ( OR  = 1.027, 95% CI : 1.001–1.055, p  < 0.05). Conclusion Air pollution is a significant environmental determinant of both CKM incidence and progression. Combined and cumulative effects of multiple pollutants may further enhance CKM risk.

Fall detection is of great significance in safeguarding human health. By monitoring the motion data, a fall detection system (FDS) can detect a fall accident. Recently, wearable sensors-based FDSs have become the mainstream of research, which can be categorized into threshold-based FDSs using experience, machine learning-based FDSs using manual feature extraction, and deep learning (DL)-based FDSs using automatic feature extraction. However, most FDSs focus on the global information of sensor data, neglecting the fact that different segments of the data contribute variably to fall detection. This shortcoming makes it challenging for FDSs to accurately distinguish between similar human motion patterns of actual falls and fall-like actions, leading to a decrease in detection accuracy. This study aims to develop and validate a DL framework to accurately detect falls using acceleration and gyroscope data from wearable sensors. We aim to explore the essential contributing features extracted from sensor data to distinguish falls from activities of daily life. The significance of this study lies in reforming the FDS by designing a weighted feature representation using DL methods to effectively differentiate between fall events and fall-like activities. Based on the 3-axis acceleration and gyroscope data, we proposed a new DL architecture, the dual-stream convolutional neural network self-attention (DSCS) model. Unlike previous studies, the used architecture can extract global feature information from acceleration and gyroscope data. Additionally, we incorporated a self-attention module to assign different weights to the original feature vector, enabling the model to learn the contribution effect of the sensor data and enhance classification accuracy. The proposed model was trained and tested on 2 public data sets: SisFall and MobiFall. In addition, 10 participants were recruited to carry out practical validation of the DSCS model. A total of 1700 trials were performed to test the generalization ability of the model. The fall detection accuracy of the DSCS model was 99.32% (recall=99.15%; precision=98.58%) and 99.65% (recall=100%; precision=98.39%) on the test sets of SisFall and MobiFall, respectively. In the ablation experiment, we compared the DSCS model with state-of-the-art machine learning and DL models. On the SisFall data set, the DSCS model achieved the second-best accuracy; on the MobiFall data set, the DSCS model achieved the best accuracy, recall, and precision. In practical validation, the accuracy of the DSCS model was 96.41% (recall=95.12%; specificity=97.55%). This study demonstrates that the DSCS model can significantly improve the accuracy of fall detection on 2 publicly available data sets and performs robustly in practical validation.

Electronic patches, based on various mechanisms, allow continuous and noninvasive monitoring of biomolecules on the skin surface. However, to date, such devices are unable to sense biomolecules in deep tissues, which have a stronger and faster correlation with the human physiological status than those on the skin surface. Here, we demonstrate a photoacoustic patch for three-dimensional (3D) mapping of hemoglobin in deep tissues. This photoacoustic patch integrates an array of ultrasonic transducers and vertical-cavity surface-emitting laser (VCSEL) diodes on a common soft substrate. The high-power VCSEL diodes can generate laser pulses that penetrate >2 cm into biological tissues and activate hemoglobin molecules to generate acoustic waves, which can be collected by the transducers for 3D imaging of the hemoglobin with a high spatial resolution. Additionally, the photoacoustic signal amplitude and temperature have a linear relationship, which allows 3D mapping of core temperatures with high accuracy and fast response. With access to biomolecules in deep tissues, this technology adds unprecedented capabilities to wearable electronics and thus holds significant implications for various applications in both basic research and clinical practice. The authors present a wearable photoacoustic patch, which integrates laser diodes and piezoelectric transducers for three-dimensional imaging of hemoglobin and temperature in deep tissues.

Functional dyspepsia (FD) is a common functional gastrointestinal disorder. The pathophysiology remains poorly understood; however, alterations in the small intestinal microbiome have been observed. Current treatments for FD with drugs are limited, and there are certain safety problems. A class of active probiotic bacteria can control gastrointestinal homeostasis, nutritional digestion and absorption, and the energy balance when taken in certain dosages. Probiotics play many roles in maintaining intestinal microecological balance, improving the intestinal barrier function, and regulating the immune response. The presence and composition of intestinal microorganisms play a vital role in the onset and progression of FD and serve as a critical factor for both regulation and potential intervention regarding the management of this condition. Thus, there are potential advantages to alleviating FD by regulating the intestinal flora using probiotics, targeting intestinal microorganisms. This review summarizes the research progress of probiotics regarding improving FD by regulating intestinal flora and provides a reference basis for probiotics to improve FD.

Multi-modal sensor fusion-based LiDAR SLAM is a key capability for reliable mobile robot operation in complex indoor environments. However, it remains susceptible to localization drift and global inconsistency in typical degenerate scenarios such as feature sparsity, repetitive structures, and dynamic disturbances. To address these challenges, we propose a LiDAR-inertial SLAM method enhanced with visual QR-code landmarks. The front-end employs a lightweight EKF-based LiDAR-IMU odometry to ensure real-time and robust motion estimation, while the back-end constructs a unified factor graph that tightly couples LiDAR, IMU, loop-closure, and QR-code landmark factors within a single state space to achieve globally consistent cross-modal constraints. QR codes are further incorporated as persistent artificial landmarks to provide strong global anchoring in long corridors and repetitive or feature-degraded environments. In addition, an adaptive covariance and hierarchical weighting mechanism dynamically adjusts factor influence based on residual statistics and observation quality, thereby improving robustness under occlusion, degradation, and sensor noise variations. Experimental results demonstrate that the proposed system significantly improves localization accuracy and mapping stability across various challenging indoor scenarios. These findings validate the effectiveness of deeply integrating visual landmarks with LiDAR-inertial information, offering new scientific evidence and practical value for robust multi-modal SLAM in indoor robotic perception—fully aligning with the research scope of Scientific Reports.

To investigate the correlation of air-conduction thresholds between automated audiometry in a non-isolated environment and manual audiometry in participants with normal hearing and different degrees of hearing loss. Eighty-three participants aged 11-88 years old underwent automated pure-tone audiometry in a non-acoustically isolated environment, and the results were compared with those of manual pure-tone audiometry performed in a standard acoustically isolated booth, with the order of testing randomised. Six frequencies of 250, 500, 1,000, 2000, 4,000 and 8,000 Hz were tested. All 166 ears were completed and 996 valid hearing threshold data were obtained, with 28 data exceeding the 95% confidence interval in the Bland-Altman plot, accounting for 2.81% of all data. The means and standard deviations of the differences for the six frequencies from 250 to 8,000 Hz were, respectively, 0.63 ± 5.31, 0.69 ± 4.50, 0.45 ± 4.99, 0.3 ± 6.2, -0.15 ± 4.8, and 0.21 ± 4.97 dB. The correlation coefficients of the two test results for normal hearing, mild, moderate, severe and above hearing loss groups were 0.95, 0.92, 0.97, and 0.96, respectively. The correlation coefficient of the automated and manual audiometry thresholds for the age groups under 40 years, 40-60 years, and 60 years above, were 0.98, 0.97 and 0.97, respectively, with all being statistically significant (  < 0.01). The response time of the three age groups were 791 ± 181 ms, 900 ± 190 ms and 1,063 ± 332 ms, respectively, and there was a significant difference between the groups under 40 years and over 60 years. There was good consistency between automated pure-tone audiometry in a non-acoustically isolated environment and manual pure-tone audiometry in participants with different hearing levels and different age groups.