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Abstract Blood pressure is a crucial indicator of cardiovascular disease, and arterial blood pressure (ABP) waveforms contain information that reflects the cardiovascular status. We propose a novel deep-learning method that converts photoplethysmogram (PPG) signals into ABP waveforms. We used $$\hbox {U}^{2}$$ -Net as a feature extractor and designed a Bi-block to capture individualised time information in encoder feature extraction. We further enhanced the prediction accuracy of the ABP waveforms by applying a combined loss function to each layer of deep supervision. We also propose a total error index (TEI) to measure overall performance. Furthermore, we extended our method from the UCI dataset to the VitalDB dataset, achieving mean absolute error ± standard deviation (MAE ± STD) values of 2.48 ± 1.95, 1.42 ± 1.42, and 1.48 ± 1.36 mmHg for systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP) in UCI dataset, and 2.16 ± 1.53, 1.12 ± 0.59, and 1.35 ± 0.84 mmHg in VitalDB dataset, respectively. The mean ± STD values of the TEI index are 0.29 ± 0.10 in UCI dataset and 0.29 ± 0.15 in VitalDB dataset. These results demonstrate the superiority of the proposed method over existing methods and its robustness to different sampling frequencies and devices.

Abstract To investigate the intervention effect of an intelligent rehabilitation training system on patients with functional ankle instability (FAI) and to advance the research to optimise the effect of FAI rehabilitation training. Thirty-four FAI patients who participated in this trial in Guilin City from April 2023 to June 2023 were recruited as research subjects, and all subjects were randomly divided into the control group (n = 17) and the observation group (n = 17). Both groups received the conventional rehabilitation training intervention for 6 weeks, and the observation group received the additional training using the intelligent rehabilitation training system training invented by our team. Visual analogue scale (VAS), ankle active mobility, ankle muscle strength and Y-balance test (YBT) were assessed before and after treatment. Two-way repeated measures ANOVA shows that the interaction effect between time and group of VAS scores was significant (F = 35.644, P

Electrocardiography (ECG) is the gold standard for monitoring vital signs and for diagnosing, controlling, and preventing cardiovascular diseases (CVDs). However, ECG requires continuous user participation, and cannot be used for continuous cardiac monitoring. In contrast to ECG, photoplethysmography (PPG) devices do not require continued user involvement, and can offer ongoing and long-term detection capabilities. However, from a medical perspective, ECG can provide more information about the heart. Currently, most existing work contains different signals recorded from the same subject in training and test sets. This study proposes a neural network model based on a 1D convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network. This neural network model can directly reconstruct ECG signals from PPG signals. The learned features are captured from the CNN model and fed into the BiLSTM model. In order to verify the validity of the model, it is evaluated using the MIMIC II dataset in the completely subject-independent model (records are placed in a training set, and a test set appears once, but the test signal belongs to a record that is not in the training set). The Pearson’s correlation coefficient between the reconstructed ECG and the reference ECG of the proposed model is 0.963 in the completely subject-independence model. The results of the proposed model are better than those of several cited state-of-the-art models. The results of our trained model indicate that we can obtain reconstructed ECGs that are highly similar to reference ECGs in the completely subject-independent model.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants are a fatal pathogen resulting in substantial morbidity and mortality, and posing a great threat to human health with epidemics and pandemics. Methods: Next-generation sequencing (NGS) was performed to investigate the SARS-CoV-2 genomic characterization. Phylogenetic analysis of SARS-CoV-2 genomes was used to probe the evolutionary. Homology protein structure modelling was done to explore potential effect of the mutations. Results: The eighty genome sequences of SARS-CoV-2 obtained from the thirty-nine patients with COVID-19. A novel variant with mutation H625R concomitant with S50L in spike glycoprotein had been identified. Phylogenetic analysis revealed that SARS-CoV-2 variants belong to several distinct lineages. Homology modelling indicated that variant with mutation H625R and S50L increases flexibility of S1 subunit. Conclusions: SARS-CoV-2 genomes are constantly evolving by accumulation of point mutations. The amino acid H625R in combination with S50L may have a significant impact on the interaction between spike glycoprotein and ACE2.

Abstract Background Kidney tumors have become increasingly prevalent among adults and are now considered one of the most common types of tumors. Accurate segmentation of kidney tumors can help physicians assess tumor complexity and aggressiveness before surgery. However, segmenting kidney tumors manually can be difficult because of their heterogeneity. Methods This paper proposes a 2.5D MFFAU-Net (multi-level Feature Fusion Attention U-Net) to segment kidneys, tumors and cysts. First, we propose a 2.5D model for learning to combine and represent a given slice in 2D slices, thereby introducing 3D information to balance memory consumption and model complexity. Then, we propose a ResConv architecture in MFFAU-Net and use the high-level and low-level feature in the model. Finally, we use multi-level information to analyze the spatial features between slices to segment kidneys and tumors. Results The 2.5D MFFAU-Net was evaluated on KiTS19 and KiTS21 kidney datasets and demonstrated an average dice score of 0.924 and 0.875, respectively, and an average Surface dice (SD) score of 0.794 in KiTS21. Conclusion The 2.5D MFFAU-Net model can effectively segment kidney tumors, and the results are comparable to those obtained with high-performance 3D CNN models, and have the potential to serve as a point of reference in clinical practice.

A multifunctional switchable metamaterial device based on graphene, a gold layer, polyimide, vanadiµm dioxide (VO2), and the sapphire substrate is designed in this paper. The top layer consists of a gold wire, graphene, and two split-ring resonators with the same parameters. By adjusting the Fermi level of graphene, the regulation of BIC and quasi-BIC is realized, and the conversion between BIC and absorber is realized by adjusting the conductivity of VO2. When the device is converted into a wave-absorbing device with single-band absorption characteristics, the Fermi level of graphene at this time is 0.001 eV, the absorption peak at 0.820 THz is higher than 99.5%, and when the Fermi level of regulated graphene is 1 eV, the absorption peak at 0.667 THz is also higher than 99.5%. The peak frequency of the device is 0.640 THz when it converts to quasi-BIC. To the best of our knowledge, this is the first time that the conversion and regulation of BIC and absorber have been achieved using these two phase change materials. Moreover, by adjusting the parameters of the metamaterial structure, the working efficiency and frequency of BIC and absorber can be dynamically adjusted. The electric field distribution and surface current of metamaterials are further studied, and the physical mechanism of effective absorption and BIC is discussed. These results show that the metamaterials proposed in this paper have many advantages, such as terahertz absorption, BIC, and active device control, and are of great significance for developing terahertz multifunctional devices.

ECG helps in diagnosing heart disease by recording heart activity. During long-term measurements, data loss occurs due to sensor detachment. Therefore, research into the reconstruction of missing ECG data is essential. However, ECG requires user participation and cannot be used for continuous heart monitoring. Continuous monitoring of PPG signals is conversely low-cost and easy to carry out. In this study, a deep neural network model is proposed for the reconstruction of missing ECG signals using PPG data. This model is an end-to-end deep learning neural network utilizing WNet architecture as a basis, on which a bidirectional long short-term memory network is added in establishing a second model. The performance of both models is verified using 146 records from the MIMIC III matched subset. Compared with the reference, the ECG reconstructed using the proposed model has a Pearson’s correlation coefficient of 0.851, root mean square error (RMSE) of 0.075, percentage root mean square difference (PRD) of 5.452, and a Fréchet distance (FD) of 0.302. The experimental results demonstrate that it is feasible to reconstruct missing ECG signals from PPG.

As an essential physiological indicator within the human body, noninvasive continuous blood pressure (BP) measurement is critical in the prevention and treatment of cardiovascular disease. However, traditional methods of blood pressure prediction using a single-wavelength Photoplethysmographic (PPG) have bottlenecks in further improving BP prediction accuracy, which limits their development in clinical application and dissemination. To this end, this study proposed a method to fuse a four-wavelength PPG and a BP prediction model based on the attention mechanism of a convolutional neural network and bidirectional long- and short-term memory (ACNN-BiLSTM). The effectiveness of a multi-wavelength PPG fusion method for blood pressure prediction was evaluated by processing PPG signals from 162 volunteers. The study compared the performance of the PPG signals with different individual wavelengths and using a multi-wavelength PPG fusion method in blood pressure prediction, assessed using mean absolute error (MAE), root mean squared error (RMSE) and AAMI-related criteria. The experimental results showed that the ACNN-BiLSTM model achieved a better MAE ± RMSE for a systolic BP and diastolic BP of 1.67 ± 5.28 and 1.15 ± 2.53 mmHg, respectively, when using the multi-wavelength PPG fusion method. As a result, the ACNN-BiLSTM blood pressure model based on multi-wavelength PPG fusion could be considered a promising method for noninvasive continuous BP measurement.

Abstract Background Pulmonary cryptococcosis (PC) and mixed pulmonary infection are difficult to be diagnosed due to the non-specificity and their overlapping clinical manifestations. In terms of the clinical diagnosis of PC and mixed pulmonary infection, conventional tests have limitations such as a long detection period, a limited range of pathogens, and low sensitivity. Metagenomics next-generation sequencing (mNGS) is a nascent and powerful method that can detect pathogens without culture, to diagnose known and unexplained infections in reduced time. Case presentation A 43-year-old female was admitted to the hospital after suffering from a cough for one month. At the time of admission, a contrast-enhanced chest CT revealed multiple nodules and plaques in her right lung, as well as the formation of cavities. The blood routine assays showed evidently increased white blood cell count (mainly neutrophils), CRP, and ESR, which suggested she was in the infection phase. The serum CrAg-LFA test showed a positive result. Initially, she was diagnosed with an unexplained pulmonary infection. Bronchoalveolar lavage fluid (BALF) samples were collected for microbial culture, immunological tests and the mNGS. Microbial culture and immunological tests were all negative, while mNGS detected Corynebacterium striatum, Pseudomonas aeruginosa, Streptococcus pneumoniae, and Cryptococcus neoformans. The diagnosis was revised to PC and bacterial pneumonia. Lung infection lesions were healed after she received targeted anti-infection therapy with mezlocillin and fluconazole. In a follow-up after 2 months, the patient’s symptoms vanished. Conclusions Here, we demonstrated that mNGS was capable of accurately distinguishing Cryptococcus from M. tuberculosis in pulmonary infection, and notably mNGS was capable of swiftly and precisely detecting pathogens in mixed bacterial and fungal pulmonary infection. Furthermore, the results of mNGS also have the potential to adjust anti-infective therapies.

The progression of Alzheimer’s disease (AD) is positively correlated with the phosphorylation damage of Tau-441 protein, which is the marker with the most potential for the early detection of AD. The low content of Tau-441 in human serum is a major difficulty for the realization of content detection. Herein, we prepared an electrochemical immunosensor modified with Poly(3,4-ethylene-dioxythiophene)-poly (styrene sulfonate) (PEDOT: PSS)/Carboxylated multi-walled carbon nanotube (MWCNTs-COOH) nanocomposites based on electrochemical immunoassay technology for the low-concentration detection of Tau-441. The immunosensor based on the nanocomposite can take advantage of the characteristics of conductive polymers to achieve electrical signal amplification and use MWCNTs-COOH to increase the contact area of the active site and bond with the Tau-441 antibodies on the electrode. The physicochemical and electrical properties of PEDOT: PSS/MWCNTs-COOH were studied by in situ characterization techniques and electrochemical characterization methods, indicating that the immunosensor has high selectivity and sensitivity to the Tau-441 immune reaction. Under optimized optimal conditions, the electrochemical immunosensor detected a range of concentrations of Tau-441 to obtain a low detection of limit (0.0074 ng mL−1) and demonstrated good detection performance through actual human serum sample testing experiments. Therefore, the study provides an effective reference value for the early diagnosis of AD.

The measurement of hemoglobin is a vital index for diagnosing and monitoring diseases in clinical practice. At present, solutions need to be found for the soreness, high risk of infection, and inconvenient operation associated with invasive detection methods. This paper proposes a method for non-invasively detecting hemoglobin levels based on multi-wavelength photoplethysmography (PPG) signals. AFE4490 and TMUX1109 were used to implement the low-cost collection of an eight-LED transmissive PPG signal. We used seven regular LEDs and one broadband LED (Osram SFH4737) as light sources. Additionally, a finger clip integrating multiple sensors was designed and manufactured via 3D printing to simultaneously monitor the LED–sensor distance and the pressure from the tester’s finger during PPG signal acquisition. We used a method to extract features from PPG signals using a sliding-window’s variance and an evaluation metric for PPG signals based on the AdaCost classification. Data were gathered from 56 participants from the Nephrology department, including 16 anemic patients. Pearson correlation analysis was conducted on the collected data to remove any data with a weak correlation. The advantage of using a broadband LED as a light source was also demonstrated. Several non-invasive hemoglobin regression models were created by applying AdaBoost, BPNN, and Random Forest models. The study’s results indicate that the AdaBoost model produced the best performance, with a mean absolute error (MAE) of 2.67 g/L and a correlation coefficient (R2) of 0.91 The study results show that the device we designed and manufactured can achieve effective non-invasive hemoglobin detection and represents a new methodological approach to obtaining measurements that can be applied in a clinical setting.

Introduction: Globally, hypertension (HT) is a substantial risk factor for cardiovascular disease and mortality; hence, rapid identification and treatment of HT is crucial. In this study, we tested the light gradient boosting machine (LightGBM) machine learning method for blood pressure stratification based on photoplethysmography (PPG), which is used in most wearable devices.Methods: We used 121 records of PPG and arterial blood pressure (ABP) signals from the Medical Information Mart for Intensive Care III public database. PPG, velocity plethysmography, and acceleration plethysmography were used to estimate blood pressure; the ABP signals were used to determine the blood pressure stratification categories. Seven feature sets were established and used to train the Optuna-tuned LightGBM model. Three trials compared normotension (NT) vs. prehypertension (PHT), NT vs. HT, and NT + PHT vs. HT.Results: The F1 scores for these three classification trials were 90.18%, 97.51%, and 92.77%, respectively. The results showed that combining multiple features from PPG and its derivative led to a more accurate classification of HT classes than using features from only the PPG signal.Discussion: The proposed method showed high accuracy in stratifying HT risks, providing a noninvasive, rapid, and robust method for the early detection of HT, with promising applications in the field of wearable cuffless blood pressure measurement.

We aimed to estimate cardiac output (CO) from photoplethysmography (PPG) and the arterial pressure waveform (ART) using a deep learning approach, which is minimally invasive, does not require patient demographic information, and is operator-independent, eliminating the need to artificially extract a feature of the waveform by implementing a traditional formula. We aimed to present an alternative to measuring cardiac output with greater accuracy for a wider range of patients. Using a publicly available dataset, we selected 543 eligible patients and divided them into test and training sets after preprocessing. The data consisted of PPG and ART waveforms containing 2048 points with the corresponding CO. We achieved an improvement based on the U-Net modeling framework and built a two-channel deep learning model to automatically extract the waveform features to estimate the CO in the dataset as the reference, acquired using the EV1000, a commercially available instrument. The model demonstrated strong consistency with the reference values on the test dataset. The mean CO was 5.01 ± 1.60 L/min and 4.98 ± 1.59 L/min for the reference value and the predicted value, respectively. The average bias was −0.04 L/min with a −1.025 and 0.944 L/min 95% limit of agreement (LOA). The bias was 0.79% with a 95% LOA between −20.4% and 18.8% when calculating the percentage of the difference from the reference. The normalized root-mean-squared error (RMSNE) was 10.0%. The Pearson correlation coefficient (r) was 0.951. The percentage error (PE) was 19.5%, being below 30%. These results surpassed the performance of traditional formula-based calculation methods, meeting clinical acceptability standards. We propose a dual-channel, improved U-Net deep learning model for estimating cardiac output, demonstrating excellent and consistent results. This method offers a superior reference method for assessing cardiac output in cases where it is unnecessary to employ specialized cardiac output measurement devices or when patients are not suitable for pulmonary-artery-catheter-based measurements, providing a viable alternative solution.

Electrocardiography and photoplethysmography are non-invasive techniques that measure signals from the cardiovascular system. While the cycles of the two measurements are highly correlated, the correlation between the waveforms has rarely been studied. Measuring the photoplethysmogram (PPG) is much easier and more convenient than the electrocardiogram (ECG). Recent research has shown that PPG can be used to reconstruct the ECG, indicating that practitioners can gain a deep understanding of the patients’ cardiovascular health using two physiological signals (PPG and ECG) while measuring only PPG. This study proposes a subject-based deep learning model that reconstructs an ECG using a PPG and is based on the bidirectional long short-term memory model. Because the ECG waveform may vary from subject to subject, this model is subject-specific. The model was tested using 100 records from the MIMIC III database. Of these records, 50 had a circulatory disease. The results show that a long ECG signal could be effectively reconstructed from PPG, which is, to our knowledge, the first attempt in this field. A length of 228 s of ECG was constructed by the model, which was trained and validated using 60 s of PPG and ECG signals. To segment the data, a different approach that segments the data into short time segments of equal length (and that do not rely on beats and beat detection) was investigated. Segmenting the PPG and ECG time series data into equal segments of 1-min width gave the optimal results. This resulted in a high Pearson’s correlation coefficient between the reconstructed 228 s of ECG and referenced ECG of 0.818, while the root mean square error was only 0.083 mV, and the dynamic time warping distance was 2.12 mV per second on average.

The incidence rate of kidney tumors increases year by year, especially for some incidental small tumors. It is challenging for doctors to segment kidney tumors from kidney CT images. Therefore, this paper proposes a deep learning model based on FR2PAttU-Net to help doctors process many CT images quickly and efficiently and save medical resources. FR2PAttU-Net is not a new CNN structure but focuses on improving the segmentation effect of kidney tumors, even when the kidney tumors are not clear. Firstly, we use the R2Att network in the “U” structure of the original U-Net, add parallel convolution, and construct FR2PAttU-Net model, to increase the width of the model, improve the adaptability of the model to the features of different scales of the image, and avoid the failure of network deepening to learn valuable features. Then, we use the fuzzy set enhancement algorithm to enhance the input image and construct the FR2PAttU-Net model to make the image obtain more prominent features to adapt to the model. Finally, we used the KiTS19 data set and took the size of the kidney tumor as the category judgment standard to enhance the small sample data set to balance the sample data set. We tested the segmentation effect of the model at different convolution and depths, and we got scored a 0.948 kidney Dice and a 0.911 tumor Dice results in a 0.930 composite score, showing a good segmentation effect.

Electrocardiograms (ECGs) provide crucial information for evaluating a patient’s cardiovascular health; however, they are not always easily accessible. Photoplethysmography (PPG), a technology commonly used in wearable devices such as smartwatches, has shown promise for constructing ECGs. Several methods have been proposed for ECG reconstruction using PPG signals, but some require signal alignment during the training phase, which is not feasible in real-life settings where ECG signals are not collected at the same time as PPG signals. To address this challenge, we introduce PPG2ECGps, an end-to-end, patient-specific deep-learning neural network utilizing the W-Net architecture. This novel approach enables direct ECG signal reconstruction from PPG signals, eliminating the need for signal alignment. Our experiments show that the proposed model achieves mean values of 0.977 mV for Pearson’s correlation coefficient, 0.037 mV for the root mean square error, and 0.010 mV for the normalized dynamic time-warped distance when comparing reconstructed ECGs to reference ECGs from a dataset of 500 records. As PPG signals are more accessible than ECG signals, our proposed model has significant potential to improve patient monitoring and diagnosis in healthcare settings via wearable devices.

As an essential indicator of liver function, bilirubin is of great significance for clinical diagnosis. A non-enzymatic sensor has been established for sensitive bilirubin detection based on the bilirubin oxidation catalyzed by unlabeled gold nanocages (GNCs). GNCs with dual-localized surface plasmon resonance (LSPR) peaks were prepared by a one-pot method. One peak around 500 nm was ascribed to gold nanoparticles (AuNPs), and the other located in the near-infrared region was the typical peak of GNCs. The catalytic oxidation of bilirubin by GNCs was accompanied by the disruption of cage structure, releasing free AuNPs from the nanocage. This transformation changed the dual peak intensities in opposite trend, and made it possible to realize the colorimetric sensing of bilirubin in a ratiometric mode. The absorbance ratios showed good linearity to bilirubin concentrations in the range of 0.20~3.60 μmol/L with a detection limit of 39.35 nM (3σ, n = 3). The sensor exhibited excellent selectivity for bilirubin over other coexisting substances. Bilirubin in real human serum samples was detected with recoveries ranging from 94.5 to 102.6%. The method for bilirubin assay is simple, sensitive and without complex biolabeling.

BackgroundImmunotherapy has evolved as a critical option to treat diverse cancers. The active response to immunotherapy relies on the unique interaction between cancer and the tumor microenvironment (TME). Angiogenesis is one of the hallmarks of cancer. However, the association between angiogenesis and clinical outcome, immune cell infiltration, and immunotherapy remains unknown in gastric cancer (GC).MethodsWe systematically assessed 36 angiogenesis-associated genes (AAGs) and comprehensively identified the correlation between angiogenesis and transcriptional patterns, prognosis, and immune cell infiltration. The AAG_score was applied to quantify the angiogenesis subtypes of each patient. We then evaluated their values in prognostic prediction and therapeutic responses in GC.ResultsWe discussed the mutations of AAGs in GC specimens from genetic levels and identified their expression patterns from TCGA and GEO cohorts. We determined two different molecular subtypes and observed that AAG mutations were related to patients’ clinicopathological characteristics, prognosis, and infiltrating TME. Next, an AAG_score for predicting overall survival (OS) was established and its reliable predictive ability in GC patients was confirmed. Furthermore, we created a highly reliable nomogram to facilitate the clinical viability of the AAG_score. A low AAG_score, characterized by elevated microsatellite instability-high, mutation burden, and immune activation, demonstrated a superior OS. Additionally, the AAG_score was remarkedly correlated with the cancer stem cell index and drug susceptibility.ConclusionCollectively, we identified a prognostic AAG signature for GC patients. This signature may contribute to clarifying the characteristics of TME and enable the exploration of more potent immunotherapy strategies.

Due to the simplicity and convenience of PPG signal acquisition, the detection of the respiration rate based on the PPG signal is more suitable for dynamic monitoring than the impedance spirometry method, but it is challenging to achieve accurate predictions from low-signal-quality PPG signals, especially in intensive-care patients with weak PPG signals. The goal of this study was to construct a simple model for respiration rate estimation based on PPG signals using a machine-learning approach fusing signal quality metrics to improve the accuracy of estimation despite the low-signal-quality PPG signals. In this study, we propose a method based on the whale optimization algorithm (WOA) with a hybrid relation vector machine (HRVM) to construct a highly robust model considering signal quality factors to estimate RR from PPG signals in real time. To detect the performance of the proposed model, we simultaneously recorded PPG signals and impedance respiratory rates obtained from the BIDMC dataset. The results of the respiration rate prediction model proposed in this study showed that the MAE and RMSE were 0.71 and 0.99 breaths/min, respectively, in the training set, and 1.24 and 1.79 breaths/min, respectively, in the test set. Compared without taking signal quality factors into account, MAE and RMSE are reduced by 1.28 and 1.67 breaths/min, respectively, in the training set, and reduced by 0.62 and 0.65 breaths/min in the test set. Even in the nonnormal breathing range below 12 bpm and above 24 bpm, the MAE reached 2.68 and 4.28 breaths/min, respectively, and the RMSE reached 3.52 and 5.01 breaths/min, respectively. The results show that the model that considers the PPG signal quality and respiratory quality proposed in this study has obvious advantages and application potential in predicting the respiration rate to cope with the problem of low signal quality.

The automatic semantic segmentation of point cloud data is important for applications in the fields of machine vision, virtual reality, and smart cities. The processing capability of the point cloud segmentation method with PointNet++ as the baseline needs to be improved for extremely imbalanced point cloud scenes. To address this problem, in this study, we designed a weighted sampling method based on farthest point sampling (FPS), which adjusts the sampling weight value according to the loss value of the model to equalize the sampling process. We also introduced the relational learning of the neighborhood space of the sampling center point in the feature encoding process, where the feature importance is distinguished by using a self-attention model. Finally, the global–local features were aggregated and transmitted using the hybrid pooling method. The experimental results of the six-fold crossover experiment showed that on the S3DIS semantic segmentation dataset, the proposed network achieved 9.5% and 11.6% improvement in overall point-wise accuracy (OA) and mean of class-wise intersection over union (MIoU), respectively, compared with the baseline. On the Vaihingen dataset, the proposed network achieved 4.2% and 3.9% improvement in OA and MIoU, respectively, compared with the baseline. Compared with the segmentation results of other network models on public datasets, our algorithm achieves a good balance between OA and MIoU.

Atrial fibrillation (AF) is the most common persistent arrhythmia and is likely to cause strokes and damage to heart function in patients. Electrocardiogram (ECG) is the gold standard for detecting AF. However, ECGs have short boards with short monitoring cycles and problems with gathering. It is also difficult to detect a burst AF through ECG. In contrast, photoplethysmography (PPG) is easy to perform and suitable for long-term monitoring. In this study, we propose a method that combines time-frequency analysis with deep learning and identifies AF based on PPG. The advantage of the method is that there is no need for the noise filtering and feature extraction of PPG, and it has a high generalization capability. The data for the experiment came from three publicly accessible databases. The first part of the experimental method uses data augmentation to convert the 10 s PPG segment into a time-frequency chromatograph by means of time-frequency analysis. The second part inputs the chromatograph into a hybrid framework that combines a convolutional neural network (CNN) and long short-term memory (LSTM) for AF/nonAF classification. The experimental results show that the method has a high classification accuracy, sensitivity, specificity, and F1 score, which are equal to 98.21%, 98.00%, 98.07% and 98.13%, respectively. The area under the receiver operating characteristic curve (AUC) is 0.9959. The model we propose not only aids doctors in diagnosing AF but also provides a method for identifying AF through portable wearable devices.

Lung cancer is the leading cause of cancer death in the world. In particular, non-small-cell lung cancer (NSCLC) represents the majority of the lung cancer population. Advances in DNA sequencing technologies have significantly contributed to revealing the roles, functions and mechanisms of gene mutations. However, the driver mutations that cause cancers and their pathologies remain to be explored. Here, we performed next-generation sequencing (NGS) on tumor tissues isolated from 314 Chinese NSCLC patients and established the mutational landscape in NSCLC. Among 656 mutations, we identified TP53-p.Glu358Val as a driver mutation in lung cancer and found that it activates mitophagy to sustain cancer cell growth. In support of this finding, mice subcutaneously implanted with NSCLC cells expressing TP53-p.Glu358Val developed larger tumors compared to wild-type cells. The pharmaceutical inhibition of autophagy/mitophagy selectively suppresses the cell proliferation of TP53-null or TP53-p.Glu358Val-expressing lung cancer cells. Together, our study characterizes a new TP53 mutation identified from Chinese lung cancer patients and uncovers its roles in regulating mitophagy, providing a new insight into NSCLC treatment.

The continuous prediction of arterial blood pressure (ABP) waveforms via non-invasive methods is of great significance for the prevention and treatment of cardiovascular disease. Photoplethysmography (PPG) can be used to reconstruct ABP signals due to having the same excitation source and high signal similarity. The existing methods of reconstructing ABP signals from PPG only focus on the similarities between systolic, diastolic, and mean arterial pressures without evaluating their global similarity. This paper proposes a deep learning model with a W-Net architecture to reconstruct ABP signals from PPG. The W-Net consists of two concatenated U-Net architectures, the first acting as an encoder and the second as a decoder to reconstruct ABP from PPG. Five hundred records of different lengths were used for training and testing. The experimental results yielded high values for the similarity measures between the reconstructed ABP signals and their reference ABP signals: the Pearson correlation, root mean square error, and normalized dynamic time warping distance were 0.995, 2.236 mmHg, and 0.612 mmHg on average, respectively. The mean absolute errors of the SBP and DBP were 2.602 mmHg and 1.450 mmHg on average, respectively. Therefore, the model can reconstruct ABP signals that are highly similar to the reference ABP signals.

Photoplethysmography (PPG) is increasingly used in digital health, exceptionally in smartwatches. The PPG signal contains valuable information about heart activity, and there is lots of research interest in its means and analysis for cardiovascular diseases. Unfortunately, to our knowledge, there is no arrhythmic PPG dataset publicly available—this paper attempt to provide a toolbox that can generate synthesized arrhythmic PPG signals. The model of a single PPG pulse in this toolbox utilizes two combined Gaussian functions. This toolbox supports synthesizing PPG waveform with regular heartbeats and three irregular heartbeats: compensation, interpolation, and reset. The user can generate a large amount of PPG data with a certain irregularity, with different sampling frequency, time length, and a range of noise types (Gaussian noise and multi-frequency noise) can be added to the synthesized PPG which can all be modified from the interface, and different types of arrhythmic PPGs (as calculated by the model) generated. The generation for large PPG datasets that simulate PPG collected from real humans could be used for testing the robustness of developed algorithms that are targeting arrhythmic PPG signals. Our PPG synthesis tool is publicly available.

Cardiovascular diseases (CVDs) have become the number 1 threat to human health. Their numerous complications mean that many countries remain unable to prevent the rapid growth of such diseases, although significant health resources have been invested toward their prevention and management. Electrocardiogram (ECG) is the most important non-invasive physiological signal for CVD screening and diagnosis. For exploring the heartbeat event classification model using single- or multiple-lead ECG signals, we proposed a novel deep learning algorithm and conducted a systemic comparison based on the different methods and databases. This new approach aims to improve accuracy and reduce training time by combining the convolutional neural network (CNN) with the bidirectional long short-term memory (BiLSTM). To our knowledge, this approach has not been investigated to date. In this study, Database I with single-lead ECG and Database II with 12-lead ECG were used to explore a practical and viable heartbeat event classification model. An evolutionary neural system approach (Method I) and a deep learning approach (Method II) that combines CNN with BiLSTM network were compared and evaluated in processing heartbeat event classification. Overall, Method I achieved slightly better performance than Method II. However, Method I took, on average, 28.3 h to train the model, whereas Method II needed only 1 h. Method II achieved an accuracy of 80, 82.6, and 85% compared with the China Physiological Signal Challenge 2018, PhysioNet Challenge 2017, and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia datasets, respectively. These results are impressive compared with the performance of state-of-the-art algorithms used for the same purpose.

Sleep apnea (SA) is a common sleep disorder which could impair the human physiological system. Therefore, early diagnosis of SA is of great interest. The traditional method of diagnosing SA is an overnight polysomnography (PSG) evaluation. When PSG has limited availability, automatic SA screening with a fewer number of signals should be considered. The primary purpose of this study is to develop and evaluate a SA detection model based on electrocardiogram (ECG) and blood oxygen saturation (SpO2). We adopted a multimodal approach to fuse ECG and SpO2 signals at the feature level. Then, feature selection was conducted using the recursive feature elimination with cross-validation (RFECV) algorithm and random forest (RF) classifier used to discriminate between apnea and normal events. Experiments were conducted on the Apnea-ECG database. The introduced algorithm obtained an accuracy of 97.5%, a sensitivity of 95.9%, a specificity of 98.4% and an AUC of 0.992 in per-segment classification, and outperformed previous works. The results showed that ECG and SpO2 are complementary in detecting SA, and that the combination of ECG and SpO2 enhances the ability to diagnose SA. Therefore, the proposed method has the potential to be an alternative to conventional detection methods.

Quercetin (Qu) is one of the most abundant flavonoids in the human diet. High concentrations of Qu can easily cause adverse effects and induce inflammation, joint pain and stiffness. In this study, Heme was used as a sensitive element and deposited and formed nanorods on a glassy carbon electrode (GCE) for the detection of Qu. The Heme/GCE sensor was characterized using scanning electron microscopy (SEM), cyclic voltammetry (CV), differential pulse voltammetry (DPV) and electrochemical impedance spectroscopy (EIS) techniques. Under optimized conditions, the developed sensor presented a linear concentration ranging from 0.1 to 700 μmol·L−1 according to the CV and DPV methods. The detection limit for the sensor was 0.134 μmol·L−1 and its sensitivity was 0.12 μA·μM−1·cm−2, which were obtained from CV analysis. Through DPV analysis we obtained a detection limit of 0.063 μmol·L−1 and a sensitivity of 0.09 μA·μM−1·cm−2. Finally, this sensor was used to detect the Qu concentration in loquat leaf powder extract, with recovery between 98.55–102.89% and total R.S.D. lower than 3.70%. The constructed electrochemical sensor showed good anti-interference, repeatability and stability, indicating that it is also usable for the rapid detection of Qu in actual samples.

Blood glucose (BG) concentration monitoring is essential for controlling complications arising from diabetes, as well as digital management of the disease. At present, finger-prick glucometers are widely used to measure BG concentrations. In consideration of the challenges of invasive BG concentration measurements involving pain, risk of infection, expense, and inconvenience, we propose a noninvasive BG concentration detection method based on the conservation of energy metabolism. In this study, a multisensor integrated detection probe was designed and manufactured by 3D-printing technology to be worn on the wrist. Two machine-learning algorithms were also applied to establish the regression model for predicting BG concentrations. The results showed that the back-propagation neural network model produced better performance than the multivariate polynomial regression model, with a mean absolute relative difference and correlation coefficient of 5.453% and 0.936, respectively. Here, about 98.413% of the predicted values were within zone A of the Clarke error grid. The above results proved the potential of our method and device for noninvasive glucose concentration detection from the human wrist.

High-field asymmetric ion mobility spectrometry (FAIMS) spectra of single chemicals are easy to interpret but identifying specific chemicals within complex mixtures is difficult. This paper demonstrates that the FAIMS system can detect specific chemicals in complex mixtures. A homemade FAIMS system is used to analyze pure ethanol, ethyl acetate, acetone, 4-methyl-2-pentanone, butanone, and their mixtures in order to create datasets. An EfficientNetV2 discriminant model was constructed, and a blind test set was used to verify whether the deep-learning model is capable of the required task. The results show that the pre-trained EfficientNetV2 model completed convergence at a learning rate of 0.1 as well as 200 iterations. Specific substances in complex mixtures can be effectively identified using the trained model and the homemade FAIMS system. Accuracies of 100%, 96.7%, and 86.7% are obtained for ethanol, ethyl acetate, and acetone in the blind test set, which are much higher than conventional methods. The deep learning network provides higher accuracy than traditional FAIMS spectral analysis methods. This simplifies the FAIMS spectral analysis process and contributes to further development of FAIMS systems.

We designed a portable direct-current (DC) jet sterilizer that discharges self-excited plasma through a needle-rectangular loop sterilizer. The device is sized ∅ 30 mm × 40 mm. The tungsten needle is connected to the high voltage source and to the copper rectangular loop with earthing wire. Under high-voltage DC (±9.6 kV) at atmospheric pressure, the plasma jets killed Escherichia coli and Staphylococcus aureus in the absence of an externally connected carrier gas. Under the same conditions, the E. coli and S. aureus were destroyed after 90 s and 120 s respectively by the positive plasma jet, and after 120 s and 150 s respectively by the negative plasma jet. Thus, the sterilization efficiency of the positive plasma jet exceeds that of the negative plasma jet.

An annotated photoplethysmogram (PPG) is required when evaluating PPG algorithms that have been developed to detect the onset and systolic peaks of PPG waveforms. However, few publicly accessible PPG datasets exist in which the onset and systolic peaks of the waveforms are annotated. Therefore, this study developed a MATLAB toolbox that stitches predetermined annotated PPGs in a random manner to generate a long, annotated PPG signal. With this toolbox, any combination of four annotated PPG templates that represent regular, irregular, fast rhythm, and noisy PPG waveforms can be stitched together to generate a long, annotated PPG. Furthermore, this toolbox can simulate real-life PPG signals by introducing different noise levels and PPG waveforms. The toolbox can implement two stitching methods: one based on the systolic peak and the other on the onset. Additionally, cubic spline interpolation is used to smooth the waveform around the stitching point, and a skewness index is used as a signal quality index to select the final signal output based on the stitching method used. The developed toolbox is free and open-source software, and a graphical user interface is provided. The method of synthesizing by stitching introduced in this paper is a data augmentation strategy that can help researchers significantly increase the size and diversity of annotated PPG signals available for training and testing different feature extraction algorithms.

A needle-to-post ionization source was designed for high-field asymmetric waveform ion mobility spectrometry (FAIMS). The needle-to-post ion source includes asymmetric electrode comprised of a copper post with a diameter of 2 mm and a stainless-steel needle with 200-μm tip radius and length of 28 mm. With the discharge voltage of -5.6 kV and N2 gas flow, glow discharge was realized at atmospheric pressure. The mass spectra of ionized ions about acetone, ethanol and ethyl acetate were gotten by Thermo Scientific LTQ XL ion trap mass spectrometer (MS). The MS experimental results show that the main ions are protonated and dimer ions. The needle-to-post ion source was mounted on the FAIMS system and FAIMS spectra are gotten successfully. Separation of p-xylene, o-xylene and m-xylene was realized. It shows that the needle-to-post electrode could be used as the ion source in a FAIMS system.

In this article, a highly sensitive label-free immunosensor based on a graphene oxide (GO)/Fe3O4/Prussian blue (PB) nanocomposite modified electrode was developed for the determination of human hepatitis B surface antigen (HBsAg). In this electrochemical immunoassay system, PB was used as a redox probe, while GO/Fe3O4/PB nanocomposites and AuNPs were prepared and coated on screen-printed electrodes to enhance the detection sensitivity and to immobilize the hepatitis B surface antibody (HBsAb). The immunosensor was fabricated based on the principle that the decrease in peak currents of PB is proportional to the concentration of HBsAg captured on the modified immunosensor. The experimental results revealed that the immunosensor exhibited a sensitive response to HBsAg in the range of 0.5 pg·mL−1 to 200 ng·mL−1, and with a low detection limit of 0.166 pg·mL−1 (S/N = 3). Furthermore, the proposed immunosensor was used to detect several clinical serum samples with acceptable results, and it also showed good reproducibility, selectivity and stability, which may have a promising potential application in clinical immunoassays.

Objective:To observe the differences in the importance of the items in the conscious state assessment of the Coma Recovery Scale-Revised (CRS-R).Methods:A total of 480 patients with disturbance of consciousness were assessed by Chinese version of CRS-R. The data were introduced into SPSS 17.0 statistical software, and the correlation between each project and the state of consciousness was analyzed by using the correlation analysis function and decision tree function. It was analyzed whether the difference of this correlation was statistically significant.Results:The correlation between exercise, hearing, visual and consciousness status was the highest; the difference between exercise and other items was statistically significant (PPP>0.05).Conclusion:The CRS-R are different in the importance of the state of mind assessment, and the exercise, hearing and visual needs are more important than other items.

A needle-to-cylinder electrode, adopted as an ion source for high-field asymmetric ion mobility spectrometry (FAIMS), is designed and fabricated by lithographie, galvanoformung and abformung (LIGA) technology. The needle, with a tip diameter of 20 μm and thickness of 20 μm, and a cylinder, with a diameter of 400 μm, were connected to the negative high voltage and ground, respectively. A negative corona and glow discharge were realized. For acetone with a density of 99.7 ppm, ethanol with a density of 300 ppm, and acetic ether with a density of 99.3 ppm, the sample gas was ionized by the needle-to-cylinder chip and the ions were detected by an LTQ XL™ (Thermo Scientific Corp.) mass spectrometer. The mass spectra show that the ions are mainly the protonated monomer, the proton bound dimer, and an ion-H2O molecule cluster. In tandem with a FAIMS system, the FAIMS spectra show that the resolving power increases with an increase in the RF voltage. The obtained experimental results showed that the micro needle-to-cylinder chip may serve as a miniature, low cost and non-radioactive ion source for FAIMS.

Serum bilirubin is an important biomarker for the diagnosis of various types of liver diseases and blood disorders. A polydopamine/gold nanoclusters composite film was fabricated for the fluorescent sensing of free bilirubin. Bovine serum albumin (BSA)-stabilized gold nanoclusters (AuNCs) were used as probes for biorecognition. The polydopamine film was utilized as an adhesion layer for immobilization of AuNCs. When the composite film was exposed to free bilirubin, due to the complex that was formed between BSA and free bilirubin, the fluorescence intensity of the composite film was gradually weakened as the bilirubin concentration increased. The fluorescence quenching ratio (F0/F) was linearly proportional to free bilirubin over the concentration range of 0.8~50 μmol/L with a limit of detection of 0.61 ± 0.12 μmol/L (S/N = 3). The response was quick, the film was recyclable, and common ingredients in human serum did not interfere with the detection of free bilirubin.

Simple and facile electrochemical sensors for nitrite detection were fabricated by directly depositing ferrocenoyl cysteine conjugates Fc[CO-Cys(Trt)-OMe]2 [Fc(Cys)2] or Fc[CO-Glu-Cys-Gly-OH] [Fc-ECG] on screen-printed electrodes (SPEs). The modified carbon electrodes were characterized by scanning electron microscopy (SEM), cyclic voltammetry (CV) and differential pulse voltammetry (DPV). Results indicated that Fc-ECG/SPE sensor showed enhanced current response and a lower overpotential than Fc(Cys)2/SPE sensor for nitrite detection. Optimal operating conditions were estimated for nitrite detection by DPV. The concentration of nitrite showed a good linear relationship with the current response in the range of 1.0–50 μmol·L−1 and with 0.3 μmol·L−1 as the concentration for limit of detection. There were no interferences from most common ions. The development of this electrochemical sensor was used for nitrite detection in pickled juice with a R.S.D. lower than 2.1% and average recovery lower than 101.5%, which indicated that disposable electrochemical sensor system can be applied for rapid and precise nitrite detection in foods.

Blood pressure is a basic physiological parameter in the cardiovascular circulatory system. Long-term abnormal blood pressure will lead to various cardiovascular diseases, making the early detection and assessment of hypertension profoundly significant for the prevention and treatment of cardiovascular diseases. In this paper, we investigate whether or not deep learning can provide better results for hypertension risk stratification when compared to the classical signal processing and feature extraction methods. We tested a deep learning method for the classification and evaluation of hypertension using photoplethysmography (PPG) signals based on the continuous wavelet transform (using Morse) and pretrained convolutional neural network (using GoogLeNet). We collected 121 data recordings from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Database, each containing arterial blood pressure (ABP) and photoplethysmography (PPG) signals. The ABP signals were utilized to extract blood pressure category labels, and the PPG signals were used to train and test the model. According to the seventh report of the Joint National Committee, blood pressure levels are categorized as normotension (NT), prehypertension (PHT), and hypertension (HT). For the early diagnosis and assessment of HT, the timely detection of PHT and the accurate diagnosis of HT are significant. Therefore, three HT classification trials were set: NT vs. PHT, NT vs. HT, and (NT + PHT) vs. HT. The F-scores of these three classification trials were 80.52%, 92.55%, and 82.95%, respectively. The tested deep method achieved higher accuracy for hypertension risk stratification when compared to the classical signal processing and feature extraction method. Additionally, the method achieved comparable results to another approach that requires electrocardiogram and PPG signals.

Cardiovascular diseases (CVDs) have become the biggest threat to human health, and they are accelerated by hypertension. The best way to avoid the many complications of CVDs is to manage and prevent hypertension at an early stage. However, there are no symptoms at all for most types of hypertension, especially for prehypertension. The awareness and control rates of hypertension are extremely low. In this study, a novel hypertension management method based on arterial wave propagation theory and photoplethysmography (PPG) morphological theory was researched to explore the physiological changes in different blood pressure (BP) levels. Pulse Arrival Time (PAT) and photoplethysmogram (PPG) features were extracted from electrocardiogram (ECG) and PPG signals to represent the arterial wave propagation theory and PPG morphological theory, respectively. Three feature sets, one containing PAT only, one containing PPG features only, and one containing both PAT and PPG features, were used to classify the different BP categories, defined as normotension, prehypertension, and hypertension. PPG features were shown to classify BP categories more accurately than PAT. Furthermore, PAT and PPG combined features improved the BP classification performance. The F1 scores to classify normotension versus prehypertension reached 84.34%, the scores for normotension versus hypertension reached 94.84%, and the scores for normotension plus prehypertension versus hypertension reached 88.49%. This indicates that the simultaneous collection of ECG and PPG signals could detect hypertension.

The injection and ionization of volatile organic compounds (VOA) by an integrated chip is experimentally analyzed in this paper. The integrated chip consists of a needle-to-cylinder electrode mounting on the Polymethyl Methacrylate (PMMA) substrate. The needle-to-cylinder electrode is designed and fabricated by Lithographie, Galvanoformung and Abformung (LIGA) technology. In this paper, the needle is connected to a negative power supply of −5 kV and used as the cathode; the cylinder electrodes are composed of two arrays of cylinders and serve as the anode. The ionic wind is produced based on corona and glow discharges of needle-to-cylinder electrodes. The experimental setup is designed to observe the properties of the needle-to-cylinder discharge and prove its functions as an ion source and air pump. In summary, the main results are as follows: (1) the ionic wind velocity produced by the chip is about 0.79 m/s at an applied voltage of −3300 V; (2) acetic acid and ammonia water can be injected through the chip, which is proved by pH test paper; and (3) the current measured by a Faraday cup is about 10 pA for acetic acid and ammonia with an applied voltage of −3185 V. The integrated chip is promising for portable analytical instruments, such as ion mobility spectrometry (IMS), field asymmetric ion mobility spectrometry (FAIMS), and mass spectrometry (MS).