Construction and evaluation of AuNPs enhanced electrochemical immunosensors with [Fe(CN)6]3-/4- and PPy probe for highly sensitive detection of human chorionic gonadotropin.

Human chorionic gonadotropin (HCG), a vital protein for pregnancy determination and a marker for trophoblastic diseases, finds application in monitoring early pregnancy and ectopic pregnancy. This study presents an innovative approach employing electrochemical immunosensors for enhanced HCG detection, utilizing Anti-HCG antibodies and gold nanoparticles (AuNPs) in the sensor platform. Two sensor configurations were optimized: BSA/Anti-HCG/c-AuNPs/MEL/e-AuNPs/SPCE with [Fe(CN)6]3-/4- as a redox probe (1) and BSA/Anti-HCG/PPy/e-AuNPs/SPCE using polypyrrole (PPy) as a redox probe (2). The first sensor offers linear correlation in the 0.10-500.00 pg∙mL-1 HCG range, with a limit of detection (LOD) of 0.06 pg∙mL-1, sensitivity of 32.25 µA∙pg-1∙mL∙cm-2, RSD <2.47 %, and a recovery rate of 101.03-104.81 %. The second sensor widens the HCG detection range (40.00 fg∙mL-1-5.00 pg∙mL-1) with a LOD of 16.53 fg∙mL-1, ensuring precision (RSD <1.04 %) and a recovery range of 94.61-106.07 % in serum samples. These electrochemical immunosensors have transformative potential in biomarker detection, offering enhanced sensitivity, selectivity, and stability for advanced healthcare diagnostics.

Advanced Glycation End Products and Health: A Systematic Review.

Advanced glycation end products (AGEs) have garnered significant attention due to their association with chronic diseases and the aging process. The prevalence of geriatric diseases among young individuals has witnessed a notable surge in recent years, potentially attributed to the accelerated pace of modern life. The accumulation of AGEs is primarily attributed to their inherent difficulty in metabolism, which makes them promising biomarkers for chronic disease detection. This review aims to provide a comprehensive overview of the recent advancements and findings in AGE research. The discussion is divided into two main sections: endogenous AGEs (formed within the body) and exogenous AGEs (derived from external sources). Various aspects of AGEs are subsequently summarized, including their production pathways, pathogenic mechanisms, and detection methods. Moreover, this review delves into the future research prospects concerning AGEs. Overall, this comprehensive review underscores the importance of AGEs in the detection of chronic diseases and provides a thorough understanding of their significance. It emphasizes the necessity for further research endeavors to deepen our comprehension of AGEs and their implications for human health.

ACNN-BiLSTM: A Deep Learning Approach for Continuous Noninvasive Blood Pressure Measurement Using Multi-Wavelength PPG Fusion.

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.

Reconstruction of Missing Electrocardiography Signals from Photoplethysmography Data Using Deep Neural Network.

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.

SADSNet: A robust 3D synchronous segmentation network for liver and liver tumors based on spatial attention mechanism and deep supervision.

Highlights: • Introduce a data augmentation strategy to expand the required different morphological data during the training and learning phase, and improve the algorithm's feature learning ability for complex and diverse tumor morphology CT images.• Design attention mechanisms for encoding and decoding paths to extract fine pixel level features, improve feature extraction capabilities, and achieve efficient spatial channel feature fusion.• The deep supervision layer is used to correct and decode the final image data to provide high accuracy of results.• The effectiveness of this method has been affirmed through validation on the LITS, 3DIRCADb, and SLIVER datasets. BACKGROUND: Accurately extracting liver and liver tumors from medical images is an important step in lesion localization and diagnosis, surgical planning, and postoperative monitoring. However, the limited number of radiation therapists and a great number of images make this work time-consuming. OBJECTIVE: This study designs a spatial attention deep supervised network (SADSNet) for simultaneous automatic segmentation of liver and tumors. METHOD: Firstly, self-designed spatial attention modules are introduced at each layer of the encoder and decoder to extract image features at different scales and resolutions, helping the model better capture liver tumors and fine structures. The designed spatial attention module is implemented through two gate signals related to liver and tumors, as well as changing the size of convolutional kernels; Secondly, deep supervision is added behind the three layers of the decoder to assist the backbone network in feature learning and improve gradient propagation, enhancing robustness. RESULTS: The method was testing on LITS, 3DIRCADb, and SLIVER datasets. For the liver, it obtained dice similarity coefficients of 97.03%, 96.11%, and 97.40%, surface dice of 81.98%, 82.53%, and 86.29%, 95% hausdorff distances of 8.96 mm, 8.26 mm, and 3.79 mm, and average surface distances of 1.54 mm, 1.19 mm, and 0.81 mm. Additionally, it also achieved precise tumor segmentation, which with dice scores of 87.81% and 87.50%, surface dice of 89.63% and 84.26%, 95% hausdorff distance of 12.96 mm and 16.55 mm, and average surface distances of 1.11 mm and 3.04 mm on LITS and 3DIRCADb, respectively. CONCLUSION: The experimental results show that the proposed method is effective and superior to some other methods. Therefore, this method can provide technical support for liver and liver tumor segmentation in clinical practice.

Randomized controlled trial study of intelligent rehabilitation training system for functional ankle instability.

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 < 0.05). The interaction effect between time and group of plantar flexion mobility was significant (F = 23.948, P < 0.05), the interaction effect between time and group of dorsiflexion mobility was significant (F = 6.570, P < 0.05), the interaction effect between time and group of inversion mobility was significant (F = 8.360, P < 0.05), the interaction effect between time and group of eversion mobility was significant (F = 10.113, P < 0.05). The interaction effect between time and group of inversion muscle strength was significant (F = 18.107, P < 0.05). The interaction effect between time and group of YBT scores was significant (F = 33.324, P < 0.05). The Intelligent Rehabilitation Training System can effectively reduce pain in FAI patients, improve joint range of motion, increase inversion strength, and improve dynamic balance of the affected limb.

Electrochemical immuno-biosensors for the detection of the tumor marker alpha-fetoprotein: A review.

Alpha-fetoprotein (AFP) is a glycoprotein that has many important physiological functions, including transportation, immunosuppression, and induction of apoptosis by T lymphocytes. AFP is closely related to the development of hepatocellular carcinoma and many kinds of tumors, all of which can show high concentrations, so it is used as a positive test indicator for many kinds of tumors. This paper reviews recent advances in the detection of the tumor marker AFP based on three immuno-biosensors: electrochemical (EC), photoelectrochemical (PEC), and electrochemical luminescence (ECL). The electrodes are modified by different materials or homemade composites, different signaling molecules are selected as single probes or dual probes for the detection of AFP. The detection limit was as low as 3 fg/mL, which indicated that the AFP immunosensor had achieved highly sensitive detection. In addition, we also reviewed and summarized the current development status and application prospect of AFP immunoelectrochemical sensors. There are not too many researches on immunosensors based on dual-signal ratios, and the commonly used probes are methylene blue (MB) and ferrocene (Fc). It would be more innovative to have more novel signaling molecules as probes to prepare dual-signal ratio sensors.

Genomic epidemiology reveals early transmission of SARS-CoV-2 and mutational dynamics in Nanning, China.

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.

Improved U-Net Model to Estimate Cardiac Output Based on Photoplethysmography and Arterial Pressure Waveform.

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.

MTAN: A semi-supervised learning model for kidney tumor segmentation.

BACKGROUND: Medical image segmentation is crucial in disease diagnosis and treatment planning. Deep learning (DL) techniques have shown promise. However, optimizing DL models requires setting numerous parameters, and demands substantial labeled datasets, which are labor-intensive to create. OBJECTIVE: This study proposes a semi-supervised model that can utilize labeled and unlabeled data to accurately segment kidneys, tumors, and cysts on CT images, even with limited labeled samples. METHODS: An end-to-end semi-supervised learning model named MTAN (Mean Teacher Attention N-Net) is designed to segment kidneys, tumors, and cysts on CT images. The MTAN model is built on the foundation of the AN-Net architecture, functioning dually as teachers and students. In its student role, AN-Net learns conventionally. In its teacher role, it generates objects and instructs the student model on their utilization to enhance learning quality. The semi-supervised nature of MTAN allows it to effectively utilize unlabeled data for training, thus improving performance and reducing overfitting. RESULTS: We evaluate the proposed model using two CT image datasets (KiTS19 and KiTS21). In the KiTS19 dataset, MTAN achieved segmentation results with an average Dice score of 0.975 for kidneys and 0.869 for tumors, respectively. Moreover, on the KiTS21 dataset, MTAN demonstrates its robustness, yielding average Dice scores of 0.977 for kidneys, 0.886 for masses, 0.861 for tumors, and 0.759 for cysts, respectively. CONCLUSION: The proposed MTAN model presents a compelling solution for accurate medical image segmentation, particularly in scenarios where the labeled data is scarce. By effectively utilizing the unlabeled data through a semi-supervised learning approach, MTAN mitigates overfitting concerns and achieves high-quality segmentation results. The consistent performance across two distinct datasets, KiTS19 and KiTS21, underscores model's reliability and potential for clinical reference.

Electrochemical sensor for ultrasensitive sensing of biotin based on heme conjugated with gold nanoparticles and its electrooxidation mechanism.

We report the fabrication of a facile sensor using heme conjugated with gold nanoparticles (AuNPs) in situ on a glass carbon electrode (GCE) for the ultrasensitive determination of biotin without antibody or streptavidin. The use of heme and AuNPs as dual amplifiers allows a very broad detection range from 0.0050 to 50.0000 µmol·L-1 and a very low detection limit of 0.0016 µmol·L-1. The mechanistic aspects were elucidated using electrochemical analyses and frontier orbital calculations showing that the electrooxidation of biotin involves a one-electron and a one-proton transfer, generating biotin sulfoxide. The heme/AuNPs/GCE sensor exhibited excellent selectivity, reproducibility and stability, indicating high robustness. The recovery was between 97.20 and 105.70% with RSD less than 8.71%, suggesting good practicability. Our studies demonstrate that this approach can be used to detect and quantify biotin in a range of foods, including milk, infant formula, flour, orange juice, mango juice, egg white and egg yolk. Furthermore, all measurements do not require any intricate preparation or pre-treatment of the foods, thus representing a great potential for point-of-care testing.

PPG2ECGps: An End-to-End Subject-Specific Deep Neural Network Model for Electrocardiogram Reconstruction from Photoplethysmography Signals without Pulse Arrival Time Adjustments.

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.

Integrated Device Based on a Sudomotor Nanomaterial for Sweat Detection.

The compositions of sweat and blood are related. Therefore, sweat is an ideal noninvasive test body fluid that could replace blood for linear detection of many biomarkers, especially blood glucose. However, access to sweat samples remains limited to physical exercise, thermal stimulation, or electrical stimulation. Despite intensive research, a continuous, innocuous, and stable method for sweat stimulation and detection has not yet been developed. In this study, a nanomaterial for a sweat-stimulating gel based on the transdermal drug delivery system is presented, which transports acetylcholine chloride into the receptors of sweat glands to achieve the function of biological stimulation of skin sweating. The nanomaterial was applied to a suitable integrated sweat glucose detection device for noninvasive blood glucose monitoring. The total amount of evaporated sweat enabled by the nanomaterial is up to 35 µL·cm-2 for 24 h, and the device detects up to 17.65 µM glucose under optimal conditions, showing stable performance regardless of the user's activity level. In addition, the in vivo test was performed and compared with several studies and products, which showed excellent detection performance and osmotic relationship. The nanomaterial and associated integrated device represent a significant advance in continuous passive sweat stimulation and noninvasive sweat glucose measurement for point-of-care applications.

A facile indole probe for ultrasensitive immunosensor fabrication toward C-reactive protein sensing.

C-reactive protein (CRP) is a protein biomarker for acute phase response. Herein, we fabricate a highly sensitive electrochemical immunosensor for CRP on a screen-printed carbon electrode (SPCE) with indole as a novel electrochemical probe and Au nanoparticles for signal amplification. Amongst, indole appeared as transparent nanofilms on the electrode surface, and underwent a one-electron and one-proton transfer to form oxindole during the oxidation process. Upon optimization of experimental conditions, a logarithmic correlation between CRP concentration (0.0001-100 µg∙mL-1) and response current was revealed with a detection limit of 0.03 ng∙mL-1 and a sensitivity of 5.7055 µA∙µg-1∙mL∙cm-2. The sensor exhibited exceptionally distinction selectivity, reproducibility and stability of the electrochemical immunosensor studied. The recovery rate of CRP in human serum samples determined by the standard addition method, ranged between 98.2-102.2%. Overall, the developed immunosensor is promising, and has the potential for CRP detection in real human serum samples.

2.5D MFFAU-Net: a convolutional neural network for kidney segmentation.

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.

Fluorescence turn-on detection of human serum albumin based on the assembly of gold nanoclusters and bromocresol green.

As the most abundant protein in plasma, human serum albumin plays a vital role in physiological processes, such as maintaining blood osmotic pressure and carrying small-molecule ligands. Since the content of albumin in the human serum can reflect the status of liver and renal function, albumin quantitation is significant in clinical diagnosis. In this work, fluorescence turn-on detection of human serum albumin (HSA) had been performed based on the assembly of gold nanoclusters and bromocresol green. Gold nanoclusters (AuNCs) capped by reduced glutathione (GSH) were assembled with bromocresol green (BCG), and the assembly was used as a fluorescent probe for HSA. After BCG assembling, the fluorescence of gold nanoclusters was nearly quenched. In acidic solution, HSA can selectively bind to BCG on the assembly and recover the fluorescence of the solution. Based on this turn-on fluorescence, ratiometric HSA quantification was realized. Under optimal conditions, HSA detection by the probe possessed a good linear relationship in the range of 0.40-22.50 mg·mL-1, and the detection limit was 0.27 ± 0.04 mg·mL-1 (3σ, n = 3). Common coexisting components in serum and blood proteins did not interfere with the detection of HSA. This method has the advantages of easy manipulation and high sensitivity, and the fluorescent response is insensitive to reaction time.

Non-Enzymatically Colorimetric Bilirubin Sensing Based on the Catalytic Structure Disruption of Gold Nanocages.

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.

Blood pressure stratification using photoplethysmography and light gradient boosting machine.

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.

Whale Optimization Algorithm with a Hybrid Relation Vector Machine: A Highly Robust Respiratory Rate Prediction Model Using Photoplethysmography Signals.

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.

Specific detection of n-propanol gas via terahertz metasurface sensor modified by molecularly imprinted polymer.

As an organic substance, n-propanol gas has been paid attention to in environmental monitoring and exhalation of lung cancer patient. In this paper a rapid detection method for n-propanol gas is developed based on molecularly imprinted polymers (MIP) and terahertz (THz) metasurface sensors. We first prepared a MIP suitable for detecting the n-propanol gas. And then the n-propanol MIP was modified to the THz metasurface sensor for detecting the n-propanol gas. Since the MIP adsorbed with n-propanol changes the dielectric environment of the sensor, the resonance frequency of the sensor also change. So we based on the n-propanol concentration was analyzed according to the change in resonance frequency. The experimental results showed that the sensor can effectively detect the n-propanol concentration in the range of 50-500 ppm (parts per million). In addition, we also verified the specificity and repeatability of the sensor. This work provides a new idea and method for the sensitive and specific detection of n-propanol gas.