Diabetes is a chronic disease with exponential growth and poses significant challenges to global healthcare. Regular blood glucose (BG) monitoring is key for avoiding diabetic complications. Traditional BG measurement techniques are invasive and minimally invasive, causing pain, discomfort, cost, and infection risks. To address these issues, we developed a noninvasive BG monitoring approach on photoplethysmography (PPG) signals using multi-view attention and cascaded BiLSTM hierarchical feature fusion approach. Firstly, we implemented a convolutional multi-view attention block to extract the temporal features through adaptive contextual information aggregation. Secondly, we built a cascaded BiLSTM network to efficiently extract the fine-grained features through bidirectional learning. Finally, we developed a hierarchical feature fusion with bilinear polling through cross-layer interaction to obtain higher-order features for BG monitoring. For validation, we conducted comprehensive experimentation on up to 6 days of PPG and BG data from 21 participants. The proposed approach showed competitive results compared to existing approaches by RMSE of 1.67 mmol/L and MARD of 17.88%. Additionally, the clinical accuracy using Clarke error grid (CEG) analysis showed 98.80% of BG values in Zone A+B. Therefore, the proposed approach offers a favorable solution in diabetes management by noninvasively monitoring the BG levels.

Cuffless blood pressure (BP) estimation is critical for managing growing concerns about hypertension and cardiovascular diseases. Despite recent advancements in multimodal (ECG and PPG) BP estimation methods, which have achieved varying degrees of success, several challenges remain to be addressed. These include capturing the full spectrum of BP-relevant information, redundant feature spaces, and handling the multigrade classification. To address these issues, we propose a Multimodal Fusion BP Network (MuFuBP-Net), featuring a novel dual-feature pipeline architecture designed to extract hierarchical and modality-specific features from both ECG and PPG signals. Additionally, the Cascading Cross-Feature Enhancer (CCFE) module integrates multiple fusion strategies with a squeeze-and-excitation mechanism to apply channel-wise attention to spatial features, enabling dynamic re-weighting. We also employed a Sequence Context Network (SCN) module to capture global sequential features. Subsequently, a Probabilistic Feature Encoder (PFE) encodes the multilevel features from both pipelines into a compact latent space, preserving their discriminative characteristics. Our approach achieved MAE $\pm$ SDE of 2.99 $\pm$ 4.37 mmHg (SBP) and 2.63 $\pm$ 4.19 mmHg (DBP) on MIMIC-II, and 2.27 $\pm$ 4.15 mmHg (SBP) and 1.63 $\pm$ 2.96 mmHg (DBP) on MIMIC-III dataset, meeting AAMI, BHS, and IEEE grade A standards. The proposed approach demonstrated competitive results compared to existing techniques, highlighting its significance as a reliable solution for cuffless BP monitoring.

change of blood glucose (BG) level stimulates the autonomic nervous system leading to variation in both human's electrocardiogram (ECG) and photoplethysmogram (PPG). In this article, we aimed to construct a novel multimodal framework based on ECG and PPG signal fusion to establish a universal BG monitoring model. This is proposed as a spatiotemporal decision fusion strategy that uses weight-based Choquet integral for BG monitoring. Specifically, the multimodal framework performs three-level fusion. First, ECG and PPG signals are collected and coupled into different pools. Second, the temporal statistical features and spatial morphological features in the ECG and PPG signals are extracted through numerical analysis and residual networks, respectively. Furthermore, the suitable temporal statistical features are determined with three feature selection techniques, and the spatial morphological features are compressed by deep neural networks (DNNs). Lastly, weight-based Choquet integral multimodel fusion is integrated for coupling different BG monitoring algorithms based on the temporal statistical features and spatial morphological features. To verify the feasibility of the model, a total of 103 days of ECG and PPG signals encompassing 21 participants were collected in this article. The BG levels of participants ranged between 2.2 and 21.8 mmol/L. The results obtained show that the proposed model has excellent BG monitoring performance with a root-mean-square error (RMSE) of 1.49 mmol/L, mean absolute relative difference (MARD) of 13.42%, and Zone A + B of 99.49% in tenfold cross-validation. Therefore, we conclude that the proposed fusion approach for BG monitoring has potentials in practical applications of diabetes management.

Irregular splicing was associated with tumor formation and progression in renal cell carcinoma (RCC) and many other cancers. By using splicing data in the TCGA SpliceSeq database, RCC subtype classification was performed and splicing features and their correlations with clinical course, genetic variants, splicing factors, pathways activation and immune heterogeneity were systemically analyzed. In this research, alternative splicing was found useful for classifying RCC subtypes. Splicing inefficiency with upregulated intron retention and cassette exon was associated with advanced conditions and unfavorable overall survival of patients with RCC. Splicing characteristics like splice site strength, guanine and cytosine content and exon length may be important factors disrupting splicing balance in RCC. Other than cis-acting and trans-acting regulation, alternative splicing also differed in races and tissue types and is also affected by mutation conditions, pathway settings and the response to environmental changes. Severe irregular splicing in tumor not only indicated terrible intra-cellular homeostasis, but also changed the activity of cancer-associated pathways by different splicing effects including isoforms switching and expression regulation. Moreover, irregular splicing and splicing-associated antigens were involved in immune reprograming and formation of immunosuppressive tumor microenvironment. Overall, we have described several clinical and molecular features in RCC splicing subtypes, which may be important for patient management and targeting treatment.

Autonomic nervous system (ANS) can maintain homeostasis through the coordination of different organs including heart. The change of blood glucose (BG) level can stimulate the ANS, which will lead to the variation of Electrocardiogram (ECG). Considering that the monitoring of different BG ranges is significant for diabetes care, in this paper, an ECG-based technique was proposed to achieve non-invasive monitoring with three BG ranges: low glucose level, moderate glucose level, and high glucose level. For this purpose, multiple experiments that included fasting tests and oral glucose tolerance tests were conducted, and the ECG signals from 21 adults were recorded continuously. Furthermore, an approach of fusing density-based spatial clustering of applications with noise and convolution neural networks (DBSCAN-CNN) was presented for ECG preprocessing of outliers and classification of BG ranges based ECG. Also, ECG's important information, which was related to different BG ranges, was graphically visualized. The result showed that the percentages of accurate classification were 87.94% in low glucose level, 69.36% in moderate glucose level, and 86.39% in high glucose level. Moreover, the visualization results revealed that the highlights of ECG for the different BG ranges were different. In addition, the sensitivity of prediabetes/diabetes screening based on ECG was up to 98.48%, and the specificity was 76.75%. Therefore, we conclude that the proposed approach for BG range monitoring and prediabetes/diabetes screening has potentials in practical applications.

Continuous blood glucose (BG) monitoring is necessary to avoid the deadly health complications from diabetes mellitus. The conventional method of measuring and monitoring BG is by pricking the finger which causes pain and discomfort to patients. To tackle this issue, there are research focusing on physiological signals, such as an electrocardiogram (ECG) to create a model capable of continuous glucose measurement. However, there are ECG segments that have not been considered that have the possibility of improving the performance for non-invasive BG monitoring. In this paper, we perform an oral glucose tolerance test (OGTT) on thirteen adults while continuously recording the ECG signal. A control experiment was also performed without the consumption of glucose. We captured continuous ECG signals and extracted 9 ECG segments. Boxplot and correlation coefficient analysis was performed on the extracted segments to observe the changes for BG. The result reveals a consistent pattern among QT, ST, QTC segments from each participant. HR and RR-I segments have dominant inverse behavior with a 92% correlation with the QT segment. While PRQ and QRS segments can also be included due to 85% and 77% correlation respectively with QTC segments. Whereas R-H and P-H segments have weak results with most of their values below 50%.

Noninvasive blood glucose monitoring (NBGM) provides patient with diabetes a prospective approach with the characteristics of painless and sustainable monitoring. In this study, the dielectric properties of glucose aqueous solutions, namely, deionized water solutions and saline solutions were studied. Specifically, the conductivity and the dielectric constant were measured by using impedance analyzer in the frequencies range of 500 KHz to 5MHz at 25°C. The results showed that both the dielectric constant and the conductivity for the two types of glucose aqueous solutions decreased with the increase of glucose concentration in the above frequency band. By comparing the two solutions with human blood, it was found that the dielectric properties of physiological saline are closer to that of human blood. In the study, saline solution was adopted as research object. In order to formulate the relationship between the dielectric properties of saline solutions and glucose concentration, the third-order Cole-Cole model for each glucose concentration was developed and the coefficient of determination R2was as higher as 0.99. In addition, a second-order polynomial is used to model the glucose concentration dependence for the Cole-Cole parameters. The result indicates that the NBGM at low frequencies has potentials in practical applications.

Human body communication (HBC) is a short-range, wireless communication in the vicinity of, or inside a human body. In this paper, biometric authentication based on capacitive coupled HBC is presented for the wearable devices. In-situ experiments were conducted with 20 volunteers to investigate the feasibility. The S21 parameters of the HBC channel from one palm to the other within the frequency range of 300 KHz-50 MHz were measured. A total of 2,561,600 data are acquired. The data are analyzed by the support vector machines (SVM) including C-SVM and nu-SVM, where 2,241,400 data are used to train the SVM model and 320,200 data are used to estimate the authentication rate. Linear, polynomial, and radial basis function (RBF) are adopted as the kernel functions, respectively. In addition, to verify whether the features in low frequency band will affect the performance of HBC authentication, the features in four frequency bands, i.e., from 300 KHz to 50 MHz, from 3.4 MHz to 50 MHz, from 5.6 MHz to 50 MHz, and from 9.6 MHz to 50 MHz are used as the biometric trait, respectively. The experiment results show that, in biometric identification mode, identification rate of 98% is achieved, and in biometric verification mode, the equal error rate (EER) is 0.24%, the average area under the curve (AUC) of receiver operating characteristic (ROC) reaches 0.9993.