Search Results (75)

This article reviews recent advances in microwave and radar techniques for non-contact measurement of human vital signs in dynamic conditions. The focus is on solutions that work when the subject is moving or performing everyday activities, rather than lying motionless in clinical settings. This review covers innovative biodegradable and flexible antenna designs for wearable devices operating in multiple frequency bands and supporting efficient 5G/IoT connectivity. Particular attention is paid to ultra-wideband (UWB) radar, Doppler sensors, and microwave reflectometry combined with advanced signal-processing and deep learning algorithms for robust estimation of respiration, heart rate, and other cardiopulmonary parameters in the presence of body motion. Applications in telemedicine, home monitoring, sports, and search and rescue are discussed, including localization of people trapped under rubble by detecting their vital sign signatures at a distance. This paper also highlights key challenges such as inter-subject anatomical variability, motion artifacts, hardware miniaturization, and energy efficiency, which still limit widespread deployment. Finally, related developments in microwave imaging and early detection of pathological tissue changes are briefly outlined, highlighting the shared components and processing methods. In general, microwave techniques show strong potential for unobtrusive, continuous, and environmentally sustainable monitoring of human physiological activity, supporting future healthcare and safety systems. Full article

Damage-associated molecular patterns (DAMPs) are endogenous molecules released during cellular stress or injury that trigger sterile inflammation. In perioperative settings, common triggers include surgical trauma, ischemia–reperfusion injury, cardiopulmonary bypass, blood transfusion, and mechanical ventilation. When released extracellularly, DAMPs activate innate immune receptors such as Toll-like receptors (TLRs) and the receptor for advanced glycation end products (RAGE), initiating signaling cascades that amplify inflammation, disrupt endothelial integrity, and promote coagulation and metabolic imbalance. This sterile inflammatory response may extend local tissue injury into systemic organ dysfunction, manifesting clinically as acute lung injury, acute kidney injury, myocardial dysfunction, disseminated intravascular coagulation, and perioperative neurocognitive disorders. Recognizing the central role of DAMPs reframes these complications as predictable consequences of endogenous danger signaling rather than solely as results of infection or hemodynamic instability. This understanding supports the use of established strategies such as protective ventilation and restrictive transfusion to minimize DAMP release. Emerging evidence also suggests that anesthetic agents may influence DAMP-mediated inflammation: propofol and dexmedetomidine appear to exert anti-inflammatory effects, whereas volatile anesthetics show variable results. Although clinical data remain limited, anesthetic choice and perioperative management may significantly affect systemic inflammatory burden and recovery. Future research validating DAMPs as biomarkers and therapeutic targets may inform precision anesthetic strategies aimed at modulating sterile inflammation, ultimately enhancing perioperative outcome. Full article

This paper proposes an adaptive layer-dependent threshold function (ALDTF) for denoising electrocardiogram (ECG) and multimode optical fiber-based cardiopulmonary signals. Based on wavelet transform, the method employs a layer-dependent threshold function strategy that utilizes the non-zero periodic peak (NZOPP) of the signal’s normalized autocorrelation function to adaptively determine the optimal threshold for each decomposition layer. The core idea applies soft thresholding at lower layers (high-frequency noise) to suppress pseudo-Gibbs oscillations, and hard thresholding at higher layers (low-frequency noise) to preserve signal amplitude and morphology. The experimental results show that for ECG signals contaminated with baseline wander (BW), electrode motion (EM) artifacts, muscle artifacts (MA), and mixed (MIX) noise, ALDTF outperforms existing methods—including SWT, DTCWT, and hybrid approaches—across multiple metrics. It achieves a ΔSNR improvement of 1.68–10.00 dB, ΔSINAD improvement of 1.68–9.98 dB, RMSE reduction of 0.02–0.56, and PRD reduction of 2.88–183.29%. The method also demonstrates excellent performance on real ECG and optical fiber cardiopulmonary signals, preserving key diagnostic features like QRS complexes and ST segments while effectively suppressing artifacts. ALDTF provides an efficient, versatile solution for physiological signal denoising with strong potential in wearable real-time monitoring systems. Full article

MicroRNA-483 regulates multiple human disease categories, spanning oncology, cardiopulmonary, metabolic, immune, neurological, and musculoskeletal pathologies. We integrate experimentally validated interactions from 146 studies to construct a comprehensive regulatory network, encompassing transcription factors, long non-coding RNAs, circular RNAs, and messenger RNA targets. Our analysis reveals that miR-483 promotes tumorigenesis by suppressing tumor-suppressive checkpoints, yet it protects cardiopulmonary, metabolic, and neurological tissues from pathological injury. This functional duality arises from tissue-specific modulation of shared signaling pathways, particularly TGF-β and MAPK cascades, which function as the core hubs driving its context-dependent activity across six disease categories. By mapping miR-483 regulatory circuits across multiple diseases, we define the molecular determinants of its context-dependent activity. These findings establish miR-483 as both a diagnostic biomarker and a therapeutic target whose function is dictated by cellular context. Full article

Pulmonary hypertension (PH) is a progressive cardiopulmonary disorder characterized by vascular remodeling and right ventricular (RV) failure. Recently, attention to lipid metabolism in PH has revealed multiple mechanisms that drive disease progression, including alterations in energy supply, oxidative stress, inflammatory signaling, and epigenetic regulation. Notably, lipid metabolism in PH exhibits marked spatiotemporal heterogeneity. This creates a therapeutic paradox in which the same metabolic intervention may exert opposing effects depending on tissue type and disease stage. Despite these challenges, targeting lipid metabolism remains an attractive therapeutic strategy. Preclinical and early clinical studies suggest that both small-molecule metabolic modulators and natural compounds hold promise for reversing pulmonary vascular remodeling and improving RV function. This review summarizes current advances in lipid metabolic reprogramming in PH and highlights the challenges of developing tissue- and time-specific interventions. Full article

Assessment of microcirculation status during surgical interventions is of significant interest for monitoring tissue perfusion and controlling the effectiveness of systemic hemodynamics. This study investigated the applicability of the electrical impedance method for the quantitative assessment of changes in the blood supply to superficial head tissues during cardiac surgeries. Impedance signal recording was performed synchronously with laser Doppler flowmetry, allowing for the comparison of parameter dynamics reflecting microcirculatory processes. Analysis of a set of impedance parameters reflecting the amplitude and temporal characteristics of the pulse signal revealed consistent changes with microvascular indicators obtained by laser Doppler flowmetry. The most pronounced changes in impedance parameters were observed during transitions between key physiological states—induction of anesthesia, initiation of cardiopulmonary bypass, and its termination. This indicates the informativeness of the electrical impedance method for assessing the dynamics of scalp perfusion. The obtained results demonstrate its potential for non-invasive, continuous, and safe monitoring of microcirculation in superficial tissues in the operating room. This approach can be considered as an additional tool for comprehensive assessment of microcirculatory changes and improving the accuracy of tissue perfusion monitoring during cardiac surgeries. Full article

Optical neuromonitoring has matured from descriptive oxygenation trends to model-informed quantification of cerebral physiology. This review synthesizes evidence on near-infrared spectroscopy (NIRS), diffuse correlation spectroscopy (DCS), and laser Doppler flowmetry (LDF) for monitoring cerebral blood flow (CBF), blood volume (CBV), and cerebral metabolic rate of oxygen (CMRO₂) during cardiac arrest (CA) and cardiopulmonary resuscitation (CPR). We focus on using hemo-metabolic models, especially Coherent Hemodynamic Spectroscopy (CHS) and the BrainSignals models, as a framework to explain what optical signals do (and do not) tell us about microvascular oxygen transport and mitochondrial metabolism. We compare linear vs. non-linear CHS formulations for large perturbations (e.g., CA/CPR), summarize emerging depth-sensitivity and extracerebral-signal suppression strategies, and outline how DCS pairs with NIRS to link oxygen delivery with use. Across animal and human studies, we highlight convergent patterns (rapid oxygenation collapse, partial reperfusion during CPR, lagging metabolic recovery), recurring limitations (extracerebral contamination, calibration to absolutes, motion), and standardization efforts required for translation. We conclude with a pragmatic roadmap for bedside implementation: harmonized physiological endpoints (CBF, CMRO₂, rCCO), reporting standards, and model-informed thresholds to guide resuscitation. This review aims to bridge instrumentation, physiology, and modeling to enhance neuroprotective care in CA/CPR. Full article

Monitoring electrocardiogram (ECG) and respiration continuously and non-invasively is essential for managing cardiopulmonary health. An effective wearable device can be used to regularly monitor key vitals, reducing the need for clinical visits. In this work, we propose a custom device for real-time continuous ECG by inkjet printed (IJP) dry electrodes and respiration monitoring by using a novel single 6-axis inertial measurement unit (IMU). The proposed system can extract the heart rate (HR) and respiration rate (RR) during static and dynamic postures. The respiration process implements a quaternion-based update and multiple filtering stages to estimate the signal. The custom device uses Bluetooth protocol to send the raw and processed data to a mobile application. The RR is investigated in stationary, i.e., sitting and standing, and dynamic, i.e., walking, running, and cycling, postures. The proposed device is evaluated with commercial Go Direct^® respiration belt from Vernier^® for RR and offers an overall accuracy of 99.3% and 98.6% for static and dynamic conditions, respectively. The wearable also offers 98.9% and 97.9% accuracy for HR measurements, respectively, in static and active postures when compared with the Kardia^® device. Furthermore, the device is assessed in an ambulatory monitoring setup in both indoor and outdoor environments. The low-power wearable consumes an average of only 7.4 mA of current during data processing. The device performs effectively and efficiently in both stationary and active states, offering a low complexity, portable solution for real-time monitoring. The proposed system can benefit from the continuous monitoring and early detection of pulmonary and cardio-respiratory health issues. Full article

Chronic inflammation conducts an irreplaceable role in the aging process. More importantly, the impact is particularly significant in scenarios involving cardiac arrest and cardiopulmonary resuscitation (CA/CPR), where elderly individuals are inclined to suffer from more severe inflammatory injuries when compared to younger counterparts. Network pharmacology demonstrated a tight correlation between epicatechin (EC), aging, and the NRG1-NF-κB signaling pathway. With an aim to investigate whether EC suppressing inflammatory aging and alleviating post-CA/CPR brain injury is associated with the inhibition of the NRG1-NF-κB pathway, we established a model of naturally aged 21-month-old rats subjected to CA/CPR. A network pharmacology method was employed to pinpoint possible pathways that connect EC to neuroinflammation associated with aging. Sixty rats were randomly divided into three groups for feeding: a control group (pure water) and EC groups (EC was administered by gavage at doses of 1 mg/kg and 2 mg/kg respectively from the 12th month). Those groups underwent a CA/CPR procedure. At 24-h post-resuscitation, neurological scores, cortical pathology staining and assessments of neural injury were conducted. Expression levels of NRG1-NF-κB pathway-relevant inflammatory factors and proteins underwent systematic investigation by carrying out ELISA, RT-PCR, and Western blotting. In comparison with the 21-month-old groups treated with water, the 21-month-old groups treated with EC at 1 mg/kg and 2 mg/kg demonstrated decreased β-galactosidase staining, aging-correlated proteins and pro-inflammatory factors and NF-κB pathway-relevant proteins, as well as reinforced NRG1-ErbB4 expression. EC lessened inflammatory aging and mitigates post-CA/CPR brain injury in aged rats, associated with the inhibition of the NRG1-NF-κB pathway. Full article

Neurologic complications remain a major cause of morbidity in patients undergoing surgical repair of acute type A aortic dissection (ATAAD). Near-infrared spectroscopy (NIRS) is used for continuous, noninvasive monitoring of cerebral oxygenation during cardiopulmonary bypass; however, its utility in predicting perioperative stroke remains inadequately defined. A retrospective cohort study was conducted in 175 patients who underwent ATAAD repair between 2015 and 2023. Patients were stratified by the occurrence of perioperative stroke (n = 47, 26.9%). Intraoperative NIRS data, including cerebral regional oxygen saturation (crSO₂) values at key procedural timepoints and signal variability with band power and crest factor, were analyzed in conjunction with demographic, anatomic, and postoperative variables. Patients with stroke exhibited significantly lower minimum NIRS values during deep hypothermic circulatory arrest (DHCA) (left: 46.7 (15.7–69.4) vs. 52.2 (22.0–81.6); right: 47.0 (23.3–78.5) vs. 56.3 (20.2–85.0); p = 0.03 and p < 0.01). Within the stroke group, NIRS signal variability was significantly greater (crest factor and standard deviation; p < 0.05) and showed blunted recovery post-DHCA. crSO₂ values below 50% were more frequent in the stroke group (p = 0.04). Right common carotid artery dissection was more prevalent in the stroke group (40% vs. 23%, p = 0.04). ICU length of stay was significantly increased in patients with stroke. Cerebral desaturation and NIRS signal instability during DHCA are significantly associated with perioperative stroke in ATAAD repair. These findings support the prognostic value of intraoperative cerebral oximetry in detecting critical ischemic thresholds and identifying at-risk perfusion patterns. Full article

Cardiopulmonary bypass (CPB) is associated with significant neurological complications, yet the mechanisms underlying brain injury remain unclear. This study investigated the role of interleukin-17A (IL-17A) in exacerbating CPB-induced neuronal apoptosis and identified vulnerable brain regions. Utilizing a rat CPB model and an oxygen–glucose deprivation/reoxygenation (OGD/R) cellular model, we demonstrated that IL-17A levels were markedly elevated in the hippocampus post-CPB, correlating with endoplasmic reticulum stress (ERS)-mediated apoptosis. Transcriptomic analysis revealed the enrichment of IL-17 signaling and apoptosis-related pathways. IL-17A-Neutralizing monoclonal antibody (mAb) and the ERS inhibitor 4-phenylbutyric acid (4-PBA) significantly attenuated neurological deficits and hippocampal neuronal damage. Mechanistically, IL-17A activated the Act1-IRE1-JNK1 axis, wherein heat shock protein 90 (Hsp90) competitively regulated Act1-IRE1 interactions. Co-immunoprecipitation confirmed the enhanced Hsp90-Act1 binding post-CPB, promoting IRE1 phosphorylation and downstream caspase-12 activation. In vitro, IL-17A exacerbated OGD/R-induced apoptosis via IRE1-JNK1 signaling, reversible by IRE1 inhibition. These findings identify the hippocampus as a key vulnerable region and delineate a novel IL-17A/Act1-IRE1-JNK1 pathway driving ERS-dependent apoptosis. Targeting IL-17A or Hsp90-mediated chaperone switching represents a promising therapeutic strategy for CPB-associated neuroprotection. This study provides critical insights into the molecular crosstalk between systemic inflammation and neuronal stress responses during cardiac surgery. Full article

Dynamic oxygen uptake (VO₂) reflects moment-to-moment changes in oxygen consumption during exercise and underpins training design, performance enhancement, and clinical decision-making. We tackled two key obstacles—the limited fusion of heterogeneous sensor data and inadequate modeling of long-range temporal patterns—by integrating wearable accelerometer and heart-rate streams with a convolutional neural network–LSTM (CNN-LSTM) architecture and optional attention modules. Physiological signals and VO₂ were recorded from 21 adults through resting assessment and cardiopulmonary exercise testing. The results showed that pairing accelerometer with heart-rate inputs improves prediction compared with considering the heart rate alone. The baseline CNN-LSTM reached R² = 0.946, outperforming a plain LSTM (R² = 0.926) thanks to stronger local spatio-temporal feature extraction. Introducing a spatial attention mechanism raised accuracy further (R² = 0.962), whereas temporal attention reduced it (R² = 0.930), indicating that attention success depends on how well the attended features align with exercise dynamics. Stacking both attentions (spatio-temporal) yielded R² = 0.960, slightly below the value for spatial attention alone, implying that added complexity does not guarantee better performance. Across all models, prediction errors grew during high-intensity bouts, highlighting a bottleneck in capturing non-linear physiological responses under heavy load. These findings inform architecture selection for wearable metabolic monitoring and clarify when attention mechanisms add value. Full article

Respiratory rate (RR) changes in the elderly can indicate serious diseases. Thus, accurate estimation of RRs for cardiopulmonary function is essential for home health monitoring systems. However, machine learning (ML) algorithm errors embedded in health monitoring systems can be problematic in medical decision-making because some data have much larger sample sizes in the training set than others. This difference in sample size implies biosignal data imbalance. Therefore, we propose a novel methodology that combines bootstrap-based imbalanced continuous power spectral generation (IPSG) with ML approaches to estimate RRs and uncertainty to address data imbalance. The sample differences between normal breathing (12–20 breaths per minute (brpm)), dyspnea (≥20 brpm), and hypopnea (<8 brpm) show significant data imbalance, which can affect the learning of ML algorithms. Hence, the normal breathing part with a large amount of data is well-trained. In contrast, the dyspnea and hypopnea parts with relatively fewer data are not well-trained, and this data imbalance makes it difficult to estimate the reference variables of the actual dyspnea and hypopnea data parts, thus generating significant errors. Hence, we apply ML models by mixing artificial feature curves generated using a bootstrap model with the original feature curves to estimate RRs and solve this problem. As a result, the nonparametric bootstrap approach significantly increases the number of artificial feature curves. The generated artificial feature curves are selectively utilized in the highly imbalanced parts. Therefore, we confirm that IPSG is efficiently trained to predict the complex nonlinear relationship between the feature vectors obtained from the photoplethysmography signal and the reference RR. The proposed methodology shows more accurate prediction performance and uncertainty. Combining the proposed Gaussian process regression (GPR) with IPSG based on the Beth Israel Deaconess Medical Center dataset, the mean absolute error of the RR is 0.79 and 1.47 brpm. Our approach achieves high stability and accuracy by randomly mixing original and artificial feature curves. The proposed GPR-IPSG model can improve the performance of clinical home-based monitoring systems and design a reliable framework. Full article

Biological signals such as respiration (RSP) and heart rate (HR) are oscillatory and physiologically coupled, maintaining homeostasis through regulatory mechanisms. This report models the dynamic relationship between RSP and HR in 45 healthy volunteers at rest. Cross-correlation between RSP and HR was computed, along with regression analysis to predict HR from RSP and its first-order time derivative in continuous signals. A simulation model tested the possibility of replicating the RSP–HR relationship. Cross-correlation results showed a time lag in the sub-second range of these signals (849.21 ms ± SD 344.84). The possible modulation of HR by RSP was mediated by the RSP amplitude and its first-order time derivative (in 45 of 45 cases). A simulation of this process allowed us to replicate the physiological relationship between RSP and HR. These results provide support for understanding the dynamic interactions in cardiorespiratory coupling at rest, showing a short time lag between RSP and HR and a modulation of the HR signal by the first-order time derivative of the RSP. This dynamic would optionally be incorporated into dynamic models of resting cardiopulmonary coupling and suggests a mechanism for optimizing respiration in the alveolar system by promoting synchrony between the gases and hemoglobin in the alveolar pulmonary system. Full article

Congenital heart defects (CHDs) are one of the most common congenital malformations and often require heart surgery with cardiopulmonary bypass (CPB). Children undergoing cardiac surgery with CPB are especially at greater risk of post-operative complications due to a systemic inflammatory response caused by innate inflammatory mediators. However, the pathophysiological response is not fully understood and warrants further investigation. Therefore, we investigated the inflammatory response in macrophages initiated by peri-operative serum samples obtained from patients with CHD undergoing CPB cardiac surgery. Human differentiated THP-1 macrophages were pretreated with Stattic, a STAT3 (Tyr705) inhibitor, before stimulation with serum samples. STAT3 and NF-κB activation were investigated via a Western blot, IL-1β, TNFα, IL-10, mediators for vascular permeability (VEGF-A, ICAM), and SOCS3 gene expressions via RT-qPCR. CPB induced an inflammatory response in macrophages via the activation of the STAT3 but not NF-κB signaling pathway. Longer duration on the CPB correlated with increased cytokine, VEGF, and ICAM expressions, relative to individual pre-operation levels. Patients that did not require CPB showed no significant immune response. Pretreatment with Stattic significantly attenuated all inflammatory mediators investigated except for TNFα in the macrophages. CPB induces an increased expression of cytokines and mediators of vascular permeability via the activation of STAT3 by IL-6 and IL-8 in the serum samples. Stattic attenuates all mediators investigated but promotes TNFα expression. Full article