Search Results (1,269)

Background: Anthracycline- and taxane-based chemotherapy regimens are widely used in the treatment of breast cancer; however, their effects on body composition and fluid distribution are not fully elucidated. Conventional assessment methods are often insufficient to distinguish true tissue changes from treatment-related fluid shifts. The primary objective of this study was to evaluate longitudinal changes in body composition and fluid distribution during chemotherapy in breast cancer patients using bioelectrical impedance spectroscopy. The secondary objective was to investigate the impact of anthracycline and docetaxel exposure on these changes and to identify patterns suggestive of masked sarcopenia. Methods: This prospective, single-center, longitudinal observational study was conducted between October 2024 and October 2025. Follow-up assessments at 3 and 6 months were completed by October 2025. A total of 51 female breast cancer patients undergoing systemic chemotherapy were evaluated using multifrequency bioelectrical impedance spectroscopy (BCM^®). Measurements were performed at baseline, 3 months, and 6 months. Changes in total body water (TBW), extracellular water (ECW), intracellular water (ICW), extracellular-to-intracellular water ratio (E/I), lean tissue mass (LTM), adipose tissue mass (ATM), and volume status were analyzed longitudinally and according to treatment exposure. Results: The cohort consisted of 51 women (median age, 55 years), of whom 70.6% were postmenopausal, and the majority had stage II–III disease. While TBW remained stable, significant alterations in fluid distribution and body composition were observed. ECW increased, and ICW decreased, resulting in a significant rise in the E/I ratio. LTM declined significantly, particularly during the first 3 months, whereas ATM showed a gradual increase. Volume status increased progressively over time, indicating fluid accumulation. Anthracycline exposure was associated with greater reductions in LTM, while docetaxel treatment was linked to significant increases in extracellular fluid and volume, especially during the 3–6-month interval. At 6 months, a median increase of +1100 mL in volume was observed alongside a decrease in muscle mass (−1.4 kg), consistent with a pattern of masked sarcopenia. Conclusions: Chemotherapy in breast cancer patients is associated with concurrent muscle loss and fluid redistribution, which may obscure clinically relevant changes in body composition. Bioelectrical impedance spectroscopy enables differentiation between fluid and tissue compartments and provides a more accurate assessment than conventional methods. Early recognition of these changes may facilitate timely nutritional support and appropriate fluid management strategies. Full article

Real-time monitoring of lithium-ion capacitors (LICs) is crucial for ensuring reliability and predictive maintenance in dynamic applications such as electric transportation. However, traditional electrochemical impedance spectroscopy (EIS) techniques are complex and costly for onboard diagnostics due to their reliance on external excitation signals and dedicated hardware. Therefore, this paper presents an innovative framework for online state of health (SOH) estimation that bypasses these limitations by utilizing fast Fourier transform (FFT)-based passive impedance extraction directly from operational current and voltage signals. From experimental data, the equivalent circuit model (ECM) is developed, as well as its parameters, such as ohmic resistance, charge-transfer resistance, and Warburg diffusion. These parameters are identified through the extraction of impedance points in the low frequency region through FFT and the series resistance point using ohmic measurement, then performing a periodic curve fitting to these points. These curve fittings provide extracted ECM parameters. These parameters are used with a trained model to estimate the SOH of the monitored cell and are updated online. The proposed method was experimentally validated on five LIC cells aged under various C-rates (1C, 4C, 7C) and temperatures (35 °C, 40 °C, 50 °C), showing consistent impedance evolution with capacity fade. Validation of the utilized machine learning models, such as Polynomial Regression (PR), principal components analysis (PCA), and random forest (RF) regression, achieved SOH prediction errors as low as 2.23% compared to experimental results. The developed framework is particularly suitable for applications such as flash-charged electric buses but is broadly applicable across other energy storage systems as well. This advanced method enables real-time diagnostics without hardware modification, offering significant potential for integration into existing battery management systems (BMSs). Full article

The detection and quantification of reactive oxygen and nitrogen species (RONS) in plasma-treated water (PTW) are essential for advancing plasma applications in biomedical and agricultural fields. However, RONS characterization remains challenging, as conventional techniques often require chemical reagents that can alter the sample. Electrochemical impedance spectroscopy (EIS) offers a non-destructive alternative by probing the electrical response of aqueous systems and providing information on ionic concentration, charge transfer, and diffusion processes. This study investigates the feasibility of EIS as a diagnostic tool for characterizing physicochemical changes in PTW. Calibration experiments were performed using saline solutions with different ionic concentrations to evaluate the sensitivity of impedance measurements. Impedance spectra were recorded over a frequency range of 0.1 Hz to 10 kHz and analyzed using Nyquist and Bode plots with equivalent circuit modeling. Deionized water was treated with cold atmospheric plasma at different discharge powers (3.53–10.15 W) and treatment times (5–30 min) to generate RONS. The results show that EIS can monitor plasma-induced changes in conductivity and interfacial properties associated with variations in ionic content. In particular, systematic changes in solution resistance and admittance were observed and were correlated with plasma-induced changes in ionic composition. These findings demonstrate that EIS is a sensitive and non-invasive diagnostic method for PTW analysis. Full article

Nursing students in their training process often suffer from anxiety due to stressful situations, and emotional intelligence can help them to manage these situations. The aim of this study is to analyse the associations between the dimensions of perceived emotional intelligence and anxiety in students undergoing their training cycles in different special services in order to check if there are differences between them. It is an observational, cross-sectional and correlational study with a sample of 85 nursing students who had not received training in emotional intelligence. Two measurement instruments were used: the Trait-State Anxiety Inventory (STAI) to assess anxiety and the Trait Meta-Mood Scale (TMMS-24) to measure EI. Data were analysed using Pearson’s coefficient when the distribution was normal, and Spearman’s coefficient in the non-normal distribution. The results showed in the group—ER-Emergency and Oncology—there was a significant negative relationship between state and trait anxiety and emotional understanding and regulation. In contrast, in the Primary Care setting there was also a positive relationship between emotional perception and trait anxiety. The study concludes that nursing students who understand and manage their emotions may have a lower risk of anxiety. Furthermore, if they identify emotions appropriately, the risk of suffering from anxiety in the long term may be lower. This finding was observed when the student did the internship in Primary Care. So there is a difference depending on the clinical context. Full article

Organic coatings are the most widely utilized corrosion protection strategy for metallic materials. Nevertheless, they can degrade over time through the effects of UV, moisture, and corrosive media, compromising their protective performance. In order to monitor the coating performance for predictive maintenance, an electrochemical sensor was fabricated using 6005A aluminum alloy and coated with four coating systems: (1) epoxy primer, (2) epoxy primer/polyurethane topcoat, (3) epoxy primer/polyurethane topcoat/aluminum-powder-containing polyester resin, and (4) epoxy primer/polyurethane topcoat/aluminum-powder-containing polyester resin/acrylic coat. The sensors and corresponding coupon samples were exposed for 24 months at two sites in Thailand: Pathum Thani (PTI, suburban) and Chon Buri (CBI, mild marine). Electrochemical impedance spectroscopy (EIS) measurements were conducted at a fixed frequency of 117 Hz, synchronized with on-site meteorological monitoring. Impedance data were converted into a coating aging index (AI) to quantitatively assess the coating degradation. Coating deterioration was observed in PTI as early as at 6 months of exposure. Machine learning modeling revealed that cumulative rainfall was the dominant environmental factor influencing coating degradation. The single epoxy primer layer exhibited the poorest durability, while the incorporation of polyurethane, aluminum-pigmented polyester, and acrylic layers significantly prolonged the protective service life of the coating system. Full article

Epilepsy is a prevalent neurological disorder characterized by recurrent seizures, and electroencephalogram (EEG) signals provide a direct measure of brain activity for detection. Although deep learning achieves high accuracy, it often lacks physiological interpretability. We propose the Excitation/Inhibition Dynamic Polynomial Network (E/I-DynPolyNet), a biologically grounded framework for interpretable seizure detection. Specifically, E/I-DynPolyNet introduces a dual excitatory/inhibitory (E/I) pathway with sign-constrained synaptic weights, encouraging the learned activations to reflect latent E/I representations. Furthermore, a differentiable Wilson-Cowan (WC) module is embedded to govern the temporal evolution of E/I interactions, ensuring consistency with neurophysiological principles. A physics-informed optimization strategy integrates supervised learning with dynamical residual constraints and E/I balance regularization, guiding the model to learn physiologically consistent representations. Experimental results on the CHB-MIT and Bonn datasets demonstrate competitive accuracies of 95.81% and 98.5%, respectively. Crucially, E/I-DynPolyNet enables quantitative estimation of E/I imbalance, revealing that E/I ratios increase from 1.01 in the pre-ictal phase to 1.38 during seizures—a finding consistent with clinical observations of ictogenesis. These results indicate that E/I-DynPolyNet not only improves detection performance but also provides a mechanistic description of seizure dynamics, bridging the gap between data-driven learning and neurophysiological interpretation. Full article

This work presents the realization of a compact and embedded impedance-based sensor system for the characterization of lithium-ion batteries by means of electrical impedance spectroscopy (EIS). The analog magnitude-ratio and phase-difference detection (MRPDD) method is implemented and extended through a generalized formulation that models the shunt element as a frequency-dependent impedance and compensates the parasitic contributions of the printed circuit board. This reformulation corrects magnitude and phase errors introduced by the measurement hardware without increasing the overall complexity. The prototype comprises two main functional blocks: current-mode excitation and voltage-mode measurement. The excitation stage uses an operational transconductance amplifier and a power MOSFET to generate a voltage-controlled current source, whereas the sinusoidal voltage signal is generated by means of a direct digital synthesizer. The measurement chain relies on differential acquisition using instrumentation amplifiers and analog magnitude/phase detection based on the AD8302 vector detector under microcontroller control. The proposed method has been first validated by simulations using both a linear RC equivalent model and an extended Randles-type battery-equivalent model, and then experimentally characterized using a linear RC equivalent model of the device under test. Measurements show that the generalized formulation recovers the ideal impedance response in the presence of parasitic effects, both in the shunt device and in the printed circuit board. In the experimental validation with the RC model, a magnitude error of 1.65% is obtained at 1 kHz, which is adopted as the upper frequency limit for battery characterization, even though operation up to 10 kHz is possible. Phase measurements revealed that the input capacitive coupling of the vector detector, conceived for operation in the RF range, requires an adaptation for appropriate operation in the intended frequency range. The prototype has been also applied to the characterization of a commercial lithium-ion 18650 cell, enabling the measurement of battery impedance and the analysis of its dependence on the state-of-charge and on the discharge current. Full article

This study investigates the microstructural evolution of the CoCrFeNi system after incorporating Gallium (Ga) at varying concentrations (0, 15, and 20 at.%). The systems were synthesized by Vacuum Arc Melting (VAM) and characterized through X-ray Diffraction diffraction (XRD) and Scanning Electron Microscopy (SEM/EDS). Findings showed that the CoCrFeNi medium medium-entropy alloy stabilizes in a single-phase Face-Centered Cubic (FCC) structure. Upon the addition of 15 at.% Ga a dendritic morphology with a transition towards a duplex FCC + BCC microstructure was induced, a trend which was further solified in the equiatomic FeCoNiCrGa system. In this case the proportion of the Ga-rich BCC phase was increased from 18–22% to 31–34% for the Ga15 and Ga20 systems respectively. A combined approach of Electrochemical Frequency Modulation (EFM), Cyclic Potentiodynamic Polarization (CPP), and Electrochemical Impedance Spectroscopy (EIS) was selected for studying the electrochemical corrosion behavior of the produced systems. EFM results indicated a progressive deterioration of corrosion resistance when increasing Ga concentration (Icorr: 4.142, 5.619 and 10.01 μA/cm², and Rp: 12,035, 10,736 and 7254 Ω for the Ga0, Ga15 and Ga20 alloys respectively). Surface inhomogeneity, rapid passivation, and diffusion-controlled processes caused deviations from the ideal causality factors’ values. CPP measurements revealed increasing corrosion current densities with Ga addition within the Tafel region (2.81 × 10⁻⁷, 3.72 × 10⁻⁷ and 5.11 × 10⁻⁷A/cm² for the Ga0, Ga15 and Ga20 alloys respectively). All alloys showed positive hysteresis loops and an absence of repassivation, indicating susceptibility to pitting corrosion. Nevertheless, detailed analysis of the forward polarization region highlighted a more complex aspect. Reverse polarization scans confirmed stable pit growth in all alloys, with the absence of a repassivation tendency. EIS tests, performed after the completion of CPP measurements, further clarified the corrosion mechanisms. Equivalent circuit modeling revealed that although Ga-containing alloys exhibited relatively improved film characteristics in the forward polarization stage, the charge transfer resistance (Rct) was highest for the CoCrFeNi alloy, followed by Ga15 and Ga20 (22,620, 11,380, 10,060 Ω respectively). The overall impedance ranking (Ga0 > Ga15 > Ga20, i.e., 27,139 > 20,279.5 > 16,341 ohms respectively) showed that, despite microstructural and entropic effects enhancing certain passivation aspects, the reduced Cr content highly impacted long-term corrosion resistance. This holistic electrochemical approach showcases the complex interactions between compositional alterations, phase structure, grain refinement, passive film chemistry, and diffusion trends in establishing the corrosion performance of Ga-modified CoCrFeNi HEAs. Full article

In this study, polyhedral self-assembled spherical titania (TS) photocatalyst was successfully synthesized via a one-step hydrothermal method from titanium chloride, sodium dodecyl sulfate and sulfuric acid. Titania modification with iron was carried out through the same procedure by the addition of different amounts of iron(III) chloride to the substrate mixture. Various methods were applied for sample characterization, e.g., XRD, SEM, TEM, XPS, UV-vis DRS, and photo-electrochemical measurements, such as EIS, CV, transient photocurrent, whereas photocatalytic activity was investigated for hydrogen evolution under UV/vis and oxidative decomposition of antibiotics under UV and/or vis, including also tests with scavengers. It has been found that iron was both incorporated in the titania structure (doping) and adsorbed on its surface. Although iron presence has hardly influenced the properties (slight changes in morphology, bandgap energy, and crystallite size), the photocatalytic activity has increased significantly. Therefore, it is proposed that iron might work as an electron sink, hindering the charge carriers’ recombination. Linear evolution of hydrogen, recycling experiments and characterization of samples after recycling have confirmed a good stability of iron-modified titania. Full article

The corrosion behavior and time-dependent mechanism of 22MnB5 steel featuring a thinned Al-Si coating (60 g/m²) were systematically investigated in a chloride ion wet–dry cyclic environment, motivated by the demand for thinning and toughening development of aluminum-silicon coatings. A periodic immersion accelerated corrosion test using 3.5% NaCl solution was conducted, together with macro/microscopic morphology observation (SEM/EDS), phase analysis (XRD, FTIR), and electrochemical measurements (polarization curves, EIS). The Al-Si coated steel was studied over corrosion periods of 1, 8, 10, and 20 days to elucidate its corrosion behavior, interfacial evolution, and failure mechanism. The results indicated that the corrosion process exhibited a three-stage evolution: stable protection, rapid failure, and dynamic equilibrium. At the initial stage (1 day), a dense Al₂O₃ passive film formed on the coating surface, providing excellent substrate protection, with a corrosion current density of only 1.77 µA/cm² and a maximum charge-transfer resistance (R₂) of 652 Ω·cm². In the middle stage (8 days), Cl⁻ permeated through the cracked film, triggering selective dissolution of Al, while Si was enriched in situ to form a porous residual layer; the corrosion current density (I_corr) sharply increased to 13.25 µA/cm², and R₂ dropped to its minimum of 156.6 Ω·cm². Corrosion products at this stage were mainly Al₂O₃ and SiO₂, accompanied by small amounts of iron oxyhydroxides and hydroxides, and local coating failure began to appear. During the later stage (10–20 days), the corrosion products evolved into γ-FeOOH, α-FeOOH, and Fe₂O₃, which, together with an amorphous SiO₂ gel network enriched at the interface, formed a dual-layer composite rust layer. R2 consequently recovered from 156.6 Ω·cm² at 8 days to 424 Ω·cm² at 20 days, indicating a reduced corrosion rate and entry into a stable inhibition stage. The critical failure mechanism is that Cl⁻ preferentially penetrates the surface of the Al₂O₃ passive film, disrupting the metastable state of the coating and thereby creating pathways for corrosive media intrusion. The findings of this study can provide technical support for the safe application of such as-received coatings in non-load-bearing components with heat and corrosion resistance requirements. Full article

C-reactive protein (CRP) is one of the most essential biomarkers for the early detection of inflammation and infection. In this study, we developed a sensitive and selective electrochemical immunosensor for CRP detection, leveraging the unique properties of gold nanoparticles (AuNPs). A nanostructured layer of AuNPs was deposited onto a screen-printed carbon electrode (SPCE), followed by the formation of a self-assembled monolayer (SAM) of L-cysteine and EDC/sulfo-NHS chemistry. The antibody was covalently immobilized onto the modified electrode through optimized dual-crosslinking chemistry. Detection conditions were systematically optimized, with pH 8.0 in Tris buffer providing the best electrochemical response. Electrochemical characterization was performed using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and differential pulse voltammetry (DPV) in a 5 mM K₃[Fe(CN)₆]/K₄[Fe(CN)₆] redox probe solution containing 0.1 M KCl. CRP detection was achieved by monitoring the increase in charge transfer resistance (Rct) upon specific binding of the target CRP antigen to the immobilized antibody. Spiked recovery experiments showed spiked recovery rates ranging from 98.01% to 107.14%, with a standard deviation below 4%. Regeneration studies demonstrated high efficiency, confirming the suitability of the sensor interface for repeated and reliable measurements. Under optimized conditions, the immunosensor exhibited excellent analytical performance, including a low limit of detection (LOD) of 0.16 µg/mL, a wide linear detection range of 5–100 µg/mL, high selectivity against 13 potential interferents (including inflammatory cytokines), and good reproducibility with a relative standard deviation (RSD) of 3.69%. The sensor also showed strong stability, retaining more than 95% of its signal after 15 days, and high regeneration efficiency of 97% over seven cycles. These results highlight the strong potential of the proposed immunosensor for point-of-care (POC) applications due to its simple fabrication, cost-effectiveness, user accessibility, and robust analytical performance. Full article

Against the global carbon neutrality backdrop, amine-based CO₂ capture technology is critical for industrial greenhouse gas emission reduction. However, mixed amine absorbents can cause severe corrosion of Q235B carbon steel, restricting the stable operation of carbon capture, utilization, and storage (CCUS) projects. This study systematically investigated the corrosion behavior of Q235B carbon steel in a novel mixed amine system under simulated industrial conditions using weight loss tests, electrochemical measurements (EIS, potentiodynamic polarization), and advanced characterizations (FT-IR, ¹³C NMR, SEM-EDS, XRD). The temperature was the dominant factor: corrosion rate increased significantly with rising temperature. Under CO₂-saturated conditions, 15–30% absorbent concentrations showed no significant effect on corrosion rate owing to similar molar loading and pH. At 60 °C and 30% concentration, the corrosion rate peaked at 30 L/L CO₂ loading. Carbamate accumulation promoted corrosion at low loading, while increased bicarbonate inhibited corrosion at high loading. The main corrosion products (Fe₃O₄, Fe₂O₃) formed loose, porous films with poor protectiveness. This work clarifies the electrochemical corrosion mechanism and provides data support for corrosion prevention in CCUS equipment. Full article

Background: Echo intensity (EI) has emerged as a promising and accessible tool for tracking changes in skeletal muscle quality; however, its utility during early-phase rehabilitation has not been studied. Using an observational cohort design, we examined changes in quadriceps muscle strength, size, and quality, along with self-reported knee function, 2, 6, and/or 10 weeks following anterior cruciate ligament reconstruction (ACLR). Methods: Thirteen participants (4 males, 9 females; mean age = 23 years) were assessed for bilateral isometric peak torque and cross-sectional area (CSA) and corrected EI of the vastus lateralis and rectus femoris. Self-reported knee function was measured using the International Knee Documentation Committee (IKDC) questionnaire. Results: Quadriceps peak torque was significantly lower in the surgical limb at 2 weeks following surgery but increased from weeks 2 to 10, while the nonsurgical limb remained stable. IKDC scores improved significantly over time. Vastus lateralis CSA decreased in the surgical limb between weeks 2 and 6, while rectus femoris CSA increased between weeks 6 and 10 in both limbs. Corrected EI values did not change over time. No significant correlations were observed among changes in muscle strength, size, quality, or self-reported knee function. Conclusions: We conclude that quadriceps strength, size, quality, and self-reported knee function change independently and do not follow a shared recovery trajectory. Full article

The deterioration of mirror coatings in aggressive environments is one of the main causes of staining, which is a manifestation of corrosion. Electrochemical Impedance Spectroscopy (EIS) allows the electrochemical behavior of these coatings to be evaluated. However, an issue with ANNs is that to perform predictions with high accuracy, it is necessary to adjust their parameters using a large amount of samples. Depending on the ANN architecture, the requirement can range from hundreds to thousands of data points. This is a problem in many real cases, since measurements are expensive in terms of time and resources. In this study, we use a Transfer Learning approach. First, we generate and use synthetic data to train a neural network; then, real data are used to fine-tune the model. The results show that the ANN can identify patterns of coating deterioration with high accuracy and provides an effective mechanism for early performance evaluation. A high accuracy of 0.98 is achieved in the advanced stage, which means that the ANN detects severely damaged protective coatings. For the initial stage, an accuracy of 0.71 and a recall of 0.56 are obtained, indicating that the model has significant difficulty detecting initial damage due to very subtle changes, in the low signal versus noise ratio and the behavior of the protective coatings’ properties when they are close to their intact state. Full article

Background/Objectives: In critically ill patients, endotracheal intubation (EI) is often performed to secure the airway or mechanical ventilation. Accurately predicting the timing of intubation significantly affects patient outcomes. We developed an artificial intelligence (AI) model designed for real-time risk stratification of patients requiring EI. Methods: We utilized the Medical Information Mart for Intensive Care-IV (MIMIC-IV) 2.2 dataset and performed model development using 15 clinical variables, including vital signs, Glasgow Coma Scale (GCS) score, and arterial blood gas analysis results. Patients intubated before or within 1 h of intensive care unit (ICU) admission were excluded. Clinical data from the ICU inherently consists of continuous time-series measurements. Traditional machine learning models often treat this information as static tabular data, neglecting vital temporal dynamics and patient history. Conversely, deep learning time-series approaches can capture these complex patterns over time. Thus, we applied the Gated Recurrent Unit with Decay++ (GRU-D++) model to predict the need for EI. GRU-D++ is an extension of the GRU and GRU-D. It builds upon the GRU-D to provide improved performance when handling datasets with exceptionally high rates of missing values. GRU-D++ is a time series deep learning model with an automatic mechanism for imputing missing values. This built-in capability eliminates the need for additional data preprocessing and has previously demonstrated high predictive performance. Using the 15 variables, we evaluated the optimal timing for EI in ICU-admitted patients by applying various AI models. Results: Among these, the GRU-D++ model demonstrated AUROC of 0.888, AUPR of 0.481, sensitivity of 0.474, specificity of 0.995, precision of 0.511, and F1 score of 0.491 on MIMIC-IV dataset. For KNUH dataset, the model demonstrated AUROC of 0.913, AUPR of 0.063, sensitivity of 0.162, specificity of 0.997, precision of 0.137, and F1 score of 0.147 within the 2 h in advance scenario. Furthermore, when compared with conventional scoring systems such as the Heart rate, Acidosis, Consciousness, Oxygenation, Respiratory rate (HACOR) score and Respiratory rate-Oxygenation (ROX) index, the GRU-D++ model also showed better performance predictive accuracy. Conclusions: The AI-based intubation prediction model developed in this study holds potential as a real-time risk stratification tool, providing timely risk assessments regarding the need EI. While operational threshold recalibration is essential prior to clinical deployment, further prospective multicenter studies are required to validate the clinical utility of this model in real-time practice. Full article