Search Results (71)

Colorectal liver metastasis (CRLM) poses a significant challenge in oncology due to its high incidence and poor prognosis in unresectable cases. Current treatments, including surgical resection, systemic chemotherapy, and liver-directed therapies, often fail to effectively target hypoxic tumor regions, which are inherently more resistant to these interventions. This review examines the potential of a novel therapeutic strategy combining irreversible electroporation (IRE) ablation and Clostridium novyi-nontoxic (C. novyi-NT) bacterial therapy. IRE is a non-thermal tumor ablation technique that uses high-voltage electric pulses to create permanent nanopores in cell membranes, leading to cell death while preserving surrounding structures, and is often associated with temporary tumor hypoxia due to disrupted perfusion. C. novyi-NT is an attenuated, anaerobic bacterium engineered to selectively germinate and proliferate in hypoxic tumor regions, resulting in localized tumor cell lysis while sparing healthy, oxygenated tissue. The synergy between IRE-induced hypoxia and hypoxia-sensitive C. novyi-NT may enhance tumor destruction and stimulate systemic antitumor immunity. Furthermore, the integration of advanced imaging and artificial intelligence can support precise treatment planning and real-time monitoring. This integrated approach holds promise for improving outcomes in patients with CRLM, though further preclinical and clinical validation is needed. Full article

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) has emerged as a transformative tool for probing cortical dynamics with millisecond precision. This review examines the state-dependent nature of TMS-EEG, a critical yet underexplored dimension influencing measurement reliability and clinical applicability. By integrating TMS’s neuromodulatory capacity with EEG’s temporal resolution, this synergy enables real-time analysis of brain network dynamics under varying neural states. We delineate foundational mechanisms of TMS-evoked potentials (TEPs), discuss challenges posed by temporal and inter-individual variability, and evaluate advanced paradigms such as closed-loop and task-embedded TMS-EEG. The former leverages real-time EEG feedback to synchronize stimulation with oscillatory phases, while the latter aligns TMS pulses with task-specific cognitive phases to map transient network activations. Current limitations—including hardware constraints, signal artifacts, and inconsistent preprocessing pipelines—are critically analyzed. Future directions emphasize adaptive algorithms for neural state prediction, phase-specific stimulation protocols, and standardized methodologies to enhance reproducibility. By bridging mechanistic insights with personalized neuromodulation strategies, state-dependent TMS-EEG holds promise for advancing both basic neuroscience and precision medicine, particularly in psychiatric and neurological disorders characterized by dynamic neural dysregulation. Full article

Although thick electrodes hold significant potential for enhancing battery energy density, their practical application is limited by restricted ion transport kinetics. Constructing porous structures within thick electrodes is a widely adopted strategy to address this limitation, but it often compromises mass retention and mechanical integrity. In this study, a microchannel structure that balances the electrochemical and mechanical properties of the electrode was identified through simulation and precisely fabricated using femtosecond laser technology. Furthermore, the ultra-short pulse duration and high pulse energy of femtosecond lasers introduce oxygen vacancies into the electrode material, thereby enhancing its electrical conductivity. The obtained electrode exhibited excellent electrochemical performance under high-rate charging and discharging conditions, achieving significantly enhanced cycling stability and capacity retention, with a capacity 1.99 times greater than that of the unstructured electrode after 100 cycles. Meanwhile, the mechanical stability of the laser-processed electrode was maintained. This study provides new insights into the structural design and processing of the thick electrode and contributes to advancements in the field of energy storage. Full article

This study systematically investigates the dynamic evolution of chemical regimes in pulsed coaxial dielectric barrier discharge (DBD) plasma under atmospheric pressure using mass spectrometry. An innovative real-time mass spectrometric monitoring methodology was established, enabling the dynamic tracking of the formation and consumption processes of key reactive species such as ozone (O₃) and nitrogen oxides (NO_x). Energy density was the critical parameter governing the evolution of gaseous chemical components, with a quantitative elucidation of the regulatory mechanisms of air flow rate and control voltage on plasma chemical regime transition kinetics. Experimental results revealed significant parametric correlations: Under a constant control voltage of 140 V, increasing the gas flow rate from 0.5 to 5.5 L/min prolonged the transition duration from O₃-NO_x coexistence regime to a NO_x-dominant regime from 408 s to 1210 s. Conversely, at a fixed flow rate of 3.5 L/min, elevating the control voltage from 120 V to 140 V accelerated this transition, reducing the required time from 2367 s to 718 s. Parametric sensitivity analysis demonstrated that control voltage exerts approximately 3.3 times greater influence on transition kinetics than flow rate variation. Through comprehensive analysis of the formation and consumption mechanisms of N, O, O₃, and NO_x species, we established a complete plasma chemical reaction network. This scheme provides fundamental insights into reaction pathways while offering practical optimization strategies for DBD systems. For aerospace applications, this work holds particular significance by demonstrating that the identified control parameters can be directly applied to plasma-assisted treatment of propellant wastewater at launch sites, where the efficient removal of nitrogen-containing pollutants is crucial. These findings advance both the fundamental understanding of atmospheric-pressure plasma chemistry and the engineering applications of plasma-based environmental remediation technologies in aerospace operations. Full article

In this work, an amino-functionalized graphene oxide/polypyrrole (AMGO/PPy) composite-based novel sensing platform was established to monitor lead ions (Pb²⁺) at high sensitivity. AMGO was synthesized through a hydrothermal process and later formed a composite with PPy at varying concentrations. A physicochemical investigation of the synthesized materials was carried out using various characterization tools, while the electrochemical properties were examined by cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS) methods. The AMGO/PPy composite was deposited on a glassy carbon electrode (GCE), which was used for the real-time electrochemical detection of Pb²⁺. The AMGO/PPy sensor exhibited lower limits of detection (LOD) of 0.91 nM. In addition, the developed Pb²⁺ sensor exhibited excellent reproducibility, repeatability, selectivity, sensitivity, and long-term stability for 25 days. The AMGO/PPy composite emerges as a ground-breaking material for the electrochemical detection of Pb²⁺, holding significant potential for environmental monitoring and the protection of human health. Full article

This study introduces a radar-based model for estimating blood pressure (BP) in a touch-free manner. The model accurately detects cardiac activity, allowing for contactless and continuous BP monitoring. Cardiac motions are considered crucial components for estimating blood pressure. Unfortunately, because these movements are extremely subtle and can be readily obscured by breathing and background noise, accurately detecting these motions with a radar system remains challenging. Our approach to radar-based blood pressure monitoring in this research primarily focuses on cardiac feature extraction. Initially, an integrated-spectrum waveform is implemented. The method is derived from the short-time Fourier transform (STFT) and has the ability to capture and maintain minute cardiac activities. The integrated spectrum concentrates on energy changes brought about by short and high-frequency vibrations, in contrast to the pulse-wave signals used in previous works. Hence, the interference caused by respiration, random noise, and heart contractile activity can be effectively eliminated. Additionally, we present two approaches for estimating cardiac characteristics. These methods involve the application of a hidden semi-Markov model (HSMM) and a U-net model to extract features from the integrated spectrum. In our approach, the accuracy of extracted cardiac features is highlighted by the notable decreases in the root mean square error (RMSE) for the estimated interbeat intervals (IBIs), systolic time, and diastolic time, which were reduced by 87.5%, 88.7%, and 73.1%. We reached a comparable prediction accuracy even while our subject was breathing normally, despite previous studies requiring the subject to hold their breath. The diastolic BP (DBP) error of our model is $3.98\pm 5.81$ mmHg (mean absolute difference ± standard deviation), and the systolic BP (SBP) error is $6.52\pm 7.51$ mmHg. Full article

Goal: Current methodologies for assessing cerebral compliance using pressure sensor technologies are prone to errors and issues with inter- and intra-observer consistency. RAP, a metric for measuring intracranial compensatory reserve (and therefore compliance), holds promise. It is derived using the moving correlation between intracranial pressure (ICP) and the pulse amplitude of ICP (AMP). RAP remains largely unexplored in cases of moderate to severe acute traumatic neural injury (also known as traumatic brain injury (TBI)). The goal of this work is to explore the general description of (a) RAP signal patterns and behaviors derived from ICP pressure transducers, (b) temporal statistical relationships, and (c) the characterization of the artifact profile. Methods: Different summary and statistical measurements were used to describe RAP’s pattern and behaviors, along with performing sub-group analyses. The autoregressive integrated moving average (ARIMA) model was employed to outline the time-series structure of RAP across different temporal resolutions using the autoregressive (p-order) and moving average orders (q-order). After leveraging the time-series structure of RAP, similar methods were applied to ICP and AMP for comparison with RAP. Finally, key features were identified to distinguish artifacts in RAP. This might involve leveraging ICP/AMP signals and statistical structures. Results: The mean and time spent within the RAP threshold ranges ([0.4, 1], (0, 0.4), and [−1, 0]) indicate that RAP exhibited high positive values, suggesting an impaired compensatory reserve in TBI patients. The median optimal ARIMA model for each resolution and each signal was determined. Autocorrelative function (ACF) and partial ACF (PACF) plots of residuals verified the adequacy of these median optimal ARIMA models. The median of residuals indicates that ARIMA performed better with the higher-resolution data. To identify artifacts, (a) ICP q-order, AMP p-order, and RAP p-order and q-order, (b) residuals of ICP, AMP, and RAP, and (c) cross-correlation between residuals of RAP and AMP proved to be useful at the minute-by-minute resolution, whereas, for the 10-min-by-10-min data resolution, only the q-order of the optimal ARIMA model of ICP and AMP served as a distinguishing factor. Conclusions: RAP signals derived from ICP pressure sensor technology displayed reproducible behaviors across this population of TBI patients. ARIMA modeling at the higher resolution provided comparatively strong accuracy, and key features were identified leveraging these models that could identify RAP artifacts. Further research is needed to enhance artifact management and broaden applicability across varied datasets. Full article

To reduce the risk of syncope, trained breath-hold divers (BHDs) use a specialized breathing technique after surfacing called “hook breathing” (HB). It consists of a full inspiration followed by a Valsalva-like maneuver and with subsequent exhalation performed against resistance to generate continuous positive airway pressure during exhalation. This study analyzed the influence of HB on oxygen saturation recovery after a −40 m depth apnea dive in trained BHDs. Thirteen BHDs performed two dives to −40 m at different days, one followed by HB after a dive and the other using usual breathing (UB). To detect signs of lung edema, ultrasound B-line measurements were conducted before, 10 min after the dive, and within 1 h after the dive. To detect oxygen saturation recovery, pulse oximetry was recorded before and immediately after surfacing. Both groups exhibited significant increases in SpO₂ over time (UB: F (2.25, 24.7) = 22.1, p < 0.001, ηg2 = 0.612; HB: F (2.11, 23.2) = 29.0, p < 0.001, ηg2 = 0.688). Significant differences in SpO₂ were observed between the HB and UB groups at 30–45 s post-apnea, with higher SpO₂ values in the HB group; between 1.64 and 5.08% of SpO₂ in favor of the HB intervention. Four participants showed ultrasound B-lines within ten minutes post-dive. After a 40 m apnea dive, the results revealed significant SpO₂ recovery from 30 s to 45 s, with the HB recovering more rapidly. No differences were found at earlier (10–25 s) or later time points (50–60 s). Full article

Resistance spot-welded joints are crucial parts in contemporary manufacturing technology due to their ubiquitous use in the automobile industry. The necessity of improving manufacturing efficiency and quality at an affordable cost requires deep knowledge of the resistance spot welding (RSW) process and the development of artificial neural network (ANN)- and machine learning (ML)-based modelling techniques, apt for providing essential tools for design, planning, and incorporation in the welding process. Tensile shear force and nugget diameter are the most crucial outputs for evaluating the quality of a resistance spot-welded specimen. This study uses ML and ANN models to predict shear force and nugget diameter responses to RSW parameters. The RSW analysis was executed on similar and dissimilar AISI 304 and grade 2 titanium alloy joints with equal and unequal thicknesses. The input parameters included welding current, pressure, welding duration, squeezing time, holding time, pulse welding, and sheet thickness. Linear regression, Decision tree, Support vector machine (SVM), Random forest (RF), Gradient-boosting, CatBoost, K-Nearest Neighbour (KNN), Ridge, Lasso, and ElasticNet machine learning algorithms, along with two different structures of Multilayer Perceptron, were utilized for studying the impact of the RSW parameters on the shear force and nugget diameter. Different validation metrics were applied to assess each model’s quality. Two equations were developed to determine the shear force and nugget diameter based on the investigation parameters. The current research also presents a prediction of the Relative Importance (RI) of RSW factors. Shear force and nugget diameter predictions were examined using SHapley (SHAP) Additive Explanations for the first time in the RSW field. Trainbr as the training function and Logsig as the transfer function delivered the best ANN model for predicting shear force in a one-output structure. Trainrp with Tansig made the most accurate predictions for nugget diameter in a one-output structure and for shear force and diameter in a two-output structure. Depending on validation metrics, the Random forest model outperformed the other ML algorithms in predicting shear force or nugget diameter in a one-output model, while the Decision tree model gave the best prediction using a two-output structure. Linear regression made the worst ML predictions for shear force, while ElasticNet made the worst nugget diameter forecasts in a one-output model. However, in two-output models, Lasso made the worst predictions. Full article

This report evaluates the pulsed breakdown performance of $C_4{F}_{7}N$ under a 6.8 kV/ns voltage excitation. The pulsed dielectric strength of $C_4{F}_{7}N$ is compared to $S{F}_6$ in the same experimental setup, and it is found that $C_4{F}_{7}N$ concentrations of 50% or greater are required to achieve a dielectric strength greater than or equal to $S{F}_6$. Pure $C_4{F}_{7}N$ demonstrated higher electric field hold-off for longer time periods and less statistical variance under pulsed conditions when compared to $S{F}_6$. Mixtures of 50%$C_4{F}_{7}N$ with $N_2$ or $C{O}_2$ as buffer gases showed no appreciable difference in pulsed dielectric strength. Full article

Advancing neuromorphic computing technology requires the development of versatile synaptic devices. In this study, we fabricated a high-performance Al/LiNbO₃/Pt memristive synapse and emulated various synaptic functions using its primary key operating mechanism, known as oxygen vacancy-mediated valence charge migration (V_O-VCM). The voltage-controlled V_O-VCM induced space-charge-limited conduction and self-rectifying asymmetric hysteresis behaviors. Moreover, the device exhibited voltage pulse-tunable multi-state memory characteristics because the degree of V_O-VCM was dependent on the applied pulse parameters (e.g., polarity, amplitude, width, and interval). As a result, synaptic functions such as short-term memory, dynamic range-tunable long-term memory, and spike time-dependent synaptic plasticity were successfully demonstrated by modulating those pulse parameters. Additionally, simulation studies on hand-written image pattern recognition confirmed that the present device performed with high accuracy, reaching up to 95.2%. The findings suggest that the V_O-VCM-based Al/LiNbO₃/Pt memristive synapse holds significant promise as a brain-inspired neuromorphic device. Full article

Understanding magma plumbing systems hinges upon an intricate comprehension of crystal populations concerning size, chemistry, and origin. We introduce an innovative, yet elegantly simple approach—the ‘number–length of crystals (N-LoC) multifractal model’—to classify crystal sizes, unveiling compelling insights into their distribution dynamics. This model, a departure from conventional crystal size distribution (CSD) diagrams, reveals multifractal patterns indicative of distinct class sizes within igneous rock crystals. By synthesizing multiple samples from experimental studies, natural occurrences, and numerical models, we validate this method’s efficacy. Our bi-logarithmic N-LoC diagrams for cooling-driven crystallized samples transcend the confines of traditional CSD plots, identifying variable thresholds linked to cooling rates and quenching temperatures. These thresholds hint at pulsative nucleation and size-dependent growth events, offering glimpses into crystallization regimes and post-growth modifications like coalescence and coarsening. Examining multifractal log–log plots across time-series samples unravels crystallization histories during cooling or decompression. Notably, microlites within volcano conduits delineate thresholds influenced by decompression rate and style, mirroring nucleation and growth dynamics observed in experimental studies. Our fractal methodology, presenting a more direct approach with fewer assumptions than the classic CSD method, stands poised as a potent alternative or complementary tool. We delve into its potential, facilitating comparisons between eruptive styles in volcanoes while deliberating on inherent limitations. This work not only advances crystal size analysis methodologies but also holds promise for inferring nuanced volcanic processes and offers a streamlined avenue for crystal size evaluation in igneous rocks. Full article

Metal thickness measurements are essential in various industrial applications, yet current non-contact ultrasonic methods face limitations in range and accuracy, hindering the widespread adoption of electromagnetic ultrasonics. This study introduces a novel combined thickness measurement method employing fuzzy logic, with the aim of broadening the applicational scope of the EMAT. Leveraging minimal hardware, this method utilizes the short pulse time-of-flight (TOF) technique for initial thickness estimation, followed by secondary measurements guided by fuzzy logic principles. The integration of measurements from the resonance, short pulse echo, and linear frequency modulation echo extends the measurement range while enhancing accuracy. Rigorous experimental validation validates the method’s effectiveness, demonstrating a measurement range of 0.3–1000.0 mm with a median error within ±0.5 mm. Outperforming traditional methods like short pulse echoes, this approach holds significant industrial potential. Full article

Gastric acid secretion is closely associated with the development and treatment of chronic gastritis, gastric ulcers, and reflux esophagitis. However, gastric acid secretion is affected by complex physiological and pathological factors, and real-time detection and control are complicated and expensive. A gastric delivery system for antacids and therapeutics in response to low pH in the stomach holds promise for smart and personalized treatment of stomach diseases. In this study, pH-responsive modular units were used to assemble various modular devices for self-regulation of pH and drug delivery to the stomach. The modular unit with a release window of 50 mm² could respond to pH and self-regulate within 10 min, which is related to its downward floatation and internal gas production. The assembled devices could stably float downward in the medium and detach sequentially at specific times. The assembled devices loaded with antacids exhibited smart pH self-regulation under complex physiological and pathological conditions. In addition, the assembled devices loaded with antacids and acid suppressors could multi-pulse or prolong drug release after rapid neutralization of gastric acid. Compared with traditional coating technology, 3D printing can print the shell layer by layer, flexibly adjust the internal and external structure and composition, and assemble it into a multi-level drug release system. Compared with traditional coating, 3D-printed shells have the advantage of the flexible adjustment of internal and external structure and composition, and are easy to assemble into a complex drug delivery system. This provides a universal and flexible strategy for the personalized treatment of diseases with abnormal gastric acid secretion, especially for delivering acid-unstable drugs. Full article

Continuous non-invasive blood pressure (CNBP) monitoring is of the utmost importance in detecting and managing hypertension, a leading cause of death in the United States. Extensive research has delved into pioneering methods for predicting systolic and diastolic blood pressure values by leveraging pulse arrival time (PAT), the time difference between the proximal and distal signal peaks. The most widely employed pairing involves electrocardiography (ECG) and photoplethysmography (PPG). Possessing similar characteristics in terms of measuring blood flow changes, a recently investigated optical signal known as speckleplethysmography (SPG) showed its stability and high signal-to-noise ratio compared with PPG. Thus, SPG is a potential surrogate to pair with ECG for CNBP estimation. The present study aims to unlock the untapped potential of SPG as a signal for non-invasive blood pressure monitoring based on PAT. To ascertain SPG’s capabilities, eight subjects were enrolled in multiple recording sessions. A third-party device was employed for ECG and PPG measurements, while a commercial device served as the reference for arterial blood pressure (ABP). SPG measurements were obtained using a prototype smartphone-based system. Following the completion of three scenarios—sitting, walking, and running—the subjects’ signals and ABP were recorded to investigate the predictive capacity of systolic blood pressure. The collected data were processed and prepared for machine learning models, including support vector regression and decision tree regression. The models’ effectiveness was evaluated using root-mean-square error and mean absolute percentage error. In most instances, predictions utilizing ${PAT}_{SPG}$ exhibited comparable or superior performance to ${PAT}_{PPG}$ (i.e., SPG Rest ± 12.4 mmHg vs. PPG Rest ± 13.7 mmHg for RSME, and SPG 8% vs. PPG 9% for MAPE). Furthermore, incorporating an additional feature, namely the previous SBP value, resulted in reduced prediction errors for both signals in multiple model configurations (i.e., SPG Rest ± 12.4 mmHg to ±3.7 mmHg for RSME, and SPG Rest 8% to 3% for MAPE). These preliminary tests of SPG underscore the remarkable potential of this novel signal in PAT-based blood pressure predictions. Subsequent studies involving a larger cohort of test subjects and advancements in the SPG acquisition system hold promise for further improving the effectiveness of this newly explored signal in blood pressure monitoring. Full article