Biomedical Engineering Letters最新文献

[This corrects the article DOI: 10.1007/s13534-023-00295-7.].

Physiological swelling in human calf has been imaged under stocking compression by the sparse Bayesian learning implemented into electrical impedance tomography (SBL-EIT) to evaluate the in situ treatment effect of various compression pressures. SBL-EIT reconstructs conductivity distribution [Formula: see text] to image excessive extracellular fluid in subcutaneous adipose tissue (SAT), indicating the susceptibilities to physiological swelling due to various compression pressures. The SBL-EIT was applied to the imaging of eight-subject calves during prolonged standing under three types of net compression pressures P ^net measured by pressure sensor - strong pressure: P ^{net, Strong} = 11.9 [Formula: see text] 2.0 mmHg, weak pressure: P ^{net, Weak} = 4.47 [Formula: see text] 3.1 mmHg, and control pressure: P ^{net, Control} = 0.00 [Formula: see text] 0.0 mmHg, respectively. From the experimental results, the spatial-mean conductivity ⟨σ⟩^α2 with two pre-processing steps to eliminate undesirable effects, i.e., the difference in skin condition and effect of wearing stockings itself, is the highest in the case of stocking with control pressure, followed by weak and strong pressures across all subjects. Moreover, the ⟨σ⟩^α2 has a strong positive correlation with the conventional inversed impedance 1/z ^BIA by bioelectrical impedance analysis (BIA) (a correlation coefficient 0.528 < R < 0.990; n = 19 and p < 0.05), which is mainly increased during the prolonged standing. Moreover, various susceptibilities to physiological swelling are investigated based on the increase in [Formula: see text] for each subject, which is associated with subject external factors such as postural changes and circumference and internal factors like SAT.

Fibroblast Growth Factor plays a crucial role in neurological health, contributing to neuron protection, injury recovery, and angiogenesis. It is also significantly involved in the onset and progression of neurodegenerative disorders such as Huntington's, Alzheimer's, Parkinson's disease, and stroke, making FGF a vital target for therapeutic interventions. Despite its importance, no computational tool has been developed to predict FGF proteins. In this study, we present the first novel deep learning-based computational approach designed for the prediction of FGF proteins. We constructed two novel, high-quality datasets curated from the UniProt database for training and evaluation. Sequences were transformed into numerical representations using three complementary feature encoding methods including Dipeptide Composition, Dipeptide Deviation from Expected Mean, and Grouped Amino Acid Composition. These features capture both local and global sequence information. Multiple deep learning models were explored, including Convolutional Neural Network, Bidirectional Long Short-Term Memory, Generative Adversarial Network, and Gated Recurrent Unit. Among these, our proposed Convolutional Neural Network-based model outperformed all others, achieving an accuracy of 83.50%, sensitivity of 84.30%, specificity of 82.67%, F1 score of 83.42%, and a Matthews Correlation Coefficient of 0.671. The proposed approach has the potential to advance therapeutic discovery by enabling accurate identification of Fibroblast Growth Factor and improving our understanding of their role in neurological health and disease.

This study proposes a visualization and analysis method for eye blinking pattern using high-frame-rate videos. The high-frame-rate video clips for visualization are taken using a camera without additional equipment. The partial video clips of eye blinking except for eyelid flutters and microsleeps are extracted from the entire video clip. The changes in shapes and positions of the upper eyelid during the eye blinking sequences are evaluated, and each eye blinking is visualized as a single image. The various parameters regarding eye blinking are calculated to analyze blinking patterns. The single eye blinking sequence is divided into phases to analyze and classify eye blinking patterns in more detail. In this experiment conducted on 80 volunteers, the proposed method was able to quantitatively analyze eyelid movements, and various parameters related to eye blinking were calculated. Additionally, different types of eye blinking patterns were visualized as graph images, and incomplete eye blinking and consecutive eye blinking were defined and detected. The proposed method can overcome the spatial and situational limitations of conventional bio-signal analysis methods, as it allows non-contact measurement in ordinary environments. In addition, since quantitative eye blink data obtained from high-frame-rate video contain more information than data obtained from bio-signals, it is expected that analysis methods using videos can be easily applied to a wider range of fields.

There has been growing interest in sleep tracking technologies utilizing ring-shaped wearable devices. This study aimed to develop a method for accurately estimating sleep duration, onset, and offset using such a device. Conventional wrist-worn accelerometer-based devices often show limited accuracy, particularly during periods of low movement. Likewise, algorithms relying solely on electrodermal activity (EDA) signals struggle to detect frequent wake episodes due to their low temporal resolution. To address these limitations, we developed a ring-shaped wearable device and a set of algorithms that integrate both accelerometer and EDA signals. The performance of the proposed algorithms was evaluated through a clinical study involving 25 participants, the majority of whom had sleep disorders. Results showed that combining these complementary signals enabled accurate detection of sleep onset and offset within 10 min and maintained high accuracy even in conditions such as minimal movement or frequent wake episodes. These findings suggest that multimodal sensing may offer a promising direction for enhancing the reliability of sleep monitoring in real-world settings.

This study investigated the effects of trigeminal nerve (TN) stimulation on cardiovascular responses in healthy individuals. Sixty-one participants received electrical stimulation to the ophthalmic and maxillary branches of the trigeminal nerve at different frequencies (2 Hz, 20 Hz, and 200 Hz) while heart rate (HR), pulse arrival time (PAT), and heart rate variability (HRV) were monitored. Results demonstrated frequency-dependent cardiovascular responses, with higher frequencies (particularly 200 Hz) producing more pronounced effects on both HR and PAT. HR showed significant decreases during stimulation, with recovery times proportional to stimulation frequency. PAT changes, which inversely reflect blood pressure alterations, occurred more rapidly than HR changes, suggesting baroreflex-mediated regulation. Notably, habituation effects were observed with repeated stimulation at 2-min intervals, but these effects were minimized when using shorter (30-s) stimulation periods. HRV analysis revealed a significant negative correlation between resting LF/HF ratio and stimulation-induced changes, indicating that TN stimulation particularly influences autonomic balance in individuals with sympathetic hyperactivity. These findings provide insights into the mechanisms of TN stimulation on cardiovascular function and suggest potential therapeutic applications for conditions characterized by autonomic dysregulation.

To improve the long-term monitoring of patients receiving catheterized urination support, it is necessary to develop an automated tool that can monitor variations in urine color and void patterns during hospitalization. In this study, a novel intravenous (IV) pole-integrated urination-status monitoring technique was developed to detect the color and volume of in-bag liquids using a deep learning technique and to detect urinary disease symptoms, and performed a proof-of-concept simulation study using various simulated urine samples. In experiments, the error rates of in-bag liquid volume prediction were 5.14 ± 3.72%, 2.93 ± 5.70%, 2.48 ± 5.57%, and 2.00 ± 4.93%, at normal, hematuria, bilirubinuria, and purple urinary bag syndrome, respectively. The range of the average error rate between the threshold of the bag-flush request alarm and the model prediction was 0.71-1.08%. During the long-term testing over 24 h, the prototype IV pole classified the types of urinary disease symptoms with 100% accuracy and estimated the total volume of void with an error rate of 14.47 ± 6.29%, 8.75 ± 4.61%, 15.43 ± 8.23%, 14.22 ± 8.13%, and 11.86 ± 4.73% at normal, polyuria, oliguria, anuria, and nocturnal polyuria, respectively. Based on these results, we conclude that the proposed IV pole-integrated urinary monitoring technique has the potential to be used as a tool for real-time, simplified urination-status monitoring of patients with catheterized urination support, and for improving the safety of patients with renal and urological diseases. Nevertheless, further clinical evaluations using actual urine samples are required in future studies.

Supplementary information: The online version contains supplementary material available at 10.1007/s13534-025-00525-0.

As artificial intelligence (AI) becomes increasingly central to modern healthcare, medical education must move beyond passive knowledge transfer and adopt a system-wide approach to convergence training. This narrative review shares a 5-year case study from Seoul National University College of Medicine (SNU Medicine), which developed a comprehensive, multi-level model for integrating AI into medical education. Instead of relying on pilot programs or piecemeal curriculum updates, SNU Medicine established a governance-driven, modular framework that includes institutional infrastructure, interdisciplinary teaching strategies, cross-campus credit integration, and alignment with national digital health policies. Based on this long-term case, we propose four key design principles-modularity, transdisciplinary alignment, infrastructure-curriculum coupling, and policy embeddedness-as a framework for creating scalable and sustainable convergence education in medical AI. While rooted in Korea's unique policy environment, this model provides transferable insights for medical institutions worldwide, particularly those operating within public or policy-constrained environments.

Chronic tympanic membrane (TM) perforations often persist due to oxidative stress and hypoxia in the middle ear. A minimally invasive, biocompatible hydrogel addressing these challenges could serve as an effective therapeutic dressing. A thiolated chitosan (CS-SH) and manganese porphyrin (MnP)-conjugated polyethylene glycol maleimide (MnP-PEG-MAL) hydrogel (Ch_MnP) was developed through in situ gelation via Michael addition reaction. Its mechanical properties and antioxidant activities (SOD- and CAT-like), were evaluated in vitro. For in vivo testing, Ch_MnP hydrogel was transtympanically injected into a chronic TM perforation rat model. Efficacy and safety were assessed using endoscopy, 3D computed tomography (CT), auditory brainstem response (ABR) testing, and histological analysis. The hydrogel exhibited optimal porosity and a swelling ratio of ~ 155%, making it well-suited as a wound healing scaffold. MnP incorporation enhanced reactive oxygen species (ROS) scavenging and O₂ generation under oxidative conditions. However, in vivo application showed no apparent improvement in TM regeneration, ABR thresholds, or histological outcomes. CT revealed a substantial hydrogel volume loss over 3 weeks, indicating significant water loss. This dehydration compromised the hydrogel's structural integrity and functionality, diminishing its role as a scaffold and therapeutic agent. The Ch_MnP hydrogel exhibited excellent biocompatibility and antioxidant properties, with potential to alleviate chronic inflammation in TM perforation. However, healing of the perforation was not observed during the study period, primarily due to dehydration in the dry middle ear. These findings underscore the importance of maintaining a hydrated environment to enhance the therapeutic efficacy of hydrogel-based TM perforation treatments.

Supplementary information: The online version contains supplementary material available at 10.1007/s13534-025-00519-y.

The integration of Micro Light-Emitting Diodes (µLEDs) into biomedical systems holds significant promise for applications such as optogenetics, neural stimulation, and implantable biosensing. A critical challenge in realizing the clinical translation of such devices lies in ensuring the long-term biocompatibility of their encapsulation materials. In this study, we systematically evaluated the short-term (4-week) biocompatibility of three candidate encapsulation materials-Polydimethylsiloxane (PDMS), Ecoflex, and Kapton-and their respective µLEDs composites through a combination of in vitro and in vivo assays. In vitro cytotoxicity was assessed using direct contact morphology analysis, MTS mitochondrial activity assay, and Annexin-V/Propidium Iodide(PI)-based flow cytometry on L-929 fibroblasts. All materials demonstrated minimal cytotoxicity and apoptosis, with cell viability exceeding 90% and apoptotic indices remaining below 2.1%, meeting ISO 10993-5 criteria. Additionally, arsenic elution testing via inductively coupled plasma-mass spectrometry (ICP-MS) revealed concentrations far below toxicological thresholds, with Ecoflex and Kapton exhibiting undetectable levels. For in vivo evaluation, the materials were subcutaneously implanted into Sprague-Dawley rats. Histological analysis (Hematoxylin and eosin staining) conducted after 4 weeks revealed no signs of necrosis or severe inflammatory response. Semiquantitative scoring indicated low fibrosis, inflammatory cell infiltration, and angiogenesis, with all materials falling within acceptable biocompatibility ranges. Collectively, these findings confirm that PDMS, Ecoflex, and Kapton, both as standalone films and in LED-integrated forms, exhibit excellent biocompatibility in short-term implantation models. This work provides a comparative foundation for selecting safe encapsulation materials in the development of implantable µLEDs bioelectronic systems and underscores the importance of multi-dimensional evaluation frameworks in preclinical safety assessment.