Bioengineering最新文献

Magnetic nanoparticles (MNPs) are widely applied in biomedicine, including bioimaging, drug delivery, and cell tracking. As central mediators of immune surveillance, macrophages phagocytize foreign substances, rendering their interactions with MNPs particularly consequential. During MNP uptake, macrophages undergo cytoplasmic remodeling that can lead to morphological alterations. Although prior studies have predominantly focused on MNP uptake efficiency and cytotoxicity, systematic quantitative assessments of macrophage morphological alterations following MNP treatment remain scarce. In this study, phase-contrast microscopy images of macrophages before and after MNP treatment were analyzed using unsupervised variational autoencoder (VAE)-based frameworks. Specifically, the β-VAE, β-total correlation VAE, and multi-encoder VAE frameworks were employed to extract latent representations of cellular morphology. The analysis revealed that MNP-treated macrophages exhibited pronounced structural alterations, including membrane expansion, central density shifts, and shape distortions. These findings were further substantiated through quantitative evaluations, including effect size analysis, kernel density estimation, latent traversal, and difference mapping. Collectively, these results demonstrate that VAE-based unsupervised learning provides a robust framework for detecting subtle morphological responses of macrophages to nanoparticle exposure and highlights its broader applicability across varied cell types, treatment conditions, and imaging platforms.

Functional electrical stimulation (FES) is widely used to improve gait in individuals with neurological impairments; however, early responses in adults with congenital conditions, such as cerebral palsy, who are newly exposed to FES, remain poorly understood. This study investigated the orthotic and therapeutic effects of FES in short- and long-term users using standardized three-dimensional gait analysis. In this longitudinal study, short-term users (G1; n = 13; mean age 31.7 ± 18.1 years) were evaluated both without and with FES and followed over a 4-12-week insurance-covered trial period. Long-term users (G2; n = 11; mean age 32.2 ± 11.0 years), who had used FES for at least one year, were reassessed over a standardized 12-week interval. Linear mixed-effects models assessed the effects of FES and time, with subjects included as random effects to account for inter-individual variability. G1 showed significant therapeutic adaptations, including increased walking speed and step length and reduced step width, accompanied by decreased dorsiflexion during stance and swing, while no significant orthotic effects were observed. G2 demonstrated clear orthotic responses, such as increased dorsiflexion at heel strike and during swing and improved walking speed and step length, with minimal evidence of additional therapeutic adaptation. The initial reduction in dorsiflexion in G1 warrants further investigation. These findings suggest that evaluation timelines may need to be extended and that outcome measures beyond foot clearance should be considered, particularly given the heterogeneity and severity of congenital neurological conditions.

Pulmonary electrical impedance tomography (EIT) offers non-invasive and real-time imaging in a compact device size, making it valuable for pulmonary ventilation monitoring. However, conventional EIT stimulation patterns face a trade-off dilemma between anti-noise performance and image interpretability. To address this challenge, we propose a novel mixed stimulation pattern that integrates opposite and adjacent stimulation patterns with a tunable weight ratio. The results of simulations and human experiments (involving 30 subjects) demonstrated that the mixed stimulation pattern uses 200 stimulation-measurement channels, preserves a high signal-to-noise ratio, improves lung separation, and reduces artifacts compared with the opposite and adjacent stimulation patterns. It maintained stable imaging at 600 μA of stimulation current amplitude (equivalent to 1 mA) and preserved most imaging and clinical indicators' stability at 200 μA (except GI/RVD_SD). The adjustable weight ratio enables imaging performance to be flexibly adjusted according to different noise levels in acquisition environments. In conclusion, the pattern we proposed offers a superior alternative to traditional patterns, achieving a favorable balance of real-time capability, anti-noise performance, and image interpretability for pulmonary EIT imaging.

This pilot study investigated multimodal physiological monitoring using minimally invasive and wearable sensors across two experimental settings. Experiment 1 involved five healthy adults (1 female) who simultaneously wore an interstitial fluid glucose (ISFG) sensor and a ring-type wearable device during sleep (00:00-06:00). Time-series analyses revealed that ISFG levels decreased during sleep in four of the five participants. ISFG values were significantly lower in the latter half of the sleep period compared with the first half (0-3 h vs. 3-6 h, p = 0.01, d = 2.056). Four participants also exhibited a mild reduction in SpO₂ between 03:00-04:00. These results suggest that nocturnal ISFG decline may be associated with subtle oxygen-saturation dynamics. Experiment 2 examined whether wearable sensors can detect physiological changes across meal-related phases. Nine male participants were monitored for heart rate (HR) and skin temperature during three periods: pre-meal (Phase 1: 09:00-09:30), during meal consumption (Phase 2: 12:30-13:00), and post-meal (Phase 3: 13:00-13:30). A paired comparison demonstrated a significant difference in median HR between Phase 1 and Phase 2 (p = 0.029, d = 0.812), indicating a large effect size. In contrast, HR-temperature correlation was weak and not statistically significant (Pearson r = 0.067, p = 0.298). Together, these findings demonstrate that multimodal wearable sensing can capture both nocturnal glucose fluctuations and meal-induced cardiovascular changes. This integrative approach may support real-time physiological risk assessment and future development of remote healthcare applications.

Intervertebral disc (IVD) herniation is a complex and multifactorial condition with a challenging diagnosis and limited therapeutic options, highlighting the need for reliable biomarkers to improve clinical decision-making. The aim of this study was to identify circulating prognostic biomarkers of IVD herniation regression. The plasma proteomic profile and the expression of circulating non-coding RNAs were analysed in a rat model of IVD herniation and were correlated with herniation size. Four candidate proteins (TNC, COPS3, JUP, and GNAI2) were significantly correlated with herniation size, with TNC further validated by ELISA. Additionally, miR-143-3p, miR-10b-5p, miR-27a-3p, miR-140-5p, miR-155-5p, miR-146a-5p, and miR-21-5p were positively correlated with herniation size. Moreover, TNC, COPS3, JUP, and GNAI2 were found to be potential targets of miR-155-5p. This study provides the first combined proteomic and miRNA account of preclinical plasma biomarkers of IVD herniation size, where TNC-miR-155-5p emerge as promising elements of a regulatory module with IVD herniation prognostic potential.

Background: Early diagnosis of Alzheimer's disease (AD) is essential for slowing disease progression and mitigating cognitive decline. However, conventional diagnostic methods are often invasive, time-consuming, and costly, limiting their utility in large-scale screening. There is an urgent need for scalable, non-invasive, and accessible screening tools. Methods: We propose a novel screening framework combining a pre-trained multimodal large language model with structured MMSE speech tasks. An artificial intelligence-assisted multilingual Mini-Mental State Examination system (AAM-MMSE) was utilized to collect voice data from 1098 participants in Sichuan and Chongqing. CosyVoice2 was used to extract speaker embeddings, speech labels, and acoustic features, which were converted into statistical representations. Fourteen machine learning models were developed for subject classification into three diagnostic categories: Healthy Control (HC), Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD). SHAP analysis was employed to assess the importance of the extracted speech features. Results: Among the evaluated models, LightGBM and Gradient Boosting classifiers exhibited the highest performance, achieving an average AUC of 0.9501 across classification tasks. SHAP-based analysis revealed that spectral complexity, energy dynamics, and temporal features were the most influential in distinguishing cognitive states, aligning with known speech impairments in early-stage AD. Conclusions: This framework offers a non-invasive, interpretable, and scalable solution for cognitive screening. It is suitable for both clinical and telemedicine applications, demonstrating the potential of speech-based AI models in early AD detection.

Artificial intelligence (AI) and machine learning (ML) are transforming medical diagnostics, but human nail, an easily accessible and rich biological substrate, is still not fully exploited in the digital health field. Nail pathologies are easily diagnosed, non-invasive disease biomarkers, including systemic diseases such as anemia, diabetes, psoriasis, melanoma, and fungal diseases. This review presents the first big synthesis of image analysis for nail lesions incorporating AI/ML for diagnostic purposes. Where dermatological reviews to date have been more wide-ranging in scope, our review will focus specifically on diagnosis and screening related to nails. The various technological modalities involved (smartphone imaging, dermoscopy, Optical Coherence Tomography) will be presented, together with the different processing techniques for images (color corrections, segmentation, cropping of regions of interest), and models that range from classical methods to deep learning, with annotated descriptions of each. There will also be additional descriptions of AI applications related to some diseases, together with analytical discussions regarding real-world impediments to clinical application, including scarcity of data, variations in skin type, annotation errors, and other laws of clinical adoption. Some emerging solutions will also be emphasized: explainable AI (XAI), federated learning, and platform diagnostics allied with smartphones. Bridging the gap between clinical dermatology, artificial intelligence and mobile health, this review consolidates our existing knowledge and charts a path through yet others to scalable, equitable, and trustworthy nail based medically diagnostic techniques. Our findings advocate for interdisciplinary innovation to bring AI-enabled nail analysis from lab prototypes to routine healthcare and global screening initiatives.

Low back pain (LBP) is a common cause of activity limitation in individuals that can result in socioeconomic costs up to $200 billion per year. Most cases of LBP lack a known underlying pathology. The L4/L5 motion segment is the most impaired lumbar segment, likely due to high load-bearing function. The ability to model L4/L5 compressive loading from surface electromyography (sEMG) data during dynamic activity may add to the understanding of LBP. Eight volunteers with no history of LBP participated in this study. Muscle activity of the erector spinae, rectus abdominus, and external obliques were recorded by a wireless EMG system (Trigno, Delsys, Natick, MA, USA) during a straight-leg stoop-to-stand task. L4/L5 compressive loading was estimated using a subject-specific sEMG model and validated by comparison with an AnyBody model and publicly available data from OrthoLoad. A specific trendline showed a significant decrease in percent error of estimated force for all muscles. Significantly lower impulse values were estimated by the AnyBody model than the sEMG subject-specific model (p = 0.007). Although our sEMG model was subject to high variability, loading values largely remained within those reported in the literature. Significant variation was found comparing the sEMG model with the AnyBody model, which may validate continued development and testing of personalized measurements of L4/L5 loading.

The goal of this study was to assess elevated spinal loading conditions and their effect on the polyethylene stresses of a lumbar total joint replacement (L-TJR). A previously validated lumbar spine finite element model was virtually implanted with an L-TJR at L4-L5 and exposed to three elevated loading conditions: (1) 95th-percentile male body weight while bending forward, (2) combined ±7.5 Nm axial torsion and lateral bending, and (3) ASTM F2423 aggressive loading (1850 N plus 10-12 Nm bending). Combined torsion and lateral bending were considered because these loads and moments may be coupled in demanding real-world scenarios. Across all conditions, contact at the bearing remained confined to the intended spherical surfaces, consistent with Mode I in vitro wear tests, with no evidence of impingement. Contact stresses and von Mises stresses were considered acceptable based on the simulated results of Mode IV impingement tests. Only in one scenario-95th-percentile male body weight with multiaxial torsion-did von Mises stress in the polyethylene slightly exceed the stresses associated with impingement (<5%). These findings are useful in establishing the upper biomechanical loading limits for the L-TJR design beyond the 50th-percentile loading levels employed by standard in vitro tests. Future validation efforts such as a comparison with retrieval analyses or clinical data will further strengthen the model's applicability to current and future questions of interest and contexts of use. Additional work may expand the modeling framework to incorporate patient-specific anatomy, variable implant positioning conditions, and a broader range of physiological load scenarios.

Scalp high-frequency oscillations (HFOs) are promising noninvasive biomarkers of epileptogenicity, but their phenotypic diversity and clinical relevance in absence epilepsy (AE) remain unclear. This study aimed to classify scalp HFOs in AE using k-means clustering based on multiple morphological characteristics, and to evaluate their distribution across electroencephalogram (EEG) epochs and seizure control statuses. We analyzed scalp EEG recordings from 14 children and adolescents with AE. After excluding outliers, 163 scalp HFOs were characterized by average frequency, duration, amplitude, and number of cycles. Amplitude and cycle count were log-transformed prior to clustering, and k-means clustering was applied to identify distinct HFO phenotypes. Three clusters were identified: Cluster 1 (short duration, low amplitude), Cluster 2 (low frequency), and Cluster 3 (long duration, high cycle count). Cluster 2 and Cluster 3 were significant predictors of ictal HFOs in active AE, with odds ratios (ORs) of 0.33 (95% confidence interval [CI]: 0.14-0.74) and 5.00 (CI: 2.02-17.73), respectively. Cluster 2 also predicted interictal HFOs in active AE (OR [95% CI] = 2.71 [1.23-5.67]). These findings support the utility of scalp HFO phenotypes as EEG-based biomarkers for seizure detection and disease monitoring, potentially guiding treatment strategies in pediatric AE.