Bioengineering最新文献

Educating deafblind children is a highly specialized field that requires computer-assisted learning tools to address the challenges of auditory and visual impairments. The objective is to reduce their difficulties in communication with their peers and to empower them to learn independently in a classroom environment. Braille and assistive tools have become profoundly beneficial for deafblind children, serving as an essential means of communication and knowledge acquisition, enabling them to live independently. This study aims to develop an assistive tool that bridges the limitations of conventional tactile methodologies by incorporating the latest artificial intelligence techniques, enabling children to learn with greater ease. The research leverages Morse code technology to facilitate communication with deafblind children. The speaker's lip movements are converted into text using the deep learning techniques of a 3D convolutional neural network and a bidirectional long short-term memory neural network. Experimental evaluations of this text conversion model show a word error rate of 2% and an accuracy rate of 98%. The text is then converted into Morse code and communicated to the deafblind child through a wearable device. The significance of this assistive tool lies in its discreet design, resembling a smartwatch. Adolescents can wear the proposed wearable device confidently without feeling self-conscious or embarrassed.

Pancreatitis is a prominent and severe type of inflammatory disorder that has grabbed a lot of scientific and clinical interest to prevent its onset. It should be detected early to avoid the development of serious complications, which occur due to long-term damage to the pancreas. The accurate measurement of biomarkers that are released from the pancreas during inflammation is essential for the detection and early treatment of patients with severe acute and chronic pancreatitis, but this is sub-optimally performed in clinically relevant practices, mainly due to the complexity of the procedure and the cost of the treatment. Clinically available tests for the early detection of pancreatitis are often time-consuming. The early detection of pancreatitis also relates to disorders of the exocrine pancreas, such as cystic fibrosis in the hereditary form and cystic fibrosis-like syndrome in the acquired form of pancreatitis, which are genetic disorders with symptoms that can be correlated with the overexpression of specific markers such as creatinine in biological fluids like urine. In this review, we studied how to develop a minimally invasive system using hydrogel-based biosensors, which are highly absorbent and biocompatible polymers that can respond to specific stimuli such as enzymes, pH, temperature, or the presence of biomarkers. These biosensors are helpful for real-time health monitoring and medical diagnostics since they translate biological reactions into quantifiable data. This paper also sheds light on the possible use of Ayurvedic formulations along with hydrogels as a treatment strategy. These analytical devices can be used to enhance the early detection of severe pancreatitis in real time.

Emerging radar sensing technology is revolutionizing cardiovascular monitoring by eliminating direct skin contact. This approach captures vital signs through electromagnetic wave reflections, enabling contactless blood pressure (BP) tracking while maintaining user comfort and privacy. We present a hierarchical neural framework that synergizes spatial and temporal feature learning for radar-driven, contactless BP monitoring. By employing advanced preprocessing techniques, the system captures subtle chest wall vibrations and their second-order derivatives, feeding dual-channel inputs into a hierarchical neural network. Specifically, Stage 1 deploys convolutional depth-adjustable lightweight residual blocks to extract spatial features from micro-motion characteristics, while Stage 2 employs a transformer architecture to establish correlations between these spatial features and BP periodic dynamic variations. Drawing on the intrinsic link between systolic (SBP) and diastolic (DBP) blood pressures, early estimates from Stage 2 are used to expand the feature set for the second-stage network, boosting its predictive power. Validation achieved clinically acceptable errors (SBP: -1.09 ± 5.15 mmHg, DBP: -0.26 ± 4.35 mmHg). Notably, this high degree of accuracy, combined with the ability to estimate BP at 2 s intervals, closely approximates real-time, beat-to-beat monitoring, representing a pivotal breakthrough in non-contact BP monitoring.

Perhaps the most innovative branch of medicine is represented by regenerative medicine. It deals with regenerating or replacing tissues damaged by disease or aging. The innovative frontier of this branch is represented by bioprinting. This technology aims to reconstruct tissues, organs, and anatomical structures, such as those in the head and neck region. This would mean revolutionizing therapeutic and surgical approaches in the management of multiple conditions in which a conspicuous amount of tissue is lost. The application of bioprinting for the reconstruction of anatomical areas removed due to the presence of malignancy would represent a revolutionary new step in personalized and precision medicine. This review aims to investigate recent advances in the use of biomaterials for the reconstruction of anatomical structures of the head-neck region, particularly those of the oral cavity. The characteristics and properties of each biomaterial currently available will be presented, as well as their potential applicability in the reconstruction of areas affected by neoplasia damaged after surgery. In addition, this study aims to examine the current limitations and challenges and to analyze the future prospects of this technology in maxillofacial surgery.

Contaminants of emerging concern (CECs) pose a potential risk to human and environmental health. Microalgae bioremediation is a promising approach for transforming or removing contaminants from the environment, while contributing to the circular economy. In this study, Nannochloropsis sp. was effectively used for the simultaneous removal of six CECs: paracetamol, ibuprofen, imidacloprid, methylparaben and bisphenol A at 10 µg mL^-1 and triclosan at 0.5 µg mL^-1 from synthetic wastewater, which were able to survive under such concentrations, higher than those commonly found in the environment (up to 2.82 µg mL^-1 of methylparaben). High removal efficiencies were reached for methylparaben (100%) and bisphenol A (93 ± 2%), while for imidacloprid, paracetamol and ibuprofen, 30 ± 1%, 64 ± 2% and 49 ± 5% were removed, respectively. Subsequently, lipids were extracted, and the FAME profile was characterised using GS-MS. The main fatty acids identified after bioremediation were hexadecadienoic acid isomers (C16:2), palmitic acid (C16), linoleic acid (C18:2) and γ-linolenic acid (C18:3). The absence of oleic acid and stearic acid was noticed, suggesting an alteration in the lipidic profile due to contaminant exposure. By exploring the quantification of fatty acids in future work, potential applications for the extracted lipids can be explored, further demonstrating the feasibility of this circular process.

There is no established detecting tool for hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM). This study aimed to develop a deep-learning-based model for identifying HCM and DCM using standard 12-lead electrocardiogram (ECG) images. We obtained a cohort of patients with HCM (171 ECG images) or DCM (364 ECG images), confirmed by cardiovascular magnetic resonance (CMR) examinations, who underwent both ECG and CMR within 30 days at our institution. Age- and sex-matched healthy controls (2314 ECG images) were selected from our Health Check Center. A total of 2849 ECG images were processed via a fine-tuned ResNet50 architecture, with stratified five-fold cross-validation for model training, validation, and testing. The proposed model demonstrated strong performance in distinguishing DCM, achieving an area under the receiver operating curve (AUROC) of 0.996 and an area under the precision-recall curve (AUPRC) of 0.940. For the detection of HCM, the model also achieved an AUROC of 0.980 and an AUPRC of 0.953, respectively. The model prospectively exhibited stability in temporal validation. Furthermore, representative images of the Gradient-weighted Class Activation Mapping (Grad-CAM) technique analysis showed the regions corresponding to the anterior and anteroseptal leads were the most important areas for the prediction of HCM or DCM. This temporally validated fine-tuned ResNet50 model shows promise to inexpensively detect individuals with HCM or DCM.

Here, we present a novel 3D bioprinted model of the forebrain cortex designed to quantify neurite outgrowth across a hydrogel bridge. To validate this model, we cultured Alzheimer's disease (AD) forebrain cortical populations derived from human iPSCs carrying APP (amyloid precursor protein) mutations (K670M/N671L + V717F). Neurite and synapse formation were significantly impaired in 3D AD mutant cultures compared to controls, but this was not replicated in 2D, highlighting deficits in these traditional 2D cell culture models. To investigate the mechanisms underlying impaired neurite outgrowth in 3D and 2D models of AD, we assessed amyloid-β dysfunction, mitochondrial health, and oxidative stress in both conditions. In the 3D model, APP mutant cultures exhibited reduced mitochondrial membrane potential and fragmented networks, indicating dysfunction and potential cellular energy deficits. Additionally, elevated oxidative stress and proteostasis disruption were identified in the 3D AD models as indicators of cellular damage, which may be limiting neurite extension. Furthermore, transcriptomic (bulk RNA-Seq) analysis revealed distinct differences in gene expression pathways between 2D and 3D models of AD, suggesting alternate underlying mechanisms of disease pathology between the culture conditions. This study demonstrates the functionality of this novel 3D bioprinted model for quantifying neurite connectivity and identifying underlying disease mechanisms.

Material Jetting (MJT) 3D printing (3DP) is a specific technology that deposits photocurable droplets of material and colored inks to fabricate objects layer-by-layer. The high resolution and full color capability render MJT 3DP an ideal technology for 3DP in medicine as evidenced by the 3DP literature. The technology has been adopted globally across the Americas, Europe, Asia, and Australia. While MJT 3D printers can be expensive, their ability to fabricate highly accurate and multi-color parts provides a lucrative opportunity in the creation of advanced prototypes and medical models. The literature on MJT 3DP has expanded greatly as of late, in part aided by the lowering costs of the technology, and this report is the first review to document the applications of MJT in medicine. Additionally, this report portrays the technological information behind MJT 3DP, cases involving fabricated MJT 3DP models from the University of Cincinnati 3DP lab, as well as the challenges of MJT in a clinical setting, including cost, expertise in managing the machines, and scalability issues. It is expected that MJT 3DP, as imaging and segmentation technologies undergo future improvement, will be best poised with representing the voxel-level-variations captured by radiologic-image-sets due to its capacity for voxel-level-control.

Continuous metrics of cerebral autoregulation (CA) assessment have been developed using various multimodal cerebral physiological monitoring devices. However, CA regional disparity remains unclear in states of health and disease. Leveraging existing archived data sources, we preliminarily evaluated regional hemispheric disparity in CA using the near infrared spectroscopy (NIRS)-derived cerebral oximetry index (COx/COx-a). Along with bilateral NIRS, regional cerebral oxygen saturation, arterial blood pressure, cerebral perfusion pressure, and bilateral COx/COx-a were derived using three different temporal resolutions-10 s, 1 min, and 5 min-based on non-overlapping mean values. The regional disparity between hemispheres was evaluated based on median and median absolute deviation. Further, patient-level autoregressive integrative moving average models were calculated for each signal stream and used to generate personalized vector autoregressive models. Multi-variate cerebral physiologic relationships between hemispheres were assessed via impulse response functions and Granger causality analyses. Data from 102 healthy control volunteers, 27 spinal surgery patients, and 95 TBI patients (varying in frontal lobe pathology impacting the optode path; 64 without bifrontal lobe pathology, 15 without left frontal lobe pathology, 11 without right frontal lobe pathology, and 5 with bifrontal lobe pathology) were retrospectively analyzed. For subjects with or without cranial pathology, no difference in COx/COx-a was found between hemispheres regardless of the analytic method. In TBI patients without pathology underneath the NIRS sensor, distant parenchymal injury does not seem to have an effect on the CA of uninjured frontal lobes. Further work is required to characterize regional disparities with multi-channel CA measurements in healthy and disease states.

The advancement of Vital Pulp Therapy (VPT) in dentistry has shown remarkable progress, with a focus on innovative materials and scaffolds to facilitate reparative dentin formation and tissue regeneration. A comprehensive search strategy was performed across PubMed, Scopus, and Web of Science using keywords such as "vital pulp therapy", "biomaterials", "dentin regeneration", and "growth factors", with filters for English language studies published in the last 10 years. The inclusion criteria focused on in vitro, in vivo, and clinical studies evaluating traditional and next-generation biomaterials for pulp capping and tissue regeneration. Due to the limitations of calcium-based cements in tissue regeneration, next-generation biomaterials like gelatin, chitosan, alginate, platelet-rich fibrins (PRF), demineralized dentin matrix (DDM), self-assembling peptides, and DNA-based nanomaterials were explored for their enhanced biocompatibility, antibacterial properties, and regenerative potential. These biomaterials hold great potential in enhancing VPT outcomes, but further research is required to understand their efficacy and impact on dentin reparative properties. This review explores the mechanisms and properties of biomaterials in dentin tissue regeneration, emphasizing key features that enhance tissue regeneration. These features include biomaterial sources, physicochemical properties, and biological characteristics that support cells and functions. The discussion also covers the biomaterials' capability to encapsulate growth factors for dentin repair. The development of innovative biomaterials and next-generation scaffold materials presents exciting opportunities for advancing VPT in dentistry, with the potential to improve clinical outcomes and promote tissue regeneration in a safe and effective manner.