Bioengineering最新文献

Nanotechnology plays a pivotal role in the biomedical field, especially in the synthesis and regulation of drug particle size. Reducing drug particles to the micron or nanometer scale can enhance bioavailability. Supercritical fluid technology, as a green drug development strategy, is expected to resolve the challenges of thermal degradation, uneven particle size, and organic solvent residue faced by traditional methods such as milling and crystallization. This paper provides an insight into the application of super-stable homogeneous intermix formulating technology (SHIFT) and super-table pure-nanomedicine formulation technology (SPFT) developed based on supercritical fluids for drug dispersion and micronization. These technologies significantly enhance the solubility and permeability of hydrophobic drugs by controlling the particle size and morphology, and the modified drugs show excellent therapeutic efficacy in the treatment of hepatocellular carcinoma, pathological scarring, and corneal neovascularization, and their performance and efficacy are highlighted when administered through multiple routes of administration. Overall, supercritical fluids have opened a green and efficient pathway for clinical drug development, which is expected to reduce side effects and enhance therapeutic efficacy.

The rising prevalence of mental illness is straining global mental health systems, particularly affecting older adults who often face deteriorating physical health and decreased autonomy and quality of life. Early detection and targeted rehabilitation are crucial in mitigating these challenges. Mindfulness acceptance and commitment therapy (ACT) holds promise for enhancing motivation and well-being among the elderly, although delivering such psychological interventions is hindered by limited access to services, prompting exploration of remote delivery options like mobile applications. In this paper, we introduce the BrainHeart App (v.1.1.8), a mobile application tailored to improve physical and mental well-being in seniors. The app features a 10-day ACT program and other sections promoting healthy lifestyle. In a pilot study involving twenty participants, individuals engaged in daily mental exercises for 10 days using the app. Clinical evaluations, including assessments of psychological flexibility, overall cognitive profile, mindfulness disposition, cognitive fusion, and heart rate collected with Polar H10, were conducted at baseline (T0) and one month post-intervention (T1). Analysis revealed significant improvements in almost all neuropsychological scores, with high usability reported (system usability scale average score: 82.3 ± 9.31). Additionally, a negative correlation was found between usability and experiential avoidance (r = -0.51; p = 0.026), and a notable difference in heart rate was observed between baseline and post-intervention (F-value = 3.06; p-value = 0.09). These findings suggest that mindfulness-ACT exercises delivered via the BrainHeart App can enhance the well-being of elderly individuals, highlighting the potential of remote interventions in addressing mental health needs in this population.

A novel, user-friendly bioreactor for the cultivation of cellularised membranes for tissue engineering has been successfully designed, manufactured, and validated. This bioreactor features a culture vessel and a cover, the latter equipped with one or more sidewalls to ensure airtightness in two distinct zones, thereby maintaining sterile conditions. The cover, designed to integrate seamlessly with the culture vessel, includes several ports compatible with commercial connectors. This design allows the introduction of cells and culture medium without requiring the opening of the cover, thus preserving sterility. Additionally, the cover is equipped with flanges that effectively press the membrane against the bottom surface of the culture vessel, preventing it from shrinking or shifting. This ensures that cells can properly adhere to the membrane and proliferate. Manufactured under Good Manufacturing Practice (GMP) conditions, the bioreactor supports cultivation in optimal aseptic environments, thereby preventing external contamination. This feature is critical for the safe transportation of cultivated tissue to clinical settings. Validation tests have confirmed the bioreactor's excellent performance, endorsing its suitability for intended applications in tissue engineering.

The use of real-world data (RWD) for healthcare decision-making is complicated by concerns regarding whether RWD is fit-for-purpose or is of sufficient validity to support the creation of credible RWE. An efficient mechanism for screening the quality of RWD is needed as regulatory agencies begin to use real-world evidence (RWE) to inform decisions about treatment effectiveness and safety. First, we provide an overview of RWD and RWE. Data quality frameworks (DQFs) in the US and EU were examined, including their dimensions and subdimensions. There is some convergence of the conceptual DQFs on specific assessment criteria. Second, we describe a list of screening criteria for assessing the quality of RWD sources. The curation and analysis of RWD will continue to evolve in light of developments in digital health and artificial intelligence (AI). In conclusion, this paper provides a perspective on the utilization of RWD and RWE in healthcare decision-making. It covers the types and uses of RWD, data quality frameworks (DQFs), regulatory landscapes, and the potential impact of RWE, as well as the challenges and opportunities for the greater leveraging of RWD to create credible RWE.

Cordyceps militaris is considered to be of great medicinal potential due to its remarkable pharmacological effects, safety, and edible characteristics. With the completion of the genome sequence and the advancement of efficient gene-editing technologies, coupled with the identification of gene functions in Cordyceps militaris, this fungus is poised to emerge as an outstanding strain for medicinal engineering applications. This review focuses on the development and application of genomic editing techniques, including Agrobacterium tumefaciens-mediated transformation (ATMT), PEG-mediated protoplast transformation (PMT), and CRISPR/Cas9. Through the application of these techniques, researchers can engineer the biosynthetic pathways of valuable secondary metabolites to boost yields; such metabolites include cordycepin, polysaccharides, and ergothioneine. Furthermore, by identifying and modifying genes that influence the growth, disease resistance, and tolerance to environmental stress in Cordyceps militaris, it is possible to stimulate growth, enhance desirable traits, and increase resilience to unfavorable conditions. Finally, the green sustainable industrial development of C. militaris using agricultural waste to produce high-value-added products and the future research directions of C. militaris were discussed. This review will provide future directions for the large-scale production of bioactive ingredients, molecular breeding, and sustainable development of C. militaris.

Medical image registration has become pivotal in recent years with the integration of various imaging modalities like X-ray, ultrasound, MRI, and CT scans, enabling comprehensive analysis and diagnosis of biological structures. This paper provides a comprehensive review of registration techniques for medical images, with an in-depth focus on 2D-2D image registration methods. While 3D registration is briefly touched upon, the primary emphasis remains on 2D techniques and their applications. This review covers registration techniques for diverse modalities, including unimodal, multimodal, interpatient, and intra-patient. The paper explores the challenges encountered in medical image registration, including geometric distortion, differences in image properties, outliers, and optimization convergence, and discusses their impact on registration accuracy and reliability. Strategies for addressing these challenges are highlighted, emphasizing the need for continual innovation and refinement of techniques to enhance the accuracy and reliability of medical image registration systems. The paper concludes by emphasizing the importance of accurate medical image registration in improving diagnosis.

Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EEG is a non-invasive monitoring technique that records the brain's electrical activity through electrodes placed on the scalp's surface. It is used in clinical and research contexts to explore how the human brain responds to emotions and cognitive stimuli. Recently, its use has gained interest in real-time emotion detection, offering a direct approach independent of facial expressions or voice. This is particularly useful in resource-limited scenarios, such as brain-computer interfaces supporting mental health. The objective of this work is to evaluate the classification of emotions (positive, negative, and neutral) in EEG signals using machine learning and deep learning, focusing on Graph Convolutional Neural Networks (GCNN), based on the analysis of critical attributes of the EEG signal (Differential Entropy (DE), Power Spectral Density (PSD), Differential Asymmetry (DASM), Rational Asymmetry (RASM), Asymmetry (ASM), Differential Causality (DCAU)). The electroencephalography dataset used in the research was the public SEED dataset (SJTU Emotion EEG Dataset), obtained through auditory and visual stimuli in segments from Chinese emotional movies. The experiment employed to evaluate the model results was "subject-dependent". In this method, the Deep Neural Network (DNN) achieved an accuracy of 86.08%, surpassing SVM, albeit with significant processing time due to the optimization characteristics inherent to the algorithm. The GCNN algorithm achieved an average accuracy of 89.97% in the subject-dependent experiment. This work contributes to emotion detection in EEG, emphasizing the effectiveness of different models and underscoring the importance of selecting appropriate features and the ethical use of these technologies in practical applications. The GCNN emerges as the most promising methodology for future research.

Wear simulation aims to assess wear rates and their dependence on factors like load, kinematics, temperature, and implant orientation. Despite its significance, there is a notable gap in research concerning advancements in simulator control systems and the testing of clinically relevant waveforms. This study addresses this gap by focusing on enhancing the conventional proportional-integral-derivative (PID) controller used in joint simulators through the development of a fuzzy logic-based controller. Leveraging a single-input multiple-output (SIMO) fuzzy logic control system, this study aimed to improve displacement control, augmenting the traditional proportional-integral (PI) tuning approach. The implementation and evaluation of a novel Fuzzy-PI control algorithm were conducted on the Leeds spine wear simulator. This study also included the testing of dailyliving (DL) profiles, particularly from the hip joint, to broaden the scope of simulation scenarios. While both the conventional PI controller and the Fuzzy-PI controller met ISO tolerance criteria for the spine flexion-extension (FE) profile at 1 Hz, the Fuzzy-PI controller demonstrated superior performance at higher frequencies and with DL profiles due to its real-time adaptive tuning capability. The Fuzzy-PI controller represents a significant advancement in joint wear simulation, offering improved control functionalities and more accurate emulation of real-world physiological dynamics.

The chair-mounted passive trunk orthosis (CMPTO) is designed to enhance wheelchair safety for individuals with dystrophinopathy during their daily activities. Given the disease's progressive nature, it is crucial to ensure that assistive devices are carefully evaluated to prevent overexertion. This study aims to assess the CMPTO's user experience and its impact on fatigue-related psychophysiological measurements. We conducted electromyography (EMG) evaluations of four trunk muscles and assessed perceived exertion using the Borg CR-10 scale in 40 healthy subjects while they performed seated maximal reaching tasks with the CMPTO. Additionally, fifteen dystrophinopathy patients evaluated the CMPTO for usability with the System Usability Scale. Paired t-tests were employed to compare the median frequency (MDF) of EMG signals, the Wilcoxon signed-rank test for evaluating exertion, and the Mann-Whitney U test to compare the usability reported by patients to those of healthy subjects. The 4-way ANOVA revealed that MDF patterns were significantly influenced by task orientation for each muscle. The CMPTO did not cause a significant reduction in the MDF. Tasks requiring greater trunk rotation were perceived as more exhaustive. Patients reported acceptable usability with the CMPTO, with scores higher than those of healthy subjects. The CMPTO's usability was comprehensively evaluated in both healthy subjects and patients with dystrophinopathy. Our findings indicate that the CMPTO can be safely used by individuals with dystrophinopathy as an assistive device to improve seated comfort and functional abilities.

Event-related optical signals (EROS) measure fast modulations in the brain's optical properties related to neuronal activity. EROS offer a high spatial and temporal resolution and can be used for brain-computer interface (BCI) applications. However, the ability to classify single-trial EROS remains unexplored. This study evaluates the performance of neural network methods for single-trial classification of motor response-related EROS. EROS activity was obtained from a high-density recording montage covering the motor cortex during a two-choice reaction time task involving responses with the left or right hand. This study utilized a convolutional neural network (CNN) approach to extract spatiotemporal features from EROS data and perform classification of left and right motor responses. Subject-specific classifiers trained on EROS phase data outperformed those trained on intensity data, reaching an average single-trial classification accuracy of around 63%. Removing low-frequency noise from intensity data is critical for achieving discriminative classification results with this measure. Our results indicate that deep learning with high-spatial-resolution signals, such as EROS, can be successfully applied to single-trial classifications.