Bioengineering最新文献

Hallux limitus pathology is defined as a limitation of the dorsiflexion movement of the first toe without degenerative involvement of the first metatarsophalangeal joint, which produces pain and generates functional impairment, especially in the propulsive phase of gait. It is very common to find this pathology in adulthood accompanied by other compensations at a biomechanical level as a consequence of blockage of the main pivot in the sagittal plane. The aim was to determine the symmetry index that occurs in dynamics affiliated with other gait parameters in subjects with and without hallux limitus. A total of 70 subjects were part of the sample, and these were separated into two groups, each consisting of 35 subjects, depending on whether they had bilateral hallux limitus or if they were healthy subjects. In this study, a platform was used to assess the load symmetry index and walking phases. The results showed significant differences in the symmetry index for lateral load (p = 0.023), the initial contact phase (p = 0.003), and the flatfoot phase (p < 0.001). The adults who had bilateral hallux limitus exhibited changes in the symmetry index during the lateral load as well as in the initial contact and flatfoot contact phases, demonstrating increased instability when compared to individuals with normal feet.

Because of their nature, biomarkers for neuropsychiatric diseases were out of the reach of medical diagnostic technology until the past few decades. In recent years, the confluence of greater, affordable computer power with the need for more efficient diagnoses and treatments has increased interest in and the possibility of their discovery. This review will focus on the progress made over the past ten years regarding the search for electroencephalographic biomarkers for neuropsychiatric diseases. This includes algorithms and methods of analysis, machine learning, and quantitative electroencephalography as applied to neurodegenerative and neurodevelopmental diseases as well as traumatic brain injury and COVID-19. Our findings suggest that there is a need for consensus among quantitative electroencephalography researchers on the classification of biomarkers that most suit this field; that there is a slight disconnection between the development of increasingly sophisticated methods of analysis and what they will actually be of use for in the clinical setting; and finally, that diagnostic biomarkers are the most favored type in the field with a few caveats. The main goal of this state-of-the-art review is to provide the reader with a general panorama of the state of the art in this field.

Purpose: We aimed to develop a simple nomogram and online calculator that can identify the optimal subpopulation of pediatric acute myeloid leukemia (AML) patients who would benefit most from gemtuzumab ozogamicin (GO) therapy. Methods: Within the framework of the phase Ⅲ AAML0531 randomized trial for GO, the event-free survival (EFS) probability was calculated using a predictor-based nomogram to evaluate GO treatment impact on EFS in relation to baseline characteristics. Nomogram performance was assessed by the area under the receiver operating characteristic curve (AUC) and the calibration curve with 500 bootstrap resample validations. Decision curve analysis (DCA) was performed to evaluate the clinical utility of the nomogram. Results: A total of 705 patients were randomly assigned to two arms: the No-GO arm (n = 358) and the GO arm (n = 347). We performed a nomogram model for EFS among childhood AML. The AUC (C statistic) of the nomogram was 0.731 (95%CI: 0.614-0.762) in the development group and 0.700 (95% CI: 0.506-0.889) in the validation group. DCA showed that the model in the development and validation groups had a net benefit when the risk thresholds were 0-0.75 and 0-0.75, respectively. Notably, an intriguing observation emerged wherein pediatric patients with AML exhibited a favorable outcome in the GO arm when the predicted 5-year EFS probability fell below 60%, demonstrating a superior EFS compared to the No-GO Arm. Conclusions: We have developed a nomogram and online calculator that can be used to predict EFS among childhood AML based on trial AAML0531, and this might help deciding which patients can benefit from GO.

Strength training machines incorporating advanced electro-mechanical technologies can produce hybrid resistances with variable inertia, such as a resistance that progressively changes from gravitational (inertial) to pneumatic (non-inertial) across the range of motion (ROM). To explore the biomechanical effects of these innovative resistances, a robotic chest press machine was programmed to offer three distinct inertial profiles: gravitational-type constant inertia throughout the ROM (I_FULL); no inertia (I_ZERO); and linearly descending inertia across the ROM (I_VAR). Ten healthy adults performed five maximal-effort, explosive chest press movements under each inertial profile at 30, 50 and 70% of their one-repetition maximum. During each trial, muscle activity of the pectoralis major, anterior deltoid, and triceps brachii was recorded, along with force, velocity and power outputs from the machine. Statistical non-parametric maps based on two-way repeated measures ANOVA were used to assess the effects of load level and inertial profile on the collected time series. Higher load levels consistently led to increased force and reduced velocity and power outcomes over large parts of the ROM. Compared to I_FULL, I_ZERO allowed for greater velocity at the expense of lower force throughout the ROM, while I_VAR produced higher force and power outputs despite having lower velocity than I_ZERO. Additionally, both I_ZERO and I_VAR significantly increased triceps brachii activity at the end of the ROM compared to I_FULL. I_VAR outperformed both I_FULL and I_ZERO in terms of force and power. Coaches and therapists are advised to consider variable inertial profiles as a key parameter when designing exercise programs for athletes or patients.

Objective: The cancer death rate has accelerated at an alarming rate, making accurate diagnosis at the primary stages crucial to enhance prognosis. This has deepened the issue of cancer mortality, which is already at an exponential scale. It has been observed that concentration on datasets drawn from supporting primary sources using machine learning algorithms brings the accuracy expected for cancer diagnosis. Methods: This research presents an innovative cancer classification technique that combines fast minimum redundancy-maximum relevance-based feature selection with Binary Portia Spider Optimization Algorithm to optimize features. The features selected, with the aid of fast mRMR and tested with a range of classifiers, Support Vector Machine, Weighted Support Vector Machine, Extreme Gradient Boosting, Adaptive Boosting, and Random Forest classifier, are tested for comprehensively proofed performance. Results: The classification efficiency of the advanced model is tested on six different cancer datasets that exhibit classification challenges. The empirical analysis confirms that the proposed methodology FmRMR-BPSOA is effective since it reached the highest accuracy of 99.79%. The result is of utmost significance as the proposed model emphasizes the need for alternative and highly efficient greater precision cancer diagnosis. The classification accuracy concludes that the model holds great promise for real-life medical implementations.

Accurate sleep staging is critical for assessing sleep quality and diagnosing sleep disorders. Recent research efforts on automated sleep staging have focused on complex deep learning architectures that have achieved modest improvements in classification accuracy but have limited real-world applicability due to the complexity of model training and deployment and a lack of interpretability. This paper presents an effective and interpretable sleep staging scheme that follows a classical machine learning pipeline. Multi-domain features were extracted from preprocessed electroencephalogram (EEG) signals, and novel electrooculogram (EOG) features were created to characterize different sleep stages. A two-step feature selection strategy combining F-score pre-filtering and XGBoost feature ranking was designed to select the most discriminating feature subset, which was then fed into an XGBoost model for sleep stage classification. Through a rigorous double-cross-validation procedure, our approach achieved competitive classification performance on the public Sleep-EDF dataset (accuracy 87.0%, F1-score 86.6%, Kappa coefficient 0.81) compared with the state-of-the-art deep learning methods and provided interpretability through feature importance analysis. These promising results demonstrate the effectiveness of the proposed sleep staging model and show its potential in practical applications due to its low complexity, interpretability, and transparency.

Neurological diseases leading to motor deficits constitute significant challenges to healthcare systems. Despite technological advancements in data acquisition, sensor development, data processing, and virtual reality (VR), a suitable framework for patient-centered neuromotor robot-assisted rehabilitation using collective sensor information does not exist. An extensive literature review was achieved based on 124 scientific publications regarding different types of sensors and the usage of the bio-signals they measure for neuromotor robot-assisted rehabilitation. A comprehensive classification of sensors was proposed, distinguishing between specific and non-specific parameters. The classification criteria address essential factors such as the type of sensors, the data they measure, their usability, ergonomics, and their overall impact on personalized treatment. In addition, a framework designed to collect and utilize relevant data for the optimal rehabilitation process efficiently is proposed. The proposed classifications aim to identify a set of key variables that can be used as a building block for a dynamic framework tailored for personalized treatments, thereby enhancing the effectiveness of patient-centered procedures in rehabilitation.

Coronary heart disease, a leading global cause of mortality, has witnessed significant advancement through robotic coronary artery bypass grafting (CABG), with the internal mammary artery (IMA) emerging as the preferred "golden conduit" for its exceptional long-term patency. Despite these advances, robotic-assisted IMA harvesting remains challenging due to the absence of force feedback, complex surgical maneuvers, and proximity to the beating heart. This study introduces a novel virtual simulation platform for robotic IMA harvesting that integrates dynamic anatomical modeling and real-time haptic feedback. By incorporating a dynamic cardiac model into the surgical scene, our system precisely simulates the impact of cardiac pulsation on thoracic cavity operations. The platform features high-fidelity representations of thoracic anatomy and soft tissue deformation, underpinned by a comprehensive biomechanical framework encompassing fascia, adipose tissue, and vascular structures. Our key innovations include a topology-preserving cutting algorithm, a bidirectional tissue coupling mechanism, and dual-channel haptic feedback for electrocautery simulation. Quantitative assessment using our newly proposed Spatial Asymmetry Index (SAI) demonstrated significant behavioral adaptations to cardiac motion, with dynamic scenarios yielding superior SAI values compared to static conditions. These results validate the platform's potential as an anatomically accurate, interactive, and computationally efficient solution for enhancing surgical skill acquisition in complex cardiac procedures.

Microalgae are valuable sources of bioactive compounds. However, their production requires strategies to enhance metabolic responses. This study explores how Chlorella vulgaris responds to different salinity conditions using a two-stage cultivation strategy, assessing the change in amino acid and carotenoid content on microalgae over time. First, microalgae were cultivated under optimal conditions, followed by exposure to different salinity levels (150 mM and 300 mM NaCl). Growth kinetics, nutrient uptake, and biochemical composition were analysed, revealing distinct salinity-induced responses. Similar specific growth rates were achieved across all assays, while nitrate removal improved under salinity and phosphate uptake decreased. Amino acid profiling showed significant declines in the content of several compounds and carotenoid content also presented declining trends, although moderate salinity mitigated degradation in key pigments. Principal component analysis identified high correlations between amino acids and carotenoids contents, forming groups of compounds with similar variations. These findings contribute to a better understanding of the salinity-induced response of C. vulgaris, offering insights for biotechnology applications. By optimising cultivation conditions, salinity could enhance bioactive compound retention, supporting the development of sustainable microalgae-based products.

Current routine diagnostic procedures for back pain mainly focus on static spinal analyses. Dynamic Surface Topography (DST) is an easy-to-use, radiation-free addition, allowing spine analyses under dynamic conditions. Until now, it is unclear if this method is applicable to back pain patients, and data reports are missing. Within a prospective observational study, 32 patients suffering from thoracic and lumbar back pain were examined while walking, randomized at four speeds (2, 3, 4, 5 km/h), using a DST measuring device (DIERS 4Dmotion^® Lab). The measurement results were compared with those of a healthy reference group. We calculated the intrasegmental rotation for every subject and summed up the spinal motion in a standardized gait cycle. The Mann-Whitney U Test was used to compare the painful and healthy reference groups at the four different speeds. In a subgroup analysis, the painful group was divided into two groups: one with less pain (≤3 points on the Visual Analogue Scale) and one with more pain (>3 points on the Visual Analogue Scale). The Kruskal-Wallis Test was used to compare these subgroups with the healthy reference group. Of the 32 included patients, not all could walk at the intended speeds (5 km/h: 28/32). At speeds of 2-4 km/h, our results point to greater total segmental rotation of back pain patients compared to the healthy reference group. At a speed of 3 km/h, we observed more movement in the patients with more pain. Overall, we monitored small differences on average between the groups but large standard deviations. We conclude that the DST measuring approach is eligible for back pain patients when they feel confident enough to walk on a treadmill. Initial results suggest that DST can be used to obtain interesting therapeutic information for an individual patient.