Biomedical Engineering / Biomedizinische Technik最新文献

Bioreactors are important tools for the pre-conditioning of tissue-engineered heart valves. The current state of the art mostly provides for timed, physical and biochemical stimulation in the bioreactor systems according to standard protocols (SOP). However, this does not meet to the individual biological variability of living tissue-engineered constructs. To achieve this, it is necessary to implement (i) sensory systems that detect the actual status of the implant and (ii) controllable bioreactor systems that allow patient-individualized pre-conditioning. During the maturation process, a pulsatile transvalvular flow of culture medium is generated within the bioreactor. For the improvement of this conditioning procedure, the relationship between the mechanical and biochemical stimuli and the corresponding tissue response has to be analyzed by performing reproducible and comparable experiments. In this work, a technological framework for maturation experiments of tissue-engineered heart valves in a pulsating bioreactor is introduced. The aim is the development of a bioreactor system that allows for continuous control and documentation of the conditioning process to increase reproducibility and comparability of experiments. This includes hardware components, a communication structure and software including online user communication and supervision. Preliminary experiments were performed with a tissue-engineered heart valve to evaluate the function of the new system. The results of the experiment proof the adequacy of the setup. Consequently, the concept is an important step for further research towards controlled maturation of tissue-engineered heart valves. The integration of molecular and histological sensor systems will be the next important step towards a fully automated, self-controlled preconditioning system.

The principle of microwave ablation (MWA) is to cause irreversible damage (protein coagulation, necrosis, etc.) to tumor cells at a certain temperature by heating, thereby destroying the tumor. We have long used functional near-infrared spectroscopy (fNIRs) to monitor clinical thermal ablation efficacy. After a lot of experimental verification, it can be found that there is a clear correlation between the reduced scattering coefficient and the degree of tissue damage. During the MWA process, the reduced scattering coefficient has a stable change. Therefore, both temperature (T) and reduced scattering coefficient ( ${\mu }_s^{\text{'}}$ ) are related to the thermal damage of the tissue. This paper mainly studies the changing law of T and ${\mu }_s^{\text{'}}$ during MWA and establishes a relationship model. The two-parameter simultaneous acquisition system was designed and used to obtain the T and ${\mu }_s^{\text{'}}$ of the ex vivo porcine liver during MWA. The correlation model between T and ${\mu }_s^{\text{'}}$ is established, enabling the quantitative estimation of ${\mu }_s^{\text{'}}$ of porcine liver based on T. The maximum and the minimum relative errors of ${\mu }_s^{\text{'}}$ are 79.01 and 0.39%, respectively. Through the electromagnetic simulation of the temperature field during MWA, 2D and 3D fields of reduced scattering coefficient can also be obtained using this correlation model. This study contributes to realize the preoperative simulation of the optical parameter field of microwave ablation and provide 2D/3D therapeutic effect for clinic.

The aim of the work was to develop a new transfer method for indirect bonding of brackets to improve the bond strength by applying a uniform contact pressure over the entire dental arch. This has a great potential to reduce the bracket loss rate during clinical treatment. A suitable shape memory polymer (SMP) was selected and prepared in the chemistry laboratory. This SMP applies a force to the brackets during bonding and thus increases the bond strength by applying uniform contact pressure. Various transfer trays were equipped with SMP platelets and the transfer of brackets from the plaster model to the real human tooth model was performed in vitro. The transfer accuracy and bond strength of the bonded brackets were investigated by 3D-overlay and shear tests, respectively. The transfer accuracy was technique sensitive and showed higher accuracy for the trays with SMPs and self-curing silicones than for the vacuum formed trays with SMPs. The bond strength of the indirectly bonded brackets with SMPs was on average 1-2 MPa higher than the bond strength of the brackets indirectly bonded with a conventional two-layer vacuum formed tray without SMPs. Thus, transfer trays with SMPs can provide a significant improvement in bond strength during indirect bonding after appropriate adjustment.

This study's purpose is to assess the stress distribution in the peri-implant bone, implants, and prosthetic framework using two different posterior implant angles. All-on-four maxillary prostheses fabricated from feldspathic-ceramic-veneered zirconia-reinforced lithium silicate (ZLS) and feldspathic-ceramic-veneered cobalt-chromium (CoCr) were designed with 17 or 30-degree-angled posterior implants. Posterior cantilever and frontal vertical loads were applied to all models. The distribution of maximum and minimum principal stresses (σmax and σmin) and von Mises stress (σVM) was evaluated. Under posterior cantilever load, with an increase in posterior implant angle, σmax decreased by 4 and 7 MPa in the cortical bone when ZLS and CoCr were used as a prosthetic framework, respectively. Regardless of the framework material, 17-degree-angled posterior implants showed the highest σVM (541.36 MPa under posterior cantilever load; 110.79 MPa under frontal vertical load) values. Regardless of the posterior implant angle, ZLS framework showed the highest σVM (91.59 MPa under posterior cantilever load; 218.99 MPa under frontal vertical load) values. Increasing implant angle from 17 to 30° caused a decrease in σmax values in the cortical bone. Designs with 30-degree posterior implant angles and ZLS framework material may be preferred in All-on-four implant-supported fixed complete dentures.

Transcranial magnetic stimulation (TMS) has widespread clinical applications from diagnosis to treatment. We combined TMS with non-contact magnetic detection of TMS-evoked muscle activity in peripheral limbs to explore a new diagnostic modality that enhances the utility of TMS as a clinical tool by leveraging technological advances in magnetometry. We recorded measurements in a regular hospital room using an array of optically pumped magnetometers (OPMs) inside a portable shield that encloses only the forearm and hand of the subject. We present magnetomyograms (MMG)s of TMS-evoked movement in a human hand, together with a simultaneous surface electromyograph (EMG) data. The biomagnetic signals recorded in the MMG provides detailed spatial and temporal information that is complementary to that of the electric signal channels. Moreover, we identify features in the magnetic recording beyond that of the EMG. This system demonstrates the value of biomagnetic signals in TMS-based clinical approaches and widens its availability and practical potential.

In lung sound classification using deep learning, many studies have considered the use of short-time Fourier transform (STFT) as the most commonly used 2D representation of the input data. Consequently, STFT has been widely used as an analytical tool, but other versions of the representation have also been developed. This study aims to evaluate and compare the performance of the spectrogram, scalogram, melspectrogram and gammatonegram representations, and provide comparative information to users regarding the suitability of these time-frequency (TF) techniques in lung sound classification. Lung sound signals used in this study were obtained from the ICBHI 2017 respiratory sound database. These lung sound recordings were converted into images of spectrogram, scalogram, melspectrogram and gammatonegram TF representations respectively. The four types of images were fed separately into the VGG16, ResNet-50 and AlexNet deep-learning architectures. Network performances were analyzed and compared based on accuracy, precision, recall and F1-score. The results of the analysis on the performance of the four representations using these three commonly used CNN deep-learning networks indicate that the generated gammatonegram and scalogram TF images coupled with ResNet-50 achieved maximum classification accuracies.

Sleep apnea is a sleep disorder caused by weakened or suspended breathing during sleep, which seriously affects the work and health of patients. The traditional polysomnography (PSG) detection process is complicated and expensive, which has attracted researchers to explore a rapid detection method based on single-lead ECG signals. However, existing ECG-based sleep apnea detection methods have certain limitations and complexities, mainly relying on human-crafted features. To solve the problem, the paper develops a sleep apnea detection method based on a residual attention mechanism network. The method uses the RR interval signal and the R-peak signal derived from the ECG signal as input, realizes feature extraction through the residual network (ResNet), and adds the SENet attention mechanism to deepen the mining of channel features. Experimental results show that the per-segment accuracy of the proposed method can reach 86.2%. Compared with existing works, its accuracy has increased by 1.1-8.1%. These results show that the proposed residual attention network can effectively use ECG signals to quickly detect sleep apnea. Meanwhile, compared with existing works, the proposed method overcomes the limitations and complexity of human-crafted features in sleep apnea detection research.