Different generations of biomedical materials are analyzed in this paper. The current clinical uses of plates made of metals, polymers or composite materials are evaluated, and nano hydroxyapatite/polylactic acid composites and carbon/carbon composite plates are introduced as emphasis. It is pointed out that the carbon/carbon composites are of great feasibility and advantage as a new generation of biomedical materials, especially in the field of bone plate. Compared to other biomaterials, carbon/carbon composites have a good biocompatibility and mechanical compatibility because they have similar elastic modulus, porosity and density to that of human bones. With the development of the technology in knitting and material preparation, carbon/carbon composite plates have a good application prospect.
This paper provides a brief overview of the current research activities which focused on the bio-application of gold magnetic nanocomposite particles. By combining the magnetic characteristics of the iron oxide core with the unique features of nano-gold particles such as targeting by surface modification and optical properties, such composite nanoparticles have a wide range of applications in cancer hyperthermia, CT and MRI imaging, bio-separation, biosensors, gene diagnosis, drug targeting and many other biomedical fields.
Through magnetic resonance electrical properties tomography (MR-EPT), electrical conductivity and permittivity of biological tissues could be reconstructed based on radio frequency field of magnetic resonance imaging (MRI) system. High precision and high resolution image could be obtained without current injection. In this study, XFDTD software was used to establish a finite element model of the human breast. Simulation was delivered at the Larmor frequency of 128 MHz by a 16-channel radio frequency coil. Conductivity and permittivity of the mammary tissue was reconstructed according to the B1+ field's amplitude and phase of forward problem. Anti-noise performance of the algorithm was studied by adding noise to B1+ field. The conductivity and permittivity's average relative error between simulation results and dielectric constant was 4.71% and 11.32%, respectively. With a signal-to-noise ratio of >30 dB, the noise added would not affect imaging results. This study demonstrated that high precision and high resolution image could be obtained by MR-EPT without excitation. MR-EPT had excellent feasibility and developing potential in early detection of diseases.
The present paper is to evaluate the scientific research level and development trends of biomedical engineering in China using metrology analysis on Chinese biomedical engineering scientific literatures. Pubmed is used to search the biomedical engineering publications in recent 5 years which are indexed by Science Citation Index, and the number and cited times of these publications and the impact factor of the journals are analyzed. The results show that comparing with the world, although the number of the publication in China has increased in recent 5 years, there is still much room for improvement. Among Chinese mainland, Hongkong and Taiwan, Chinese mainland maintains the obvious advantage in this subject, but Hongkong has the highest average cited number. Shanghai and Beijing have better research ability than other areas in Chinese mainland.
Bionic untethered micro-nano robots, due to their advantages of small size, low weight, large thrust-to-weight ratio, strong wireless mobility, high flexibility and high sensitivity, have very important application values in the fields of biomedicine, such as disease diagnosis, minimally invasive surgery, targeted therapy, etc. This review article systematically introduced the manufacturing methods and motion control, and discussed the biomedical applications of bionic untethered micro-nano robots. Finally, the article discussed the possible challenges for bionic untethered micro-nano robots in the future. In summary, this review described bionic untethered micro-nano robots and their potential applications in biomedical fields.
Traditional biomedical data analysis technology faces enormous challenges in the context of the big data era. The application of deep learning technology in the field of biomedical analysis has ushered in tremendous development opportunities. In this paper, we reviewed the latest research progress of deep learning in the field of biomedical data analysis. Firstly, we introduced the deep learning method and its common framework. Then, focusing on the proposal of biomedical problems, data preprocessing method, model building method and training algorithm, we summarized the specific application of deep learning in biomedical data analysis in the past five years according to the chronological order, and emphasized the application of deep learning in medical assistant diagnosis. Finally, we gave the possible development direction of deep learning in the field of biomedical data analysis in the future.
In photoacoustic imaging the ultrasonic signals are usually detected by contacting transducers. For some applications, contact with the tissue should be avoided, e.g. in those of brain functional imaging. As alternatives to contacting transducers interferometric techniques can be used to acquire photoacoustic signals remotely. Here, a system for non-contact photoacoustic tomography imaging (NCPAT) has been established. This approach enables NCPAT not to exceed laser exposure safety limits. The stimulated source of NCPAT utilized a laser with center wavelength of 532 nm and output intensity of 17.5 mJ/cm2, and a laser heterodyne interferometry was used to receive the photoacoustic signals. The NCPAT was used to implement on a rotational imaging geometry for photoacoustic tomography with a real-tissue phantom. The photoacoustic imaging was obtained by applying a reconstruction algorithm to the data acquired for NCPAT. Experiments results showed that the NCPAT system with detection 15 dB bandwidth of 2.25 MHz could resolve spherical optical inclusions with dimension of 500 μm and multi-layered structure with optical contrast in strongly scattering medium. The method could expand the scope of photoacoustic and ultrasonic technology to in-vivo biomedical applications where contact is impractical.
【Abstract】ObjectiveTo study the applicatble value of absorbable biomedical membrane (ABMM) in thyroid operation.MethodsABMM (size 0.02 cm) was placed between trachea and the skin flap trachea and the anterior muscles of the neck, and between anterior muscles of neck and the skin flap after radical cure for thyroid cancer of thyroidectomy including one or two lobes. ResultsConglutination under the incision was significantly lighter in experiment group (153 cases ) than control group (139 cases) ( P＜0.05）, but the difference of complication between two groups was insignificant after postoperative followup for 6 weeks. ConclusionABMM can effectively prevent conglutination under the incision without increasing of postoperative complications.
The impulsive electroencephalograph (EEG) noises in evoked potential (EP) signals is very strong, usually with a heavy tail and infinite variance characteristics like the acceleration noise impact, hypoxia and etc., as shown in other special tests. The noises can be described by α stable distribution model. In this paper, Wigner-Ville distribution (WVD) and pseudo Wigner-Ville distribution (PWVD) time-frequency distribution based on the fractional lower order moment are presented to be improved. We got fractional lower order WVD (FLO-WVD) and fractional lower order PWVD (FLO-PWVD) time-frequency distribution which could be suitable for α stable distribution process. We also proposed the fractional lower order spatial time-frequency distribution matrix (FLO-STFM) concept. Therefore, combining with time-frequency underdetermined blind source separation (TF-UBSS), we proposed a new fractional lower order spatial time-frequency underdetermined blind source separation (FLO-TF-UBSS) which can work in α stable distribution environment. We used the FLO-TF-UBSS algorithm to extract EPs. Simulations showed that the proposed method could effectively extract EPs in EEG noises, and the separated EPs and EEG signals based on FLO-TF-UBSS were almost the same as the original signal, but blind separation based on TF-UBSS had certain deviation. The correlation coefficient of the FLO-TF-UBSS algorithm was higher than the TF-UBSS algorithm when generalized signal-to-noise ratio (GSNR) changed from 10 dB to 30 dB and α varied from 1.06 to 1.94, and was approximately equal to 1. Hence, the proposed FLO-TF-UBSS method might be better than the TF-UBSS algorithm based on second order for extracting EP signal under an EEG noise environment.
UK Biobank (UKB) is a forward-looking epidemiological project with over 500, 000 people aged 40 to 69, whose image extension project plans to re-invite 100, 000 participants from UKB to perform multimodal brain magnetic resonance imaging. Large-scale multimodal neuroimaging combined with large amounts of phenotypic and genetic data provides great resources to conduct brain health-related research. This article provides an in-depth overview of UKB in the field of neuroimaging. Firstly, neuroimage collection and imaging-derived phenotypes are summarized. Secondly, typical studies of UKB in neuroimaging areas are introduced, which include cardiovascular risk factors, regulatory factors, brain age prediction, normality, successful and morbid brain aging, environmental and genetic factors, cognitive ability and gender. Lastly, the open challenges and future directions of UKB are discussed. This article has the potential to open up a new research field for the prevention and treatment of neurological diseases.