Critical Reviews in Biomedical Engineering最新文献

Numerous studies have stressed the importance of exercise in promoting physical and mental health and for aiding in cognition. Encouragingly, physical exercise has been shown to reduce the risk of developing Alzheimer's disease and to mitigate hemiparesis experienced by stroke patients. Additionally, today where over 1.9 billion are overweight, physical exercise is imperative to save lives and to mitigate the burden on the healthcare system. Although the benefits of physical exercise have been explored, the underlying mechanisms to enact these benefits have not been well-characterized. Here we review exercise-induced changes in regional brain activation and modulation. Paradigms differing in intensity, duration, and type of motor movement have been used to assess exercise effects on memory, cognition, and disease mitigation in youth and elderly populations. To evaluate exercise-induced changes in neural activity, the noninvasive imaging technique, functional magnetic resonance imaging (fMRI), is employed. fMRI is recorded either during or after exercise intervention. Post-exercise fMRI is often paired with in-bore tests of cognition to provide insight into the associated brain regions. Whereas, during intervention, fMRI is used to detail muscle-associated neural activation profiles. Characterization of the region and magnitude of brain activation has been used to perform comparative studies and identify specific characteristics from individuals with varying motor and cognitive abilities. Further fMRI and exercise research, with the use of these metrics, could facilitate the development of tools for disease diagnosis or to assess level of dysfunction or progression.

Aiming at the difficulty of accurate prediction due to the randomness and nonstationary nature of blood glucose concentration series, a blood glucose concentration prediction model based on complementary ensemble empirical mode decomposition (CEEMD) and least squares support vector machine (LSSVM) is proposed. Firstly, CEEMD is used to convert the blood glucose concentration sequence into a series of intrinsic mode functions (IMFs) to reduce the impact of randomness and nonstationary signals on prediction performance. Then, a LSSVM prediction model is established for each mode IMF. The comprehensive learning particle swarm optimization (CLPSO) algorithm is used to optimize the kernel parameters of LSSVM. Finally, the prediction results of all IMFs are superimposed to yield the final blood glucose concentration prediction value. The experimental results show that the proposed prediction model has higher prediction accuracy in short-term blood glucose concentration values.

Polymethylmethacrylate (PMMA) bone cement is increasingly being used for percutaneous minimally invasive treatments of patients suffering from bone malignancies. PMMA is composed of a polymeric powder and a monomeric liquid. Once mixed, the polymerization process begins and leads to a viscous fluid that can be injected through a bone trocar. Cement progressively hardens within the bone, leading to a viscoelastic solid material. PMMA interacts with the surrounding cancellous bone through mechanical interlocking via interdigitations in trabecular bone. It can also bond with hardware, such as titanium screws, as it penetrates the macro- and micro-irregularities of the hardware. PMMA itself has no antineoplastic effects but may be used as a stand-alone treatment to provide pain palliation and bone consolidation through mechanical reinforcement, notably in areas with high compression load. It can also be used to reinforce the anchorage of screws in case of a landing zone with poor bone quality due to underlying malignant osteolysis.

The outbreak of coronavirus disease 2019 (COVID-19) has resulted in a world-wide crisis. To contain the virus, it is important to find infected individuals and isolate them to stop transmission. Various diagnostic techniques are used to check for infection. With the havoc that severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has created, it is imperative to work on alternative diagnostic techniques that can be used at both point of care with little or no expertise and at mass testing (i.e., when screening). Despite extensive research, to this date no specific effective treatment or cure is available to neutralize this viral infection. Globally, researchers are working to develop effective treatments, and several vaccines have been approved for public use. We found the studies that we explored for this review using appropriate key words for indexing in PubMed and Google Scholar from 2019 to 2020. We compile various techniques that have been used worldwide to diagnose and treat SARS-CoV-2 and discuss novel methods that may be modified for use in diagnosis and treatment. It is crucial to develop a more specific serological test for diagnosis that can rule out the possibility of COVID-19 and be used for mass testing. An affordable, safe, targeted, effective treatment must be developed to cure this disease, which has created a public health emergency of international concern.

Diffusion-weighted imaging (DWI) allows white matter quantification of the white matter tracts of the brain. However, at a high b-value (≥ 2000 s/mm2), DWI acquisition suffers from noise due to longer acquisition times obscuring white matter interpretation. DWI denoising techniques can be used to acquire high b-value DWI without increasing the number of signal averages. We used a residual learning-based convolutional neural network (DnCNN) to reduce noise in high b-value DWI based on the literature review. We applied the proposed denoising method on high b-value, retrospectively collected DWI data with multiple noise levels. Experimental results show an improved image quality after denoising in retrospective DWI (average PSNR before and after denoising: 27.63 ± 1.06 dB and 51.76 ± 1.95 dB, respectively). The prospective DWI included one and two signal averages for denoising. DWI with four signal averages was used as the reference. Representative images show high b-value prospective DW images denoised using the DnCNN. We demonstrated DnCNN for cases of multiple noise levels and signal averages. For the prospective study, the PSNR values for 1-NEX before and after denoising were 27.39 ± 3.75 dB and 27.68 ± 3.75 dB. For 2-NEX, the PSNR values before and after denoising were 27.51 ± 4.18 dB and 27.75 ± 4.05 dB.