Critical Reviews in Biomedical Engineering最新文献

Surface modification is the science of manipulating surface morphology and interfacial properties and also plays a vital role in biomedical implantation. A few of the interfacial properties are biocompatibility, protein adsorption, wettability, cell proliferation, collagen, etc. These properties depend on surface modification strategies and significantly impact the implant response within the host body. Generally, the corrosion, surface wear, and degradation in the physiological environment limit the application of different biomaterials and can address through various surface modification strategies. These surface modifications developed over the years to improve the morphology and interfacial properties to meet the specific functional surface application in biomedical implantation. It can be done through surface roughening, patterning/texturing, coating with different materials, and hybrid modification. Further, the process development for bio-medical application, process capabilities, limitations, challenges, and characterization aspects are correlated to identify the effectiveness of different surface modification strategies. Finally, various innovative biomedical applications and surface characteristics are also present with future scope in the direction of surface modification for biomedical implantation.

Bone resection is a common technique in modern surgery, which can be divided into contact (such as mechanical osteotomy and ultrasonic osteotomy) and non-contact (such as laser osteotomy). Irrespective of the excision method, it causes processing damage to natural bone material, thus affecting bone healing. To reduce the machining damage in bone resection, different machining variables (cutting fluid temperature, feed rate, rotational speed, and ultrasonic frequency) were considered to explore the selection of various cutting conditions. This paper reviews the excision of natural bone materials including mechanical osteotomy, laser osteotomy, and ultrasonic osteotomy, especially traditional drilling and ultrasonic cutting, which represent the traditional and prospective methods of bone excision technology, respectively. Finally, the differences between methods are emphasized and the future trends in osteotomy technology and condition control during osteotomy are analyzed.

Investigation concerning the bioinspired pumping flow of viscous fluids in the porous region using Darcy's law is demonstrated in the present article. The rhythmic membrane contraction propels fluids in the porous microchannel. The periodic contraction of the membrane is utilized in the present analysis to introduce the unique pumping mechanism. For small pattern, width to channel height ratio (i.e., the channel is substantially longer than its width) and at low Reynolds numbers, the governing equations are solved by an analytical approach. In light of porous effects, we noticed the implications of rheological limitations on pumping and trapping processes. The porosity has a dynamic role in the augmentation of membrane-based pumping. These outcomes may be productive in various bioengineering (drug delivery schemes) applications.

Coronavirus is a RNA type virus, which makes various respiratory infections in both human as well as animals. In addition, it could cause pneumonia in humans. The Coronavirus affected patients has been increasing day to day, due to the wide spread of diseases. As the count of corona affected patients increases, most of the regions are facing the issue of test kit shortage. In order to resolve this issue, the deep learning approach provides a better solution for automatically detecting the COVID-19 disease. In this research, an optimized deep learning approach, named Henry gas water wave optimization-based deep generative adversarial network (HGWWO-Deep GAN) is developed. Here, the HGWWO algorithm is designed by the hybridization of Henry gas solubility optimization (HGSO) and water wave optimization (WWO) algorithm. The pre-processing method is carried out using region of interest (RoI) and median filtering in order to remove the noise from the images. Lung lobe segmentation is carried out using U-net architecture and lung region extraction is done using convolutional neural network (CNN) features. Moreover, the COVID-19 detection is done using Deep GAN trained by the HGWWO algorithm. The experimental result demonstrates that the developed model attained the optimal performance based on the testing accuracy of 0.9169, sensitivity of 0.9328, and specificity of 0.9032.

A simple computational approach to simulation of healing in long bone fractures is presented. In particular, an algorithm that could simulate the formation, maturation, and resorption of fracture callus is developed and validated. The simplicity of the approach lies in the fact that the algorithm uses only the applied load and a single constraint parameter for the entire simulation. The work hypothesizes bone healing as a comprehensive energy minimization process where mechanical stimulation is proposed as the primary precursor for the beginning of different stages (i.e., callus formation, mineralization, and resorption). As such, the hypothesis is derived from the second law of thermodynamics which states that the energy of a closed system should be minimum at equilibrium. Alternatively, each stage of healing bone healing may be termed a state of homeostasis. The validation is done through a multi-material, time-based simulation of bone healing in a damaged tibia. The simulation uses a cross-section-based finite element model and an advanced version of an already validated structural optimization algorithm. The optimization objective is to minimize overall strain energy for the entire process, subject to a polar first moment of mass constraint. The simulation results show different stages of healing, where the algorithm generates a callus geometry similar to those observed experimentally. Eventually, a geometry similar to that in an intact cross-section is achieved by resorption of the callus from the unwanted sites.

This overview shows the mapping of specific visualization techniques, depth assessment of the structure of the underlying tissues and used wavelengths of radiation. Medical imaging is currently one of the most dynamically developing areas of medical science. The main aim of the review is a systematization of information on the current status of the microwave imaging of biological objects, primarily of body tissues. The main options of microwave sensing of biological objects are analyzed. Two basic techniques for sensing differing evaluation parameters are characterized. They are microwave thermometry (passive) and near-field resonance imaging. The physical principles of microwave sensing application are discussed. It is shown that the resonant near-field microwave tomography allows visualization of the structure of biological tissues on the basis of the spatial distribution of their electrodynamic characteristics - permittivity and conductivity. Potential areas for this method in dermatology, including dermatooncology, are shown. The known results of applying the method to patients with dermatoses are given. The informativeness of the technology in the early diagnosis of melanoma is shown. The prospects of microwave diagnostics in combustiology, reconstructive and plastic surgery are demonstrated. Thus, microwave sensing is a modern, dynamically developing method of biophysical assessment of body tissues. There is a strong indication of the feasibility of application of microwave sensing in combustiology (in different periods of burn disease), as well as in reconstructive surgery. Further research in this and other areas of biomedicine will significantly expand the range of possibilities of modern technologies of visualization.

Urologists and nephrologists attribute pyelonephritis of pregnant women to the category of complicated upper urinary tract infections that threaten the development of a severe purulent-septic process. The frequency of pyelonephritis in pregnant women ranges from 12.2 to 33.8%. In this research, laboratory indicators of the state of immunity and lipid peroxidation using fuzzy decision logic are used to improve the quality of differential diagnosis of serous and purulent pyelonephritis in pregnant women. A space of informative indicators was formed that characterize the state of immune changes, making it possible to carry out the differential diagnosis of pyelonephritis forms in pregnant women with high accuracy. Results of the operation of the obtained decision rules in the control sample showed that the diagnostic efficiency of the proposed method reaches 93%, which is acceptable for use in medical practice.