Critical Reviews in Biomedical Engineering最新文献

Malignant tumors of the pancreas are the fourth leading cause of cancer-related deaths. This is mainly because they are often diagnosed at a late stage. One of the challenges in diagnosing focal lesions in the pancreas is the difficulty in distinguishing them from other conditions due to the unique location and anatomy of the organ, as well as the similarity in their ultrasound characteristics. One of the most sensitive imaging modalities of the pancreas is endoscopic ultrasonography. However, clinicians recognize that EUS is a difficult and highly operator-dependent method, while its results are highly dependent on the experience of the investigator. Hybrid technologies based on artificial intelligence methods can improve the accuracy and objectify the results of endosonographic diagnostics. Endoscopic ultrasonography was performed on 272 patients with focal lesions of the pancreatobiliary zone, who had been treated in the surgical section of the Kursk Regional Clinical Hospital in 2014-2023. The study utilized an Olympus EVIS EXERA II video information endoscopic system, along with an EU-ME1 ultrasound unit equipped with GF UM160 and GF UC140P-AL5 echo endoscopes. Out of the focal formations in the pancreatobiliary zone, pancreatic cancer was detected in 109 patients, accounting for 40.1% of the cases. Additionally, 40 patients (14.7%) were diagnosed with local forms of chronic pancreatitis. The reference sonograms displayed distinguishable focal pancreatic pathologies, leading to the development of hybrid fuzzy mathematical decision-making rules at the South-West State University in Kursk, Russian Federation. This research resulted in the creation of a fuzzy hybrid model for the differential diagnosis of chronic focal pancreatitis and pancreatic cancer. Endoscopic ultrasonography, combined with hybrid fuzzy logic methodology, has made it possible to create a model for differentiating between chronic focal pancreatitis and pancreatic ductal adenocarcinoma. Statistical testing on control samples has shown that the diagnostic model, based on reference endosonograms of the echographic texture of pancreatic focal pathology, has a confidence level of 0.6 for the desired diagnosis. By incorporating additional information about the contours of focal formations obtained through endosonography, the reliability of the diagnosis can be increased to 0.9. This level of reliability is considered acceptable in clinical practice and allows for the use of the developed model, even with data that is not well-structured.

The efficacy of every neuromodulation modality depends upon the characteristics of the electrodes used to stimulate the chosen target. The geometrical, chemical, mechanical and physical configuration of electrodes used in neurostimulation affects several performance attributes like stimulation efficiency, selectivity, tissue response, etc. The efficiency of stimulation in relation to electrode impedance is influenced by the electrode material and/or its geometry. The nature of the electrode material determines the charge transfer across the electrode-tissue interface, which also relates to neuronal tissue damage. Electrode morphology or configuration pattern can facilitate the modulation of extracellular electric field (field shaping). This enables selective activation of neurons and minimizes side effects. Biocompatibility and biostability of the electrode materials or electrode coating have a role in glial formation and tissue damage. Mechanical and electrochemical stability (corrosion resistance) determines the long-term efficacy of any neuromodulation technique. Here, a review of electrodes typically used for implantable neuromodulation is discussed. Factors affecting the performance of electrodes like stimulation efficiency, selectivity and tissue responses to the electrode-tissue interface are discussed. Technological advancements to improve electrode characteristics are also included.

Vestibular evoked myogenic potentials (VEMPs) in individuals with diabetes mellitus (DM) provide evidence as how diabetes can bring about changes in the peripheral nervous system. Cervical VEMP (cVEMP) evaluates the function and integrity of the sacullo- collic pathway and ocular VEMP (oVEMP) evaluates the utriculo-collic pathway. cVEMP is an ipsilateral inhibitory response of the sternocleidomastoid muscle. cVEMP is recorded at higher intensity above 80-85 dBnHL with biphasic waveforms having initial peak positivity P13 followed by a negativity N23. We performed a systematic review following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines to evaluate cVEMP in diabetes mellitus. The search was conducted in the databases: PubMed, Cumulative Index of Nursing and Allied Health Literature, Scopus, and Cochrane library using the keywords "diabetes mellitus" and "vestibular evoked myogenic potential" or "cVEMP." A two-phase selection process was used for the final inclusion of studies, and the methodological quality of these studies was assessed using the Newcastle Ottawa scale (NCOS). Meta-analysis was performed using a random-effects model. For comparisons between DM and healthy controls, a significant difference was observed for cVEMP amplitude (P = 0.01). Our meta-analysis's results suggest peripheral vestibular dysfunction can be observed in DM. It appears that cVEMPs may be useful in the early detection of neuropathy in DM.

Cerebrospinal fluid (CSF) leakage is a common postoperative complication of neurosurgical procedures, with iatrogenic causes accounting for 16% of CSF leakages. This complication increases healthcare costs and patient morbidity. The focus of this review is to analyze the rates of CSF leakage of some of the most commonly used xenogeneic and synthetic dural substitutes following surgeries in the infratentorial region of the brain where surgical repair can be most challenging. A systematic literature search was conducted using studies detailing duraplasty procedures performed with nonautologous grafts in the infratentorial region in PubMed. Studies were identified using the following search terms: "posterior fossa" or "infratentorial" were used in combination with "CSF leak," "CSF leakage," "cerebrospinal fluid leakage," "duraplasty" or "dura graft." The outcome of interest was a measure of the prevalence of CSF leakage rates following posterior fossa neurosurgery. Studies that contributed data to this review were published between 2006 and 2021. The dural graft materials utilized included: bovine collagen, acellular dermis, equine collagen, bovine pericardium, collagen matrix, and expanded polytetrafluoroethylene (ePTFE). The number of subjects in studies on each of these grafts ranged from 6 to 225. CSF leak rates ranged from 0% to 25% with the predominance of studies reporting between 3% and 15%. The studies that utilize bovine collagen, equine collagen, and acellular dermis reported higher CSF leakage rates; whereas studies that utilized ePTFE, bovine pericardium, and collagen matrix reported lower CSF leakage rates. Due to the heterogeneity of methodologies used across these studies, it is difficult to draw a direct correlation between the dural patch products used and CSF leaks. Larger prospective controlled studies that evaluate various products in a head-to-head fashion, using the same methods and animal models, are needed to conclude the relative efficacy of these dural patch products.

Bone is one of the most complex, inaccessible body structures, responsible for calcium storage and haematopoiesis. The second highest cause of death across the world is cancer. Amongst all the types of cancers, bone cancer treatment modalities are limited due to the structural complexity and inaccessibility of bones. The worldwide incidence of bone diseases and bone defects due to cancer, infection, trauma, age-related bone degeneration is increasing. Currently different conventional therapies are available for bone cancer such as chemotherapy, surgery and radiotherapy, but they have several disadvantages associated with them. Nanomedicine is being extensively researched as viable therapeutics to mitigate drug resistance in cancer therapy and promote bone regeneration. Several natural polymers such as chitosan, dextran, alginate, hyaluronic acid, and synthetic polymers like polyglycolic acid, poly(lactic-co-glycolic acid), polycaprolactone are investigated for their application in nanomedicine for bone cancer treatment and bone regeneration. Nanocarriers have shown promising results in preclinical experimental studies. However, they still face a major drawback of inadequate targetability. The paper summarizes the status of research and the progress made so far in modifications and functionalization of natural polymers for improving their site specificity and targeting for effective treatment of bone cancer and enhancing bone regeneration.

A major challenge of gene therapy is to achieve highly specific transgene expression in tissues of interest with minimized off-target expression. Ultrasound in combination with microbubbles can transiently increase permeability of desired cells or tissues and thereby facilitate gene transfer. This kind of ultrasound-driven transgene expression has gained increasing attention due to its deep tissue penetration and high spatiotemporal resolution. However, successful genetic manipulation in vivo with ultrasound need to well optimize various aspects involved in this process. Ultrasound parameters, microbubble dose, and gene vectors need to be optimized for highly increased transgene expression in the cells of interest. Conversely, the potential off-target transgene expression and toxicities need to be reduced by modification of gene vectors and/or promoter sequence. This review will discuss some major strategies for enhanced specificity of the ultrasound-mediated gene transfer in vivo. Five major strategies will be discussed, including the integration of real-time imaging methods, local injection, targeted microbubbles loaded with nucleic acids, stealth nanocarriers, and cell-specific promoter. The advantages and limitations of each strategy were outlined, hoping to provide a guideline for researchers in achieving high specific ultrasound-driven gene expression.

The landscape of breast cancer diagnostics has significantly evolved over the past decade. With these changes, it is possible to provide a comprehensive assessment of both benign and malignant breast calcifications. The biochemistry of breast cancer and calcifications are thoroughly examined to describe the potential to characterize better different calcium salts composed of calcium carbonate, calcium oxalate, or calcium hydroxyapatite and their associated prognostic implications. Conventional mammographic imaging techniques are compared to available ones, including breast tomosynthesis and contrast-enhanced mammography. Additional methods in computed tomography and magnetic resonance imaging are discussed. The concept of using magnetic resonance imaging particularly magnetic susceptibility to characterize the biochemical characteristics of calcifications is described. As we know magnetic resonance imaging is safe and there is no ionization radiation. Experimental findings through magnetic resonance susceptibility imaging techniques are discussed to illustrate the potential for integrating this technique to provide a quantitative assessment of magnetic susceptibility. Under the right magnetic resonance imaging conditions, a distinct phase variability was isolated amongst different types of calcium salts.

In public health, the transmission characteristics and laws of highly infectious virus-carrying particles in the air environment have become a hot topic. The study on the spread characteristics of human virus-carrying droplets in a typical densely populated space is necessary. As such, a classroom space lattice Boltzmann method (LBM) model with a dense population is established to simulate and analyze the spreading and diffusing behavior of pathogenic droplets. The results show that the dispersion density is mainly affected by the mainstream wind direction in the area of concern, and particle aggregation is more likely to form in the area close to the wind disturbance. Due to the dense thermal plumes, the droplet movement is a clear convergence towards the upper space of the classroom. This could explain the fact that people living above confirmed cases are now more likely to be infected.

Cancer, a leading cause of mortality, is distinguished by the multi-stage conversion of healthy cells into cancer cells. Discovery of the disease early can significantly enhance the possibility of survival. Histology is a procedure where the tissue of interest is first surgically removed from a patient and cut into thin slices. A pathologist will then mount these slices on glass slides, stain them with specialized dyes like hematoxylin and eosin (H&E), and then inspect the slides under a microscope. Unfortunately, a manual analysis of histopathology images during breast cancer biopsy is time consuming. Literature suggests that automated techniques based on deep learning algorithms with artificial intelligence can be used to increase the speed and accuracy of detection of abnormalities within the histopathological specimens obtained from breast cancer patients. This paper highlights some recent work on such algorithms, a comparative study on various deep learning methods is provided. For the present study the breast cancer histopathological database (BreakHis) is used. These images are processed to enhance the inherent features, classified and an evaluation is carried out regarding the accuracy of the algorithm. Three convolutional neural network (CNN) models, visual geometry group (VGG19), densely connected convolutional networks (DenseNet201), and residual neural network (ResNet50V2), were employed while analyzing the images. Of these the DenseNet201 model performed better than other models and attained an accuracy of 91.3%. The paper includes a review of different classification techniques based on machine learning methods including CNN-based models and some of which may replace manual breast cancer diagnosis and detection.

Autism spectrum disorder (ASD) is a complex neurological condition that limits an individual's capacity for communication and learning throughout their life. Although symptoms of Autism can be diagnosed in individuals of different ages, it is labeled as a developmental disorder because symptoms typically start to show up in the initial 2 years of childhood. Autism has no single known cause but multiple factors contribute to its etiology in children. Because symptoms and severity of ASD vary in every individual, there could be many causes. Detection of ASD in the early stages is crucial for providing a path for rehabilitation that enhances the quality of life and integrates the ASD person into the social, family, and professional spheres. Assessment of ASD includes experienced observers in neutral environments, which brings constraints and biases to a lack of credibility and fails to accurately reflect performance in terms of real-world scenarios. To get around these limitations, the conducted review offers a thorough analysis of the impact on the individual and the ones living around them and most recent research on how these techniques are implemented in the diagnosis of ASD. As a result of improvements in technology, assessments now include processing unconventional data than can be collected from measurements arising out of laboratory chemistry or of electrophysiological origin. Examples of these technologies include virtual reality and sensors including eye-tracking imaging. Studies have been conducted towards recognition of emotion and brain networks to identify functional connectivity and discriminate between people with ASD and people who are thought to be typically developing. Diagnosis of Autism has recently made substantial use of long short term memory (LSTM), convolutional neural network (CNN) and its variants, the random forest (RF) and naive Bayes (NB) machine learning techniques. It is hoped that researchers will develop methodologies that increase the probability of identification of ASD in its varied forms and contribute towards improved lifestyle for patients with ASD and those affected by the pathology.