Applications In Medical Research Research Articles

Hyaluronic Acid-Extraction Methods, Sources and Applications.

In this review, a compilation of articles in databases on the extraction methods and applications of hyaluronic acid (HA) was carried out. HA is a highly hydrated component of different tissues, including connective, epithelial, and neural. It is an anionic, linear glycosaminoglycan (GAG) primarily found in the native extracellular matrix (ECM) of soft connective tissues. Included in the review were studies on the extraction methods (chemical, enzymatical, combined) of HA, describing advantages and disadvantages as well as news methods of extraction. The applications of HA in food are addressed, including oral supplementation, biomaterials, medical research, and pharmaceutical and cosmetic industry applications. Subsequently, we included a section related to the structure and penetration routes of the skin, with emphasis on the benefits of systems for transdermal drug delivery nanocarriers as promoters of percutaneous absorption. Finally, the future trends on the applications of HA were included. This final section contains the effects before, during, and after the application of HA-based products.

Unlocking the Potential of Zebrafish Research with Artificial Intelligence: Advancements in Tracking, Processing, and Visualization.

Zebrafish have become a widely accepted model organism for biomedical research due to their strong cortisol stress response, behavioral strain differences, and sensitivity to both drug treatments and predators. However, experimental zebrafish studies generate substantial data that must be analyzed through objective, accurate, and repeatable analysis methods. Recently, advancements in artificial intelligence (AI) have enabled automated tracking, image recognition, and data analysis, leading to more efficient and insightful investigations. In this review, we examine key AI applications in zebrafish research, including behavior analysis, genomics, and neuroscience. With the development of deep learning technology, AI algorithms have been used to precisely analyze and identify images of zebrafish, enabling automated testing and analysis. By applying AI algorithms in genomics research, researchers have elucidated the relationship between genes and biology, providing a better basis for the development of disease treatments and gene therapies. Additionally, the development of more effective neuroscience tools could help researchers better understand the complex neural networks in the zebrafish brain. In the future, further advancements in AI technology are expected to enable more extensive and in-depth medical research applications in zebrafish, improving our understanding of this important animal model. This review highlights the potential of AI technology in achieving the full potential of zebrafish research by enabling researchers to efficiently track, process, and visualize the outcomes of their experiments.

Artificial intelligence and medicine: Mounting risks amid the promise

This news section is written by a medical journalist and offers Cancer Cytopathology readers timely information on events, issues, and personalities of interest to the subspecialty. In this second of a two‐part series, developers have touted artificial intelligence as a boon for both medical research and clinical applications, but plenty of pitfalls are giving ethicists and other experts considerable pause in their use.

Oversized liposomes boost macrophage-targeted RNA delivery to regulate macrophage polarity

Oversized liposomes boost macrophage-targeted RNA delivery to regulate macrophage polarity

2D medical image synthesis using transformer-based denoising diffusion probabilistic model

Objective. Artificial intelligence (AI) methods have gained popularity in medical imaging research. The size and scope of the training image datasets needed for successful AI model deployment does not always have the desired scale. In this paper, we introduce a medical image synthesis framework aimed at addressing the challenge of limited training datasets for AI models. Approach. The proposed 2D image synthesis framework is based on a diffusion model using a Swin-transformer-based network. This model consists of a forward Gaussian noise process and a reverse process using the transformer-based diffusion model for denoising. Training data includes four image datasets: chest x-rays, heart MRI, pelvic CT, and abdomen CT. We evaluated the authenticity, quality, and diversity of the synthetic images using visual Turing assessments conducted by three medical physicists, and four quantitative evaluations: the Inception score (IS), Fréchet Inception Distance score (FID), feature similarity and diversity score (DS, indicating diversity similarity) between the synthetic and true images. To leverage the framework value for training AI models, we conducted COVID-19 classification tasks using real images, synthetic images, and mixtures of both images. Main results. Visual Turing assessments showed an average accuracy of 0.64 (accuracy converging to indicates a better realistic visual appearance of the synthetic images), sensitivity of 0.79, and specificity of 0.50. Average quantitative accuracy obtained from all datasets were IS = 2.28, FID = 37.27, FDS = 0.20, and DS = 0.86. For the COVID-19 classification task, the baseline network obtained an accuracy of 0.88 using a pure real dataset, 0.89 using a pure synthetic dataset, and 0.93 using a dataset mixed of real and synthetic data. Significance. A image synthesis framework was demonstrated for medical image synthesis, which can generate high-quality medical images of different imaging modalities with the purpose of supplementing existing training sets for AI model deployment. This method has potential applications in many data-driven medical imaging research.

Current Perspective of Metaverse Application in Medical Education, Research and Patient Care

As virtual and augmented reality simulation technologies advance, the use of such technologies in medicine is widespread. The advanced virtual and augmented systems coupled with a complex interactive, immersive environment create a metaverse. The metaverse enables us to connect with others in a virtual world free of spatial restrictions and time constraints. In the educational aspect, it allows collaboration among peers and educators in an immersive 3D environment that can imitate the actual classroom setting with learning tools. Metaverse technology enables visualization of virtual 3D structures, facilitates collaboration and small group activities, improves mentor–mentee interactions, provides opportunities for self-directed learning experiences, and helps develop teamwork skills. The metaverse will be adapted rapidly in healthcare, boost digitalization, and grow in use in surgical procedures and medical education. The potential advantages of using the metaverse in diagnosing and treating patients are tremendous. This perspective paper provides the current state of technology in the medical field and proposes potential research directions to harness the benefits of the metaverse in medical education, research, and patient care. It aims to spark interest and discussion in the application of metaverse technology in healthcare and inspire further research in this area.

Nano-Strategies for Lignin Biomaterials toward Cancer Therapy.

Lignin is a nontoxic and biocompatible biopolymer with many promising characteristics, including a high tensile strength and antioxidant properties. This natural polymer can be processed through several chemical methods and modified into lignin nanomaterials for potential biomedical applications. This review summarizes the latest developments in nanolignin (NL)-based biomaterials for cancer therapy; various NL applications related to cancer therapy are considered, including drug and gene delivery, biosensing, bioimaging, and tissue engineering. The manuscript also outlines the potential use of these materials to improve the therapeutic potency of chemotherapeutic drugs by decreasing their dose and reducing their adverse effects. Due to its high surface area-to-volume ratio and the easy modification of its chemical components, NL could serve as an appropriate matrix for the binding and controlled release of various pharmaceutical agents. Moreover, the challenges in the utilization of NL-based materials for cancer therapy are discussed, along with the prospects of advances in such nanomaterials for medical research applications.

From fetus to neonate: A review of cardiovascular modeling in early life.

Computational modeling has well-established utility in the study of cardiovascular hemodynamics, with applications in medical research and, increasingly, in clinical settings to improve the diagnosis and treatment of cardiovascular diseases. Most cardiovascular models developed to date have been of the adult circulatory system; however, the perinatal period is unique as cardiovascular physiology undergoes drastic changes from the fetal circulation, during the birth transition, and into neonatal life. There may also be further complications in this period: for example, preterm birth (defined as birth before completed weeks of gestation) carries risks of short-term cardiovascular instability and is associated with increased lifetime cardiovascular risk. Here, we review computational models of the cardiovascular system in early life, their applications to date and potential improvements and enhancements of these models. We propose a roadmap for developing an open-source cardiovascular model that spans the fetal, perinatal, and postnatal periods. This article is categorized under: Cardiovascular Diseases > Computational Models Cardiovascular Diseases > Biomedical Engineering Congenital Diseases > Computational Models.

Characterization and modeling of the anisotropic behavior of the porcine dermis

This work uses a combined experimental and modeling investigation into the anisotropic behavior of porcine dermis. Porcine skin tissues have been used in various medical, cosmetic, and biomedical research applications. However, their tensile behavior still needs to be fully characterized. Much of the existing work uses the uniaxial experimental data from two orthogonally oriented specimens to inform and model the anisotropic behavior of the tissue. However, the skin has a complex fibrous architecture with several non-symmetric fiber families. Accordingly, we have (i) mechanically characterized the behavior of the porcine dermis using the experimental data from three differently orientated uniaxial tensile specimens (two orthogonal and one diagonal) and (ii) developed an enhanced constitutive model to better capture the observed behavior from three different orientations. The enhanced model takes into account the non-symmetric planar fiber distribution, the non-linearity of the ground substance, as well as the varying fiber responses along three orientations. We demonstrate significant improvement of the enhanced model over the prior literature to capture the observed nonlinear and anisotropic behavior from three differently oriented specimens simultaneously.

Application Progress of the Single Domain Antibody in Medicine.

The camelid-derived single chain antibody (sdAb), also termed VHH or nanobody, is a unique, functional heavy (H)-chain antibody (HCAb). In contrast to conventional antibodies, sdAb is a unique antibody fragment consisting of a heavy-chain variable domain. It lacks light chains and a first constant domain (CH1). With a small molecular weight of only 12~15 kDa, sdAb has a similar antigen-binding affinity to conventional Abs but a higher solubility, which exerts unique advantages for the recognition and binding of functional, versatile, target-specific antigen fragments. In recent decades, with their unique structural and functional features, nanobodies have been considered promising agents and alternatives to traditional monoclonal antibodies. As a new generation of nano-biological tools, natural and synthetic nanobodies have been used in many fields of biomedicine, including biomolecular materials, biological research, medical diagnosis and immune therapies. This article briefly overviews the biomolecular structure, biochemical properties, immune acquisition and phage library construction of nanobodies and comprehensively reviews their applications in medical research. It is expected that this review will provide a reference for the further exploration and unveiling of nanobody properties and function, as well as a bright future for the development of drugs and therapeutic methods based on nanobodies.

Spheroid Engineering in Microfluidic Devices.

Two-dimensional (2D) cell culture techniques are commonly employed to investigate biophysical and biochemical cellular responses. However, these culture methods, having monolayer cells, lack cell-cell and cell-extracellular matrix interactions, mimicking the cell microenvironment and multicellular organization. Three-dimensional (3D) cell culture methods enable equal transportation of nutrients, gas, and growth factors among cells and their microenvironment. Therefore, 3D cultures show similar cell proliferation, apoptosis, and differentiation properties to in vivo. A spheroid is defined as self-assembled 3D cell aggregates, and it closely mimics a cell microenvironment in vitro thanks to cell-cell/matrix interactions, which enables its use in several important applications in medical and clinical research. To fabricate a spheroid, conventional methods such as liquid overlay, hanging drop, and so forth are available. However, these labor-intensive methods result in low-throughput fabrication and uncontrollable spheroid sizes. On the other hand, microfluidic methods enable inexpensive and rapid fabrication of spheroids with high precision. Furthermore, fabricated spheroids can also be cultured in microfluidic devices for controllable cell perfusion, simulation of fluid shear effects, and mimicking of the microenvironment-like in vivo conditions. This review focuses on recent microfluidic spheroid fabrication techniques and also organ-on-a-chip applications of spheroids, which are used in different disease modeling and drug development studies.

Microwave-Based Electrochemical Sensor Design by SRR Approach for ISM Sensing Applications

This paper presents a transmission line and a split ring resonator (SRR) based sensor structure with a high sensitivity capacity to discriminate various methanol mixtures. The height of the dielectric layer and thickness of copper are assigned as 1.6 mm and 0.035 mm, respectively. The overall dimensions of the sensor structure are defined as 16 mm × 16 mm × 1.6 mm . The operating frequency is selected for the ISM bands, especially around 2.45 GHz. Different methanol-water mixtures are prepared at various ratios, and then, the complex permittivity values are measured. Different sensor structures are modelled and investigated using a two-port transmission line approach. Various types of SRR based sensors are designed, and an optimum design is proposed for methanol mixture detection applications. The observed quality factor of the proposed sensor is 16.5. The resonance shifts of the transmission value ( S 21 ) are used for sensing capability around 2.45 GHz at -45 dB and 90 MHz resonance shifts. The sensitivity of the sensor has been evaluated as 1 MHz. Finally, the electric field distributions of the proposed SRR integrated transmission line are investigated. The novelty of the proposed design is to exactly sense the ratio of methanol in water with a very simple design. The proposed sensor structure can be used for methanol detection applications in medical, military, and chemical research.

Smart Visualization of Medical Images as a Tool in the Function of Education in Neuroradiology.

The smart visualization of medical images (SVMI) model is based on multi-detector computed tomography (MDCT) data sets and can provide a clearer view of changes in the brain, such as tumors (expansive changes), bleeding, and ischemia on native imaging (i.e., a non-contrast MDCT scan). The new SVMI method provides a more precise representation of the brain image by hiding pixels that are not carrying information and rescaling and coloring the range of pixels essential for detecting and visualizing the disease. In addition, SVMI can be used to avoid the additional exposure of patients to ionizing radiation, which can lead to the occurrence of allergic reactions due to the contrast media administration. Results of the SVMI model were compared with the final diagnosis of the disease after additional diagnostics and confirmation by neuroradiologists, who are highly trained physicians with many years of experience. The application of the realized and presented SVMI model can optimize the engagement of material, medical, and human resources and has the potential for general application in medical training, education, and clinical research.

Perceived barriers towards the importance and application of medical research: a source of gender disparity among medical undergraduates

BackgroundLittle is known about gender disparity among medical undergraduates in the developing world. Therefore, this study aims to explore the attitudes and perceived barriers among Jordanian medical students, particularly women.MethodsAn online, self-administered questionnaire, developed after an extensive literature review, was disseminated across all six Jordanian medical schools targeting more than 5000 medical students. Student t-test and ANOVA were used to document mean differences among different groups. Linear and logistic regression models were used to find predictors of publication and number of publications.ResultsA total of 636 students participated in the survey with a women to men ratio of 1.1. Women medical students report significantly higher knowledge (t(634) = 2.47, p = 0.013), personal (t(634) = 3.31, p = 0.001), and total barriers scores than men (t(634) = 3.02, p = 0.003). Moreover, compared to men, women were less likely to find same-sex mentorship (t(634) = 3.18, p = 0.001) or receive credited authorship (t(634) = 2.12, p = 0.011). Overall, women medical students were more likely to perceive that their gender (t(634) = 3.58, p < 0.001) and people’s perception of their gender (t(634) = 4.25, p < 0.001) are barriers to their career advancement. Binary logistic regression demonstrated that gender is a significant predictor of being able to publish (OR: 1.645; 95%CI: 1.002–2.731), while linear regression demonstrated that gender is a predictor of number of publications (ß: 0.113; 95%CI: 0.063–0.288).ConclusionA significant gender disparity exists in terms of both attitudes and overall barriers among Jordanian medical undergraduates which calls for immediate policy changes as to produce successful clinicians and researchers.

Time Series Analysis and Some Applications in Medical Research

Time series have applications in many fields. Time Series Analysis is used for many applications, such as economic forecasting, sales forecasting, budgetary analysis, stock market analysis, yield projections, process and quality control, etc. In this study, we examined the applications of time series in the field of health. We gave some examples of applications in the field of health. We mentioned that many more studies could be done in the field of health with time series analysis. In this paper, we reviewed recent works and present a summary and a brief theoretical introduction to time series ARIMA modeling. We have seen that this model is the most widely used method in research in the field of health. As a result, time series analysis, a statistical technique that may be appropriate when data are measured repeatedly and at nearly equal intervals of time, has potential research applications in the study of chronic diseases such as diabetes, hypertension, and herpes simplex. We believe that this study will guide researchers working in this field in their research.

ARTIFICIAL INTELLIGENCE IN HEALTH CARE AND THE OVIEDO CONVENTION

Applications of AI technology are being developed in healthcare. The prospects for the deployment of targeted AI applications in medical treatment, clinical trials, drug research, and public health are promising and groundbreaking.&#x0D; But the deployment of these new technologies in healthcare also raises legal questions, especially from a human rights perspective. This contribution therefore focuses on the human rights analysis of AI applications in healthcare and how these applications relate to the Oviedo Convention.

A state-of-the-art guide to the sterilization of thermoplastic polymers and resin materials used in the additive manufacturing of medical devices

Continuous development of additive manufacturing technologies has reached new areas, including medical research and healthcare applications, where precision, highly accurate reproducibility, disinfection, or even sterility of the end product is essential. The study aims to critically evaluate the effect of different sterilization and disinfection methods for most commonly used additive manufacturing technologies and materials in medical device development: PLA (polylactic acid), PETG (polyethylene terephthalate glycol), ABS (acrylonitrile butadiene styrene), and HIPS (high impact polystyrene) for material extrusion technology (FFF; fused filament fabrication), polyamide (PA2200) for powder bed fusion technology (SLS; selective laser sintering), MED610™ for PolyJet™ material jetting, and standard Formlabs™ white resin (V4) for SLA (stereolithography) technology. The materials were tested before and after different sterilization and disinfection protocols, including treatment with 70% ethanol, chlorine solution, H2O2 plasma sterilization, autoclave and dry heat sterilization, applying 10 and 20 cycles in all cases. Bacteriostatic/fungistatic and sterility tests were performed using Escherichia coli, Enterococcus faecalis, and Candida albicans as reference strains. Cytotoxicity was evaluated using A549 cells. The samples showed significant structural or aesthetic alterations detected by various mechanical tests, scanning electron microscopy, or differential scanning calorimetry (DSC-TGA). No significant cytotoxic effect was revealed during the experiment. Our results can serve as a guide for the selection of effective sterilization and disinfection methods for the most common materials used in 3D printing in medical and healthcare applications.

Nanoporous Atomically Thin Graphene Filters for Nanoscale Aerosols

Filtering nanoparticulate aerosols from air streams is important for a wide range of personal protection equipment (PPE), including masks used for medical research, healthcare, law enforcement, first responders, and military applications. Conventional PPEs capable of filtering nanoparticles <300 nm are typically bulky and sacrifice breathability to maximize protection from exposure to harmful nanoparticulate aerosols including viruses ∼20-300 nm from air streams. Here, we show that nanopores introduced into centimeter-scale monolayer graphene supported on polycarbonate track-etched supports via a facile oxygen plasma etch can allow for filtration of aerosolized SiO2 nanoparticles of ∼5-20 nm from air steams while maintaining air permeance of ∼2.28-7.1 × 10-5 mol m-2 s-1 Pa-1. Furthermore, a systematic increase in oxygen plasma etch time allows for a tunable size-selective filtration of aerosolized nanoparticles. We demonstrate a new route to realize ultra-compact, lightweight, and conformal form-factor filters capable of blocking sub-20 nm aerosolized nanoparticles with particular relevance for biological/viral threat mitigation.

A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural Networks.

Recent improvements in current technology have had a significant impact on a wide range of image processing applications, including medical imaging. Classification, detection, and segmentation are all important aspects of medical imaging technology. An enormous need exists for the segmentation of diagnostic images, which can be applied to a wide variety of medical research applications. It is important to develop an effective segmentation technique based on deep learning algorithms for optimal identification of regions of interest and rapid segmentation. To cover this gap, a pipeline for image segmentation using traditional Convolutional Neural Network (CNN) as well as introduced Swarm Intelligence (SI) for optimal identification of the desired area has been proposed. Fuzzy C-means (FCM), K-means, and improvisation of FCM with Particle Swarm Optimization (PSO), improvisation of K-means with PSO, improvisation of FCM with CNN, and improvisation of K-means with CNN are the six modules examined and evaluated. Experiments are carried out on various types of images such as Magnetic Resonance Imaging (MRI) for brain data analysis, dermoscopic for skin, microscopic for blood leukemia, and computed tomography (CT) scan images for lungs. After combining all of the datasets, we have constructed five subsets of data, each of which had a different number of images: 50, 100, 500, 1000, and 2000. Each of the models was executed and trained on the selected subset of the datasets. From the experimental analysis, it is observed that the performance of K-means with CNN is better than others and achieved 96.45% segmentation accuracy with an average time of 9.09 seconds.

Bessel beam induced deep-penetrating bioimaging and self-monitored heating using Nd/Yb heavily doped nanocrystals

Biological probes facilitate optical imaging and disease diagnosis and treatment. However, the large absorption and scattering loss in the tissue highly limit the depth during the application. In the present research, an NIR-I bioprobing system, which utilizes the Bessel beam to excite heavily doping nanocrystals, has been developed for deep tissue applications. On the one hand, the capillary mode selection method generates the Bessel excitation beam, lowering the excitation energy loss. On the other hand, a strong energy harvest of NaYbF4:90%Nd nanocrystals enables effective fluorescence and heat generation upon 800 nm excitation. By considering the advantages of Bessel excitation and heavily doping nanocrystals, up to ∼3 cm penetration depth for ex vivo bioimaging and the potential self-monitored photothermal treatment are demonstrated. The resultant bioprobing system allows deep tissue imaging and photothermal therapy, showcasing broad prospects in medical research and clinical applications.