Medical Diagnostic Applications Research Articles

Polysaccharides Based Biosensors for Medical Applications: Prospective and Future Aspects

AbstractBiopolymers have attracted much of interest in various applications needed for sustainable and biodegradable solutions. Indeed, biopolymers lessen the requirement for petroleum‐based resources, municipal solid waste output, and carbon dioxide emissions. Polysaccharides are a unique category of biopolymers that are especially characteristic such as biocompatibility and editability. Polysaccharides functionalization plays a significant role in various applications and their suitability for additive applications. Sensor‐based polysaccharides are widely used with many drawbacks localized in durability, moldability, and sensitivity. Otherwise, biosensors are devices monitoring a specimen's biological phenomena or measurements, usually not disposable devices. For that, biosensors are needed urgently to address the high cost of disposable supplies in the medical diagnostic industry, which span many elements of medical diagnostic applications. Additionally, manipulating of polysaccharide‐based materials is considered accessible and usable and requires low equipment requirements. Herein, the main features of polysaccharides will be discussed, along with potential applications as biosensors for medical diagnosis and new lines of inquiry with such focus on the main required to formulate polysaccharides biosensors.

Nano-structuring metal organic frameworks on semiconductor nanowire arrays for highly sensitive and selective chemical sensing

Chemiresistive sensing of gas molecules has been widely investigated for application in medical diagnostics and environmental monitoring, showing high sensitivity and low limits of detection toward various volatile organic compounds. While metal oxide semiconductors offer numerous advantages, such as ease of fabrication, high sensitivity, and fast response times, they often suffer of high insufficient selectivity. Here, we report the engineering of a low-temperature sensing platform consisting of nanostructured zeolitic imidazolate framework (ZIF-8) metal organic frameworks (MOFs) over InP semiconducting nanowire (NW) arrays. These devices were fabricated via top-down etching of InP NW arrays, aerosol deposition of flame-made ZnO nanoparticles, and their chemical vapor conversion to ZIF-8. The presence of ZIF-8 significantly enhances the device sensitivity over that of the pristine InP NW arrays by providing a high density of adsorption sites and faster reduction kinetics. Our optimal sensors can detect NO2 in a large concentration range from 0.1 to 8 ppm, in addition to showing relatively higher responses toward various gas molecules, including CO2, methanol, ethanol, acetone, and propane, in comparison with pristine InP NW sensors. Given the large family of MOFs with controllable pore size and chemical composition, our findings provide a flexible approach for engineering the selectivity of highly sensitive and miniaturized gas sensors for integration in miniaturized devices.

Microfluidic pumps for cell sorting.

Microfluidic cell sorting has shown promising advantages over traditional bulky cell sorting equipment and has demonstrated wide-reaching applications in biological research and medical diagnostics. The most important characteristics of a microfluidic cell sorter are its throughput, ease of use, and integration of peripheral equipment onto the chip itself. In this review, we discuss the six most common methods for pumping fluid samples in microfluidic cell sorting devices, present their advantages and drawbacks, and discuss notable examples of their use. Syringe pumps are the most commonly used method for fluid actuation in microfluidic devices because they are easily accessible but they are typically too bulky for portable applications, and they may produce unfavorable flow characteristics. Peristaltic pumps, both on- and off-chip, can produce reversible flow but they suffer from pulsatile flow characteristics, which may not be preferable in many scenarios. Gravity-driven pumping, and similarly hydrostatic pumping, require no energy input but generally produce low throughputs. Centrifugal flow is used to sort cells on the basis of size or density but requires a large external rotor to produce centrifugal force. Electroosmotic pumping is appealing because of its compact size but the high voltages required for fluid flow may be incompatible with live cells. Emerging methods with potential for applications in cell sorting are also discussed. In the future, microfluidic cell sorting methods will trend toward highly integrated systems with high throughputs and low sample volume requirements.

Nanostructures and biomaterials based on silk polymer for medical diagnostic and therapeutic applications

Nanostructures and biomaterials based on silk polymer for medical diagnostic and therapeutic applications

Recent Progress of Spectroscopic Probes for Peroxynitrite and Their Potential Medical Diagnostic Applications.

Peroxynitrite (ONOO-) is a crucial reactive oxygen species that plays a vital role in cellular signal transduction and homeostatic regulation. Determining and visualizing peroxynitrite accurately in biological systems is important for understanding its roles in physiological and pathological activity. Among the various detection methods, fluorescent probe-based spectroscopic detection offers real-time and minimally invasive detection, high sensitivity and selectivity, and easy structural and property modification. This review categorizes fluorescent probes by their fluorophore structures, highlighting their chemical structures, recognition mechanisms, and response behaviors in detail. We hope that this review could help trigger novel ideas for potential medical diagnostic applications of peroxynitrite-related molecular diseases.

Physico-Chemical Characterization of Green Synthesized Nanomaterials by UV-Visible Spectroscopy

Abstract: Nanomaterials (NMs) particularly synthesized by green routes have attracted researchers and scientists for their multifunctional industrial applications. NMs have not only revolutionized research, but also our daily life because of numerous applications in medical diagnostics, consumer products, and energy-related applications. Their unique properties are directly related to chemical composition, structure, size and shape. There are several characterization techniques used to determine the size, composition, crystalline structure and other physical properties of NMs. Prominent among them are spectroscopic techniques such as UV-Visible, FTIR, EDX; diffraction techniques such as XRD, SAED; microscopic techniques such as SEM, TEM, AFM and others such as Zeta potential measurements. Every technique has its own merit and demerit. This mini review describes the uses of UV-Vis spectroscopy in characterization of NMs.

NGS Analysis to Detect Mutation in Brain Tumor Diagnostic

Abstract: This study presents an integrated computational approach for analyzing protein sequences and their 3D structures. By leveraging the MMDB Macromolecular database, homologs of a protein sequence of interest are identified, and interactive visualization of their structural properties is provided. The computational alignment method, using BLASTP, allows for efficient determination of sequence similarities and identification of conserved regions among multiple protein sequences. COBALT is employed to refine sequence alignments and facilitate graphical analysis of sequence relationships. RASMOL, a computational analysis program, generates 2-D representations of protein-ligand complexes, enabling visual exploration of their interactions. ORF finder is used to identify coding regions in mRNA sequences, aiding in the prediction of protein-coding regions. The approach is applied to brain tumor diagnostics using human biological samples, exploring the structural properties of brain tumor-related proteins with the help of the 2RHU protein structure and PYMOL visualization software. Overall, this integrated computational framework offers a comprehensive toolkit for protein sequence analysis, structure visualization, and homology modeling, with potential applications in drug discovery, molecular biology, and medical diagnostics

Separation of Cells From Plasma by Means of Ultrasonics

This paper presents an analysis of use of ultrasonic standing wave in cell separation from bodily fluids based on the example of erythrocyte separation from plasma. It describes movement of red blood cells in plasma under the influence of the acoustic field (whose forces result from interaction of red blood cells with plasma as the vibrating medium) and under the influence of resistance forces in Stokes’ and Oseen’s approximation. The general properties of solutions of the motion equation are given. The solutions for the parameters of the ultrasonic wave and blood cells which are interesting in terms of practical applications in medical diagnostics are discussed. Time constants of the cell transportation to the regions of stable equilibrium in the field of ultrasonic standing wave are estimated. The formulas which determine the time needed to obtain the assumed concentration increase in plasma in nodes and/or anti-nodes of the standing wave are derived.

Nanoreactor based on Cu nanoparticles confined in B, N co-doped porous carbon nanotubes for glutathione biosensing.

Acost-effective approach has been developedto synthesize Cu nanoparticles encapsulated into B and N double-doped carbon nanotubes (Cu@BCNNTs) by one-step pyrolysis. According to the specific binding of Cu-Cl and Cu-glutathione (GSH), we employed Cu@BCNNTs to build an electrochemical sensing platform to detect GSH. The unique space-confined structure can prevent Cu nanoparticles from agglomeration. In addition, B and N co-doped porous hollow tubes can improve the electrochemical conductivity, expand the number of active sites, enhance surface adsorption, and shorten the transport path. These favorable characteristics of Cu@BCNNTs make them have excellent electrocatalytic properties. These results display that the prepared sensor can detect GSH from 0.5 to 120 μM with adetection limit of 0.024 μM. Theobtained sensors can be successfully applied in the human serum with recovery of GSH ranging from 100.2 to 103.9%. This work provides a new vision to synthesize nanoparticles confined in ahollow tube for the applications in biosensing and medical diagnostics.

CRISPR-Cas Systems: Programmable Nuclease Revolutionizing the Molecular Diagnosis.

CRISPR-Cas system has evolved as a highly preferred genetic engineering tool to perform target gene manipulation via alteration of the guide RNA (gRNA) sequence. The ability to recognize and cleave a specific target with high precision has led to its applicability in multiple frontiers pertaining to human health and medicine. From basic research focused on understanding the molecular basis of disease to translational approach leading to early and precise disease diagnosis as well as developing effective therapeutics, the CRISPR-Cas system has proved to be a quite versatile tool. The coupling of CRISPR-Cas mediated cleavage with isothermal amplification (ISA) of target DNA, followed by a read-out using fluorescent or colorimetric reporters appears quite promising in providing a solution to the urgent need for nucleic acid-based point-of-care diagnostic. Hence, it has been recognized as a highly sophisticated molecular diagnostic tool for the detection of disease-specific biomarkers not limited to nucleic acids-based detection but also of non-nucleic acid targets such as proteins, exosomes, and other small molecules. In this review, we have presented salient features and principles of class 2 type II, V, and VI CRISPR-Cas systems represented by Cas9, Cas12, and Cas13 endonucleases which are frequently used in molecular diagnosis. The article then highlights different medical diagnostic applications of CRISPR-Cas systems focusing on the diagnosis of SARS-CoV-2, Dengue, Mycobacterium tuberculosis, and Listeria monocytogenes. Lastly, we discuss existing obstacles and potential future pathways concerning this subject in a concise manner.

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges.

Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients' healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs' adaptation for complex applications. Specifically, we investigate ANNs' advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.

The lipid composition of extracellular vesicles: applications in diagnostics and therapeutic delivery.

Extracellular vesicles (EVs), including exosomes, with nanoscale sizes, biological origins, various functions, and unique lipid and protein compositions have been introduced as versatile tools for diagnostic and therapeutic medical applications. Numerous studies have reported the importance of the lipid composition of EVs and its influence on their mechanism of action. For example, changes in the lipidomic profile of EVs have been shown to influence the progression of various diseases, including ovarian malignancies and prostate cancer. In this review, we endeavored to examine differences in the lipid content of EV membranes derived from different cell types to characterize their capabilities as diagnostic tools and treatments for diseases like cancer and Alzheimer's disease. We additionally discuss designing functionalized vesicles, whether synthetically by hybrid methods or by changing the lipid composition of natural EVs. Lastly, we provide an overview of current and potential biomedical applications and perspectives on the future of this growing field.

Enhancing Medical Image Fusion and Diagnostic Accuracy Using Vision Transformers: A Novel Approach Leveraging Generative Adversarial Networks

Developed by Ian Goodfellow, Generative Adversarial Networks, GANs in short, are a great advance in machine learning. They have shown exceptional precision and accuracy results in classification methods across a wide range of applications, including computer vision. This paper presents a novel method for medical image fusion that leverages the potential of Vision Transformers, enhancing diagnostic accuracy. Our method, which addresses the growing demand for advanced analytic tools in medical image analysis, combines the informative features from different imaging modalities - specifically X-rays and MRI - into a comprehensive, interpretable format. Our method uses a ViT-based architecture capable of handling images with varying resolutions to produce fused pictures that preserve both the texture and thermal radiation from the source images. The experiments conducted show a significant improvement in the preservation of detailed information. This could be crucial for medical diagnostic applications. Our approach encourages the integration of artificial intelligence into healthcare, setting up the future of medical imaging. Our experiments reveal that the quality of the merged images has improved significantly. Quantified by entropy, mean gradient, and structural similarity index, the results show a dynamic shift of EN and MG as the number image pairs changes. These advances could have a significant impact on medical diagnostic applications. They also highlight the potential benefits of AI integration.

Digital subtraction angiography image segmentation based on multiscale Hessian matrix applied to medical diagnosis and clinical nursing of coronary stenting patients

ObjectiveTo introduce an improved method of digital subtraction angiography image segmentation based on a multi-scale Hessian matrix, to accurately segment vascular images before and after coronary stent implantation, which will have strong applications in medical diagnostics and clinical nursing of patients who underwent coronary stents implantation. MethodsFirstly, a vessel edge enhancement algorithm is proposed, which enhances the gradient of the vessel edge, which not only makes the obtained vessel edge smoother, but also enhances the visual effect of the intersection of vessels; secondly, introduces noise filtering based on morphology, which can detect and remove linear noise similar to blood vessels; finally, in view of the problem that each image in the DSA blood vessel sequence can only show part of the blood vessels, which is not conducive to observing the overall state of the blood vessels, a blood vessel sequence image fusion is designed to display the blood vessels in each image on the same image. The state of blood vessels can be understood as a whole in one image. ResultsCompared with the segmentation method in the literature, the blood vessel overlap rate increased by 0.0089, and the mis-segmentation rate decreased by 0.0334. In the quantitative analysis, the improved blood vessel segmentation algorithm in this paper improves the overlapping rate and reduces the mis-segmentation rate. ConclusionFor the comparison of visual effects, the blood vessels segmented by the algorithm in this paper have higher clarity, which is helpful for doctors and nurses to comprehensively synthesize blood vessel clinical information. There is a more objective image basis in the preoperative diagnosis and postoperative rehabilitation nursing of coronary angiographic stent implantation.

Non-invasive detection of haemoglobin, platelets, and total bilirubin using hyperspectral cameras

Hyperspectral imaging has emerged as a promising high-resolution and real-time imaging technology with potential applications in medical diagnostics and surgical guidance. In this study, we developed a high-speed hyperspectral camera by integrating a Fabry-Perot cavity filter on each CMOS pixel. We used it to non-invasively detect three blood components (haemoglobin, platelet, and total bilirubin). Specifically, we acquired transmission images of the subject's fingers, extracted spectral signals at each wavelength, and used dynamic spectroscopy to obtain non-invasive blood absorption spectra. The prediction models were established using the PLSR method and were modelled and validated based on the standard clinical-biochemical test values. The experimental results demonstrated excellent performance. The best predictions were obtained for haemoglobin, with a high related coefficient (R) of 0.85 or more in both the calibration and prediction sets and a mean absolute percentage error (MAPE) of only 5.7%. The results for total bilirubin were also ideal, with R values exceeding 0.8 in both sets and a MAPE of 10.6%. Although the prediction results for platelets were slightly less satisfactory, the error was still less than 15%, indicating that the results were also acceptable. Overall, our study highlights the potential of hyperspectral imaging technology for the development of portable and affordable devices for blood analysis, which can be used in various settings.

Impedimetric DNA Sensor Based on Electropolymerized N-Phenylaminophenothiazine and Thiacalix[4]arene Tetraacids for Doxorubicin Determination.

Electrochemical DNA sensors are highly demanded for fast and reliable determination of antitumor drugs and chemotherapy monitoring. In this work, an impedimetric DNA sensor has been developed on the base of a phenylamino derivative of phenothiazine (PhTz). A glassy carbon electrode was covered with electrodeposited product of PhTz oxidation obtained through multiple scans of the potential. The addition of thiacalix[4]arene derivatives bearing four terminal carboxylic groups in the substituents of the lower rim improved the conditions of electropolymerization and affected the performance of the electrochemical sensor depending on the configuration of the macrocyclic core and molar ratio with PhTz molecules in the reaction medium. Following that, the deposition of DNA by physical adsorption was confirmed by atomic force microscopy and electrochemical impedance spectroscopy. The redox properties of the surface layer obtained changed the electron transfer resistance in the presence of doxorubicin due to its intercalating DNA helix and influencing charge distribution on the electrode interface. This made it possible to determine 3 pM-1 nM doxorubicin in 20 min incubation (limit of detection 1.0 pM). The DNA sensor developed was tested on a bovine serum protein solution, Ringer-Locke's solution mimicking plasma electrolytes and commercial medication (doxorubicin-LANS) and showed a satisfactory recovery rate of 90-105%. The sensor could find applications in pharmacy and medical diagnostics for the assessment of drugs able to specifically bind to DNA.

Organic near‐infrared photodetectors with photoconductivity‐enhanced performance

AbstractOrganic near‐infrared (NIR) photodetectors with essential applications in medical diagnostics, night vision, remote sensing, and optical communications have attracted intensive research interest. Compared with most conventional inorganic counterparts, organic semiconductors usually have higher absorption coefficients, and their thin active layer could be sufficient to absorb most incident light for effective photogeneration. However, due to the relatively poor charge mobility of organic materials, it remains challenging to inhibit the photogenerated exciton recombination and effectively extract carriers to their respective electrodes. Herein, this challenge was addressed by increasing matrix conductivities of a ternary active layer (D–A–D structure NIR absorber [2TT‐oC6B]:poly(N,N′‐bis‐4‐butylphenyl‐N,N′‐bisphenyl)benzidin [PolyTPD]:[6,6]‐phenyl‐C61‐butyric acid methyl ester [PCBM] = 1:1:1) upon in situ incident light illumination, significantly accelerating charge transport through percolated interpenetrating paths. The greatly enhanced photoconductivity under illumination is intrinsically related to the unique donor–acceptor molecular structures of PolyTPD and 2TT‐oC6B, whereas stable intermolecular interaction has been verified by systematic molecular dynamics simulation. In addition, an ultrafast charge transfer time of 0.56 ps from the NIR aggregation‐induced luminogens of 2TT‐oC6B absorber to PolyTPD and PCBM measured by femtosecond transient absorption spectroscopy is beneficial for effective exciton dissociation. The solution‐processed organic NIR photodetector exhibits a fast response time of 83 μs and a linear dynamic range value of 111 dB under illumination of 830 nm. Therefore, our work has opened up a pioneering window to enhance photoconductivity through in situ photoirradiation and benefit NIR photodetectors as well as other optoelectronic devices.

Brief Insight on Nanovesicular Liposomes as Drug-delivery Carriers for Medical Applications

Nanotechnology has revolutionized and improved the potential of drug-delivery systems. Lipidic nanovesicles, termed liposomes, offer several benefits as drug-delivery carriers due to their compositional similarity with human biological membranes. Some of the advantages offered by liposomes include targeted drug delivery and improved pharmacokinetics of an entrapped drug substance, leading to enhanced bioavailability. Additionally, the reduction in the drug dose or dosing frequency, reduced drug toxicity or side effects, longer and controlled therapeutic response of a drug essential for the treatment of chronic diseases, and improved patient acceptance margins are some other benefits offered by liposomes. The easily modifiable surface of liposomes makes it an ideal vehicle for targeting a drug to the desired site. The current review provides insight on liposomes as drug-delivery carriers. This review summarizes the classification of compositional constituents of liposomes based on their chemical nature and structure. Morphology-based classification of liposomes along with methods of preparation and characterization for liposomes are also summarized. The current review emphasizes the medical applications of liposomes, specifically as delivery carriers for therapeutic and diagnostic agents. The article features detailed therapeutic applications of liposomes based on routes of administration and specific disease conditions. Diagnostic applications of liposomes for improving the efficiency of available techniques to treat diseases such as cancer are also discussed. Liposomes are thus reviewed as multifaceted nanovesicular carriers with potential therapeutic and diagnostic medical applications. The prospective multifunctional application of combining imaging functionality with therapeutic agents in a single liposome for diagnosis and real-time treatment is anticipated to be the future of liposomal formulations.

Electrochemical Sweat Sensors

Sweat analysis by means of minimally invasive wearable sensors is considered a potentially disruptive method for assessing clinical parameters, with exciting applications in early medical diagnostics and high-performance sports. Electrochemical sensors and biosensors are especially attractive because of the possibility of the electronic integration of wearable devices. In this article, we review several aspects regarding the potentialities and present limitations of electrochemical sweat (bio)sensors, including: the main target analytes and their relationships with clinical conditions; most usual electrochemical techniques of transduction used according to the nature of the target analytes; issues connected to the collection of representative sweat samples; aspects regarding the associated, miniaturized electronic instrumentation used for signal processing and communication; and signal processing by machine learning.

Metabolomics: A New Era in the Diagnosis or Prognosis of B-Cell Non-Hodgkin's Lymphoma.

A wide range of histological as well as clinical properties are exhibited by B-cell non-Hodgkin's lymphomas. These properties could make the diagnostics process complicated. The diagnosis of lymphomas at an initial stage is essential because early remedial actions taken against destructive subtypes are commonly deliberated as successful and restorative. Therefore, better protective action is needed to improve the condition of those patients who are extensively affected by cancer when diagnosed for the first time. The development of new and efficient methods for early detection of cancer has become crucial nowadays. Biomarkers are urgently needed for diagnosing B-cell non-Hodgkin's lymphoma and assessing the severity of the disease and its prognosis. New possibilities are now open for diagnosing cancer with the help of metabolomics. The study of all the metabolites synthesised in the human body is called "metabolomics." A patient's phenotype is directly linked with metabolomics, which can help in providing some clinically beneficial biomarkers and is applied in the diagnostics of B-cell non-Hodgkin's lymphoma. In cancer research, it can analyse the cancerous metabolome to identify the metabolic biomarkers. This review provides an understanding of B-cell non-Hodgkin's lymphoma metabolism and its applications in medical diagnostics. A description of the workflow based on metabolomics is also provided, along with the benefits and drawbacks of various techniques. The use of predictive metabolic biomarkers for the diagnosis and prognosis of B-cell non-Hodgkin's lymphoma is also explored. Thus, we can say that abnormalities related to metabolic processes can occur in a vast range of B-cell non-Hodgkin's lymphomas. The metabolic biomarkers could only be discovered and identified as innovative therapeutic objects if we explored and researched them. In the near future, the innovations involving metabolomics could prove fruitful for predicting outcomes and bringing out novel remedial approaches.