Diagnosis Of Several Diseases Research Articles

Quantification of Ions in Human Urine-A Review for Clinical Laboratories.

Urine is an organic fluid produced by the kidney, and its analysis is one of the most requested laboratory tests by clinicians. The ionic composition of urine has been shown to be a good health indicator: it is useful for the diagnosis of several diseases, as well as monitoring therapeutics. This review considers laboratorial techniques that have been used throughout time for the quantification of ions in urine, and also considers some methodologies that can potentially be used in clinical laboratories for this kind of analysis. Those methods include gravimetry, titration, flame emission spectrophotometry (flame photometry), fluorimetry, potentiometry (ion selective electrodes), ion chromatography, electrophoresis, kinetic colorimetric tests, enzymatic colorimetric tests, flow cytometry, atomic absorption, plasma atomic emission spectrometry, and paper-based devices. Sodium, potassium, chloride, calcium, and magnesium are among the most important physiological ions, and their determination is frequently requested in hospitals. There have been many advances regarding the analysis of these ions in 24 h urine. However, there is still some way to go concerning the importance of intracellular ions in this type of sample as well as the use of occasional urine for monitoring these parameters.

Label-Free Metal-Oxide Transistor Biosensors for Metabolite Detection in Human Saliva.

Metabolites are essential molecules involved in various metabolic processes, and their deficiencies and excessive concentrations can trigger significant physiological consequences. The detection of multiple metabolites within a non-invasively collected biofluid could facilitate early prognosis and diagnosis of severe diseases. Here, a metal oxide heterojunction transistor (HJ-TFT) sensor is developed for the label-free, rapid detection of uric acid (UA) and 25(OH)Vitamin-D3 (Vit-D3) in human saliva. The HJ-TFTs utilize a solution-processed In2O3/ZnO channel functionalized with uricase enzyme and Vit-D3 antibody for the selective detection of UA and Vit-D3, respectively. The ultra-thin tri-channel architecture facilitates strong coupling between the electrons transported along the buried In2O3/ZnO heterointerface and the electrostatic perturbations caused by the interactions between the surface-immobilized bioreceptors and target analytes. The biosensors can detect a wide range of concentrations of UA (from 500nm to 1000µM) and Vit-D3 (from 100 pM to 120nm) in human saliva within 60s. Moreover, the biosensors exhibit good linearity with the physiological concentration of metabolites and limit of detections of ≈152nm for UA and ≈7 pM for Vit-D3 in real saliva. The specificity is demonstrated against various interfering species, including other metabolites and proteins found in saliva, further showcasing its capabilities.

Impacts of environmental factors on the aetiological diagnosis and disease severity of community-acquired pneumonia in China: a multicentre, hospital-based, observational study.

Environmental exposures are known to be associated with pathogen transmission and immune impairment, but the association of exposures with aetiology and severity of community-acquired pneumonia (CAP) are unclear. A retrospective observational study was conducted at nine hospitals in eight provinces in China from 2014 to 2019. CAP patients were recruited according to inclusion criteria, and respiratory samples were screened for 33 respiratory pathogens using molecular test methods. Sociodemographic, environmental and clinical factors were used to analyze the association with pathogen detection and disease severity by logistic regression models combined with distributed lag nonlinear models. A total of 3323 CAP patients were included, with 709 (21.3%) having severe illness. 2064 (62.1%) patients were positive for at least one pathogen. More severe patients were found in positive group. After adjusting for confounders, particulate matter (PM) 2.5 and 8-h ozone (O3-8h) were significant association at specific lag periods with detection of influenza viruses and Klebsiella pneumoniae respectively. PM10 and carbon monoxide (CO) showed cumulative effect with severe CAP. Pollutants exposures, especially PM, O3-8h, and CO should be considered in pathogen detection and severity of CAP to improve the clinical aetiological and disease severity diagnosis.

Age- and sex-adjusted reference intervals for steroid hormones measured by liquid chromatography-tandem mass spectrometry using a widely available kit.

Measurements utilizing commercially available sets of reagents for determination of steroid hormone profiles by liquid chromatography-tandem mass spectrometry (LC-MS/MS) have become increasingly important for routine laboratories. However, method-specific publications of reference intervals obtained from sufficiently large studies are often missing. After validation of performance characteristics, a widely available kit for steroid analysis by LC-MS/MS was used to measure concentrations of 15 endogenous steroids (aldosterone, cortisol, cortisone, corticosterone, 11-deoxycortisol, 21-deoxycortisol, dehydroepiandrosterone sulfate, estradiol, testosterone, androstenedione, dihydrotestosterone, dehydroepiandrosterone, 17-hydroxyprogesterone, 11-deoxycorticosterone, progesterone) in more than 500 blood samples from a population-based study. While randomly selected from a larger cohort, the samples equally represented both sexes and covered a wide range of adult age groups. Age- and sex-specific reference intervals were calculated, and correlation with BMI was assessed. Performance characteristics of the assay matched expectations for 9 of 15 steroids. For most of them, reference intervals obtained from our study population were comparable to those reported by others, with age and sex being the major determinants. A sex-specific correlation with BMI was found for seven steroids. We identified limitations regarding sensitivity of the method for quantification of progesterone in males and postmenopausal females. Concentrations of aldosterone, 21-deoxycortisol, estradiol, 11-deoxycorticosterone, and dihydrotestosterone could not be quantified in a large percentage of samples. The reference intervals for nine steroids will support meaningful interpretation for steroid profiles as measured by a widely used kit for LC-MS/MS-based quantification. Laboratories using such kits must be aware of potential limitations in sensitivity for some steroids included in the profile. Quantification of steroid hormones is a cornerstone for diagnosis of several diseases. Commonly used immunoassays have limitations in specificity. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a promising alternative, particularly if methods are harmonized across laboratories. The use of kits from commercial suppliers might support this. Clinical interpretation of steroid concentrations requires availability of appropriate reference intervals (RIs), but studies on RIs reported in the literature differ in preanalytical and analytical procedures. Here, we provide RIs for steroids measured by a widely available kit under preanalytical conditions mirroring common clinical practice. Such RIs might facilitate interpretation for those using the same method and comparable conditions in clinical routine.

(Eu-MOF)-derived Smart luminescent sensing for Ultrasensitive on-site detection of MiR-892b

MicroRNAs (miRNAs) are important biomacromolecules used as biomarkers for the diagnosis of several diseases. However, current detection strategies are limited by expensive equipment and complicated procedures. Here, we develop a portable, sensitive, and stable (Eu-MOF)-based sensing platform to detect miRNA via smartphone. The Eu-MOF absorbs the carboxyfluorescein (FAM)-tagged probe DNA (pDNA) to generate hybrid pDNA@Eu-MOF, which can efficiently quench the fluorescence of FAM through a photoinduced electron transfer (PET) process. When integrated with a smartphone, the nonemissive pDNA@ Eu-MOF hybrid could be utilized as a portable and sensitive platform to sense miRNA (miR-892b) with a detection limit of 0.32 pM, which could be even distinguished by the naked eye. Moreover, this system demonstrates high selectivity for identifying miRNA family members with single-base mismatches. Furthermore, the expression levels of miRNA in cancer cell samples could be analyzed accurately. Therefore, the proposed method offers a promising guideline for the design of MOF-based sensing strategies and expands their potential applications for diagnostic purposes.

Molecularly Imprinted Polymer Interface on Screen-Printed ZnO Nanorod Field Effect Transistors for Serotonin Detection in Clinical Samples.

Ultrasensitive detection of serotonin is crucial for the early diagnosis of several diseases like Parkinson's and Alzheimer's. Most of the existing detection strategies are still not suitable for sensitive point-of-care applications. This study presents direct molecular imprinting of serotonin on the surface of three-dimensional zinc oxide (ZnO) nanorod devices connected in a field effect transistor (FET) configuration to achieve ultrasensitive, real-time, and rapid detection with a convenient and affordable approach, which has significant potential for translation to clinical settings. This strategy has enabled pushing the detection limit to 0.1 fM in a physiological analyte in real time with screen-printed electrodes, thereby resulting in the convenient batch fabrication of sensors for clinical validation. The response of the sensor with the clinical sample has been correlated with that of the gold standard and has been observed to be statistically similar.

Applications and challenges of large language models in critical care medicine

The rapid development of big data methods and technologies has provided more and more new ideas and methods for clinical diagnosis and treatment. The emergence of large language models (LLM) has made it possible for human-computer interactive dialogues and applications in complex medical scenarios. Critical care medicine is a process of continuous dynamic targeted treatment. The huge data generated in this process needs to be integrated and optimized through models for clinical application, interaction in teaching simulation, and assistance in scientific research. Using the LLM represented by generative pre-trained transformer ChatGPT can initially realize the application in the diagnosis of severe diseases, the prediction of death risk and the management of medical records. At the same time, the time and space limitations, illusions and ethical and moral issues of ChatGPT emerged as the times require. In the future, it is undeniable that it may play a huge role in the diagnosis and treatment of critical care medicine, but the current application should be combined with more clinical knowledge reserves of critical care medicine to carefully judge its conclusions.

A Comprehensive Review on Disease Related Applications of Nanotechnology

Nanotechnology serves as a new approach in the treatment of many diseases which has very large number in the morbidity and mortality. Biosensing and molecular imaging assisted with nanotechnology helps in identifying and diagnosis of many diseases. These nanomaterials also helps in the targeted drug delivery to the specific region. The existence of nanomaterials have become increased over the period of 40 years. Nanoparticles helps in the diagnosis of several diseases especially detection of cancer biomarkers. This technology also has a greater ability in the management of neurodegenerative diseases such as Alzheimer’s disease, autoimmune diseases, skin diseases, renal diseases, tuberculosis and cancer. It also extended its application to targeted drug delivery, diagnosis and pharmacogenetics to individualize the treatment options for several diseases and disorder. Apart from this, nanoparticles also showed more efficiency for antimicrobial, antifungal and antiviral therapies. Nanotechnology has greater applications in the medical and pharmaceutical field due to its unique characteristics. Due to their smaller size, these nanomaterials have the ability to penetrate deeper into the biological tissues or skin and can elicit their therapeutic activities. Nanotechnology extended its application in the recent pandemic COVID-19 by the production of vaccines using lipid nanoparticles and also conquered the difficulties faced by the normal drug delivery systems. In this article, we can discuss about the various types and applications of nanotechnology.

A step ahead to enhancing routine breast cancer resection: Spheroid and hen's egg chorioallantoic membrane models to assess the photodynamic diagnosis efficiency of ALA and PSI-ALA-hex.

Aminolevulinic acid (ALA) and its derivatives have been used in the diagnosis of several diseases through topical, intravesical, and oral administration. However, their intravenous use for the theranostics of cancers has not raised interest despite its potential advantages. In this study, we compared the efficacy of ALA, its hexyl ester ALA-Hex, and our new derivative PSI-ALA-Hex to induce a fluorescent protoporphyrin IX (PpIX) overproduction in breast cancers. First, we tested the drugs on four subtypes of breast cancer spheroids in vitro. Our results demonstrated the capacity of ALA-Hex and PSI-ALA-Hex to produce PpIX in all breast spheroids, although ALA struggled in half of the models. We applied the chick embryo in vivo model to investigate the intravenous administration route of ALA and PSI-ALA-Hex, ALA-Hex being toxic. We engrafted breast cancer nodules having various hormonal profiles onto the chorioallantoic membrane of the eggs. They were all detected by fluorescence imaging with mild efficacy using PSI-ALA-Hex, which displayed a maximum selectivity of 2.2 to 2.9, whereas ALA showed a higher selectivity from 3.2 to 5.1 at 300μmol/kg. PSI-ALA-Hex was less appropriate for the diagnosis of breast cancer by intravenous administration. We show for the first time, to the best of our knowledge, the photodetection and imaging of a wide range of breast tumors in vivo upon intravenous treatment with ALA.

The Role of Nano-Sensors in Breath Analysis for Early and Non-Invasive Disease Diagnosis

Early-stage, precise disease diagnosis and treatment has been a crucial topic of scientific discussion since time immemorial. When these factors are combined with experience and scientific knowledge, they can benefit not only the patient, but also, by extension, the entire health system. The development of rapidly growing novel technologies allows for accurate diagnosis and treatment of disease. Nanomedicine can contribute to exhaled breath analysis (EBA) for disease diagnosis, providing nanomaterials and improving sensing performance and detection sensitivity. Through EBA, gas-based nano-sensors might be applied for the detection of various essential diseases, since some of their metabolic products are detectable and measurable in the exhaled breath. The design and development of innovative nanomaterial-based sensor devices for the detection of specific biomarkers in breath samples has emerged as a promising research field for the non-invasive accurate diagnosis of several diseases. EBA would be an inexpensive and widely available commercial tool that could also be used as a disease self-test kit. Thus, it could guide patients to the proper specialty, bypassing those expensive tests, resulting, hence, in earlier diagnosis, treatment, and thus a better quality of life. In this review, some of the most prevalent types of sensors used in breath-sample analysis are presented in parallel with the common diseases that might be diagnosed through EBA, highlighting the impact of incorporating new technological achievements in the clinical routine.

On-Field Test of Tuberculosis Diagnosis through Exhaled Breath Analysis with a Gas Sensor Array

Tuberculosis (TB) is among the more frequent causes of death in many countries. For pulmonary TB, early diagnosis greatly increases the efficiency of therapies. Although highly sensitive tests based on nucleic acid amplification tests (NAATs) and loop-mediated isothermal amplification (TB-LAMP) are available, smear microscopy is still the most widespread diagnostics method in most low-middle-income countries, and the true positive rate of smear microscopy is lower than 65%. Thus, there is a need to increase the performance of low-cost diagnosis. For many years, the use of sensors to analyze the exhaled volatile organic compounds (VOCs) has been proposed as a promising alternative for the diagnosis of several diseases, including tuberculosis. In this paper, the diagnostic properties of an electronic nose (EN) based on sensor technology previously used to identify tuberculosis have been tested on-field in a Cameroon hospital. The EN analyzed the breath of a cohort of subjects including pulmonary TB patients (46), healthy controls (38), and TB suspects (16). Machine learning analysis of the sensor array data allows for the identification of the pulmonary TB group with respect to healthy controls with 88% accuracy, 90.8% sensitivity, 85.7% specificity, and 0.88 AUC. The model trained with TB and healthy controls maintains its performance when it is applied to symptomatic TB suspects with a negative TB-LAMP. These results encourage the investigation of electronic noses as an effective diagnostic method for future inclusion in clinical practice.

In-silico prediction of RT-qPCR-high resolution melting for broad detection of emaraviruses.

Twenty-four species of RNA viruses contain members infecting economically important crops that are classified within the genus Emaravirus, family Fimoviridae. There are at least two other non-classified species that may be added. Some of these viruses are spreading rapidly and cause economically important diseases on several crops, raising a need for a sensitive diagnostic technique for taxonomic and quarantine purposes. High-resolution melting (HRM) has shown to be reliable for the detection, discrimination, and diagnosis of several diseases of plants, animals, and humans. This research aimed to explore the ability to predict HRM outputs coupled to reverse transcription-quantitative polymerase chain reaction (RT-qPCR). To approach this goal a pair of degenerate genus-specific primers were designed for endpoint RT-PCR and RT-qPCR-HRM and the species in the genus Emaravirus were selected to framework the development of the assays. Both nucleic acid amplification methods were able to detect in-vitro several members of seven Emaravirus species with sensitivity up to one fg of cDNA. Specific parameters for in-silico prediction of the melting temperatures of each expected emaravirus amplicon are compared to the data obtained in-vitro. A very distinct isolate of the High Plains wheat mosaic virus was also detected. The high-resolution DNA melting curves of the RT-PCR products predicted in-silico using uMeltSM allowed saving time while designing and developing the RT-qPCR-HRM assay since the approach avoided extensive searching for optimal HRM assay regions and rounds of HRM tests in-vitro for optimization. The resultant assay provides sensitive detection and reliable diagnosis for potentially any emaravirus, including new species or strains.

Tongue Color Analysis and Diseases Detection Based on a Computer Vision System

The tongue reflects the abnormal condition and behavior of the internal organs of the body, such as problems of the heart, liver, pancreas, stomach, intestines, blood diseases and others, which lead to changes in some of the features and characteristics of the tongue. The most important of these is tongue color, which can be adopted as a biometric that can be used in Computerized Tongue Diagnostic Systems (CTDS). Quantitative diagnosis of the tongue requires some devices, including image acquisition devices such as cameras, light sources, filters, color checkers, image analysis and processing devices through the application of some algorithms or image processing and color correction software, as well as a computer. This study proposes a real-time imaging system to analyze tongue color and diagnose diseases using a webcam under specific conditions. The proposed system was designed in a Matlab GUI environment. After testing the system on a data set of more than 100 images, the preliminary results showed that the proposed system gives a disease diagnosis with an accuracy rate of no less than 86.667%. The proposed system contributed to the diagnosis of several diseases in real time, with an accuracy of 95.45%, with ease of use, implementation and low cost. This gives impetus to further studies to apply computerized diagnosis in medical applications, to enhance the medical reality, monitor patient health, and make an accurate diagnosis.

Identifying elevated plasma free triiodothyronine levels: age-adapted reference intervals for pediatrics.

Elevated free T3 (FT3) is an important feature for the early diagnosis of several diseases among which Grave's disease or Allan-Hernon-Dudley syndrome. However, there is a lack of age-adapted reference intervals for plasma thyroid hormones in children. We conducted a study to define reference values of peripheral FT3 in children using a commonly used automated immunoassay. All thyroid function test (TFT) results from our lab collected during 9 months were extracted anonymously, and reference intervals establishment followed recommendations validated by International Federation of Clinical Chemistry (IFCC). We defined five reference intervals covering the whole pediatric period. Overall, 26.1% of peripheral FT3 measured in children with normal TSH are out of the adult reference range, and 22.2% are upper it leading to misinterpretation. In a 9-month old patient with severe neurodevelopmental disorders, a pathological elevated FT3 has been securely interpreted using the newly established interval. The study highlights the poor relevance of adult intervals in pediatric cares, as it confirms that plasmatic FT3 is higher during the whole pediatric period. This work reports useful age-adapted reference intervals for free T3 in pediatrics using a widely used electrochemiluminescent Immunoassay (ECLIA) kit.

Computational Intelligence-Based Disease Severity Identification: A Review of Multidisciplinary Domains.

Disease severity identification using computational intelligence-based approaches is gaining popularity nowadays. Artificial intelligence and deep-learning-assisted approaches are proving to be significant in the rapid and accurate diagnosis of several diseases. In addition to disease identification, these approaches have the potential to identify the severity of a disease. The problem of disease severity identification can be considered multi-class classification, where the class labels are the severity levels of the disease. Plenty of computational intelligence-based solutions have been presented by researchers for severity identification. This paper presents a comprehensive review of recent approaches for identifying disease severity levels using computational intelligence-based approaches. We followed the PRISMA guidelines and compiled several works related to the severity identification of multidisciplinary diseases of the last decade from well-known publishers, such as MDPI, Springer, IEEE, Elsevier, etc. This article is devoted toward the severity identification of two main diseases, viz. Parkinson's Disease and Diabetic Retinopathy. However, severity identification of a few other diseases, such as COVID-19, autonomic nervous system dysfunction, tuberculosis, sepsis, sleep apnea, psychosis, traumatic brain injury, breast cancer, knee osteoarthritis, and Alzheimer's disease, was also briefly covered. Each work has been carefully examined against its methodology, dataset used, and the type of disease on several performance metrics, accuracy, specificity, etc. In addition to this, we also presented a few public repositories that can be utilized to conduct research on disease severity identification. We hope that this review not only acts as a compendium but also provides insights to the researchers working on disease severity identification using computational intelligence-based approaches.

Enhancing deep learning techniques for the diagnosis of the novel coronavirus (COVID-19) using X-ray images

Deep learning techniques combined with radiological imaging provide precision in the diagnosis of diseases that can be utilised for the classification and diagnosis of several diseases in the medical sector. Several research studies have focused on binary classification of COVID-19, and there is limited research focusing on multiclass classification of COVID-19. The purpose of this study is to develop a model that can enhance the multiclass classification of COVID-19 using raw chest X-ray images. The study involved using convolutional neural networks as the classifier. Five pre-trained deep learning models including VGG16, MobileNet, EfficientNetB0, NasNetMobile and ResNet50V2 are used to distinguish between COVID-19 infection and other lung diseases. Data Augmentation and Normalization techniques have been used to improve the models’ performance and avoid training problems. The study findings revealed that it is possible to distinguish between COVID-19 infection and other lung diseases using pre-trained deep learning models. The proposed technique successfully classifies five classes (normal, COVID-19, lung opacity, viral pneumonia, and bacterial pneumonia). It is found that NasNetMobile model outperformed the rest of the models and achieved the highest results. It achieved an overall accuracy, sensitivity, specificity and precision of 91%, 91%, 97.7% and 91%, respectively. The VGG16 model produced better results in detecting COVID-19 infection, resulting in an accuracy of 95.8%. The suggested technique is more accurate in comparison to the other newly developed techniques presented in the literature. This provides healthcare staff with a powerful tool for the diagnosis of COVID-19 based on deep learning.

Proteolytic Biosensors with Functional Nanomaterials: Current Approaches and Future Challenges.

Proteolytic enzymes are one of the important biomarkers that enable the early diagnosis of several diseases, such as cancers. A specific proteolytic enzyme selectively degrades a certain sequence of a polypeptide. Therefore, a particular proteolytic enzyme can be selectively quantified by changing detectable signals causing degradation of the peptide chain. In addition, by combining polypeptides with various functional nanomaterials, proteolytic enzymes can be measured more sensitively and rapidly. In this paper, proteolytic enzymes that can be measured using a polypeptide degradation method are reviewed and recently studied functional nanomaterials-based proteolytic biosensors are discussed. We anticipate that the proteolytic nanobiosensors addressed in this review will provide valuable information on physiological changes from a cellular level for individual and early diagnosis.

Analysis of the microelement composition of saliva in patients with comorbidities

The microelement composition of saliva makes it possible to analyze the processes occurring in the oral cavity and the body as a whole, both under conditions of normal functioning and in the presence of pathological processes. At the same time, the technical simplicity of performing trace element analysis of salivary fluid as a possible diagnostic criterion for various diseases contributes to a significant number of studies devoted to the search for possible correlations between the trace element composition of salivary fluid and various types of pathologies. The possibility of using the analysis of the microelement composition of saliva in determining concomitant pathologies of organs and systems of the body as a whole is a promising direction in scientific research, including applied research. The article analyzes the possibilities of using microelement analysis of saliva in such pathologies as psoriasis, Parkinson's disease, schizophrenia, pathology of the gastrointestinal tract, diabetes mellitus, pathology of the cardiovascular system, giardiasis, oncological diseases. It is noted that at present, the determination of the microelement composition of saliva in various pathological conditions is considered as a promising clinical direction that allows for the simultaneous diagnosis of several diseases in real time.

NEW TECHNOLOGIES IN RADIOLOGY DIAGNOSTICS

Today, modern medical imaging techniques are in the process of developing and implementing machine learning and neural network-based programs to improve diagnosis of several diseases, including cancer. Such methods of image processing are superior to traditional methods of image assessment and reduce the time required and diagnostic error rate. In addition, more advanced programs, in the foreseeable future, will be able to act as a second opinion for the doctor, helping him in making decisions on the choice of techniques for treating patients. In this paper some aspects related to radiomics and radiogenomics, machine learning and the challenges of implementing the latest image evaluation algorithms are discussed.

The Emerging Roles of MicroRNAs as Biomarkers in Diabetic Nephropathy

Diabetic nephropathy (DN) is one of the most common complications of diabetes mellitus (DM), as well as the most common health issue among End Stage Renal Diseases (ESRD). Recent studies have shown that this concern will likely to become a global phenomenon in the future. Early diagnosis of DN is vital for its treatment. MicroRNAs (miRNAs or miRs) are the most promising for new biomarker candidates proposed for DN. MiRNAs, known as non protein-coding short-chain RNA sequences, have a regulatory role in many cellular events. Advances in molecular genetics and successful genomic techniques allow miRNAs to be used in the diagnosis of several diseases. As the DN diagnostic markers used today are insufficient for some cases, identifying new diagnostic markers is the basis of recent studies. Successful use of miRNAs in the areas of cancer, immunity and diabetes indicates that the DN perspective should be based on miRNA. In this review, miRNAs and their role in DN will be reviewed.