Prediction Of Many Diseases Research Articles

Medical imaging consultation practices and challenges at public hospitals in the Amhara regional state, Northwest Ethiopia: a descriptive phenomenological study

BackgroundMedical imaging plays a vital role in the accurate diagnosis, treatment and outcome prediction of many diseases and injuries. However, in many African countries, deserving populations do not have access to the proper medical imaging specialists’ services. As a result, clinicians continue to struggle to provide medical imaging via consultation. However, little is known about conventional referral consultation practices and their challenges. This study, therefore, aimed to explore the practice and challenges of medical imaging service consultation among health professionals and patients in the context of the Ethiopian public healthcare delivery system.MethodsDescriptive phenomenological study was employed to explore the practice of medical imaging service consultation among health professionals and patients in public hospitals of Amhara region from October 12, 2021 to December 29, 2021. Semi-structured interview guides were prepared separately for key-informant and in-depth interviews. A total of 21 participants (6 hospital managers, 4 medical directors, 4 department heads, 3 medical imaging coordinators and 4 patients) were selected using the maximum variation sampling technique. All interviews were audio-recorded, transcribed verbatim and subjected to inductive thematic analysis using Open Code 4.02 software.ResultsSix major themes emerged following the thematic analysis: (1) medical image service delivery practices; (2) medical imaging consultation modalities; (3) benefits and drawbacks of the consultation modalities; (4) challenges; (5) challenge mitigation strategies; and (6) future recommendations. Image films, compact disks, and telegram apps were the consultation modalities used by the referring clinicians to send the medical images to radiologists. Frequent failure of imaging machines, delayed equipment maintenance, inadequate infrastructure, shortage of budget, lack of radiologists, and low-quality of printed image films were among the challenges influencing the medical imaging consultation service.ConclusionsThis research explored onsite and referral imaging consultation practices. However, there are many challenges encountered by the referring clinicians and the radiologists during the consultation process. These challenges could potentially affect clinicians’ ability to provide timely diagnosis and treatment services which would ultimately affects patient health status and service delivery. Virtual consultation via teleradiology and enhancing clinicians’ competence through long-term and short-term trainings are recommended to improve the referral consultation practice.

Challenges and Opportunities for Extracellular Vesicles in Clinical Oncology Therapy

Extracellular vesicles (EVs) are membrane-bound vesicles that can be released by all cell types. They may have different biogenesis, physical features, and cargo. EVs are important biomarkers for the diagnosis and prediction of many diseases due to their essential role in intercellular communication, their highly variable cargoes, and their accumulation in various body fluids. These natural particles have been investigated as potential therapeutic materials for many diseases. In our previous studies, the clinical usage of tumor-cell-derived microparticles (T-MPs) as a novel medication delivery system was examined. This review summarizes the clinical translation of EVs and related clinical trials, aiming to provide suggestions for safer and more effective oncology therapeutic systems, particularly in biotherapeutic and immunotherapeutic systems.

Hybrid Feature Selection Method for Predicting Alzheimer’s Disease Using Gene Expression Data

Gene expression (GE) classification is a research trend as it has been used to diagnose and prognosis many diseases. Employing machine learning (ML) in the prediction of many diseases based on GE data has been a flourishing research area. However, some diseases, like Alzheimer’s disease (AD), have not received considerable attention, probably owing to data scarcity obstacles. In this work, we shed light on the prediction of AD from GE data accurately using ML. Our approach consists of four phases: preprocessing, gene selection (GS), classification, and performance validation. In the preprocessing phase, gene columns are preprocessed identically. In the GS phase, a hybrid filtering method and embedded method are used. In the classification phase, three ML models are implemented using the bare minimum of the chosen genes obtained from the previous phase. The final phase is to validate the performance of these classifiers using different metrics. The crux of this article is to select the most informative genes from the hybrid method, and the best ML technique to predict AD using this minimal set of genes. Five different datasets are used to achieve our goal. We predict AD with impressive values for MultiLayer Perceptron (MLP) classifier which has the best performance metrics in four datasets, and the Support Vector Machine (SVM) achieves the highest performance values in only one dataset. We assessed the classifiers using seven metrics; and received impressive results, allowing for a credible performance rating. The metrics values we obtain in our study lie in the range [.97, .99] for the accuracy (Acc), [.97, .99] for F1-score, [.94, .98] for kappa index, [.97, .99] for area under curve (AUC), [.95, 1] for precision, [.98, .99] for sensitivity (recall), and [.98, 1] for specificity. With these results, the proposed approach outperforms recent interesting results. With these results, the proposed approach outperforms recent interesting results.

Serum soluble T cell immunoglobulin mucin domain-3 as an early predictive marker for severity of acute pancreatitis; a retrospective analysis

BackgroundEarly prediction of severe acute pancreatitis (SAP) plays an important role in timely treatment decisions. Soluble T cell immunoglobulin and mucin domain-3 (sTIM-3) has been applied as a potential biomarker for the prediction of many diseases, while its predictive ability for AP severity remains largely unexplored. In this study, we aimed to identify whether serum sTIM-3 could be used as an indicator of AP severity in the early stage of the disease.MethodsA retrospective study was conducted. The enrolled AP patients should meet the 2012 Atlanta guideline and have an onset to admission ≤ 48 h.ResultsA total of 94 AP patients were enrolled in the current analysis, including 42 (45%), 35 (37%), and 17 (18%) patients were diagnosed as mild AP (MAP), moderately SAP (MSAP), and SAP, respectively. SAP patients had significantly higher the white blood cells (WBCs) count, red blood cells (RBCs) count, C-reactive protein (CRP) level, direct bilirubin level, creatinine and procalcitonin levels compared with MAP and MSAP patients. Among SAP and MSAP patients, significantly higher APACHE II, BISAP, and MCTSI scores were observed compared with MAP patients, and there was significant difference in APACHE II and BISAP scores between SAP and MSAP patients. Stepwise multivariate linear regression analysis showed that the concentrations of serum sTIM-3, as well as the BISAP and MCTSI scores, were significantly associated with the severity of AP. The areas under the ROC curve were 0.914 (95% CI, 0.865-0.963), 0.855 (95%CI, 0.742-0.968) 0.853 (95%CI, 0.768-0.938), and 0.746 (95%CI, 0.633-0.860) for BISAP score, APACHE II score, sTIM-3 level, and MCTSI score, respectively.ConclusionsSerum sTIM-3 might be ultimately incorporated into a predictive system for assessing the severity of AP.

Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation

The technical improvements in healthcare sector today have given rise to many new inventions in the field of artificial intelligence. Patterns for disease identification are carried out, and the onset of prediction of many diseases is detected. Diseases include diabetes mellitus disease, fatal heart diseases, and symptomatic cancer. There are many algorithms that have played a critical role in the prediction of diseases. This paper proposes an ML based approach for diabetes mellitus disease prediction. For diabetes prediction, many ML algorithms are compared and used in the proposed work, and finally the three ML classifiers providing the highest accuracy are determined: RF, GBM, and LGBM. The accuracy of prediction is obtained using two types of datasets. They are Pima Indians dataset and a curated dataset. The ML classifiers LGBM, GB, and RF are used to build a predictive model, and the accuracy of each classifier is noted and compared. In addition to the generalized prediction mechanism, the data augmentation technique is also used, and the final accuracy of prediction is obtained for the classifiers LGBM, GB, and RF. A comparative study and demonstration between augmentation and non-augmentation are also discussed for the two datasets used in order to further improve the performance accuracy for predicting diabetes disease.

Mathematical Modelling to Simulate Biological Fluid Flow in a Collapsible Tube

The purpose of this paper is to advance a mathematical model for reviewing to simulate biological flows such as blood flow in arteries or veins, flow of urine in urethras and air flow in the bronchial airways. They can also be used to study and prediction of many diseases, as the lung disease (asthma and emphysema), or the cardiovascular diseases (heart stroke), Makinde (2005). In this work, laminar flow of an incompressible viscous fluid through a collapsible tube of circular cross section is considered. Collapsible tubes are easily deformed by negative transmural pressure owing to marked reduction of rigidity. Thus, they show a considerable nonlinearity and reveal various complicated phenomena Our objectives are to study the effect of temperature along the tube as the fluid Prandtl number and Reynolds number increases. We launch the mathematical formulation of the problem. The problem is solved by using power series and perturbation techniques with help of boundary conditions and results are displayed graphically for different flow characteristics, velocity profile.

Salivary proteins in health and disease.

Besides their structural catalytic and diverse regulatory functions, proteins are also precursors of many important biological compounds essential for normal functioning of humans. Many of these compounds may be used as markers for identification of specific pathological states. A comprehensive knowledge about the metabolism of salivary proteins and the mechanisms of action of their metabolites allowed the development of effective treatment for many disorders. However, it should not be forgotten that in some pathological conditions, these compounds not only could be involved in the pathogenesis but also could be used as tool in the prediction of many diseases. This paper is a review of the published literature on selected salivary proteins in the context of the physiological processes of the human body and chosen chronic disorders, such as diabetes, diabetic nephropathy, mucositis, oral mycoses and caries.

Activity of selected aromatic amino acids in biological systems.

Besides the structural function in proteins, aromatic amino acids are precursors of many important biological compounds essential for normal functioning of the human organism. Many of these compounds may be used as markers for identification of specific pathological states. Comprehensive knowledge about the metabolism of aromatic amino acids and mechanisms of action of their metabolites made it possible to develop effective treatments for many disorders. However, it should not be forgotten that in some pathological conditions, these compounds could not only be involved in the pathogenesis of many disease entities but could also be used as an important tool in prediction of many diseases. This paper contains a review of published literature on aromatic amino acids in the context of physiological processes of the human body and chosen social disorders, such as cancers; psychiatric disorders: depression, anxiety states, schizophrenia, bipolar affective disorders; neurodegenerative, and cardiovascular diseases; chronic kidney insufficiency or diabetes.

Familial frontotemporal dementia: from gene discovery to clinical molecular diagnostics.

Genetic testing is important for diagnosis and prediction of many diseases. The development of a clinical genetic test can be rapid for common disorders, but for rare genetic disorders this process can take years, if it occurs at all. We review the path from gene discovery to development of a clinical genetic test, using frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17) as an example of a complex, rare genetic condition. An Institutional Review Board-approved multidisciplinary research program was developed to identify patients with familial frontotemporal dementia. Genetic counseling is provided and DNA obtained to identify mutations associated with FTDP-17. In some cases it may be appropriate for individuals to be given the opportunity to learn information from the research study to prevent unnecessary diagnostic studies or the utilization of inappropriate therapies, and to make predictive testing possible. Mutations identified in a research laboratory must be confirmed in a clinical laboratory to be used clinically. To facilitate the development of clinical genetic testing for a rare disorder, it is useful for a research laboratory to partner with a clinical laboratory. Most clinical molecular assays are developed in research laboratories and must be properly validated. We conclude that the transition of genetic testing for rare diseases from the research laboratory to the clinical laboratory requires a validation process that maintains the quality-control elements necessary for genetic testing but is flexible enough to permit testing to be developed for the benefit of patients and families.