• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2904-2926
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• 2022

Currently, diabetes is the most common chronic disease in the world, affecting 23.7% of the population in the Kingdom of Saudi Arabia. Diabetes may be the cause of lower-limb amputations, kidney failure and blindness among adults. Therefore, diagnosing the disease in its early stages is essential in order to save human lives. With the revolution in technology, Artificial Intelligence (AI) could play a central role in the early prediction of diabetes by employing Machine Learning (ML) technology. In this paper, we developed a diagnosis system using machine learning models for the detection of type 2 diabetes among adults, through the adoption of two different diabetes datasets: one for training and the other for the testing, to analyze and enhance the prediction accuracy. This work offers an enhanced classification accuracy as a result of employing several pre-processing methods before applying the ML models. According to the obtained results, the implemented Random Forest (RF) classifier offers the best classification accuracy with a classification score of 98.95%.

• Tuberculosis and Respiratory Diseases
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• v.77 no.4
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• pp.161-166
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• 2014

Since tuberculosis (TB) remains a major global health concern and the incidence of multi-drug resistant (MDR)-TB is increasing globally, new modalities for the detection of TB and drug resistant TB are needed to improve TB control. The Xpert MTB/RIF test can be a valuable new tool for early detection of TB and rifampicin resistance, with a high sensitivity and specificity. Late-generation fluoroquinolones, levofloxacin, and moxifloxacin, which are the principal drugs for the treatment of MDR-TB, show equally high efficacy and safety. Systemic steroids may reduce the overall TB mortality attributable to all forms of TB across all organ systems, although inhaled corticosteroids can increase the risk of TB development. Although fixed dose combinations were expected to reduce the risk of drug resistance and increase drug compliance, a recent meta-analysis found that they might actually increase the risk of relapse and treatment failure. Regarding treatment duration, patients with cavitation and culture positivity at 2 months of TB treatment may require more than 6 months of standard treatment. New anti-TB drugs, such as linezolid, bedaquiline, and delamanid, could improve the outcomes in drug-resistant TB. Nontuberculous mycobacterial lung disease has typical clinical and immunological phenotypes. Mycobacterial genotyping may predict disease progression, and whole genome sequencing may reveal the transmission of Mycobacterium abscessus. In refractory Mycobacterium avium complex lung disease, a moxifloxacin-containing regimen was expected to improve the treatment outcome.

• Journal of Ginseng Research
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• v.48 no.2
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• pp.113-121
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• 2024

Since its outbreak in late 2019, the Coronavirus disease 2019 (COVID-19) pandemic has profoundly caused global morbidity and deaths. The COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has major complications in cardiovascular and pulmonary system. The increased rate of mortality is due to delayed detection of certain biomarkers that are crucial in the development of disease. Furthermore, certain proteins and enzymes in cellular signaling pathways play an important role in replication of SARS-CoV-2. Most cases are mild to moderate symptoms, however severe cases of COVID-19 leads to death. Detecting the level of biomarkers such as C-reactive protein, cardiac troponin, creatine kinase, creatine kinaseMB, procalcitonin and Matrix metalloproteinases helps in early detection of the severity of disease. Similarly, through downregulating Renin-angiotensin system, interleukin, Mitogen-activated protein kinases and Phosphoinositide 3-kinases pathways, COVID-19 can be effectively controlled and mortality could be prevented. Ginseng and ginsenosides possess therapeutic potential in cardiac and pulmonary complications, there are several studies performed in which they have suppressed these biomarkers and downregulated the pathways, thereby inhibiting the further spread of disease. Supplementation with ginseng or ginsenoside could act on multiple pathways to reduce the level of biomarkers significantly and alleviate cardiac and pulmonary damage. Therefore, this review summarizes the potential of ginseng extract and ginsenosides in controlling the cardiovascular and pulmonary diseases by COVID-19.

• Journal of Chest Surgery
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• v.47 no.3
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• pp.294-297
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• 2014

Cardiac complications such as arrhythmia and heart failure are common in Graves disease. Early detection and proper treatment of hyperthyroidism are important because cardiac complications are reported to be reversible if the thyroid function is normalized by medical treatment. We report here a case of cardiac complication of Graves disease that was too late to reverse with medical treatment and required surgical treatment.

• BMB Reports
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• v.40 no.2
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• pp.135-141
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• 2007

Conjugation of the methyl group at the fifth carbon of cytosines within the palindromic dinucleotide 5'-CpG-3' sequence (DNA methylation) is the best studied epigenetic mechanism, which acts together with other epigenetic entities: histone modification, chromatin remodeling and microRNAs to shape the chromatin structure of DNA according to its functional state. The cancer genome is frequently characterized by hypermethylation of specific genes concurrently with an overall decrease in the level of 5-methyl cytosine, the pathological implication of which to the cancerous state has been well established. While the latest genome-wide technologies have been applied to classify and interpret the epigenetic layer of gene regulation in the physiological and disease states, the epigenetic testing has also been seriously explored in clinical practice for early detection, refining tumor staging and predicting disease recurrence. This critique reviews the latest research findings on the use of DNA methylation in cancer diagnosis, prognosis and staging/classification.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.sup1
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• pp.110-112
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• 2008

Endometrial carcinoma is one of the most common gynecologic malignancies and which is predominant in postmenopausal women. Clinically many patients are hospitalized in early stage due to clinical sign and symptom such as vaginal bleeding and in this case, patient's prognosis is known to be good. However, considerable number of patients with advanced and relapsed disease reveal poor prognosis. Therefore, exact staging work up is essential for proper treatment as is primary lesion detection. $^{18}F-FDG-PET$ has been widely used for the evaluation of gynecologic malignancies such as cervical carcinoma and ovarian cancer. In contrast, FDG PET application to endometrial carcinoma is limited until now and there is no sufficient data to validate the usefulness of FDG PET for this disease yet. However, several studies showed promising results that FDG PET is sensitive and specific in detection of recurrent or metastatic lesions. Therefore further active investigation in this field can facilitate the use of FDG PET for endometrial carcinoma.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.245-257
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• 2023

The representative crop in the Republic of Korea, rice, is cultivated over extensive areas every year, which resulting in reduced resistance to pests and diseases. One of the major rice diseases, rice blast disease, can lead to a significant decrease in yields when it occurs on a large scale, necessitating early detection and effective control of rice blast disease. Drone-based crop monitoring techniques are valuable for detecting abnormal growth, but frequent image capture for potential rice blast disease occurrences can consume significant labor and resources. The purpose of this study is to early detect rice blast disease using remote sensing data, such as drone and satellite images, along with weather data. Satellite images was helpful in identifying rice cultivation fields. Effective detection of paddy fields was achieved by utilizing vegetation and water indices. Subsequently, air temperature, relative humidity, and number of rainy days were used to calculate the risk of rice blast disease occurrence. An increase in the risk of disease occurrence implies a higher likelihood of disease development, and drone measurements perform at this time. Spectral reflectance changes in the red and near-infrared wavelength regions were observed at the locations where rice blast disease occurred. Clusters with low vegetation index values were observed at locations where rice blast disease occurred, and the time series data for drone images allowed for tracking the spread of the disease from these points. Finally, drone images captured before harvesting was used to generate spatial information on the incidence of rice blast disease in each field.

• Korean Journal of Clinical Laboratory Science
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• v.54 no.4
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• pp.249-255
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• 2022

Alzheimer's disease (AD) represents a major public health concern and has been identified as a research priority. Clinical research evidence supports that the core cerebrospinal fluid (CSF) biomarkers for AD, including amyloid-β (Aβ42), total tau (T-tau), and phosphorylated tau (P-tau), reflect key elements of AD pathophysiology. Nevertheless, advances in the clinical identification of new indicators will be critical not only for the discovery of sensitive, specific, and reliable biomarkers of preclinical AD pathology, but also for the development of tests that facilitate the early detection and differential diagnosis of dementia and disease progression monitoring. The early detection of AD in its presymptomatic stages would represent a great opportunity for earlier therapeutic intervention. The chance of successful treatment would be increased since interventions would be performed before extensive synaptic damage and neuronal loss would have occurred. In this study, the importance of developing an early diagnostic method using cognitive decline biomarkers that can discriminate between normal, mild cognitive impairment (MCI), and AD preclinical stages has been emphasized.

• The Journal of the Korea Contents Association
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• v.21 no.12
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• pp.322-330
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• 2021

In modern aquaculture, mass mortality is a very important issue that determines the success of aquaculture business. If a fish disease is not detected at an early stage in the farm, the disease spreads quickly because the farm is a closed environment. Therefore, early detection of diseases is crucial to prevent mass mortality of fish raised in farms. Recently deep learning-based automatic identification of fish diseases has been widely used, but there are many difficulties in identifying objects due to insufficient images of fish diseases. Therefore, this paper suggests a method to generate a large number of fish disease images by synthesizing normal images and disease images using SinGAN deep learning model in order to to solve the lack of fish disease images. We generate images from the three most frequently occurring Paralichthys Olivaceus diseases such as Scuticociliatida, Vibriosis, and Lymphocytosis and compare them with the original image. In this study, a total of 330 sheets of scutica disease, 110 sheets of vibrioemia, and 110 sheets of limphosis were made by synthesizing 10 disease patterns with 11 normal halibut images, and 1,320 images were produced by quadrupling the images.

• Journal of Veterinary Science
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• v.23 no.1
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• pp.17.1-17.10
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• 2022

Background: Inspection of livestock farms using surveillance cameras is emerging as a means of early detection of transboundary animal disease such as African swine fever (ASF). Object tracking, a developing technology derived from object detection aims to the consistent identification of individual objects in farms. Objectives: This study was conducted as a preliminary investigation for practical application to livestock farms. With the use of a high-performance artificial intelligence (AI)-based 3D depth camera, the aim is to establish a pathway for utilizing AI models to perform advanced object tracking. Methods: Multiple crossovers by two humans will be simulated to investigate the potential of object tracking. Inspection of consistent identification will be the evidence of object tracking after crossing over. Two AI models, a fast model and an accurate model, were tested and compared with regard to their object tracking performance in 3D. Finally, the recording of pig pen was also processed with aforementioned AI model to test the possibility of 3D object detection. Results: Both AI successfully processed and provided a 3D bounding box, identification number, and distance away from camera for each individual human. The accurate detection model had better evidence than the fast detection model on 3D object tracking and showed the potential application onto pigs as a livestock. Conclusions: Preparing a custom dataset to train AI models in an appropriate farm is required for proper 3D object detection to operate object tracking for pigs at an ideal level. This will allow the farm to smoothly transit traditional methods to ASF-preventing precision livestock farming.