• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.183-189
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• 2023

A stroke is a medical disease where a blood vessel in the brain ruptures, causes damage to the brain. If the flow of blood and different nutrients to the brain is intermittent, symptoms may occur. Stroke is other reason for loss of life and widespread disorder. The prevalence of stroke is high in growing countries, with ischemic stroke being the high usual category. Many of the forewarning signs of stroke can be recognized the seriousness of a stroke can be reduced. Most of the earlier stroke detections and prediction models uses image examination tools like CT (Computed Tomography) scan or MRI (Magnetic Resonance Imaging) which are costly and difficult to use for actual-time recognition. Machine learning (ML) is a part of artificial intelligence (AI) that makes software applications to gain the exact accuracy to predict the end results not having to be directly involved to get the work done. In recent times ML algorithms have gained lot of attention due to their accurate results in medical fields. Hence in this work, Stroke disease identification system by using Machine Learning algorithm is presented. The ML algorithm used in this work is Artificial Neural Network (ANN). The result analysis of presented ML algorithm is compared with different ML algorithms. The performance of the presented approach is compared to find the better algorithm for stroke identification.

• Journal of Veterinary Science
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• v.25 no.1
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• pp.10.1-10.11
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• 2024

In livestock industry, there is growing interest in methods to increase the production efficiency of livestock to address food shortages, given the increasing global population. With the advancements in gene engineering technology, it is a valuable tool and has been intensively utilized in research specifically focused on human disease. In historically, this technology has been used with livestock to create human disease models or to produce recombinant proteins from their byproducts. However, in recent years, utilizing gene editing technology, cattle with identified genes related to productivity can be edited, thereby enhancing productivity in response to climate change or specific disease instead of producing recombinant proteins. Furthermore, with the advancement in the efficiency of gene editing, it has become possible to edit multiple genes simultaneously. This cattle breed improvement has been achieved by discovering the genes through the comprehensive analysis of the entire genome of cattle. The cattle industry has been able to address gene bottlenecks that were previously impossible through conventional breeding systems. This review concludes that gene editing is necessary to expand the cattle industry, improving productivity in the future. Additionally, the enhancement of cattle through gene editing is expected to contribute to addressing environmental challenges associated with the cattle industry. Further research and development in gene editing, coupled with genomic analysis technologies, will significantly contribute to solving issues that conventional breeding systems have not been able to address.

• Smart Media Journal
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• v.13 no.8
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• pp.49-57
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• 2024

In this paper, we propose an algorithm that can recommend personalized health functional foods considering diseases due to the high interest in health functional foods and the development of machine learning as society enters an aging phase. By applying the KNN algorithm, we presented a foundational framework for a platform for personalized health functional food recommendations through disease analysis, matching techniques of publicly available health functional food information, and national public data. To ensure reliable matching between diseases and health functional foods, we analyzed correlations, assessed the appropriateness and accuracy of variables for enhancing the KNN algorithm, and derived improvement directions for the proposed system through the improvement of learning models and information to be disclosed in the future.

• Molecules and Cells
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• v.25 no.1
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• pp.1-6
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• 2008

Osteoarthritis (OA), one of the most common skeletal disorders characterized by cartilage degradation and osteophyte formation in joints, is induced by accumulated mechanical stress; however, little is known about the underlying molecular mechanism. Several experimental OA models in mice by producing instability in the knee joints have been developed to apply approaches from mouse genetics. Although proteinases like matrix metalloproteinases and aggrecanases have now been proven to be the principal initiators of OA progression, clinical trials of proteinase inhibitors have not been successful for the treatment, turning the interest of researchers to the upstream signals of proteinase induction. These signals include undegraded and fragmented matrix proteins like type II collagen or fibronection that affects chondrocytes through distinct receptors. Another signal is proinflammatory factors that are produced by chondrocytes and synovial cells; however, recent studies that used mouse OA models in knockout mice did not support that these factors have a role in the central contribution to OA development. Our mouse genetic approaches found that the induction of a transcriptional activator Runx2 in chondrocytes under mechanical stress contributes to the pathogenesis of OA through chondrocyte hypertrophy. In addition, chondrocyte apoptosis has recently been identified as being involved in OA progression. We hereby propose that these endochondral ossification signals may be important for the OA progression, suggesting that the related molecules can clinically be therapeutic targets of this disease.

• Advances in Traditional Medicine
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• v.10 no.3
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• pp.222-230
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• 2010

Trichosanthes cucumerina Linn. (cucurbitaceae) is widely used in Indian folk medicine for variety of disease conditions. The aim of present study was to evaluate the antiulcer activity of 50% ethanolic extract of fruits of Trichosanthes cucumerina Linn. (TCFE) using various experimental models of gastric and duodenal ulceration in rats. Oral administration of 50% ethanolic extract of fruits of Trichosanthes cucumerina Linn. was evaluated in rats against ethanol, aspirin and pylorus ligated gastric ulcers as well as cysteamine-induced duodenal ulcers. In all the models studied, the antiulcer activity of TCFE compared with that of cimetidine (100 mg/kg, p.o.), an $H_2$ receptor antagonist. TCFE showed significant antiulcer activity in ethanol-induced and aspirin-induced gastric ulcer models. In 19 h pylorus ligated rats, significant reduction in ulcer index, total acidity and pepsin activity was observed with TCFE, when compared with the control group. Mucosal defensive factors such as pH, mucin activity and gastric wall mucous content was found to be increased with TCFE. TCFE was also, afforded remarkable protection in cysteamine-induced duodenal lesions. The antiulcer activity of TCFE was comparable with that of cimetidine. Thus, TCFE possess significant antiulcer activity against both gastric and duodenal ulcers in rats. The antiulcer activity may be attributed to its cytoprotective action and inhibition of acid secretary parameters.

• Journal of Preventive Medicine and Public Health
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• v.45 no.4
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• pp.259-266
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• 2012

Objectives: This study aims to determine the risk factors predicting rehospitalization by comparing three models and selecting the most successful model. Methods: In order to predict the risk of rehospitalization within 28 days after discharge, 11 951 inpatients were recruited into this study between January and December 2009. Predictive models were constructed with three methods, logistic regression analysis, a decision tree, and a neural network, and the models were compared and evaluated in light of their misclassification rate, root asymptotic standard error, lift chart, and receiver operating characteristic curve. Results: The decision tree was selected as the final model. The risk of rehospitalization was higher when the length of stay (LOS) was less than 2 days, route of admission was through the out-patient department (OPD), medical department was in internal medicine, 10th revision of the International Classification of Diseases code was neoplasm, LOS was relatively shorter, and the frequency of OPD visit was greater. Conclusions: When a patient is to be discharged within 2 days, the appropriateness of discharge should be considered, with special concern of undiscovered complications and co-morbidities. In particular, if the patient is admitted through the OPD, any suspected disease should be appropriately examined and prompt outcomes of tests should be secured. Moreover, for patients of internal medicine practitioners, co-morbidity and complications caused by chronic illness should be given greater attention.

• Journal of Korea Multimedia Society
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• v.24 no.7
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• pp.849-859
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• 2021

Prostate cancer (PCa) is a fatal disease that occurs in men. In general, PCa cells are found in the prostate gland. Early diagnosis is the key to prevent the spreading of cancers to other parts of the body. In this case, deep learning-based systems can detect and distinguish histological patterns in microscopy images. The histological grades used for the analysis were benign, grade 3, grade 4, and grade 5. In this study, we attempt to use transfer learning and fine-tuning methods as well as different model architectures to develop and compare the models. We implemented MobileNet, ResNet50, and DenseNet121 models and used three different strategies of freezing layers techniques of fine-tuning, to get various pre-trained weights to improve accuracy. Finally, transfer learning using MobileNet with the half-layer frozen showed the best results among the nine models, and 90% accuracy was obtained on the test data set.

• Laboraroty Animal Research
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• v.34 no.4
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• pp.264-269
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• 2018

Cell cycle dysfunction can cause severe diseases, including neurodegenerative disease and cancer. Mutations in cyclin-dependent kinase inhibitors controlling the G1 phase of the cell cycle are prevalent in various cancers. Mice lacking the tumor suppressors $p16^{Ink4a}$ (Cdkn2a, cyclin-dependent kinase inhibitor 2a), $p19^{Arf}$ (an alternative reading frame product of Cdkn2a,), and $p27^{Kip1}$ (Cdkn1b, cyclin-dependent kinase inhibitor 1b) result in malignant progression of epithelial cancers, sarcomas, and melanomas, respectively. Here, we generated knockout mouse models for each of these three cyclin-dependent kinase inhibitors using engineered nucleases. The $p16^{Ink4a}$ and $p19^{Arf}$ knockout mice were generated via transcription activator-like effector nucleases (TALENs), and $p27^{Kip1}$ knockout mice via clustered regularly interspaced short palindromic repeats/CRISPR-associated nuclease 9 (CRISPR/Cas9). These gene editing technologies were targeted to the first exon of each gene, to induce frameshifts producing premature termination codons. Unlike preexisting embryonic stem cell-based knockout mice, our mouse models are free from selectable markers or other external gene insertions, permitting more precise study of cell cycle-related diseases without confounding influences of foreign DNA.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.79-86
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• 2021

Alzheimer's is a chronic progressive disease which exhibits varied symptoms and behavioural traits from person to person. The deterioration in cognitive abilities is more noticeable through their Activities and Instrumental Activities of Daily Living rather than biological markers. This information discussed in social media communities was collected and features were extracted by using the proposed fuzzy logic based algorithm to address the uncertainties and imprecision in the data reported. The data thus obtained is used to train machine learning models in order to predict the longevity of the patients. Models built on features extracted using the proposed algorithm performs better than models trained on full set of features. Important findings are discussed and Support Vector Regressor with RBF kernel is identified as the best performing model in predicting the longevity of Alzheimer's patients. The results would prove to be of high value for healthcare practitioners and palliative care providers to design interventions that can alleviate the trauma faced by patients and caregivers due to chronic diseases.

• Korean Journal of Veterinary Research
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• v.61 no.2
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• pp.12.1-12.9
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• 2021

The mosquito-borne pathogen Zika virus may result in neurological disorders such as Guillain-Barré syndrome and microcephaly. The virus is classified as a member of the Flaviviridae family and its wide spread in multiple continents is a significant threat to public health. So, there is a need to develop animal models to examine the pathogenesis of the disease and to develop vaccines. To examine the clinical profile during Zika virus infection, we infected neonatal and adult wild-type mice (C57BL/6 and Balb/c) and compared the clinical signs of African-lineage strain (MR766) and Asian-lineage strain (PRVABC59, MEX2-81) of Zika virus. Consistent with previous reports, eight-week-old female Balb/c mice infected with these viral strains showed no changes in body weight, survival rate, and neurologic signs, but demonstrated increases in the weights of spleens and hearts. However, one-day-old neonates showed significantly lower survival rate and body weight with the African-lineage strain than the Asian-lineage strain. These results confirmed the pathogenic differences between Zika virus strains. We also evaluated the clinical responses in neonatal and adult mice of different strains. Our findings suggest that these are useful mouse models for characterization of Zika virus for vaccine development.