• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.231-243
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• 2021

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model "A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction" to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

• Tuberculosis and Respiratory Diseases
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• v.87 no.3
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• pp.221-233
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• 2024

The 2023 Global Initiative for Chronic Obstructive Lung Disease (GOLD) revised the definition of chronic obstructive pulmonary disease (COPD) to broadly include a variety of etiologies. A new taxonomy, composed of etiotypes, aims to highlight the heterogeneity in causes and pathogenesis of COPD, allowing more personalized management strategies and emphasizing the need for targeted research to understand and manage COPD better. However, controversy arises with including some diseases under the umbrella term of COPD, as their clinical presentations and treatments differ from classical COPD, which is smoking-related. COPD due to infection (COPD-I) and COPD due to environmental exposure (COPD-P) are classifications within the new taxonomy. Some disease entities in these categories show distinct clinical features and may not benefit from conventional COPD treatments, raising questions about their classification as COPD subtypes. There is also controversy regarding whether bronchiectasis with airflow limitations should be classified as an etiotype of COPD. This article discusses controversial issues associated with the proposed etiotypes for COPD in terms of COPD-I, COPD-P, and bronchiectasis. While the updated COPD definition by GOLD 2023 is a major step towards recognizing the disease's complexity, it also raises questions about the classification of related respiratory conditions. This highlights the need for further research to improve our understanding and approach to COPD management.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.19
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• pp.8035-8040
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• 2014

Gastric cancer is the second after lung cause of cancer-related mortality in the world. Early detection and treatment can lead to a long survival time. Recently microarrays and next generation sequencing (NGS) have become very useful tools of comprehensive research into gastric cancer, facilitating the identification of treatment targets and personalized treatments. However, there are numerous challenges from cancer target discovery to practical clinical benefits. Although there are many biomarkers and target agents, only a minority of patients are tested and treated accordingly. Microarray technology with maturity was established more than 10 years ago, and has been widely used in the study of functional genomics, systems biology, and genomes in medicine. Second generation sequencing technology is more recent, but development is very fast, and it has been applied to the genome, including sequencing and epigenetics and many aspects of functional genomics. Here we review insights gained from these studies regarding the technology of microarray and NGS, how to elucidate the molecular basis of gastric cancer and identify potential therapeutic targets, and how to analyse candidate genes. We also discuss the challenges and future directions of such efforts.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.26 no.2
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• pp.75-85
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• 2015

Internet gaming disorder (IGD), one of the common subtypes of internet addiction, is now classified in Section 3 of DSM-5 and is increasingly regarded as a growing health concern in many parts of the world. Consequently, many psychotherapeutic and psychopharmacological approaches have been considered and some research regarding therapeutic strategies has been conducted. However, treatment of IGD is in its early stages and therefore is not yet well established. This article reviews multiple therapeutic modalities including our own treatment model for IGD according to clinical and biological effects, thus providing suggestions for standard treatment strategies. The two main streams are psychopharmacological treatment and cognitive-behavior treatment, and the cognitive-behavior approach includes cognitive reconstruction, psychoeducation, and parenting coach. Many other non-pharmacological treatments are also recommended for personalized treatment of IGD.

• Molecules and Cells
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• v.46 no.10
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• pp.579-588
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• 2023

Sarcomas are rare and heterogeneous mesenchymal neoplasms originating from the bone or soft tissues, which pose significant treatment challenges. The current standard treatment for sarcomas consists of surgical resection, often combined with chemo- and radiotherapy; however, local recurrence and metastasis remain significant concerns. Although immunotherapy has demonstrated promise in improving long-term survival rates for certain cancers, sarcomas are generally considered to be relatively less immunogenic than other tumors, presenting substantial challenges for effective immunotherapy. In this review, we examine the possible opportunities for sarcoma immunotherapy, noting cancer testis antigens expressed in sarcomas. We then cover the current status of immunotherapies in sarcomas, including progress in cancer vaccines, immune checkpoint inhibitors, and adoptive cellular therapy and their potential in combating these tumors. Furthermore, we discuss the limitations of immunotherapies in sarcomas, including a low tumor mutation burden and immunosuppressive tumor microenvironment, and explore potential strategies to tackle the immunosuppressive barriers in therapeutic interventions, shedding light on the development of effective and personalized treatments for sarcomas. Overall, this review provides a comprehensive overview of the current status and potential of immunotherapies in sarcoma treatment, highlighting the challenges and opportunities for developing effective therapies to improve the outcomes of patients with these rare malignancies.

• International Journal of Heart Failure
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• v.6 no.1
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• pp.11-19
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• 2024

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

• Journal of Gastric Cancer
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• v.19 no.4
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• pp.408-416
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• 2019

Purpose: To study the efficacy of capecitabine or S-1 plus oxaliplatin (CAPOX or SOX) for treating thymidine phosphorylase (TP)- or dihydropyrimidine dehydrogenase (DPD)-positive advanced gastric cancer. Materials and Methods: Eighty-six patients with stage IIIC to IV gastric cancer were assessed for TP and DPD expression by immunohistochemistry. The association between CAPOX or SOX efficacy and TP/DPD expression was retrospectively analyzed. Results: There were no significant differences in the objective remission rate (ORR, 52.27% vs. 47.62%; P>0.05), disease control rate (72.73% vs. 73.81%, P>0.05), progression-free survival (hazard ratio [HR], 1.119; 95% confidence interval [CI], 0.739-1.741; P=0.586), and overall survival (OS; HR, 0.855; 95% CI, 0.481-1.511; P=0.588) between CAPOX and SOX. A higher number of stage IV patients showed TP positivity, while DPD-positive patients predominantly showed intestinal type of gastric cancer. In TP-positive patients, the ORRs associated with CAPOX and SOX treatments were 57.14% and 38.10%, respectively; OS was better with CAPOX than with SOX (HR, 0.447; 95% CI, 0.179-0.978; P=0.046). Among DPD-positive patients, the SOX treatment-associated ORR (60.87%) was significantly higher than the CAPOX treatment-associated ORR (43.48%). Furthermore, SOX treatment resulted in better OS than did CAPOX treatment (HR, 2.020; 95% CI, 1.019-4.837; P=0.049). Conclusions: No significant difference in clinical efficacy was found between CAPOX and SOX. TP-positive patients might respond better to CAPOX while DPD-positive patients may respond better to SOX. Our findings might serve as a guide for personalized chemotherapy for gastric cancer.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.309-327
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• 2021

Dynamic treatment regimes (DTRs) are decision-making rules designed to provide personalized treatment to individuals in multi-stage randomized trials. Unlike classical methods, in which all individuals are prescribed the same type of treatment, DTRs prescribe patient-tailored treatments which take into account individual characteristics that may change over time. The Q-learning method, one of regression-based algorithms to figure out optimal treatment rules, becomes more popular as it can be easily implemented. However, the performance of the Q-learning algorithm heavily relies on the correct specification of the Q-function for response, especially in observational studies. In this article, we examine a number of double-robust weighted least-squares estimating methods for Q-learning in high-dimensional settings, where treatment models for propensity score and penalization for sparse estimation are also investigated. We further consider flexible ensemble machine learning methods for the treatment model to achieve double-robustness, so that optimal decision rule can be correctly estimated as long as at least one of the outcome model or treatment model is correct. Extensive simulation studies show that the proposed methods work well with practical sample sizes. The practical utility of the proposed methods is proven with real data example.

• BMB Reports
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• v.56 no.10
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• pp.569-574
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• 2023

Aberrant DNA methylation plays a pivotal role in the onset and progression of colorectal cancer (CRC), a disease with high incidence and mortality rates in Korea. Several CRC-associated diagnostic and prognostic methylation markers have been identified; however, due to a lack of comprehensive clinical and methylome data, these markers have not been validated in the Korean population. Therefore, in this study, we aimed to obtain the CRC methylation profile using 172 tumors and 128 adjacent normal colon tissues of Korean patients with CRC. Based on the comparative methylome analysis, we found that hypermethylated positions in the tumor were predominantly concentrated in CpG islands and promoter regions, whereas hypomethylated positions were largely found in the open-sea region, notably distant from the CpG islands. In addition, we stratified patients by applying the CpG island methylator phenotype (CIMP) to the tumor methylome data. This stratification validated previous clinicopathological implications, as tumors with high CIMP signatures were significantly correlated with the proximal colon, higher prevalence of microsatellite instability status, and MLH1 promoter methylation. In conclusion, our extensive methylome analysis and the accompanying dataset offers valuable insights into the utilization of CRC-associated methylation markers in Korean patients, potentially improving CRC diagnosis and prognosis. Furthermore, this study serves as a solid foundation for further investigations into personalized and ethnicity-specific CRC treatments.