• Molecules and Cells
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• v.42 no.8
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• pp.604-616
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• 2019

Phosphoserine phosphatase (PSPH) is one of the key enzymes of the L-serine synthesis pathway. PSPH is reported to affect the progression and survival of several cancers in an L-serine synthesis-independent manner, but the mechanism remains elusive. We demonstrate that PSPH promotes lung cancer progression through a noncanonical L-serine-independent pathway. PSPH was significantly associated with the prognosis of lung cancer patients and regulated the invasion and colony formation of lung cancer cells. Interestingly, L-serine had no effect on the altered invasion and colony formation by PSPH. Upon measuring the phosphatase activity of PSPH on a serine-phosphorylated peptide, we found that PSPH dephosphorylated phospho-serine in peptide sequences. To identify the target proteins of PSPH, we analyzed the protein phosphorylation profile and the PSPH-interacting protein profile using proteomic analyses and found one putative target protein, IRS-1. Immunoprecipitation and immunoblot assays validated a specific interaction between PSPH and IRS-1 and the dephosphorylation of phospho-IRS-1 by PSPH in lung cancer cells. We suggest that the specific interaction and dephosphorylation activity of PSPH have novel therapeutic potential for lung cancer treatment, while the metabolic activity of PSPH, as a therapeutic target, is controversial.

• Tuberculosis and Respiratory Diseases
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• v.75 no.5
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• pp.181-187
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• 2013

The emergence of new therapeutic agents for non-small cell lung cancer (NSCLC) implies that histologic subtyping and molecular predictive testing are now essential for therapeutic decisions. Histologic subtype predicts the efficacy and toxicity of some treatment agents, as do genetic alterations, which can be important predictive factors in treatment selection. Molecular markers, such as epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement, are the best predictors of response to specific tyrosine kinase inhibitor treatment agents. As the majority of patients with NSCLC present with unresectable disease, it is therefore crucial to optimize the use of tissue samples for diagnostic and predictive examinations, particularly for small biopsy and cytology specimens. Therefore, each institution needs to develop a diagnostic approach requiring close communication between the pulmonologist, radiologist, pathologist, and oncologist in order to preserve sufficient biopsy materials for molecular analysis as well as to ensure rapid diagnosis. Currently, personalized medicine in NSCLC is based on the histologic subtype and molecular status. This review summarizes strategies for tissue acquisition, histologic subtyping and molecular analysis for predictive testing in NSCLC.

• Journal of Oral Medicine and Pain
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• v.47 no.2
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• pp.107-108
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• 2022

Artificial intelligence (AI) refers to the use of machines to mimic intelligent human behavior. It involves interactions with humans in clinical settings, and augmented intelligence is considered as a cognitive extension of AI. The importance of AI in healthcare and medicine has been emphasized in recent studies. Machine learning models, such as genetic algorithms, artificial neural networks (ANNs), and fuzzy logic, can learn and examine data to execute various functions. Among them, ANN is the most popular model for diagnosis based on image data. AI is rapidly becoming an adjunct to healthcare professionals and is expected to be human-independent in the near future. The introduction of AI to the diagnosis and treatment of oral diseases worldwide remains in the preliminary stage. AI-based or assisted diagnosis and decision-making will increase the accuracy of the diagnosis and render treatment more precise and personalized. Therefore, dental professionals must actively initiate and lead the development of AI, even if they are unfamiliar with it.

• Journal of Digestive Cancer Research
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• v.6 no.2
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• pp.45-49
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• 2018

Colorectal cancer (CRC) is the third most common cause of cancer-related death in the world. Although the long-term outcome of patients with metastatic CRC is still poor, target therapy including anti EGFR agents and anti VEGF agents and immunotherapy including anti PD-1 antibody and anti CTLA-4 antibody have shown clinical benefits in the treatment of patient with metastatic CRC. In the future, the personalized treatment strategy based on the clinical characteristics and biologic features of patients with metastatic CRC will be necessary. In this review, we summarized the mechanisms and clinical evidences of target therapy and immunotherapy, and the guideline of clinical practice in patients with metastatic CRC.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.6
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• pp.2933-2934
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• 2014

Involvement of a multidisciplinary team in cancer care may have added benefits over the existing system of patient management. A paradigm shift in the current patient management would allow more focus on nutritional support, in addition to clinical care. Malnutrition, a common problem in cancer patients, needs special attention from the early days of cancer care to improve quality of life and treatment outcomes. Patient management teams with trained oncology dietitians may provide quality personalized nutritional care to cancer patients.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.6
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• pp.303-308
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• 2017

Nowadays, living standard is improved and people have high interest to the personal health care problem. Accordingly, people desire to know the personal physical condition and the related medical treatment. Thus, there is the necessary of the personalized medical treatment, and there are many studies about the automatic disease diagnosis and the related services. Those studies focus on the particular disease prediction which is based on the related particular data. However, there is no studies about the medical treatment prediction. In our study, national health data based medical treatment predictor is built by using SVM, and the performance is evaluated by comparing with other prediction methods. The experimental results show that the health data based medical treatment prediction resulted in the average accuracy of 80%, and the SVM performs better than other prediction algorithms.

• Journal of the Korean Society of Physical Medicine
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• v.18 no.3
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• pp.73-84
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• 2023

PURPOSE: This study analyzed the impact of AI and biosensors on physical therapy, identifying the stage of customized technology development and future prospects. AI and biosensors improve the efficiency, establish customized treatment plans, and expand patient treatment opportunities. The study employed a literature review by searching databases and collecting research. METHODS: This study searched various databases related to the topic, collected existing research, papers, and reports, evaluated the literature, and summarize the results. RESULTS: Exercise therapy utilizing artificial intelligence can provide personalized and optimal exercise plans while monitoring rehabilitation progress. In addition, biosensors such as EMG sensors and accelerometers can monitor the individual progress in physical therapy, particularly in stroke patients, which can help improve physical therapy strategy and promote patient recovery. CONCLUSION: This study suggested that artificial intelligence can be applied in many areas of physical therapy, such as exercise therapy, customized treatment plans, rehabilitation and management, pain management, neuro rehabilitation, and auxiliary devices. Using AI technology, it is possible to analyze and improve exercise and posture, retrain the central nervous system, establish customized treatment plans for individual patients, predict and compare patient progress before and after treatment, and provide customized pain analysis and treatment methods. In addition, AI can provide neuro rehabilitation programs and customized auxiliary devices.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.34 no.4
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• pp.450-455
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• 2008

A Single Nucleotide Polymorphism (SNP) is a small genetic change or variation that can occur within a DNA sequence. It's the difference of one base at specific base pair position. SNP variation occurs when a single nucleotide, such as an A, replaces one of the other three nucleotide letters-C, G, or T. On average, SNP occur in the human population more than 1 percent of the time. They occur once in every 300 nucleotides on average, which means there are roughly 10 million SNPs in the human genome. Because SNPs occur frequently throughout the genome and tend to be relatively stable genetically, they serve as excellent biological markers. They can help scientists locate genes that are associated with disease such as heart disease, cancer, diabetes. They can also be used to track the inheritance of disease genes within families. SNPs may also be associated with absorbance and clearance of therapeutic agents. In the future, the most appropriate drug for an individual could be determined in advance of treatment by analyzing a patient's SNP profile. This pharmacogenetic strategy heralds an era in which the choice of drugs for a particular patient will be based on evidence rather than trial and error (so called "personalized medicine").

• Genomics & Informatics
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• v.11 no.4
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• pp.174-179
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• 2013

The large amount of data on cancer genome research has contributed to our understanding of cancer biology. Indeed, the genomics approach has a strong advantage for analyzing multi-factorial and complicated problems, such as cancer. It is time to think about the actual usage of cancer genomics in the clinical field. The clinical cancer field has lots of unmet needs in the management of cancer patients, which has been defined in the pre-genomic era. Unmet clinical needs are not well known to bioinformaticians and even non-clinician cancer scientists. A personalized approach in the clinical field will bring potential additional challenges to cancer genomics, because most data to now have been population-based rather than individualbased. We can maximize the use of cancer genomics in the clinical field if cancer scientists, bioinformaticians, and clinicians think and work together in solving unmet clinical needs. In this review, we present one imaginary case of a cancer patient, with which we can think about unmet clinical needs to solve with cancer genomics in the diagnosis, prediction of prognosis, monitoring the status of cancer, and personalized treatment decision.