• Title/Summary/Keyword: health care system

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Differential Expression of Chemokine MCP-1, MIP-1α, MIP-2 in Lipopolysaccharide-stimulated Neonatal and Adult Rat Brain (LPS 유도에 의한 신생쥐에서 chemokine의 단계별 발현)

  • Lee, Jong-Hwan
    • Journal of Life Science
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    • v.16 no.5
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    • pp.840-849
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    • 2006
  • Severe brain injuries induced by toxin pose one of the most important problems on our health care because of their high morbidity and mortality, are implicated to leucocyte infiltration more premature or immature brain than mature brain. Chemokines are the induction meditators for infiltration of inflammatory cells to the inflammation sites. In order to study the mechanism of leucocyte infiltration, the expression of several chemokines, MCP-1, $MIP-1{\alpha}$ and MIP-2 was studied in lipopolysaccharide(LPS)-stimulated neonatal and adult brain. One week old Sprague-Dawley rats or adult male rats weighing 300-350 g were used for the experiment. After anesthetization, $1\;{\mu}l$ LPS (0.5 mg/ml) subsequently was injected in the right caudate nucleus of the brain with stereotaxic frame. Animals were sacrificed at 6 hours, 24 hours, and 72 hours after injection. The present study was carried out using RT-PCR for the mRNA and immunohistochemistry for the expression of the proteins. In the neonatal rat brain, prominent interstitial edema with significant accumulation of leukocytes was detected at 24 and 72 hours after LPS injection. A semiquantitative analysis of RT-PCR revealed that the MCP-1, $MIP-1{\alpha}$, and MIP-2 mRNA expression peaked at 24 hours in neonatal and adult rat brain. Neonatal rats showed about 2.6, 1.4, and 1.2 times more expression of the MCP-1, $MIP-1{\alpha}$, and MIP-2 than that of the adult rats in the brain tissue. Immunohistochemical analysis also showed that MCP-1 immunoreactivity was paralleled with the RT-PCR results. MCP-1 protein was significantly detected at 24 and 72 hours in the brain parenchyma. $MIP-1{\alpha}$protein was highly expressed at 24 hours. The results of leukocyte infiltration in H&E stain was parallelled with that of the immunohistochemistry. Chemokine proteins were markedly detected at 24 hours after injection of LPS and neutrophil influx into intraparenchymal was prominent at 24 hours. These results suggest that the leukocyte infiltration in the intracranial infection may be controlled by mechanisms influenced by chemokine producing cells in the central nervous system such as microglia, astrocyte and endothelial cell.

Effects Of Environmental Factors And Individual Traits On Work Stress And Ethical Decision Making (간호사의 환경적 요소와 개인적 특성이 직무스트레스와 윤리적 의사결정에 미치는 영향)

  • Kim, Sang Mi L.;Shake ketefian
    • Journal of Korean Academy of Nursing
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    • v.23 no.3
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    • pp.417-430
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    • 1993
  • 이 연구는 환경적 요소(간호사의 자율성, 조직의 표준화)와 개인의 특성(통제위, 나이, 경험. 간호역할개념, 도덕성), 직무 스트레스, 윤리적 의사결정 사이의 관계를 이론적 틀을 구성하여 테스트함으로써 그 인과관계를 탐구하였다. 본 연구를 위해 개발된 모형은 1) Katz와 Kahn의 조직에 대한 개방체계 이론(open systems theory of organization) ; 2) Kahn. Wolfe, Quinn, Snoek의 스트레스 이론 (theory of stress) : 3) Kohlberg의 도덕발달 이론(theory of moral develop-ment): 그리고 4) 여러 문헌고찰을 기초로 하였다. 본 연구의 모형은 2가지의 주요 종속변수(직무 스트레스, 윤리적 간호행위), 2가지 매개변수(간호 역할개념, 도덕성 발달정도) 그리고 여러 독립변수들(조직의 표준화, 자율성, 통제위, 교육, 나이, 경험 등)로 구성되었다. 간단히 말해, 간호사의 스트레스와 윤리적 간호행위 를 개인 자신과 환경이라는 두 요소의 결과로 간주한 것이다. 미국(2개주)의 여러 건강관리기관에 근무하는 224명의 정규 간호사를 대상으로 하였고. 가설 검증을 위하여 1) 변수간의 인과관계를 조사하기 위한 Linear Structural Relationships(LISREL)기법과 2) 나이, 경험, 교육이 변수간의 관계에 미치는 중간역할을 알아보기 위해 상관분석을 이용하였다. LISREL결과를 보면 제시된 모델이 각 내재 변수에 상당한 설명력을 가지면서 자료에 잘 맞는 것으로 나타났다. 이 연구에서 가장 뚜렷한 점으로 나타난 것은 개인의 특성보다 환경적 요소로서의 자율성이 직무스트레스와 윤리적 의사결정을 예견하는데 훨씬 중요한 변수로 부각되었다는 점이다. 또한 간호사의 전문적 역할개념과 봉사적 역할개념이 간호사의 윤리적 의사결정을 예견하는 가장 중요한 요소로 나타났다. 중간영향(moderation effect)을 보면, 젊고 경험이 적은 간호사일수록 나이가 많고 경험있는 간호사보다 환경적 요소(자율성)에 더 큰 영향을 받는다는 것을 암시하고 있다. 또한 4년제 대학 이상을 졸업한 간호사의 윤리 적 간호행 위 는 2, 3년제 를 졸업 한 간호사 보다 환경적 요소에 의해 덜 영향을 받는 것으로 나타났다. 한편 자율성의 부족은 2, 3년제 졸업 간호사보다 4년제 졸업 간호사에게 더 심한 스트레스가 되고 있음을 시사하였다. 이 연구의 결과로부터 적어도 다음과 같은 두 가지 실제적인 제언을 도출할 수 있다. 첫째, 이 연구는 환경적요소로서의 자율성이 다른 어떤 개인적인 요소보다 직무 스트레스를 예견하는 데 중요한 요소라는 것을 제시하였다. 이것은 간호행정가들에게, 간호사의 직무 스트레스를 감소시키기 위해선 “자율성”이 아주 중요히 다루어져야 한다는 것을 의미한다. 만일 간호사들의 직무스트레스가 그 개인의 복지에 큰 해가 되고 환자를 간호하는 데 직접적으로 관계된다면, 간호행정가는 그 조직의 직무체계를 다시 평가해서 일에 대한 새로운 설계가 필요한지를 파악해야 한다. 또한 이 연구는 직무를 다시 설계할 경우, 누구에게 먼저 촛점을 두고 시작해야 하는지를 밝혀주고 있다. 즉, 젊고 경험이 미숙한 간호사들에게 촛점을 두고 시작해야 하며, 작업환경의 가장 중요한 차원중의 하나인 사회적 지원(social support)을 조심스럽게 고려해 보아야 한다. 둘째, 간호사의 윤리적 간호행위를 높히기 위해 전문적 역할개념과 봉사적 역할개념이 재강조될 필요가 있다. 이 두 역할개념 들을 교육을 통하여 효과적으로 가르칠 필요가 있다고 본다. 이 두 개념들이 간호사의 바람직한 간호행 위에 영향을 미치는 가장 중요한 요소로 나타났기 때문이다. 또한, 본 연구결과에 따르면, 경험이 많을수록 일에 싫증을 느껴 바람직한 윤리적 간호행위가 감소되는 경향이 있었다. 따라서, 건강관리체제 (health care system) 안에서의 간호사의 역할이-전문직으로서의, 그리고 환자를 위한 옹호자로서의-학교와 임상에서 효과적으로 교육되어져야 한다고 본다. 간호사들의 역할에 대한 계속적인 교육이 학생은 물론 임상 간호사들에게도 실시되어져야 할 것이다. 미래연구의 방향을 제시해 보면 첫째로 연구의 일반화를 높히기 위해 더 많은 대상자를 포함시켜야 한다. 이는 여러 종류의 표본을 반드시 한번에 전부 포함시켜야 한다는 것을 의미하는 것이 아니고, 특정한 여러 표본들을 연속적으로 연구함으로서 이 목표를 성취할 수 있다고 생각한다. 둘째는 여러 construct들(윤리적 간호행위, 직무 스트레스, 간호 역할개념 등)에 대한 적절한 측정도구를 개발해야 한다. 측정도구를 개발하기 위해서는 풍부하고 세세한 통찰력을 제공하는 질적인 정보를 얻는 것이 선행되어야 한다. 셋째, 윤리적 간호행위와 직무 스트레스에 관한 연구를 증진시키기 위해 실험설계 및 종단적 연구(expel-imental, longitudinal design)가 시도될 필요가 있다. 마지막으로, 윤리적 간호행위와 직무 스트레스를 예견할 수 있는 이론적 탐구(theoretical exploration), 즉 이론정립을 위하여, 환경적 요소와 개인의 특성에 대한 자세한 정보를 제공해 줄 수 있는 질적 연구들이 요구된다.

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A Study on the Effect of Person-Job Fit and Organizational Justice Recognition on the Job Competency of Small and Medium Enterprises Workers (중소기업 종사자들의 직무 적합성과 조직 공정성 인식이 직무역량에 미치는 영향에 관한 연구)

  • Jung, Hwa;Ha, Kyu Soo
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.14 no.3
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    • pp.73-84
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    • 2019
  • Despite decades of work experience, workers at small- and medium-sized enterprises(SME) here have yet to make inroads into the self-employed sector that utilizes the job competency they have accumulated at work after retirement. Unlike large companies, SME do not have a proper system for improving the long-term job competency of their employees as they focus on their immediate performance. It is necessary to analyse the independent variables affecting the job competency of employees of SME to derive practical implications for the personnel of SME. In the preceding studies, there are independent variable analyses that affect job competency in specialized industries, such as health care, public officials and IT, but the analysis of workers at SME is insufficient. This study set the person-job fit and organizational justice based on the prior studies of the independent variables that affect the job competency of SME general workers as a dependent variable. The sub-variables of each variable derived knowledge, skills, experience, and desire for person-job fit, and distribution, procedural and deployment justice for organizational justice, respectively. The survey of employees of SME in Korea was conducted from February to March 2019 by Likert 5 scales, and the survey was retrieved from 323 people and analyzed in a demonstration using the SPSS and AMOS statistics package. Among the four sub-independent variables of person-job fit, knowledge, skills and experience were shown to have a significant impact on the job competency, and desire was not shown to be so. Among the three sub-independent variables of organizational justice, deployment justice has a significant impact on job competency, but distribution and procedural justices have not. Personnel managers of SME need to improve the job competency of their employees by appropriately utilizing independent variables such as knowledge, skills, experience and deployment at each stage, including recruitment, deployment, and promotion. Future job competency modeling studies are needed to overcome the limitations of this study, which fails to objectively measure job competency.

Nonlinear Vector Alignment Methodology for Mapping Domain-Specific Terminology into General Space (전문어의 범용 공간 매핑을 위한 비선형 벡터 정렬 방법론)

  • Kim, Junwoo;Yoon, Byungho;Kim, Namgyu
    • Journal of Intelligence and Information Systems
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    • v.28 no.2
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    • pp.127-146
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    • 2022
  • Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

A Study on Medical Waste Generation Analysis during Outbreak of Massive Infectious Diseases (대규모 감염병 발병에 따른 의료폐기물 발생량 예측에 관한 연구)

  • Sang-Min Kim;Jin-Kyu Park;In-Beom Ko;Byung-Sun Lee;Sang-Ryong Shin;Nam-Hoon Lee
    • Journal of the Korea Organic Resources Recycling Association
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    • v.31 no.4
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    • pp.29-39
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    • 2023
  • In this study, an analysis of medical waste generation characteristics was conducted, differentiating between ordinary situation and the outbreaks of massive infectious diseases. During ordinary situation, prediction models for medical waste quantities by type, general medical waste(G-MW), hazardous medical waste(H-MW), infectious medical waste(I-MW), were established through regression analysis, with all significance values (p) being <0.0001, indicating statistical significance. The determination coefficient(R2) values for prediction models of each category were analyzed as follows : I-MW(R2=0.9943) > G-MW(R2=0.9817) > H-MW(R2=0.9310). Additionally, factors such as GDP(G-MW), the number of medical institutions (H-MW), and the elderly population ratio(I-MW), utilized as influencing factors and consistent with previous literature, showed high correlations. The total MW generation, evaluated by combining each model, had an MAE of 2,615 and RMSE of 3,353. This indicated accuracy levels similar to the medical waste models of H-MW(2,491, 2,890) and I-MW(2,291, 3,267). Due to limitations in accurately estimating the quantity of medical waste during the rapid and outbreaks of massive infectious diseases, the generation unit of I-MW was derived to analyze its characteristics. During the early unstable stage of infectious disease outbreaks, the generation unit was 8.74 kg/capita·day, 2.69 kg/capita·day during the stable stage, and an average of 0.08 kg/capita·day during the reduction stage. Correlation analysis between generation unit of I-MW and lethality rates showed +0.99 in the unstable stage, +0.52 in the stable stage, and +0.96 in the reduction period, demonstrating a very high positive correlation of +0.95 or higher throughout the entire outbreaks of massive infectious diseases. The results derived from this study are expected to play a useful role in establishing an effective medical waste management system in the field of health care.

A Study on Risk Parity Asset Allocation Model with XGBoos (XGBoost를 활용한 리스크패리티 자산배분 모형에 관한 연구)

  • Kim, Younghoon;Choi, HeungSik;Kim, SunWoong
    • Journal of Intelligence and Information Systems
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    • v.26 no.1
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    • pp.135-149
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    • 2020
  • Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

Halitosis and Related Factors among Rural Residents (농촌지역 주민들의 구취실태와 유발요인)

  • Lee, Young-Ok;Hong, Jung-Pyo;Lee, Tae-Yong
    • Journal of Oral Medicine and Pain
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    • v.32 no.2
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    • pp.157-175
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    • 2007
  • This study was conducted through an interview process in which questionnaires were administered to 293 people. The questionnaires related to the behaviors of oral hygiene care, and disease history related to halitosis, and status of halitosis, halitosis measurement, oral examination, and caries activity tests such as the snyder test, Salivary flow rate test, and Salivary buffering capacity test. Our sample was taken from 293 rural residents within the period from 4th to 21st of January 2006. This was done in order to provide basic data to prepare both policies of halitosis prevention and a device to efficiently measure halitosis status and investigate the factors related therein. The major findings of this study results are as follows: 1. As for frequency of tooth brushing, twice a day occupied the greatest portion at 46.1 % Women exceeded men in frequency of tooth brushing. Tongue brushing everyday produced a 25.6 % result among subjects and The use of auxiliary oral hygiene devices occupied 9.2 %. 2. As for degree of usual self-awareness of halitosis: 62.5 %. This result also demonstrate that the severest time of self-awareness in regards to halitosis is wake up time in the morning. The time period produced the highest portion of 72.7 % in times of self-awareness. In terms of the area in which halitosis was observed, gum resulted in 23.0 %. As for types of halitosis, fetid smell was the most frequent at 37.2 %. 3. As for the result of halitosis measurement, values of OG less than 50 ppm occupied 54.3 % and $50{\sim}100ppm$ occupied 41.6 %. As for $NH_3$ values, $20{\sim}60ppm$ showed the highest value range of 52.6 %. 4. As for OG per disease history related to halitosis, values of OG were significantly high in the ranges of $50{\sim}100ppm$ within family history groups of food impaction by dental caries, diabetes mellitus and halitosis. As for values of $NH_3$, there showed a significant difference in respiratory system disease groups. 5 Value range of OG per ordinary halitosis self-awareness degree: values ranging less than 50 ppm were recorded at 55.9 % from the group realizing not aware of smell. 57.5 % from groups only realizing sometimes, while values range of $50{\sim}100ppm$ were recorded at 52.0 % from groups always aware of smell. 63.6 % from groups always strongly aware of smell. Meanwhile as for the values ranges of $NH_3$, $20{\sim}60ppm$. they occupied high portions for all groups of exams. 6. Values of OG per oral examination: the more pulp-exposed teeth and food impaction and the higher the tongue plaque index, values of OG increased within the range of $50{\sim}100ppm$. As for values of $NH_3$, the more prosthetic teeth and the higher the tongue plaque index, this value increased significantly, and the values increased up to no less than 60 ppm for groups of mandibular partial denture. 7. Within the realm of caries activity test: as for the Snyder test, high activity was highest by 43.0 % wherewith the higher the activity of acidogenic bacteria the higher the OG values. As for the salivary flow rate test, the number of cases below 8.0 ml showed the highest tendency by 62.5 %. The larger the salivary flow rate the more decreased OG values distribution. As for the salivary buffering capacity test, $6{\sim}10$ drops of 0.1N lactic acid showed the overwhelming trend by 58.7 % whereby the higher the salivary buffering capacity the greater distribution occupancy ratio of OG values below 50 ppm which is scentless to on ordinary person. 8. As for the correlation between oral environment and halitosis, OG showed the positive correlation with pulp exposed teeth, filled teeth, present teeth, tongue plaque index, and food impaction, while the negative correlation with salivary flow rate and prosthetic teeth. $NH_3$ showed a positive correlation with prosthetic teeth and frequency of tooth brushing, while decayed teeth was negative correlation. 9. As for the multiple regression analysis result, there have been selected female, pulp exposed teeth, prosthetic teeth, food impaction, salivary flow rate, tongue plaque index and severe activities in the Snyder test as factors affecting OG wherein explanatory power on it was 45.1 %. There have been selected females, pulp exposed teeth, tongue plaque index, and prosthetic teeth as factors affecting on $NH_3$ wherein explanatory power on it was 6.6 %. With the aforementioned results in mind, the status of halitosis among rural residents is considered to bare a close relation with oral environments and other factors related to halitosis such as the Snyder test from caries activity test, and salivary flow rate test. For the prevention of halitosis of residents in rural areas, we have to focus on correct tooth brushing methods and tongue brushing, with using auxiliary oral hygiene devices to remove fur of tongue plaque and food impaction. Also, when the cause and ingredients of halitosis are diverse and complex, in order to analyze exactly the factors of individual halitosis development, we need continuous and systematic study in order to provide rural residents with programs of oral hygiene education and encourage the use of dental hygienists in public health centers.