Search

Abstract This study reviews the French examination of the College Scholastic Ability Test (CSAT), the most influential and highest-stakes test in Korea. The CSAT score has considerable influence on university admissions, and admission to a highly ranked university is generally a prerequisite for socioeconomic success. From this perspective, the significance of the CSAT is enormous. In general, the assessment is influenced by the goals and content of the curriculum, and it also plays an important role in curriculum implementation. In Korea, the national curriculum has been recently reformed. The main goal of the reformed French curriculum is to promote communicative skills and cultural literacy. However, the examination has been criticized for not uniformly or adequately evaluating overall communicative skills. Considering the importance of the CSAT and the interdependence of assessment and curriculum, the CSAT French examination has to be reviewed from the perspective of test quality. Due to its limited assessment of communicative skills, the validity of the French examination is debatable. The problem of low reliability also arises due to the uncertainty of assessment criteria and guidelines. Authenticity and interactiveness could be improved by avoiding fragmentary knowledge and memorization-oriented assessment. The negative washback effect needs to be addressed to resolve the low relevance of the curriculum to the test and the relatively low applicability of the CSAT French score in university admission policy.

Abstract We investigate regional features nearby the subway station using the clustering method called the funFEM and propose a two-step procedure to predict a subway passenger transport flow by incorporating the geographical information from the cluster analysis to functional time series prediction. A massive smart card transaction dataset is used to analyze the daily number of passengers for each station in Seoul Metro. First, we cluster the stations into six categories with respect to their patterns of passenger transport. Then, we forecast the daily number of passengers with respect to each cluster. By comparing our predicted results with the actual number of passengers, we demonstrate the predicted number of passengers based on the clustering results is more accurate in contrast to the result without considering the regional properties. The result from our data-driven approach can be applied to improve the subway service plan and relieve infectious diseases as we can reduce the congestion by controlling train intervals based on the passenger flow. Furthermore, the prediction result can be utilized to plan a ‘smart city’ which seeks shorter commuting time, comfortable ridership, and environmental sustainability.

Immunotherapy has fundamentally changed the landscape of cancer treatment. However, only a subset of patients respond to immunotherapy, and a significant portion experience immune-related adverse events (irAEs). In addition, the predictive ability of current biomarkers such as programmed death-ligand 1 (PD-L1) remains unreliable and establishing better potential candidate markers is of great importance in selecting patients who would benefit from immunotherapy. Here, we focus on the role of serum-based proteomic tests in predicting the response and toxicity of immunotherapy. Serum proteomic signatures refer to unique patterns of proteins which are associated with immune response in patients with cancer. These protein signatures are derived from patient serum samples based on mass spectrometry and act as biomarkers to predict response to immunotherapy. Using machine learning algorithms, serum proteomic tests were developed through training data sets from advanced non-small cell lung cancer (Host Immune Classifier, Primary Immune Response) and malignant melanoma patients (PerspectIV test). The tests effectively stratified patients into groups with good and poor treatment outcomes independent of PD-L1 expression. Here, we review current evidence in the published literature on three liquid biopsy tests that use biomarkers derived from proteomics and machine learning for use in immuno-oncology. We discuss how these tests may inform patient prognosis as well as guide treatment decisions and predict irAE of immunotherapy. Thus, mass spectrometry-based serum proteomics signatures play an important role in predicting clinical outcomes and toxicity.

ABSTRACT Oral squamous cell carcinoma (OSCC) is the most common malignancy of the head and neck worldwide. Dysbiosis of the microbiome has increasingly been linked to the development of different kinds of cancer. Applying 16S rRNA gene sequence analysis and metatranscriptomic analyses, we characterized the longitudinal changes in the profiles and the function of the oral microbiome in a 4-nitroquinoline-1-oxide (4-NQO)-induced model of OSCC in gnotobiotic mice. We characterized the dynamics of the oral microbiome in this model using two different microbiome inocula: one from healthy mice and the other from mice bearing a 4-NQO-induced tumor. Mice colonized with different oral microbiomes and exposed to 4-NQO had increased tumor numbers and sizes compared to controls exposed to 4-NQO but lacking a microbiome. We observed an overall increase in diversity in the tumorigenic samples compared to that in the nontumor group not exposed to 4-NQO. Despite the variability in community dynamics, specific patterns emerged during the progression of the disease. In the two groups that were inoculated with the OSCC-associated microbiome, we observed opposite profiles of abundance in Parabacteroides and Corynebacterium. While the percentage of Parabacteroides bacteria decreased in the control group, it increased in the OSCC group, and the opposite was observed for Corynebacterium. The metatranscriptomic analysis revealed overexpression of the same metabolic signatures associated with OSCC regardless of the community profile. These included nitrogen transport, response to stress, interspecies interactions, Wnt pathway modulation, and amino acid and lipid biosynthesis. Thus, these results seem to suggest that certain collective physiological activities are critical for microbiome-mediated OSCC progression. IMPORTANCE There is growing evidence that changes in the microbiome are associated with carcinogenesis. To date, no consistent oral microbiome composition associated with OSCC has been identified. Longitudinal and functional studies like the study presented here should yield a better understanding of the role that the oral microbiome plays in OSCC. Our findings, obtained using a germ-free mouse model, indicate that the presence of different oral microbiomes enhances tumorigenesis and increases the final number of tumors in mice. By studying community-wide expression profiles, we found that regardless of the phylogenetic composition of the microbiome, the same metabolic activities were consistently associated with OSCC. Therefore, due to the functional redundancy of the microbiome, the critical element in explaining the contribution of the microbiota in OSCC is the collective physiological activity of the community, thus accounting for the previous inability to identify a consensus community profile or etiologic agents for OSCC.

Background: Systematic protocols for the management of outpatient parenteral antimicrobial therapy (OPAT) and information on the current status of a prescription of parenteral antibiotics for outpatients and referred patients are lacking in the Korea. This study aimed to describe the current status of OPAT at a tertiary care hospital in Korea. Materials and Methods: This was a retrospective study of outpatients and referral patients who were prescribed parenteral antibiotics from July to December 2019. We reviewed the prescribed antimicrobials, indications for antimicrobial therapy, institution administering the antimicrobial injections, and pre- and post-prescription management. Results: Of the 577 prescriptions assessed in this study, 399 (69.2%) and 178 (30.8%) were delivered using the referral and outpatient models, respectively. About 70% of OPATs were prescribed in the pulmonology, infectious diseases, orthopedics, gastroenterology, and hematology departments. Five antibiotics (ertapenem [26.0%], ceftriaxone [12.8%], kanamycin [11.8%], amikacin [10.1%], and cefazolin [8.5%]) accounted for 69.2% of the total OPATs. Urinary tract (27.3%), respiratory (20.8%), and intra-abdominal (15.9%) infections were the most frequent indications for OPAT. After prescription, there were 295 (73.9%) and 150 (84.3%) follow-up visits in the referral and outpatient models, respectively (P <0.05). Laboratory tests necessary for monitoring were fully performed for 274 (47.5%) prescriptions. Conclusion: We found that a significant number of OPATs were prescribed, follow-up visits were not performed in the case of about a quarter of prescriptions, and laboratory monitoring was not fully conducted in more than half of the cases. Therefore, it is necessary to establish an appropriate management program for OPAT. Considering the limited resources and the distribution of OPAT prescriptions, an effective strategy may be to select the frequently-used antibiotics or frequently-prescribing departments and start the program with them [ABSTRACT FROM AUTHOR]