• Korean Journal of Construction Engineering and Management
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• v.22 no.6
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• pp.76-86
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• 2021

Construction projects have high potentials of claims and disputes due to inherent risks where a variety of stakeholders are involved. Since disputes could cause losses in terms of cost and time, it is a critical issue for contractors to forecast and pro-actively manage disputes in advance in order to secure project efficiency and higher profits. The objective of the study is to develop a decision tree-based predictive analytics model for forecasting dispute types and their probabilities according to construction project conditions. It can be a useful tool to forecast potential disputes and thus provide opportunities for proactive management.

• Journal of Microbiology and Biotechnology
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• v.33 no.10
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• pp.1351-1360
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• 2023

Endocrine-disrupting chemicals (EDCs) are compounds that disturb hormonal homeostasis by binding to receptors. EDCs are metabolized through hepatic enzymes, causing altered transcriptional activities of hormone receptors, and thus necessitating the exploration of the potential endocrine-disrupting activities of EDC-derived metabolites. Accordingly, we have developed an integrative workflow for evaluating the post-metabolic activity of potential hazardous compounds. The system facilitates the identification of metabolites that exert hormonal disruption through the integrative application of an MS/MS similarity network and predictive biotransformation based on known hepatic enzymatic reactions. As proof-of-concept, the transcriptional activities of 13 chemicals were evaluated by applying the in vitro metabolic module (S9 fraction). Identified among the tested chemicals were three thyroid hormone receptor (THR) agonistic compounds that showed increased transcriptional activities after phase I+II reactions (T3, 309.1 ± 17.3%; DITPA, 30.7 ± 1.8%; GC-1, 160.6 ± 8.6% to the corresponding parents). The metabolic profiles of these three compounds showed common biotransformation patterns, particularly in the phase II reactions (glucuronide conjugation, sulfation, GSH conjugation, and amino acid conjugation). Data-dependent exploration based on molecular network analysis of T3 profiles revealed that lipids and lipid-like molecules were the most enriched biotransformants. The subsequent subnetwork analysis proposed 14 additional features, including T4 in addition to 9 metabolized compounds that were annotated by prediction system based on possible hepatic enzymatic reaction. The other 10 THR agonistic negative compounds showed unique biotransformation patterns according to structural commonality, which corresponded to previous in vivo studies. Our evaluation system demonstrated highly predictive and accurate performance in determining the potential thyroid-disrupting activity of EDC-derived metabolites and for proposing novel biotransformants.

• Journal of Korean Neurosurgical Society
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• v.67 no.1
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• pp.14-21
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• 2024

Objective : The primary treatment goal of current endovascular thrombectomy (EVT) for emergent large-vessel occlusion (ELVO) is complete recanalization after a single maneuver, referred to as the 'first-pass effect' (FPE). Hence, we aimed to identify the predictive factors of FPE and assess its effect on clinical outcomes in patients with ELVO of the anterior circulation. Methods : Among the 129 patients who participated, 110 eligible patients with proximal ELVO (intracranial internal carotid artery and proximal middle cerebral artery) who achieved successful recanalization after EVT were retrospectively reviewed. A comparative analysis between patients who achieved FPE and all others (defined as a non-FPE group) was performed regarding baseline characteristics, clinical variables, and clinical outcomes. Multivariate logistic regression analyses were subsequently conducted for potential predictive factors with p<0.10 in the univariate analysis to determine the independent predictive factors of FPE. Results : FPE was achieved in 31 of the 110 patients (28.2%). The FPE group had a significantly higher level of functional independence at 90 days than did the non-FPE group (80.6% vs. 50.6%, p=0.002). Pretreatment intravenous thrombolysis (IVT) (odds ratio [OR], 3.179; 95% confidence interval [CI], 1.025-9.861; p=0.045), door-to-puncture (DTP) interval (OR, 0.959; 95% CI, 0.932-0.987; p=0.004), and the use of balloon guiding catheter (BGC) (OR, 3.591; 95% CI, 1.231-10.469; p=0.019) were independent predictive factors of FPE. Conclusion : In conclusion, pretreatment IVT, use of BGC, and a shorter DTP interval were positively associated with FPE, increasing the chance of acquiring better clinical outcomes.

• Experimental and Molecular Medicine
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• v.50 no.12
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• pp.10.1-10.11
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• 2018

Cancer growth and progression are associated with immune suppression. Cancer cells have the ability to activate different immune checkpoint pathways that harbor immunosuppressive functions. Monoclonal antibodies that target immune checkpoints provided an immense breakthrough in cancer therapeutics. Among the immune checkpoint inhibitors, PD-1/PD-L1 and CTLA-4 inhibitors showed promising therapeutic outcomes, and some have been approved for certain cancer treatments, while others are under clinical trials. Recent reports have shown that patients with various malignancies benefit from immune checkpoint inhibitor treatment. However, mainstream initiation of immune checkpoint therapy to treat cancers is obstructed by the low response rate and immune-related adverse events in some cancer patients. This has given rise to the need for developing sets of biomarkers that predict the response to immune checkpoint blockade and immune-related adverse events. In this review, we discuss different predictive biomarkers for anti-PD-1/PD-L1 and anti-CTLA-4 inhibitors, including immune cells, PD-L1 overexpression, neoantigens, and genetic and epigenetic signatures. Potential approaches for further developing highly reliable predictive biomarkers should facilitate patient selection for and decision-making related to immune checkpoint inhibitor-based therapies.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.6
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• pp.3819-3823
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• 2013

Background: Hepatocellular carcinoma (HCC) is a major cause of cancer mortality worldwide. The outcome of HCC depends mainly on its early diagnosis. To date, the performance of traditional biomarkers is unsatisfactory. Talins were firstly identified as cytoplasmic protein partners of integrins but Talin-1 appears to play a crucial role in cancer formation and progression. Our study was conducted to assess the diagnostic value of serum Talin-1 (TLN1) compared to the most feasible traditional biomarker alpha-fetoprotein (AFP) for the diagnosis of HCC. Methods: TLN1 was detected using enzyme linked immunosorbent assay (ELISA) in serum samples from 120 Egyptian subjects including 40 with HCC, 40 with liver cirrhosis (LC) and 40 healthy controls (HC). Results: ROC curve analysis was used to create a predictive model for TLN1 relative to AFP in HCC diagnosis. Serum levels of TLN1 in hepatocellular carcinoma patients were significantly higher compared to the other groups (p<0.0001). The diagnostic accuracy of TLN1 was higher than that of AFP regarding sensitivity, specificity, positive predictive value and negative predictive value in diagnosis of HCC. Conclusions: The present study showed for the first time that Talin-1 (TLN1) is a potential diagnostic marker for HCC, with a higher sensitivity and specificity compared to the traditional biomarker AFP.

• Toxicological Research
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• v.28 no.2
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• pp.81-91
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• 2012

Many risk factors exist for chemotherapy-induced nausea and vomiting (CINV). This study utilized a multivariate projection technique to identify which risk factors were predictive of CINV in clinical practice. A single-centre, prospective, observational study was conducted from January 2007~July 2010 in Singapore. Patients were on highly (HECs) and moderately emetogenic chemotherapies with/without radiotherapy. Patient demographics and CINV risk factors were documented. Daily recording of CINV events was done using a standardized diary. Principal component (PC) analysis was performed to identify which risk factors could differentiate patients with and without CINV. A total of 710 patients were recruited. Majority were females (67%) and Chinese (84%). Five risk factors were potential CINV predictors: histories of alcohol drinking, chemotherapy-induced nausea, chemotherapy-induced vomiting, fatigue and gender. Period (ex-/current drinkers) and frequency of drinking (social/chronic drinkers) differentiated the CINV endpoints in patients on HECs and anthracycline-based, and XELOX regimens, respectively. Fatigue interference and severity were predictive of CINV in anthracycline-based populations, while the former was predictive in HEC and XELOX populations. PC analysis is a potential technique in analyzing clinical population data, and can provide clinicians with an insight as to what predictors to look out for in the clinical assessment of CINV. We hope that our results will increase the awareness among clinician-scientists regarding the usefulness of this technique in the analysis of clinical data, so that appropriate preventive measures can be taken to improve patients' quality of life.

• Molecular & Cellular Toxicology
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• v.1 no.3
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• pp.157-163
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• 2005

Because of their widespread occurrence and substantial biological activity, halogenated aromatic hydrocarbons are one of the important classes of contaminants in the environment. We have performed comparative molecular field analysis (CoMFA) on structurally diverse ligands of Ah (dioxin) receptor to explore the physico-chemical requirements for binding. All CoMFA models have given $q^{2}$ value of more than 0.5 and $r^{2}$ value of more than 0.83. The predictive ability of the models was validated by an external test set, which gave satisfactory predictive $r^{2}$ values. Best predictions were obtained with CoMFA model of combined modified training set ($q^{2}=0.631,\;r^{2}=0.900$), giving predictive residual value = 0.002 log unit for the test compound. We have suggested a model comprises of four structurally different compounds, which offers a good predictability for various ligands. Our QSAR model is consistent with all previously established QSAR models with less structurally diverse ligands. The implications of the CoMFA/QSAR model presented herein are explored with respect to quantitative hazard identification of potential toxicants.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.1
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• pp.347-352
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• 2016

Background: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) adenosquamous carcinoma data to identify predictive models and potential disparities in outcome. Materials and Methods: This study analyzed socio-economic, staging and treatment factors available in the SEER database for adenosquamous carcinoma. For the risk modeling, each factor was fitted by a generalized linear model to predict the cause specific survival. An area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. Results: A total of 20,712 patients diagnosed from 1973 to 2009 were included in this study. The mean follow up time (S.D.) was 54.2 (78.4) months. Some 2/3 of the patients were female. The mean (S.D.) age was 63 (13.8) years. SEER stage was the most predictive factor of outcome (ROC area of 0.71). 13.9% of the patients were un-staged and had risk of cause specific death of 61.3% that was higher than the 45.3% risk for the regional disease and lower than the 70.3% for metastatic disease. Sex, site, radiotherapy, and surgery had ROC areas of about 0.55-0.65. Rural residence and race contributed to socioeconomic disparity for treatment outcome. Radiotherapy was underused even with localized and regional stages when the intent was curative. This under use was most pronounced in older patients. Conclusions: Anatomic stage was predictive and useful in treatment selection. Under-staging may have contributed to poor outcome.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.1
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• pp.353-356
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• 2016

Background: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) for glassy cell carcinoma data to identify predictive models and potential disparities in outcome. Materials and Methods: This study analyzed socio-economic, staging and treatment factors. For risk modeling, each factor was fitted by a generalized linear model to predict the cause specific survival. Area under the receiver operating characteristic curves (ROCs) were computed. Similar strata were combined to construct the most parsimonious models. A random sampling algorithm was used to estimate modeling errors. Risk of glassy cell carcinoma death was computed for the predictors for comparison. Results: There were 79 patients included in this study. The mean follow up time (S.D.) was 37 (32.8) months. Female patients outnumbered males 4:1. The mean (S.D.) age was 54.4 (19.8) years. SEER stage was the most predictive factor of outcome (ROC area of 0.69). The risks of cause specific death were, respectively, 9.4% for localized, 16.7% for regional, 35% for the un-staged/others category, and 60% for distant disease. After optimization, separation between the regional and unstaged/others category was removed with a higher ROC area of 0.72. Several socio-economic factors had small but measurable effects on outcome. Radiotherapy had not been used in 90% of patients with regional disease. Conclusions: Optimized SEER stage was predictive and useful in treatment selection. Underuse of radiotherapy may have contributed to poor outcome.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.4
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• pp.128-136
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• 2005

The use of technology is increasing within the service industry, but there is some doubt as to whether the benefits of employing this technology have been efficiently harnessed such as data mining. Data mining is the process of extracting certain predictive information from databases that can evolve from currently used restaurant management systems. The potential of harnessing this predictive information can have an enormous impact on the restaurant's operation on the whole, particularly in the area customer retention and competition. Since there is insufficient literature on the use of data mining in the restaurant industry, this study is both seminal and investigative, done via a Delphi survey to explore and describe the current and future applications of this process.