Insurance Fraud in Korea, Its Seriousness, and Policy Implications

Several characteristics of insurance fraud including its chronic nature justifies the need for identifying feasible proposals which can be expected to bring about significant impacts. Recent statistics show that insurance fraud is now consistently on the increase. However, insurance fraud is highly fragmented and each offence is not significant enough to elicit active interest among the public or interventions from the police. Three problems have been identified and diagnosed. These were a lack of awareness, an absence of a national leadership and also limited attention directed to insurance fraud by the investigating authorities. Based on these, three recommendations have been suggested. (1) Embarking on and developing a national initiative by central government, (2) Taking a dynamic concentration approach to send deterrent threats to potential fraudsters, and (3) Using big data technologies to detect clandestine activities by organised groups.

Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort

Endocrine-disrupting chemicals (EDCs) are widespread environmental chemicals that are often considered as risk factors with weak activity on the hormone-dependent process of pregnancy. However, the adverse effects of EDCs in the body of pregnant women were underestimated. The interaction between dynamic concentration of EDCs and endogenous hormones (EHs) on gestational age and delivery time remains unclear. To define a temporal interaction between the EDCs and EHs during pregnancy, comprehensive, unbiased, and quantitative analyses of 33 EDCs and 14 EHs were performed for a longitudinal cohort with 2317 pregnant women. We developed a machine learning model with the dynamic concentration information of EDCs and EHs to predict gestational age with high accuracy in the longitudinal cohort of pregnant women. The optimal combination of EHs and EDCs can identify when labor occurs (time to delivery within two and four weeks, AUROC of 0.82). Our results revealed that the bisphenols and phthalates are more potent than partial EHs for gestational age or delivery time. This study represents the use of machine learning methods for quantitative analysis of pregnancy-related EDCs and EHs for understanding the EDCs’ mixture effect on pregnancy with potential clinical utilities.

Generation and Characterization of a New FRET-Based Ca2+ Sensor Targeted to the Nucleus

Calcium (Ca2+) exerts a pivotal role in controlling both physiological and detrimental cellular processes. This versatility is due to the existence of a cell-specific molecular Ca2+ toolkit and its fine subcellular compartmentalization. Study of the role of Ca2+ in cellular physiopathology greatly benefits from tools capable of quantitatively measuring its dynamic concentration ([Ca2+]) simultaneously within organelles and in the cytosol to correlate localized and global [Ca2+] changes. To this aim, as nucleoplasm Ca2+ changes mirror those of the cytosol, we generated a novel nuclear-targeted version of a Föster resonance energy transfer (FRET)-based Ca2+ probe. In particular, we modified the previously described nuclear Ca2+ sensor, H2BD3cpv, by substituting the donor ECFP with mCerulean3, a brighter and more photostable fluorescent protein. The thorough characterization of this sensor in HeLa cells demonstrated that it significantly improved the brightness and photostability compared to the original probe, thus obtaining a probe suitable for more accurate quantitative Ca2+ measurements. The affinity for Ca2+ was determined in situ. Finally, we successfully applied the new probe to confirm that cytoplasmic and nucleoplasmic Ca2+ levels were similar in both resting conditions and upon cell stimulation. Examples of simultaneous monitoring of Ca2+ signal dynamics in different subcellular compartments in the very same cells are also presented.

Co-Doped ZnO Nano-Agglomerates as a Potential Scaffold for Non-Enzymatic Hydrogen Peroxide Sensing

Co-doped ZnO nano-agglomerates were synthesized by a facile solution process. Several characterization techniques revealed the successful doping of the ZnO by Co ions. FESEM results showed the agglomeration of the Co-doped ZnO nanoparticles to form large-sized nano-agglomerates. The diameters of the spherical nanoparticles and the agglomerates were not found to be uniform. The diameters of the nano-agglomerates ranged from ~25 nm–120 nm. XRD spectrum confirmed the Wurtzite hexagonal phase of ZnO in Co-doped ZnO nanoagglomerates. The average particle size for Co-doped ZnO nano-agglomerates was 20.68 nm. The sensing parameters were examined by using Co-doped ZnO nano-agglomerates modified gold electrode through cyclic voltammetric and amperometric analysis. The sensitivity of 70.73 μAmM−1cm−2 and very low-detection limit of 0.2 μM was observed for H2O2. The corresponding linear dynamic concentration range was 0.2–1633 μM. The excellent sensing activities of the Co-doped ZnO nano-agglomerates for H2O2 were attributed to the improved intrinsic electric properties and increased inner defects density, particularly near the interface region.

Pipelines and Systems for Threshold Avoiding Quantification of LC-MS/MS data

The accurate processing of complex LC-MS/MS data from biological samples is a major challenge for metabolomics, proteomics and related approaches. Here we present the Pipelines and Systems for Threshold Avoiding Quantification (PASTAQ) LC-MS/MS pre-processing toolset, which allows highly accurate quantification of data-dependent acquisition (DDA) LC-MS/MS datasets. PASTAQ performs compound quantification using single-stage (MS1) data and implements novel algorithms for high-performance and accurate quantification, retention time alignment, feature detection, and linking annotations from multiple identification engines. PASTAQ offers straightforward parametrization and automatic generation of quality control plots for data and pre-processing assessment. This design results in smaller variance when analyzing replicates of proteomes mixed with known ratios, and allows the detection of peptides over a larger dynamic concentration range compared to widely used proteomics preprocessing tools. The performance of the pipeline is also demonstrated in a biological human serum dataset for the identification of gender related proteins.

Deiodinases and Cancer

Hormones are key drivers of cancer development, and alteration of the intratumoral concentration of thyroid hormone (TH) is a common feature of many human neoplasias. Besides the systemic control of TH levels, the expression and activity of deiodinases constitute a major mechanism for the cell-autonomous, prereceptoral control of TH action. The action of deiodinases ensures tight control of TH availability at intracellular level in a time- and tissue-specific manner, and alterations in deiodinase expression are frequent in tumors. Research over the past decades has shown that in cancer cells, a complex and dynamic expression of deiodinases is orchestrated by a network of growth factors, oncogenic proteins, and miRNA. It has become increasingly evident that this fine regulation exposes cancer cells to a dynamic concentration of TH that is functional to stimulate or inhibit various cellular functions. This review summarizes recent advances in the identification of the complex interplay between deiodinases and cancer and how this family of enzymes is relevant in cancer progression. We also discuss whether deiodinase expression could represent a diagnostic tool with which to define tumor staging in cancer treatment or even a therapeutic tool against cancer.