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Fulltext Simultaneous quantification method for multiple antiviral drugs in serum using isotope dilution liquid chromatography-tandem mass spectrometry

Rosli NB, Kwon HJ, Jeong JS

J Chromatogr B Analyt Technol Biomed Life Sci, 2023 Dec 01;1231:123925.
PMID: 37992562 DOI: 10.1016/j.jchromb.2023.123925

We describe the simultaneous quantification of six antiviral drugs in serum based on high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). The target drugs-hydroxychloroquine, chloroquine, favipiravir, umifenovir, ritonavir, and lopinavir-were extracted and purified from serum with 75 % v/v methanol as the precipitant reagent. The six analytes were clearly separated within 15 min using gradient elution and mixed-mode stationary phase. The measurement accuracy and precision were assured by adopting isotopes as internal standards. The optimized measurement procedure was strictly validated in linearity, sensitivity, accuracy, and precision. To confirm the robustness of the method in matrix, the method was additionally applied to various types of serum, namely hyperlipidemic and hyperglycemic serum. The method was then applied to assess the stability of the drugs in serum in order to set sample handling and storage guides for laboratory testing. Lastly, the method was implemented in different LC-MS systems to confirm its applicability across similar equipment commonly used in clinical testing laboratories. The overall results show that the optimized protocol is suitable for the accurate, simultaneous quantification of the six antiviral drugs in serum, and it is anticipated to satisfactorily serve as a reference protocol for the analysis of a wide range of other antiviral drugs for drug monitoring with various purposes.
Fulltext Pathway linking health information behaviors to mental health condition during the COVID-19 infodemic: A moderated mediation analysis

Zhang TH, Tham JS, Waheed M, Kim JN, Jeong JS, Chang PK, et al.

Front Public Health, 2022;10:924331.
PMID: 36106161 DOI: 10.3389/fpubh.2022.924331

BACKGROUND: The COVID-19 outbreak is no longer a pure epidemiological concern but a true digital infodemic. Numerous conflicting information and misinformation occupy online platforms and specifically social media. While we have lived in an infodemic environment for more than 2 years, we are more prone to feel overwhelmed by the information and suffer from long-term mental health problems. However, limited research has concentrated on the cause of these threats, particularly in terms of information processing and the context of infodemic.
OBJECTIVE: This study proposed and tested moderated mediation pathways from two types of health information behaviors (social media engagement and interpersonal communication) on information overload and mental health symptoms-long-term stress.
METHODS: We conducted a cross-sectional online survey between May and June of 2021 among the Malaysian public. The final sample size was 676 (N = 676). A conceptual model was built to guide the data analysis. We conducted structural equation modeling (SEM), moderation and mediation analyses to examine each direct pathway, moderating and mediating effects.
RESULTS: According to the pathway analysis, we found that, during the infodemic period, engaging COVID-19 information on social media positively associated with information overload, but interpersonal communication was negatively related to it. As the proximal outcome, there was also a positive association between information overload and the final outcome, perceived stress. The moderation analysis only reported one significant interaction: risk perception weakened the association between social media engagement and information overload. A conditional indirect effect was demonstrated and the indirect associated between social media engagement and perceived stress mediated through information overload was further moderated by COVID-19 risk perception.
CONCLUSION: This research offers new grounds for understanding health information behaviors and their consequences in the COVID-19 infodemic. We particularly highlighted the distinct functions of health information behaviors in causing information overload, as well as the importance of personal health belief in this process. Our proposed model contributes to the strategies of developing health messaging strategies that may be utilized by public health researchers and health educators in the future.
Rapid quantitative high-throughput mouse embryoid body model for embryotoxicity assessment

Quah Y, Jung S, Ham O, Jeong JS, Kim S, Kim W, et al.

Arch Toxicol, 2024 Sep 05.
PMID: 39235594 DOI: 10.1007/s00204-024-03845-9

Individuals are exposed to a wide arrays of hazardous chemicals on a daily basis through various routes, many of which have not undergone comprehensive toxicity assessments. While traditional developmental toxicity tests involving pregnant animals are known for their reliability, they are also associated with high costs and time requirements. Consequently, there is an urgent demand for alternative, cost-efficient, and rapid in vitro testing methods. This study aims to address the challenges related to automating and streamlining the screening of early developmental toxicity of chemicals by introducing a mouse embryoid body test (EBT) model in a 384-ultra low attachment well format. Embryoid bodies (EBs) generated in this format were characterized by a spontaneous differentiation trajectory into cardiac mesoderm by as analyzed by RNA-seq. Assessing prediction accuracy using reference compounds suggested in the ICH S5(R3) guideline and prior studies resulted in the establishment of the acceptance criteria and applicability domain of the EBT model. The results indicated an 84.38% accuracy in predicting the developmental toxicity of 23 positive and 9 negative reference compounds, with an optimized cutoff threshold of 750 µM. Overall, the developed EBT model presents a promising approach for more rapid, high-throughput chemical screening, thereby facilitating well-informed decision-making in environmental management and safety assessments.
Predictive biomarkers for embryotoxicity: a machine learning approach to mitigating multicollinearity in RNA-Seq

Quah Y, Jung S, Chan JY, Ham O, Jeong JS, Kim S, et al.

Arch Toxicol, 2024 Sep 06.
PMID: 39242367 DOI: 10.1007/s00204-024-03852-w

Multicollinearity, characterized by significant co-expression patterns among genes, often occurs in high-throughput expression data, potentially impacting the predictive model's reliability. This study examined multicollinearity among closely related genes, particularly in RNA-Seq data obtained from embryoid bodies (EB) exposed to 5-fluorouracil perturbation to identify genes associated with embryotoxicity. Six genes-Dppa5a, Gdf3, Zfp42, Meis1, Hoxa2, and Hoxb1-emerged as candidates based on domain knowledge and were validated using qPCR in EBs perturbed by 39 test substances. We conducted correlation studies and utilized the variance inflation factor (VIF) to examine the existence of multicollinearity among the genes. Recursive feature elimination with cross-validation (RFECV) ranked Zfp42 and Hoxb1 as the top two among the seven features considered, identifying them as potential early embryotoxicity assessment biomarkers. As a result, a t test assessing the statistical significance of this two-feature prediction model yielded a p value of 0.0044, confirming the successful reduction of redundancies and multicollinearity through RFECV. Our study presents a systematic methodology for using machine learning techniques in transcriptomics data analysis, enhancing the discovery of potential reporter gene candidates for embryotoxicity screening research, and improving the predictive model's predictive accuracy and feasibility while reducing financial and time constraints.