Association Between Risk Perceptions of COVID-19, Political Ideology, and Mask-Wearing Behavior After the Outbreak: A Cross-Sectional Survey in South Korea.

Purpose: After the declaration by the World Health Organization signaling the conclusion of the COVID-19 pandemic, most countries lifted mandatory mask-wearing regulations. This study aimed to investigate factors such as risk perception and political ideology associated with continued adherence to mask-wearing among specific populations, particularly when it is no longer deemed necessary. Methods: We conducted a cross-sectional study including a sample of 1001 respondents stratified by sex, age (≥ 18 years), and region from January 31 to February 2, 2023, after the mandatory mask regulation was lifted in South Korea. Multivariate logistic regression models were applied to estimate the relationships between risk perceptions, political ideology, and mask-wearing maintenance, adjusting for factors such as sex, age, occupation, and trust in the government. Results: Our results indicated significant associations between age, self-reported household economic status, political ideology, affective risk perception, and perceived effectiveness of the government's COVID-related measures with indoor mask-wearing. Specifically, liberals were more likely to keep mask-wearing indoors than conservatives (adjusted odds ratio [aOR]: 2.19; 95% confidence interval [CI]: 1.33-3.59); and those who perceived a greater affective risk of COVID-19 (aOR: 2.47; 95% CI: 1.96-3.10), along with those who perceived the government's countermeasures as inadequate, were more inclined to maintain the habit of wearing masks indoors (aOR: 1.90; 95% CI: 1.19-3.03). Conclusion: Our study highlighted the multifaceted factors influencing mask-wearing behavior in the post-COVID-19 era. Even after adjusting for various confounding factors, such as age, sex, and trust in the government, an association remained between affective risk perception, political ideology, and mask-wearing behavior. However, further research for psychological mechanisms is needed to foster a culture of preventive behaviors proportional to the risk of infection.

Comparative analysis of salivary cortisol measurements using different assay methods in relation to serum-free cortisol measurement.

Objectives: Salivary cortisol reflects the biologically active form of serum cortisol, offering a noninvasive evaluation method for the diurnal rhythm of the hypothalamic-pituitary-adrenal (HPA) axis. While liquid chromatography-tandem mass spectrometry (LC-MS/MS) is known for its specificity, immunoassays (IA) are commonly used because of their simplicity. This study aimed to assess the performance of salivary cortisol measurement using both IA and LC-MS/MS in comparison to serum-free cortisol measurement. Methods: Assay results for 188 saliva and 94 serum samples from 47 participants were analyzed. Salivary samples collected at different time points were analyzed using IA and LC-MS/MS. Serum samples were analyzed for cortisol, cortisol-binding globulin, and free cortisol. The statistical analyses included correlations and method comparisons. Results: The diurnal salivary cortisol profiles exhibited a comparable circadian rhythm pattern; however, the concentrations measured using IA were consistently higher than those measured using LC-MS/MS. The correlation analysis revealed robust associations among salivary cortisol (IA), salivary cortisol (LC-MS/MS), and serum-free cortisol levels (LC-MS/MS). However, the method comparison revealed a systematic bias between IA and LC-MS/MS in salivary cortisol measurement. Conclusions: This study contributes to the ongoing debate on assay techniques by affirming the suitability of IA and LC-MS/MS for salivary cortisol measurement to assess dynamic changes in HPA axis activity. The identified systematic bias emphasizes the importance of selecting methods based on specific research or clinical requirements.

Microalgal and activated sludge processing for biodegradation of textile dyes.

The textile industry contributes substantially to water pollution. To investigate bioremediation of dye-containing wastewater, the decolorization and biotransformation of three textile azo dyes, Red HE8B, Reactive Green 27, and Acid Blue 29, were considered using an integrated remediation approach involving the microalga Chlamydomonas mexicana and activated sludge (ACS). At a 5 mg L-1 dye concentration, using C. mexicana and ACS alone, decolorization percentages of 39%-64% and 52%-54%, respectively, were obtained. In comparison, decolorization percentages of 75%-79% were obtained using a consortium of C. mexicana and ACS. The same trend was observed for the decolorization of dyes at higher concentrations, but the potential for decolorization was low. The toxic azo dyes adversely affect the growth of microalgae and at high concentration 50 mg L-1 the growth rate inhibited to 50-60% as compared to the control. The natural textile wastewater was also treated with the same pattern and got promising results of decolorization (90%). Moreover, the removal of BOD (82%), COD (72%), TN (64%), and TP (63%) was observed with the consortium. The HPLC and GC-MS confirm dye biotransformation, revealing the emergence of new peaks and the generation of multiple metabolites with more superficial structures, such as N-hydroxy-aniline, naphthalene-1-ol, and sodium hydroxy naphthalene. This analysis demonstrates the potential of the C. mexicana and ACS consortium for efficient, eco-friendly bioremediation of textile azo dyes.

Machine learning-based analysis for prediction of surgical necrotizing enterocolitis in very low birth weight infants using perinatal factors: a nationwide cohort study.

Early prediction of surgical necrotizing enterocolitis (sNEC) in preterm infants is important. However, owing to the complexity of the disease, identifying infants with NEC at a high risk for surgical intervention is difficult. We developed a machine learning (ML) algorithm to predict sNEC using perinatal factors obtained from the national cohort registry of very low birth weight (VLBW) infants. Data were collected from the medical records of 16,385 VLBW infants registered in the Korean Neonatal Network (KNN). Infants who underwent surgical intervention were identified with sNEC, and infants who received medical treatment, with medical NEC (mNEC). We used 38 variables, including maternal, prenatal, and postnatal factors that were obtained within 1 week of birth, for training. A total of 1085 patients had NEC (654 with sNEC and 431 with mNEC). VLBW infants showed a higher incidence of sNEC at a lower gestational age (GA) (p < 0.001). Our proposed ensemble model showed an area under the receiver operating characteristic curve of 0.721 for sNEC prediction.    Conclusion: Proposed ensemble model may help predict which infants with NEC are likely to develop sNEC. Through early prediction and prompt intervention, prognosis of sNEC may be improved. What is Known: • Machine learning (ML)-based techniques have been employed in NEC research for prediction, diagnosis, and prognosis, with promising outcomes. • While most studies have utilized abdominal radiographs and clinical manifestations of NEC as data sources, and have demonstrated their usefulness, they may prove weak in terms of early prediction. What is New: • We analyzed the perinatal factors of VLBW infants acquired within 7 days of birth and used ML-based analysis to identify which infants with NEC are vulnerable to clinical deterioration and at high risk for surgical intervention using nationwide cohort data.