[Contamination levels of perfluorinated and polyfluoroalkyl compounds in breast milk and assessment of their exposure risk to infants].

The purposes of this study are to explore the contamination levels of perfluorinated and polyfluoroalkyl substances (PFASs) in breast milk and assess their exposure risk to infants. Based on data from a birth cohort study conducted in Yingcheng, Hubei Province, from 2018 to 2021, the contents of 23 types of PFASs in the breast milk of 324 pregnant women were determined using isotope dilution-high performance liquid chromatography-tandem mass spectrometry. Multiple linear regression was then performed to analyze the effects of various demographic characteristics and eating habits on the concentration of PFASs in breast milk. The daily PFASs intake of infants through breast milk was estimated, and the exposure risk of infants was also assessed. The results revealed that 23 types of PFASs in breast milk had good linear relationships in the range of 0.2-100 ng/mL, with correlation coefficients greater than 0.992. The limits of detection were 5-42 pg/mL, the limits of quantification were 15-126 pg/mL, the recoveries were 65.6%-108.1%, and the relative standard deviations were 1.6%-12.8%. Perfluorooctane sulfonic acid (PFOS), perfluorooctanoate acid (PFOA), and perfluorohexanesulfonic acid (PFHxS), with median concentrations of 200.7, 63.5, and 25.2 pg/mL, respectively, were the main PFASs found in breast milk. The detection rates of these three contaminants were higher than 80%, whereas the detection rates of other compounds were lower than 45%. The results of multiple linear regression showed that older pregnant women and a higher frequency of pickled food intake may be related to increased PFAS levels in breast milk whereas a higher frequency of legume intake may be related to decreased PFAS levels in breast milk. The median estimated daily intakes (EDIs) of PFOS, PFOA, and PFHxS for infants were 25.1, 7.9, and 3.2 ng/(kg·d), respectively. In summary, this study found notable PFAS levels in breast milk in Yingcheng, Hubei Province. Among these PFASs, PFOS, PFOA, and PFHxS were the main contaminants. Maternal age as well as pickled food and legume intake may affect the PFAS level in breast milk. The health risk of PFAS intake through breast milk to some infants is worthy of attention and further study.

Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study.

BACKGROUND: COVID-19 has overwhelmed health systems worldwide. It is important to identify severe cases as early as possible, such that resources can be mobilized and treatment can be escalated. OBJECTIVE: This study aims to develop a machine learning approach for automated severity assessment of COVID-19 based on clinical and imaging data. METHODS: Clinical data-including demographics, signs, symptoms, comorbidities, and blood test results-and chest computed tomography scans of 346 patients from 2 hospitals in the Hubei Province, China, were used to develop machine learning models for automated severity assessment in diagnosed COVID-19 cases. We compared the predictive power of the clinical and imaging data from multiple machine learning models and further explored the use of four oversampling methods to address the imbalanced classification issue. Features with the highest predictive power were identified using the Shapley Additive Explanations framework. RESULTS: Imaging features had the strongest impact on the model output, while a combination of clinical and imaging features yielded the best performance overall. The identified predictive features were consistent with those reported previously. Although oversampling yielded mixed results, it achieved the best model performance in our study. Logistic regression models differentiating between mild and severe cases achieved the best performance for clinical features (area under the curve [AUC] 0.848; sensitivity 0.455; specificity 0.906), imaging features (AUC 0.926; sensitivity 0.818; specificity 0.901), and a combination of clinical and imaging features (AUC 0.950; sensitivity 0.764; specificity 0.919). The synthetic minority oversampling method further improved the performance of the model using combined features (AUC 0.960; sensitivity 0.845; specificity 0.929). CONCLUSIONS: Clinical and imaging features can be used for automated severity assessment of COVID-19 and can potentially help triage patients with COVID-19 and prioritize care delivery to those at a higher risk of severe disease.

[Study on optimal model of hypothetical work injury insurance scheme].

OBJECTIVE: To explore an optimal model of hypothetical work injury insurance scheme, which is in line with the wishes of workers, based on the problems in the implementation of work injury insurance in China and to provide useful information for relevant policy makers. METHODS: Multistage cluster sampling was used to select subjects: first, 9 small, medium, and large enterprises were selected from three cities (counties) in Zhejiang Province, China according to the economic development, transportation, and cooperation; then, 31 workshops were randomly selected from the 9 enterprises. Face-to-face interviews were conducted by trained interviewers using a pre-designed questionnaire among all workers in the 31 workshops. RESULTS: After optimization of hypothetical work injury insurance scheme, the willingness to participate in the scheme increased from 73.87%to 80.96%; the average willingness to pay for the scheme increased from 2.21% (51.77 yuan) to 2.38% of monthly wage (54.93 Yuan); the median willingness to pay for the scheme increased from 1% to 1.2% of monthly wage, but decreased from 35 yuan to 30 yuan. The optimal model of hypothetical work injury insurance scheme covers all national and provincial statutory occupational diseases and work accidents, as well as consultations about occupational diseases. The scheme is supposed to be implemented worldwide by the National Social Security Department, without regional differences. The premium is borne by the state, enterprises, and individuals, and an independent insurance fund is kept in the lifetime personal account for each of insured individuals. The premium is not refunded in any event. Compensation for occupational diseases or work accidents is unrelated to the enterprises of the insured workers but related to the length of insurance. The insurance becomes effective one year after enrollment, while it is put into effect immediately after the occupational disease or accident occurs. CONCLUSION: The optimal model of hypothetical work injury insurance scheme actually realizes cross-regional mobility of workers, minimizes regional differences, and embodies the fairness. The proposed model will, to some extent, protect the rights and interests of enterprises, as well as the healthy rights and interests of workers when they are unemployed.

[Sexual hormone and traditional Chinese patent medicine for early postmenopausal women: effect on quality of life and cost-utility analysis].

OBJECTIVE: To evaluate the effect of Premarin and Kuntai capsule (a traditional Chinese patent medicine) on the quality of life (QOL) and their cost-utility in early postmenopausal women. METHODS: Fifty-seven women with menopausal syndrome in the early postmenopausal stage were randomly allocated into Premarin group (0.3 mg/day and 0.6 mg/day alternately, n=29) and Kuntai group (4 g/day, n=28). The therapies lasted for one year and the patients were followed up every 3 months. The QOL of the patients was evaluated and the utility scores were obtained from rating scale to conduct a cost-utility analysis (CUA). RESULTS: At each follow-up examination, no significant difference was found in the QOL between the two groups (P>0.05). The QOL obviously increased after the 1-year-long therapy in both the groups, and Kuntai required longer treatment time than Premarin to take effect. The cost-utility ratio of Premarin and Kuntai were 13581.45 yuan/QALY (quality adjusted life year) and 25105.12 yuan/QALY, respectively. Both incremental cost analysis and sensitivity analysis showed that Kuntai was more costly than Premarin. The result of per-protocol analysis was consistent with that of intention-to-treat analysis. CONCLUSION: At early stage of menopause, the QOL of women with menopausal syndrome can be significantly improved by low-dose Premarin and Kuntai capsule, but the latter is more costly.