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Human thermal comfort measures the combined effects of temperature, humidity, and wind speed, etc., and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generate the first high spatial resolution (1 km-1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China from 2003 to 2020. In this collection, 12 commonly used thermal indicators are generated by the LGBM machine learning algorithm from multisource gridded data, including MODIS land surface temperature, topography, land cover and land use, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable performance, with mean R2, root mean square error, mean absolute error, and bias of 0.996, 0.693°C, 0.512°C, and 0.003°C, respectively, by averaging the 12 indicators. Moreover, the predictions exhibit high agreements with observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. The comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) study. Further investigation reveals that nearly all indicators exhibit increasing trends in most parts of China during the year 2003~2020. The increase is especially stronger in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, and in the spring and summer seasons. The HiTIC-Monthly dataset is publicly available via https://zenodo.org/record/6895533 (Zhang et al., 2022a). [ABSTRACT FROM AUTHOR]

Background and Objectives: Post embolization fever (PEF) is one of the most common symptoms of post embolization syndrome (PES). This study aimed to determine and validate a model to predict PEF after transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma (HCC) patients. Methods: Clinical data of HCC patients who underwent TACE with platinum was retrospectively collected in our center from 2017 to 2018. Predictive factors were screened by multivariate logistic regression. The accuracy and discriminative ability of these factors were evaluated by the receiver operating characteristic (ROC) curve using the derivation cohort and an independent validation cohort. Results: A total of 367 patients were included, of whom 53 (14.4%) patients had PEF. Fevers were detected in 44 of 252 patients in the derivation cohort and 9 of 115 patients in the validation cohort. Predictors for PEF identified in multivariate logistic regression included Lipiodol emulsion dose (OR, 1.081; 95%CI, 1.006-1.162), number of concomitants uses of hepatoprotectants (OR, 0.619; 95%CI, 0.419-0.914), K+ levels (OR, 2.992; 95%CI, 1.225-7.308), and albumin-bilirubin (ALBI) grade (OR, 2.249; 95%CI, 1.040-4.862). Furthermore, the area under the ROC curve of the derivation and validation cohorts were 0.798 and 0.874, respectively. Conclusions: Our study demonstrated that Lipiodol emulsion dose, number of concomitant uses of hepatoprotectants, K+ levels, and ALBI grade are independent risk factors for PEF. The multivariate logistic model of these factors shows a discriminative ability to predict PEF in the patients who underwent TACE. [ABSTRACT FROM AUTHOR]

Background: Epidemiological studies suggest that proxies of higher lifetime estrogen exposure are associated with better cognitive function in postmenopausal women, but this has not been found consistently.Objective: To determine whether reproductive history, an important modifier of estrogen exposure across the lifetime, is associated with risk of cognitive impairment in postmenopausal women.Methods: We analyzed the baseline data from Zhejiang Major Public Health Surveillance Program (ZPHS) including 4,796 postmenopausal women. Cognitive impairment was assessed through the application of Mini-Mental State Examination questionnaire. Logistic regression models, controlled for an extensive range of potential confounders, were generated to examine the associations between women's reproductive history and risk of cognitive impairment in their later life.Results: The length of reproductive period was inversely associated with risk of cognitive impairment (p = 0.001). Odds ratio (OR) of cognitive impairment were 1.316 (95% CI 1.095∼1.582) for women with 5 or more times of full-term pregnancies, compared with those with 1∼4 times of full-term pregnancies. Women without incomplete pregnancy had a significant higher risk of cognitive impairment (OR = 1.194, 95% CI 1.000∼1.429), compared with the reference (1∼2 times of incomplete pregnancies). Oral contraceptive use (OR = 0.489, 95% CI 0.263∼0.910) and intrauterine device (IUD) use (OR = 0.684, 95% CI 0.575∼0.815) were associated with significantly reduced risk of cognitive impairment.Conclusion: Our results indicated that shorter reproductive period, higher number of full-term pregnancies and no incomplete pregnancy history were associated with an increased risk of cognitive impairment. In contrast, oral contraceptive and IUD use corresponded to reduced risk of cognitive impairment. [ABSTRACT FROM AUTHOR]