In recent years, the situation of climate change has intensified, posing a threat to public health. There is an urgent need to promote public health adaptation actions to climate change. In January 2025, the National Disease Control and Prevention Administration issued the "Guidelines for Public Health Adaptation Actions to Climate Change" (hereinafter referred to as the "Guidelines"). The Guidelines put forward 20 items of guidance on six categories of public health adaptation actions, including understanding basic concepts, comprehending important policies, learning core knowledge, paying attention to key populations, practicing a low-carbon lifestyle, and mastering protection skills. It elaborates on the key concepts and the latest policies that the public needs to understand, and also provides the behavioral concepts and protection skills that should be mastered to adapt to climate change. This article provides a systematic interpretation of the Guidelines, introducing the background, ideas, connotations, and applications of their compilation, with the aim of enhancing society′s cognitive understanding of the Guidelines.

In recent years, the situation of climate change has intensified, posing a threat to public health. There is an urgent need to promote public health adaptation actions to climate change. In January 2025, the National Disease Control and Prevention Administration issued the "Guidelines for Public Health Adaptation Actions to Climate Change" (hereinafter referred to as the "Guidelines"). The Guidelines put forward 20 items of guidance on six categories of public health adaptation actions, including understanding basic concepts, comprehending important policies, learning core knowledge, paying attention to key populations, practicing a low-carbon lifestyle, and mastering protection skills. It elaborates on the key concepts and the latest policies that the public needs to understand, and also provides the behavioral concepts and protection skills that should be mastered to adapt to climate change. This article provides a systematic interpretation of the Guidelines, introducing the background, ideas, connotations, and applications of their compilation, with the aim of enhancing society′s cognitive understanding of the Guidelines.

ObjectiveTo investigate the therapeutic effect of Baihe Wuyaotang （BWT） on non-alcoholic fatty liver disease （NAFLD） and elucidate its underlying mechanism. MethodC57BL/6J mice were randomly assigned to six groups： normal control， model， positive drug （pioglitazone hydrochloride 1.95×10^-3 g·kg^-1）， and low-， medium-， and high-dose BWT （1.3，2.5 and 5.1 g·kg^-1）. Following a 12-week high-fat diet （HFD） inducement， the mice underwent six weeks of therapeutic intervention with twice-daily drug administration. Body weight was monitored weekly throughout the treatment period. At the fifth week， glucose tolerance （GTT） and insulin tolerance （ITT） tests were conducted. Subsequently， the mice were euthanized for the collection of liver tissue and serum， and the subcutaneous adipose tissue （iWAT） and epididymal adipose tissue （eWAT） were weighed. Serum levels of total triglycerides （TG） and liver function indicators，such as alanine aminotransferase （ALT） and aspartate aminotransferase （AST）， were determined. Histological examinations， including oil red O staining， hematoxylin-eosin （HE） staining， Masson staining， and transmission electron microscopy， were performed to evaluate hepatic lipid deposition， pathological morphology， and ultrastructural changes， respectively. Meanwhile， Western blot and real-time quantitative polymerase chain reaction （Real-time PCR） were employed to analyze alterations， at both gene and protein levels， the insulin signaling pathway molecules， including insulin receptor substrate 1/2/protein kinase B/forkhead box gene O1 （IRS1/2/Akt/FoxO1）， glycogen synthesis enzymes phosphoenolpyruvate carboxy kinase （Pepck） and glucose-6-phosphatase （G6Pase）， lipid metabolism-related genes stearoyl-coA desaturase-1 （SCD-1） and carnitine palmitoyltransferase-1 （CPT-1）， fibrosis-associated molecules α-smooth muscle actin （α-SMA）， type Ⅰ collagen （CollagenⅠ）， and the fibrosis canonical signaling pathway transforming growth factor-β₁/drosophila mothers against decapentaplegic protein2/3（TGF-β₁/p-Smad/Smad2/3）， inflammatory factors such as interleukin（IL）-6， IL-8， IL-11， and IL-1β， autophagy markers LC3B Ⅱ/Ⅰ and p62/SQSTM1， and the expression of mammalian target of rapamycin （mTOR）. ResultCompared with the model group， BWT reduced the body weight and liver weight of NAFLD mice（P<0.05， P<0.01）， inhibited liver lipid accumulation， and reduced the weight of white fat： it reduced the weight of eWAT and iWAT（P<0.05， P<0.01） as well as the serum TG content（P<0.05， P<0.01）. BWT improved the liver function as reflected by the reduced ALT and AST content（P<0.05， P<0.01）. It improved liver insulin resistance by upregulating IRS2， p-Akt/Akt， p-FoxO1/FoxO1 expressions（P<0.05）. Besides， it improved glucose and lipid metabolism disorders： it reduced fasting blood glucose and postprandial blood glucose（P<0.05， P<0.01）， improved GTT and ITT（P<0.05， P<0.01）， reduced the expression of Pepck， G6Pase， and SCD-1（P<0.01）， and increased the expression of CPT-1（P<0.01）. The expressions of α-SMA， Collagen1， and TGF-β₁ proteins were down-regulated（P<0.05， P<0.01）， while the expression of p-Smad/Smad2/3 was downregulated（P<0.05）， suggesting BWT reduced liver fibrosis. BWT inhibited inflammation-related factors as it reduced the gene expression of IL-6， IL-8， IL-11 and IL-1β（P<0.01） and it enhanced autophagy by upregulating LC3B Ⅱ/Ⅰ expression（P<0.05）while downregulating the expression of p62/SQSTM1 and mTOR（P<0.05）. ConclusionBWT ameliorates NAFLD by multifaceted improvements， including improving IR and glucose and lipid metabolism， anti-inflammation， anti-fibrosis， and enhancing autophagy. In particular， BWT may enhance liver autophagy by inhibiting the mTOR-mediated signaling pathway.

Background::Substantial progress in air pollution control has brought considerable health benefits in China, but little is known about the spatio-temporal trends of economic burden from air pollution. This study aimed to explore their spatio-temporal features of disease burden from air pollution in China to provide policy recommendations for efficiently reducing the air pollution and related disease burden in an era of a growing economy.Methods::Using the Global Burden of Disease method and willingness to pay method, we estimated fine particulate matter (PM 2.5) and/or ozone (O 3) related premature mortality and its economic burden across China, and explored their spatio-temporal trends between 2005 and 2017. Results::In 2017, we estimated that the premature mortality and economic burden related to the two pollutants were RMB 0.94 million (68.49 per 100,000) and 1170.31 billion yuan (1.41% of the national gross domestic product [GDP]), respectively. From 2005 to 2017, the total premature mortality was decreasing with the air quality improvement, but the economic burden was increasing along with the economic growth. And the economic growth has contributed more to the growth of economic costs than the economic burden decrease brought by the air quality improvement. The premature mortality and economic burden from O 3 in the total loss from the two pollutants was substantially lower than that of PM 2.5, but it was rapidly growing. The O 3-contribution was highest in the Yangtze River Delta region, the Fen-Wei Plain region, and some western regions. The proportion of economic burden from PM 2.5 and O 3 to GDP significantly declined from 2005 to 2017 and showed a decreasing trend pattern from northeast to southwest. Conclusion::The disease burden from O 3 is lower than that of PM 2.5, the O 3-contribution has a significantly increasing trend with the growth of economy and O 3 concentration.

Objective:To analyze the risk of diabetes mellitus related to immune checkpoint inhibitors (ICI).Methods:The adverse event (AE) reports on fulminant type 1 diabetes mellitus (FT1DM), type 1 diabetes mellitus (T1DM), diabetic ketoacidosis (DKA), which were related to duvalizumab, pabolizumab, nivolumab, and atezolizumab in the US FDA Adverse Event Reporting System from the first quarter of 2004 to the second quarter of 2021, were collected.The correlation between the 4 drugs and FT1DM, T1DM,and DKA were evaluated using proportional reporting odds ratio ( PRR) method.AE with reports ≥3, PRR value≥2, and χ2≥4 were judged to have statistical correlations with drugs. The greater the PRR value, the stronger the correlation between AE and drugs and the stronger the risk signals. Results:A total of 1 468 AE reports on diabetes mellitus were collected, 53, 386, 957, and 72 of which were related toduvalizumab, pabolizumab, nivolumab, and atezolizumab, respectively. For duvalizumab, pabolizumab, nivolumab, and atezolizumab, the PRR reflecting the correlation with FT1DM were 21.97 ( χ2=40.71), 71.50 ( χ2=3 531.21), 294.30 ( χ2=4 3915.75), and 33.58 ( χ2=279.70), respectively; the PRR reflecting correlation with T1DM were 12.12 ( χ2=162.08), 21.04 ( χ2=3391.17), 20.99 ( χ2=5816.11), and 9.71 ( χ2=224.81), respectively; the PRR reflecting correlation with DKA were 6.93 ( χ2=161.26), 4.78 ( χ2=426.52), 6.82 ( χ2=1797.15), and 3.04 ( χ2=41.84), respectively. The 4 drugs were statistically correlated with their corresponding AE. The order of risk signal intensity for corresponding AE was FT1DM > T1DM > DKA. The order of risk signal intensity for FT1DM were nivolumab > pabolizumab > duvalizumab > atezolizumab, for T1DM were pabolizumab ≈ nivolumab > duvalizumab > atezolizumab, for DKA were duvalizumab ≈ nivolumab > pabolizumab > atezolizumab. Conclusions:Duvalizumab, pabolizumab, nivolumab, and atezolizumab all can cause diabetes mellitus. The risk signal intensity was the strongest for FT1DM, followed by T1DM and DKA in order.

Objective:To analyze the risk of diabetes mellitus related to immune checkpoint inhibitors (ICI).Methods:The adverse event (AE) reports on fulminant type 1 diabetes mellitus (FT1DM), type 1 diabetes mellitus (T1DM), diabetic ketoacidosis (DKA), which were related to duvalizumab, pabolizumab, nivolumab, and atezolizumab in the US FDA Adverse Event Reporting System from the first quarter of 2004 to the second quarter of 2021, were collected.The correlation between the 4 drugs and FT1DM, T1DM,and DKA were evaluated using proportional reporting odds ratio ( PRR) method.AE with reports ≥3, PRR value≥2, and χ2≥4 were judged to have statistical correlations with drugs. The greater the PRR value, the stronger the correlation between AE and drugs and the stronger the risk signals. Results:A total of 1 468 AE reports on diabetes mellitus were collected, 53, 386, 957, and 72 of which were related toduvalizumab, pabolizumab, nivolumab, and atezolizumab, respectively. For duvalizumab, pabolizumab, nivolumab, and atezolizumab, the PRR reflecting the correlation with FT1DM were 21.97 ( χ2=40.71), 71.50 ( χ2=3 531.21), 294.30 ( χ2=4 3915.75), and 33.58 ( χ2=279.70), respectively; the PRR reflecting correlation with T1DM were 12.12 ( χ2=162.08), 21.04 ( χ2=3391.17), 20.99 ( χ2=5816.11), and 9.71 ( χ2=224.81), respectively; the PRR reflecting correlation with DKA were 6.93 ( χ2=161.26), 4.78 ( χ2=426.52), 6.82 ( χ2=1797.15), and 3.04 ( χ2=41.84), respectively. The 4 drugs were statistically correlated with their corresponding AE. The order of risk signal intensity for corresponding AE was FT1DM > T1DM > DKA. The order of risk signal intensity for FT1DM were nivolumab > pabolizumab > duvalizumab > atezolizumab, for T1DM were pabolizumab ≈ nivolumab > duvalizumab > atezolizumab, for DKA were duvalizumab ≈ nivolumab > pabolizumab > atezolizumab. Conclusions:Duvalizumab, pabolizumab, nivolumab, and atezolizumab all can cause diabetes mellitus. The risk signal intensity was the strongest for FT1DM, followed by T1DM and DKA in order.

Objective: To investigate the association of ambient fine particulate matters (PM_2.5) exposure with anxiety in middle-aged and elderly people in China. Methods: Using a stratified random sampling method, 5 997 middle-aged and elderly people (aged 40-89) who resided in the region for more than 2 years and had no hearing or language impairment were selected from 32 districts/counties in the key areas for air pollution prevention and control in China from October 10^th, 2017 to February 7^th, 2018. Information about demographic characteristics, socioeconomic factors and health status were collected by questionnaire survey and physical examination. The anxiety symptoms were assessed by 7-item Generalized Anxiety Disorder Scales. Three-year moving average concentrations of PM_2.5 were calculated to estimate exposure level. The multivariate logistic regression model was conducted to assess the association between PM_2.5 exposure and anxiety. The interaction of age, gender, overweight, education, smoking, drinking and chronic diseases was also analyzed by likelihood ratio test. Results: There were 2 995 (49.94%) males subjects, 4 092 (68.23%) subjects with education of secondary school or above and 2 576 (42.95%) subjects with self-reported chronic diseases among the 5 997 middle-aged and elder participants. The prevalence of anxiety was 6.64% (n=398). The mean±SD of 3-year moving average concentrations of PM_2.5, O₃ and SO₂ were (53.50±13.38), (90.58±13.26) and (40.29±12.56) μg/m³, respectively. PM_2.5 had significantly association with anxiety, and the corresponding OR value was 1.17 (95%CI: 1.05,1.31) with a 10 μg/m³ increment of 3-year moving average concentrations of PM_2.5. Compared with female (OR (95%CI)=1.07 (0.93,1.23)), those with no chronic diseases (OR (95%CI)=1.06 (0.93,1.20)) and those with primary school degree or below (OR (95% CI)=0.90 (0.75,1.09)), the association between PM_2.5 and anxiety was stronger among male (OR (95%CI)=1.35 (1.12,1.63)) and those with chronic diseases (OR (95%CI)=1.77 (1.31,2.38)), middle school education (OR (95%CI)=1.43 (1.22,1.67)), college education and above (OR (95%CI)=1.68 (1.05, 2.67)), all the P interaction values were <0.05. Conclusion PM_2.5 exposure has significantly positive association with anxiety. The associations are stronger in male, people with higher educational qualifications and patients with chronic diseases.

Objective: To investigate the impact of persistent high ambient fine particulate matters (PM_2.5) exposures on mortality in the polluted areas of 40 districts/counties in China. Methods: Using a convenient sampling method, we selected 40 districts/counties as research sites from the Beijing-Tianjin-Hebei Metropolitan Region, the Yangtze River Delta, the Pearl River Delta, and Heilongjiang, Shanxi, and Sichuan province. The daily concentrations of PM_2.5, meteorological data and population death data from January 1, 2013 to December 31, 2015 were collected. The six persistent PM₂.5 pollution episode scenarios were defined by the average daily concentration of PM_2.5 (75 μg/m³, P₇₅ and P₉₀ of the average daily concentration of each district/county respectively) and the duration (≥2 days or 3 days). Generalized linear models and meta analyses were used to explore the impact of PM_2.5 pollution episodes on mortality in 40 districts/counties. Results: The mean±SD and P₅₀ (P₂₅, P₇₅) of average daily temperature, relative humidity and PM_2.5 were (15.26±10.48) ℃, 17.20 (7.50, 23.70) ℃, (67.31±19.26)%, 72.00% (57.00%, 81.00%), (72.81±60.93) μg/m³ and 55.38 (33.77, 91.45) μg/m³, respectively in 40 districts/counties during 2013-2015. The average number of non-accidental, cardiovascular and cerebrovascular diseases deaths per day were (12±7), (5±4) and (2±2) in each district/county, respectively. When the daily concentrations of PM_2.5 were ≥75 μg/m³ (≥2 days), ≥P₇₅ (≥2 days), ≥P₉₀ (≥2 days), ≥75 μg/m³ (≥3 days), and ≥P₇₅ (≥3 days), the excess risk (95%CI) of the total non-accidental deaths and cardiovascular diseases deaths were 1.77% (0.89%,2.66%), 2.69% (1.06%,4.35%), 1.67% (0.59%,2.76%), 2.31% (0.67%, 3.97%), 0.71% (-0.75%, 2.20%), 1.95% (0.08%, 3.86%), 1.15% (0.12%, 2.18%), 1.85% (0.25%, 3.47%), 1.39% (0.15%, 2.64%), 2.29% (0.39%, 4.23%), respectively. Conclusion Persistently high PM_2.5 exposures were associated with total non-accidental deaths and cardiovascular disease deaths.

Objective To summarize the progress in the research of the association between air pollution and adverse birth outcomes in China.Methods A literature retrieval was conducted by using the databases of CNKI,Wanfang,Pubmed,Science Direct,and Web of Science to select relevant research papers published before 30th,June 2016 in China according to inclusion criteria.Finally,27 papers were included in analysis.Results Exposure to particulate matter (PM10),sulfur dioxide (SO2),nitrogen dioxide (NO2),total suspended particles (TSP) during pregnancy might increase risk for low birth weight;exposure to PM10,SO2,NO2 during pregnancy might increase risk for premature birth;and exposure to SO2,NO2,ozone (O3),and PM10 during pregnancy might increase risk for congenital heart diseases and other birth defects.Conclusion Exposure to air pollutants during pregnancy might increase the risk for adverse birth outcomes,and further studies are needed to prove the association.