ObjectiveTo investigate the contamination status of ochratoxin A (OTA) in commercially available food products in Shanghai, and to assess OTA exposure levels and the associated non-carcinogenic and carcinogenic risks among pregnant women by integrating dietary consumption data of this population. MethodsThe levels of OTA contamination in 1 520 food samples collected in Shanghai from 2022 to 2023 were determined using liquid chromatography-tandem mass spectrometry. An exposure assessment model was developed based on the dietary consumption levels of pregnant women from the 2016‒2017 Shanghai Pregnant Women Dietary Monitoring Survey to calculate the estimated daily intake (EDI) of OTA, the margin of exposure for non-carcinogenic toxicity (MOE₁), and the margin of exposure for carcinogenic toxicity (MOE₂). An MOE₁ greater than 200 and an MOE₂ greater than 10 000 indicate that the non-carcinogenic toxicity and carcinogenic toxicity resulting from exposure are negligible, respectively. For samples with OTA contamination levels below the limit of detection (LOD), which accounted for more than 80% of the samples, the OTA levels were assigned values of 0 and LOD, respectively, for subsequent calculations. ResultsThe detection rates of OTA in cereals, nuts, dried fruits, and alcohol samples collected in 2022 were 2.03%, 0, 0, and 0, respectively. The OTA detection rates in cereals, nuts, dried fruits, beans, and alcohol samples collected in 2023 were 2.50%, 0.39%, 2.47%, 1.67%, and 13.33%, respectively. For pregnant women in Shanghai in 2022, simulation results indicated that when assigning a value of 0 and the LOD, theP₅₀ values of EDI for dietary OTA exposure were 0.05 and 0.72 ng·(kg·d)^-1, respectively, and the P₉₅ values of EDI for dietary OTA exposure were 0.25 and 2.40 ng·(kg·d)^-1, respectively. For pregnant women in Shanghai in 2023, the P₅₀ values of EDI for dietary OTA exposure were 0.04 and 1.00 ng·(kg·d)^-1, respectively, and the P₉₅ values of EDI for dietary OTA exposure were 0.23 and 2.67 ng·(kg·d)^-1, respectively, both substantially below the tolerable daily intake (TDI) for OTA [17 ng·(kg·d)^-1]. The EDI for dietary OTA exposure in 100.0% of Shanghai pregnant women was lower than the TDI, indicating an overall low level of dietary OTA exposure among this population. For 100.0% of pregnant women, the MOE₁ for dietary OTA exposure exceeded 200. When assigned a value of 0, the MOE₂ for 100.0% of pregnant women in both 2022 and 2023 exceeded10 000. When assigned the LOD value, 72.3% and 81.8% of pregnant women in 2022 and 2023, respectively, had an MOE₂ exceeding 10 000. ConclusionFrom 2022 to 2023, samples of cereals, nuts, dried fruits, beans, and alcohol sold in Shanghai exhibited varying degrees of OTA contamination. The overall EDI of OTA exposure among pregnant women in Shanghai remained at a low level. The non-carcinogenic and carcinogenic risks associated with OTA exposure were generally low and at controllable levels.

Objective To establish a standardized quality criterion for the formula of the Liuwei Runfu oil recipe. Methods The qualitative identification of the components, such as Cannabis sativa, Ligusticum chuanxiong, Angelica sinensis, Coix lacryma-jobi, and Glycyrrhiza uralensis, was conducted by thin layer chromatography （TLC）. The concentration of Senkyunolide A, ligustilide, and linoleic acid was determined by high-performance liquid chromatography （HPLC）. Results The TLC identification results demonstrated excellent differentiation and high selectivity, without any disruption from the negative samples. The reference standard for Senkyunolide A exhibited excellent linearity in the range of 25 to 300 µg （r=0.9999）, with an average recovery rate of 102.3% and a relative standard deviation （RSD） of 2.3%. The ligustilide reference standard exhibited strong linearity between the concentrations of 0.6875 and 8.25 µg （r=0.9993）, with an average recovery rate of 100.1% and a relative standard deviation （RSD） of 2.8%. The linoleic acid reference standard demonstrated strong linear correlation within the range of 2.8125 to 33.75 µg （r=0.9990）, with an average spiking recovery rate of 102.7% and a relative standard deviation （RSD） of 1.7%. Conclusion The established method with strong specificity was simple，accurate，reliable，which coulod provide a reference for the quality control of the Liuwei Runfu oil recipe.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43％(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38％,9.73％,and 8.47％,respectively.The prevalence of CRE strains in Escherichia coli was 1.99％.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)％,followed by blood(20.88±3.40)％and urine(18.40±3.45)％.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)％.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.

Objective To characterize the changing species distribution and antibiotic resistance profiles of respiratory isolates in hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods Commercial automated antimicrobial susceptibility testing systems and disk diffusion method were used to test the susceptibility of respiratory bacterial isolates to antimicrobial agents following the standardized technical protocol established by the CHINET program.Results A total of 589 746 respiratory isolates were collected from 2015 to 2021.Overall,82.6％of the isolates were Gram-negative bacteria and 17.4％were Gram-positive bacteria.The bacterial isolates from outpatients and inpatients accounted for(6.0±0.9)％and(94.0±0.1)％,respectively.The top microorganisms were Klebsiella spp.,Acinetobacter spp.,Pseudomonas aeruginosa,Staphylococcus aureus,Haemophilus spp.,Stenotrophomonas maltophilia,Escherichia coli,and Streptococcus pneumoniae.Each microorganism was isolated from significantly more males than from females(P＜0.05).The overall prevalence of methicillin-resistant S.aureus(MRSA)was 39.9％.The prevalence of penicillin-resistant S.pneumoniae was 1.4％.The prevalence of extended-spectrum β-lactamase(ESBL)-producing E.coli and K.pneumoniae was 67.8％and 41.3％,respectively.The overall prevalence of carbapenem-resistant E.coli,K.pneumoniae,Enterobacter cloacae,Pseudomonas aeruginosa,and Acinetobacter baumannii was 3.7％,20.8％,9.4％,29.8％,and 73.3％,respectively.The prevalence of β-lactamase was 96.1％in Moraxella catarrhalis and 60.0％in Haemophilus influenzae.The H.influenzae isolates from children(＜18 years)showed significantly higher resistance rates to β-lactam antibiotics than the isolates from adults(P＜0.05).Conclusions Gram-negative bacteria are still predominant in respiratory isolates associated with serious antibiotic resistance.Antimicrobial resistance surveillance should be strengthened in clinical practice to support accurate etiological diagnosis and appropriate antimicrobial therapy based on antimicrobial susceptibility testing results.

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

Background Deoxynivalenol (DON), a priority contaminant for food safety risk monitoring, is produced by Fusarium spp. infesting crops, and its common derivatives are 3-acetyl-DON (3A-DON) and 15-acetyl-DON (15A-DON), which have been shown to possess gastrointestinal toxicity, immunotoxicity, reproductive toxicity, and cytotoxicity. Due to the stable physicochemical properties of the DON family of toxins (DONs), they cannot be effectively removed during food processing, thus following the food chain, entering the human body, and posing health risks. Objective To understand the contamination status of DONs in commercial foods (cereals and bakery products) in Shanghai in 2022–2023, and to assess the exposure risk of DONs in pregnant women by combining their dietary consumption data. Methods Liquid chromatography tandem mass spectrometry (LC-MS/MS) was used to determine the contamination level of DONs in 1 100 food samples (cereals and baked goods) collected in 2022 and 944 samples collected in 2023 from Shanghai. The dietary monitoring data of pregnant women in Shanghai from 2016 to 2017 were adopted. The monitoring employed the food frequency questionnaire distributed among pregnant women through a combination of online telephone enquiry and offline on-site face-to-face survey to estimate their food consumption levels. An exposure assessment model was established to calculate the exposure level to DONs, and the probability distribution of the DONs exposure level in the pregnant women group in Shanghai was obtained by applying @Risk 7.5 software and simulating the calculation according to the Monte Carlo principle. With reference to the tolerable daily intake (TDI) of DONs [1.00 µg·(kg·d)⁻¹] proposed by the Joint FAO/WHO Expert Committee on Food Additives, the risk of exposure to DONs from commercial cereals and bakery products in pregnant women in Shanghai was assessed. Results DONs were detected in cereal and bakery samples collected in 2022 and 2023 with different levels of contamination. The level of DONs in cereal foods in 2023 (mean: 36.33 µg·kg⁻¹) decreased compared to 2022 (mean: 23.64 µg·kg⁻¹). However, the positive rate (71.67%) and level (mean: 51.22 µg·kg⁻¹) of DONs in bakery products increased significantly compared with 2022 (positive rate: 10.00%, mean: 24.39 µg·kg⁻¹). The mean consumption of cereals in 783 pregnant women was 222.48 g·d⁻¹ and the mean consumption of bakery products was 36.07 g·d⁻¹, and there was no statistically significant difference in the intake of all types of cereals and bakery products across the early, middle, and late stages of pregnancy. The modelled intakes of DONs via commercial cereals and bakery products for pregnant women in Shanghai were calculated to be 0.20 and 0.57 µg·(kg·d)⁻¹ in 2022 for the mean level and the 95th percentile level, respectively, and 0.16 µg·(kg·d)⁻¹ and 0.35 µg·(kg·d)⁻¹ in 2023, respectively. The results of the health risk assessment showed that pregnant women in Shanghai had 2.6% and 1.4% probability of exposure to DONs from cereal consumption in 2022 and 2023, respectively. Conclusion The risk of exposure of pregnant women in Shanghai to DONs via commercial cereals and bakery products is relatively low (1.4%-2.6%). However, considering the physical sensitivity of pregnant women, they should avoid consuming moldy grains and appropriately reduce intake of bakery products.

A novel analytical method was developed in this study by combining online solid phase extraction with ultra performance liquid chromatography-tandem mass spectrometry(Online SPE-UPLC-MS/MS)for simultaneous determination of 50 kinds of steroid hormones in surface water.Specifically,after high-speed centrifugation of 4 mL water samples,the supernatant was directly injected into an Oasis HLB online SPE column for enrichment and purification.Subsequently,the target compounds were transferred to the analytical column via valve switching for separation and analysis.The chromatographic separation was performed on a Thermo Acclaim RSLC C18 column(100 mm×2.1 mm,2.2 μm),using a mobile phase composed of 5 mmol/L ammonium fluoride aqueous solution and acetonitrile.Mass spectrometric detection was conducted in positive ion mode,utilizing multiple reaction monitoring(MRM)with quantification achieved by the internal standard method.The method validation demonstrated that the limits of detection(LOD)for the 50 kinds of steroid hormones ranged from 0.02 to 0.50 ng/L,while the limits of quantification(LOQ)were between 0.08 and 1.67 ng/L.The average recoveries in surface water samples at spiked concentrations of 5,20 and 200 ng/L were between 74.1％and 119％,with relative standard deviations(RSDs)of 0.2％to 9.9％.This method was applied to analyze 11 surface water samples collected from sites surrounding a pharmaceutical and chemical industrial park.A total of 44 kinds of steroid hormones were detected,with concentrations ranging from 0.11 to 88.6 ng/L,revealing the presence of hormone contamination in the environmental waters surrounding industrial areas.Compared with the traditional offline SPE methods,the proposed online SPE technique significantly reduced sample volume requirements and pretreatment time,while minimizing the loss of target compounds during the pretreatment process.Moreover,compared to reported online SPE techniques,this method achieved high-throughput analysis of multiple classes of steroid hormones,with lower detection limits and higher recoveries.Overall,this method provided rapid sample preparation,high sensitivity,and excellent stability,making it suitable for the direct analysis of trace steroid hormones in surface water.

Gender recognition based on the analysis of fingerprint residue can assist investigators in narrowing down the scope of investigation and play an important role in the field of criminal investigation.This study established a quantitative analysis method for lipid substances in fingerprints based on gas chromatography-mass spectrometry(GC-MS).Fatty acids in fingerprints were methylated using sulfuric acid methanol derivatization reagent(7%,V/V),the extraction reagent was dichloromethane-methanol(1∶1,V/V)solution,the reaction temperature was 70℃and the heating time was 45 min.Quantitative analysis of the relative content of 23 kinds of fatty acids and squalene in fingerprints residue by different genders was conducted,and orthogonal partial least squares-discriminant analysis(OPLS-DA)was used to reduce the dimensionality of the quantitative results.A total of 13 kinds of components in the fingerprints were selected to maximize the difference in relative content between male and female fingerprints.Three machine learning models,including binary logistic regression(BLR),support vector machine(SVM)and random forest(RF),were further used as feature variables to classify the gender of fingerprints.The classification performance of each model was compared through five indicators,and it was found that the most suitable model for binary classification of fingerprint gender was SVM model.The results showed that the SVM fingerprint residual gender binary classification model established based on the relative content data of 13 kinds of lipid substances in fingerprints achieved a classification accuracy of 90%and an area under the receiver operating characteristic curve(AUC)value of 0.98.This study provided a new research method for detecting lipid components in fingerprints and a methodological basis for gender recognition of fingerprints.

The integrin alpha 2（ITGA2）gene locates on chromosome 5q11.2，encodes the α 2 subunit of the integrin family.The α 2 subunit is found on many cell surfaces.The α 2 subunit plays an important role in the regulation of platelet aggregation，cell migration，cell proliferation and angiogenesis.Through a wide range of studies conducted in recent years，researchers have discovered that this gene is linked to some diseases，such as rheumatoid arthritis，pediatric sickle cell disease，Kawasaki disease，malignant tumor and biliary atresia.These studies have contributed to the early identification of diseases associated with this gene and the exploration of diagnostic and therapeutic solutions for them.This article reviews the advancements in research concerning the association of ITGA2 gene with various diseases.