Objective To investigate whether P12，a kind of lipopolysaccharide（LPS）-binding protein（LBP） inhibitory peptide，could suppress the binding of LPS to alveolar macrophages（AMs） in a mouse model of endotoxemia in vivo.Methods Forty mice were randomly divided into five groups，ie.a control group，an endotoxemia group，a low dose P12-treated group，a middle dose P12-treated group and a high dose P12-treated group.Mouse model of endotoxemia was established by LPS injection intraperitoneally in the endotoxemia group and P12-treated groups.P12 was instilled via the tail vein.The effects of P12 on the binding of LPS to AMs were determined by flow cytometric analysis and quantization by mean fluorescence intensity(MFI).The productions of tumor necrosis factor α(TNF-α) in serum of mice were measured by enzyme-linked immunosorbent assay(ELISA).Results MFI in AMs from low，middle and high dose P12-treated groups was 40.08%，30.76% and 24.45%，respectively，which was higher than that of the control group(4.61%)，but less than that of the endotoxemic mice(45.31%).The concentration of TNF-α in serum of low，media and high dose P12-treated mice was (112.69±19.78)pg/mL，(86.34±9.25) pg/mL，(70.48±8.48)pg/mL respectively，which was higher than that of the control group[(24.88±5.82)pg/mL]，but less than that of the endotoxemic mice[(180.17±39.14)pg/mL].Conclusion The results suggest that P12 inhibit the binding of LPS and AMs，thus reduce the proudction of TNF-α stimulated by LPS.
With the increasing availability of clinical and biomedical big data, machine learning is being widely used in scientific research and academic papers. It integrates various types of information to predict individual health outcomes. However, deficiencies in reporting key information have gradually emerged. These include issues like data bias, model fairness across different groups, and problems with data quality and applicability. Maintaining predictive accuracy and interpretability in real-world clinical settings is also a challenge. This increases the complexity of safely and effectively applying predictive models to clinical practice. To address these problems, TRIPOD+AI (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis+artificial intelligence) introduces a reporting standard for machine learning models. It is based on TRIPOD and aims to improve transparency, reproducibility, and health equity. These improvements enhance the quality of machine learning model applications. Currently, research on prediction models based on machine learning is rapidly increasing. To help domestic readers better understand and apply TRIPOD+AI, we provide examples and interpretations. We hope this will support researchers in improving the quality of their reports.
ObjectiveTo analyze and compare the incidence, mortality, temporal trends, and cancer spectrum differences between China and the United States (US), providing theoretical support for cancer prevention and control in China. MethodsAge standardized incidence rate (ASIR), age standardized mortality rate (ASMR), and cancer site composition were extracted from GLOBOCAN, Cancer Statistics 2025, the China Cancer Registry Annual Report, and other epidemiological sources. Spatial (urban-rural, sex specific) and temporal distributions were described, and average annual growth rate (AAGR) were calculated. ResultsFrom 2005 onward, China exhibited a modest rise in ASIR, whereas the United States showed a decline (AAGR: 0.58 vs –0.42); nevertheless, China’s overall incidence remained lower (2022 ASIR = 201.61/100 000) than that of the United States (303.60/100 000). Both countries experienced decreasing ASMRs (AAGR: –1.03 vs –1.72). Prior to 2020, US mortality exceeded that of China, but by 2022 China’s ASMR (96.47/100 000) had slightly surpassed the US figure (81.80/100 000). In both nations, male ASIR and ASMR were higher than female. Since 2005, the top three US cancers had remained prostate (men) or breast (women), lung and colorectal. In China, incidences of lung, colorectal, female breast and thyroid cancers had continued to rise, while stomach and liver cancer incidences had declined yet still rank high among men. Urban ASIR in China exceeded rural rates, whereas rural ASMR was higher than urban counterparts. ConclusionsAccelerating population ageing and lifestyle transitions have driven an upward incidence trend in China, accompanied by a shift towards a mixed pattern of traditional and emerging cancer risks. Drawing on US experience, China should intensify tobacco control measures, expand organized screening and early detection programs, implement comprehensive interventions for priority cancers, strengthen primary level capacity and improve treatment access in rural areas, thereby establishing a more effective national cancer prevention and control system.