Objective:To investigate the potential causal relationships between gut microbiota composition and the risk of developing various subtypes of breast cancer by using bidirectional two-sample Mendelian randomization(MR).Methods:The research utilized genome-wide association studies(GWAS) data on gut microbiota from the MiBioGen database and GWAS data on breast cancer from the Breast Cancer Association Consortium (BCAC). In this MR study, inverse variance weighted (IVW), weighted median, MR Egger, and MR-PRESSO methods were used. Additionally, reverse MR and stratified analyses were conducted to assess reverse causality and the impact on different subtypes of breast cancer.Results:Adlercreutzia (IVW OR=0.92, 95% CI: 0.87-0.98, P=0.01) and Parabacteroides (IVW OR=0.87, 95% CI: 0.79-0.96, P=0.007) exhibited a statistically significant protective effect on breast cancer. Conversely, Sellimonas (IVW OR=1.05, 95% CI: 1.01-1.09, P=0.01) was significantly associated with an increased risk of breast cancer. Desulfovibrio (IVW OR=0.94, 95% CI: 0.88-1.00, P=0.04) and Ruminococcaceae (UCG013) (IVW OR=0.92, 95% CI: 0.86-0.99, P=0.03) presented suggestive protective effects against breast cancer. Furthermore, stratified analysis revealed that the protective effect of Adlercreutzia against breast cancer persisted in the estrogen receptor(ER)-positive subtypes, while Desulfovibrio persisted in the ER-negative subtypes. Sellimonas was causally associated with the risk of ER-positive subtypes. CACNA1S was identified as the functional gene of Adlercreutzia, and associated with favorable prognosis in breast cancer, while ERBB4 was identified as the functional gene of Sellimonas and associated with poor prognosis in breast cancer. Conclusions:This study identifies the causal relationships between gut microbiota and breast cancer, suggesting a novel target for early clinical intervention and treatment, with potential implications for future functional analysis.

Objective To validate the diagnostic performance and risk stratification ability of an AI-based recognition system(PE-AI)for pulmonary embolism(PE)using computed tomography pulmonary angiography(CTPA)so as to analyze its diagnostic value in clinical practice.Methods A total of 416 patients with suspected PE who underwent CTPA from January 1,2023 to December 10,2023 at our hospital were included in this study.Two junior radiologists and PE-AI separately detected and diagnosed emboli in the collected cases by double-blind method,and recorded the diagnosis time respectively.Three senior radiologists reviewing with clinical follow-up results were used as the gold standard in this study.Diagnostic performance was evaluated by using the receiver operating characteristic(ROC)curve analysis and Delong-t test.For positive cases,the pulmonary artery obstruction index(PAOI)calculated by AI and manually were collected respectively and consistency analysis was performed.Results The area under the curve(AUC)of PE-AI,manual and combined diagnosis was 85.6％,90.8％and 95.1％,respectively,which differed significantly(P＜0.05).The reading time of PE-AI[(0.16±0.07)min]was significantly lower than the time of manual[(4.42±1.85)min,P＜0.001]and combined diagnosis[(4.58±1.84)min,P＜0.001].The PAOI measured by PE-AI and manually had high consistency(intraclass correlation efficient,ICC=0.80)in the subgroup analysis of confirmed cases.Conclusion AI can quickly identify pulmonary artery emboli in a short time and assist radiologists to improve diagnostic efficiency.At the same time,through the intelligent detection of PAOI,it is helpful for the risk stratification of patients with PE and optimizing the diagnosis and treatment pathway for pulmonary embolism.

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:How to improve the expansion of cells,reduce cell loss,increase homing rate and reduce apoptosis is the main problem in the preclinical research of human umbilical cord mesenchymal stem cells.Ginsenoside Rg1 can promote the proliferation and differentiation of mesenchymal stem cells in different microenvironments in vitro or in vivo,which may be a candidate drug to improve the efficiency of human umbilical cord mesenchymal stem cell transplantation.OBJECTIVE:To investigate the effect of human umbilical cord mesenchymal stem cells co-culture combined with ginsenoside Rg1 on pentobarbital sodium induced heart failure cell model.METHODS:H9C2 cells were divided into five groups:Control group,pentobarbital sodium group,human umbilical cord mesenchymal stem cell group,ginsenoside Rg1 group,and human umbilical cord mesenchymal stem cell+ginsenoside Rg1 group.H9C2 cells in the control group were cultured in normal DMEM for 24 hours.H9C2 cells in the other groups were cultured in DMEM containing 0.8％pentobarbital sodium for 7 minutes to establish a heart failure cell model.After modeling,above models were treated with human umbilical cord mesenchymal stem cells,ginsenoside Rg1,or their combination.CCK-8 assay and EdU staining were used to detect cell proliferation.TUNEL assay was used to detect cell apoptosis.Na+-K+-ATPase and Ca2+-Mg2+-ATPase activities were detected according to kit instructions.The mRNA levels of tumor necrosis factor α,interleukin 1β,and interleukin 6 in the supernatant were determined by ELISA.The mRNA levels of tumor necrosis factor α,interleukin 1β,interleukin 6,Bax,and Bcl2 in the cells were determined by RT-qPCR.The protein levels of Bax,Bcl2,Toll-like receptor 4,p65,and p-p65 were determined by western blot assay.RESULTS AND CONCLUSION:(1)Compared with the pentobarbital sodium group,H9C2 cell viability and EdU positive rate were increased;TUNEL positive rate and Bax mRNA and protein expression were decreased,and Bcl-2 mRNA and protein expression were increased;Na+-K+-ATPase activity decreased;Ca2+-Mg2+-ATPase activity increased;tumor necrosis factor α,interleukin-1β,and interleukin-6 levels decreased in H9C2 cell supernatant,and tumor necrosis factor α,interleukin-1β,and interleukin-6 mRNA expression decreased in H9C2 cells;the expression of toll-like receptor 4 and P-P65 protein decreased with significant difference in human umbilical cord mesenchymal stem cell group,ginsenoside Rg1 group and human umbilical cord mesenchymal stem cell+ginsenoside Rg1 group(P＜0.05).(2)Compared with human umbilical cord mesenchymal stem cell group and ginsenoside Rg1 group,the above indexes in human umbilical cord mesenchymal stem cells+ginsenoside Rg1 group were further improved(P＜0.05).The results showed that human umbilical cord mesenchymal stem cells combined with ginsenoside Rg1 promoted the viability of heart failure cells induced by pentobarbital sodium and inhibited inflammation mediated by the Toll-like receptor 4/nuclear factor κB pathway.

Objective:To apply statistical process control (SPC) techniques to the quality assurance of non-normal radiotherapy plans through Johnson transformation, establishing patient-specific tolerance and action limits based on treatment sites and dose/distance assessment criteria, thereby enhancing the intensity-modulated radiation therapy (IMRT) verification accuracy and dose delivery precision.Methods:In this study, 951 gamma analysis data of patient-specific quality assurance (PSQA) executed on the Halcyon accelerator platform were selected and categorized into six groups based on treatment sites, including brain (102 cases), head and neck (100 cases), breast (229 cases), lung (154 cases), esophagus (223 cases), and pelvic (143 cases) groups. The six groups of data were statistically analyzed through Anderson-Darling normality tests ( α = 0.05) using Minitab 21 software. Non-normal data were transformed into normal data through Johnson transformation and then were used to establish treatment site-specific tolerance and action limits, which were compared with the Shewhart control charts based on normal distributions. Results:The PSQA result of the six groups all exhibited non-normal distributions ( P < 0.05). Through Johnson transformation, the tolerance and action limits for the head and neck, breast, lung, esophagus, and pelvic areas under the 3%/2 mm criterion ranged from 95.13% to 96.16% and 94.19% to 95.91%, respectively. In contrast, the tolerance and action limits ranged from 91.15% to 94.86% and 89.94% to 94.78% under the 2%/2 mm criterion. Directly applying Shewhart control charts without normality assumptions yielded higher tolerance limits compared to the application of Johnson transformation, increasing the false positive rate in the non-normal PSQA process. Conclusions:Applying the SPC techniques directly to a non-normal process can lead to an increased false alarm rate and wrong process interpretation. The SPC techniques combined with Johnson transformation enable more effective monitoring of a non-normal PSQA process, facilitating timely identification of potential factors that may lead to an out-of-control process based on the treatment site-specific limits.

Objective To evaluate the value of deep learning(DL)-based artificial intelligence(AI)automatic reconstruction for evaluation of grafts in patients who underwent coronary artery bypass grafting(CABG).Methods Coronary CT angiography data of 90 patients who underwent CABG with a total of 197 grafts were retrospectively analyzed.Taken manual evaluation results(manual group)as the standards,the efficacy of AI(AI group)for evaluating the degree of stenosis of graft and distal autologous blood vessels were assessed.The consistency between calculating unprotected coronary territory(UCT)and the total time for image post-processing and diagnosis were compared between groups.Results AI group showed average consistency with manual group for evaluating the number of grafts([intra-class correlation coefficient,ICC]=0.743,P＜0.05),average to excellent for evaluating the maximum degree of graft stenosis(Kappa=0.310-1.000,all P＜0.05),also average to good consistency for evaluating the maximum degree of stenosis of the native vessel distal to the graft insertion(Kappa=0.292-0.795,all P＜0.05).AI group had moderate consistency with manual group for UCT(ICC=0.469,P＜0.05),achieved an area under the curve of 0.811.The overall time of image post-processing and diagnosis in AI group were both significantly shorter than that in manual group(P＜0.05).Conclusion Having acceptable consistency with manual evaluation and ability for assistant,AI was efficient for automatic reconstructing coronary artery bypass graft and quantifying the degree of graft stenosis.

Objective:To apply statistical process control (SPC) techniques to the quality assurance of non-normal radiotherapy plans through Johnson transformation, establishing patient-specific tolerance and action limits based on treatment sites and dose/distance assessment criteria, thereby enhancing the intensity-modulated radiation therapy (IMRT) verification accuracy and dose delivery precision.Methods:In this study, 951 gamma analysis data of patient-specific quality assurance (PSQA) executed on the Halcyon accelerator platform were selected and categorized into six groups based on treatment sites, including brain (102 cases), head and neck (100 cases), breast (229 cases), lung (154 cases), esophagus (223 cases), and pelvic (143 cases) groups. The six groups of data were statistically analyzed through Anderson-Darling normality tests ( α = 0.05) using Minitab 21 software. Non-normal data were transformed into normal data through Johnson transformation and then were used to establish treatment site-specific tolerance and action limits, which were compared with the Shewhart control charts based on normal distributions. Results:The PSQA result of the six groups all exhibited non-normal distributions ( P < 0.05). Through Johnson transformation, the tolerance and action limits for the head and neck, breast, lung, esophagus, and pelvic areas under the 3%/2 mm criterion ranged from 95.13% to 96.16% and 94.19% to 95.91%, respectively. In contrast, the tolerance and action limits ranged from 91.15% to 94.86% and 89.94% to 94.78% under the 2%/2 mm criterion. Directly applying Shewhart control charts without normality assumptions yielded higher tolerance limits compared to the application of Johnson transformation, increasing the false positive rate in the non-normal PSQA process. Conclusions:Applying the SPC techniques directly to a non-normal process can lead to an increased false alarm rate and wrong process interpretation. The SPC techniques combined with Johnson transformation enable more effective monitoring of a non-normal PSQA process, facilitating timely identification of potential factors that may lead to an out-of-control process based on the treatment site-specific limits.

BACKGROUND:How to improve the expansion of cells,reduce cell loss,increase homing rate and reduce apoptosis is the main problem in the preclinical research of human umbilical cord mesenchymal stem cells.Ginsenoside Rg1 can promote the proliferation and differentiation of mesenchymal stem cells in different microenvironments in vitro or in vivo,which may be a candidate drug to improve the efficiency of human umbilical cord mesenchymal stem cell transplantation.OBJECTIVE:To investigate the effect of human umbilical cord mesenchymal stem cells co-culture combined with ginsenoside Rg1 on pentobarbital sodium induced heart failure cell model.METHODS:H9C2 cells were divided into five groups:Control group,pentobarbital sodium group,human umbilical cord mesenchymal stem cell group,ginsenoside Rg1 group,and human umbilical cord mesenchymal stem cell+ginsenoside Rg1 group.H9C2 cells in the control group were cultured in normal DMEM for 24 hours.H9C2 cells in the other groups were cultured in DMEM containing 0.8％pentobarbital sodium for 7 minutes to establish a heart failure cell model.After modeling,above models were treated with human umbilical cord mesenchymal stem cells,ginsenoside Rg1,or their combination.CCK-8 assay and EdU staining were used to detect cell proliferation.TUNEL assay was used to detect cell apoptosis.Na+-K+-ATPase and Ca2+-Mg2+-ATPase activities were detected according to kit instructions.The mRNA levels of tumor necrosis factor α,interleukin 1β,and interleukin 6 in the supernatant were determined by ELISA.The mRNA levels of tumor necrosis factor α,interleukin 1β,interleukin 6,Bax,and Bcl2 in the cells were determined by RT-qPCR.The protein levels of Bax,Bcl2,Toll-like receptor 4,p65,and p-p65 were determined by western blot assay.RESULTS AND CONCLUSION:(1)Compared with the pentobarbital sodium group,H9C2 cell viability and EdU positive rate were increased;TUNEL positive rate and Bax mRNA and protein expression were decreased,and Bcl-2 mRNA and protein expression were increased;Na+-K+-ATPase activity decreased;Ca2+-Mg2+-ATPase activity increased;tumor necrosis factor α,interleukin-1β,and interleukin-6 levels decreased in H9C2 cell supernatant,and tumor necrosis factor α,interleukin-1β,and interleukin-6 mRNA expression decreased in H9C2 cells;the expression of toll-like receptor 4 and P-P65 protein decreased with significant difference in human umbilical cord mesenchymal stem cell group,ginsenoside Rg1 group and human umbilical cord mesenchymal stem cell+ginsenoside Rg1 group(P＜0.05).(2)Compared with human umbilical cord mesenchymal stem cell group and ginsenoside Rg1 group,the above indexes in human umbilical cord mesenchymal stem cells+ginsenoside Rg1 group were further improved(P＜0.05).The results showed that human umbilical cord mesenchymal stem cells combined with ginsenoside Rg1 promoted the viability of heart failure cells induced by pentobarbital sodium and inhibited inflammation mediated by the Toll-like receptor 4/nuclear factor κB pathway.

Objective To validate the diagnostic performance and risk stratification ability of an AI-based recognition system(PE-AI)for pulmonary embolism(PE)using computed tomography pulmonary angiography(CTPA)so as to analyze its diagnostic value in clinical practice.Methods A total of 416 patients with suspected PE who underwent CTPA from January 1,2023 to December 10,2023 at our hospital were included in this study.Two junior radiologists and PE-AI separately detected and diagnosed emboli in the collected cases by double-blind method,and recorded the diagnosis time respectively.Three senior radiologists reviewing with clinical follow-up results were used as the gold standard in this study.Diagnostic performance was evaluated by using the receiver operating characteristic(ROC)curve analysis and Delong-t test.For positive cases,the pulmonary artery obstruction index(PAOI)calculated by AI and manually were collected respectively and consistency analysis was performed.Results The area under the curve(AUC)of PE-AI,manual and combined diagnosis was 85.6％,90.8％and 95.1％,respectively,which differed significantly(P＜0.05).The reading time of PE-AI[(0.16±0.07)min]was significantly lower than the time of manual[(4.42±1.85)min,P＜0.001]and combined diagnosis[(4.58±1.84)min,P＜0.001].The PAOI measured by PE-AI and manually had high consistency(intraclass correlation efficient,ICC=0.80)in the subgroup analysis of confirmed cases.Conclusion AI can quickly identify pulmonary artery emboli in a short time and assist radiologists to improve diagnostic efficiency.At the same time,through the intelligent detection of PAOI,it is helpful for the risk stratification of patients with PE and optimizing the diagnosis and treatment pathway for pulmonary embolism.