Objective: To investigate the role of ferroptosis in transfusion-related acute lung injury (TRALI) and evaluate the efficacy of the specific inhibitor Ferrostatin-1 (Fer-1), thereby to provide a basis for the prevention and treatment of TRALI. Methods: This study utilized a ”2-hit” model to induce TRALI in mice. The mouse model of TRALI was validated through survival curve analysis, lung tissue wet/dry weight ratio (W/D), myeloperoxidase (MPO) activity, and total protein concentration in lung tissue. Samples from the TRALI model group, LPS group, and control group (n=6) were collected. The occurrence of ferroptosis in TRALI was confirmed by measuring key ferroptosis indicators, including iron concentration in lung tissue, malondialdehyde (MDA) level, lipid peroxidation products (LPO) level, and expression levels of related proteins (GPX4, ACSL4). Additionally, a Fer-1 intervention group was added to evaluate its preventive and therapeutic effects. The survival rates and clinical symptoms of the four groups (n=6) were dynamically monitored, and the degrees of lung injury were assessed. Ferroptosis-related indicators were also measured to elucidate the protective mechanism of Fer-1. Results: A mouse model of TRALI was successfully established. Compared to the control and LPS groups, the TRALI group showed significantly higher levels of ferrous iron [(18.32±1.11) nmol/well, MDA [(14.68±0.96) μmol/L], and LPO [(1.60±0.02) μmol/L] in lung tissue (all P<0.01), along with a downregulation of GPX4 and an upregulation of ACSL4. Fer-1 pretreatment significantly reversed these abnormalities: the W/D ratio decreased to 4.01±0.43, and MPO activity significantly decreased [Fer-1 group: (21 606±4 235) pg/mL vs TRALI group: (30 724±2 616) pg/mL], the total protein concentration in lung tissue of the Fer-1 group decreased by approximately 40.8% compared to the TRALI group (all P<0.01). These changes indicate that the lung injury in mice was alleviated after treatment. Following Fer-1 intervention, ferrous iron concentration [(7.46±1.83) nmol/well] was restored to a level close to that of the control group [(5.48±0.70) nmol/well]. Lipid peroxidation tests further revealed that Fer-1 intervention reduced MDA and LPO levels by 35.8% and 29.4%, respectively (P<0.001). Additionally, the expression levels of GPX4 and ACSL4 proteins returned to near-normal levels in the treated mice (both P>0.05). Conclusion: The progression of TRALI is closely related to the activation of ferroptosis, characterized by iron overload, lipid peroxidation accumulation, and the imbalance of GPX4/ACSL4. Ferrostatin-1 significantly alleviates pulmonary edema and inflammatory damage by inhibiting the ferroptosis pathway, suggesting that targeting ferroptosis may provide a new therapeutic strategy for TRALI.

Objective:To develop an artificial intelligence（AI）-based sequential ultrasound-magnetic resonance imaging（US-MRI）diagnostic strategy to optimize the imaging workflow for ovarian masses.Methods:A total of 1 120 patients with pathologically confirmed ovarian masses who underwent both preoperative pelvic ultrasound and MRI between January 2021 and December 2023 at Women's Hospital，Zhejiang University School of Medicine were retrospectively included. Patients were randomly divided into the training（ n=672）and internal test set（ n=448）at a ratio of 6∶4. An external test set（ n=128）was established at the Forth Affiliated Hospital of School of Medicine. Deep learning was used for automated segmentation of MRI lesions，followed by radiomic feature extraction and machine learning classification to construct both a US-MRI multimodal model and sequential US-MRI strategy. Diagnostic performance and potential healthcare cost-saving effects were evaluated across strategies. Results:In the internal test set（ n=448），the AI-based sequential US-MRI strategy achieved a F1 score of 0.863 and a diagnostic accuracy of 82.14%，with no significant difference compared to the US-MRI multi-modal model（ P>0.05）. The sequential strategy identified 82 cases（18.30%，82/448）of patients as low-risk true negatives during initial ultrasound screening，suggesting a potential to reduce the need for MRI examinations in future clinical practice. In the external test set（ n=128），the strategy achieved an F1 score of 0.800 and a confirmed diagnosis rate of 85.94%，with a theoretical reduction of 26.56%（34 cases）in MRI utilization while maintaining a diagnostic accuracy rate higher than that of the multi-modal model（82.18%）. Conclusions:The AI-based US-MRI sequential diagnostic strategy demonstrates favorable diagnostic accuracy while offering the potential to optimize MRI utilization. This approach may enhance the efficiency of imaging resource allocation and reduce healthcare burden in the management of ovarian masses.

Objective To explore the value of dynamic contrast-enhanced(DCE)-MRI quantitative parameters based on differential sub-sampling with Cartesian ordering(DISCO)technology for differentiating high-and low-grade breast cancer.Methods A total of 80 patients with single breast cancer confirmed by biopsy pathology were retrospectively enrolled,including 40 cases of low-grade(L group)and 40 cases of high-grade breast cancer(H group).Then quantitative parameters obtained from DISCO-DCE-MRI before treatment were compared between groups,including extravascular extracellular volume fraction(Ve),rate constant(Kep),contrast enhancement ratio(CER),maximum slope(MaxSlope)and volume transfer constant(Ktrans),and their correlations with histological grade were analyzed.Receiver operating characteristic(ROC)curves of DISCO-DCE-MRI quantitative parameters being significantly different between groups were plotted,and the area under the curves(AUC)were calculated to evaluate their efficacy for differentiating high-and low-grade breast cancer.Results Ve(0.91[0.59,0.99]),CER(2.76±0.54)and MaxSlope(0.02[0.01,0.03])in L group were all higher than those in H group(0.52[0.34,0.73],[2.31±0.74],0.01[0.01,0.02],all P＜0.05),and no significant difference of Kep nor Ktrans was found between groups(both P＞0.05).Ve,CER and MaxSlope of breast cancer were all negatively correlated with histological grade(rs=-0.43,-0.39,-0.35,all P＜0.05),while Kep andKtranshad no significant correlation with histological grade(both P＞0.05).The AUC of Ve,CER and MaxSlope for differentiating high-and low-grade breast cancer was 0.749,0.725 and 0.700,respectively.Conclusion Among DISCO-DCE-MRI quantitative parameters,Ve,CER and MaxSlope could be used for differentiating high-and low-grade breast cancer.

Objective:To explore the correlation between quantitative parameters based on SUV acquired by dynamic SPECT/CT imaging of parotid glands and pathological grading of labial gland in patients with primary Sj?gren syndrome (pSS).Methods:Seventy-two patients (6 males, 66 females, age (51.5±13.8) years) with confirmed pSS diagnosed at Hebei General Hospital between August 2022 and March 2024 were prospectively included. The clinical data and pathological grading information from labial gland biopsies were analyzed. Dynamic SPECT/CT imaging of the parotid glands was performed, and quantitative parameters based on SUV were obtained using Q-metrix software: SUV max, SUV mean, uptake volume of parotid glands (UVP) and total parotid uptake (TPU) pre/post-acid stimulation, as well as the differences in quantitative parameters before and after acid stimulation (ΔSUV max, ΔSUV mean, ΔUVP, and ΔTPU). The independent-sample t test or Mann-Whitney U test was performed to evaluate the differences in parameters between patients with pathological grade 1-2 and those with pathological grade 3-4. Spearman rank correlation was used to analyze the correlation between quantitative parameters and pathological grading. The performance of quantitative parameters in distinguishing pathological grade 1-2 from grade 3-4 was assessed using ROC curve analysis with Delong test. Results:The SUV max pre/post-acid stimulation in patients with pathological grade 1-2 ( n=30) were higher than those in patients with grade 3-4 ( n=42) (36.38(27.81, 44.17) vs 15.45(10.77, 24.51), Z=-5.51, P<0.001(pre-acid stimulation); 21.53(16.93, 26.21) vs 11.33(7.32, 15.89), Z=-5.27, P<0.001 (post-acid stimulation)). SUV mean, UVP and TPU pre/post-acid stimulation in patients with pathological grade 1-2, as well as ΔSUV max, ΔSUV mean and ΔTPU, were all significantly higher ( Z values: from -4.73 to -3.04, t values: 6.39, 4.50, all P<0.01). Moreover, these parameters were negatively correlated with the pathological grading ( rs values: from -0.66 to -0.36, all P<0.05). No significant difference in ΔUVP was observed between patients with pathological grade 1-2 and those with grade 3-4 ( Z=-1.05, P=0.293), and ΔUVP showed no correlation with pathological grading ( rs=-0.13, P=0.297). Among all parameters, SUV max pre/post-acid stimulation and TPU pre-acid stimulation exhibited better diagnostic performance in differentiating pathological grade 1-2 from grade 3-4, with AUC values of 0.883, 0.866, and 0.888, respectively. Delong test showed that those 3 AUC values were all higher than AUC values of SUV mean, UVP post-acid stimulation and ΔUVP (all AUC<0.800; Z values: 2.09-4.65, all P<0.05). Conclusion:The quantitative parameters of parotid glands based on SUV acquired by dynamic SPECT/CT can reflect the damage degree of parotid glands in patients with pSS, providing novel quantitative analytical tools for the functional diagnosis and assessment of pSS.

Objective To explore the value of dynamic contrast-enhanced(DCE)-MRI quantitative parameters based on differential sub-sampling with Cartesian ordering(DISCO)technology for differentiating high-and low-grade breast cancer.Methods A total of 80 patients with single breast cancer confirmed by biopsy pathology were retrospectively enrolled,including 40 cases of low-grade(L group)and 40 cases of high-grade breast cancer(H group).Then quantitative parameters obtained from DISCO-DCE-MRI before treatment were compared between groups,including extravascular extracellular volume fraction(Ve),rate constant(Kep),contrast enhancement ratio(CER),maximum slope(MaxSlope)and volume transfer constant(Ktrans),and their correlations with histological grade were analyzed.Receiver operating characteristic(ROC)curves of DISCO-DCE-MRI quantitative parameters being significantly different between groups were plotted,and the area under the curves(AUC)were calculated to evaluate their efficacy for differentiating high-and low-grade breast cancer.Results Ve(0.91[0.59,0.99]),CER(2.76±0.54)and MaxSlope(0.02[0.01,0.03])in L group were all higher than those in H group(0.52[0.34,0.73],[2.31±0.74],0.01[0.01,0.02],all P＜0.05),and no significant difference of Kep nor Ktrans was found between groups(both P＞0.05).Ve,CER and MaxSlope of breast cancer were all negatively correlated with histological grade(rs=-0.43,-0.39,-0.35,all P＜0.05),while Kep andKtranshad no significant correlation with histological grade(both P＞0.05).The AUC of Ve,CER and MaxSlope for differentiating high-and low-grade breast cancer was 0.749,0.725 and 0.700,respectively.Conclusion Among DISCO-DCE-MRI quantitative parameters,Ve,CER and MaxSlope could be used for differentiating high-and low-grade breast cancer.

Objective:To explore the correlation between quantitative parameters based on SUV acquired by dynamic SPECT/CT imaging of parotid glands and pathological grading of labial gland in patients with primary Sj?gren syndrome (pSS).Methods:Seventy-two patients (6 males, 66 females, age (51.5±13.8) years) with confirmed pSS diagnosed at Hebei General Hospital between August 2022 and March 2024 were prospectively included. The clinical data and pathological grading information from labial gland biopsies were analyzed. Dynamic SPECT/CT imaging of the parotid glands was performed, and quantitative parameters based on SUV were obtained using Q-metrix software: SUV max, SUV mean, uptake volume of parotid glands (UVP) and total parotid uptake (TPU) pre/post-acid stimulation, as well as the differences in quantitative parameters before and after acid stimulation (ΔSUV max, ΔSUV mean, ΔUVP, and ΔTPU). The independent-sample t test or Mann-Whitney U test was performed to evaluate the differences in parameters between patients with pathological grade 1-2 and those with pathological grade 3-4. Spearman rank correlation was used to analyze the correlation between quantitative parameters and pathological grading. The performance of quantitative parameters in distinguishing pathological grade 1-2 from grade 3-4 was assessed using ROC curve analysis with Delong test. Results:The SUV max pre/post-acid stimulation in patients with pathological grade 1-2 ( n=30) were higher than those in patients with grade 3-4 ( n=42) (36.38(27.81, 44.17) vs 15.45(10.77, 24.51), Z=-5.51, P<0.001(pre-acid stimulation); 21.53(16.93, 26.21) vs 11.33(7.32, 15.89), Z=-5.27, P<0.001 (post-acid stimulation)). SUV mean, UVP and TPU pre/post-acid stimulation in patients with pathological grade 1-2, as well as ΔSUV max, ΔSUV mean and ΔTPU, were all significantly higher ( Z values: from -4.73 to -3.04, t values: 6.39, 4.50, all P<0.01). Moreover, these parameters were negatively correlated with the pathological grading ( rs values: from -0.66 to -0.36, all P<0.05). No significant difference in ΔUVP was observed between patients with pathological grade 1-2 and those with grade 3-4 ( Z=-1.05, P=0.293), and ΔUVP showed no correlation with pathological grading ( rs=-0.13, P=0.297). Among all parameters, SUV max pre/post-acid stimulation and TPU pre-acid stimulation exhibited better diagnostic performance in differentiating pathological grade 1-2 from grade 3-4, with AUC values of 0.883, 0.866, and 0.888, respectively. Delong test showed that those 3 AUC values were all higher than AUC values of SUV mean, UVP post-acid stimulation and ΔUVP (all AUC<0.800; Z values: 2.09-4.65, all P<0.05). Conclusion:The quantitative parameters of parotid glands based on SUV acquired by dynamic SPECT/CT can reflect the damage degree of parotid glands in patients with pSS, providing novel quantitative analytical tools for the functional diagnosis and assessment of pSS.

Objective:To develop an artificial intelligence（AI）-based sequential ultrasound-magnetic resonance imaging（US-MRI）diagnostic strategy to optimize the imaging workflow for ovarian masses.Methods:A total of 1 120 patients with pathologically confirmed ovarian masses who underwent both preoperative pelvic ultrasound and MRI between January 2021 and December 2023 at Women's Hospital，Zhejiang University School of Medicine were retrospectively included. Patients were randomly divided into the training（ n=672）and internal test set（ n=448）at a ratio of 6∶4. An external test set（ n=128）was established at the Forth Affiliated Hospital of School of Medicine. Deep learning was used for automated segmentation of MRI lesions，followed by radiomic feature extraction and machine learning classification to construct both a US-MRI multimodal model and sequential US-MRI strategy. Diagnostic performance and potential healthcare cost-saving effects were evaluated across strategies. Results:In the internal test set（ n=448），the AI-based sequential US-MRI strategy achieved a F1 score of 0.863 and a diagnostic accuracy of 82.14%，with no significant difference compared to the US-MRI multi-modal model（ P>0.05）. The sequential strategy identified 82 cases（18.30%，82/448）of patients as low-risk true negatives during initial ultrasound screening，suggesting a potential to reduce the need for MRI examinations in future clinical practice. In the external test set（ n=128），the strategy achieved an F1 score of 0.800 and a confirmed diagnosis rate of 85.94%，with a theoretical reduction of 26.56%（34 cases）in MRI utilization while maintaining a diagnostic accuracy rate higher than that of the multi-modal model（82.18%）. Conclusions:The AI-based US-MRI sequential diagnostic strategy demonstrates favorable diagnostic accuracy while offering the potential to optimize MRI utilization. This approach may enhance the efficiency of imaging resource allocation and reduce healthcare burden in the management of ovarian masses.

Healthy lifestyles and good living habits are effective strategies and important approaches to prevent chronic non-communicable diseases. With the development of evidence-based medicine, the evidence translation system has made some achievements in clinical practice. There is, however, no comprehensive, professional and efficient system for translating lifestyle evidence globally. Therefore, the Health Lifestyle Evidence (HLSE) Group of Lanzhou University constructed the HLSE Evidence Translation System (https://hlse.cn/), with the aim of synthesizing, evaluating, translating, and applying lifestyle-related evidence resources. This paper aims to introduce the functional module design of the evidence translation system, the system development process and testing, introduce the interface design and function demonstration, and explain the significance of constructing the HLSE.

Healthy lifestyles and good living habits are effective strategies and important approaches to prevent chronic non-communicable diseases. With the development of evidence-based medicine, the evidence translation system has made some achievements in clinical practice. There is, however, no comprehensive, professional and efficient system for translating lifestyle evidence globally. Therefore, the Health Lifestyle Evidence (HLSE) Group of Lanzhou University constructed the HLSE Evidence Translation System (https://hlse.cn/), with the aim of synthesizing, evaluating, translating, and applying lifestyle-related evidence resources. This paper aims to introduce the functional module design of the evidence translation system, the system development process and testing, introduce the interface design and function demonstration, and explain the significance of constructing the HLSE.

Objective To investigate the value of extracellular volume fraction(ECV)based on CT for predicting macrotrabecular-massive hepatocellular carcinoma(MTM-HCC).Methods Data of 23 MTM-HCC(MTM-HCC group)and 56 non-MTM-HCC(nMTM-HCC group)patients were retrospectively analyzed,and CT manifestations were compared between groups.CT values of abdominal aorta(P-CTabdominal aorta,E-CTabdominal aorta),tumors(P-CTtumor,E-CTtumor)and non-tumor liver parenchyma(P-CTliver,E-CTliver)in plain phase(P)and enhancement equilibrium phase(E)CT were measured,then ECV of tumors and liver parenchyma were calculated,and ECV-related parameters were compared between groups.Receiver operating characteristic curves were drawn,and area under the curve(AUC)was calculated to evaluate the predictive efficacy of ECV-related parameters for predicting MTM-HCC.Results No significant difference of CT manifestations was found between groups(all P＞0.05).E-CTtumor,Δltumor(absolute enhancement CT value of the tumor area)and ECVtumor in MTM-HCC group were all lower than those in nMTM-HCC group(all P＜0.01).The AUC of E-CTtumor,Δtumor and ECVtumor for predicting MTM-HCC was 0.74,0.77 and 0.87,respectively,and the AUC of ECVtumor was higher than that of E-CTtumor and Δtumor(Z=2.271,2.557,P=0.023,0.011).Conclusion ECV based on CT could be used to effectively predict MTM-HCC.