Radiopharmaceuticals play an important role in the medical field,but they also carry certion risks and potential safety concerns.Medical institutions implement pharmacovigilance to ensure the safety of patients'drug use,including the safety of Radiopharmaceuticals.The operation and management of the pharmacovigilance system in the United States and the European Union are relatively mature.China can learn from their advanced concepts and establish our own radiopharmaciligence system.

Objective To investigate the improvement effect and related mechanism of roxadustat on myocardial ischemia-reperfusion(I/R)injury in mice.Methods Twenty four male C57BL/6N mice were randomly divided into the sham operation group,the control group and the roxadustat group,with eight mice in each group.A mouse myocardial I/R model was established.The control group was given 100 μL saline injection containing 5%dimethyl sulfoxide by gavage.The roxadustat group was given 25 mg/kg roxadustat by gavage.The left anterior descending coronary artery of mice in both groups was ligated for 40 minutes,and then reperfusion for 24 hours to establish the myocardial I/R model.In the sham operation group,only the left anterior coronary artery was pierced without ligation.The area of myocardial infarction in mice was detected by triphenyltetrazolium chloride(TTC)staining.The apoptosis of mouse cardiomyocytes was detected by TdT-mediated dUTP nick and labeling(TUNEL)staining.The expression of apoptosis-related proteins bcl-2 associated X protein(Bax),Caspase3 and inflammatory cell markers F4/80 and myeloperoxidase(MPO)were detected by immunohistochemistry staining.The damage of myocardial cells was observed by hematoxylin-eosin(HE)staining.Results The area of myocardial infarction after myocardial I/R was reduced in the roxadustat group compared to the control group and the sham operation group(P＜0.05).The number of apoptotic cells was higher in the control group and the roxadustat group than that in the sham operation group,and the number of apoptotic cells was lower in the roxadustat group than that in the control group(P＜0.05).The expression levels of Bax and Caspase3 proteins in myocardial tissue were higher in the control group and the roxadustat group than those in the sham operation group,while those of the roxadustat group was lower than those of the control group(P＜0.05).The expression levels of F4/80 and MPO proteins in myocardial tissue were lower in the roxadustat group than those in the control group(P＜0.05).In the control group,the myocardial tissue arrangement was disordered,and there was an increase in interstitial vacuoles.Compared with the control group,the myocardial cells were arranged more neatly in the roxadustat group,and the interstitial vacuoles were reduced.Conclusion Roxadustat can reduce the myocardial infarction area after I/R injury,inhibit myocardial cell apoptosis,alleviate myocardial injury,reduce infiltration of myocardial macrophages and neutrophils,and reduce inflammatory injury.

Objective To evaluate the effect of acupuncture at Yin Wei point on postoperative nausea and vomiting(PONV)in female patients undergoing abdominal surgery.The clinical efficacy of postoperative nausea and vomiting(PONV)and its impact on gut microbiota.Methods This study included 184 patients who underwent gynecological laparoscopic surgery in Jinhua Central Hospital from January 2021 to April 2024.They were randomly divided into control group(n=93)and acupuncture group(n=91)using a random number method.At the completion of surgery,the control group received intravenous injection of 5mg of tropisetron hydrochloride.In acupuncture group,on the basis of control group,intervention was performed by needling the palmar Yin Wei points of both forearms for 30 minutes before surgery.The incidence and severity of PONV were compared between two groups of patients.In addition,fecal samples were collected from two groups of patients before and after surgery,and differential analysis of gut microbiota community structure was performed using 16S amplicon absolute quantitative sequencing technology.Results In the 0-24 hours after surgery,40 cases of the acupuncture group and 56 cases of control group experienced PONV.The acupuncture group's PONV incidence was lower than control group's(P＜0.05).The nausea severity of acupuncture group after surgery was significantly lower than that of control groups.The proportion of patients taking antiemetic drugs after surgery in acupuncture group was also significantly lower than that in control group(P＜0.05).Before surgery,the two groups have no significant difference regarding Chao1,ACE,Shannon,and Simpson indices(P＞0.05).After surgery,the acupuncture group's Chao1,ACE,and Shannon indices were significantly higher than control group's(P＜0.05).The Simpson scores of two groups of patients were compared after surgery,and no significant difference was found(P＞0.05).The Observed,Chao1,ACE,and Shannon indices were significantly higher in the acupuncture group after surgery than before(P＜0.05).The incidence of adverse reactions in acupuncture group and control group were 8.8％and 8.6％,respectively,and there was no statistical significance(P＞0.05).Conclusion Acupuncture at Yin Wei point combined with intravenous injection of tropisetron can reduce the incidence of PONV in patients undergoing gynecological laparoscopic surgery.

Liver size is regulated by circadian clock and exhibits a diurnal rhythm. Pregnane X receptor (PXR) and peroxisome proliferator-activated receptor α (PPARα), members of nuclear receptor superfamily, are important regulators of liver size. We previously demonstrated that mPXR agonist pregnenolone 16α-carbonitrile (PCN) and mPPARα agonist pirinixic acid (WY-14643) promoted liver enlargement and liver regeneration after partial hepatectomy. However, whether PXR or PPARα activation-induced liver enlargement exhibits diurnal rhythm with normal liver diurnal oscillations remains unclear. The aim of this study was to investigate the diurnal rhythm of PXR or PPARα activation-induced liver enlargement. Male C57BL/6 mice were intraperitoneally injected with corn oil, PCN or WY-14643 for three days, and liver samples were weighed and collected at various time points for analysis. The animal experiment protocol was reviewed and approved by Institutional Animal Care and Use Committee of Sun Yat-sen University (approval No. SYSU-IACUC-2023-001613, SYSU-IACUC-2023-001783). The results showed that PXR or PPARα activation at various time points significantly induced liver enlargement, and liver size maintained normal diurnal oscillations during PXR or PPARα activation-induced hepatomegaly, without significant effects on the expression of core clock genes. This study reveals PXR or PPARα activation-induced liver enlargement exhibits diurnal rhythm with normal liver diurnal oscillations, and provides novel data for nuclear receptor-induced liver enlargement and liver diurnal rhythm.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: Lynch syndrome (LS) is a hereditary cancer predisposition syndrome with a significantly increased risk of colorectal and endometrial cancers. Current standard practice involves universal screening for LS in patients with newly diagnosed colorectal or endometrial cancer using a multi-step screening protocol (MSP). However, MSP may not always accurately identify LS cases. To address this limitation, we compared the diagnostic performance of immediate germline sequencing (IGS) with MSP in a high-risk group. Methods: A total of 31 Taiwanese women with synchronous or metachronous endometrial and colorectal malignancies underwent MSP which included immunohistochemical staining of DNA mismatch repair (MMR) proteins, MLH1 promoter hypermethylation analysis, and germline sequencing to identify pathogenic variants. All patients who were excluded during MSP received germline sequencing for MMR genes to simulate IGS for the detection of LS. Results: Our findings indicate that IGS surpassed MSP in terms of diagnostic yield (29.0% vs.19.4%, respectively) and sensitivity (90% vs. 60%, respectively). Specifically, IGS successfully identified nine LS cases, which is 50% more than the number detected through MSP.Additionally, germline methylation analysis revealed one more LS case with constitutional MLH1 promoter hypermethylation, bringing the total LS cases to ten (32.3%). Intriguingly, we observed no significant differences in clinical characteristics or overall survival between patients with and without LS in our cohort. Conclusion Our study suggests that IGS may potentially offer a more effective approach compared to MSP in identifying LS among high-risk patients. This advantage is evident when patients have been pre-selected utilizing specific clinical criteria.

Objective: Lynch syndrome (LS) is a hereditary cancer predisposition syndrome with a significantly increased risk of colorectal and endometrial cancers. Current standard practice involves universal screening for LS in patients with newly diagnosed colorectal or endometrial cancer using a multi-step screening protocol (MSP). However, MSP may not always accurately identify LS cases. To address this limitation, we compared the diagnostic performance of immediate germline sequencing (IGS) with MSP in a high-risk group. Methods: A total of 31 Taiwanese women with synchronous or metachronous endometrial and colorectal malignancies underwent MSP which included immunohistochemical staining of DNA mismatch repair (MMR) proteins, MLH1 promoter hypermethylation analysis, and germline sequencing to identify pathogenic variants. All patients who were excluded during MSP received germline sequencing for MMR genes to simulate IGS for the detection of LS. Results: Our findings indicate that IGS surpassed MSP in terms of diagnostic yield (29.0% vs.19.4%, respectively) and sensitivity (90% vs. 60%, respectively). Specifically, IGS successfully identified nine LS cases, which is 50% more than the number detected through MSP.Additionally, germline methylation analysis revealed one more LS case with constitutional MLH1 promoter hypermethylation, bringing the total LS cases to ten (32.3%). Intriguingly, we observed no significant differences in clinical characteristics or overall survival between patients with and without LS in our cohort. Conclusion Our study suggests that IGS may potentially offer a more effective approach compared to MSP in identifying LS among high-risk patients. This advantage is evident when patients have been pre-selected utilizing specific clinical criteria.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.