Background::Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely intervention play a pivotal role in enhancing survival rates. However, there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD.Methods::In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) were used to select the clinical features for further training with machine learning (ML) methods, including random forest (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression trees (CART), and C5.0 models. The performances of these models were subsequently validated using a multicenter validation cohort.Results::In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the training cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer levels alone.Conclusion::ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE.Trial Registration::Chictr.org.cn: ChiCTR2200059599.

Heart failure is the leading cause of mortality in cardiovascular diseases and represents the ultimate common manifestation of most cardiovascular conditions,impacting over 60 million individuals globally.Currently,heart transplantation remains the standard treatment for heart failure patients.Adherence to fundamental pharmacotherapy can improve quality of life and extend survival time for heart failure patients.However,due to the complex mechanism of heart failure and numerous complications,the limitations of conventional heart failure treatment strategies in clinical work are gradually magnified.In recent years,interventional therapy has emerged as an innovative approach for managing heart failure,attracting significant attention and achieving substantial breakthroughs that offer new hope for affected individuals.Injectable hydrogel has garnered considerable interest in biomedicine due to its minimally invasive nature and capacity for efficient therapeutic drug delivery.In the context of chronic heart failure,injectable hydrogel finds application primarily in tissue regeneration,drug delivery,and immunotherapy.This review mainly describes the application and research progress of injectable hydrogel in the treatment of heart failure.

Objective To establish a rat model of hyperuricemic nephropathy(HN)using a multifactorial induction method of potassium oxazinate combined with adenine and yeast feed to observe the intervention effect of Qiling granules(QLG).Methods Fifty-eight SPF-grade male SD rats were selected,and 10 rats were randomly allocated to the normal control(NC)group.The remaining rats were induced by multiple factors to establish HN rat models.After 2 weeks of modeling,submandibular blood samples were taken to detect serum UA,CREA,BUN,TG,and TC.Forty HN rats with bleeding clearance UA and body weight close to the mean were selected.They were randomly divided into a model(M)group,QLG low dose(QLG-L)groups,QLG high dose(QLG-H)group,and a positive control(PC)group,with 10 rats in each group,using a stratified randomization method.Each group was given corresponding drugs by gavage daily,and after continuous administration for 4 weeks,submandibular blood samples were taken to detect serum UA,CREA,BUN,TG,and TC.After euthanasia of the rats,liver tissue was taken to detect XOD and ADA activity.Renal tissue was taken for HE and Gomori hexamine silver staining,and the protein expression of GLUT9,OAT1,VCAM-1,and TGF-β in the kidneys was observed using immunohistochemistry and Western blot method.Results Compared with the NC group,the M group's serum levels of UA,CREA,BUN,TC,and TG,as well as liver XOD and ADA activities,were significantly increased(P＜0.01).The renal tissue of the model rats showed significant pathological changes.The area of renal tubules positive for urate and the expression of GLUT9,VCAM-1,and TGF-β proteins in the kidneys were significantly increased(P＜0.01,P＜0.05),while the expression of OAT1 was significantly reduced(P＜0.01).Compared with the M group,each treatment group showed significantly reduced serum UA levels,liver XOD,ADA activity,and renal VCAM-1 protein expression(P＜0.01,P＜0.05).The serum CREA and BUN levels and renal TGF-β protein expression of rats in the QLG-L group were significantly reduced(P＜0.05,P＜0.01).The serum CREA and BUN levels and renal GLUT9 protein expression of rats in the QLG-H group were also significantly reduced(P＜0.01,P＜0.05).The urate deposition and renal injury caused by each treatment were reduced to varying degrees,but there were no significant differences among groups(P＞0.05).Conclusions A stable HN rat model can be induced by gavage of potassium oxyzinate and adenine in combination with yeast feed.QLG can effectively treat HN by improving UA metabolic disorders,reducing the renal inflammation and urate deposition that cause renal damage in HN model rats.Its mechanism of action is related to a reduction in serum UA,CREA,BUN,and TG levels;liver XOD and ADA activities;and the expression of GLUT9,OAT1,VCAM-1,and TGF-β proteins in the kidneys.

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.

Hepatocellular carcinoma(HCC)is one of the most common tumor types and remains a major clinical challenge.Increasing evidence has revealed that mitophagy inhibitors can enhance the effect of chemotherapy on HCC.However,few mitophagy inhibitors have been approved for clinical use in humans.Pyrimethamine(Pyr)is used to treat infections caused by protozoan parasites.Recent studies have reported that Pyr may be beneficial in the treatment of various tumors.However,its mechanism of action is still not clearly defined.Here,we found that blocking mitophagy sensitized cells to Pyr-induced apoptosis.Mechanistically,Pyr potently induced the accumulation of autophagosomes by inhibiting autophagosome-lysosome fusion in human HCC cells.In vitro and in vivo studies revealed that Pyr blocked autophagosome-lysosome fusion by upregulating BNIP3 to inhibit synaptosomal-associated protein 29(SNAP29)-vesicle-associated membrane protein 8(VAMP8)interaction.Moreover,Pyr acted synergistically with sorafenib(Sora)to induce apoptosis and inhibit HCC proliferation in vitro and in vivo.Pyr enhances the sensitivity of HCC cells to Sora,a common chemotherapeutic,by inhibiting mitophagy.Thus,these results provide new insights into the mechanism of action of Pyr and imply that Pyr could potentially be further developed as a novel mitophagy inhibitor.Notably,Pyr and Sora combination therapy could be a promising treatment for malignant HCC.

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 To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.

With the awareness of the hazards of ionizing radiation to human health and the consequent research on the biological effects and protective measures against ionizing radiation, the radiological health has gradually developed. Therefore, as one of the five major areas of health under the traditional public health system, the study on radiological health mainly involves the harmful effects of ionizing radiation on human health and its comprehensive protection measures. After nearly a century of vigorous development ince the 1930 s and 1940 s, the level of the discipline of the radiological health and the effectiveness of radiological hygiene work in China have been greatly improved at this stage. This paper provides a brief overview of the history, current status, and work content of the radiological health research and radiological hygiene work in China, with a view to contributing to the future development of radiological hygiene work in China.

Objective:To investigate the biological effects of acute high-dose radon exposure on mice.Methods:BALB/c male mice aged 6 to 8 weeks were exposed once in an HD-3 ecological radon chamber with an average radon concentration of 7 × 10 5 Bq/m 3 for 10 h. Mice were weighed, their lung tissues and blood samples were collected at 1, 2 and 3 months after exposure. Control groups were set up at the three time points with four mice in each group. For these mice, the lung tissue pathology was observed using hematoxylin-eosin (HE) staining method, routine blood tests were conducted using a hematology analyzer and the levels of superoxide dismutase (SOD) and malondialdehyde (MDA) in the serum and lung tissues were measured using corresponding assay kits. Results:The HE staining result revealed that compared to the control groups, the experimental groups exhibited thickening of alveolar walls and increased infiltration of granulocyte, whose degrees, however, reduced over time and displayed no significant difference at 3 months after exposure. There was no significant difference in body weight or blood routine between the experimental and control groups. The detection result revealed decreased SOD levels in the lung tissues at 2 months after exposure, which were (11.34 ± 1.03) U/mgprot and (9.75 ± 0.71) U/mgprot, respectively for the control and experimental groups ( t = 2.54, P < 0.05). The MDA levels in lung tissue increased at 1 month after exposure, which were(2.30 ± 0.24) and (2.77 ± 0.29) nmol/mgprot, respectively for the control and experimental groups ( t = 2.49, P < 0.05). At 3 months after exposure, the SOD and MDA levels differed insignificantly between the control and experimental groups ( P > 0.05). Conclusions:After acute high-dose radon exposure, the mice suffered damage to the lung tissue, with changes in their oxidative stress indicators being detected. However, these effects gradually diminished at 3 months after exposure. Additionally, acute high-dose radon exposure did not give rise to significant changes in the body weight or routine blood result of the mice.