ObjectiveTo evaluate the feasibility of various strength levels of deep learning image reconstruction （DLIR） algorithms for improving non-contrast abdominal CT image quality at ultra-low radiation doses， by comparing ultra-low-dose DLIR images with low-dose filtered back projection （FBP） images. MethodsA prospective collection of 85 patients undergoing non-contrast abdominal CT scans was performed， and a self-controlled study method was employed to conduct low-dose （LD） group and ultra-low-dose （ULD） group scans. The LD group used a noise index of 10 and employed FBP for image reconstruction （LD-FBP group）. The ULD group used a noise index of 30 and employed DLIR at different levels （low， medium， high）， resulting in three subgroups of reconstructed images： ULD-DLIR-L， ULD-DLIR-M， and ULD-DLIR-H. For each group， CT values， standard devia-tion （SD）， signal-to-noise ratio （SNR）， and contrast-to-noise ratio （CNR） were measured and calculated for the liver， spleen， kidneys， aorta， psoas major， and subcutaneous fat. Effective dose （ED） was also recorded. Two radiologists independently performed subjective evaluations of image quality using a 5-point scale. ResultsCompared with the LD-FBP group， the ULD-DLIR-L group showed significantly lower SNR and CNR values in the liver， spleen， kidneys， aorta， and psoas major （P<0.001）， while the ULD-DLIR-H group exhibited significantly higher values （P<0.001）. The difference of SNR and CNR values for the ULD-DLIR-M group showed no statistically significant difference. For subjective evaluation， the scores of the ULD-DLIR-L and ULD-DLIR-M groups were lower than those of the LD-FBP group， while there was no statistically significant difference in scores between the ULD-DLIR-H group and the LD-FBP group. The ED value of the ULD group was approximately 88% lower than that of the LD group. ConclusionCompared with the LD-FBP group， the ULD-DLIR-H group significantly reduces SD values while increasing SNR and CNR values， effectively improving the image quality of non-contrast abdominal CT scans.

Objective To evaluate the safety and therapeutic efficacy of three-dimensional(3D)-printed personalized cervical correction pillows for treating cervical spondylotic radiculopathy.Methods A finite element model was established to simulate and analyze the biomechanical changes in cervical spine before and after using the pillow.Additionally,20 patients with chronic neck pain were included to analyze changes in visual analogue scale(VAS)scores,neck disability index(NDI),pressure pain threshold(PPT),Borden value,cervical lordosis,T1 slope,cervical slope,and thoracic inlet angle before and after using the pillow.Results Finite element analysis indicated that the maximum stress on vertebral bodies increased by 64.35％and the maximum stress on cartilage tissues by 5.09％after using the pillow.The Borden value improved by 45.75％.Clinical studies showed a significant reduction in VAS scores,NDI,and PPT after treatment(P＜0.05),while PPT,Borden value,cervical lordosis,T1 slope,and thoracic inlet angle significantly increased(P＜0.05).Conclusions The 3D-printed personalized cervical correction pillow is safe and effective in alleviating neck pain and improving cervical curvature,and it provides a new and effective non-surgical treatment option for cervical spondylotic radiculopathy,with significant clinical implications.

Transcription factor EB(TFEB),which belongs to the microphthalmia/transcription factor E(MiTF/TFE)family,is mainly functioned as regulator involved in regulating lysosomal function and autophagy.It plays an im-portant role in lipid metabolism via modulating lysosomal lipid degradation,mitochondrial β-oxidation of fatty acid,and in-tracellular cholesterol efflux.TFEB inhibits the development of metabolic associated fatty liver disease(MAFLD)and o-besity by regulating the autophagy and the expression of lipid metabolism-related genes.Additionally,it prevents the for-mation of foam cell from macrophages and vascular smooth muscle cells by restraining lipid accumulation,thereby attenua-ting the progression of atherosclerosis.TFEB promotes lipophagy to relieve lipid accumulation and lipid accumulation-in-duced insulin resistance,and β-cell failure,deferring diabetes-related lipid metabolic disorders.In summary,TFEB plays a key role in lipid metabolism and associated lipid disorder diseases,and serves as a potential clinical target to correct lipid dysmetabolism in vivo.

The deep learning reconstruction(DLR)algorithm is a new CT image reconstruction technology in recent years,which can be used as an alternative to filtered back projection and iterative reconstruction in clinical practice.Compared with traditional image reconstruction algorithms,the DLR algorithm can reduce image noise and radiation dose while preserving image texture,shortening reconstruction time,and improving diagnostic efficiency.Therefore,it has broad clinical application prospects in the field of image reconstruction.This article summarizes the basic principles of the DLR algorithm and its new progress in the clinical application of abdominal CT.

Objective A Cobb angle prediction model for adolescent idiopathic scoliosis(AIS)based on three-point mechanical data from three-dimensional(3D)-printed orthotics and various machine learning algorithms was developed,so as to provide an innovative,radiation-free method for early clinical screening and monitoring of AIS.Methods Clinical data from AIS patients and mechanical data from 3D-printed orthotics were collected to construct a comprehensive dataset with features such as gender,age,disease type,weight,and Risser score.Six algorithms,namely,random forest,support vector regression,gradient boosting regressor,extreme gradient boosting,lightgbm,and catboost,were used to construct and evaluate the performance of Cobb angle prediction models.Results The gradient boosting regressor model had the best performance on several evaluation metrics,achieving a precision rate of 0.937,recall rate of 0.818,F1-score of 0.949,and an area under curve(AUC)value of 0.843.In the validation set,the model's predictions reached an accuracy rate of 0.942,fitting well with the actual Cobb values.Conclusions The Cobb angle prediction model based on mechanical data and machine learning effectively avoids the radiation risks associated with traditional full-spine X-ray examinations in early clinical screening.It provides a non-invasive assessment for AIS patients,enhancing the safety and efficiency of screening and monitoring,and offering a powerful decision-making tool for clinicians,with a great clinical significance.

ObjectiveTo study the mechanism of compound Xishu granules （CXG） in inhibiting the proliferation of hepatocellular carcinoma cells by regulating ferroptosis. MethodsThe transplanted tumor model of human Huh7 was established with nude mice and the successfully modeled mice were randomized into model， Fufang Banmao （0.21 g·kg^-1）， low-dose （1.87 g·kg^-1） CXG， medium-dose （3.74 g·kg^-1） CXG， and high-dose （7.49 g·kg^-1） CXG groups. Mice were administrated with drinking water or CXG for 28 days， and the body weight and tumor volume were measured every 4 days. Hematoxylin-eosin staining was employed to observe the histopathological changes of tumors. The cell-counting kit-8 （CCK-8） was used to examine the survival rate of Huh7 cells treated with different concentrations （0， 31.25， 62.5， 125， 250， 500， 1 000 mg·L^-1） of CXG for 24 h and 48 h. CA-AM， DCFH-DA， and C11-BODIPY^581/591 fluorescent probes were used to determine the intracellular levels of ferrous ion （Fe²⁺）， reactive oxygen species （ROS）， and lipid peroxide （LPO）， respectively. The colorimetric method was employed to measure the levels of glutathione （GSH） and superoxide dismutase （SOD）. Western blot was employed to determine the protein levels of glutathione peroxidase 4 （GPX4）， transferrin receptor 1 （TFR1）， and ferritin heavy chain 1 （FTH1）， respectively. ResultsIn the animal experiment， compared with the model group， the drug treatment groups showed reductions in the tumor volume from day 12 （P<0.01）. After treatment， the Fufang Banmao and low-， medium-， and high-dose CXG groups had lower tumor volume， relative tumor volume， and tumor weight than the model group （P<0.05）， with tumor inhibition rates of 48.99%， 79.93%， 91.38%， and 97.36%， respectively. Moreover， the CXG groups had lower tumor volume and relative tumor volume （P<0.05 in all the three dose groups） and lower tumor weight （P<0.05 in medium-dose and high-dose groups） than the Fufang Banmao group. Compared with the model group， the drug treatment groups showed reduced number of tumor cells， necrotic foci with karyopyknosis， nuclear fragmentation， and nucleolysis， and the high-dose CXG group showed an increase in the proportion of interstitial fibroblasts. In the cell experiment， compared with the blank group， CXG reduced the survival rate of Huh7 cells in a dose-dependent manner after incubation for 24 h and 48 h （P<0.05）. Compared with the blank group， the RSL3 group and the low-， medium-， and high-dose CXG groups showed a decrease in the relative fluorescence intensity of CA-AM and increases in the fluorescence intensity of DCFH-DA and fluorescence ratio of C11-BODIPY^581/591， which indicated elevations in the levels of Fe²⁺ （P<0.01）， ROS （P<0.05）， and LPO （P<0.01）， respectively. Compared with the blank group， the RSL3 and low-， medium-， and high-dose CXG groups showed lowered levels of GSH and SOD （P<0.05）. In addition， the RSL3 group and the medium- and high-dose CXG groups showed down-regulated expression of GPX4 and FTH1 （P<0.05）， and the low- and high-dose CXG groups presented up-regulated expression of TFR1 （P<0.05）. ConclusionCXG suppresses the proliferation of hepatocellular carcinoma cells by inducing ferroptosis via downregulating the GSH-GPX4 signaling axis and increasing intracellular Fe²⁺and LPO levels.

Objective A Cobb angle prediction model for adolescent idiopathic scoliosis(AIS)based on three-point mechanical data from three-dimensional(3D)-printed orthotics and various machine learning algorithms was developed,so as to provide an innovative,radiation-free method for early clinical screening and monitoring of AIS.Methods Clinical data from AIS patients and mechanical data from 3D-printed orthotics were collected to construct a comprehensive dataset with features such as gender,age,disease type,weight,and Risser score.Six algorithms,namely,random forest,support vector regression,gradient boosting regressor,extreme gradient boosting,lightgbm,and catboost,were used to construct and evaluate the performance of Cobb angle prediction models.Results The gradient boosting regressor model had the best performance on several evaluation metrics,achieving a precision rate of 0.937,recall rate of 0.818,F1-score of 0.949,and an area under curve(AUC)value of 0.843.In the validation set,the model's predictions reached an accuracy rate of 0.942,fitting well with the actual Cobb values.Conclusions The Cobb angle prediction model based on mechanical data and machine learning effectively avoids the radiation risks associated with traditional full-spine X-ray examinations in early clinical screening.It provides a non-invasive assessment for AIS patients,enhancing the safety and efficiency of screening and monitoring,and offering a powerful decision-making tool for clinicians,with a great clinical significance.

Objective:This article studies the abilities and quality that medical technical managers should possess and provides a reference for promoting the professional training and development of medical technical managers.Methods:The data were obtained through semi-structured interviews and literature collection. The interview subjects were 20 scientific researchers with transformation projects and 10 management staffs with technical manager certificates in medical colleges. The documents are 6 articles related to ″technical manager capabilities″ collected on open academic platforms. Grounded theory was used to code and analyze above data.Results:After three-level coding and combining with the iceberg competency model, the knowledge, skills, self-awareness, traits and motivation of medical technology managers were sorted out, totalling 5 core categories, 10 main categories, and 50 initial categories, to construct a competency model for medical technology managers.Conclusions:Based on the complex knowledge structure and high occupational requirements of medical technology managers, policy insights such as systematic knowledge training, raising skill requirements in practice, and enriching assessment standards and communication channels are proposed.

To analyse the role and mechanism of lipid metabolism disorder in vascular aging caused by endothelial cells,smooth muscle cells,and macrophages.Lipid metabolism disorder damages endothelial cells and promotes vascular aging through the reactive oxygen species(ROS)pathway,endothelial nitric oxide synthase(eNOS)activity,oxidized low density lipoprotein(ox-LDL),inflammasomes,and various inflammatory factors.Lipid metabolism disorder accelerates vascular aging process through autophagy,DNA damage,and nuclear factor-κB(NF-κB)in vascular smooth muscle cells.Lipid metabolism disorder stimulates macrophages in the vascular wall to secrete various inflammatory factors that act on the Toll-like receptor(TLR)pathway,promoting oxidative stress and causing DNA damage,thereby promoting vascular aging.Lipid metabolism disorder promotes oxidative stress and chronic inflammation in endothelial cells,smooth muscle cells,and macrophages,leading to vascular aging.

Objective To evaluate the safety and therapeutic efficacy of three-dimensional(3D)-printed personalized cervical correction pillows for treating cervical spondylotic radiculopathy.Methods A finite element model was established to simulate and analyze the biomechanical changes in cervical spine before and after using the pillow.Additionally,20 patients with chronic neck pain were included to analyze changes in visual analogue scale(VAS)scores,neck disability index(NDI),pressure pain threshold(PPT),Borden value,cervical lordosis,T1 slope,cervical slope,and thoracic inlet angle before and after using the pillow.Results Finite element analysis indicated that the maximum stress on vertebral bodies increased by 64.35％and the maximum stress on cartilage tissues by 5.09％after using the pillow.The Borden value improved by 45.75％.Clinical studies showed a significant reduction in VAS scores,NDI,and PPT after treatment(P＜0.05),while PPT,Borden value,cervical lordosis,T1 slope,and thoracic inlet angle significantly increased(P＜0.05).Conclusions The 3D-printed personalized cervical correction pillow is safe and effective in alleviating neck pain and improving cervical curvature,and it provides a new and effective non-surgical treatment option for cervical spondylotic radiculopathy,with significant clinical implications.