In order to investigate the mechanical response of lumbar vertebrae during gait cycle in adolescents with idiopathic scoliosis (AIS), the present study was based on computed tomography (CT) data of AIS patients to construct model of the left support phase (ML) and model of the right support phase (MR), respectively. Firstly, material properties, boundary conditions and load loading were set to simulate the lumbar vertebra-pelvis model. Then, the difference of stress and displacement in the lumbar spine between ML and MR was compared based on the stress and displacement cloud map. The results showed that in ML, the lumbar stress was mostly distributed on the convex side, while in MR, it was mostly distributed on the concave side. The stress of the two types of stress mainly gathered near the vertebral arch plate, and the stress of the vertebral arch plate was transmitted to the vertebral body through the pedicle with the progress of gait. The average stress of the intervertebral tissue in MR was greater than that in ML, and the difference of stress on the convex and convex side was greater. The displacement of lumbar vertebrae in ML decreased gradually from L1 to L5. The opposite is true in MR. In conclusion, this study can accurately quantify the stress on the lumbar spine during gait, and may provide guidance for brace design and clinical decision making.
Humans ; Lumbar Vertebrae/diagnostic imaging* ; Scoliosis/diagnostic imaging* ; Adolescent ; Gait/physiology* ; Biomechanical Phenomena ; Tomography, X-Ray Computed ; Stress, Mechanical ; Female ; Male

Allergen-specific immunotherapy（AIT） remains the only therapeutic approach with the potential to modify the natural course of allergic rhinitis（AR）. Nevertheless, considerable inter-individual variability exists in patients'responses to AIT. To facilitate more reliable assessment of treatment efficacy, the China Rhinopathy Research Cooperation Group（CRRCG） convened young and middle-aged nasal experts in China to formulate the present consensus. The recommended subjective outcome measures for AIT comprise symptom scores, medication scores, combined symptom and medication scores, quality-of-life assessments, evaluation of disease control, and assessment of comorbidities. Objective indicators may supplement these measures. Currently available objective approaches include skin prick testing, nasal provocation testing, and allergen exposure chambers. However, these methods remain constrained by practical limitations and are not yet appropriate for routine implementation in clinical efficacy evaluation. In addition, several biomarkers, including sIgE and the sIgE/tIgE ratio, sIgG4, serum IgE-blocking activity, IgA, cytokines and chemokines, as well as immune cell surface molecules and their functional activity, have been shown to have associations with AIT outcomes. While these biomarkers may complement subjective assessments, they are subject to significant limitations. Consequently, large-scale multicenter trials and real-world evidence are required to strengthen the evidence base. The present consensus underscores the necessity of integrating patients'subjective experiences with objective testing throughout the treatment process, thereby providing a more comprehensive and accurate framework for efficacy evaluation. Looking forward, future investigations should prioritize the incorporation of multi-omics data and artificial intelligence methodologies, which hold promise for overcoming current limitations in assessment strategies and for advancing both the standardization and personalization of AIT.
Humans ; Allergens/immunology* ; China ; Consensus ; Desensitization, Immunologic ; Immunoglobulin E ; Quality of Life ; Rhinitis, Allergic/therapy* ; Treatment Outcome ; East Asian People

Integrin α _v β ₃ is overexpressed in various tumor cells and angiogenesis. To date, no drug has been proven to target it for therapy. A first-in-human study was designed to investigate the safety, pharmacokinetics, and dosimetry of ¹⁷⁷Lu-AB-3PRGD2, a novel integrin α _v β ₃-targeting radionuclide drug with an albumin-binding motif to optimize the pharmacokinetics. Ten patients (3 men, 7 women; aged 45 ± 16 years) with integrin α _v β ₃-avid tumors were recruited to accept ¹⁷⁷Lu-AB-3PRGD2 injection in a dosage of 1.57 ± 0.08 GBq (42.32 ± 2.11 mCi), followed by serial scans to obtain its dynamic distribution in the body. Safety tests were performed before and every 2 weeks after the treatment for 6-8 weeks. No adverse event over grade 3 was observed. ¹⁷⁷Lu-AB-3PRGD2 was excreted mainly through the urinary system, with intense radioactivity in the kidneys and bladder. Moderate distribution was found in the liver, spleen, and intestines. The estimated blood half-life was 2.85 ± 2.17 h. The whole-body effective dose was 0.251 ± 0.047 mSv/MBq. The absorbed doses were 0.157 ± 0.032 mGy/MBq in red bone marrow and 0.684 ± 0.132 mGy/MBq in kidneys. This first-in-human study of ¹⁷⁷Lu-AB-3PRGD2 treatment indicates its promising potential for targeted radionuclide therapy of integrin α _v β ₃-avid tumors. It merits further studies in more patients with escalating doses and multiple treatment courses.

Objective To construct and validate machine learning-based models to predict the risk of acute kidney injury(AKI)following percutaneous coronary intervention(PCI)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods A total of 2 315 STEMI patients who underwent PCI between January 2020 and June 2023 were included;306(13.2％)of them developed AKI.Baseline variables were screened using LASSO regression,with the optimal λ value selected via 10-fold cross-validation to identify AKI-associated features.Subsequently,eight distinct machine learning models were constructed and evaluated for their predictive performance.SHAP value analysis was employed to assess the impact of key variables on model predictions.Results LASSO regression identified seven variables significantly associated with AKI,including age,multivessel disease,preoperative creatinine,heart failure,white blood cell count,hemoglobin,and albumin levels.Among all the models,the light gradient boosting machine(LGBM)and extreme gradient boosting(XGB)demonstrated the best predictive performance,with training set AUCs being 0.899(95％CI:0.877-0.921)and 0.893(95％CI:0.868-0.918),and validation set AUCs being 0.809(95％CI:0.763-0.856)and 0.871(95％CI:0.833-0.909),respectively.SHAP analysis revealed that albumin,age,preoperative creatinine,and white blood cell count were the primary contributors to AKI risk.Conclusion This study successfully developed and validated machine learning-based predictive models capable of effectively identifying the risk of AKI following PCI in STEMI patients,thus providing valuable support for clinical decision-making.

To explore the interaction between ASFV capsid protein M1249L and host from the host cellular perspective,M1249L was selected for constructing the bait plasmid(pGBKT7-M1249L)to screen the bone marrow-derived macrophages(BMDMs)cDNA library.After again co-transform and sequence alignment,20 candidate interacting host proteins were screened,such as IL-1β,CTSB and DNAJA3.And then,co-immunoprecipitation assay was performed to verify the interaction be-tween M1249L and host proteins.GO ontology(GO)and KEGG pathway enrichment analyses re-vealed that biological regulation,cellular communication and response to stimulus and others were enriched in biological processes.And these host proteins could share some pathways,including toll-like receptor signaling pathway and Nod-like receptor signaling pathway.Therefore,the results provides the theoretical basis for further research on the mechanism of ASFV M1249L in viral in-fection and immune regulation.

Purpose Based on a variety of combinations of cranial MR Dixon images,the deep learning method is used to generate CT images,and the reconstruction efficiency is evaluated by comparing with the corresponding CT images.Materials and Methods A total of 77 cranial CT and MR images were collected retrospectively in Ruijin Hospital,Shanghai Jiaotong University School of Medicine from June to December 2021.The U-Net neural network was used for network training,with 62 cases in the training set and 15 cases in the test set.CT image reconstruction was performed using four kinds of Dixon images and a total of seven models among the various combinations.Mean absolute error,mean squared error,Pearson correlation coefficient and skull area Dice similarity coefficient were used to evaluate the image reconstruction efficiency.Results The generated CT images of the various Dixon image combination models showed strong correlation with the corresponding CT images(R>0.75,P<0.05),and the CT images reconstructed by the four-channel model had the closest value to the actual CT images[mean absolute error=147.516±30.802,mean squared error=(8.648±3.403)×104],the highest correlation coefficient(R=0.796±0.055),and the highest similarity coefficient in the cranial region(Dice similarity coefficient=0.800±0.036).Conclusion Deep learning training through Dixon images can be used to generate CT images,and the combination of four kinds of Dixon contrast images can improve the CT image reconstruction efficiency.

Objective To construct and validate machine learning-based models to predict the risk of acute kidney injury(AKI)following percutaneous coronary intervention(PCI)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods A total of 2 315 STEMI patients who underwent PCI between January 2020 and June 2023 were included;306(13.2％)of them developed AKI.Baseline variables were screened using LASSO regression,with the optimal λ value selected via 10-fold cross-validation to identify AKI-associated features.Subsequently,eight distinct machine learning models were constructed and evaluated for their predictive performance.SHAP value analysis was employed to assess the impact of key variables on model predictions.Results LASSO regression identified seven variables significantly associated with AKI,including age,multivessel disease,preoperative creatinine,heart failure,white blood cell count,hemoglobin,and albumin levels.Among all the models,the light gradient boosting machine(LGBM)and extreme gradient boosting(XGB)demonstrated the best predictive performance,with training set AUCs being 0.899(95％CI:0.877-0.921)and 0.893(95％CI:0.868-0.918),and validation set AUCs being 0.809(95％CI:0.763-0.856)and 0.871(95％CI:0.833-0.909),respectively.SHAP analysis revealed that albumin,age,preoperative creatinine,and white blood cell count were the primary contributors to AKI risk.Conclusion This study successfully developed and validated machine learning-based predictive models capable of effectively identifying the risk of AKI following PCI in STEMI patients,thus providing valuable support for clinical decision-making.

To explore the interaction between ASFV capsid protein M1249L and host from the host cellular perspective,M1249L was selected for constructing the bait plasmid(pGBKT7-M1249L)to screen the bone marrow-derived macrophages(BMDMs)cDNA library.After again co-transform and sequence alignment,20 candidate interacting host proteins were screened,such as IL-1β,CTSB and DNAJA3.And then,co-immunoprecipitation assay was performed to verify the interaction be-tween M1249L and host proteins.GO ontology(GO)and KEGG pathway enrichment analyses re-vealed that biological regulation,cellular communication and response to stimulus and others were enriched in biological processes.And these host proteins could share some pathways,including toll-like receptor signaling pathway and Nod-like receptor signaling pathway.Therefore,the results provides the theoretical basis for further research on the mechanism of ASFV M1249L in viral in-fection and immune regulation.

Purpose Based on a variety of combinations of cranial MR Dixon images,the deep learning method is used to generate CT images,and the reconstruction efficiency is evaluated by comparing with the corresponding CT images.Materials and Methods A total of 77 cranial CT and MR images were collected retrospectively in Ruijin Hospital,Shanghai Jiaotong University School of Medicine from June to December 2021.The U-Net neural network was used for network training,with 62 cases in the training set and 15 cases in the test set.CT image reconstruction was performed using four kinds of Dixon images and a total of seven models among the various combinations.Mean absolute error,mean squared error,Pearson correlation coefficient and skull area Dice similarity coefficient were used to evaluate the image reconstruction efficiency.Results The generated CT images of the various Dixon image combination models showed strong correlation with the corresponding CT images(R>0.75,P<0.05),and the CT images reconstructed by the four-channel model had the closest value to the actual CT images[mean absolute error=147.516±30.802,mean squared error=(8.648±3.403)×104],the highest correlation coefficient(R=0.796±0.055),and the highest similarity coefficient in the cranial region(Dice similarity coefficient=0.800±0.036).Conclusion Deep learning training through Dixon images can be used to generate CT images,and the combination of four kinds of Dixon contrast images can improve the CT image reconstruction efficiency.

Objective:To investigate the morphological difference and clinical significance of bilateral lumbar multifidus muscles in patients with unilateral lumbosacral radiculopathy due to lumbar disc herniation and lumbar spondylolisthesis.Methods:A retrospective analysis was conducted on patients with low back pain, lumbar disc herniation and lumbar spondylolisthesis. Patients with lumbar disc herniation or lumbar spondylolisthesis underwent single segment lesion either at L 4, 5 or L 5S 1, while those accompanied with unilateral lumbosacral radiculopathy underwent percutaneous endoscopic lumbar discectomy or conventional open surgery at Qingdao Municipal Hospital between January 2017 and January 2023. Patients with lumbar spondylolisthesis were subdivided into degenerative lumbar spondylolisthesis and isthmic spondylolisthesis. 53 patients with low back pain met the inclusion criteria. 170 patients with lumbar disc herniation met the inclusion criteria, with 101 at L 4, 5 and 69 at L 5S 1 level. 129 patients with lumbar spondylolisthesis met the inclusion criteria, including 91 of degenerative lumbar spondylolisthesis at L 4, 5 level and 9 at L 5S 1 level, and 11 of isthmic spondylolisthesis at L 4, 5 level and 18 at L 5S 1 level. Cross-sectional images at the mid-disc of L 3, 4, L 4, 5 and L 5S 1 segments in MRI were acquired. Relative total cross-sectional area (rTCSA), relative functional cross-sectional area (rFCSA), fat infiltration rate (FIR), relative fat distance (rFD) and differential value FIR (D-FIR) in bilateral lumbar multifidus muscle were measured respectively by using Image J software, and were then used to evaluate the atrophy and fat infiltration of bilateral lumbar multifidus muscles. Results:No significant difference was found between the both sides of multifidus muscle in low back pain patients. L 4, 5 lumbar disc herniation group had smaller rFCSA (0.34±0.10 and 0.35±0.10) and larger FIR [29.92(22.21, 36.46) and 26.48(17.54, 34.55)] and rFD [0.39(0.29, 0.54) and 0.32(0.21, 0.43)] on the affected side compared to the unaffected side in L 4, 5 segment, and had larger FIR (34.83±11.34 and 31.44±10.94) and rFD [0.59(0.43, 0.77) and 0.51(0.37, 0.69)] on the affected side in L 5S 1 segment. L 5S 1 lumbar disc herniation group had smaller rFCSA (0.41±0.11 and 0.42±0.12) and larger FIR [26.84(22.92, 35.29) and 24.02(20.03, 32.87)] and rFD (0.51±0.28 and 0.42±0.26) on the affected side in L 5S 1 segment. L 4, 5 degenerative lumbar spondylolisthesis group had larger FIR (36.49±9.76 and 34.72±9.86) on the affected side in L 4, 5 segment, and had larger FIR [35.03(28.64, 41.85) and 33.34(26.37, 39.76)] on the affected side in L 5S 1 segment. L 5S 1 degenerative lumbar spondylolisthesis group had larger FIR [42.53(37.94, 46.81) and 40.79(30.84, 43.53)] and rFD (1.12±0.79 and 0.94±0.79) on the affected side in L 5S 1 segment. L 4, 5 isthmic spondylolisthesis group had smaller rFCSA [0.24(0.20, 0.30) and 0.29(0.23, 0.34)]and larger FIR [34.19 31.30, 42.39) and 29.43(28.82, 36.89)] and rFD (0.39±0.15 and 0.29±0.15) on the affected side in L 4, 5 segment, and had larger FIR (43.18±12.71 and 34.12±11.63) on the affected side in L 5S 1 segment. L 5S 1 isthmic spondylolisthesis group had larger FIR (40.24±9.34 and 36.37±10.70) on the affected side in L 5S 1 segment. No significant difference was found of the multifidus muscle between the affected and unaffected sides in the proximal adjacent segment of the responsible segment in lumbar disc herniation or lumbar spondylolisthesis group patients. L 4, 5 isthmic spondylolisthesis group had larger D-FIR (6.75±8.46 and 1.78±5.77) in L 4, 5 segment, and had larger D-FIR (9.06±11.59 and 1.54±7.08) in L 5S 1 segment compared to L 4, 5 degenerative lumbar spondylolisthesis group. Grade Ⅱ L 4, 5 lumbar spondylolisthesis group had larger D-FIR (10.73±13.61 and 1.92±7.43) in L 5S 1 segment compared to grade Ⅰ L 4, 5 lumbar spondylolisthesis group. Conclusion:L 4, 5 or L 5S 1 lumbar disc herniation and lumbar spondylolisthesis patients with unilateral lumbosacral radiculopathy had asymmetric atrophy and fat infiltration of multifidus muscle. The atrophy and fat infiltration on the affected side showed greater. The asymmetry appeared in the responsible segment and its distal adjacent lumbar segment. Lumbar spondylolisthesis patients with a lager degree of slip or with isthmic type could be accompanied by more severe asymmetry of multifidus muscle.