Thyroid cancer is caused by multiple factors, including genetics, environment, metabolism, and the immune microenvironment, among which ionizing radiation exposure is an important risk factor for thyroid cancer. As one of the most sensitive target organs of ionizing radiation, the thyroid gland may have different risks of thyroid cancer caused by different types of ionizing radiation exposures, such as medical exposure, occupational exposure, and emergency exposure. The sensitivity of children and adolescents are higher than that of adults. The dose-response relationship still needs to be further explored. The molecular mechanism between ionizing radiation and the increased risk of thyroid cancer is complex, which may involve DNA damage and repair abnormalities, gene mutations, non-coding RNA regulation, DNA methylation, cell cycle regulation imbalance, and immune microenvironment changes. This article reviews the risk and molecular mechanisms associated with different types of ionizing radiation exposure in thyroid cancer, based on literature retrieved from CNKI and PubMed databases. It aims to provide a theoretical basis for the early monitoring, prevention, and intervention of thyroid cancer related to ionizing radiation exposure.

ObjectiveTo observe the therapeutic effect of Qiwei Baizhusan（QWBZS） on diabetic encephalopathy（DE） rat model， and to explore the possible mechanism of QWBZS in the treatment of DE based on phosphatidylinositol 3-kinase（PI3K）/protein kinase B（Akt）/glycogen synthase kinase-3β（GSK-3β） signaling pathway. MethodForty-eight SPF male Wistar rats were randomly divided into blank group（8 rats） and high-fat diet group（40 rats）. After 12 weeks of feeding， rats in the high-fat diet group were intraperitoneally injected with 35 mg·kg^-1 of 1% streptozotocin（STZ） for 2 consecutive days to construct a DE model， and rats in the blank group were injected with the same amount of sodium citrate buffer. After successful modeling， according to blood glucose and body weight， model rats were randomly divided into model group， low， medium and high dose groups of QWBZS（3.15， 6.3， 12.6 g·kg^-1）， combined western medicine group（metformin+rosiglitazone， 0.21 g·kg^-1）， with 6 rats in each group. The administration group was given the corresponding dose of drug by gavage， and the blank group and the model group were given an equal volume of 0.9% sodium chloride solution by gavage， 1 time/day for 6 weeks. Morris water maze was used to detect the spatial memory ability of DE rats. Fasting insulin （FINS） level was detected by enzyme-linked immunosorbent assay（ELISA） and insulin resistance index（HOMA-IR） was calculated. Hematoxylin-eosin（HE） staining was used to observe the morphological changes of hippocampus in rats， ELISA was used to detect the indexes of oxidative stress in hippocampal tissues， real-time fluorescence quantitative polymerase chain reaction（Real-time PCR） was used to detect mRNA expression levels of PI3K， Akt， nuclear transcription factor-κB（NF-κB）， tumor necrosis factor-α（TNF-α） and interleukin-1β（IL-1β） in hippocampus， and Western blot was used to detect the protein expression of PI3K， Akt， phosphorylated（p）-Akt， GSK-3β and p-GSK-3β in hippocampus of rats. ResultCompared with the blank group， FINS and HOMA-IR values of the model group were significantly increased（P<0.01）， the path of finding the original position of the platform was significantly increased， and the escape latency was significantly prolonged（P<0.01）， the morphology of neuronal cells in hippocampal tissues was disrupted， the levels of reactive oxygen species（ROS） and malondialdehyde（MDA） in hippocampus of rats were increased， and the activity of superoxide dismutase（SOD） was decreased（P<0.05， P<0.01）， mRNA expression levels of PI3K and Akt were decreased（P<0.01）， mRNA expression levels of NF-κB， TNF-α and IL-1β were increased（P<0.05， P<0.01）， the protein expression levels of PI3K， p-Akt and p-GSK-3β were significantly decreased， and the protein expression of GSK-3β was significantly increased（P<0.01）. Compared with the model group， the FINS and HOMA-IR values of the medium dose group of QWBZS and the combined western medicine group were significantly decreased（P<0.01）， the path of finding the original position of the platform and the escape latency were significantly shortened（P<0.01）， the hippocampal tissue structure of rats was gradually recovered， and the morphological damage of nerve cells was significantly improved， the contents of ROS and MDA in hippocampus of rats decreased and the level of SOD increased（P<0.01）， the mRNA expression levels of PI3K and Akt were increased（P<0.01）， and the mRNA expression levels of NF-κB， TNF-α and IL-1β were decreased （P<0.05， P<0.01）， the protein expression levels of PI3K， p-Akt and p-GSK-3β were significantly increased（P<0.01）， and the expression of GSK-3β was significantly decreased（P<0.01）. ConclusionQWBZS can alleviate insulin resistance in DE rats， it may repair hippocampal neuronal damage and improve learning and cognitive ability of DE rats by activating PI3K/Akt/GSK-3β signaling pathway.

A major impedance to neuronal regeneration after peripheral nerve injury(PNI)is the activation of various programmed cell death mechanisms in the dorsal root ganglion.Ferroptosis is a form of pro-grammed cell death distinguished by imbalance in iron and thiol metabolism,leading to lethal lipid peroxidation.However,the molecular mechanisms of ferroptosis in the context of PNI and nerve regeneration remain unclear.Ferroportin(Fpn),the only known mammalian nonheme iron export protein,plays a pivotal part in inhibiting ferroptosis by maintaining intracellular iron homeostasis.Here,we explored in vitro and in vivo the involvement of Fpn in neuronal ferroptosis.We first delineated that reactive oxygen species at the injury site induces neuronal ferroptosis by increasing intracellular iron via accelerated UBA52-driven ubiquitination and degradation of Fpn,and stimulation of lipid peroxidation.Early administration of the potent arterial vasodilator,hydralazine(HYD),decreases the ubiquitination of Fpn after PNI by binding to UBA52,leading to suppression of neuronal cell death and significant ac-celeration of axon regeneration and motor function recovery.HYD targeting of ferroptosis is a promising strategy for clinical management of PNI.

Objective To observe the value of clinical and CT radiomics features for predicting microsatellite instability-high(MSI-H)status of gastric cancer.Methods Totally 150 gastric cancer patients including 30 cases of MSI-H positive and 120 cases of MSI-H negative were enrolled and divided into training set(n=105)or validation set(n=45)at the ratio of 7∶3.Based on abdominal vein phase enhanced CT images,lesions radiomics features were extracted and screened,and radiomics scores(Radscore)was calculated.Clinical data and Radscores were compared between MSI-H positive and negative patients in training set and validation set.Based on clinical factors and Radscores being significant different between MSI-H positive and negative ones,clinical model,CT radiomics model and clinical-CT radiomics combination model were constructed,and their predictive value for MSI-H status of gastric cancer were observed.Results Significant differences of tumor location and Radscore were found between MSI-H positive and negative patients in both training and validation sets(all P＜0.05).The area under the curve(AUC)of clinical model,CT radiomics model and combination model for evaluating MSI-H status of gastric cancer in training set was 0.760,0.799 and 0.864,respectively,of that in validation set was 0.735,0.812 and 0.849,respectively.AUC of clinical-CT radiomics combination model was greater than that of the other 2 single models(all P＜0.05).Conclusion Clinical-CT radiomics combination model based on tumor location and Radscore could effectively predict MSI-H status of gastric cancer.

Objective:To establish and validate a clinical and CT radiomics combined model for predicting lymph node metastasis (LNM) risk in patients with hilar cholangiocarcinoma (HCCA).Methods:This was a case-control study. Data from 158 pathologically confirmed HCCA patients between January 2016 and January 2022 at the First Affiliated Hospital of Zhengzhou University were retrospectively analyzed. Using stratified random sampling, the patients were randomly divided into a training set ( n=95) and an internal validation set ( n=63) at a 6∶4 ratio. According to postoperative pathology, 31 LNM-positive cases and 64 LNM-negative cases were in the training set, and 22 LNM-positive cases and 41 LNM-negative cases were in the internal validation set. A cohort of 50 HCCA patients was retrospectively collected from West China Hospital of Sichuan University between October 2018 and June 2021 as an external validation set, including 21 LNM-positive and 29 LNM-negative cases. Clinical features were selected by multivariate logistic regression analysis to establish a clinical model. Radiomics features were extracted from portal venous phase CT images using 3D Slicer software. A radiomics model was developed using the least absolute shrinkage and selection operator regression algorithm. A clinical-radiomics model was constructed by integrating clinical features and Radscore, and a nomogram was developed. The prediction performance of models was evaluated by the area under the receiver operating characteristic curve (AUC). The AUC values were compared using the DeLong test. Calibration curves and decision curves were plotted to assess calibration and clinical net benefit. Results:Clinical N (cN) staging was an independent risk factor for LNM ( OR=6.86, 95% CI 2.70-18.49, P<0.001). Totally 12 optimal features were selected to construct the radiomics model, and the clinical-radiomics nomogram model was constructed by combining cN staging and Radscore. In the external validation set, the AUC (95% CI) of the clinical model, radiomics model, and clinical-radiomics nomogram were 0.706 (0.576-0.836), 0.768 (0.637-0.899), and 0.803 (0.680-0.926), respectively. The nomogram achieved higher AUC than clinical and radiomics models with statistical significance ( Z=2.01, 2.21; P=0.044, 0.027). The calibration and decision curves demonstrated good model fit, providing clinical net benefits for patients. Conclusion:The clinical-radiomics nomogram model combining cN staging and CT radiomics features can effectively predict LNM risk in HCCA patients.

Objective:To compare Al 18F-1, 4, 7-trizacyclononane-1, 4, 7-triacetic acid (NOTA)-fibroblast activation protein inhibitor (FAPI)-04 PET/CT with 18F-FDG PET/CT in the evaluation of patients with initial gastric cancer. Methods:Twenty patients (13 males, 7 females, age: 27-77 years) with histologically proven gastric cancer were recruited prospectively between March 2021 and July 2022 in the First Affiliated Hospital of Zhengzhou University. Each patient underwent both 18F-FDG and Al 18F-NOTA-FAPI-04 PET/CT within one week. SUV max, tumor background ratio (TBR) and positive detection rate of the two methods were compared (Wilcoxon signed rank sum test, McNemar χ2 test). Results:Al 18F-NOTA-FAPI-04 showed higher SUV max and TBR than those of 18F-FDG in primary tumors (10.2(8.0, 13.7) vs 5.2(3.3, 7.7), z=-3.47, P=0.001; 7.6(5.6, 10.3) vs 2.4(1.8, 3.0), z=-3.85, P<0.001). For the detection of primary gastric cancer, the positive detection rate of Al 18F-NOTA-FAPI-04 PET/CT showed the trend of being higher than that of 18F-FDG PET/CT (95%(19/20) and 75%(15/20); χ2=2.25, P=0.125). For assessing lymph node metastasis, the detection rate of Al 18F-NOTA-FAPI-04 PET/CT was higher than that of 18F-FDG PET/CT (78.9%(101/128) vs 64.8%(83/128); χ2=13.47, P<0.001). The SUV max and TBR of Al 18F-NOTA-FAPI-04 in lymph node were higher than those of 18F-FDG (5.3(3.5, 9.2) vs 2.8(1.8, 4.7), z=-7.31, P<0.001; 4.6(2.6, 6.5) vs 1.7(1.0, 3.0), z=-8.44, P<0.001). For the detection of peritoneal carcinomatosis, Al 18F-NOTA-FAPI-04 PET/CT showed higher peritoneal cancer index (PCI), SUV max, and TBR compared to 18F-FDG PET/CT (PCI: 12.0(3.0, 29.8) vs 5.5(0.5, 17.5), z=-2.22, P=0.026; SUV max: 8.2(4.4, 12.5) vs 2.7(1.9, 4.0); z=-2.52, P=0.012; TBR: 5.1(2.9, 13.3) vs 1.1(0.9, 2.0); z=-2.52, P=0.012). Conclusion:Al 18F-NOTA-FAPI-04 PET/CT outperforms 18F-FDG PET/CT in primary and metastatic lesions of gastric cancer and might be a potential novel modality for imaging patients with gastric cancer.

Objective To explore the application value of deep learning image reconstruction(DLIR)combined with Smart metal artifact reduction(Smart MAR)algorithm in maxillofacial CT images.Methods A total of 34 patients with maxillofacial lesions affected by oral metal implants who underwent maxillofacial enhanced CT scans were included.The images of four groups in venous phase were reconstructed with 50％adaptive statistical iterative reconstruction(ASIR-V)(IR group),50％ASIR-V combined with Smart MAR(IR+S group),DLIR(at medium strength)combined with Smart MAR(D-M+S group)and DLIR(at high strength)combined with Smart MAR(D-H+S group)respectively.The artifact index(AI)was worked out by measuring the standard deviation(SD)of CT values in maxillofacial lesions and longhead muscle.The subjective scores of overall image quality,lesion conspicuity and diagnostic confidence were assessed.The image quality of different algorithms was compared.Results Compared with IR+S group,the AI value of IR group was significantly increased(P＜0.05),while the noise had no significant difference(P＞0.05).Compared with IR+S group,the AI value and noise of D-M+S group and D-H+S group both were significantly decreased(P＜0.05),and the AI value of D-M+S group and D-H+S group reduced by 13.70％and 19.06％respectively,the noise reduced by 16.37％and 30.78％respectively.The subjective scores of overall image quality,lesion conspicuity and diagnostic confidence in IR+S group were significantly lower than those in D-M+S group and D-H+S group,but significantly higher than those in IR group(P＜0.05).There were 6 patients'(17.64％)lesions were detected only in the groups with Smart MAR algorithm,while 9 patients(26.47％)had introduced new artifacts in the tongue with Smart MAR algorithm.Conclusion DLIR combined with Smart MAR can improve the CT image quality of maxillofacial region,enhance the conspicuity and diagnosis confidence of maxillofacial lesions in patients with oral metal implants.Smart MAR algorithm may produce new artifacts that need to be analyzed along with the images not added Smart MAR algorithm.

Objective To evaluate the correlation and consistency between an artificial intelligence(AI)bone mineral density(BMD)measurement system based on 3D-Densenet neural network technology and quantitative computed tomography(QCT)in measuring BMD,as well as to assess its effectiveness in diagnosing osteoporosis(OP).Methods A total of 1 201 participants who underwent low dose computed tomography(LDCT)were retrospectively included.The AI BMD measurement system and QCT were utilized to measure the BMD of T12,L1,L2 vertebrae,and the average BMD.Consistency and correlation of BMD measurements between the two methods were assessed using Bland-Altman,Pearson,and Kappa analyses.With QCT results as the reference standard,the receiver operating characteristic(ROC)curve was drawn to evaluate the accuracy of AI BMD measurement system in diagnosing OP.Results The r and r2 for the average BMD measured by the two methods were 0.997 and 0.993,respectively.The Kappa value for the diagnosis of normal BMD,low bone mass,and OP using the AI BMD measurement system was 0.905.The area under the curve(AUC)for diagnosing OP using the AI BMD measurement system was 0.998,with a sensitivity of 0.888 and specificity of 0.997.Conclusion The AI BMD measurement system based on 3D-Densenet neural network technology has a high correlation and consistency with the QCT measurement result,which can accurately diagnose normal BMD,low bone mass,and OP.

Objective To observe the value of CT radiomics-based nomogram for predicting difference of Lauren types of gastric cancers between endoscopic biopsy and postoperative pathology.Methods Totally 126 patients with gastric cancer diagnosed by surgical pathology were retrospectively analyzed.The patients were divided into concordant group(n=77)and inconsistent group(n=49)according to the concordance between endoscopic biopsy and postoperative pathology results or not,also divided into training set and validation set at the ratio of 2∶1.Clinical predictors were screened,then a clinical prediction model was constructed.Radiomics features were extracted based on venous-phase CT images and screened using L1 regularization.Radiomics models were constructed using 3 machine learning(ML)algorithms,i.e.decision trees,random forests and logistic regression.The nomogram based on clinical and the best ML radiomics model was constructed,and the efficacy and clinical utility of the above models and nomogram for predicting inconsistency of Lauren types of gastric cancers between endoscopic biopsy and postoperative pathology were evaluated.Results Patients'age,platelet count,and arterial-phase CT values of tumors were all independent predictors of inconsistency between endoscopic biopsy and postoperative pathology of Lauren types of gastric cancer.CT radiomics model using random forests algorithm showed better predictive efficacy among 3 ML models,with the area under the curve(AUC)of 0.835 in training set and 0.724 in validation set,respectively.The AUC of clinical model,radiomics model and the nomogram in training set was 0.764,0.835 and 0.884,while was 0.760,0.724 and 0.841 in validation set,respectively.In both training set and validation set,the nomogram showed a good fit and considerable clinical utility.Conclusion CT radiomics-based nomogram had potential clinical application value for predicting inconsistency of Lauren types of gastric cancers between endoscopic biopsy and postoperative pathology.

Objective To observe the value of spectral CT multi-parameter imaging for preoperative predicting lymph node metastasis(LNM)of gastric cancer.Methods Totally 136 patients with gastric adenocarcinoma were retrospectively enrolled.The patients were further divided into LNM group(n=74)and non-LNM group(n=62)according to postoperative pathological findings of lymph nodes status.Clinical data,conventional CT findings and spectral CT parameters were compared between groups.Factors being significant different between groups were included in multivariate logistic regression analysis to screen independent predictors of gastric cancer LNM.Clinical+conventional CT model(model 1),spectrum CT model(model 2)and combined model(model 3)were constructed based on the above independent predictors,respectively.Receiver operating characteristic curve was drawn,and the area under the curve(AUC)was calculated to evaluate the efficacy of each model for preoperative predicting LNM of gastric cancer.Results CT-N stage,CT-T stage,70,100 and 140 keV CT valuestumor at arterial phase(AP),arterial enhancement fraction(AEF)and normalized iodine concentration at venous phase(NICVP)were all independent predictors of gastric cancer LNM(all P＜0.05).AUC of model 3 was 0.846,higher than that of model 1 and model 2(AUC=0.767,0.774,Z=-0.368,-2.373,both P＜0.05)for preoperative predicting LNM of gastric cancer,while the latter two were not significantly different(Z=-0.152,P=0.879).Conclusion Spectral CT multi-parameter imaging could effectively predict LNM of gastric cancer preoperatively.