Objective: To investigate the causal association between amino acids and the primary malignant bone tumor and its underlying mechanism. Methods: Genome-wide association study (GWAS) data of glycine, serine, arginine, glutamine, methionine, and leucine was sourced from the IEU OpenGWAS database and the GWAS Catalog. GWAS data of primary malignant bone tumor were obtained from the FinnGen database. Using each of the six amino acids as the exposure and primary malignant bone tumor as the outcome, two-sample Mendelian randomization (MR) analysis was performed with the inverse-variance weighted method as the primary approach. Multivariable MR analysis was employed to control for collinearity among amino acids. Sensitivity analyses were conducted using Cochran's Q test, MR-Egger regression and the MR Steiger test. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction network analysis were explored to explore potential mechanisms and identify key genes. Results: MR analysis results indicated a statistically significant causal association between glycine and primary malignant bone tumor (OR=1.719, 95%CI: 1.083-2.728). No significant causal associations were found for the other five amino acids (all P>0.05). Multivariable MR analysis revealed that, after adjusting for the other five amino acids, confirmed a positive causal association between glycine and primary malignant bone tumor (OR=1.512, 95%CI: 1.125-2.031). Sensitivity analyses revealed no significant heterogeneity, horizontal pleiotropy, or reverse causality (all P>0.05). Genes associated with both glycine metabolism and primary malignant bone tumor were enriched in the JAK-STAT signaling pathway, with serine hydroxymethyltransferase 2 (SHMT2) identified as a key gene. Conclusion Higher glycine levels may increase the risk of primary malignant bone tumor via the SHMT2-JAK-STAT pathway.

Objective: To examine the causal association and potential mechanisms between bone mineral density in different age groups and primary malignant bone tumor based on two sample Mendelian randomization (MR), so as to provide a reference for the prevention and treatment of primary malignant bone tumor. Methods: The genome-wide association study (GWAS) of bone mineral density was obtained from the GEFOS database,which included 66 628 subjects divided into five age groups (0-15, 15-30, 30-45, 45-60, and >60 years) based on the phases of human bone development. The GWAS of primary malignant bone tumor was sourced from the FinnGen database, including 648 cases and 378 749 controls. Using bone mineral density of five age groups as the exposure and primary malignant bone tumor as the outcome, an MR analysis was performed with the inverse-variance weighted (IVW) method. Sensitivity analysis were conducted using Cochran's Q test, MR-Egger regression, MR-PRESSO test and MR Steiger test. The potential mechanisms underlying the causal association between bone density and primary malignant bone tumors were explored using Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Results: The MR analysis results showed that there was a negative causal association between bone density and primary malignant bone tumors in the 30-45 age group (OR=0.301, 95%CI: 0.126-0.721). No statistically significant associations between bone density and primary malignant bone tumors were found in the 0-15, 15-30, 45-60, and >60 age groups (all P>0.05). Sensitivity analysis did not detect heterogeneity, pleiotropy (all P>0.05) and reverse causality. KEGG enrichment analysis revealed that genes highly associated with bone density and primary malignant bone tumors were enriched in the mTOR signaling pathway and the Wnt signaling pathway, among which Low Density lipoprotein Receptor Related protein 5 and Wnt Family Member 16 are key regulatory genes. Conclusion The decrease in bone mineral density among individuals aged 30-45 may increase the risk of primary malignant bone tumors through the mTOR signaling pathway and the Wnt signaling pathway.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

Objective:To explore the status of psychological resilience in female infertility patients and analyze its influencing factors, providing a basis for developing effective intervention measures in clinical practice.Methods:A convenient sampling method was used to select female infertility patients who visited the Reproductive Medicine Center of the First Affiliated Hospital of the University of South China from March to October 2024 as the research objects. A cross-sectional survey was conducted using the General Data Questionnaire, Connor-Davidson Resilience Scale, Herth Hope Index, the Infertility Stigma Scale, Family Resilience Scale and Perceived Social Support Scale. The random forest algorithm was used to rank the importance of variables, Lasso regression was used to further screen variables, and the selected variables were included in multiple stepwise regression analysis to analyze the influencing factors.Results:Ultimately, 322 female infertility patients aged (30.40 ± 4.50) were included. The psychological resilience score was (64.29 ± 10.05) points, which was above the medium level. The top 6 influential factors in the importance of variables were stigma, family resilience, age, social support, infertility cause and hope level. Multiple stepwise regression analysis showed that age, infertility cause, hope level, stigma, family resilience and social support were the main influencing factors of mental resilience of female infertility patients ( t values were -8.32 to 6.85, all P<0.05). Conclusions:The psychological resilience of female infertility patients is above the medium level, and the psychological resilience of infertile women is affected by many factors such as individual characteristics, family environment and social support. Medical staff should take targeted intervention measures to improve the psychological resilience of female infertility patients.

Objective:To develop and validate a combined model integrating radiomics, dosiomics, and clinical parameters based on CT simulation and dosimetric images in order to predict the occurrence of radiation pneumonitis (RP) in patients with non-small cell lung cancer (NSCLC).Methods:A retrospective study was conducted on the clinic data of 143 NSCLC patients who received radiotherapy at the Affiliated Hospital of Jining Medical University from January 2016 to December 2022. Patients were randomly stratified into a training group ( n = 100) and an internal validation group ( n = 43) at a 7∶3 ratio. Moreover, clinic data were collected from 34 NSCLC patients who received radiotherapy at the Jining Cancer Hospital between January 2019 and December 2022 as an external validation group. All three groups (the training group, internal validation, and external validation groups) were further categorized into two groups based on the RP severity (i.e., RP ≥ grade 2 and RP < grade 2). Their radiotherapy dose, CT simulation, and 3D dose distribution images were collected. Then, the total lung minus planning target volume (TL-PTV) was defined as the region of interest (ROI) for radiomics and dosiomic feature extraction, followed by feature dimensionality reduction. Consequently, key features associated with RP were determined. Four predictive models were developed using machine learning approaches (especially multilayer perceptron, MLP): a clinical model (CM), a radiomics model (RM), a dosiomics model (DM), and a radiomics and dosiomics nomogram (RDN), with a nomogram subsequently constructed. Ultimately, the performance and clinical feasibility of these models were assessed using receiver operating characteristic (ROC), area under the curve (AUC), and decision curve analysis (DCA). Results:A total of 1 834 radiomic features and 1 834 dosiomic features were extracted. Using the occurrence of RP ≥ grade 2 as the marker variable, 14 radiomic features, 15 dosiomic features, and three clinical features were selected from the training group to construct the prediction models (CM, RM, DM, and RDN). The performance and generalizability of these models were subsequently validated in both the internal validation and external validation groups. Specifically, the RDN exhibited AUCs of 0.915 (95% CI: 0.852-0.978), 0.879 (95% CI: 0.777-0.982), and 0.838 (95% CI: 0.701-0.975) in the three groups, respectively. A nomogram was established for RDN by integrating the radiomics score (R-score), dosiomics score (D-score), mean lung dose (MLD), V20, and V30. This nomogram allowed for individualized risk estimation of RP and facilitated personalized radiotherapy planning. Conclusions:The RDN model that is developed based on CT simulation and 3D dose distribution images and integrates radiomics, dosiomics, and clinical features can effectively predict the RP risk of NSCLC patients. The integration of multidimensional data contributes to the formation of the optimal predictive model, offering guidance for clinicians.

Objective:To explore the status of psychological resilience in female infertility patients and analyze its influencing factors, providing a basis for developing effective intervention measures in clinical practice.Methods:A convenient sampling method was used to select female infertility patients who visited the Reproductive Medicine Center of the First Affiliated Hospital of the University of South China from March to October 2024 as the research objects. A cross-sectional survey was conducted using the General Data Questionnaire, Connor-Davidson Resilience Scale, Herth Hope Index, the Infertility Stigma Scale, Family Resilience Scale and Perceived Social Support Scale. The random forest algorithm was used to rank the importance of variables, Lasso regression was used to further screen variables, and the selected variables were included in multiple stepwise regression analysis to analyze the influencing factors.Results:Ultimately, 322 female infertility patients aged (30.40 ± 4.50) were included. The psychological resilience score was (64.29 ± 10.05) points, which was above the medium level. The top 6 influential factors in the importance of variables were stigma, family resilience, age, social support, infertility cause and hope level. Multiple stepwise regression analysis showed that age, infertility cause, hope level, stigma, family resilience and social support were the main influencing factors of mental resilience of female infertility patients ( t values were -8.32 to 6.85, all P<0.05). Conclusions:The psychological resilience of female infertility patients is above the medium level, and the psychological resilience of infertile women is affected by many factors such as individual characteristics, family environment and social support. Medical staff should take targeted intervention measures to improve the psychological resilience of female infertility patients.

Objective:To investigate the diagnostic value and imaging characteristics of MRI combined with 18F-fluorodeoxyglucose (FDG) positron-emission tomography (PET)/CT in focal cortical dysplasia (FCD) complicated with refractory epilepsy. Methods:A retrospective analysis was performed on 42 patients with FCD complicated with refractory epilepsy who were admitted to the Affiliated Hospital of Jining Medical University from January 2017 to December 2022. All patients underwent preoperative MRI and 18F-FDG PET/CT, and PET/MRI fusion was performed on the images. Chi-square test and Kappa consistency test were used to compare the localization diagnostic efficacy of PET/CT, MRI and PET/MRI fusion for epileptic foci. The patients were categorized based on gender, lesion location, pathological type, seizure type, and efficacy. Independent sample t-test and analysis of variance were used to compare maximum standardized uptake (SUVmax) values and asymmetry index (AI) of the patients between different groups. Results:Among the 42 patients, the positive rates of MRI, PET/CT, PET/MRI fusion examinations were 85.7%(36/42), 95.2%(40/42), 100.0%(42/42), the lateral localization rates were 71.4%(30/42), 92.9%(39/42), 95.2%(40/42), and the localization rates were 57.1%(24/42), 81.0%(34/42), 88.1%(37/42), respectively. There were significant differences in the lateral localization rates and localization rates of epileptogenic foci between MRI and PET/CT (χ 2=6.574, P=0.010; χ 2=5.570, P=0.018). There were significant differences in the positive rates of lesions, the lateral localization rates and the localization rates of epileptogenic foci between MRI and PET/MRI fusion (χ 2=6.385, P=0.012; χ 2=8.571, P=0.003; χ 2=10.118, P=0.001). There were no significant differences in the positive rates of lesions between MRI and PET/CT, and in the positive rates of lesions, the lateral localization rates and localization rates of epileptogenic foci between PET/CT and PET/MRI fusion (χ 2=2.184, P=0.139; χ 2=2.024, P=0.155; χ 2=0.210, P=0.647; χ 2=0.819, P=0.365). The Kappa consistency test of PET/CT and PET/MRI fusion imaging was performed for the location of epileptogenic foci, and the Kappa=0.721 was obtained, indicating that they were consistent in the location of epileptogenic foci. The SUVmax values of patients with temporal lobe epilepsy were lower, and the AI values were higher than that of patients with extra temporal lobe epilepsy (7.4±1.3 vs 9.6±1.6, 15.5±2.6 vs 12.9±2.4; t=5.154, 6.083; P=0.001, 0.001). The SUVmax values of patients with good efficacy (according to the Engel efficacy grading system, grades Ⅰ-Ⅱ indicating good efficacy) were higher, and the AI values were lower than that of patients with poor efficacy (according to the Engel efficacy grading system, grades Ⅲ-Ⅳ indicating poor efficacy; 9.5±1.9 vs 7.9±2.1, 13.5±3.3 vs 14.8±3.0; t=2.789, 3.722; P=0.042, 0.029). There were no significant differences in SUVmax and AI values among different genders, pathological types and seizure types (all P>0.05). Conclusions:The imaging characteristics of patients with different types of FCD complicated with refractory epilepsy are different. PET/MRI fusion is better than MRI in the diagnosis of FCD complicated with refractory epilepsy, and is consistent with PET/CT in the location of epileptogenic foci.

Objective:To investigate the value of traditional metabolic parameters, CT features and intratumoral heterogeneity parameters measured by 18F-FDG PET/CT in predicting the mutation status of the epidermal growth factor receptor (EGFR) gene in patients with adenocarcinoma. Methods:A total of 147 patients (73 males, 74 females, age (59.8±10.2) years) with pathological confirmed adenocarcinoma between January 2016 and June 2020 in the Affiliated Hospital of Jining Medical University were retrospectively included. The differences of clinical data (smoking history, tumor location and clinical stage), CT features (maximum diameter, ground-glass opacity content, lobulation, speculation, cavitation, air-bronchogram, pleural retraction and bronchial cut-off sign), 18F-FDG PET/CT traditional metabolic parameters (SUV max, SUV mean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG)) and intratumoral heterogeneity parameters ( CV, heterogeneity index (HI)) were analyzed between patients with EGFR mutation and patients with EGFR wild-type. Independent-sample t test, Mann-Whitney U test and χ2 test were used to analyze the data. Multivariate logistic regression was used to analyze the predictors of EGFR mutation. ROC curve analysis was used to evaluate the predictive value of clinical and PET/CT information. Results:Among 147 patients, 87 were with EGFR mutation and 60 were with EGFR wild-type. There were significant differences in gender (male/female), smoking history (with/without), location (peripheral lesion/central lesion), pleural retraction (with/without), SUV max, SUV mean, TLG, CV and HI ( χ2 values: 4.72-23.89, z values: from -2.31 to 5.74, all P<0.05). Multivariate logistic regression analysis showed that smoking history (odds ratio ( OR)=0.167, 95% CI: 0.076-0.366; P<0.001), pleural retraction ( OR=1.404, 95% CI: 1.115-3.745; P=0.012), SUV max ( OR=0.922, 95% CI: 0.855-0.995; P=0.003), TLG ( OR=0.991, 95% CI: 0.986-0.996; P=0.001) and HI ( OR=0.796, 95% CI: 0.700-0.859; P<0.001) were predictors of EGFR mutation. ROC curve analysis showed the AUC of HI was 0.779, with the sensitivity of 76.67%(46/60) and the specificity of 79.31%(69/87). The predictive model was constructed by combining smoking history, pleural retraction, TLG, SUV max and HI, and the AUC was 0.908, with the sensitivity of 88.33%(53/60) and the specificity of 68.97%(60/87). The difference of AUCs between HI and the predictive model was statistically significant ( z=3.71, P<0.001). Conclusion:HI can predict EGFR mutations better, and the predictive value for EGFR mutations can be enhanced when combining HI with smoking history, pleural retraction, TLG and SUV max.

Objective:To explore the diagnostic value of 18F-FDG PET/CT imaging in etiology of patients with hemophagocytic lymphohistiocytosis (HLH). Methods:Retrospective analysis was performed on 49 patients newly diagnosed as HLH (32 males, 17 females; age 19-61 years) who received 18F-FDG PET/CT imaging in Affiliated Hospital of Jining Medical University from January 2017 to January 2023. PET/CT images and clinical parameters were observed and recorded. Based on the pathological examination and clinical follow-up results, diagnostic efficacies for HLH etiology of PET/CT, PET and CT imaging were calculated. χ2 test, independent-sample t test and Mann-Whitney U test were used to compare the differences between hematologic tumors associated HLH and non-hematologic tumor associated HLH. Multivariate logistic regression was used to analyze the predictors of secondary HLH in hematologic tumors. ROC curve analysis was used to calculate AUCs and optimal threshold of lymph node SUV max and soluble CD25 (sCD25) to predict secondary HLH in patients with hematologic tumors. Results:The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of PET/CT, PET and CT in the etiological diagnosis of HLH were 85.7%(30/35), 8/10, 84.4%(38/45), 93.8%(30/32), 8/13; 77.1%(27/35), 6/10, 73.3%(33/45), 87.1%(27/31), 6/14; 62.9%(22/35), 5/10, 60.0%(27/45), 81.5%(22/27), 5/18, respectively. There were differences in lymph node distribution and boundary, liver and spleen and bone lesions, SUV max of lymph node and liver and spleen and bone, gender, age, WBC, neutrophil (ANC), PLT, lactate dehydrogenase (LDH), total bilirubin (TBIL), C-reactive protein (CRP) and sCD25 between different etiology groups ( χ2 values: 3.91-9.66, t values: 3.75-7.90, z values: 3.82-4.01, all P<0.05). SUV max of lymph nodes and sCD25 were predictive factors for secondary HLH of hematological tumors (odds ratio ( OR): 1.28 (95% CI: 1.09-1.72), 1.56 (95% CI: 1.17-2.49), P values: 0.004, 0.013). The optimal thresholds were 12.6 and 40 028 ng/L, with the AUC of 0.87 and 0.76, with the sensitivity and specificity of 88.6%(31/35) and 8/10, 65.7%(23/35) and 7/10, respectively. The combined AUC was 0.83 and the sensitivity and specificity were 74.3% (26/35) and 9/10. Conclusions:18F-FDG PET/CT imaging is of high value for the diagnosis of the cause of HLH. SUV max of lymph node and sCD25 are predictive factors for secondary HLH of hematologic tumors.

Objective:To investigate the mechanism of crossed cerebellar diaschisis(CCD) induced by supratentorial tumors and the characteristics of 18F-FDG PET/CT imaging. Methods:Eighty-six patients with supratentorial tumors who underwent 18F-FDG PET/CT whole-body imaging from January 2017 to June 2021 were retrospectively analyzed.Placement, number, size, SUVmax, CT values, relationship with basal ganglia, edema, and cerebellar asymmetry index (AI) were observed and recorded.The imaging differences between patients with CCD and patients without CCD were compared, and the correlations between SUVmax, maximum diameter and cerebellar AI were analyzed.SPSS 21.0 software was used for statistical analysis.Chi-square test, independent sample t-test and Pearson correlation analysis were used for data statistics. Results:Among the 86 patients, 14 were patients with CCD and 72 were patients without CCD.The incidence of CCD was 16.3%.There were statistically significant differences in whether the primary lesions involved the basal ganglia region between patients with CCD and patients without CCD ( χ2=7.637, P=0.006). The cerebellar AI ((0.27±0.09), (0.05±0.02), t=6.847, P=0.003)and maximum diameter of primary lesions((3.98±1.09)cm, (2.36±1.61)cm, t=2.011, P=0.040) in patients with CCD were both larger than those in patients without CCD.There was a significant positive correlation between cerebellar AI and the maximum diameter of primary lesions in patients with CCD ( r=0.375 P=0.028). Conclusion:18F-FDG PET/CT imaging can assist in the diagnosis of crossed cerebellar diaschisis.The primary lesion of supratentorial tumor involving the basal ganglia is more likely to cause crossed cerebellar diaschisis, and the size of the primary lesion is correlated with cerebellar AI.