Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective To explore the efficiency improvement in segmenting neural network with the application of Transformer + U-Net artificial intelligence (AI) and modeling with the application of Python scripts in three-dimensional (3D) printing brachytherapy. Methods A Transformer + U-Net AI neural network model was constructed, and Adam optimizer was used to ensure rapid gradient descent. Computed tomography or magnetic resonance imaging data of patients were standardized and processed as self-made data sets. The training set was used to train AI and the optimal result weight parameters were saved. The test set was used to evaluate the AI ability. Python programming language was used to write an automated script to obtain the output segmentation image and convert it to the STL file for import. The source applicator and needle could be automatically modeled. The time of automatic segmentation and modeling and the time of manual segmentation and modeling were entered by two people, and the difference was verified by paired t-test. Results Dice similarity coefficient (DSC), mean intersection over union (MIOU), and Hausdorff distance (HD95) were used for evaluation. DSC was 0.9341, MIOU was 0.8762, and mean HD95 was 2.516. The mean time of manual segmentation was 1187 s, and the mean time of AI segmentation was 145 s (P < 0.01). The mean time of manual modeling was 321 s, and the mean time of modeling with Python automated scripts was 18 s (P < 0.01). Conclusion The application of Transformer + U-Net automatic segmentation and Python automated scripts can effectively improve work efficiency in the 3D printing brachytherapy for cervical cancer.

AIM: To investigate the genetic association and potential causal relationship between diabetic nephropathy(DN)and diabetic retinopathy(DR), and to elucidate their shared molecular mechanisms through differential gene expression analysis and Mendelian randomization(MR).METHODS: Transcriptomic data of DN and DR were obtained from the Gene Expression Omnibus(GEO)database and analyzed for differentially expressed genes(DEGs). Genes meeting the significance threshold(log2FC>1, P<0.05)were identified, followed by Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis to explore shared biological pathways. Using genome-wide association study(GWAS)summary statistics for DN and DR, two-sample MR analysis was performed, with DN as the exposure and DR as the outcome. The causal effect was primarily estimated with the inverse-variance weighted(IVW)method, and sensitivity analyses were conducted to assess robustness.RESULTS: MR analysis revealed that DN significantly increased the risk of DR. IVW estimates indicated that the odds ratio(OR)for non-proliferative DR(NPDR)was 3.23(95% CI: 2.12-4.95, P<0.001), and the OR for proliferative DR(PDR)was 1.10(95% CI: 1.06-1.15, P<0.001). DEG analysis identified several key genes, including FN1, COL1A2, and THBS2. FN1 and COL1A2 are involved in extracellular matrix remodeling and fibrosis, contributing to vascular permeability alterations and microvascular damage in diabetic complications. THBS2 is closely associated with angiogenesis and vascular homeostasis, suggesting its potential role in DR. KEGG enrichment analysis showed that these DEGs were mainly enriched in advanced glycation end products(AGEs)-RAGE signaling, extracellular matrix degradation, and oxidative stress pathways, all of which are highly relevant to the pathogenesis of DN and DR.CONCLUSION: This study demonstrates the genetic association between DN and DR using MR and DEGs analyses. The shared mechanisms, particularly involving extracellular matrix remodeling, inflammatory response, and angiogenesis, may serve as novel therapeutic targets and provide a theoretical basis for the early diagnosis and targeted treatment of diabetic complications.

Objective: To investigate the influencing factors for cognitive function among aluminum workers, so as to provide the basis for intervention and prevention of cognitive function among aluminum-exposed populations. Methods: From July to August 2019, male aluminum workers in the electrolytic aluminum workshop of an aluminum factory in Shanxi Province were selected using the cluster sampling method. Demographic information, prevalence of chronic diseases, lifestyle behaviors, night shifts, and sleep quality were collected through questionnaire surveys. Blood aluminum levels were measured using inductively coupled plasma-mass spectrometry. Cognitive function was investigated using the Montreal Cognitive Assessment. Factors affecting cognitive function among aluminum workers were analyzed by a quantile regression model. Results: A total of 142 aluminum workers were surveyed, including 57 workers aged 20 to <40 years (40.14%) and 85 workers aged 40 to 60 years (59.86%). The median blood aluminum level was 38.23 (interquartile range, 21.82) μg/L. The median cognitive function score was 24.00 (interquartile range, 3.00) points. Quantile regression analysis revealed that older age (βQ5=-0.186, 95%CI: -0.269 to -0.102), lower educational level (βQ5=1.933, 95%CI: 1.029 to 2.838; βQ10=1.743, 95%CI: 0.480 to 3.006; βQ50=1.038, 95%CI: 0.141 to 1.935; βQ75=1.006, 95%CI: 0.437 to 1.575; βQ90=1.111, 95%CI: 0.291 to 1.930), smoking (βQ5=-2.056, 95%CI: -3.264 to -0.849), alcohol consumption (βQ5=-1.821, 95%CI: -3.247 to -0.396) and higher blood aluminum level (βQ5=-0.075, 95%CI: -0.110 to -0.040; βQ10=-0.078, 95%CI: -0.127 to -0.029; βQ50=-0.075, 95%CI: -0.110 to -0.040; βQ75=-0.057, 95%CI: -0.079 to -0.035; βQ90=-0.067, 95%CI: -0.099 to -0.035) were associated with cognitive function decline among aluminum workers. Conclusions Educational level and blood aluminum level are the main factors affecting the cognitive function among aluminum workers. Among those with lower cognitive function scores, age, smoking and alcohol consumption are also associated with cognitive function.

The correct pairing of disulfide bonds maintains the correct folding mode and high-level structure formation of peptides and protein drugs, which is crucial for the quality control of products. In order to ensure that the disulfide bonds are correctly paired, disulfide bond analysis is an essential part of peptides and protein drug characterization. Mass spectrometry can be used to analyze disulfide bonds. However, insulin and its analogues have two pairs of disulfide bonds without restriction enzyme cutting site. Conventional collision-induced dissociation (CID) and high-energy induced cleavage (HCD) cannot accurately locate the complex disulfide bond. In our study, three methods were used to localize the complex disulfide, including enzyme digestion combined with key peptide fragment in source decay (ISD) fragmentation method, enzyme digestion combined with partial reduction alkylation method, intact protein source ISD and electron transfer dissociation (ETD) cleavage method, The applicability of insulin aspart, insulin lispro and insulin glargine were also investigated. This study provides a new way for the quality control of disulfide bonding mode of insulin and its analogues, and also provides a reference for the disulfide bond localization of peptides or proteins containing this complex disulfide bond.

Quercetin can mediate the activation of mitogen-activated protein kinase(MAPK)signaling pathways to prevent osteoporosis(OP).This paper comprehensively discusses the interrelationship between MAPK and osteoporosis-related cells based on the latest domestic and international research.Additionally,it elucidates the research progress of quercetin in mediating the MAPK signaling pathway for OP prevention.The aim is to provide an effective foundation for the clinical prevention and treatment of OP and the in-depth development of quercetin.