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 analyze the antigen recognition sites of commercial and homemade antibodies against aquaporin(AQP)9,and to identify the application effect.Methods Western blotting was used to compare the efficacy of three commercial antibodies and self-made antibody in identifying AQP9 genotypes.The antigen recognition sites of four antibodies and their specificities in practical applications were analyzed.Results Western blotting showed that protein bands of three commercial antibodies were detected in both WT and Aqp9-/-mice.The keyhole limpet hemocyanin(KLH)conjugated synthetic peptides corresponding to the three commercial antibodies were derived from rat,human and human,respectively.And The sequences of these three synthetic peptides were different from those of mice.AQP3/7 and AQP9 have similar molecular weight and were expressed in the liver with high homology.An obvious band of self-made antibody was observed at the 27 kD position in WT mice,but no band was observed at the corresponding position in Aqp9-/-mice.Conclusion Commercial antibodies 1 and 3 can be used to assist in the identification of genotypes in Aqp9-/-mice.Homemade antibodies can accurately identify genotypes at the protein level.

Objective To build the early predictive model for chronic kidney disease(CKD)in hypertension and diabetes patients in the community.Methods The CKD patients were recruited from 4 health care centers in 4 urban areas in Kunming.The control group was residents without hypertension and diabetes(n = 1267).The disease group was residents with hypertension and/or diabetes(n = 566).The questionnaire survey,physical examination,laboratory testing,and 5 SNPs gene types in the PVT1 gene.The risk factors,which were filtered with logistics regression,were used to build predictive models.Four machine learning algorithms were built:support vector machine(SVM),random forest(RF),Na?ve Bayes(NB),and artificial neural network(ANN)models.Results Thirteen indicators included in the final diagnostic model:age,disease type,ethnicity,blood urea nitrogen,creatinine,eGFR from MDRD,ACR,eGFR from EPI2009,PAM13 score,sleep quality survey,staying-up late,PVT1 SNP rs11993333 and rs2720659.The accuracy,specificity,Kappa value,AUC of ROC,and PRC of ANN are greater than those of the other 3 models.The sensitivity of RF is the highest among 4 types of machine learning.Conclusions The ANN predictive model has a good ability of efficiency and classification to predict CKD with hypertension and/or diabetes patients in the community.

Objective To explore whether ferulic acid can inhibit the progression of T-cell acute lymphoblastic leukemia in vivo and in vitro by regulating PTEN/PI3K/AKT signaling pathway.Methods The T-cell acute lymphoblastic leukemia Jurkat cells were divided into the control group,the ferulic acid treatment group and the LY294002 treatment group for in vitro experiment.The cells in the control group were given normal culture;cells in the ferulic acid treatment group were given different concentrations(1.25,2.5,5,10,20,40,80,160 μmol/L)of ferulic acid,respectively,and the cell proliferation was detected by CCK-8 method,to screen the experimental concentration;cells in the LY294002 treatment group were given 50 μmol/L PI3K/AKT inhibitor LY294002.The cells proliferation,apoptosis and invasion were detected by clone formation assay,flow cytometry and Transwell assay.The relative expression levels of nuclear protein Ki67,proliferating cell nuclear antigen(PCNA),cleaved caspase-3,cleaved caspase-9,E-cadherin,N-cadherin,Vimentin,PTEN,p-PI3K,PI3K,p-AKT and AKT proteins were detected by Western blot.The nude mice models of transplanted tumors were constructed by 30 male BALB/c nude mice,and they were averagely divided into the normal group and the ferulic acid treatment group for in vivo experiment.The normal group was given normal saline by gavage,while the ferulic acid treatment group was given 75 mg/kg ferulic acid by gavage after inoculating Jurkat cells.The weight and volume changes of transplanted tumors were compared,and the levels of Ki67,cleaved caspase-3/caspase-3,E-cadherin,N-cadherin,PTEN,p-PI3K,PI3K,p-AKT and AKT in tumor tissues were detected.Results In vitro experiment,compared with the control group,the clone formation rate of cells,number of invasion cells,Ki67,PCNA,N-cadherin,Vimentin,p-PI3K/PI3K and p-AKT/AKT in the 5,10,20 μmol/L ferulic acid treatment group and the LY294002 treatment group were significantly decreased(P<0.05),while the apoptosis rate,cleaved caspase-3/caspase-3,cleaved caspase-9/caspase-9,E-cadherin and PTEN were significantly increased(P<0.05).In vivo experiment,compared with the normal group,the weight and volume of tumors were reduced in the ferulic acid treatment group,Ki67,N-cadherin,p-PI3K/PI3K and p-AKT/AKT in tumor tissues were significantly decreased,cleaved caspase-3/caspase-3,E-cadherin and PTEN were significantly increased,with statistically significant differences(P<0.05).Conclusion Ferulic acid can inhibit the proliferation and invasion of T-cell acute lymphoblastic leukemia Jurkat cells in vivo and in vitro,and induce apoptosis,its mechanism may be related to the regulation of PTEN/PI3K/AKT signaling pathway.

Objective:To construct an explainable artificial intelligence(AI) model of risk characteristics of papillary thyroid carcinoma(PTC), and to explore its value of it combined with clinical features in predicting cervical lymph node metastasis(CLNM) in PTC patients.Methods:From January 2021 to September 2022, 422 patients(422 nodules) with pathologically confirmed PTC underwent thyroidectomy and neck lymph node dissection in the Second Affiliated Hospital of Xi′an Jiaotong University were retrospectively collected, the patients were randomly divided into training set and test set according to the ratio of 7∶3. Ultrasonographic features highly correlated with PTC risk characteristics were extracted by traditional machine learning method, and an intelligent prediction model with optimal probability of risk characteristics was established. Then, a risk model for predicting CLNM of PTC patients was constructed in combination with clinical features. The diagnostic effectiveness of the model was evaluated by drawing a ROC curve and calculating the area under curve (AUC).Results:In the AI explaineable model of PTC risk characteristics in the test set, the intelligent diagnosis model of calcification based on logistic regression classification showed the highest diagnostic efficiency, with an AUC of 0.87 ( P<0.05). Compared with the probability model of risk characteristic of PTC alone, the comprehensive model combined with clinical characteristics showed higher diagnostic efficiency in predicting CLNM of PTC patients, with AUC of 0.97, diagnostic critical value of 0.15, corresponding accuracy, sensitivity and specificity of 92.65%, 92.76% and 92.54%, respectively (all P<0.05). Conclusions:The explaineble risk characteristics of PTC AI model combined with clinical features can effectively predict the cervical lymph node metastasis of PTC, and then provide effective information for clinical decision-making of PTC patients.

Objective:To investigate the characteristics of early motor development in small for gestational age (SGA) infants at high risk of brain injury.Methods:This study retrospectively enrolled a total of 81 SGA infants and appropriate for gestational age (AGA) infants who were at high risk of brain injury and attended outpatient follow-up visits in Xi'an Children's Hospital from February to October 2022. Seventeen SGA infants (SGA group) and 24 AGA infants (AGA group) were assessed for motor development using the Test of Infant Motor Performance (TIMP) at 2-5 weeks of corrected age (CA) and 20 SGA infants (SGA group) and 20 AGA infants (AGA group) were assessed at 14-17 weeks of CA. Independent samples t-test, rank-sum test, and Chi-square test were used to compare the demographic characteristics, high-risk factors of brain injury, and TIMP scores between the two groups. Results:At 2-5 weeks and 14-17 weeks of CA, the birth weights of SGA group were both less than those of AGA group [(1 817.1±440.3) vs. (2 630.0±560.9) g, t=－4.98; (1 752.0±434.4) vs. (2 226.3±699.8) g, t=－2.58; both P<0.05], but there were no significant differences in gestational age at birth or high-risk factors of brain injury between the two groups (all P>0.05). (1) At 2-5 weeks of CA: SGA group had lower total TIMP score [(71.6±13.7) vs. (80.5±11.5) scores, t=－2.26, P=0.029], elicited item score [61.0 scores (41.0-85.0 scores) vs. 69.1 scores (49.0-96.0 scores), Z=－2.15, P=0.037], sitting position score [8.8 scores (3.0-19.0 scores) vs. 11.2 scores (5.0-22.0 scores), Z=－2.07, P=0.038], and prone position score [(9.8±3.1) vs. (12.3±3.1) scores, t=－2.19, P=0.034] when compared with AGA group. (2) At 14-17 weeks of CA: The standing position score of the SGA group was lower than that of the AGA group [6.5 scores (4.0-11.0 scores) vs. 7.7 scores (2.0-11.0 scores), Z=－2.05, P=0.040], but no statistical difference was observed in the total TIMP score or the scores of sitting, supine, prone, turning, and lateral positions between the two groups (all P>0.05). Conclusion:Early motor performance of SGA infants is inferior to AGA infants before five months of age, which is embodied in the poor head control at 2-5 weeks of CA that further affects the stability of standing posture in them at 14-17 weeks of CA.