The accumulation of uremic toxins such as urea nitrogen, blood creatinine, and uric acid of patients with renal failure in vivo would lead to aggravated kidney damage. In this study, coated aldehyde oxy-starch (CAO) was used as an adsorbent to investigate its in vitro adsorption performance on renal failure indexes for urea, indoxyl sulfate (INS), monomethylamine (MMA), dimethylamine (DMA), uric acid (UA), and creatinine (Cr). The effects of variables such as pH, temperature, concentration, dosage, and time on the adsorption capacity of CAO were systematically investigated, employing analytical techniques of high-performance liquid chromatography (HPLC) and gas chromatography (GC). The results revealed that CAO exhibited a strong adsorption capacity for urea, INS, and MMA, alongside a moderate adsorption capacity for DMA, UA, and Cr. The adsorption kinetics and thermodynamic studies indicated that the adsorption of urea and UA by CAO were fitted in pseudo-first-order kinetics, and the adsorption isotherm aligned with the Freundlich adsorption model. The enthalpy change ΔH of urea in adsorption was in the range of 40 to 60 kJ·mol^-1, which demonstrated the presence of strong adsorption force due to the interactions of coordinating groups. The ΔH of UA was greater than 80 kJ·mol^-1, indicating the generation of chemical bonds during the adsorption. Both of them, Gibbs free energy ΔG was less than 0, within the range of -20 to 0 kJ·mol^-1, suggested that the adsorption of urea and UA by CAO occurred spontaneously as physical adsorption process. The adsorption entropy ΔS of urea and UA was > 0, which indicated an increase in entropy throughout the adsorption. The infrared spectroscopygram showed the formation of a chemical bond, specifically the imine bond, following the adsorption of urea by CAO, thereby indicating a chemical reaction during the adsorption. This study elucidates the adsorption mechanism of CAO on various indexes of renal failure, providing a scientific basis for its clinical usage.

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.

BackgroundPrenatal depression has an important impact on maternal health and pregnancy outcomes. Previous studies have shown that maternal prenatal depression is associated with social support， and social support is related to fear of childbirth. However， there is limited research on the relationship among maternal prenatal depression， social support and fear of childbirth， and no studies have specifically explored the influence of social support and fear of childbirth on prenatal depression in primiparous women. ObjectiveTo investigate the current status of prenatal depression among primiparous women， and to analyze the correlation between social support and fear of childbirth， and to further explore the influence of social support and fear of childbirth on prenatal depression in this population， so as to provide references for improving their mental health. MethodsA total of 380 primiparous women admitted to the inpatient department of Chengdu Wenjiang District People's Hospital from December 2022 to September 2023 were enrolled as study subjects. A self-made questionnaire， Edinburgh Postnatal Depression Scale （EPDS）， Social Support Rating Scale （SSRS） and Childbirth Attitudes Questionnaire （CAQ） were used to conduct the survey. Pearson correlation analysis was employed to examine the relationships between scale scores. Multiple linear regression analysis was conducted to identify influencing factors of prenatal depression. ResultsA total of 380 questionnaires were distributed， with 372 （97.89%） valid responses collected. Among the participants， 222 cases （59.68%） were identified with prenatal depression. Pearson correlation analysis revealed that EPDS score was negatively correlated with SSRS score （r=-0.283， P<0.01） and positively correlated with CAQ score （r=0.341， P<0.01）. Multiple linear regression analysis indicated that social support （β=-0.166， P<0.01） and fear of childbirth （β=0.269， P<0.01） were influencing factors of prenatal depression in primiparous women. ConclusionThe prevalence of prenatal depression among primiparous women is concerning， with depression levels showing significant associations with both social support and fear of childbirth.

ObjectiveTo investigate the relationship between mitochondrial DNA copy number (mtDNAcn) and cognitive dysfunction, and its mediating role between type 2 diabetes mellitus (T2DM) and cognitive dysfunction. MethodsA case-control study was conducted from May 2019 to April 2021 at the Shanghai Yangpu District Central Hospital, China. A total of 193 subjects were recruited and divided into two groups based on the Montreal Cognitive Assessment (MoCA): normal control (NC) group (n=95) and cognitive impairment group (n=98). The prevalence of T2DM was determined on the basis of medical history, while mtDNAcn in peripheral blood samples was quantified using realtime fluorescent quantitative polymerase chain reaction. ResultsUnivariate analyses revealed that the mean mtDNAcn in the cognitive impairment group was 0.76±0.37, significantly lower than that in the NC group (1.06±0.45) (P<0.05). Logistic regression analyses showed that higher mtDNAcn was associated with a reduced risk of cognitive impairment (OR=0.315, 95%CI: 0.125‒0.795). Additionaly, a statistically significant positive correlation was observed between mtDNAcn and the total MoCA score (r=0.381, P<0.01). Morever, T2DM history (OR=2.741, 95%CI: 1.002‒7.497) and elevated glycosylated hemoglobin (HbA1c) levels (OR=1.796, 95%CI: 1.190‒2.711) were identified as risk factors for cognitive impairment. Mediation analyses indicated that mtDNAcn served as a mediator between T2DM/HbA1c and the risk of cognitive impairment, with proportions of mediating effect of 9.04% and 9.18%, respectively. ConclusionPatients with mild and moderate cognitive impairment have significantly lower mtDNAcn than those with normal cognitive function. Reduced mtDNAcn is an influencing factor for cognitive dysfunction and may play a mediating role in the association between T2DM and mild to moderate cognitive impairment.

ObjectiveTo explore the risk of different levels of pre-pregnancy obesity on trimester-specific thyroid dysfunction. MethodsQuestionnaire information， blood samples， and urine samples from a 2017 pregnancy cohort study in Shanghai， China were collected. A total of 2 455 pregnant women were included in the analysis. Pre-pregnancy BMI was calculated based on the height and self-reported pre-pregnancy weight. Serum TSH， total thyroxine （TT4）， free thyroxine （FT4）， total triiodothyronine （TT3）， free triiodothyronine （FT3）， thyroid globulin antibody（TgAb）， and Thyroid peroxidase antibody （TPOAb） were measured using the electrochemiluminescence method. Urine iodine levels were measured using the acid digestion method. Levels of thyroid function indexes of pregnant women with different degrees of obesity during pre-pregnancy were compared， and trimester-specific thyroid dysfunction was evaluated according to the reference range of trimester-specific thyroid hormone established by this cohort. Multivariate logistic regressions analysis was used to assess the correlation between pre-pregnancy obesity and trimester-specific thyroid dysfunction. ResultsAs the degree of obesity increased， maternal levels of FT3 and TT3 gradually increased during pregnancy （P<0.001， P=0.001）， while FT4 levels gradually decreased （P=0.001）. Multivariate logistic regression analysis showed that compared with the normal weight group， pregnant women who were overweight or obesity before pregnancy had a significantly higher risk of hypothyroxinemia （OR=3.85， 95%CI： 2.08‒7.14， P<0.001） and high TT3 （OR=2.78， 95%CI： 1.45‒5.26， P=0.002） during pregnancy. ConclusionPre-pregnancy overweight or obesity can increase the risk of thyroid dysfunction during pregnancy.

Background Multiple studies have shown a close relationship between changes in gut microbiota composition and obesity, and research results are influenced by factors such as race and geographical location, but there are few studies on children. Objective To analyze the diversity of gut microbiota related to obesity in a population of 2-6 years old, observe the distribution characteristics and species differences of gut microbiota between obese/overweight and normal weight groups, and explore the association betweenobese/overweight and gut microbiota diversity. Methods Fecal samples were collected from 74 children aged 2-6 years in Shanghai, including 18 obese/overweight individuals, 6 males and 12 females (male to female ratio of 1∶2), and 56 normal weight individuals, 18 males and 38 females (male to female ratio is nearly 1∶2). The 16S rDNA was extracted from bacteria in fecal samples, followed by PCR amplification, cDNA construction, and high-throughput sequencing. Naive Bayes algorithm was used to perform taxonomic analysis (phylum, class, order, family, genus, species) and community diversity analysis (Sobs index, Shannon index, Shannoneven index, Coverage index, PD index, and principal co-ordinates analysis) on representative sequences and abundance of amplicon sequence variants (ASV). Wilcoxon rank sum test, P-value multiple test correction, and analysis of similarities were used to test differences between the two groups to obtain information on the distribution characteristics and species differences of intestinal microbiota in children. Results Seventy-four fecal samples were sequenced, and the sequencing results were subjected to quality control and filtering. A total of 4905306 optimized sequences were obtained, resulting in 1860 ASVs. The diversity data analysis of ASVs generated 889 species annotation results at 8 taxonomic levels. The alpha diversity analysis showed that the richness (Sobs index), diversity (Shannon index), evenness (Shannoneven index), and phylogenetic diversity (PD index) of fecal community of the obese/overweight children were increased compared to those of the normal weight children, but there were no statistical differences between the two groups (P>0.05). The beta diversity analysis showed that there was little difference in the composition of microbial species between the two groups, and no significant clustering separation was observed. The results of species composition analysis at phylum, order, family, and genus levels of 74 samples showed a consistent core microbiota structure in the two groups of gut microbiota, but there were differences in microbiota composition. The differences in microbial community composition between the two groups were manifested at the taxonomic levels of order, family, and genus, among which phylum Firmicutes, order Erysipelotrichales, family Erysipelatocyclostridiaceae, genus Erysipelotrichaceae_ UCG-003 and genus Catenibacterium were significantly enriched in the obese/overweight group and contributed significantly to the phenotypic difference of obese/overweight [linear discriminant analysis (LDA)=3.72, P<0.01; LDA=3.29, P<0.05). Phylum Proteobacteria, order Enterobacterales, family Enterobacteriaceae, genus unclassified was significantly enriched in the normal weight group and contributed significantly to the phenotypic difference of normal body weight (LDA=3.93, P<0.05). Conclusion The richness and diversity of gut microbiota in obese/overweight children aged 2-6 years in Shanghai are increased, but there is no difference compared to normal weight children. There is a difference in the composition of gut microbiota between the obese/overweight group and the normal weight group.