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.

This study aims to investigate the protective effects and mechanisms of polysaccharide extract PCP1 from Polygonatum cyrtonema in ameliorating cerebral ischemia-reperfusion(I/R) injury in rats through modulation of the Toll-like receptor 4(TLR4)/NOD-like receptor protein 3(NLRP3) signaling pathway. In vivo, SD rats were randomly divided into the sham group, model group, PCP1 group, nimodipine(NMDP) group, and TLR4 signaling inhibitor(TAK-242) group. A middle cerebral artery occlusion/reperfusion(MCAO/R) model was established, and neurological deficit scores and infarct size were evaluated 24 hours after reperfusion. Hematoxylin-eosin(HE) and Nissl staining were used to observe pathological changes in ischemic brain tissue. Transmission electron microscopy(TEM) assessed ultrastructural damage in cortical neurons. Enzyme-linked immunosorbent assay(ELISA) was used to measure the levels of interleukin-1β(IL-1β), interleukin-6(IL-6), interleukin-18(IL-18), tumor necrosis factor-α(TNF-α), interleukin-10(IL-10), and nitric oxide(NO) in serum. Immunofluorescence was used to analyze the expression of TLR4 and NLRP3 proteins. In vitro, a BV2 microglial cell oxygen-glucose deprivation/reperfusion(OGD/R) model was established, and cells were divided into the control, OGD/R, PCP1, TAK-242, and PCP1 + TLR4 activator lipopolysaccharide(LPS) groups. The CCK-8 assay evaluated BV2 cell viability, and ELISA determined NO release. Western blot was used to analyze the expression of TLR4, NLRP3, and downstream pathway-related proteins. The results indicated that, compared with the model group, PCP1 significantly reduced neurological deficit scores, infarct size, ischemic tissue pathology, cortical cell damage, and the levels of inflammatory factors IL-1β, IL-6, IL-18, TNF-α, and NO(P<0.01). It also elevated IL-10 levels(P<0.01) and decreased the expression of TLR4 and NLRP3 proteins(P<0.05, P<0.01). Moreover, in vitro results showed that, compared with the OGD/R group, PCP1 significantly improved BV2 cell viability(P<0.05, P<0.01), reduced cell NO levels induced by OGD/R(P<0.01), and inhibited the expression of TLR4-related inflammatory pathway proteins, including TLR4, myeloid differentiation factor 88(MyD88), tumor necrosis factor receptor-associated factor 6(TRAF6), phosphorylated nuclear factor-kappaB dimer RelA(p-p65)/nuclear factor-kappaB dimer RelA(p65), NLRP3, cleaved-caspase-1, apoptosis-associated speck-like protein(ASC), GSDMD-N, IL-1β, and IL-18(P<0.05, P<0.01). The protective effects of PCP1 were reversed by LPS stimulation. In conclusion, PCP1 ameliorates cerebral I/R injury by modulating the TLR4/NLRP3 signaling pathway, exerting anti-inflammatory and anti-pyroptotic effects.
Animals ; Toll-Like Receptor 4/genetics* ; NLR Family, Pyrin Domain-Containing 3 Protein/genetics* ; Rats, Sprague-Dawley ; Rats ; Reperfusion Injury/genetics* ; Male ; Signal Transduction/drug effects* ; Polysaccharides/isolation & purification* ; Polygonatum/chemistry* ; Brain Ischemia/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Humans

OBJECTIVES: To explore the predictive factors for hemodynamically significant patent ductus arteriosus (hsPDA) in preterm infants and to construct a nomogram prediction model for hsPDA occurrence in this population. METHODS: A retrospective analysis was conducted on the clinical data of preterm infants with gestational age <32 weeks diagnosed with patent ductus arteriosus (PDA) who were delivered at Nanjing Women and Children's Healthcare Hospital from January 2020 to December 2022. The subjects were divided into an hsPDA group (52 cases) and a non-hsPDA group (176 cases) based on the presence of hsPDA. Univariate analysis and multivariate logistic regression analysis were performed to screen predictive variables regarding the general information of the infants at birth, maternal pregnancy and delivery conditions, and relevant indicators during hospitalization. A nomogram prediction model for hsPDA occurrence was constructed using R software in preterm infants. Internal validation was performed using the Bootstrap method. Finally, the predictive model was evaluated for calibration, discrimination ability, and clinical utility. RESULTS: Multivariate regression analysis showed that the ratio of the left atrium to aorta diameter (LA/AO), mode of delivery (vaginal), and duration of mechanical ventilation were independent predictive factors for hsPDA in preterm infants (P<0.05). Based on the results of univariate analysis and multivariate logistic regression analysis, variables used to construct the nomogram prediction model for hsPDA risk included: LA/AO ratio, mode of delivery (vaginal), duration of mechanical ventilation, 5-minute Apgar score, and the presence of neonatal respiratory distress syndrome requiring surfactant therapy. The area under the receiver operating characteristic curve for this model was 0.876 (95%CI: 0.824-0.927), and the calibrated curve was close to the ideal reference line, indicating good calibration. The Hosmer-Lemeshow test demonstrated that the model fit well, and the clinical decision curve was above the extreme curves. CONCLUSIONS The nomogram prediction model, constructed using five variables (LA/AO ratio, vaginal delivery, duration of mechanical ventilation, 5-minute Apgar score, and the presence of neonatal respiratory distress syndrome requiring surfactant therapy), has reference significance for predicting the occurrence of hsPDA in preterm infants and provides valuable guidance for the early clinical identification of hsPDA.
Humans ; Ductus Arteriosus, Patent/etiology* ; Nomograms ; Female ; Infant, Newborn ; Infant, Premature ; Retrospective Studies ; Male ; Hemodynamics ; Logistic Models ; Pregnancy

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Objective To investigate the diagnostic efficiency of artificial intelligence(AI)in rib frac-ture under the computed tomography(CT)images with different reconstruction slice thickness.Methods The first CT images of 100 patients with rib fractures were selected,and the interval-free recon-struction was carried out with the thickness of 0.625 mm,1.250 mm,2.500 mm and 5.000 mm,respectively.The rib fracture screening function of AI was used to automatically detect the CT images of four groups,and the diagnostic efficiency of AI for rib fracture under different reconstruction thickness conditions was com-pared.Results The sensitivity of AI in the diagnosis of rib fracture at 0.625 mm,1.250 mm,2.500 mm and 5.000 mm thickness was 99.32%(436/439),98.41%(432/439),89.52%(393/439)and 83.60%(367/439),respectively.The false positive rate was 4.80%(22/458),0.92%(4/436),0.76%(3/396)and 0.27%(1/368).The diagnostic sensitivity of AI in 0.625 mm and 1.250 mm thickness was higher than that in 2.500 mm and 5.000 mm,and the difference was statistically significant(P<0.05),while there was no significant difference in the thickness of 0.625 mm and 1.250 mm.The false positive rate of AI in the diagnosis of 0.625 mm slice thickness was higher than that of 1.250 mm,2.500 mm and 5.000 mm,and the difference was sta-tistically significant(P<0.05),while there was no significant difference in the thickness of 1.250 mm,2.500 mm and 5.000 mm(P>0.05).Conclusion The diagnostic efficiency of AI in 1.250 mm CT images is better than that in 0.625 mm,2.500 mm and 5.000 mm CT images.

A new method for simultaneous determination of 23 kinds of per-and polyfluoroalkyl substances(PFASs)(13 kinds of perfluoro carboxylic acids,4 kinds of perfluoro sulfonic acids,and 6 kinds of new substitutes)in plant leaf tissue by ultra-high performance liquid chromatography-tandem mass spectrometry(UHPLC-MS/MS)using automatic online solid phase extraction(SPE)to remove the matrix interference components in plant crude extracts was developed.The plant leaf samples were extracted twice with 1%formic acid-methanol solution,then evaporated to dry,redissolved with 70%methanol solution,and directly injected for analysis.After 23 kinds of target PFASs were purified automatically by online SPE with a WAX column,the six-way valve was switched to rinse PFASs onto an alkaline mobile phase system-compatible C18 analytical column.Then,the 23 kinds of target PFASs were separated within 16 min by gradient elution using a binary mobile phase system of methanol/water(Containing 0.4%ammonium hydroxide).Tandem mass spectrometry was performed in multiple reaction monitoring(MRM)mode for online detection of various PFASs,and quantification was carried out by internal standard method.The results of the method validation showed that satisfactory average recoveries of 23 kinds of PFASs in plant leaf samples(64.2%-125.5%),precision(relative standard deviations(RSDs)of 0.7%-12.8%),linearity(R2＞0.990),and sensitivity(the detection limits(S/N=3)were in the range of 0.02-0.50 μg/kg)were achieved.Finally,this method was used to detect PFASs in the marine green tide algae(Enteromorpha prolifera)and several tree leaves,and a total of 6 kinds of PFASs were detected,in which PFBA was the main contaminant.Compared with the reported offline SPE methods,the proposed online SPE technique significantly simplified the sample pretreatment process and provided an automatic,simple,and environment-friendly method for the routine monitoring of legacy and emerging PFASs in plant tissues.