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.

Professor LIU Qingguo's academic thoughts and clinical experience in treating pediatric tic disorders with scalp fire needling is introduced. Professor LIU believes that the core pathogenesis of this disease lies in "wind stirring and qi disorder, leading to the spirit failing to govern the body". Therefore, treatment should focus on "regulating the spirit to stabilize the form and extinguishing wind to stop movement". Clinically, the main acupoints include Shenting (GV24), Benshen (GB13), Xinhui (GV22), Baihui (GV20), Sishencong (EX-HN1), Fengchi (GB20), and Fengfu (GV16), which are rapidly punctured with fine fire needles, leading to significant therapeutic efficacy.
Humans ; Acupuncture Therapy/methods* ; Child ; Tic Disorders/therapy* ; Acupuncture Points ; Male ; Scalp ; Female ; Adolescent ; Child, Preschool

To investigate the mechanism of Jianpi Qutan Formula in regulating the balance between classically activated macrophages(M1) and alternatively activated macrophages(M2) in atherosclerotic plaques through phosphorylation and activation of the signal transducer and activator of transcription 6(STAT6), thereby reducing inflammation, increasing plaque stability, and exerting anti-atherosclerosis(AS) effects. An AS model was established by feeding apolipoprotein E(ApoE)~(-/-) mice with atherosclerotic chow for 8 weeks. The ApoE~(-/-) mice were randomly divided into a model group(Mod group), a Jianpi Qutan Formula group(JPQT group, 8.97 g·kg~(-1)), and a Atorvastatin Calcium Tablets group(ATO group, 1.3 mg·kg~(-1)) according to a random table method, with 10 mice in each group. Additionally, 10 male C57BL/6J mice of the same age, fed with a normal diet, were set as the control group(Con group). The JPQT and ATO groups received their respective treatments via oral gavage for 8 consecutive weeks, while the Con and Mod groups were administered an equivalent volume of saline. Body weight was continuously monitored, and after blood collection, total cholesterol(TC) and triglyceride(TG) levels in the serum of each group were compared. Hematoxylin-eosin(HE) staining and oil red O staining were used to observe plaque formation in aortic tissue. Enzyme-linked immunosorbent assay(ELISA) was employed to detect the expression levels of pro-inflammatory cytokines interleukin(IL)-6 and IL-12, as well as the anti-inflammatory cytokine IL-10. Immunofluorescence was used to detect the positive expression of aortic cluster of differentiation(CD)86 and CD206. Western blot analysis was conducted to detect the protein expression levels of aortic inducible nitric oxide synthase(iNOS), arginase 1(Arg1), STAT6, and p-STAT6. Compared to the Con group, the Mod group exhibited increased body weight and blood lipid levels, disordered aortic structure, significant AS plaque formation accompanied by extensive lipid deposition, and elevated serum levels of pro-inflammatory cytokines IL-6 and IL-12, as well as elevated CD86 and iNOS protein levels. In contrast, the serum levels of the anti-inflammatory cytokine IL-10, along with the protein expression levels of CD206, Arg1, and p-STAT6/STAT6, were reduced. Compared to the Mod group, the drug intervention groups showed improvements in body weight and lipid metabolism, with a more significant improvement in aortic structure, reduced lipid accumulation, decreased serum levels of IL-6 and IL-12, and lower CD86 and iNOS protein levels. Meanwhile, levels of IL-10, CD206, Arg1, and p-STAT6/STAT6 increased. Jianpi Qutan Formula improves AS by regulating the imbalance in M1/M2 macrophage polarization, and its mechanism is likely closely related to the activation of the STAT6 signaling pathway.
Animals ; Atherosclerosis/metabolism* ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Macrophages/cytology* ; Mice, Inbred C57BL ; STAT6 Transcription Factor/immunology* ; Humans ; Apolipoproteins E/genetics* ; Interleukin-6/immunology*