Thalidomide (THA) is renowned for its potent anti-inflammatory properties. This study aimed to elucidate its underlying mechanisms in the context of Crohn's disease (CD) development. Mouse colitis models were established by dextran sulfate sodium (DSS) treatment. Fecal microbiota and metabolites were analyzed by metagenomic sequencing and mass spectrometry, respectively. Antibiotic-treated mice served as models for microbiota depletion and transplantation. The expression of forkhead box P3⁺ (FOXP3⁺) regulatory T cells (Tregs) was measured by flow cytometry and immunohistochemical assay in colitis model and patient cohort. THA inhibited colitis in DSS-treated mice by altering the gut microbiota profile, with an increased abundance of probiotics Bacteroides fragilis, while pathogenic bacteria were depleted. In addition, THA increased beneficial metabolites bile acids and significantly restored gut barrier function. Transcriptomic profiling revealed that THA inhibited interleukin-17 (IL-17), IL-1β and cell cycle signaling. Fecal microbiota transplantation from THA-treated mice to microbiota-depleted mice partly recapitulated the effects of THA. Specifically, increased level of gut commensal B. fragilis was observed, correlated with elevated levels of the microbial metabolite 3alpha-hydroxy-7-oxo-5beta-cholanic acid (7-ketolithocholic acid, 7-KA) following THA treatment. This microbial metabolite may stable FOXP3 expression by targeting the receptor FMR1 autosomal homolog 1 (FXR1) to inhibit autophagy. An interaction between FOXP3 and FXR1 was identified, with binding regions localized to the FOXP3 domain (aa238-335) and the FXR1 domain (aa82-222), respectively. Conclusively, THA modulates the gut microbiota and metabolite profiles towards a more beneficial composition, enhances gut barrier function, promotes the differentiation of FOXP3⁺ Tregs and curbs pro-inflammatory pathways.

Objective To analyze the cellular and histopathological characteristics of underdiagnosed thyroid nodules of Chinese thyroid imaging reporting and data system(C-TIRADS) categories 3 and 4a,thus improving the understanding of these lesions. Methods The data of ultrasound and fine needle aspiration cytology were collected from 683 nodules diagnosed based on pathological evidence in 549 patients undergoing thyroid surgery.The cellular and histopathological characteristics of C-TIRADS 3 and 4a nodules were analyzed. Results Two hundred and sixty-eight nodules were classified as C-TIRADS category 3,including 236 benign nodules,12 low-risk ones,and 20 (7.46%) malignant ones.Two hundred and twenty-one nodules were classified as C-TIRADS category 4a,including 133 benign nodules,7 low-risk ones,and 81 (36.65%) malignant ones.The malignancy rates differed between C-TIRADS 3 and 4a nodules (χ²=58.93,P<0.001),and both were higher than the recommended malignancy rate in the guidelines for malignancy risk stratification of thyroid nodules (C-TIRADS) (both P<0.001).According to the pathological evidence,the underdiagnosed C-TIRADS 3/4a nodules were mainly papillary thyroid carcinoma,especially in patients with Hashimoto thyroiditis.There was not a consistent one-to-one match between each ultrasound result and each cytological classification of low-risk thyroid nodules.Conclusions When the malignant features in preoprative ultrasound imaging are atypical or absent,papillary thyroid carcinoma (especially with Hashimoto thyroiditis),follicular carcinoma,and medullary carcinoma are likely to be underdiagnosed as C-TIRADS 3 or 4a nodules.Therefore,efforts should be made to fully understand the cellular and pathological characteristics of these lesions.
Humans ; Thyroid Nodule/diagnostic imaging* ; Female ; Male ; Middle Aged ; Adult ; Ultrasonography ; Biopsy, Fine-Needle ; Aged ; Young Adult ; Thyroid Neoplasms/diagnostic imaging* ; Adolescent

Objective To evaluate the clinical efficacy of oral use of Jiawei Puji Xiaodu Granules(mainly composed of Lonicerae Japonicae Flos,Forsythiae Fructus,Taraxaci Herba,Violae Herba,Schizonepetae Herba,Arctii Fructus,Gleditsiae Spina,Paeoniae Radix Rubra,Moutan Cortex,and Phragmitis Rhizoma)combined with external application of Xiaozhong Sanjie Ointment(mainly composed of Scutellariae Radix,Coptidis Rhizoma,Phellodendri Chinensis Cortex,and Gleditsiae Spina,etc.)in the treatment of acute tonsillitis in children,and to observe their effects on the immune function and related inflammatory indexes of the patients.Methods A total of 116 children with acute tonsillitis of heat stagnation in the lung and stomach type were randomly divided into the control group and the observation group,with 58 cases in each group.The control group was treated with Cefixime Dispersible Tablets,while the observation group was treated with Jiawei Puji Xiaodu Granules for oral use and Xiaozhong Sanjie Ointment for external application.Both groups were treated for 14 days and then were followed-up for a period of 6 months.The changes of traditional Chinese medicine(TCM)syndrome scores,white blood cell(WBC)count,T lymphocyte subset CD3+,CD4+,CD8+ and CD4+/CD8+ levels,and serum levels of tumor necrosis factor α(TNF-α),interleukin 1β(IL-1β),interleukin 6(IL-6)and C-reactive protein(CRP)in the two groups were observed before and after the treatment.Moreover,the clinical efficacy and time for the disappearance of clinical symptoms were compared between the two groups,and the occurrence of adverse reactions and the recurrence of tonsillitis in the two groups were monitored at the same time.Results(1)During the trial,there were 8 cases falling off in the control group but none case falling off in the observation group,and eventually 50 cases in the control group and 58 cases in the observation group completed the full course of treatment.(2)After 14 days of treatment,the total effective rate of the observation group was 98.28%(57/58),while that of the control group was 90.00%(45/50).The intergroup(tested by rank sum test)showed that the clinical efficacy of the observation group was significantly superior to that of the control group(P＜0.05).(3)After treatment,the time for the disappearance of sore throat,time for the disappearance of purulent spots,time for subsiding fever and time for the tonsils recovering to normal in the observation group were all significantly shorter than those in the control group(P＜0.05).(4)After treatment,the scores of primary and secondary symptoms and the overall symptom scores in the two groups were significantly lower than those before treatment(P＜0.05),and the reduction of the scores in the observation group was significantly superior to that in the control group(P＜0.05).(5)After treatment,the levels of T lymphocyte subset CD3+,CD4+ and CD4+/CD8+ in the two groups were significantly higher(P＜0.05)while the level of CD8 + was significantly lower(P＜0.05)than those before treatment,and the increase in the levels of CD3+,CD4+ and CD4+/CD8+ and the reduction of the CD8+ level of the observation group were significantly superior to those of the control group(P＜0.05).(6)After treatment,the levels of WBC,TNF-α,IL-1β,IL-6 and CRP in the two groups were significantly lower than those before treatment(P＜0.05),and the reduction in the observation group was significantly superior to that in the control group(P＜0.05).(7)During the treatment period,no skin allergy,nausea,vomiting or other gastrointestinal adverse reactions occurred in the two groups,which showed a high degree of safety.(8)The 6-month follow-up showed that the recurrence rate of tonsillitis in the observation group was 5.17%(3/58),which was significantly lower than that of 24.00%(12/50)in the control group,and the difference was statistically significant(χ2 = 8.330,P＜0.05).Conclusion The efficacy of Jiawei Puji Xiaodu Granules combined with Xiaozhong Sanjie Ointment exert notable curative effect for children with acute tonsillitis of heat stagnation in the lung and stomach type.The combined therapy can significantly shorten the duration of the disease,improve the clinical symptoms of the children and effectively reduce the recurrence rate of tonsillitis.The therapeutic mechanism may be related to the enhancement of the immune function and the inhibition of inflammatory response.

Objective To investigate the distribution of the traditional Chinese medicine(TCM)constitution types in the patients with nonspecific low back pain(NLBP)and to explore the correlation of the thickness of ligamentum flavum with the age,body mass index(BMI),gender,the presence of diabetes mellitus,and the grading of hypertension.Methods Sixty patients with NLBP admitted to Guangdong Second Traditional Chinese Medicine Hospital from January 2023 to June 2023 were selected as the study subjects.The TCM constitution types of the patients were identified,the thickness of the ligamentum flavum at lumbar vertebrae 4/5 segment(L4/5)disc level was measured by computerized tomography(CT)scanning,and the patients'age,genders,TCM constitution types,BMI,the presence or absence of diabetes mellitus,and hypertension grading were recorded.Correlation analysis and linear regression analysis were used for the exploration of the relevant influencing factors for the thickness of the ligamentum flavum of patients with NLBP.Results(1)The average thickness of ligamentum flavum in the 60 patients with NLBP was(2.60±0.72)mm.(2)The TCM constitutions of NLBP patients were classified into four types,of which blood stasis constitution was the most common,accounting for 21 cases(35.0%),followed by 19 cases(31.7%)of damp-heat constitution,12 cases(20.0%)of phlegm-damp constitution,and 8 cases(13.3%)of qi deficiency constitution.(3)The results of correlation analysis showed that BMI,gender,TCM constitution type and the presence or absence of diabetes mellitus had no influence on the thickness of ligamentum flavum in NLBP patients(P＞0.05),while the age and hypertension grading had an influence on the thickness of ligamentum flavum(P＜0.01).(4)The results of linear regression analysis showed that the age had an influence on the thickness of the ligamentum flavum(b = 0.034,t = 6.282,P＜0.01),while the influence of the hypertension grading had no influence on the thickness of the ligamentum flavum(P＞0.05).Conclusion The TCM constitution type of NLBP patients is predominated by blood stasis constitution,the thickness of ligamentum flavum is significantly affected by the age,and hypertension may be a potential factor affecting the thickness of ligamentum flavum.

Objective To observe the clinical efficacy of"triple-posture positive bone-setting"chiropractic manipulation combined with Tongluo Huoxue Formula for the treatment of lumbar spinal stenosis(LSS)with qi deficiency and blood stasis syndrome.Methods Sixty patients with LSS of qi deficiency and blood stasis type were randomly divided into trial group and control group,with 30 cases in each group.The trial group was treated with"triple-posture positive bone-setting"chiropractic manipulation(a chiropractic manipulation performed under the positive cooperation of the patients at three postures)combined with Tongluo Huoxue Formula,while the control group was treated with"triple-posture positive bone-setting"chiropractic manipulation combined with conventional western medicine.The course of treatment for the two groups covered 4 weeks.Before and after treatment,the patients of the two groups were observed in the changes of pain visual analogue scale(VAS)score,Japanese Orthopedic Association(JOA)score of lumbar function,Oswestry Disability Index(ODI)score,straight-leg raising test results and serum interleukin 6(IL-6)and C-reactive protein(CRP)levels.After treatment,the clinical efficacy and safety of the two groups were evaluated.Results(1)After 4 weeks of treatment,the total effective rate of the trial group was 96.67％(29/30)and that of the control group was 63.33％(19/30).The intergroup comparison(tested by Fisher's exact test)showed that the clinical efficacy of the trial group was significantly superior to that of the control group(P＜0.05).(2)After treatment,the lumbar function indicators of pain VAS scores and ODI scores in the trial group were significantly lower(P＜0.05),and the JOA scores were significantly higher than those before treatment(P＜0.05),while in the control group,only the ODI scores were significantly lower than those before treatment(P＜0.05).The intergroup comparison showed that the decrease of VAS and ODI scores and the increase of JOA scores in the trial group were significantly superior to those in the control group(P＜0.05 or P＜0.01).(3)After treatment,the Laseque s sign of the trial group was significantly improved compared with that before treatment(P＜0.05),while no significant improvement was presented in the control group(P＞0.05).The intergroup comparison showed that the improvement of Laseque's sign in the trial group was significantly superior to that in the control group(P＜0.01).(4)After treatment,the levels of serum inflammatory factors of IL-6 and CRP in the two groups were lower than those before treatment(P＜0.05),and the decrease of serum IL-6 level in the trial group was significantly superior to that in the control group(P＜0.05),but CRP level in the two groups after treatment did not differ from that before treatment,no statistically significant difference was shown between the two groups after treatment,either(P＞0.05).(5)The incidence of adverse reactions in the trial group was 6.67％(2/30)and that in the control group was 13.33％(4/30),and the intergroup comparison(by Fisher's exact test)showed that there was no significant difference between the two groups(P＞0.05).Conclusion The therapeutic effect of"triple-posture positive bone-setting"chiropractic manipulation combined with Tongluo Huoxue Formula exert certain effect for the treatment of LSS patients with qi deficiency and blood stasis syndrome,and it has more obvious advantages in improving the lumbar function,promoting the rehabilitation of the patients,and lowering the level of serum inflammatory factors than"triple-posture positive bone-setting"chiropractic manipulation combined with conventional western medication.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.