Objective:To achieve precision medicine,the use of imaging methods to help the clinical detection of cerebral infarction is conducive to the clinical development of a treatment plan and increase of the cure rate and i...Objective:To achieve precision medicine,the use of imaging methods to help the clinical detection of cerebral infarction is conducive to the clinical development of a treatment plan and increase of the cure rate and improvement of the prognosis of patients.Methods:In this work,T2-weighted imaging(T2WI),diffusion-weighted imaging(DWI),susceptibility-weighted imaging(SWI),and diffusion tensor imaging(DTI)examinations were performed on 34 patients with clinically diagnosed cerebral infarction to measure the difference in signal intensity between the lesion and its mirror area and make a comparative analysis by means of the Student-Newman-Keuls method.Results:The detection rate of T2WI was 79%(27/34),the detection rate of DWI was 97%(33/34),the detection rate of SWI was 88%(30/34),and the detection rate of DTI was 94%(32/34).Conclusion:The imaging performance was in the order DWI>DTI>SWI>T2WI for the diagnosis of cerebral infarction,and combined imaging is better than single imaging.展开更多
BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation gr...BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.展开更多
BACKGROUND Neurovascular compression(NVC) is the main cause of primary trigeminal neuralgia(TN) and hemifacial spasm(HFS). Microvascular decompression(MVD) is an effective surgical method for the treatment of TN and H...BACKGROUND Neurovascular compression(NVC) is the main cause of primary trigeminal neuralgia(TN) and hemifacial spasm(HFS). Microvascular decompression(MVD) is an effective surgical method for the treatment of TN and HFS caused by NVC. The judgement of NVC is a critical step in the preoperative evaluation of MVD, which is related to the effect of MVD treatment. Magnetic resonance imaging(MRI) technology has been used to detect NVC prior to MVD for several years. Among many MRI sequences, three-dimensional time-of-flight magnetic resonance angiography(3D TOF MRA) is the most widely used. However, 3D TOF MRA has some shortcomings in detecting NVC. Therefore, 3D TOF MRA combined with high resolution T2-weighted imaging(HR T2WI) is considered to be a more effective method to detect NVC.AIM To determine the value of 3D TOF MRA combined with HR T2WI in the judgment of NVC, and thus to assess its value in the preoperative evaluation of MVD.METHODS Related studies published from inception to September 2022 based on PubMed, Embase, Web of Science, and the Cochrane Library were retrieved. Studies that investigated 3D TOF MRA combined with HR T2WI to judge NVC in patients with TN or HFS were included according to the inclusion criteria. Studies without complete data or not relevant to the research topics were excluded. The Quality Assessment of Diagnostic Accuracy Studies checklist was used to assess the quality of included studies. The publication bias of the included literature was examined by Deeks’ test. An exact binomial rendition of the bivariate mixed-effects regression model was used to synthesize data. Data analysis was performed using the MIDAS module of statistical software Stata 16.0. Two independent investigators extracted patient and study characteristics, and discrepancies were resolved by consensus. Individual and pooled sensitivities and specificities were calculated. The I_(2) statistic and Q test were used to test heterogeneity. The study was registered on the website of PROSERO(registration No. CRD42022357158).RESULTS Our search identified 595 articles, of which 12(including 855 patients) fulfilled the inclusion criteria. Bivariate analysis showed that the pooled sensitivity and specificity of 3D TOF MRA combined with HR T2WI for detecting NVC were 0.96 [95% confidence interval(CI): 0.92-0.98] and 0.92(95%CI: 0.74-0.98), respectively. The pooled positive likelihood ratio was 12.4(95%CI: 3.2-47.8), pooled negative likelihood ratio was 0.04(95%CI: 0.02-0.09), and pooled diagnostic odds ratio was 283(95%CI: 50-1620). The area under the receiver operating characteristic curve was 0.98(95%CI: 0.97-0.99). The studies showed no substantial heterogeneity(I2 = 0, Q = 0.001 P = 0.50).CONCLUSION Our results suggest that 3D TOF MRA combined with HR T2WI has excellent sensitivity and specificity for judging NVC in patients with TN or HFS. This method can be used as an effective tool for preoperative evaluation of MVD.展开更多
Purpose: To predict the diagnostic performance of combined use of T2-weighted imaging (T2W)-diffusion weighted MRI (DWI) and apparent diffusion coefficient (ADC)-proton MR spectroscopy (H-MRS) for the detection of pro...Purpose: To predict the diagnostic performance of combined use of T2-weighted imaging (T2W)-diffusion weighted MRI (DWI) and apparent diffusion coefficient (ADC)-proton MR spectroscopy (H-MRS) for the detection of prostate cancer, correlated to histopathology as the reference standard. Method: After institutional review board approval, 40 patients with prostate cancer were included in this retrospective research. Two readers evaluated the results of T2W, DWI-ADC mapping and H-MRS independently for the depiction of prostate cancer. Reference standard was the TRUS-guided biopsy and the surgical histopathological results. Statistical analysis was assessed by Fisher’s exact t-test, Wilcoxon signed rank test, variance analysis test with Kappa (k) values and receiver operating characteristics (ROC) curve for ADC values, Cho/Cit and Cho + Cre/Cit ratios for each observer. Results: Both readers declined 46% sensitivity and 68% specificity for T2W sequence, 29% sensitivity and 82% specificity for DWI-ADC mapping and 49% specificity for Cho/Cit and Cho + Cre/Cit ratios, 69% sensitivity for Cho/Cit 70% sensitivity for Cho + Cre/Cit ratios of H-MRS. T2W + DWI-ADC mapping + H-MRS (Cho/Cit and Cho + Cre/Cit ratios) regarded 81% sensitivity and 66% specificity, with significant statistical differences to the reference histopathology (p 0.05). Conclusion: Combination of T2W, DWI and H-MRS were more sensitive and more accurate than either sequences alone, for prostate cancer localization and detection.展开更多
OBJECTIVE To compare the results from breast cancer patients who undergo T2-weighted first-pass perfusion imaging after dynamic contrast-enhanced T1-weighted imaging during the same examination,and to evaluate if T2-w...OBJECTIVE To compare the results from breast cancer patients who undergo T2-weighted first-pass perfusion imaging after dynamic contrast-enhanced T1-weighted imaging during the same examination,and to evaluate if T2-weighted imaging can provide additional diagnostic information over that obtained with Tl-weiahted imaaina.METHODS Twenty-nine patients with breast lesions verified by pathology (benign 12, malignant 17) underwent MR imaging with dynamic contrast-enhanced Tl-weighted imaging of the entire breasts,immediately followed by 6-sections of T2-weighted first-pass perfusion imaging of the lesions. The diagnostic indices were acquired by individual 3D Tl-weighted enhancement rate criterion and the T2 signalintensity loss rate criterion. The sensitivity and specificity were calculated and the 2 methods were compared.RESULTS With the dynamic contrast-enhanced T1-weighted imaging there was a significant differences breast lesions (t=2.563, P=0.016)overlap between the signal intensitybetween the benign and malignant However we found a considerable increase in the carcinomas and thatin the benign lesions, for a sensitivity of 94% and a specificity of 25%.With T2-weighted first-pass perfusion imaging, there was a very significant difference between the benign and malignant breast lesions(t=4.777,P<0.001), and the overlap between the signal intensity decrease in the carcinomas and that of the benign lesions on the T2-weighted images was less pronounced than the overlap in the T1-weighted images, for a sensitivity of 88% and a specificity of 75%.CONCLUSION T2-weighted first-pass perfusion imaging may help differentiate between benign and malignant breast lesions with a higher level of specificity. The combination of T1-weighted and T2-weighted imaging is feasible in a single patient examination and may improve breast MR imaging.展开更多
BACKGROUND Colorectal cancer is a malignancy with a high risk of lymph node metastasis and poor prognosis,and thus requires an accurate diagnosis.AIM To assess the diagnostic value of combined magnetic resonance T2-we...BACKGROUND Colorectal cancer is a malignancy with a high risk of lymph node metastasis and poor prognosis,and thus requires an accurate diagnosis.AIM To assess the diagnostic value of combined magnetic resonance T2-weighted imaging(T2WI)and diffusion-weighted imaging(DWI)in colorectal cancer.METHODS We included 120 patients with suspected colorectal cancer who underwent magnetic resonance imaging.Surgical pathology was used as the gold standard for comparison.Combined T2WI and DWI showed higher diagnostic efficacy than either of the two methods used individually.RESULTS The combined method achieved 94.74%sensitivity,95.45%specificity,95.00%accuracy,94.74%positive predictive value,and 95.45%negative predictive value in qualitative diagnosis.It showed 94.44%sensitivity,95.00%specificity,94.74%accuracy,94.44%positive predictive value,and 95.00%negative predictive value in clinical staging.Finally,it showed 94.74%sensitivity,94.59%specificity,94.74%accuracy,94.74%positive predictive value,and 94.59%negative predictive value in diagnosing lymph node metastasis.These results were highly consistent with that of the gold standard.CONCLUSION This study combined T2WI and DWI for accurate diagnosis of colorectal cancer,aiding clinical staging and lymph node metastasis assessment.This approach is promising for clinical application.展开更多
目的:通过对C3H10T1/2细胞分化为成熟棕色脂肪细胞的培养、诱导分化及鉴定,深入探讨棕色脂肪细胞的生物学特性,为人类棕色脂肪细胞的相关研究提供实验参考与理论依据。方法:C3H10T1/2细胞经细胞接种、培养及诱导分化处理,利用光学显微...目的:通过对C3H10T1/2细胞分化为成熟棕色脂肪细胞的培养、诱导分化及鉴定,深入探讨棕色脂肪细胞的生物学特性,为人类棕色脂肪细胞的相关研究提供实验参考与理论依据。方法:C3H10T1/2细胞经细胞接种、培养及诱导分化处理,利用光学显微镜观察细胞形态变化,并通过油红O染色、免疫荧光法、线粒体探针法以及线粒体电镜技术对分化细胞进行鉴定分析。结果:未分化的C3H10T1/2细胞形态多样,具有突触伸展特征;分化后的细胞逐渐变为圆形或椭圆形,形成环形脂滴;未分化组细胞形态无明显变化。油红O染色显示,未分化组细胞基本无染色,而分化组中约90%的细胞红染,脂滴分布于细胞核周围,吸光度值显著升高(P<0.05)。分化组解偶联蛋白1(uncoupling protein 1,UCP1)、过氧化物酶体增殖激活受体γ(peroxisome proliferator-activated receptorγ,PPARγ)辅激活因子1α(PPARγco-activator-1α,PGC-1α)、PPARγ和转录因子PRDM16的相对荧光强度和蛋白表达量显著高于未分化组(P<0.05),且线粒体活性增强。结论:本研究成功诱导C3H10T1/2细胞分化为成熟棕色脂肪细胞,结合细胞形态观察、关键蛋白表达检测及线粒体功能分析进行综合评估,证实了细胞的有效分化及成熟棕色脂肪细胞的功能特性。展开更多
文摘Objective:To achieve precision medicine,the use of imaging methods to help the clinical detection of cerebral infarction is conducive to the clinical development of a treatment plan and increase of the cure rate and improvement of the prognosis of patients.Methods:In this work,T2-weighted imaging(T2WI),diffusion-weighted imaging(DWI),susceptibility-weighted imaging(SWI),and diffusion tensor imaging(DTI)examinations were performed on 34 patients with clinically diagnosed cerebral infarction to measure the difference in signal intensity between the lesion and its mirror area and make a comparative analysis by means of the Student-Newman-Keuls method.Results:The detection rate of T2WI was 79%(27/34),the detection rate of DWI was 97%(33/34),the detection rate of SWI was 88%(30/34),and the detection rate of DTI was 94%(32/34).Conclusion:The imaging performance was in the order DWI>DTI>SWI>T2WI for the diagnosis of cerebral infarction,and combined imaging is better than single imaging.
基金the Fujian Province Clinical Key Specialty Construction Project,No.2022884Quanzhou Science and Technology Plan Project,No.2021N034S+1 种基金The Youth Research Project of Fujian Provincial Health Commission,No.2022QNA067Malignant Tumor Clinical Medicine Research Center,No.2020N090s.
文摘BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.
基金Supported by the Key Research and Development Plan of Shaanxi Province,No.2021SF-298.
文摘BACKGROUND Neurovascular compression(NVC) is the main cause of primary trigeminal neuralgia(TN) and hemifacial spasm(HFS). Microvascular decompression(MVD) is an effective surgical method for the treatment of TN and HFS caused by NVC. The judgement of NVC is a critical step in the preoperative evaluation of MVD, which is related to the effect of MVD treatment. Magnetic resonance imaging(MRI) technology has been used to detect NVC prior to MVD for several years. Among many MRI sequences, three-dimensional time-of-flight magnetic resonance angiography(3D TOF MRA) is the most widely used. However, 3D TOF MRA has some shortcomings in detecting NVC. Therefore, 3D TOF MRA combined with high resolution T2-weighted imaging(HR T2WI) is considered to be a more effective method to detect NVC.AIM To determine the value of 3D TOF MRA combined with HR T2WI in the judgment of NVC, and thus to assess its value in the preoperative evaluation of MVD.METHODS Related studies published from inception to September 2022 based on PubMed, Embase, Web of Science, and the Cochrane Library were retrieved. Studies that investigated 3D TOF MRA combined with HR T2WI to judge NVC in patients with TN or HFS were included according to the inclusion criteria. Studies without complete data or not relevant to the research topics were excluded. The Quality Assessment of Diagnostic Accuracy Studies checklist was used to assess the quality of included studies. The publication bias of the included literature was examined by Deeks’ test. An exact binomial rendition of the bivariate mixed-effects regression model was used to synthesize data. Data analysis was performed using the MIDAS module of statistical software Stata 16.0. Two independent investigators extracted patient and study characteristics, and discrepancies were resolved by consensus. Individual and pooled sensitivities and specificities were calculated. The I_(2) statistic and Q test were used to test heterogeneity. The study was registered on the website of PROSERO(registration No. CRD42022357158).RESULTS Our search identified 595 articles, of which 12(including 855 patients) fulfilled the inclusion criteria. Bivariate analysis showed that the pooled sensitivity and specificity of 3D TOF MRA combined with HR T2WI for detecting NVC were 0.96 [95% confidence interval(CI): 0.92-0.98] and 0.92(95%CI: 0.74-0.98), respectively. The pooled positive likelihood ratio was 12.4(95%CI: 3.2-47.8), pooled negative likelihood ratio was 0.04(95%CI: 0.02-0.09), and pooled diagnostic odds ratio was 283(95%CI: 50-1620). The area under the receiver operating characteristic curve was 0.98(95%CI: 0.97-0.99). The studies showed no substantial heterogeneity(I2 = 0, Q = 0.001 P = 0.50).CONCLUSION Our results suggest that 3D TOF MRA combined with HR T2WI has excellent sensitivity and specificity for judging NVC in patients with TN or HFS. This method can be used as an effective tool for preoperative evaluation of MVD.
文摘Purpose: To predict the diagnostic performance of combined use of T2-weighted imaging (T2W)-diffusion weighted MRI (DWI) and apparent diffusion coefficient (ADC)-proton MR spectroscopy (H-MRS) for the detection of prostate cancer, correlated to histopathology as the reference standard. Method: After institutional review board approval, 40 patients with prostate cancer were included in this retrospective research. Two readers evaluated the results of T2W, DWI-ADC mapping and H-MRS independently for the depiction of prostate cancer. Reference standard was the TRUS-guided biopsy and the surgical histopathological results. Statistical analysis was assessed by Fisher’s exact t-test, Wilcoxon signed rank test, variance analysis test with Kappa (k) values and receiver operating characteristics (ROC) curve for ADC values, Cho/Cit and Cho + Cre/Cit ratios for each observer. Results: Both readers declined 46% sensitivity and 68% specificity for T2W sequence, 29% sensitivity and 82% specificity for DWI-ADC mapping and 49% specificity for Cho/Cit and Cho + Cre/Cit ratios, 69% sensitivity for Cho/Cit 70% sensitivity for Cho + Cre/Cit ratios of H-MRS. T2W + DWI-ADC mapping + H-MRS (Cho/Cit and Cho + Cre/Cit ratios) regarded 81% sensitivity and 66% specificity, with significant statistical differences to the reference histopathology (p 0.05). Conclusion: Combination of T2W, DWI and H-MRS were more sensitive and more accurate than either sequences alone, for prostate cancer localization and detection.
文摘OBJECTIVE To compare the results from breast cancer patients who undergo T2-weighted first-pass perfusion imaging after dynamic contrast-enhanced T1-weighted imaging during the same examination,and to evaluate if T2-weighted imaging can provide additional diagnostic information over that obtained with Tl-weiahted imaaina.METHODS Twenty-nine patients with breast lesions verified by pathology (benign 12, malignant 17) underwent MR imaging with dynamic contrast-enhanced Tl-weighted imaging of the entire breasts,immediately followed by 6-sections of T2-weighted first-pass perfusion imaging of the lesions. The diagnostic indices were acquired by individual 3D Tl-weighted enhancement rate criterion and the T2 signalintensity loss rate criterion. The sensitivity and specificity were calculated and the 2 methods were compared.RESULTS With the dynamic contrast-enhanced T1-weighted imaging there was a significant differences breast lesions (t=2.563, P=0.016)overlap between the signal intensitybetween the benign and malignant However we found a considerable increase in the carcinomas and thatin the benign lesions, for a sensitivity of 94% and a specificity of 25%.With T2-weighted first-pass perfusion imaging, there was a very significant difference between the benign and malignant breast lesions(t=4.777,P<0.001), and the overlap between the signal intensity decrease in the carcinomas and that of the benign lesions on the T2-weighted images was less pronounced than the overlap in the T1-weighted images, for a sensitivity of 88% and a specificity of 75%.CONCLUSION T2-weighted first-pass perfusion imaging may help differentiate between benign and malignant breast lesions with a higher level of specificity. The combination of T1-weighted and T2-weighted imaging is feasible in a single patient examination and may improve breast MR imaging.
文摘BACKGROUND Colorectal cancer is a malignancy with a high risk of lymph node metastasis and poor prognosis,and thus requires an accurate diagnosis.AIM To assess the diagnostic value of combined magnetic resonance T2-weighted imaging(T2WI)and diffusion-weighted imaging(DWI)in colorectal cancer.METHODS We included 120 patients with suspected colorectal cancer who underwent magnetic resonance imaging.Surgical pathology was used as the gold standard for comparison.Combined T2WI and DWI showed higher diagnostic efficacy than either of the two methods used individually.RESULTS The combined method achieved 94.74%sensitivity,95.45%specificity,95.00%accuracy,94.74%positive predictive value,and 95.45%negative predictive value in qualitative diagnosis.It showed 94.44%sensitivity,95.00%specificity,94.74%accuracy,94.44%positive predictive value,and 95.00%negative predictive value in clinical staging.Finally,it showed 94.74%sensitivity,94.59%specificity,94.74%accuracy,94.74%positive predictive value,and 94.59%negative predictive value in diagnosing lymph node metastasis.These results were highly consistent with that of the gold standard.CONCLUSION This study combined T2WI and DWI for accurate diagnosis of colorectal cancer,aiding clinical staging and lymph node metastasis assessment.This approach is promising for clinical application.
文摘目的:通过对C3H10T1/2细胞分化为成熟棕色脂肪细胞的培养、诱导分化及鉴定,深入探讨棕色脂肪细胞的生物学特性,为人类棕色脂肪细胞的相关研究提供实验参考与理论依据。方法:C3H10T1/2细胞经细胞接种、培养及诱导分化处理,利用光学显微镜观察细胞形态变化,并通过油红O染色、免疫荧光法、线粒体探针法以及线粒体电镜技术对分化细胞进行鉴定分析。结果:未分化的C3H10T1/2细胞形态多样,具有突触伸展特征;分化后的细胞逐渐变为圆形或椭圆形,形成环形脂滴;未分化组细胞形态无明显变化。油红O染色显示,未分化组细胞基本无染色,而分化组中约90%的细胞红染,脂滴分布于细胞核周围,吸光度值显著升高(P<0.05)。分化组解偶联蛋白1(uncoupling protein 1,UCP1)、过氧化物酶体增殖激活受体γ(peroxisome proliferator-activated receptorγ,PPARγ)辅激活因子1α(PPARγco-activator-1α,PGC-1α)、PPARγ和转录因子PRDM16的相对荧光强度和蛋白表达量显著高于未分化组(P<0.05),且线粒体活性增强。结论:本研究成功诱导C3H10T1/2细胞分化为成熟棕色脂肪细胞,结合细胞形态观察、关键蛋白表达检测及线粒体功能分析进行综合评估,证实了细胞的有效分化及成熟棕色脂肪细胞的功能特性。
