BACKGROUND:Breast cancer stem cells not only lead to theoccurrence of breast cancer, but also may cause breast cancer metastasis and recurrence. The relationship between stem cells and cell resistance is also gaining increasing attentions, and the focus on the stem cell treatment may result in unexpected results.OBJECTIVE:To explore the reversal effect of psoralen on glutathione-S-transferase π (GST-π) in human breast cancer MCF-7/ADR cells and its mechanism.METHODS:MCF-7/ADR cells were cultured and enriched in serum-free medium to obtain breast cancer stem cells.RT-PCR and western blot were used to detect the expression of GST-π at the levels of gene and protein in the MCF-7/ADR cells after treatment with 0, 4, 8, 12, 16 mg/L psoralen. To observe the activation of nuclear factor-κB,western blot was used. The expression of GST-π was detected by RT-PCR in 18 μmol/L SN50 group and 8 mg/L psoralen group. Cell counting kit-8 assay was used to detect the effect of doxorubicin on cell proliferation.RESULTS AND CONCLUSION:Compared with the control group, psoralen reduced the expression of GST-π at the mRNA and protein levels, and significantly inhibited the activation of nuclear factor-κB. It was suggested that psoralen could reverse the multidrug resistance of human breast cancer MCF-7/ADR stem cells by decreasing the expression level of GST-π. The mechanism may be achieved by inhibiting the nuclear factor-κB signal pathway.

Objective To explore the effect of psoralen on topoisomerase IIα(TopoIIα) expression of breast cancer stem cells.Methods CD44+CD24-/low breast cancer stem cells were sorted from MCF-7/ADR by magnetic-activated cell sorting(MACS).We observed the growth characteristics of these stem cells through optical microscope and detected the growth-inhibitory effects of psoralen on breast cancer stem cells by CCK-8 assay and IC50 of adriamycin and adriamycin combined with psoralen to calculate the reversal index.The mRNA and protein expressions of Topo IIα were detected using RT-PCR and Western blot, respectively.Results Under the optical microscope, breast cancer stem cells presented spheres.IC10 and IC20 of psoralen on breast cancer stem cells were (6.77±0.23)μg/mL and (10.36±0.21)μg/mL.IC50 of adriamycin and adriamycin combined with psoralen on breast cancer stem cells was (90.03±3.56)μg/mL and (21.47±0.82)μg/mL, the reversal index was 4.19.Psoralen significantly raised the expressions of Topo Ⅱα at mRNA and protein levels.Conclusion Psoralen reversed the resistance of adriamycin by increasing the gene and protein expressions of breast cancer stem cells Topo Ⅱα and the drug targets.

Objective To evaluate the stability of U6 and let-7a as internal reference genes of miRNAs in RTqPCR by using femoral head samples of cartilage tissue from inbred DA rats.Methods Total RNA was extracted from femoral head cartilage tissues of female DA rats at three different time points,i.e.at birth (D0),ablactation (D21) and maturation (D42).The expressions of different miRNAs (miR-1,-25,-26a,-140,-146a,-150,-181a,-195,-223 and-337) were detected by RT-qPCR using U6 or let-7a as the internal reference.The two sets of miR expression were compared with the results from Solexa sequencing in our pioneer work to evaluate the stability of the two internal references.Results The relative values of U6 (P =0.045) and let-7a (P =0.021 5) revealed significant difference in the D42 sample.Both in U6 and let-7a systems,miR-26a,-140,-223,and-337 showed a similar tendency in expression and quantification but miR-1 and-146a did not have significant differences.miR-25,-150,-181a and-195 differed significantly (P＜0.05).Comparison of absolute quantification results between the two generations' sequencing showed that let-7a is more stable than U6.Conclusion Let-7a is more suitable to be used as the internal reference gene in RT-qPCR for miRNAs in cartilage tissue.

Objective To explore the correlation between blood pressure variability (BPV) and arterial elasticity in elderly patients with stroke.Methods One hundred and eighty-three stroke patients (121 with cerebral ischemic stroke and 62 with hemorrhagic stroke),admitted to our hospital from May 2014 to December 2016,and 61 non-stroke individuals were enrolled according to the inclusion and exclusion criterion.Carotid-radial pulse wave velocity (CrPWV) was calculated and carotid artery ultrasound was used to measure intima-media thickness (IMT);24 h ambulatory blood pressure monitoring was performed to calculate the BPV parameters.The arterial stiffness index β was calculated.Results As compared with those in the non-stroke subjects,morning blood pressure surge (MBPS),standard deviation of systolic blood pressure (SBP),variable coefficient of SBP,systolic blood pressure fall (SBPF),CrPWV,IMT and stiffness index β in stroke patients were significant increased (P＜0.05).Correlation analysis indicated that MBPS,standard deviation of SBP,weighted standard deviation of SBP,variance of SBP,variable coefficient of SBP,SBPF,and diastolic blood pressure fall were signficantly correlated with CrPWV and stiffness index β (P＜0.05);IMT was closely related to MBPS,standard deviation of SBP,variable coefficient of SBP,and SBPF (P＜0.05).Multivariate linear regression analysis showed that MBPS,standard deviation of SBP and SBPF were the independent influencing factors of IMT,CrPWV and stiffness index β5.Conclusion BPV showed significant relationship with vascular elasticity in stroke patients,and reduction of BPV helps to delay the process of atherosclerosis.

【Objective】 To explore the relationship between changes in the entorhinal cortex （EC） of traumatic brain injury （TBI） and cognitive function based on structural magnetic resonance imaging. 【Methods】 MRI was performed in 26 patients with clinically confirmed TBI after admission， and the Mini-mental State Examination （MMSE） was followed up 6 months later. The TBI patients were classified as mild TBI and moderate to severe TBI according to the post-traumatic Glasgow coma scale （GCS）. We compared the differences in age， gender， education level， hypertension， diabetes， TBI operation history， and follow-up MMSE between the two groups. Then the morphology， surface area， volume and thickness of the patient’s EC were evaluated using the visual score and Freesurfer software， and finally the correlation between EC parameters and MMSE was analyzed. 【Results】 The study included 12 cases of mild TBI and 14 cases of moderate to severe TBI. There were no statistical differences in age， gender， years of education， hypertension， diabetes or TBI operation history. However， the two groups differed significantly in follow-up MMSE. Visual evaluation showed statistical difference in the left EC scores. Structural MRI showed that the volume and thickness of left EC were statistically different between the two groups. The correlation analysis showed that there was a positive correlation between the thickness of left EC and MMSE （r=0.430， P<0.05）. 【Conclusion】 Entorhinal cortex atrophy after TBI is related to the severity of trauma， and it can reflect the long-term cognitive level of patients， which can be used as a noninvasive and reliable imaging marker for evaluating cognitive impairment after TBI.

Using machine learning algorithms to construct an early prediction model of brucellosis to improve the diagnosis efficiency of Brucellosis. This study was a case-control study. 2 381 brucellosis patients from Beijing Ditan Hospital affiliated to Capital Medical University were retrospectively collected as case group, and healthy people from Beijing Chaoyang Hospital affiliated to Capital Medical University were collected as control group from May 9, 2011 to November 29, 2021. The relevant clinical information and full blood count results of 13 257 data were collected and five algorithms of machine learning were used to construct an early predication model of brucellosis by using machine learning: random forest, Naive Bayes, decision tree, logistic regression and support vector machine;14 074 data (2 143 cases incase group and 11 931 cases in control group) were used to establish the early predication model of brucellosis, and 1 564 (238 cases in case group and 1 326 cases in control group) data were used to test the predication efficiency of the brucellosis model. The results showed that the support vector machine algorithm has the best predication performance by comparing the five machine learning models. The area under receiver curve (AUC) of receiver operating characteristic (ROC) was 0.991, and the accuracy, precision, specificity and Recall were 95.6%, 95.5%, 95.4% and 95.9%, respectively. Based on the SHAP plot, platelet distribution width (PDW) and basophil relative value (BASO%) results were low, and men with high coefficient of variation (R-CV), erythrocyte hemoglobin concentration (MCHC), and platelet volume (MPV) were predicted to be at high risk of brucellosis. Platelet distribution width (PDW) contributed the most to the prediction model, followed by red blood cell distribution width coefficient of variation (R-CV). In conclusion, the establishment of a high-precision early predication method of brucellosis based on machine learning may be of great significance for the early detection and treatment of brucellosis patients.
Male ; Humans ; Retrospective Studies ; Case-Control Studies ; Bayes Theorem ; Algorithms ; Machine Learning

Using machine learning algorithms to construct an early prediction model of brucellosis to improve the diagnosis efficiency of Brucellosis. This study was a case-control study. 2 381 brucellosis patients from Beijing Ditan Hospital affiliated to Capital Medical University were retrospectively collected as case group, and healthy people from Beijing Chaoyang Hospital affiliated to Capital Medical University were collected as control group from May 9, 2011 to November 29, 2021. The relevant clinical information and full blood count results of 13 257 data were collected and five algorithms of machine learning were used to construct an early predication model of brucellosis by using machine learning: random forest, Naive Bayes, decision tree, logistic regression and support vector machine;14 074 data (2 143 cases incase group and 11 931 cases in control group) were used to establish the early predication model of brucellosis, and 1 564 (238 cases in case group and 1 326 cases in control group) data were used to test the predication efficiency of the brucellosis model. The results showed that the support vector machine algorithm has the best predication performance by comparing the five machine learning models. The area under receiver curve (AUC) of receiver operating characteristic (ROC) was 0.991, and the accuracy, precision, specificity and Recall were 95.6%, 95.5%, 95.4% and 95.9%, respectively. Based on the SHAP plot, platelet distribution width (PDW) and basophil relative value (BASO%) results were low, and men with high coefficient of variation (R-CV), erythrocyte hemoglobin concentration (MCHC), and platelet volume (MPV) were predicted to be at high risk of brucellosis. Platelet distribution width (PDW) contributed the most to the prediction model, followed by red blood cell distribution width coefficient of variation (R-CV). In conclusion, the establishment of a high-precision early predication method of brucellosis based on machine learning may be of great significance for the early detection and treatment of brucellosis patients.
Male ; Humans ; Retrospective Studies ; Case-Control Studies ; Bayes Theorem ; Algorithms ; Machine Learning