Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To constructCSF1R+/-mice and to analyze their genotypes, so as to provide animal model basis for disease pathological mechanism and drug target. Methods : A linearized targeting vector was designed according to Cre/Loxp system. A Loxp site was inserted upstream of the 5th exon of theCSF1Rgene, and a neomycin resistance box with Loxp sites on both sides was inserted downstream of the 5th exon. The linearized targeting vector was electroporated into embryonic stem cells. The correctly targeted embryonic stem cells were injected into the blastocysts of C57BL/6J mice to obtain chimeric mice, which were bred with Zp3-Cre mice. The newborn mice were numbered 9-14 days after birth and their tails were cut. The DNA of the mice was extracted, and the genotype of the mice was identified by polymerase chain reaction and agarose gel electrophoresis. The expression of CSF1R in mouse macrophages was detected by flow cytometry. The expression of CSF1R in mouse tissues was detected by Western blot. Results: The results of agarose gel electrophoresis showed that 453 bp bands were amplified in wild type mice, and 453 bp and 650 bp bands were amplified in heterozygous mice. The results of flow cytometry showed that the expression of CSF1R in peritoneal macrophages and bone marrow-derived macrophages of CSF1R heterozygous mice was lower than that of WT group(P<0.05). The results of Western blot showed that the expression of CSF1R in spleen, kidney and brain tissue of CSF1R heterozygous group was lower than that of WT group(P<0.05). Conclusion CSF1R+/-mice are successfully constructed, reproduced and identified, which provides an animal model basis for further revealing the potential mechanism of CSF1R in immune regulation.

Existing epidemiologic and clinical studies have demonstrated that obesity is associated with the risk of a variety of cancers. In recent years, an increasing number of experimental and clinical studies have unraveled the complex relationship between obesity and cancer risk and the underlying mechanisms. Obesity-induced abnormalities in immunity and biochemical metabolism, including chronic inflammation, hormonal disorders, dysregulation of adipokines, and microbial dysbiosis, may be important contributors to cancer development and progression. These contributors play different roles in cancer development and progression at different sites. Lifestyle changes, weight loss medications, and bariatric surgery are key approaches for weight-centered, obesity-related cancer prevention. Treatment of obesity-related inflammation and hormonal or metabolic dysregulation with medications has also shown promise in preventing obesity-related cancers. In this review, we summarize the mechanisms through which obesity affects the risk of cancer at different sites and explore intervention strategies for the prevention of obesity-associated cancers, concluding with unresolved questions and future directions regarding the link between obesity and cancer. The aim is to provide valuable theoretical foundations and insights for the in-depth exploration of the complex relationship between obesity and cancer risk and its clinical applications.
Humans ; Adipokines/metabolism* ; Bariatric Surgery ; Inflammation/therapy* ; Neoplasms/prevention & control* ; Obesity/therapy* ; Risk Factors

Objective:To analyze the clinical features of patients with COVID-19 in Chongqing Municipality.Methods:The clinical data, laboratory tests and chest imaging findings of 153 patients COVID-19 admitted in Chongqing Public Health Medical Center from January 26 to February 5, 2020 were retrospectively reviewed. According to the relevant diagnostic criteria, patients were divided into non-severe group (n=132) and severe group (n=21). The correlation between serum index changes and disease severity was analyzed.Results:The proportion of patients with underlying diabetes or chronic respiratory diseases in severe group was significantly higher than that in non-severe group ( χ2=11.04 and 6.94, P<0.05). The proportion of symptom-free patients in non-severe group was significantly higher than that in severe group ( χ2=4.09, P<0.05). The symptoms of fever, fatigue and muscle soreness in the severe group were more common than those in the non-severe group ( χ2=4.40, 14.42 and 22.67, P<0.05). Among the concomitant symptoms, the proportion of cough and shortness of breath in the severe group was higher than that in the non-severe group ( χ2=8.46 and 4.80, P<0.05). C-reactive protein and D-Dimer levels were higher in the severe group than those in the non-severe group ( Z=-4.39 and -1.96, P<0.05), and the number of CD3 + T lymphocyte cells, CD4 + T lymphocyte cells and CD8 + T lymphocyte cells in the severe group was lower than that in the non-severe group ( Z=27.25, 20.60 and 17.36, P<0.05). Compared with the non-severe group, both lungs and the right lung lower lobe were more susceptible to be involved( χ2=9.71和23.61, P<0.05). Conclusions:There are significant differences in underlying diseases, clinical symptoms, imaging manifestations and laboratory findings between severe and non-severe patients with COVID-19.

Objective: To analyze the clinical data of 153 patients with novel coronavirus pneumonia (COVID-19) in chongqing ,and provide reference and thinking for the diagnosis and treatment. Methods: Analyze the clinical data, laboratory examination and chest imaging characteristics of 153 COVID-19 patients in Chongqing Public Health Medical Center from January 26 to February 5, 2020. According to the relevant diagnostic criteria ,patients were divided into non-severe group(n=132) and severe group(n=21),and analyze the correlation between serum index changes and disease severity. Results: Combined with diabetes and chronic respiratory diseases, the severity of the disease was statistically significant (χ²=11.04和6.94, P<0.05). No symptoms were found in patients with mild illness (χ²=4.09, P<0.05) .The proportion of fever and muscle soreness in the severe group was higher than that in the non-severe group (χ²=4.40 and 22.67,P<0.05).Among the concomitant symptoms, the proportion of cough and shortness of breath in the severe group was higher than that in the non-severe group (χ²=8.46 and 4.80,P<0.05).C-reactive protein and d-dimer were higher in the severe group than in the non-severe group (t=43.44 and 37.13, P<0.05), and the number of CD₃⁺T lymphocyte cells, CD₄⁺T lymphocyte cells and CD₈⁺T lymphocyte cells in the severe group was lower than that in the non-severe group (Z=27.25, 20.60 and 17.36, P<0.05).Compared with the non-severe group, both lungs and the right lung lower lobe were more susceptible to involved (χ²=6.95和20.39, P<0.05) . Conclusion Severity of COVID-19 was associated with underlying disease, symptoms, site of involvement, C-reactive protein, d-dimer, CD₃⁺T lymphocyte count, CD₄⁺T lymphocyte count, and CD₈⁺T lymphocyte count. 

OBJECTIVE： To study the chemical constituents of ethyl acetate fraction of Panax ginseng fungal substance obtained by biotransformation， in order to obtain compounds with better activity and lower toxicity， and to provide reference for new drug R&D and the second development and utilization of P. ginseng. METHODS： Fungus of Code Name C-1 seed solution was added into the culture medium containing P. ginseng， and P. ginseng fungal substance was obtained by biotransformation； the dried P. ginseng fungal substance were weighed， extracting with 70％ ethanol solvent and concentrating to obtain thick paste. The thick paste was added with water suspension and extracted with ethyl acetate to obtain ethyl acetate fraction. TLC， silica gel column chromatography， ODS column chromatography and semi-prepared liquid phase were used to isolate and purify above ethyl acetate fraction， and the compound structure was identified according to physicochemical properties， hydrogen spectrum （1H-NMR） and carbon spectrum （13C-NMR） data. RESULTS： Eight compounds were isolated and identified from the ethyl acetate fraction of P. ginseng fungal substance and identified as ginsenoside Rs7 （1）， ginsenoside Rk3 （2）， oleanolic acid-28-O-β-D-glucopyranoside （3）， ginsenoside Rs6 （4）， 20（R）-ginsenoside Rh1 （5）， ginsenoside F1 （6）， notoginsenoside R2 （7） and ginsenoside F4 （8）. CONCLUSIONS： All the above compounds were found in P. ginseng fungal substance， which compounds 3， 5， 6， 7 and 8 were obtained after biotransformation， proving that biotransformation technology can change the chemical composition of ginseng.