Objective To study the effects of knock down midkine(MK)by siRNA on chemosinsitivity in bladder cancer cells. Methods Three MK siRNAs were designed and constructed. After transfected with MK siRNA or scrambled siRNA for different time, cultured cells were harvested to carry on the next experiments. Expression of MK was determined by real-time RT-PCR and western blot, and apoptosis were evaluated by caspase-3 activity and TUNEL assay. MTT was performed to examine the inhibition effect of Paclitaxel (PTX) on cells. Results MK siRNA could down-regulate the MK expression in a dose- and time-dependent manner. The MTT results showed that the inhibit rates were (18.21±0.36)%, (18.19±0.29)%, (17.89±0.33)%, (1.86±0.52)%, (32.56±0.53) %, (53. 83±0.38) % and (78. 95±0.55) % in different groups PTX alone(0.2μmol/L), ConA +PTX(0.2 μmol/L), Con-B +PTX(0.2 μmol/L), siRNA alone(12. 50 nmol/L), siRNA(3. 125nmol/L) + PTX (0. 2 μmol/L), siRNA (6. 25 nmol/L) + PTX (0. 2 μmol/L) and siRNA (12. 50nmol/L)+PTX(0.2 μmol/L), respectively. The TUNEL results showed that apoptosis index was (1.81 ±0. 36)%, (1. 89±0. 38)%, (5. 56±0. 58)%, (9. 68±0.55)% and (15. 25±0.56)% in different groups (Con-A, Con-B, siRNA (3. 125 nmol/L), siRNA (6. 25 nmol/L) and siRNA ( 12. 5nmol/L), respectively. The activity of caspase-3 increased significantly in transefected cells with a dose-and time-dependent manner. Conclusion MK siRNA could sensitize human bladder cancer cells to chemotherapy which might be through the apotosis.

Objective To study the therapeutic window of rapamycin(RPM) concentration in primary recipients of renal transplantation. Methods An open label, multi center study was performed. One hundred primary renal allograft recipients with cadaveric donors were enrolled from 4 transplantation centers in China. The immunosuppressive regimen was triple therapy,i.e.RPM combined with CsA and steroid. A loading dose of RPM 6 mg/d was administered within 48 hours after transplantation, then a maintaining dose of 2 mg/d was administered. The whole blood concentration of RPM was measured by HPLC method. Results The whole blood concentration of RPM in this group was (6.65?2.75)ng/ml, the 10th and 90th percentile for RPM concentration was 3 2 ng/ml and 10 26 ng/ml,respectively.9 5%(8/84)patients suffered from acute rejection during the 6 month period after transplantation in this study, and the concentration of RPM in these was lower than that in non rejection patients(P=0.001). Hyperlipidemia and liver dysfunction were the most frequently adverse events, and RPM concentration was significantly associated with the concentration of triglyceride. Conclusions 4～8 ng/ml is a suitable level for RPM concentration. Regular drug monitoring and reasonable dose modulation may increase the validity and security of RPM.

Objective:To develop early gastric cancer (EGC) detection system of magnifying blue laser imaging (ME-BLI) model and magnifying narrow-band imaging (ME-NBI) model based on deep convolutional neural network, to compare the performance differences of the two models and to explore the effects of training methods on the accuracy.Methods:The images of benign gastric lesions and EGC under ME-BLI and ME-NBI were respectively collected. A total of five data sets and three test sets were collected. Data set 1 included 2 024 noncancerous lesions and 452 EGC images under ME-BLI. Data set 2 included 2 024 noncancerous lesions and 452 EGC images under ME-NBI. Data set 3 was the combination of data set 1 and 2 (a total of 4 048 noncancerous lesions and 904 EGC images under ME-BLI and ME-NBI). Data set 4: on the basis of data set 2, another 62 noncancerous lesions and 2 305 EGC images under ME-NBI were added (2 086 noncancerous lesions and 2 757 EGC images under ME-NBI). Data set 5: on the basis of data set 3, another 62 noncancerous lesions and 2 305 EGC images under ME-NBI were added(4 110 noncancerous lesions and 3 209 EGC images under ME-NBI and ME-BLI). Test set A included 422 noncancerous lesions and 197 EGC images under ME-BLI. Test set B included 422 noncancerous lesions and 197 EGC images under ME-NBI. Test set C was the combination of test set A and B (844 noncancerous and 394 EGC images under ME-BLI and ME-NBI). Five models were constructed according to these five data sets respectively and their performance was evaluated in the three test sets. Per-lesion video was collected and used to compare the performance of deep convolutional neural network models under ME-BLI and ME-NBI for the detection of EGC in clinical environment, and compared with four senior endoscopy doctors. The primary endpoint was the diagnostic accuracy of EGG, sensitivity and specificity. Chi-square test was used for statistical analysis.Results:The performance of model 1 was the best in test set A with the accuracy, sensitivity and specificity of 76.90% (476/619), 63.96% (126/197) and 82.94% (350/422), respectively. The performance of model 2 was the best in test set B with the accuracy, sensitivity and specificity of 86.75% (537/619), 92.89% (183/197) and 83.89% (354/422), respectively. The performance of model 3 was the best in test set B with the accuracy, sensitivity and specificity of 86.91% (538/619), 84.26% (166/197) and 88.15% (372/422), respectively. The performance of model 4 was the best in test set B with the accuracy, sensitivity and specificity of 85.46% (529/619), 95.43% (188/197) and 80.81% (341/422), respectively. The performance of model 5 was the best in test set B, with the accuracy, sensitivity and specificity of 83.52% (517/619), 96.95% (191/197) and 77.25% (326/422), respectively. In terms of image recognition of EGC, the accuracy of models 2 to 5 was higher than that of model 1, and the differences were statistically significant ( χ2=147.90, 149.67, 134.20 and 115.30, all P<0.01). The sensitivity and specificity of models 2 and 3 were higher than those of model 1, the specificity of model 2 was lower than that of model 3, and the differences were statistically significant ( χ2=131.65, 64.15, 207.60, 262.03 and 96.73, all P < 0.01). The sensitivity of models 4 and 5 was higher than those of models 1 to 3, and the specificity of models 4 and 5 was lower than those of models 1 to 3, and the differences were statistically significant ( χ2=151.16, 165.49, 71.35, 112.47, 132.62, 153.14, 176.93, 74.62, 14.09, 15.47, 6.02 and 5.80, all P<0.05). The results of video test based on lesion showed that the average accuracy of doctors 1 to 4 was 68.16%. And the accuracy of models 1 to 5 was 69.47% (66/95), 69.47% (66/95), 70.53% (67/95), 76.84% (73/95) and 80.00% (76/95), respectively. There were no significant differences in the accuracy among models 1 to 5 and between models 1 to 5 and doctors 1 to 4 (all P>0.05). Conclusions:ME-BLI EGC recognition model based on deep learning has good accuracy, but the diagnostic effecacy is sligntly worse than that of ME-NBI model. The effects of EGC recognition model of ME-NBI combined with ME-BLI is better than that of a single model. A more sensitive ME-NBI model can be obtained by increasing the number of ME-NBI images, especially the images of EGG, but the specificity is worse.

Myelodysplastic syndrome(MDS)is a heterogeneous myeloid tumor that originates from hematopoietic stem cells(HSCs)and is associated with a high risk of progression to acute myeloid leukemia(AML).Studies have shown that 90%of patients with MDS have gene mutations,of whom approximately 25%have SF3B1 mutations.In patients with MDS carrying this mutation,the TGF-β pathway is upregu-lated,inducing cell cycle arrest and thereby leading to erythroid ineffective hematopoiesis and pathological hematopoiesis.Luspatercept can be used as a ligand trap to capture TGF-β ligands,inhibit SMAD2/3 pathway activation,downregulate TGF-β pathway,and promote ad-vanced red blood cell maturation.Currently,it has been approved by the Food and Drug Administration(FDA)for the treatment of anemia in patients with low-risk MDS,and studies have shown that the response rate is higher in patients with SF3B1 mutations.This article will re-view the current status of luspatercept in the treatment of SF3B1 mutation-related MDS;it will also analyze its effectiveness and safety and provide therapeutic strategies for clinical use.

Objective:To compare the ability of deep convolutional neural network-crop (DCNN-C) and deep convolutional neural network-whole (DCNN-W), 2 artificial intelligence systems based on different training methods to dignose early gastric cancer (EGC) diagnosis under magnifying image-enhanced endoscopy (M-IEE).Methods:The images and video clips of EGC and non-cancerous lesions under M-IEE under narrow band imaging or blue laser imaging mode were retrospectively collected in the Endoscopy Center of Renmin Hospital of Wuhan University, for the training set and test set for DCNN-C and DCNN-W. The ability of DCNN-C and DCNN-W in EGC identity in image test set were compared. The ability of DCNN-C, DCNN-W and 3 senior endoscopists (average performance) in EGC identity in video test set were also compared. Paired Chi-squared test and Chi-squared test were used for statistical analysis. Inter-observer agreement was expressed as Cohen′s Kappa statistical coefficient (Kappa value).Results:In the image test set, the accuracy, sensitivity, specificity and positive predictive value of DCNN-C in EGC diagnosis were 94.97%(1 133/1 193), 97.12% (202/208), 94.52% (931/985), and 78.91%(202/256), respectively, which were higher than those of DCNN-W(86.84%, 1 036/1 193; 92.79%, 193/208; 85.58%, 843/985 and 57.61%, 193/335), and the differences were statistically significant ( χ2=4.82, 4.63, 61.04 and 29.69, P=0.028, =0.035, <0.001 and <0.001). In the video test set, the accuracy, specificity and positive predictive value of senior endoscopists in EGC diagnosis were 67.67%, 60.42%, and 53.37%, respectively, which were lower than those of DCNN-C (93.00%, 92.19% and 87.18%), and the differences were statistically significant ( χ2=20.83, 16.41 and 11.61, P<0.001, <0.001 and =0.001). The accuracy, specificity and positive predictive value of DCNN-C in EGC diagnosis were higher than those of DCNN-W (79.00%, 70.31% and 64.15%, respectively), and the differences were statistically significant ( χ2=7.04, 8.45 and 6.18, P=0.007, 0.003 and 0.013). There were no significant differences in accuracy, specificity and positive predictive value between senior endoscopists and DCNN-W in EGC diagnosis (all P>0.05). The sensitivity of senior endoscopists, DCNN-W and DCNN-C in EGC diagnosis were 80.56%, 94.44%, and 94.44%, respectively, and the differences were not statistically significant (all P>0.05). The results of the agreement analysis showed that the agreement between senior endoscopists and the gold standard was fair to moderate (Kappa=0.259, 0.532, 0.329), the agreement between DCNN-W and the gold standard was moderate (Kappa=0.587), and the agreement between DCNN-C and the gold standard was very high (Kappa=0.851). Conclusion:When the training set is the same, the ability of DCNN-C in EGC diagnosis is better than that of DCNN-W and senior endoscopists, and the diagnostic level of DCNN-W is equivalent to that of senior endoscopists.

OBJECTIVE To analyze the efficacy and safety of luspatercept in the treatment of myelodysplastic syndromes （MDS） anemia， and provide reference for clinical medication. METHODS The literature related to luspatercept for MDS anemia in PubMed， Cochrane Library， Embase and Web of Science were searched by computer， and the search time was from the establishment of the database to January 2024. The quality of literature was evaluated after they were screened according to inclusion and exclusion criteria， the single-group rate meta-analysis and sensitivity analysis were performed by using RevMan 5.4 software， and the subgroup analysis was conducted. RESULTS A total of 756 patients in 9 articles were included in this study. The results of meta-analysis showed that the proportion of MDS patients who reached ≥8 weeks of red blood cell transfusion independence （RBC-TI） was 46% after using luspatercept [95%CI （0.28， 0.64）， P＜0.000 01]. The proportion of MDS patients whose hematological improvement in erythrocyte （HI-E） was 59% [95%CI （0.43， 0.74）， P＜0.000 01]. Among them， 5 articles reported that the proportion of MDS patients with grade 3-4 adverse reactions was 14% [95%CI （0.07， 0.22）， P＝0.000 2]， and the poor general condition， infection， blood and lymphatic system disease were the common adverse reactions. Subgroup analysis showed that the source of heterogeneity was the blood transfusion burden in the proportion of MDS patients with RBC-TI≥8 weeks， and the source of heterogeneity was the 0931-8356251。revised international prognostic scoring system （IPSS-R） risk grade， SF3B1 mutation status and blood transfusion burden in the proportion of MDS patients with HI-E. Sensitivity analysis showed that the results of this study were stable. CONCLUSIONS Luspatercept can significantly improve blood transfusion dependence， reduce blood transfusion burden and promote hematology improvement in MDS patients. But attention should be paid to the occurrence of grade 3-4 adverse events； adverse events such as poor general condition， infection， blood and lymphatic system diseases are more common.