Objective:To investigate the relationship between thoracic aortic calcification (TAC) and autonomic nervous system (ANS) function in patients receiving continuous ambulatory peritoneal dialysis (CAPD).Methods:It was a cross-sectional study. The CAPD patients with dialysis duration >6 months between January and December 2022 were retrospectively enrolled. The baseline clinical data, heart rate variability (HRV) data such as standard deviation of all normal to normal intervals (SDNN), root mean square of successive differences between adjacent normal-to-normal intervals (RMSSD), high frequency (HF), very low frequency (VLF), low frequency (LF), LF/HF, acceleration capacity (AC) and deceleration capacity (DC), and skin sympathetic nerve activity (SKNA) were collected. TAC was defined as TAC score (TACS) >100 AU. The patients were divided into TACS >100 AU group and TACS≤100 AU group based on whether the thoracic aorta was calcified. The differences of those data between the two groups were compared. Logistic regression model was used to analyze the related factors of TAC. Spearman correlation analysis method was used to analyze the correlation between peripheral blood neuropeptide Y, ANS parameters, average amplitude SKNA (aSKNA) and TACS.　Cox regression model was used to analyze the risk factors of all-cause mortality in patients with CAPD.Results:The study included 106 CAPD patients with 50 males (47.2%), age of (46.04±11.10) years and dialysis duration of (41.55±30.52) months. TACS>100 AU group exhibited significantly lower heart rate ( t=2.015, P=0.046), DC ( t=2.131, P=0.035), LF/HF ( Z=3.332, P<0.001) and ln(LF/HF) ( t=3.326, P=0.001), and higher AC ( t=-2.392, P=0.019) than TACS≤100 AU group. Multivariate logistic regression analysis results showed that after adjusting for age and eosinophil count, lnVLF ( OR=0.66, 95% CI 0.45-0.98, P=0.038), lnLF ( OR=0.69, 95% CI 0.49-0.97, P=0.032), DC ( OR=0.79, 95% CI 0.64-0.99, P=0.039) and AC ( OR=1.32, 95% CI 1.04-1.68, P=0.021) were independently correlated with the risk of TAC. Spearman correlation analysis showed that neuropeptide Y level in peripheral blood was correlated with aSKNA ( r=0.23, P=0.017), lnSDNN ( r=-0.20, P=0.036) and TACS ( r=0.19, P=0.048). During the follow-up period of (25.8±4.2) months, 5 patients (4.72%) died, including 1 patient in the TACS≤100 AU group and 4 patients in the TACS>100 AU group. Compared with the survival group, the death group had higher TACS ( Z=-2.262, P=0.024) and lower LF/HF ( Z=-2.750, P=0.006). Cox regression analysis results showed that increased ln(LF/HF) was an independent influencing factor for all-cause mortality in CAPD patients ( HR=0.22, 95% CI 0.05-0.83, P=0.026). Conclusions:HRV parameters (lnVLF, lnLF, AC and DC) of CAPD patients are independently associated with TAC. The dysfunction of ANS in CAPD patients (especially the decreased vagus nerve activity) may promote TAC.

Objective:To explore the construction and validation of a nomogram model for predicting poor survival prognosis in patients with clear cell renal cell carcinoma（ccRCC）based on deep learning of pathological images.Methods:This study was an observational cohort study. The original pathological images and clinicopathological data（TCGA cohort）of 378 patients with ccRCC were obtained from the Cancer Genome Atlas Database（TCGA）for model training. A total of 301 patients with ccRCC who underwent surgical treatment at Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2010 to December 2020（Renji cohort）and 214 patients with ccRCC who underwent surgical treatment at the First People’s Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2012 to December 2018（General cohort）were included for model validation. Their original pathological images and clinical pathological data were collected. A clustering-constrained attention and multi-instance learning method was used to accurately identify sub-regions of the images to classify and extract features of the pathological images. A deep learning-based disease-free survival prognosis prediction model（DL-DFS）was constructed through a weakly supervised learning strategy. The clinical pathological features and DL-DFS were further combined to construct a nomogram model for the clinical prognosis of ccRCC patients. Univariate and multivariate Cox regression analyses were employed to evaluate the independent risk factors for disease-free survival（DFS）. The efficacy of the predictive model were evaluated by the receiver operating characteristic curve（ROC）with area under the curve（AUC），respectively. Survival analysis was conducted using the Kaplan-Meier curve.Results:DL-DFS could accurately predict the DFS status of ccRCC patients in 5 years after surgery. Through ROC analysis in the training cohort，the AUC value reached 0.75（ P < 0.001）. In the Renji cohort and the General cohort，the AUC values were 0.65（ P < 0.001）and 0.81（ P < 0.001），respectively. Through Kaplan-Meier survival analysis，we found that DL-DFS could identify ccRCC patients with high survival risks. The hazard ratio in the training cohort was 3.86（95% CI 2.36-6.30， P < 0.001）. The hazard ratio in the Renji cohort and General cohort were 1.97（95% CI 1.03-3.80， P = 0.009）and 4.66（95% CI 1.80-12.06， P = 0.008），respectively. Univariate and multivariate Cox regression analyses indicated that DL-DFS risk score，tumor grade，and tumor stage could act as prognostic risk factors for patients with ccRCC（ P < 0.05）. Considering that age was a common prognostic risk factor for patients with renal cancer，a nomogram model was constructed by combining the DL-DFS risk score with patient age，tumor grade，and tumor stage. The AUC of this model for predicting the 5-year DFS of ccRCC patients after surgery was 0.87，which was significantly higher than that of DL-DFS（AUC = 0.74），tumor stage（AUC = 0.84），tumor grade（AUC = 0.72），and patient age（AUC = 0.56）in the TCGA cohort（all P<0.05）. In the Renji cohort and the General cohort，the AUC of the nomogram model were 0.78 and 0.86 respectively，which was significantly higher than that of DL-DFS（0.65 and 0.81），tumor stage（0.72 and 0.69），tumor grade（0.64 and 0.77），and patient age（0.56 and 0.63）. Conclusions:In this study a DL-DFS for ccRCC patients was constructed. Then a nomogram model was constructed by combining the DL-DFS risk value with patient age，tumor grade，and tumor stage. This nomogram model demonstrated superior predictive performance compared to DL-DFS alone in evaluating the DFS prognosis of ccRCC patients，which still needs to be further verified in prospective clinical studies.

Objective:To investigate the relationship between thoracic aortic calcification (TAC) and autonomic nervous system (ANS) function in patients receiving continuous ambulatory peritoneal dialysis (CAPD).Methods:It was a cross-sectional study. The CAPD patients with dialysis duration >6 months between January and December 2022 were retrospectively enrolled. The baseline clinical data, heart rate variability (HRV) data such as standard deviation of all normal to normal intervals (SDNN), root mean square of successive differences between adjacent normal-to-normal intervals (RMSSD), high frequency (HF), very low frequency (VLF), low frequency (LF), LF/HF, acceleration capacity (AC) and deceleration capacity (DC), and skin sympathetic nerve activity (SKNA) were collected. TAC was defined as TAC score (TACS) >100 AU. The patients were divided into TACS >100 AU group and TACS≤100 AU group based on whether the thoracic aorta was calcified. The differences of those data between the two groups were compared. Logistic regression model was used to analyze the related factors of TAC. Spearman correlation analysis method was used to analyze the correlation between peripheral blood neuropeptide Y, ANS parameters, average amplitude SKNA (aSKNA) and TACS.　Cox regression model was used to analyze the risk factors of all-cause mortality in patients with CAPD.Results:The study included 106 CAPD patients with 50 males (47.2%), age of (46.04±11.10) years and dialysis duration of (41.55±30.52) months. TACS>100 AU group exhibited significantly lower heart rate ( t=2.015, P=0.046), DC ( t=2.131, P=0.035), LF/HF ( Z=3.332, P<0.001) and ln(LF/HF) ( t=3.326, P=0.001), and higher AC ( t=-2.392, P=0.019) than TACS≤100 AU group. Multivariate logistic regression analysis results showed that after adjusting for age and eosinophil count, lnVLF ( OR=0.66, 95% CI 0.45-0.98, P=0.038), lnLF ( OR=0.69, 95% CI 0.49-0.97, P=0.032), DC ( OR=0.79, 95% CI 0.64-0.99, P=0.039) and AC ( OR=1.32, 95% CI 1.04-1.68, P=0.021) were independently correlated with the risk of TAC. Spearman correlation analysis showed that neuropeptide Y level in peripheral blood was correlated with aSKNA ( r=0.23, P=0.017), lnSDNN ( r=-0.20, P=0.036) and TACS ( r=0.19, P=0.048). During the follow-up period of (25.8±4.2) months, 5 patients (4.72%) died, including 1 patient in the TACS≤100 AU group and 4 patients in the TACS>100 AU group. Compared with the survival group, the death group had higher TACS ( Z=-2.262, P=0.024) and lower LF/HF ( Z=-2.750, P=0.006). Cox regression analysis results showed that increased ln(LF/HF) was an independent influencing factor for all-cause mortality in CAPD patients ( HR=0.22, 95% CI 0.05-0.83, P=0.026). Conclusions:HRV parameters (lnVLF, lnLF, AC and DC) of CAPD patients are independently associated with TAC. The dysfunction of ANS in CAPD patients (especially the decreased vagus nerve activity) may promote TAC.

Objective:To explore the construction and validation of a nomogram model for predicting poor survival prognosis in patients with clear cell renal cell carcinoma（ccRCC）based on deep learning of pathological images.Methods:This study was an observational cohort study. The original pathological images and clinicopathological data（TCGA cohort）of 378 patients with ccRCC were obtained from the Cancer Genome Atlas Database（TCGA）for model training. A total of 301 patients with ccRCC who underwent surgical treatment at Renji Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2010 to December 2020（Renji cohort）and 214 patients with ccRCC who underwent surgical treatment at the First People’s Hospital Affiliated to Shanghai Jiaotong University School of Medicine from January 2012 to December 2018（General cohort）were included for model validation. Their original pathological images and clinical pathological data were collected. A clustering-constrained attention and multi-instance learning method was used to accurately identify sub-regions of the images to classify and extract features of the pathological images. A deep learning-based disease-free survival prognosis prediction model（DL-DFS）was constructed through a weakly supervised learning strategy. The clinical pathological features and DL-DFS were further combined to construct a nomogram model for the clinical prognosis of ccRCC patients. Univariate and multivariate Cox regression analyses were employed to evaluate the independent risk factors for disease-free survival（DFS）. The efficacy of the predictive model were evaluated by the receiver operating characteristic curve（ROC）with area under the curve（AUC），respectively. Survival analysis was conducted using the Kaplan-Meier curve.Results:DL-DFS could accurately predict the DFS status of ccRCC patients in 5 years after surgery. Through ROC analysis in the training cohort，the AUC value reached 0.75（ P < 0.001）. In the Renji cohort and the General cohort，the AUC values were 0.65（ P < 0.001）and 0.81（ P < 0.001），respectively. Through Kaplan-Meier survival analysis，we found that DL-DFS could identify ccRCC patients with high survival risks. The hazard ratio in the training cohort was 3.86（95% CI 2.36-6.30， P < 0.001）. The hazard ratio in the Renji cohort and General cohort were 1.97（95% CI 1.03-3.80， P = 0.009）and 4.66（95% CI 1.80-12.06， P = 0.008），respectively. Univariate and multivariate Cox regression analyses indicated that DL-DFS risk score，tumor grade，and tumor stage could act as prognostic risk factors for patients with ccRCC（ P < 0.05）. Considering that age was a common prognostic risk factor for patients with renal cancer，a nomogram model was constructed by combining the DL-DFS risk score with patient age，tumor grade，and tumor stage. The AUC of this model for predicting the 5-year DFS of ccRCC patients after surgery was 0.87，which was significantly higher than that of DL-DFS（AUC = 0.74），tumor stage（AUC = 0.84），tumor grade（AUC = 0.72），and patient age（AUC = 0.56）in the TCGA cohort（all P<0.05）. In the Renji cohort and the General cohort，the AUC of the nomogram model were 0.78 and 0.86 respectively，which was significantly higher than that of DL-DFS（0.65 and 0.81），tumor stage（0.72 and 0.69），tumor grade（0.64 and 0.77），and patient age（0.56 and 0.63）. Conclusions:In this study a DL-DFS for ccRCC patients was constructed. Then a nomogram model was constructed by combining the DL-DFS risk value with patient age，tumor grade，and tumor stage. This nomogram model demonstrated superior predictive performance compared to DL-DFS alone in evaluating the DFS prognosis of ccRCC patients，which still needs to be further verified in prospective clinical studies.

Objective:To investigate the epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in patients with maintenance hemodialysis (MHD) in Jiangsu province during SARS-CoV-2 pandemic in China from December 7, 2022 to January 27, 2023, and to analyze the influencing factors of all-cause death.Methods:It was a multi-center cross-sectional investigation. Structured questionnaire was used to collect patient information by medical staff of each hemodialysis center (room) as investigators. Part of the demography data and laboratory examination data came from the Jiangsu Province Hemodialysis Data Information System. MHD patients from hemodialysis centers (rooms) at all levels of medical institutions and independent hemodialysis institutions in Jiangsu province during the outbreak of SARS-CoV-2 infection were included, and the clinical characteristics and all-cause mortality of confirmed and suspected cases of SARS-CoV-2 infection were analyzed.Results:Questionnaire surveys and data analysis on 57 278 patients in 407 hemodialysis centers (rooms) were completed, accounting for 90.41% of the total number of MHD patients (63 357 cases) in Jiangsu province during the same period. There were 24 038 cases (41.97%) of SARS-CoV-2 infection and 14 805 cases (25.85%) of suspected infection, which were widely distributed in all dialysis centers in Jiangsu province. After clinical classification of 38 843 confirmed and suspected SARS-CoV-2 infection cases, 3 662 cases were severe and critical cases, accounting for 9.43% of the infected and suspected cases. Among the patients who had completed the questionnaires, there were 1 812 all-cause deaths, with an all-cause mortality rate of 3.16%. Multivariate logistic regression analysis showed that elderly (taking ≤50 years as a reference, 51-59 years: OR=1.583, 95% CI 1.279-1.933, P=0.001; 60-69 years: OR=3.972, 95% CI 3.271-4.858, P<0.001; 70-79 years: OR=7.236, 95% CI 5.917-8.698, P<0.001; ≥80 years: OR=11.738, 95% CI 9.459-14.663, P<0.001), male ( OR=1.371, 95% CI 1.229-1.529, P<0.001), and co-infection with hepatitis B virus (HBV) (positive serum HBV surface antigen, OR=0.629, 95% CI 0.484-0.817, P<0.001) were independent influencing factors for all cause mortality. Receiver-operating characteristic curve analysis showed that the area under the curve for male, age and current HBV infection prediction of all-cause death was 0.529 ( P<0.001), 0.724 ( P<0.001) and 0.514 ( P=0.042), respectively, and the cut-off value for age prediction of all-cause death was 65.5 years old. Compared with patients without HBV infection, MHD patients with HBV infection significantly reduced the proportion of severe and critically ill patients, all-cause hospitalizations and all cause deaths when infected with SARS-CoV-2 (4.99% vs. 6.41%, χ2=6.136, P=0.013; 8.90% vs. 11.44%, χ2=11.662, P<0.001; 2.01% vs. 3.37%, χ2=10.713, P=0.001, respectively). Conclusion:The MHD patients in Jiangsu province are susceptible to SARS-CoV-2. Elderly age and male gender are independent risk factors for death in MHD patients during the epidemic, while the HBV infection may be a protective factor for death of MHD patients infected with SARS-CoV-2.

Objective:To develop an air particulate protective mask filter test device that can simulate the dynamics process of human breathing.Methods:The new device used two air pumps working alternately to simulate the dynamics process of human breathing. On March 4th to 17th, 2017, the new device and the traditional one-way airflow mask filtration test device were used to measure the internal and external particle levels of 39 masks of 13 models of 6 brands, and then the filtration efficiency of the mask was calculated and the test results were compared.Results:For the mask without breathing valve, there was no statistically significant difference between the filter efficiency test results of the new device and the traditional unidirectional airflow filter performance test device ( P>0.05) . For masks with breathing valves, the new device detected that three of them had lower filtration efficiency (99.50% vs 98.63%, P<0.01) . After sealing the mask breathing valve with glue, the filtering efficiency of the mask with a breathing valve detected by the new device significantly improved (98.63% vs 99.50%, P<0.01) . Conclusion:This new device can simulate the dynamic process of human exhalation and inhalation, and measure the filtration efficiency of the mask. For masks with breathing valves, the new device makes it easier to detect the decrease in the filtering efficiency of the mask caused by the breathing valve.

Objective:To develop an air particulate protective mask filter test device that can simulate the dynamics process of human breathing.Methods:The new device used two air pumps working alternately to simulate the dynamics process of human breathing. On March 4th to 17th, 2017, the new device and the traditional one-way airflow mask filtration test device were used to measure the internal and external particle levels of 39 masks of 13 models of 6 brands, and then the filtration efficiency of the mask was calculated and the test results were compared.Results:For the mask without breathing valve, there was no statistically significant difference between the filter efficiency test results of the new device and the traditional unidirectional airflow filter performance test device ( P>0.05) . For masks with breathing valves, the new device detected that three of them had lower filtration efficiency (99.50% vs 98.63%, P<0.01) . After sealing the mask breathing valve with glue, the filtering efficiency of the mask with a breathing valve detected by the new device significantly improved (98.63% vs 99.50%, P<0.01) . Conclusion:This new device can simulate the dynamic process of human exhalation and inhalation, and measure the filtration efficiency of the mask. For masks with breathing valves, the new device makes it easier to detect the decrease in the filtering efficiency of the mask caused by the breathing valve.

OBJECTIVETo correlate the clinical features of patients with acute myeloid leukemia (AML) with mutations of FLT3-ITD, NPM1, CEBPA, c-KIT, DNMT3A and ND4 genes as well as chromosomal aberrations.

METHODSSomatic mutations of aforementioned genes in 412 newly diagnosed AML patients were detected with PCR and direct sequencing. All patients were also subjected to R-banding chromosomal analysis. The results were correlated with the clinical features and prognosis of the patients.

RESULTSThe mutation rates of FLT3-ITD, NPM1, CEBPA, c-KIT, DNMT3A and ND4 were 9.0% (26/289), 19.1% (50/262), 18.9% (34/180), 3.4% (7/208), 6.6% (9/137) and 6.9% (4/58), respectively. Patients with poor prognosis based on genetic mutations had lower blood platelet count than those with intermediate and good prognosis (P=0.001 and P=0.001, respectively). None of the three groups attained median overall survival (OS) (P> 0.05). The complete remission (CR) was similar among the three groups (P> 0.05). For patients with different prognosis based on cytogenetic findings, white blood cell count in those with intermediate prognosis was higher than those with good and poor prognosis (P< 0.001 and P=0.004, respectively), while the blood platelet count of the intermediate group was higher than that of the group with good prognosis (P=0.018). No significant difference was found among the three groups in terms of hemoglobin level (P> 0.05). The group with poor prognosis has attained shorter OS compared with those with good and intermediate prognosis (P< 0.001 and P=0.003, respectively). However, the CR rate of the group with good prognosis was higher than that of the intermediate group (P=0.001). For the group with intermediate prognosis, presence of genetic mutations did not correlate with the clinic characteristics such as white blood cell count, blood platelet count, hemoglobin level, OS and CR rate (P> 0.05 for all comparisons).

CONCLUSIONGenetic mutations combined with cytogenetic analysis can facilitate the prognosis and personalized treatment for patients with AML.

Adolescent ; Adult ; Aged ; Aged, 80 and over ; Child ; Child, Preschool ; Female ; Humans ; Infant ; Leukemia, Myeloid, Acute ; genetics ; mortality ; Male ; Middle Aged ; Mutation ; Prognosis ; Young Adult

Chronic myeloid leukemia (CML) is a myeloproliferative disease characterized by the overproduction of granulocytes, which leads to high white blood cell counts and splenomegaly in patients. Based on clinical symptoms and laboratory findings, CML is classified into three clinical phases, often starting with a chronic phase, progressing to an accelerated phase and ultimately ending in a terminal phase called blast crisis. Blast crisis phase of CML is clinically similar to an acute leukemia; in particular, B-cell acute lymphoblastic leukemia (B-ALL) is a severe form of acute leukemia in blast crisis, and there is no effective therapy for it yet. CML is induced by the BCR-ABL oncogene, whose gene product is a BCR-ABL tyrosine kinase. Currently, inhibition of BCR-ABL kinase activity by its kinase inhibitor such as imatinib mesylate (Gleevec) is a major therapeutic strategy for CML. However, the inability of BCR-ABL kinase inhibitors to completely kill leukemia stem cells (LSCs) indicates that these kinase inhibitors are unlikely to cure CML. In addition, drug resistance due to the development of BCRABL mutations occurs before and during treatment of CML with kinase inhibitors. A critical issue to resolve this problem is to fully understand the biology of LSCs, and to identify key genes that play significant roles in survival and self-renewal of LSCs. In this review, we will focus on LSCs in CML by summarizing and discussing available experimental results, including the original studies from our own laboratory.
5-Lipoxygenase-Activating Proteins ; metabolism ; Animals ; Benzamides ; Disease Models, Animal ; Fusion Proteins, bcr-abl ; antagonists & inhibitors ; chemistry ; metabolism ; Humans ; Imatinib Mesylate ; Leukemia, Myelogenous, Chronic, BCR-ABL Positive ; drug therapy ; enzymology ; genetics ; pathology ; Male ; Mice ; Neoplastic Stem Cells ; enzymology ; pathology ; PTEN Phosphohydrolase ; metabolism ; Philadelphia Chromosome ; Piperazines ; therapeutic use ; Point Mutation ; Protein Structure, Tertiary ; Protein-Tyrosine Kinases ; antagonists & inhibitors ; chemistry ; metabolism ; Pyrimidines ; therapeutic use

Objectire:To explore and evaluate the predictors for the prognosis of acute besilar artery occlusion(BAO)and the clinical efficacy of intra-arterial thrombolysis and stenting for BAO.Methods:Intra-arterial thrombolysis was administered with recombinant tissue plasminogen activator or urokinase in 52 patients with BAO within 3 to 48 hours.Stenting was performed in patients whose partial recanalization of residual stenosis＞50%after the thrombolysis.National Institutes of Health Stroke Scale(NIHSS)score was measured before the procedure,and the modifled Rankin scale(mRS)score was obtained at 3 months after the procedure.The clinical data were evaluated with the multivariable stepwise logistic regression analysis and Fisher's exact test.Results:Complete recanalization achieved in 24 patients(46.2%),partial recanalization in 16 patients(30.7%),and non-recanalization in 12 patients(23.1%).mRS scores:22 patients(42.3%)had a favorable outcome,32 survived(61.5%),and 20 died (38.5%).The prognosis of BAO was significantly correlated with the NIHSS score(P＜0.01),therapeutic time window (P ＜0.05) and recanalization level (after the thrombolysis and stenting)(P＜0.01 );the good recanalization after the intra-arterial thrombolysis was significantly correlated with the NIHSS score (P ＜0.01) and therapeutic time window (P ＜0.05).Multivariate analysis showed that the NIHSS score ＜ 14 (P ＜ 0.01 ) and good recanalization could independently predict the favorable prognosis of BAO.Conclusions:The NIHSS score ＜ 14 and good recanalization were the independent predictors for good prognosis of BAO.The rapid and timely treatment of BAO with intra-arterial thrombolysis and stenting is a safe and effective measure.