Objective: To explore the correlation between the composition of bone marrow lymphocyte subsets and the clinical attributes observed in de novo AML patients, as well as their influence on prognosis. Methods: A detailed study was carried out on a cohort of 191 de novo acute myeloid leukemia patients who were admitted to our medical center between October 2022 and September 2024. In addition, a group of 24 patients with iron deficiency anemia individuals was carefully chosen as the control cohort. The proportions of lymphocyte subsets within the bone marrow of de novo AML patients were analyzed. Furthermore, an in-depth analysis was performed to investigate the association between the expression levels of these subsets in de novo AML patients and their clinical attributes, as well as their prognostic implications. Results: The proportion of CD19 and CD56 lymphocytes within the bone marrow of de novo AML patients significantly diminished compared to the control cohort (8.5% vs 13.2% P<0.05, and 15.5% vs 18.0%, P<0.05). Conversely, no significant discrepancies were observed in the CD3 , CD3 CD4 , and CD3 CD8 lymphocyte percentages between the AML patients and control group (71.7% vs 72.1%, 32.5% vs 33.7% and 32.8% vs 35.7%, P>0.05). When analyzing the relationships between lymphocyte subsets within the bone marrow of de novo patients and their respective clinical characteristics, patients aged 60 years and above exhibited diminished percentages of CD3 CD8 lymphocytes in the bone marrow compared to their younger counterparts (31.6% vs 34.1%, P<0.05), while the CD56 lymphocyte subsets demonstrated an increased prevalence (17.2% vs 14.4%, P<0.05). Furthermore, patients with leukocytosis (WBC≥100×10 /L) presented lower levels of CD3 and CD3 CD4 lymphocytes in the bone marrow compared with those without it (65.3% vs 72.9% P<0.05, and 28.9% vs 33.2%, P<0.05), respectively. The AML1-ETO fusion gene-positive cohort exhibited a higher prevalence of CD3 CD8 lymphocytes in the bone marrow than in the negative group (38.2% vs 32.3%, P<0.05), whereas the FLT3-ITD mutation-positive group presented a decreased prevalence of CD56 lymphocytes compared with the negative group (12.4% vs 16.8%, P<0.05). In addition, the NPM1 mutation-positive group demonstrated lower levels of CD3 CD8 lymphocytes in the bone marrow than in the negative group (29.1% vs 33.3%, P<0.05). Variables such as tumor protein p53(TP53) mutation positive, the absence of hematopoietic stem cell transplantation, and CD3 CD4 lymphocyte proportions below 25% were identified as independent adverse prognostic indicators for AML patients (P<0.05). Conclusion: The pathogenesis of AML is closely associated with an imbalance in bone marrow lymphocyte subsets. The FLT3-ITD mutation potentially contributes to the dysregulation of CD56 lymphocyte subset expression. The AML1-ETO fusion gene and NPM1 mutation are implicated in the abnormal expression of CD3 CD8 lymphocytes within the bone marrow. Moreover, the percentage of CD3 CD4 lymphocytes in the bone marrow serves as a prognostic factor for de novo AML patients.

Objective: To explore the correlation between the composition of bone marrow lymphocyte subsets and the clinical attributes observed in de novo AML patients, as well as their influence on prognosis. Methods: A detailed study was carried out on a cohort of 191 de novo acute myeloid leukemia patients who were admitted to our medical center between October 2022 and September 2024. In addition, a group of 24 patients with iron deficiency anemia individuals was carefully chosen as the control cohort. The proportions of lymphocyte subsets within the bone marrow of de novo AML patients were analyzed. Furthermore, an in-depth analysis was performed to investigate the association between the expression levels of these subsets in de novo AML patients and their clinical attributes, as well as their prognostic implications. Results: The proportion of CD19 and CD56 lymphocytes within the bone marrow of de novo AML patients significantly diminished compared to the control cohort (8.5% vs 13.2% P<0.05, and 15.5% vs 18.0%, P<0.05). Conversely, no significant discrepancies were observed in the CD3 , CD3 CD4 , and CD3 CD8 lymphocyte percentages between the AML patients and control group (71.7% vs 72.1%, 32.5% vs 33.7% and 32.8% vs 35.7%, P>0.05). When analyzing the relationships between lymphocyte subsets within the bone marrow of de novo patients and their respective clinical characteristics, patients aged 60 years and above exhibited diminished percentages of CD3 CD8 lymphocytes in the bone marrow compared to their younger counterparts (31.6% vs 34.1%, P<0.05), while the CD56 lymphocyte subsets demonstrated an increased prevalence (17.2% vs 14.4%, P<0.05). Furthermore, patients with leukocytosis (WBC≥100×10 /L) presented lower levels of CD3 and CD3 CD4 lymphocytes in the bone marrow compared with those without it (65.3% vs 72.9% P<0.05, and 28.9% vs 33.2%, P<0.05), respectively. The AML1-ETO fusion gene-positive cohort exhibited a higher prevalence of CD3 CD8 lymphocytes in the bone marrow than in the negative group (38.2% vs 32.3%, P<0.05), whereas the FLT3-ITD mutation-positive group presented a decreased prevalence of CD56 lymphocytes compared with the negative group (12.4% vs 16.8%, P<0.05). In addition, the NPM1 mutation-positive group demonstrated lower levels of CD3 CD8 lymphocytes in the bone marrow than in the negative group (29.1% vs 33.3%, P<0.05). Variables such as tumor protein p53(TP53) mutation positive, the absence of hematopoietic stem cell transplantation, and CD3 CD4 lymphocyte proportions below 25% were identified as independent adverse prognostic indicators for AML patients (P<0.05). Conclusion: The pathogenesis of AML is closely associated with an imbalance in bone marrow lymphocyte subsets. The FLT3-ITD mutation potentially contributes to the dysregulation of CD56 lymphocyte subset expression. The AML1-ETO fusion gene and NPM1 mutation are implicated in the abnormal expression of CD3 CD8 lymphocytes within the bone marrow. Moreover, the percentage of CD3 CD4 lymphocytes in the bone marrow serves as a prognostic factor for de novo AML patients.

Aim To reveal the mechanism of CIP4 homologs protein 1(RICH1)are involved in the regu-lation of myocardial fibrosis.Methods Mouse cardiac fibroblasts(MCFs)cells were treated with transforming growth factor-β(TGF-β1)to induce the formation of a myocardial fibrosis cell model;the level of the target protein was detected by Western blotting;and the RICH1 gene was detected by transfection of the cells with plasmid.The RICH1 gene was overexpressed(RICH 1 OE)using plasmid transfection;the RICH1 gene was silenced using siRNA fragment(siRICH1);and the expression levels of myocardial fibrosis marker genes,such as Col1 a1,Col3 a1,and Acta2,were de-tected using RT-qPCR.Results RICH1 was signifi-cantly down-regulated in TGF-β1-treated MCFs;the expression levels of myocardial fibrosis marker genes,such as Col1 a1,Col3a1,and Acta2,were down-regu-lated in the RICH1 OE+TGF-β1 group;and in the siRICH1+TGF-β1 group,myocardial fibrosis marker genes,such as Col1 a1,Col3a1 and Acta2 were up-regulated at the expression level;phosphorylated SMAD2(p-SMAD2)and phosphorylated SMAD3(p-SMAD3)levels were down-regulated in the siRICH1 OE+TGF-β1 group.p-SMAD2 and P-SMAD3 levels were upregulated in the siRICH1+TGF-β1 group.Conclusion RICH1 inhibits TGF-β1-induced myo-cardial fibrosis;RICH1 inhibits TGF-β1-induced myo-cardial fibrosis by negatively regulating the SMAD2/3 signaling pathway.

Aim To reveal the mechanism of CIP4 homologs protein 1(RICH1)are involved in the regu-lation of myocardial fibrosis.Methods Mouse cardiac fibroblasts(MCFs)cells were treated with transforming growth factor-β(TGF-β1)to induce the formation of a myocardial fibrosis cell model;the level of the target protein was detected by Western blotting;and the RICH1 gene was detected by transfection of the cells with plasmid.The RICH1 gene was overexpressed(RICH 1 OE)using plasmid transfection;the RICH1 gene was silenced using siRNA fragment(siRICH1);and the expression levels of myocardial fibrosis marker genes,such as Col1 a1,Col3 a1,and Acta2,were de-tected using RT-qPCR.Results RICH1 was signifi-cantly down-regulated in TGF-β1-treated MCFs;the expression levels of myocardial fibrosis marker genes,such as Col1 a1,Col3a1,and Acta2,were down-regu-lated in the RICH1 OE+TGF-β1 group;and in the siRICH1+TGF-β1 group,myocardial fibrosis marker genes,such as Col1 a1,Col3a1 and Acta2 were up-regulated at the expression level;phosphorylated SMAD2(p-SMAD2)and phosphorylated SMAD3(p-SMAD3)levels were down-regulated in the siRICH1 OE+TGF-β1 group.p-SMAD2 and P-SMAD3 levels were upregulated in the siRICH1+TGF-β1 group.Conclusion RICH1 inhibits TGF-β1-induced myo-cardial fibrosis;RICH1 inhibits TGF-β1-induced myo-cardial fibrosis by negatively regulating the SMAD2/3 signaling pathway.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To investigate the incidence of venous thromboembolism (VTE) in patients with esophageal cancer (EC).Methods:The retrospective cohort study was conducted. The clinicopathological data of 8 458 EC patients who were admitted to Sichuan Cancer Hospital from January 2017 to December 2021 were collected. There were 6 923 males and 1 535 females, aged (64±9)years. There were 3 187 patients undergoing surgical treatment, and 5 271 cases undergoing non-surgical treatment. Observation indicators: (1) incidence of VTE in EC patients; (2) treatment and outcomes of patients with VTE. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed using the t test. Measurement data with skewed distribution were represented as M(range), and comparison between groups was analyzed using the nonparameter rank sum test. Count data were expressed as absolute numbers or percentages, and comparison between groups was analyzed using the chi-square test or Fisher exact probability. Comparison of ordinal data was analyzed using the nonparameter rank sum test. Results:(1) Incidence of VTE in EC patients. Of 8 458 EC patients, 175 cases developed VTE, with an incidence rate of 2.069%(175/8 458). Among 175 VTE patients, there were 164 cases of deep venous thrombosis (DVT), 4 cases of pulmonary embolism (PE), 7 cases of DVT and PE. There were 59 surgical patients and 116 non-surgical patients. There was no significant difference in thrombus type between surgical and non-surgical EC patients with VTE ( χ2=1.95, P>0.05). Of 3 187 surgical patients, the incidence of VTE was 1.851%(59/3 187), including an incidence of 0.157%(5/3 187) of PE. PE accounted for 8.475%(5/59) of surgical patients with VTE. Of 5 271 non-surgical patients, the incidence of VTE was 2.201%(116/5 271), including an incidence of 0.114%(6/5 271) of PE. PE accounted for 5.172%(6/116) of non-surgical patients with VTE. There was no significant difference in the incidence of VTE or PE between surgical patients and non-surgical patients ( χ2=1.20, 0.05, P>0.05). (2) Treatment and outcomes of patients with VTE. Among 175 EC patients with VTE, 163 cases underwent drug treatment, and 12 cases did not receive treatment. Among 163 cases with drug therapy, 158 cases underwent anticoagulant therapy, 5 cases were treated with thrombolysis. All the 163 patients were improved and discharged from hospital. Conclusions:The incidence of VTE in patients with EC is relatively low, as 2.069%. There is no significant difference in the incidence of VTE or thrombus type between surgical EC patients and non-surgical EC patients.

AIM: To analyze the effect of full-femtosecond small incision lenticule extraction(SMILE)on the treatment of high myopia based on propensity score matching.METHODS: A total of 48 cases(48 eyes)of high myopia patients who underwent SMILE surgery in our hospital from May 2019 to May 2021 were selected as the observation group, and 48 cases(48 eyes)of high myopia patients who underwent FS-LASIK surgery were matched using propensity score matching as the control group. Follow up for 6mo after surgery, the changes in cylindrical, central corneal thickness, uncorrected visual acuity(UCVA), corneal endothelial cell related indicators [percentage of hexagonal endothelial cells(6A), coefficient of variation(CV)of endothelial cell area, central corneal endothelial cell density(ECD)] and corneal biomechanical indicators [simulated Goldman intraocular pressure(IOPg), corneal hysteresis(CH), corneal resistance factor(CRF), corneal compensated intraocular pressure(IOPcc)] between the two groups were compared, and the incidence of complications in both groups of patients was recorded.RESULTS: Both groups of patients showed significant improvements in cylindrical and UCVA at 3 and 6mo after surgery, as well as decreased central corneal thickness, corneal endothelial cells, and corneal biomechanics related indicators. The changes in the observation group were more significant(all P<0.05). During the follow-up period, there was no significant difference in the incidence of complications between the observation group and the control group(8% vs. 17%, P>0.05).CONCLUSION: SMILE has a definite effect on patients with high myopia and is helpful to improve visual acuity.

In recent years, researchers, pharmaceutical companies, and political makers gradually using more real-world data (RWD) to produce real-world evidence (RWE) for policy-making. A research team of Harvard University launched the RCT DUPLICATE project in 2018, aiming to replicate 30 randomized controlled trials using the medical claims database in order to explore methods for quantifying the efficacy-effectiveness gap and explain its potential sources, to enhance the credibility of the RWE. This paper reviews the background of RCT DUPLICATE Initiative, highlights the research purposes, research design and implementation process of the RCT DUPLICATE Initiative, to help domestic scholars better understand the scope and application value of RWE.
Humans ; Randomized Controlled Trials as Topic ; Cognition ; Databases, Factual ; Research Personnel ; Universities

With the promotion and application of big medical data, non-interventional real-world evidence (RWE) has been used by regulators to assess the effectiveness of medical products. This paper briefly introduces the latest progress and research results of the RCT DUPLICATE Initiative launched by the research team of Harvard University in 2018 and summarizes relevant research experience based on the characteristics of China's medical service to provide inspiration and reference for domestic scholars to conduct related RWE research in the future.
Humans ; Randomized Controlled Trials as Topic ; Cognition ; Big Data ; Universities